From 1bea8e1eacc68bcedbd3f207395bccf11011dae2 Mon Sep 17 00:00:00 2001
From: Juan Linietsky <reduzio@gmail.com>
Date: Fri, 1 May 2020 09:34:23 -0300
Subject: New lightmapper

-Added LocalVector (needed it)
-Added stb_rect_pack (It's pretty cool, we could probably use it for other stuff too)
-Fixes and changes all around the place
-Added library for 128 bits fixed point (required for Delaunay3D)
---
 thirdparty/oidn/.gitignore                         |    1 +
 thirdparty/oidn/common/barrier.h                   |   52 +
 thirdparty/oidn/common/exception.h                 |   45 +
 thirdparty/oidn/common/platform.cpp                |  114 +
 thirdparty/oidn/common/platform.h                  |  131 +
 thirdparty/oidn/common/ref.h                       |  163 +
 thirdparty/oidn/common/tensor.cpp                  |   83 +
 thirdparty/oidn/common/tensor.h                    |   66 +
 thirdparty/oidn/common/thread.cpp                  |  297 ++
 thirdparty/oidn/common/thread.h                    |  202 +
 thirdparty/oidn/common/timer.h                     |   49 +
 thirdparty/oidn/core/api.cpp                       |  408 ++
 thirdparty/oidn/core/autoencoder.cpp               |  519 +++
 thirdparty/oidn/core/autoencoder.h                 |  116 +
 thirdparty/oidn/core/buffer.h                      |   75 +
 thirdparty/oidn/core/common.h                      |  133 +
 thirdparty/oidn/core/device.cpp                    |  205 +
 thirdparty/oidn/core/device.h                      |   78 +
 thirdparty/oidn/core/filter.cpp                    |   27 +
 thirdparty/oidn/core/filter.h                      |   52 +
 thirdparty/oidn/core/image.h                       |  111 +
 thirdparty/oidn/core/input_reorder.h               |  232 +
 thirdparty/oidn/core/math.h                        |   78 +
 thirdparty/oidn/core/network.cpp                   |  434 ++
 thirdparty/oidn/core/network.h                     |  112 +
 thirdparty/oidn/core/node.h                        |  142 +
 thirdparty/oidn/core/output_reorder.h              |  126 +
 thirdparty/oidn/core/transfer_function.cpp         |   95 +
 thirdparty/oidn/core/transfer_function.h           |  201 +
 thirdparty/oidn/core/upsample.h                    |   92 +
 thirdparty/oidn/core/weights_reorder.h             |   99 +
 thirdparty/oidn/include/OpenImageDenoise/oidn.h    |  214 +
 thirdparty/oidn/include/OpenImageDenoise/oidn.hpp  |  468 ++
 thirdparty/oidn/include/OpenImageDenoise/version.h |   23 +
 thirdparty/oidn/mkl-dnn/LICENSE                    |  214 +
 thirdparty/oidn/mkl-dnn/include/mkldnn.h           | 1771 ++++++++
 thirdparty/oidn/mkl-dnn/include/mkldnn.hpp         | 2615 +++++++++++
 thirdparty/oidn/mkl-dnn/include/mkldnn_debug.h     |   98 +
 thirdparty/oidn/mkl-dnn/include/mkldnn_types.h     | 1415 ++++++
 thirdparty/oidn/mkl-dnn/include/mkldnn_version.h   |   32 +
 .../oidn/mkl-dnn/include/mkldnn_version.h.in       |   32 +
 .../mkl-dnn/src/common/batch_normalization.cpp     |  104 +
 .../mkl-dnn/src/common/batch_normalization_pd.hpp  |  240 ++
 thirdparty/oidn/mkl-dnn/src/common/c_types_map.hpp |  550 +++
 thirdparty/oidn/mkl-dnn/src/common/concat.cpp      |   86 +
 thirdparty/oidn/mkl-dnn/src/common/concat_pd.hpp   |  211 +
 thirdparty/oidn/mkl-dnn/src/common/convolution.cpp |  200 +
 .../oidn/mkl-dnn/src/common/convolution_pd.cpp     |   56 +
 .../oidn/mkl-dnn/src/common/convolution_pd.hpp     |  348 ++
 .../oidn/mkl-dnn/src/common/deconvolution.cpp      |  188 +
 .../oidn/mkl-dnn/src/common/deconvolution_pd.hpp   |  293 ++
 thirdparty/oidn/mkl-dnn/src/common/eltwise.cpp     |   84 +
 thirdparty/oidn/mkl-dnn/src/common/eltwise_pd.hpp  |  161 +
 thirdparty/oidn/mkl-dnn/src/common/engine.cpp      |   75 +
 thirdparty/oidn/mkl-dnn/src/common/engine.hpp      |  119 +
 .../oidn/mkl-dnn/src/common/inner_product.cpp      |  106 +
 .../oidn/mkl-dnn/src/common/inner_product_pd.cpp   |   56 +
 .../oidn/mkl-dnn/src/common/inner_product_pd.hpp   |  321 ++
 thirdparty/oidn/mkl-dnn/src/common/lrn.cpp         |   91 +
 thirdparty/oidn/mkl-dnn/src/common/lrn_pd.hpp      |  170 +
 thirdparty/oidn/mkl-dnn/src/common/math_utils.hpp  |  280 ++
 thirdparty/oidn/mkl-dnn/src/common/memory.cpp      |  238 +
 thirdparty/oidn/mkl-dnn/src/common/memory.hpp      |   63 +
 .../mkl-dnn/src/common/memory_desc_wrapper.cpp     |  212 +
 .../mkl-dnn/src/common/memory_desc_wrapper.hpp     |  400 ++
 .../oidn/mkl-dnn/src/common/memory_tracking.hpp    |  295 ++
 .../oidn/mkl-dnn/src/common/mkldnn_debug.cpp       |  131 +
 .../src/common/mkldnn_debug_autogenerated.cpp      |  365 ++
 .../oidn/mkl-dnn/src/common/mkldnn_thread.hpp      |  115 +
 .../src/common/mkldnn_thread_parallel_nd.hpp       |  277 ++
 .../oidn/mkl-dnn/src/common/mkldnn_traits.hpp      |   77 +
 thirdparty/oidn/mkl-dnn/src/common/nstl.hpp        |  193 +
 thirdparty/oidn/mkl-dnn/src/common/pooling.cpp     |  114 +
 thirdparty/oidn/mkl-dnn/src/common/pooling_pd.hpp  |  238 +
 thirdparty/oidn/mkl-dnn/src/common/primitive.cpp   |  103 +
 thirdparty/oidn/mkl-dnn/src/common/primitive.hpp   |   76 +
 .../oidn/mkl-dnn/src/common/primitive_attr.cpp     |  290 ++
 .../oidn/mkl-dnn/src/common/primitive_attr.hpp     |  183 +
 .../oidn/mkl-dnn/src/common/primitive_desc.cpp     |   78 +
 .../oidn/mkl-dnn/src/common/primitive_desc.hpp     |  174 +
 .../mkl-dnn/src/common/primitive_exec_types.cpp    |   90 +
 .../mkl-dnn/src/common/primitive_exec_types.hpp    |   68 +
 .../oidn/mkl-dnn/src/common/primitive_iterator.cpp |   89 +
 .../oidn/mkl-dnn/src/common/primitive_iterator.hpp |   79 +
 thirdparty/oidn/mkl-dnn/src/common/query.cpp       |   59 +
 thirdparty/oidn/mkl-dnn/src/common/reorder.cpp     |   68 +
 thirdparty/oidn/mkl-dnn/src/common/reorder_pd.hpp  |   85 +
 thirdparty/oidn/mkl-dnn/src/common/rnn.cpp         |  400 ++
 thirdparty/oidn/mkl-dnn/src/common/rnn_pd.hpp      |  280 ++
 thirdparty/oidn/mkl-dnn/src/common/scratchpad.cpp  |  112 +
 thirdparty/oidn/mkl-dnn/src/common/scratchpad.hpp  |   36 +
 thirdparty/oidn/mkl-dnn/src/common/shuffle.cpp     |   72 +
 thirdparty/oidn/mkl-dnn/src/common/shuffle_pd.hpp  |  121 +
 thirdparty/oidn/mkl-dnn/src/common/softmax.cpp     |   68 +
 thirdparty/oidn/mkl-dnn/src/common/softmax_pd.hpp  |  161 +
 thirdparty/oidn/mkl-dnn/src/common/stream.cpp      |   46 +
 thirdparty/oidn/mkl-dnn/src/common/stream.hpp      |   44 +
 thirdparty/oidn/mkl-dnn/src/common/sum.cpp         |   79 +
 thirdparty/oidn/mkl-dnn/src/common/sum_pd.hpp      |  143 +
 thirdparty/oidn/mkl-dnn/src/common/tag_traits.hpp  |  200 +
 .../oidn/mkl-dnn/src/common/type_helpers.hpp       |  348 ++
 thirdparty/oidn/mkl-dnn/src/common/utils.cpp       |  135 +
 thirdparty/oidn/mkl-dnn/src/common/utils.hpp       |  370 ++
 thirdparty/oidn/mkl-dnn/src/common/verbose.cpp     |  665 +++
 thirdparty/oidn/mkl-dnn/src/common/verbose.hpp     |   62 +
 thirdparty/oidn/mkl-dnn/src/common/z_magic.hpp     |   46 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_barrier.cpp    |  112 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_barrier.hpp    |   60 +
 .../mkl-dnn/src/cpu/cpu_batch_normalization_pd.hpp |   40 +
 .../src/cpu/cpu_batch_normalization_utils.cpp      |  140 +
 .../src/cpu/cpu_batch_normalization_utils.hpp      |   43 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_concat.cpp     |   51 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_concat_pd.hpp  |   41 +
 .../oidn/mkl-dnn/src/cpu/cpu_convolution_pd.hpp    |   74 +
 .../oidn/mkl-dnn/src/cpu/cpu_deconvolution_pd.hpp  |   46 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_eltwise_pd.hpp |   45 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_engine.cpp     |  324 ++
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_engine.hpp     |   70 +
 .../oidn/mkl-dnn/src/cpu/cpu_inner_product_pd.hpp  |   84 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_isa_traits.hpp |  151 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_lrn_pd.hpp     |   42 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_memory.cpp     |  277 ++
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_memory.hpp     |   89 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_pooling_pd.hpp |   40 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_primitive.hpp  |   83 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_reducer.cpp    |  544 +++
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_reducer.hpp    |  334 ++
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_reorder.cpp    |  262 ++
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_reorder_pd.hpp |   48 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_shuffle_pd.hpp |   41 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_softmax_pd.hpp |   45 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_sum.cpp        |   48 +
 thirdparty/oidn/mkl-dnn/src/cpu/cpu_sum_pd.hpp     |   39 +
 .../mkl-dnn/src/cpu/gemm/f32/gemm_utils_f32.cpp    |  372 ++
 .../mkl-dnn/src/cpu/gemm/f32/gemm_utils_f32.hpp    |   72 +
 .../cpu/gemm/f32/jit_avx512_common_gemm_f32.cpp    | 2131 +++++++++
 .../cpu/gemm/f32/jit_avx512_common_gemm_f32.hpp    |   36 +
 .../mkl-dnn/src/cpu/gemm/f32/jit_avx_gemm_f32.cpp  | 2705 ++++++++++++
 .../mkl-dnn/src/cpu/gemm/f32/jit_avx_gemm_f32.hpp  |   37 +
 .../oidn/mkl-dnn/src/cpu/gemm/f32/ref_gemm_f32.cpp |  346 ++
 .../oidn/mkl-dnn/src/cpu/gemm/f32/ref_gemm_f32.hpp |   36 +
 thirdparty/oidn/mkl-dnn/src/cpu/gemm/gemm.cpp      |  280 ++
 thirdparty/oidn/mkl-dnn/src/cpu/gemm/gemm.hpp      |   58 +
 thirdparty/oidn/mkl-dnn/src/cpu/gemm/os_blas.hpp   |   86 +
 .../oidn/mkl-dnn/src/cpu/gemm/s8x8s32/common.hpp   |  206 +
 .../oidn/mkl-dnn/src/cpu/gemm/s8x8s32/gemv.hpp     |   28 +
 .../gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32.cpp  | 1409 ++++++
 .../gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32.hpp  |   38 +
 .../s8x8s32/jit_avx512_core_gemm_s8u8s32_kern.cpp  |  539 +++
 .../s8x8s32/jit_avx512_core_gemm_s8u8s32_kern.hpp  |  101 +
 .../gemm/s8x8s32/jit_avx512_core_gemv_s8u8s32.cpp  |  290 ++
 .../jit_avx512_core_kernel_gemv_s8u8s32_kern.cpp   |  411 ++
 .../jit_avx512_core_kernel_gemv_s8u8s32_kern.hpp   |   64 +
 .../s8x8s32/jit_avx512_core_u8_copy_an_kern.cpp    |  819 ++++
 .../s8x8s32/jit_avx512_core_u8_copy_at_kern.cpp    | 2209 ++++++++++
 .../s8x8s32/jit_avx512_core_u8_copy_bn_kern.cpp    |  564 +++
 .../s8x8s32/jit_avx512_core_u8_copy_bt_kern.cpp    |  501 +++
 .../jit_avx512_core_u8_copy_sum_an_kern.cpp        | 1283 ++++++
 .../jit_avx512_core_u8_copy_sum_at_kern.cpp        | 3163 ++++++++++++++
 .../jit_avx512_core_u8_copy_sum_bn_kern.cpp        |  821 ++++
 .../jit_avx512_core_u8_copy_sum_bt_kern.cpp        |  647 +++
 .../src/cpu/gemm/s8x8s32/ref_gemm_s8x8s32.cpp      |  116 +
 .../src/cpu/gemm/s8x8s32/ref_gemm_s8x8s32.hpp      |   38 +
 .../src/cpu/gemm/s8x8s32/simple_gemm_s8s8s32.cpp   |  180 +
 .../src/cpu/gemm/s8x8s32/simple_gemm_s8s8s32.hpp   |   37 +
 .../oidn/mkl-dnn/src/cpu/gemm_convolution.cpp      |  307 ++
 .../oidn/mkl-dnn/src/cpu/gemm_convolution.hpp      |  250 ++
 .../mkl-dnn/src/cpu/gemm_convolution_utils.cpp     |  771 ++++
 .../mkl-dnn/src/cpu/gemm_convolution_utils.hpp     |   66 +
 .../oidn/mkl-dnn/src/cpu/gemm_inner_product.cpp    |  156 +
 .../oidn/mkl-dnn/src/cpu/gemm_inner_product.hpp    |  157 +
 .../mkl-dnn/src/cpu/gemm_x8s8s32x_convolution.cpp  |  740 ++++
 .../mkl-dnn/src/cpu/gemm_x8s8s32x_convolution.hpp  |  266 ++
 .../src/cpu/gemm_x8s8s32x_inner_product.cpp        |  453 ++
 .../src/cpu/gemm_x8s8s32x_inner_product.hpp        |  166 +
 .../src/cpu/jit_avx2_1x1_conv_kernel_f32.cpp       |  674 +++
 .../src/cpu/jit_avx2_1x1_conv_kernel_f32.hpp       |  110 +
 .../mkl-dnn/src/cpu/jit_avx2_1x1_convolution.cpp   |  545 +++
 .../mkl-dnn/src/cpu/jit_avx2_1x1_convolution.hpp   |  344 ++
 .../mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.cpp   | 1501 +++++++
 .../mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.hpp   |  225 +
 .../oidn/mkl-dnn/src/cpu/jit_avx2_convolution.cpp  |  410 ++
 .../oidn/mkl-dnn/src/cpu/jit_avx2_convolution.hpp  |  302 ++
 .../src/cpu/jit_avx512_common_1x1_conv_kernel.cpp  | 1255 ++++++
 .../src/cpu/jit_avx512_common_1x1_conv_kernel.hpp  |  108 +
 .../src/cpu/jit_avx512_common_1x1_convolution.cpp  |  816 ++++
 .../src/cpu/jit_avx512_common_1x1_convolution.hpp  |  344 ++
 .../src/cpu/jit_avx512_common_conv_kernel.cpp      | 4539 ++++++++++++++++++++
 .../src/cpu/jit_avx512_common_conv_kernel.hpp      |  423 ++
 .../jit_avx512_common_conv_winograd_kernel_f32.cpp | 1163 +++++
 .../jit_avx512_common_conv_winograd_kernel_f32.hpp |  179 +
 .../src/cpu/jit_avx512_common_convolution.cpp      | 1526 +++++++
 .../src/cpu/jit_avx512_common_convolution.hpp      |  302 ++
 .../cpu/jit_avx512_common_convolution_winograd.cpp | 1215 ++++++
 .../cpu/jit_avx512_common_convolution_winograd.hpp |  318 ++
 .../oidn/mkl-dnn/src/cpu/jit_avx512_common_lrn.cpp |  853 ++++
 .../oidn/mkl-dnn/src/cpu/jit_avx512_common_lrn.hpp |   96 +
 .../src/cpu/jit_avx512_core_fp32_wino_conv_2x3.cpp | 1103 +++++
 .../src/cpu/jit_avx512_core_fp32_wino_conv_2x3.hpp |  144 +
 .../src/cpu/jit_avx512_core_fp32_wino_conv_4x3.cpp | 1020 +++++
 .../src/cpu/jit_avx512_core_fp32_wino_conv_4x3.hpp |  386 ++
 .../jit_avx512_core_fp32_wino_conv_4x3_kernel.cpp  | 2596 +++++++++++
 .../jit_avx512_core_fp32_wino_conv_4x3_kernel.hpp  |  291 ++
 .../jit_avx512_core_u8s8s32x_wino_convolution.cpp  | 1284 ++++++
 .../jit_avx512_core_u8s8s32x_wino_convolution.hpp  |  128 +
 .../jit_avx512_core_x8s8s32x_1x1_conv_kernel.cpp   |  820 ++++
 .../jit_avx512_core_x8s8s32x_1x1_conv_kernel.hpp   |  131 +
 .../jit_avx512_core_x8s8s32x_1x1_convolution.cpp   |  292 ++
 .../jit_avx512_core_x8s8s32x_1x1_convolution.hpp   |  159 +
 .../jit_avx512_core_x8s8s32x_1x1_deconvolution.hpp |  140 +
 .../cpu/jit_avx512_core_x8s8s32x_conv_kernel.cpp   | 1182 +++++
 .../cpu/jit_avx512_core_x8s8s32x_conv_kernel.hpp   |  239 ++
 .../cpu/jit_avx512_core_x8s8s32x_convolution.cpp   |  423 ++
 .../cpu/jit_avx512_core_x8s8s32x_convolution.hpp   |  115 +
 .../cpu/jit_avx512_core_x8s8s32x_deconvolution.cpp | 1034 +++++
 .../cpu/jit_avx512_core_x8s8s32x_deconvolution.hpp |  237 +
 thirdparty/oidn/mkl-dnn/src/cpu/jit_generator.hpp  |  773 ++++
 .../oidn/mkl-dnn/src/cpu/jit_primitive_conf.hpp    |  481 +++
 .../src/cpu/jit_sse42_1x1_conv_kernel_f32.cpp      |  677 +++
 .../src/cpu/jit_sse42_1x1_conv_kernel_f32.hpp      |  104 +
 .../mkl-dnn/src/cpu/jit_sse42_1x1_convolution.cpp  |  134 +
 .../mkl-dnn/src/cpu/jit_sse42_1x1_convolution.hpp  |   96 +
 .../mkl-dnn/src/cpu/jit_sse42_conv_kernel_f32.cpp  |  497 +++
 .../mkl-dnn/src/cpu/jit_sse42_conv_kernel_f32.hpp  |   93 +
 .../oidn/mkl-dnn/src/cpu/jit_sse42_convolution.cpp |  136 +
 .../oidn/mkl-dnn/src/cpu/jit_sse42_convolution.hpp |  103 +
 .../mkl-dnn/src/cpu/jit_transpose_src_utils.cpp    | 1192 +++++
 .../mkl-dnn/src/cpu/jit_transpose_src_utils.hpp    |  145 +
 .../mkl-dnn/src/cpu/jit_uni_1x1_conv_utils.hpp     |  327 ++
 .../src/cpu/jit_uni_batch_normalization.cpp        | 1407 ++++++
 .../src/cpu/jit_uni_batch_normalization.hpp        |  100 +
 .../mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_f32.cpp | 1302 ++++++
 .../mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_f32.hpp |  253 ++
 .../mkl-dnn/src/cpu/jit_uni_dw_convolution.cpp     |  427 ++
 .../mkl-dnn/src/cpu/jit_uni_dw_convolution.hpp     |  266 ++
 .../oidn/mkl-dnn/src/cpu/jit_uni_eltwise.cpp       | 1142 +++++
 .../oidn/mkl-dnn/src/cpu/jit_uni_eltwise.hpp       |  193 +
 .../oidn/mkl-dnn/src/cpu/jit_uni_i8i8_pooling.cpp  |  949 ++++
 .../oidn/mkl-dnn/src/cpu/jit_uni_i8i8_pooling.hpp  |   89 +
 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn.cpp    |  305 ++
 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn.hpp    |  103 +
 .../mkl-dnn/src/cpu/jit_uni_lrn_kernel_f32.cpp     | 1487 +++++++
 .../mkl-dnn/src/cpu/jit_uni_lrn_kernel_f32.hpp     |  183 +
 .../mkl-dnn/src/cpu/jit_uni_pool_kernel_f32.cpp    |  699 +++
 .../mkl-dnn/src/cpu/jit_uni_pool_kernel_f32.hpp    |  192 +
 .../oidn/mkl-dnn/src/cpu/jit_uni_pooling.cpp       |  264 ++
 .../oidn/mkl-dnn/src/cpu/jit_uni_pooling.hpp       |  182 +
 .../oidn/mkl-dnn/src/cpu/jit_uni_reorder.cpp       | 1006 +++++
 .../oidn/mkl-dnn/src/cpu/jit_uni_reorder.hpp       |  127 +
 .../oidn/mkl-dnn/src/cpu/jit_uni_reorder_utils.cpp |  313 ++
 .../oidn/mkl-dnn/src/cpu/jit_utils/jit_utils.cpp   |  115 +
 .../oidn/mkl-dnn/src/cpu/jit_utils/jit_utils.hpp   |   32 +
 .../src/cpu/jit_utils/jitprofiling/LICENSE.BSD     |   27 +
 .../src/cpu/jit_utils/jitprofiling/README.md       |    1 +
 .../cpu/jit_utils/jitprofiling/ittnotify_config.h  |  595 +++
 .../cpu/jit_utils/jitprofiling/ittnotify_types.h   |   94 +
 .../src/cpu/jit_utils/jitprofiling/jitprofiling.c  |  293 ++
 .../src/cpu/jit_utils/jitprofiling/jitprofiling.h  |  673 +++
 thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.cpp   |  317 ++
 thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.hpp   |  147 +
 .../mkl-dnn/src/cpu/ncsp_batch_normalization.cpp   |  382 ++
 .../mkl-dnn/src/cpu/ncsp_batch_normalization.hpp   |  160 +
 thirdparty/oidn/mkl-dnn/src/cpu/nhwc_pooling.cpp   |  392 ++
 thirdparty/oidn/mkl-dnn/src/cpu/nhwc_pooling.hpp   |  210 +
 .../mkl-dnn/src/cpu/nspc_batch_normalization.cpp   |  288 ++
 .../mkl-dnn/src/cpu/nspc_batch_normalization.hpp   |  169 +
 .../mkl-dnn/src/cpu/ref_batch_normalization.cpp    |  265 ++
 .../mkl-dnn/src/cpu/ref_batch_normalization.hpp    |  127 +
 thirdparty/oidn/mkl-dnn/src/cpu/ref_concat.hpp     |   97 +
 .../oidn/mkl-dnn/src/cpu/ref_convolution.cpp       |  395 ++
 .../oidn/mkl-dnn/src/cpu/ref_convolution.hpp       |  194 +
 .../oidn/mkl-dnn/src/cpu/ref_deconvolution.cpp     |  199 +
 .../oidn/mkl-dnn/src/cpu/ref_deconvolution.hpp     |  502 +++
 thirdparty/oidn/mkl-dnn/src/cpu/ref_eltwise.cpp    |  297 ++
 thirdparty/oidn/mkl-dnn/src/cpu/ref_eltwise.hpp    |  168 +
 .../oidn/mkl-dnn/src/cpu/ref_inner_product.cpp     |  285 ++
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 thirdparty/oidn/mkl-dnn/src/cpu/xbyak/COPYRIGHT    |   47 +
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 .../oidn/mkl-dnn/src/cpu/xbyak/xbyak_mnemonic.h    | 2017 +++++++++
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 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_conv_kernel_f32.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_conv_kernel_f32.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_convolution.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_convolution.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_transpose_src_utils.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_transpose_src_utils.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_1x1_conv_utils.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_batch_normalization.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_batch_normalization.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_f32.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_f32.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_convolution.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_convolution.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_eltwise.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_eltwise.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_i8i8_pooling.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_i8i8_pooling.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn_kernel_f32.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn_kernel_f32.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pool_kernel_f32.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pool_kernel_f32.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pooling.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pooling.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder_utils.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jit_utils.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jit_utils.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/LICENSE.BSD
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/README.md
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/ittnotify_config.h
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/ittnotify_types.h
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/jitprofiling.c
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/jitprofiling.h
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ncsp_batch_normalization.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ncsp_batch_normalization.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/nhwc_pooling.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/nhwc_pooling.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/nspc_batch_normalization.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/nspc_batch_normalization.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_concat.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_deconvolution.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_deconvolution.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_eltwise.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_eltwise.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_inner_product.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_inner_product.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_lrn.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_lrn.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_pooling.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_pooling.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_shuffle.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_shuffle.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/ref_sum.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_common.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_gru.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_gru_lbr.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_lstm.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_rnn.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/cpu_rnn_pd.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/jit_uni_rnn_postgemm.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_reorders.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_utils.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_utils.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/simple_concat.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/simple_concat.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/simple_q10n.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/simple_reorder.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/simple_sum.cpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/simple_sum.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/wino_reorder.hpp
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/xbyak/COPYRIGHT
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak.h
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_bin2hex.h
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_mnemonic.h
 create mode 100644 thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_util.h
 create mode 100644 thirdparty/oidn/weights/rtlightmap_hdr.tza

(limited to 'thirdparty/oidn')

diff --git a/thirdparty/oidn/.gitignore b/thirdparty/oidn/.gitignore
new file mode 100644
index 0000000000..6be206fc29
--- /dev/null
+++ b/thirdparty/oidn/.gitignore
@@ -0,0 +1 @@
+weights/rtlightmap_hdr.cpp
diff --git a/thirdparty/oidn/common/barrier.h b/thirdparty/oidn/common/barrier.h
new file mode 100644
index 0000000000..b20f670053
--- /dev/null
+++ b/thirdparty/oidn/common/barrier.h
@@ -0,0 +1,52 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "platform.h"
+#include <mutex>
+#include <condition_variable>
+
+namespace oidn {
+
+  class Barrier
+  {
+  private:
+    std::mutex m;
+    std::condition_variable cv;
+    volatile int count;
+
+  public:
+    Barrier(int count) : count(count) {}
+
+    void wait()
+    {
+      std::unique_lock<std::mutex> lk(m);
+      count--;
+
+      if (count == 0)
+      {
+        lk.unlock();
+        cv.notify_all();
+      }
+      else
+      {
+        cv.wait(lk, [&]{ return count == 0; });
+      }
+    }
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/common/exception.h b/thirdparty/oidn/common/exception.h
new file mode 100644
index 0000000000..18069c6a7d
--- /dev/null
+++ b/thirdparty/oidn/common/exception.h
@@ -0,0 +1,45 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include <exception>
+#include "platform.h"
+
+namespace oidn {
+
+  class Exception : public std::exception
+  {
+  private:
+    Error error;
+    const char* message;
+
+  public:
+    Exception(Error error, const char* message)
+      : error(error), message(message) {}
+
+    Error code() const noexcept
+    {
+      return error;
+    }
+
+    const char* what() const noexcept override
+    {
+      return message;
+    }
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/common/platform.cpp b/thirdparty/oidn/common/platform.cpp
new file mode 100644
index 0000000000..59a14ff47c
--- /dev/null
+++ b/thirdparty/oidn/common/platform.cpp
@@ -0,0 +1,114 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#include "platform.h"
+
+namespace oidn {
+
+  // ----------------------------------------------------------------------------
+  // Common functions
+  // ----------------------------------------------------------------------------
+
+  void* alignedMalloc(size_t size, size_t alignment)
+  {
+    if (size == 0)
+      return nullptr;
+
+    assert((alignment & (alignment-1)) == 0);
+    void* ptr = _mm_malloc(size, alignment);
+
+    if (ptr == nullptr)
+      throw std::bad_alloc();
+
+    return ptr;
+  }
+
+  void alignedFree(void* ptr)
+  {
+    if (ptr)
+      _mm_free(ptr);
+  }
+
+  // ----------------------------------------------------------------------------
+  // System information
+  // ----------------------------------------------------------------------------
+
+  std::string getPlatformName()
+  {
+    std::string name;
+
+  #if defined(__linux__)
+    name = "Linux";
+  #elif defined(__FreeBSD__)
+    name = "FreeBSD";
+  #elif defined(__CYGWIN__)
+    name = "Cygwin";
+  #elif defined(_WIN32)
+    name = "Windows";
+  #elif defined(__APPLE__)
+    name = "macOS";
+  #elif defined(__unix__)
+    name = "Unix";
+  #else
+    return "Unknown";
+  #endif
+
+  #if defined(__x86_64__) || defined(_M_X64) || defined(__ia64__) || defined(__aarch64__)
+    name += " (64-bit)";
+  #else
+    name += " (32-bit)";
+  #endif
+
+    return name;
+  }
+
+  std::string getCompilerName()
+  {
+  #if defined(__INTEL_COMPILER)
+    int mayor = __INTEL_COMPILER / 100 % 100;
+    int minor = __INTEL_COMPILER % 100;
+    std::string version = "Intel Compiler ";
+    version += toString(mayor);
+    version += "." + toString(minor);
+  #if defined(__INTEL_COMPILER_UPDATE)
+    version += "." + toString(__INTEL_COMPILER_UPDATE);
+  #endif
+    return version;
+  #elif defined(__clang__)
+    return "Clang " __clang_version__;
+  #elif defined(__GNUC__)
+    return "GCC " __VERSION__;
+  #elif defined(_MSC_VER)
+    std::string version = toString(_MSC_FULL_VER);
+    version.insert(4, ".");
+    version.insert(9, ".");
+    version.insert(2, ".");
+    return "Visual C++ Compiler " + version;
+  #else
+    return "Unknown";
+  #endif
+  }
+
+  std::string getBuildName()
+  {
+  #if defined(NDEBUG)
+    return "Release";
+  #else
+    return "Debug";
+  #endif
+  }
+
+} // namespace oidn
diff --git a/thirdparty/oidn/common/platform.h b/thirdparty/oidn/common/platform.h
new file mode 100644
index 0000000000..205ac8981d
--- /dev/null
+++ b/thirdparty/oidn/common/platform.h
@@ -0,0 +1,131 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#if defined(_WIN32)
+  #define WIN32_LEAN_AND_MEAN
+  #define NOMINMAX
+  #include <Windows.h>
+#elif defined(__APPLE__)
+  #include <sys/sysctl.h>
+#endif
+
+#include <xmmintrin.h>
+#include <cstdint>
+#include <climits>
+#include <limits>
+#include <atomic>
+#include <algorithm>
+#include <memory>
+#include <cmath>
+#include <string>
+#include <sstream>
+#include <iostream>
+#include <cassert>
+#include "include/OpenImageDenoise/oidn.hpp"
+
+namespace oidn {
+
+  // ----------------------------------------------------------------------------
+  // Macros
+  // ----------------------------------------------------------------------------
+
+  #if defined(_WIN32)
+    // Windows
+    #if !defined(__noinline)
+      #define __noinline     __declspec(noinline)
+    #endif
+  #else
+    // Unix
+    #if !defined(__forceinline)
+      #define __forceinline  inline __attribute__((always_inline))
+    #endif
+    #if !defined(__noinline)
+      #define __noinline     __attribute__((noinline))
+    #endif
+  #endif
+
+  #ifndef UNUSED
+    #define UNUSED(x) ((void)x)
+  #endif
+  #ifndef MAYBE_UNUSED
+    #define MAYBE_UNUSED(x) UNUSED(x)
+  #endif
+
+  // ----------------------------------------------------------------------------
+  // Error handling and debugging
+  // ----------------------------------------------------------------------------
+
+  struct Verbose
+  {
+    int verbose;
+
+    Verbose(int v = 0) : verbose(v) {}
+    __forceinline bool isVerbose(int v = 1) const { return v <= verbose; }
+  };
+
+  #define OIDN_WARNING(message) { if (isVerbose()) std::cerr << "Warning: " << message << std::endl; }
+  #define OIDN_FATAL(message) throw std::runtime_error(message);
+
+  // ----------------------------------------------------------------------------
+  // Common functions
+  // ----------------------------------------------------------------------------
+
+  using std::min;
+  using std::max;
+
+  template<typename T>
+  __forceinline T clamp(const T& value, const T& minValue, const T& maxValue)
+  {
+    return min(max(value, minValue), maxValue);
+  }
+
+  void* alignedMalloc(size_t size, size_t alignment);
+  void alignedFree(void* ptr);
+
+  template<typename T>
+  inline std::string toString(const T& a)
+  {
+    std::stringstream sm;
+    sm << a;
+    return sm.str();
+  }
+
+#if defined(__APPLE__)
+  template<typename T>
+  bool getSysctl(const char* name, T& value)
+  {
+    int64_t result = 0;
+    size_t size = sizeof(result);
+
+    if (sysctlbyname(name, &result, &size, nullptr, 0) != 0)
+      return false;
+
+    value = T(result);
+    return true;
+  }
+#endif
+
+  // ----------------------------------------------------------------------------
+  // System information
+  // ----------------------------------------------------------------------------
+
+  std::string getPlatformName();
+  std::string getCompilerName();
+  std::string getBuildName();
+
+} // namespace oidn
diff --git a/thirdparty/oidn/common/ref.h b/thirdparty/oidn/common/ref.h
new file mode 100644
index 0000000000..de44603af2
--- /dev/null
+++ b/thirdparty/oidn/common/ref.h
@@ -0,0 +1,163 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "platform.h"
+
+namespace oidn {
+
+  class RefCount
+  {
+  private:
+    std::atomic<size_t> count;
+
+  public:
+    __forceinline RefCount(int count = 0) noexcept : count(count) {}
+
+    __forceinline size_t incRef() noexcept
+    {
+      return count.fetch_add(1) + 1;
+    }
+
+    __forceinline size_t decRef()
+    {
+      const size_t newCount = decRefKeep();
+      if (newCount == 0)
+        destroy();
+      return newCount;
+    }
+
+    __forceinline size_t decRefKeep() noexcept
+    {
+      return count.fetch_add(-1) - 1;
+    }
+
+    __forceinline void destroy()
+    {
+      delete this;
+    }
+
+  protected:
+    // Disable copying
+    RefCount(const RefCount&) = delete;
+    RefCount& operator =(const RefCount&) = delete;
+
+    virtual ~RefCount() noexcept = default;
+  };
+
+  template<typename T>
+  class Ref
+  {
+  private:
+    T* ptr;
+
+  public:
+    __forceinline Ref() noexcept : ptr(nullptr) {}
+    __forceinline Ref(std::nullptr_t) noexcept : ptr(nullptr) {}
+    __forceinline Ref(const Ref& other) noexcept : ptr(other.ptr) { if (ptr) ptr->incRef(); }
+    __forceinline Ref(Ref&& other) noexcept : ptr(other.ptr) { other.ptr = nullptr; }
+    __forceinline Ref(T* ptr) noexcept : ptr(ptr) { if (ptr) ptr->incRef(); }
+
+    template<typename Y>
+    __forceinline Ref(const Ref<Y>& other) noexcept : ptr(other.get()) { if (ptr) ptr->incRef(); }
+
+    template<typename Y>
+    __forceinline explicit Ref(Y* ptr) noexcept : ptr(ptr) { if (ptr) ptr->incRef(); }
+
+    __forceinline ~Ref() { if (ptr) ptr->decRef(); }
+
+    __forceinline Ref& operator =(const Ref& other)
+    {
+      if (other.ptr)
+        other.ptr->incRef();
+      if (ptr)
+        ptr->decRef();
+      ptr = other.ptr;
+      return *this;
+    }
+
+    __forceinline Ref& operator =(Ref&& other)
+    {
+      if (ptr)
+        ptr->decRef();
+      ptr = other.ptr;
+      other.ptr = nullptr;
+      return *this;
+    }
+
+    __forceinline Ref& operator =(T* other)
+    {
+      if (other)
+        other->incRef();
+      if (ptr)
+        ptr->decRef();
+      ptr = other;
+      return *this;
+    }
+
+    __forceinline Ref& operator =(std::nullptr_t)
+    {
+      if (ptr)
+        ptr->decRef();
+      ptr = nullptr;
+      return *this;
+    }
+
+    __forceinline operator bool() const noexcept { return ptr != nullptr; }
+
+    __forceinline T& operator  *() const noexcept { return *ptr; }
+    __forceinline T* operator ->() const noexcept { return  ptr; }
+
+    __forceinline T* get() const noexcept { return ptr; }
+
+    __forceinline T* detach() noexcept
+    {
+      T* res = ptr;
+      ptr = nullptr;
+      return res;
+    }
+  };
+
+  template<typename T> __forceinline bool operator < (const Ref<T>& a, const Ref<T>& b) noexcept { return a.ptr   <  b.ptr;   }
+
+  template<typename T> __forceinline bool operator ==(const Ref<T>& a, std::nullptr_t)  noexcept { return a.ptr   == nullptr; }
+  template<typename T> __forceinline bool operator ==(std::nullptr_t,  const Ref<T>& b) noexcept { return nullptr == b.ptr;   }
+  template<typename T> __forceinline bool operator ==(const Ref<T>& a, const Ref<T>& b) noexcept { return a.ptr   == b.ptr;   }
+
+  template<typename T> __forceinline bool operator !=(const Ref<T>& a, std::nullptr_t)  noexcept { return a.ptr   != nullptr; }
+  template<typename T> __forceinline bool operator !=(std::nullptr_t,  const Ref<T>& b) noexcept { return nullptr != b.ptr;   }
+  template<typename T> __forceinline bool operator !=(const Ref<T>& a, const Ref<T>& b) noexcept { return a.ptr   != b.ptr;   }
+
+  template<typename T, typename... Args>
+  __forceinline Ref<T> makeRef(Args&&... args)
+  {
+    return Ref<T>(new T(std::forward<Args>(args)...));
+  }
+
+  template<typename T, typename Y>
+  __forceinline Ref<Y> staticRefCast(const Ref<T>& a)
+  {
+    return Ref<Y>(static_cast<Y*>(a.get()));
+  }
+
+  template<typename T, typename Y>
+  __forceinline Ref<Y> dynamicRefCast(const Ref<T>& a)
+  {
+    return Ref<Y>(dynamic_cast<Y*>(a.get()));
+  }
+
+} // namespace oidn
diff --git a/thirdparty/oidn/common/tensor.cpp b/thirdparty/oidn/common/tensor.cpp
new file mode 100644
index 0000000000..0249f2e141
--- /dev/null
+++ b/thirdparty/oidn/common/tensor.cpp
@@ -0,0 +1,83 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#include "exception.h"
+#include "tensor.h"
+
+namespace oidn {
+
+  std::map<std::string, Tensor> parseTensors(void* buffer)
+  {
+    char* input = (char*)buffer;
+
+    // Parse the magic value
+    const int magic = *(unsigned short*)input;
+    if (magic != 0x41D7)
+      throw Exception(Error::InvalidOperation, "invalid tensor archive");
+    input += sizeof(unsigned short);
+
+    // Parse the version
+    const int majorVersion = *(unsigned char*)input++;
+    const int minorVersion = *(unsigned char*)input++;
+    UNUSED(minorVersion);
+    if (majorVersion > 1)
+      throw Exception(Error::InvalidOperation, "unsupported tensor archive version");
+
+    // Parse the number of tensors
+    const int numTensors = *(int*)input;
+    input += sizeof(int);
+
+    // Parse the tensors
+    std::map<std::string, Tensor> tensorMap;
+    for (int i = 0; i < numTensors; ++i)
+    {
+      Tensor tensor;
+
+      // Parse the name
+      const int nameLen = *(unsigned char*)input++;
+      std::string name(input, nameLen);
+      input += nameLen;
+
+      // Parse the number of dimensions
+      const int ndims = *(unsigned char*)input++;
+
+      // Parse the shape of the tensor
+      tensor.dims.resize(ndims);
+      for (int i = 0; i < ndims; ++i)
+        tensor.dims[i] = ((int*)input)[i];
+      input += ndims * sizeof(int);
+
+      // Parse the format of the tensor
+      tensor.format = std::string(input, input + ndims);
+      input += ndims;
+
+      // Parse the data type of the tensor
+      const char type = *(unsigned char*)input++;
+      if (type != 'f') // only float32 is supported
+        throw Exception(Error::InvalidOperation, "unsupported tensor data type");
+
+      // Skip the data
+      tensor.data = (float*)input;
+      input += tensor.size() * sizeof(float);
+
+      // Add the tensor to the map
+      tensorMap.emplace(name, std::move(tensor));
+    }
+
+    return tensorMap;
+  }
+
+} // namespace oidn
diff --git a/thirdparty/oidn/common/tensor.h b/thirdparty/oidn/common/tensor.h
new file mode 100644
index 0000000000..48e7d1123d
--- /dev/null
+++ b/thirdparty/oidn/common/tensor.h
@@ -0,0 +1,66 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "platform.h"
+#include <vector>
+#include <map>
+
+namespace oidn {
+
+  template<typename T>
+  using shared_vector = std::shared_ptr<std::vector<T>>;
+
+  // Generic tensor
+  struct Tensor
+  {
+    float* data;
+    std::vector<int64_t> dims;
+    std::string format;
+    shared_vector<char> buffer; // optional, only for reference counting
+
+    __forceinline Tensor() : data(nullptr) {}
+
+    __forceinline Tensor(const std::vector<int64_t>& dims, const std::string& format)
+      : dims(dims),
+        format(format)
+    {
+      buffer = std::make_shared<std::vector<char>>(size() * sizeof(float));
+      data = (float*)buffer->data();
+    }
+
+    __forceinline operator bool() const { return data != nullptr; }
+
+    __forceinline int ndims() const { return (int)dims.size(); }
+
+    // Returns the number of values
+    __forceinline size_t size() const
+    {
+      size_t size = 1;
+      for (int i = 0; i < ndims(); ++i)
+        size *= dims[i];
+      return size;
+    }
+
+    __forceinline float& operator [](size_t i) { return data[i]; }
+    __forceinline const float& operator [](size_t i) const { return data[i]; }
+  };
+
+  // Parses tensors from a buffer
+  std::map<std::string, Tensor> parseTensors(void* buffer);
+
+} // namespace oidn
diff --git a/thirdparty/oidn/common/thread.cpp b/thirdparty/oidn/common/thread.cpp
new file mode 100644
index 0000000000..48c489c57b
--- /dev/null
+++ b/thirdparty/oidn/common/thread.cpp
@@ -0,0 +1,297 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#if defined(_MSC_VER)
+  #pragma warning (disable : 4146) // unary minus operator applied to unsigned type, result still unsigned
+#endif
+
+#if defined(__APPLE__)
+  #include <mach/thread_act.h>
+  #include <mach/mach_init.h>
+#endif
+
+#include "thread.h"
+#include <fstream>
+
+namespace oidn {
+
+#if defined(_WIN32)
+
+  // --------------------------------------------------------------------------
+  // ThreadAffinity - Windows
+  // --------------------------------------------------------------------------
+
+  ThreadAffinity::ThreadAffinity(int numThreadsPerCore, int verbose)
+    : Verbose(verbose)
+  {
+    HMODULE hLib = GetModuleHandle(TEXT("kernel32"));
+    pGetLogicalProcessorInformationEx = (GetLogicalProcessorInformationExFunc)GetProcAddress(hLib, "GetLogicalProcessorInformationEx");
+    pSetThreadGroupAffinity = (SetThreadGroupAffinityFunc)GetProcAddress(hLib, "SetThreadGroupAffinity");
+
+    if (pGetLogicalProcessorInformationEx && pSetThreadGroupAffinity)
+    {
+      // Get logical processor information
+      PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX buffer = nullptr;
+      DWORD bufferSize = 0;
+
+      // First call the function with an empty buffer to get the required buffer size
+      BOOL result = pGetLogicalProcessorInformationEx(RelationProcessorCore, buffer, &bufferSize);
+      if (result || GetLastError() != ERROR_INSUFFICIENT_BUFFER)
+      {
+        OIDN_WARNING("GetLogicalProcessorInformationEx failed");
+        return;
+      }
+
+      // Allocate the buffer
+      buffer = (PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX)malloc(bufferSize);
+      if (!buffer)
+      {
+        OIDN_WARNING("SYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX allocation failed");
+        return;
+      }
+
+      // Call again the function but now with the properly sized buffer
+      result = pGetLogicalProcessorInformationEx(RelationProcessorCore, buffer, &bufferSize);
+      if (!result)
+      {
+        OIDN_WARNING("GetLogicalProcessorInformationEx failed");
+        free(buffer);
+        return;
+      }
+
+      // Iterate over the logical processor information structures
+      // There should be one structure for each physical core
+      char* ptr = (char*)buffer;
+      while (ptr < (char*)buffer + bufferSize)
+      {
+        PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX item = (PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX)ptr;
+        if (item->Relationship == RelationProcessorCore && item->Processor.GroupCount > 0)
+        {
+          // Iterate over the groups
+          int numThreads = 0;
+          for (int group = 0; (group < item->Processor.GroupCount) && (numThreads < numThreadsPerCore); ++group)
+          {
+            GROUP_AFFINITY coreAffinity = item->Processor.GroupMask[group];
+            while ((coreAffinity.Mask != 0) && (numThreads < numThreadsPerCore))
+            {
+              // Extract the next set bit/thread from the mask
+              GROUP_AFFINITY threadAffinity = coreAffinity;
+              threadAffinity.Mask = threadAffinity.Mask & -threadAffinity.Mask;
+
+              // Push the affinity for this thread
+              affinities.push_back(threadAffinity);
+              oldAffinities.push_back(threadAffinity);
+              numThreads++;
+
+              // Remove this bit/thread from the mask
+              coreAffinity.Mask ^= threadAffinity.Mask;
+            }
+          }
+        }
+
+        // Next structure
+        ptr += item->Size;
+      }
+
+      // Free the buffer
+      free(buffer);
+    }
+  }
+
+  void ThreadAffinity::set(int threadIndex)
+  {
+    if (threadIndex >= (int)affinities.size())
+      return;
+
+    // Save the current affinity and set the new one
+    const HANDLE thread = GetCurrentThread();
+    if (!pSetThreadGroupAffinity(thread, &affinities[threadIndex], &oldAffinities[threadIndex]))
+      OIDN_WARNING("SetThreadGroupAffinity failed");
+  }
+
+  void ThreadAffinity::restore(int threadIndex)
+  {
+    if (threadIndex >= (int)affinities.size())
+      return;
+
+    // Restore the original affinity
+    const HANDLE thread = GetCurrentThread();
+    if (!pSetThreadGroupAffinity(thread, &oldAffinities[threadIndex], nullptr))
+      OIDN_WARNING("SetThreadGroupAffinity failed");
+  }
+
+#elif defined(__linux__)
+
+  // --------------------------------------------------------------------------
+  // ThreadAffinity - Linux
+  // --------------------------------------------------------------------------
+
+  ThreadAffinity::ThreadAffinity(int numThreadsPerCore, int verbose)
+    : Verbose(verbose)
+  {
+    std::vector<int> threadIds;
+
+    // Parse the thread/CPU topology
+    for (int cpuId = 0; ; cpuId++)
+    {
+      std::fstream fs;
+      std::string cpu = std::string("/sys/devices/system/cpu/cpu") + std::to_string(cpuId) + std::string("/topology/thread_siblings_list");
+      fs.open(cpu.c_str(), std::fstream::in);
+      if (fs.fail()) break;
+
+      int i;
+      int j = 0;
+      while ((j < numThreadsPerCore) && (fs >> i))
+      {
+        if (std::none_of(threadIds.begin(), threadIds.end(), [&](int id) { return id == i; }))
+          threadIds.push_back(i);
+
+        if (fs.peek() == ',')
+          fs.ignore();
+        j++;
+      }
+
+      fs.close();
+    }
+
+  #if 0
+    for (size_t i = 0; i < thread_ids.size(); ++i)
+      std::cout << "thread " << i << " -> " << thread_ids[i] << std::endl;
+  #endif
+
+    // Create the affinity structures
+    affinities.resize(threadIds.size());
+    oldAffinities.resize(threadIds.size());
+
+    for (size_t i = 0; i < threadIds.size(); ++i)
+    {
+      cpu_set_t affinity;
+      CPU_ZERO(&affinity);
+      CPU_SET(threadIds[i], &affinity);
+
+      affinities[i] = affinity;
+      oldAffinities[i] = affinity;
+    }
+  }
+
+  void ThreadAffinity::set(int threadIndex)
+  {
+    if (threadIndex >= (int)affinities.size())
+      return;
+
+    const pthread_t thread = pthread_self();
+
+    // Save the current affinity
+    if (pthread_getaffinity_np(thread, sizeof(cpu_set_t), &oldAffinities[threadIndex]) != 0)
+    {
+      OIDN_WARNING("pthread_getaffinity_np failed");
+      oldAffinities[threadIndex] = affinities[threadIndex];
+      return;
+    }
+
+    // Set the new affinity
+    if (pthread_setaffinity_np(thread, sizeof(cpu_set_t), &affinities[threadIndex]) != 0)
+      OIDN_WARNING("pthread_setaffinity_np failed");
+  }
+
+  void ThreadAffinity::restore(int threadIndex)
+  {
+    if (threadIndex >= (int)affinities.size())
+      return;
+
+    const pthread_t thread = pthread_self();
+
+    // Restore the original affinity
+    if (pthread_setaffinity_np(thread, sizeof(cpu_set_t), &oldAffinities[threadIndex]) != 0)
+      OIDN_WARNING("pthread_setaffinity_np failed");
+  }
+
+#elif defined(__APPLE__)
+
+  // --------------------------------------------------------------------------
+  // ThreadAffinity - macOS
+  // --------------------------------------------------------------------------
+
+  ThreadAffinity::ThreadAffinity(int numThreadsPerCore, int verbose)
+    : Verbose(verbose)
+  {
+    // Query the thread/CPU topology
+    int numPhysicalCpus;
+    int numLogicalCpus;
+
+    if (!getSysctl("hw.physicalcpu", numPhysicalCpus) || !getSysctl("hw.logicalcpu", numLogicalCpus))
+    {
+      OIDN_WARNING("sysctlbyname failed");
+      return;
+    }
+
+    if ((numLogicalCpus % numPhysicalCpus != 0) && (numThreadsPerCore > 1))
+      return; // this shouldn't happen
+    const int maxThreadsPerCore = numLogicalCpus / numPhysicalCpus;
+
+    // Create the affinity structures
+    // macOS doesn't support binding a thread to a specific core, but we can at least group threads which
+    // should be on the same core together
+    for (int core = 1; core <= numPhysicalCpus; ++core) // tags start from 1!
+    {
+      thread_affinity_policy affinity;
+      affinity.affinity_tag = core;
+
+      for (int thread = 0; thread < min(numThreadsPerCore, maxThreadsPerCore); ++thread)
+      {
+        affinities.push_back(affinity);
+        oldAffinities.push_back(affinity);
+      }
+    }
+  }
+
+  void ThreadAffinity::set(int threadIndex)
+  {
+    if (threadIndex >= (int)affinities.size())
+      return;
+
+    const auto thread = mach_thread_self();
+
+    // Save the current affinity
+    mach_msg_type_number_t policyCount = THREAD_AFFINITY_POLICY_COUNT;
+    boolean_t getDefault = FALSE;
+    if (thread_policy_get(thread, THREAD_AFFINITY_POLICY, (thread_policy_t)&oldAffinities[threadIndex], &policyCount, &getDefault) != KERN_SUCCESS)
+    {
+      OIDN_WARNING("thread_policy_get failed");
+      oldAffinities[threadIndex] = affinities[threadIndex];
+      return;
+    }
+
+    // Set the new affinity
+    if (thread_policy_set(thread, THREAD_AFFINITY_POLICY, (thread_policy_t)&affinities[threadIndex], THREAD_AFFINITY_POLICY_COUNT) != KERN_SUCCESS)
+      OIDN_WARNING("thread_policy_set failed");
+  }
+
+  void ThreadAffinity::restore(int threadIndex)
+  {
+    if (threadIndex >= (int)affinities.size())
+      return;
+
+    const auto thread = mach_thread_self();
+
+    // Restore the original affinity
+    if (thread_policy_set(thread, THREAD_AFFINITY_POLICY, (thread_policy_t)&oldAffinities[threadIndex], THREAD_AFFINITY_POLICY_COUNT) != KERN_SUCCESS)
+      OIDN_WARNING("thread_policy_set failed");
+  }
+
+#endif
+
+} // namespace oidn
diff --git a/thirdparty/oidn/common/thread.h b/thirdparty/oidn/common/thread.h
new file mode 100644
index 0000000000..2c731367da
--- /dev/null
+++ b/thirdparty/oidn/common/thread.h
@@ -0,0 +1,202 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "platform.h"
+
+#if !defined(_WIN32)
+  #include <pthread.h>
+  #include <sched.h>
+  #if defined(__APPLE__)
+    #include <mach/thread_policy.h>
+  #endif
+#endif
+
+#include <vector>
+#include <mutex>
+
+namespace oidn {
+
+  // --------------------------------------------------------------------------
+  // ThreadLocal
+  // --------------------------------------------------------------------------
+
+  // Wrapper which makes any variable thread-local
+  template<typename T>
+  class ThreadLocal : public Verbose
+  {
+  private:
+  #if defined(_WIN32)
+    DWORD key;
+  #else
+    pthread_key_t key;
+  #endif
+
+    std::vector<T*> instances;
+    std::mutex mutex;
+
+  public:
+    ThreadLocal(int verbose = 0)
+      : Verbose(verbose)
+    {
+    #if defined(_WIN32)
+      key = TlsAlloc();
+      if (key == TLS_OUT_OF_INDEXES)
+        OIDN_FATAL("TlsAlloc failed");
+    #else
+      if (pthread_key_create(&key, nullptr) != 0)
+        OIDN_FATAL("pthread_key_create failed");
+    #endif
+    }
+
+    ~ThreadLocal()
+    {
+      std::lock_guard<std::mutex> lock(mutex);
+      for (T* ptr : instances)
+        delete ptr;
+
+    #if defined(_WIN32)
+      if (!TlsFree(key))
+        OIDN_WARNING("TlsFree failed");
+    #else
+      if (pthread_key_delete(key) != 0)
+        OIDN_WARNING("pthread_key_delete failed");
+    #endif
+    }
+
+    T& get()
+    {
+    #if defined(_WIN32)
+      T* ptr = (T*)TlsGetValue(key);
+    #else
+      T* ptr = (T*)pthread_getspecific(key);
+    #endif
+
+      if (ptr)
+        return *ptr;
+
+      ptr = new T;
+      std::lock_guard<std::mutex> lock(mutex);
+      instances.push_back(ptr);
+
+    #if defined(_WIN32)
+      if (!TlsSetValue(key, ptr))
+        OIDN_FATAL("TlsSetValue failed");
+    #else
+      if (pthread_setspecific(key, ptr) != 0)
+        OIDN_FATAL("pthread_setspecific failed");
+    #endif
+
+      return *ptr;
+    }
+  };
+
+#if defined(_WIN32)
+
+  // --------------------------------------------------------------------------
+  // ThreadAffinity - Windows
+  // --------------------------------------------------------------------------
+
+  class ThreadAffinity : public Verbose
+  {
+  private:
+    typedef BOOL (WINAPI *GetLogicalProcessorInformationExFunc)(LOGICAL_PROCESSOR_RELATIONSHIP,
+                                                                PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX,
+                                                                PDWORD);
+
+    typedef BOOL (WINAPI *SetThreadGroupAffinityFunc)(HANDLE,
+                                                      CONST GROUP_AFFINITY*,
+                                                      PGROUP_AFFINITY);
+
+    GetLogicalProcessorInformationExFunc pGetLogicalProcessorInformationEx = nullptr;
+    SetThreadGroupAffinityFunc pSetThreadGroupAffinity = nullptr;
+
+    std::vector<GROUP_AFFINITY> affinities;    // thread affinities
+    std::vector<GROUP_AFFINITY> oldAffinities; // original thread affinities
+
+  public:
+    ThreadAffinity(int numThreadsPerCore = INT_MAX, int verbose = 0);
+
+    int getNumThreads() const
+    {
+      return (int)affinities.size();
+    }
+
+    // Sets the affinity (0..numThreads-1) of the thread after saving the current affinity
+    void set(int threadIndex);
+
+    // Restores the affinity of the thread
+    void restore(int threadIndex);
+  };
+
+#elif defined(__linux__)
+
+  // --------------------------------------------------------------------------
+  // ThreadAffinity - Linux
+  // --------------------------------------------------------------------------
+
+  class ThreadAffinity : public Verbose
+  {
+  private:
+    std::vector<cpu_set_t> affinities;    // thread affinities
+    std::vector<cpu_set_t> oldAffinities; // original thread affinities
+
+  public:
+    ThreadAffinity(int numThreadsPerCore = INT_MAX, int verbose = 0);
+
+    int getNumThreads() const
+    {
+      return (int)affinities.size();
+    }
+
+    // Sets the affinity (0..numThreads-1) of the thread after saving the current affinity
+    void set(int threadIndex);
+
+    // Restores the affinity of the thread
+    void restore(int threadIndex);
+  };
+
+#elif defined(__APPLE__)
+
+  // --------------------------------------------------------------------------
+  // ThreadAffinity - macOS
+  // --------------------------------------------------------------------------
+
+  class ThreadAffinity : public Verbose
+  {
+  private:
+    std::vector<thread_affinity_policy> affinities;    // thread affinities
+    std::vector<thread_affinity_policy> oldAffinities; // original thread affinities
+
+  public:
+    ThreadAffinity(int numThreadsPerCore = INT_MAX, int verbose = 0);
+
+    int getNumThreads() const
+    {
+      return (int)affinities.size();
+    }
+
+    // Sets the affinity (0..numThreads-1) of the thread after saving the current affinity
+    void set(int threadIndex);
+
+    // Restores the affinity of the thread
+    void restore(int threadIndex);
+  };
+
+#endif
+
+} // namespace oidn
diff --git a/thirdparty/oidn/common/timer.h b/thirdparty/oidn/common/timer.h
new file mode 100644
index 0000000000..62aaaa1c33
--- /dev/null
+++ b/thirdparty/oidn/common/timer.h
@@ -0,0 +1,49 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "platform.h"
+#include <chrono>
+
+namespace oidn {
+
+  class Timer
+  {
+  private:
+    using clock = std::chrono::high_resolution_clock;
+
+    std::chrono::time_point<clock> start;
+
+  public:
+    Timer()
+    {
+      reset();
+    }
+
+    void reset()
+    {
+      start = clock::now();
+    }
+
+    double query() const
+    {
+      auto end = clock::now();
+      return std::chrono::duration_cast<std::chrono::duration<double>>(end - start).count();
+    }
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/api.cpp b/thirdparty/oidn/core/api.cpp
new file mode 100644
index 0000000000..7353fe4e25
--- /dev/null
+++ b/thirdparty/oidn/core/api.cpp
@@ -0,0 +1,408 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#ifdef _WIN32
+#  define OIDN_API extern "C" __declspec(dllexport)
+#else
+#  define OIDN_API extern "C" __attribute__ ((visibility ("default")))
+#endif
+
+// Locks the device that owns the specified object
+// Use *only* inside OIDN_TRY/CATCH!
+#define OIDN_LOCK(obj) \
+  std::lock_guard<std::mutex> lock(obj->getDevice()->getMutex());
+
+// Try/catch for converting exceptions to errors
+#define OIDN_TRY \
+  try {
+
+#define OIDN_CATCH(obj) \
+  } catch (Exception& e) {                                                                          \
+    Device::setError(obj ? obj->getDevice() : nullptr, e.code(), e.what());                         \
+  } catch (std::bad_alloc&) {                                                                       \
+    Device::setError(obj ? obj->getDevice() : nullptr, Error::OutOfMemory, "out of memory");        \
+  } catch (mkldnn::error& e) {                                                                      \
+    if (e.status == mkldnn_out_of_memory)                                                           \
+      Device::setError(obj ? obj->getDevice() : nullptr, Error::OutOfMemory, "out of memory");      \
+    else                                                                                            \
+      Device::setError(obj ? obj->getDevice() : nullptr, Error::Unknown, e.message);                \
+  } catch (std::exception& e) {                                                                     \
+    Device::setError(obj ? obj->getDevice() : nullptr, Error::Unknown, e.what());                   \
+  } catch (...) {                                                                                   \
+    Device::setError(obj ? obj->getDevice() : nullptr, Error::Unknown, "unknown exception caught"); \
+  }
+
+#include "device.h"
+#include "filter.h"
+#include <mutex>
+
+namespace oidn {
+
+  namespace
+  {
+    __forceinline void checkHandle(void* handle)
+    {
+      if (handle == nullptr)
+        throw Exception(Error::InvalidArgument, "invalid handle");
+    }
+
+    template<typename T>
+    __forceinline void retainObject(T* obj)
+    {
+      if (obj)
+      {
+        obj->incRef();
+      }
+      else
+      {
+        OIDN_TRY
+          checkHandle(obj);
+        OIDN_CATCH(obj)
+      }
+    }
+
+    template<typename T>
+    __forceinline void releaseObject(T* obj)
+    {
+      if (obj == nullptr || obj->decRefKeep() == 0)
+      {
+        OIDN_TRY
+          checkHandle(obj);
+          OIDN_LOCK(obj);
+          obj->destroy();
+        OIDN_CATCH(obj)
+      }
+    }
+
+    template<>
+    __forceinline void releaseObject(Device* obj)
+    {
+      if (obj == nullptr || obj->decRefKeep() == 0)
+      {
+        OIDN_TRY
+          checkHandle(obj);
+          // Do NOT lock the device because it owns the mutex
+          obj->destroy();
+        OIDN_CATCH(obj)
+      }
+    }
+  }
+
+  OIDN_API OIDNDevice oidnNewDevice(OIDNDeviceType type)
+  {
+    Ref<Device> device = nullptr;
+    OIDN_TRY
+      if (type == OIDN_DEVICE_TYPE_CPU || type == OIDN_DEVICE_TYPE_DEFAULT)
+        device = makeRef<Device>();
+      else
+        throw Exception(Error::InvalidArgument, "invalid device type");
+    OIDN_CATCH(device)
+    return (OIDNDevice)device.detach();
+  }
+
+  OIDN_API void oidnRetainDevice(OIDNDevice hDevice)
+  {
+    Device* device = (Device*)hDevice;
+    retainObject(device);
+  }
+
+  OIDN_API void oidnReleaseDevice(OIDNDevice hDevice)
+  {
+    Device* device = (Device*)hDevice;
+    releaseObject(device);
+  }
+
+  OIDN_API void oidnSetDevice1b(OIDNDevice hDevice, const char* name, bool value)
+  {
+    Device* device = (Device*)hDevice;
+    OIDN_TRY
+      checkHandle(hDevice);
+      OIDN_LOCK(device);
+      device->set1i(name, value);
+    OIDN_CATCH(device)
+  }
+
+  OIDN_API void oidnSetDevice1i(OIDNDevice hDevice, const char* name, int value)
+  {
+    Device* device = (Device*)hDevice;
+    OIDN_TRY
+      checkHandle(hDevice);
+      OIDN_LOCK(device);
+      device->set1i(name, value);
+    OIDN_CATCH(device)
+  }
+
+  OIDN_API bool oidnGetDevice1b(OIDNDevice hDevice, const char* name)
+  {
+    Device* device = (Device*)hDevice;
+    OIDN_TRY
+      checkHandle(hDevice);
+      OIDN_LOCK(device);
+      return device->get1i(name);
+    OIDN_CATCH(device)
+    return false;
+  }
+
+  OIDN_API int oidnGetDevice1i(OIDNDevice hDevice, const char* name)
+  {
+    Device* device = (Device*)hDevice;
+    OIDN_TRY
+      checkHandle(hDevice);
+      OIDN_LOCK(device);
+      return device->get1i(name);
+    OIDN_CATCH(device)
+    return 0;
+  }
+
+  OIDN_API void oidnSetDeviceErrorFunction(OIDNDevice hDevice, OIDNErrorFunction func, void* userPtr)
+  {
+    Device* device = (Device*)hDevice;
+    OIDN_TRY
+      checkHandle(hDevice);
+      OIDN_LOCK(device);
+      device->setErrorFunction((ErrorFunction)func, userPtr);
+    OIDN_CATCH(device)
+  }
+
+  OIDN_API OIDNError oidnGetDeviceError(OIDNDevice hDevice, const char** outMessage)
+  {
+    Device* device = (Device*)hDevice;
+    OIDN_TRY
+      return (OIDNError)Device::getError(device, outMessage);
+    OIDN_CATCH(device)
+    if (outMessage) *outMessage = "";
+    return OIDN_ERROR_UNKNOWN;
+  }
+
+  OIDN_API void oidnCommitDevice(OIDNDevice hDevice)
+  {
+    Device* device = (Device*)hDevice;
+    OIDN_TRY
+      checkHandle(hDevice);
+      OIDN_LOCK(device);
+      device->commit();
+    OIDN_CATCH(device)
+  }
+
+  OIDN_API OIDNBuffer oidnNewBuffer(OIDNDevice hDevice, size_t byteSize)
+  {
+    Device* device = (Device*)hDevice;
+    OIDN_TRY
+      checkHandle(hDevice);
+      OIDN_LOCK(device);
+      Ref<Buffer> buffer = device->newBuffer(byteSize);
+      return (OIDNBuffer)buffer.detach();
+    OIDN_CATCH(device)
+    return nullptr;
+  }
+
+  OIDN_API OIDNBuffer oidnNewSharedBuffer(OIDNDevice hDevice, void* ptr, size_t byteSize)
+  {
+    Device* device = (Device*)hDevice;
+    OIDN_TRY
+      checkHandle(hDevice);
+      OIDN_LOCK(device);
+      Ref<Buffer> buffer = device->newBuffer(ptr, byteSize);
+      return (OIDNBuffer)buffer.detach();
+    OIDN_CATCH(device)
+    return nullptr;
+  }
+
+  OIDN_API void oidnRetainBuffer(OIDNBuffer hBuffer)
+  {
+    Buffer* buffer = (Buffer*)hBuffer;
+    retainObject(buffer);
+  }
+
+  OIDN_API void oidnReleaseBuffer(OIDNBuffer hBuffer)
+  {
+    Buffer* buffer = (Buffer*)hBuffer;
+    releaseObject(buffer);
+  }
+
+  OIDN_API void* oidnMapBuffer(OIDNBuffer hBuffer, OIDNAccess access, size_t byteOffset, size_t byteSize)
+  {
+    Buffer* buffer = (Buffer*)hBuffer;
+    OIDN_TRY
+      checkHandle(hBuffer);
+      OIDN_LOCK(buffer);
+      return buffer->map(byteOffset, byteSize);
+    OIDN_CATCH(buffer)
+    return nullptr;
+  }
+
+  OIDN_API void oidnUnmapBuffer(OIDNBuffer hBuffer, void* mappedPtr)
+  {
+    Buffer* buffer = (Buffer*)hBuffer;
+    OIDN_TRY
+      checkHandle(hBuffer);
+      OIDN_LOCK(buffer);
+      return buffer->unmap(mappedPtr);
+    OIDN_CATCH(buffer)
+  }
+
+  OIDN_API OIDNFilter oidnNewFilter(OIDNDevice hDevice, const char* type)
+  {
+    Device* device = (Device*)hDevice;
+    OIDN_TRY
+      checkHandle(hDevice);
+      OIDN_LOCK(device);
+      Ref<Filter> filter = device->newFilter(type);
+      return (OIDNFilter)filter.detach();
+    OIDN_CATCH(device)
+    return nullptr;
+  }
+
+  OIDN_API void oidnRetainFilter(OIDNFilter hFilter)
+  {
+    Filter* filter = (Filter*)hFilter;
+    retainObject(filter);
+  }
+
+  OIDN_API void oidnReleaseFilter(OIDNFilter hFilter)
+  {
+    Filter* filter = (Filter*)hFilter;
+    releaseObject(filter);
+  }
+
+  OIDN_API void oidnSetFilterImage(OIDNFilter hFilter, const char* name,
+                                   OIDNBuffer hBuffer, OIDNFormat format,
+                                   size_t width, size_t height,
+                                   size_t byteOffset,
+                                   size_t bytePixelStride, size_t byteRowStride)
+  {
+    Filter* filter = (Filter*)hFilter;
+    OIDN_TRY
+      checkHandle(hFilter);
+      checkHandle(hBuffer);
+      OIDN_LOCK(filter);
+      Ref<Buffer> buffer = (Buffer*)hBuffer;
+      if (buffer->getDevice() != filter->getDevice())
+        throw Exception(Error::InvalidArgument, "the specified objects are bound to different devices");
+      Image data(buffer, (Format)format, (int)width, (int)height, byteOffset, bytePixelStride, byteRowStride);
+      filter->setImage(name, data);
+    OIDN_CATCH(filter)
+  }
+
+  OIDN_API void oidnSetSharedFilterImage(OIDNFilter hFilter, const char* name,
+                                         void* ptr, OIDNFormat format,
+                                         size_t width, size_t height,
+                                         size_t byteOffset,
+                                         size_t bytePixelStride, size_t byteRowStride)
+  {
+    Filter* filter = (Filter*)hFilter;
+    OIDN_TRY
+      checkHandle(hFilter);
+      OIDN_LOCK(filter);
+      Image data(ptr, (Format)format, (int)width, (int)height, byteOffset, bytePixelStride, byteRowStride);
+      filter->setImage(name, data);
+    OIDN_CATCH(filter)
+  }
+
+  OIDN_API void oidnSetFilter1b(OIDNFilter hFilter, const char* name, bool value)
+  {
+    Filter* filter = (Filter*)hFilter;
+    OIDN_TRY
+      checkHandle(hFilter);
+      OIDN_LOCK(filter);
+      filter->set1i(name, int(value));
+    OIDN_CATCH(filter)
+  }
+
+  OIDN_API bool oidnGetFilter1b(OIDNFilter hFilter, const char* name)
+  {
+    Filter* filter = (Filter*)hFilter;
+    OIDN_TRY
+      checkHandle(hFilter);
+      OIDN_LOCK(filter);
+      return filter->get1i(name);
+    OIDN_CATCH(filter)
+    return false;
+  }
+
+  OIDN_API void oidnSetFilter1i(OIDNFilter hFilter, const char* name, int value)
+  {
+    Filter* filter = (Filter*)hFilter;
+    OIDN_TRY
+      checkHandle(hFilter);
+      OIDN_LOCK(filter);
+      filter->set1i(name, value);
+    OIDN_CATCH(filter)
+  }
+
+  OIDN_API int oidnGetFilter1i(OIDNFilter hFilter, const char* name)
+  {
+    Filter* filter = (Filter*)hFilter;
+    OIDN_TRY
+      checkHandle(hFilter);
+      OIDN_LOCK(filter);
+      return filter->get1i(name);
+    OIDN_CATCH(filter)
+    return 0;
+  }
+
+  OIDN_API void oidnSetFilter1f(OIDNFilter hFilter, const char* name, float value)
+  {
+    Filter* filter = (Filter*)hFilter;
+    OIDN_TRY
+      checkHandle(hFilter);
+      OIDN_LOCK(filter);
+      filter->set1f(name, value);
+    OIDN_CATCH(filter)
+  }
+
+  OIDN_API float oidnGetFilter1f(OIDNFilter hFilter, const char* name)
+  {
+    Filter* filter = (Filter*)hFilter;
+    OIDN_TRY
+      checkHandle(hFilter);
+      OIDN_LOCK(filter);
+      return filter->get1f(name);
+    OIDN_CATCH(filter)
+    return 0;
+  }
+
+  OIDN_API void oidnSetFilterProgressMonitorFunction(OIDNFilter hFilter, OIDNProgressMonitorFunction func, void* userPtr)
+  {
+    Filter* filter = (Filter*)hFilter;
+    OIDN_TRY
+      checkHandle(hFilter);
+      OIDN_LOCK(filter);
+      filter->setProgressMonitorFunction(func, userPtr);
+    OIDN_CATCH(filter)
+  }
+
+  OIDN_API void oidnCommitFilter(OIDNFilter hFilter)
+  {
+    Filter* filter = (Filter*)hFilter;
+    OIDN_TRY
+      checkHandle(hFilter);
+      OIDN_LOCK(filter);
+      filter->commit();
+    OIDN_CATCH(filter)
+  }
+
+  OIDN_API void oidnExecuteFilter(OIDNFilter hFilter)
+  {
+    Filter* filter = (Filter*)hFilter;
+    OIDN_TRY
+      checkHandle(hFilter);
+      OIDN_LOCK(filter);
+      filter->execute();
+    OIDN_CATCH(filter)
+  }
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/autoencoder.cpp b/thirdparty/oidn/core/autoencoder.cpp
new file mode 100644
index 0000000000..8ae2421fa6
--- /dev/null
+++ b/thirdparty/oidn/core/autoencoder.cpp
@@ -0,0 +1,519 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#include "autoencoder.h"
+
+namespace oidn {
+
+  // --------------------------------------------------------------------------
+  // AutoencoderFilter
+  // --------------------------------------------------------------------------
+
+  AutoencoderFilter::AutoencoderFilter(const Ref<Device>& device)
+    : Filter(device)
+  {
+  }
+
+  void AutoencoderFilter::setImage(const std::string& name, const Image& data)
+  {
+    if (name == "color")
+      color = data;
+    else if (name == "albedo")
+      albedo = data;
+    else if (name == "normal")
+      normal = data;
+    else if (name == "output")
+      output = data;
+
+    dirty = true;
+  }
+
+  void AutoencoderFilter::set1i(const std::string& name, int value)
+  {
+    if (name == "hdr")
+      hdr = value;
+    else if (name == "srgb")
+      srgb = value;
+    else if (name == "maxMemoryMB")
+      maxMemoryMB = value;
+
+    dirty = true;
+  }
+
+  int AutoencoderFilter::get1i(const std::string& name)
+  {
+    if (name == "hdr")
+      return hdr;
+    else if (name == "srgb")
+      return srgb;
+    else if (name == "maxMemoryMB")
+      return maxMemoryMB;
+    else if (name == "alignment")
+      return alignment;
+    else if (name == "overlap")
+      return overlap;
+    else
+      throw Exception(Error::InvalidArgument, "invalid parameter");
+  }
+
+  void AutoencoderFilter::set1f(const std::string& name, float value)
+  {
+    if (name == "hdrScale")
+      hdrScale = value;
+
+    dirty = true;
+  }
+
+  float AutoencoderFilter::get1f(const std::string& name)
+  {
+    if (name == "hdrScale")
+      return hdrScale;
+    else
+      throw Exception(Error::InvalidArgument, "invalid parameter");
+  }
+
+  void AutoencoderFilter::commit()
+  {
+    if (!dirty)
+      return;
+
+    {
+      if (mayiuse(avx512_common))
+        net = buildNet<16>();
+      else
+        net = buildNet<8>();
+    }
+
+    dirty = false;
+  }
+
+  void AutoencoderFilter::execute()
+  {
+    if (dirty)
+      throw Exception(Error::InvalidOperation, "changes to the filter are not committed");
+
+    if (!net)
+      return;
+
+    {
+      Progress progress;
+      progress.func = progressFunc;
+      progress.userPtr = progressUserPtr;
+      progress.taskCount = tileCountH * tileCountW;
+
+      // Iterate over the tiles
+      int tileIndex = 0;
+
+      for (int i = 0; i < tileCountH; ++i)
+      {
+        const int h = i * (tileH - 2*overlap); // input tile position (including overlap)
+        const int overlapBeginH = i > 0            ? overlap : 0; // overlap on the top
+        const int overlapEndH   = i < tileCountH-1 ? overlap : 0; // overlap on the bottom
+        const int tileH1 = min(H - h, tileH); // input tile size (including overlap)
+        const int tileH2 = tileH1 - overlapBeginH - overlapEndH; // output tile size
+        const int alignOffsetH = tileH - roundUp(tileH1, alignment); // align to the bottom in the tile buffer
+
+        for (int j = 0; j < tileCountW; ++j)
+        {
+          const int w = j * (tileW - 2*overlap); // input tile position (including overlap)
+          const int overlapBeginW = j > 0            ? overlap : 0; // overlap on the left
+          const int overlapEndW   = j < tileCountW-1 ? overlap : 0; // overlap on the right
+          const int tileW1 = min(W - w, tileW); // input tile size (including overlap)
+          const int tileW2 = tileW1 - overlapBeginW - overlapEndW; // output tile size
+          const int alignOffsetW = tileW - roundUp(tileW1, alignment); // align to the right in the tile buffer
+
+          // Set the input tile
+          inputReorder->setTile(h, w,
+                                alignOffsetH, alignOffsetW,
+                                tileH1, tileW1);
+
+          // Set the output tile
+          outputReorder->setTile(alignOffsetH + overlapBeginH, alignOffsetW + overlapBeginW,
+                                 h + overlapBeginH, w + overlapBeginW,
+                                 tileH2, tileW2);
+
+          //printf("Tile: %d %d -> %d %d\n", w+overlapBeginW, h+overlapBeginH, w+overlapBeginW+tileW2, h+overlapBeginH+tileH2);
+
+          // Denoise the tile
+          net->execute(progress, tileIndex);
+
+          // Next tile
+          tileIndex++;
+        }
+      }
+    }
+  }
+
+  void AutoencoderFilter::computeTileSize()
+  {
+    const int minTileSize = 3*overlap;
+    const int estimatedBytesPerPixel = mayiuse(avx512_common) ? estimatedBytesPerPixel16 : estimatedBytesPerPixel8;
+    const int64_t maxTilePixels = (int64_t(maxMemoryMB)*1024*1024 - estimatedBytesBase) / estimatedBytesPerPixel;
+
+    tileCountH = 1;
+    tileCountW = 1;
+    tileH = roundUp(H, alignment);
+    tileW = roundUp(W, alignment);
+
+    // Divide the image into tiles until the tile size gets below the threshold
+    while (int64_t(tileH) * tileW > maxTilePixels)
+    {
+      if (tileH > minTileSize && tileH > tileW)
+      {
+        tileCountH++;
+        tileH = max(roundUp(ceilDiv(H - 2*overlap, tileCountH), alignment) + 2*overlap, minTileSize);
+      }
+      else if (tileW > minTileSize)
+      {
+        tileCountW++;
+        tileW = max(roundUp(ceilDiv(W - 2*overlap, tileCountW), alignment) + 2*overlap, minTileSize);
+      }
+      else
+        break;
+    }
+
+    // Compute the final number of tiles
+    tileCountH = (H > tileH) ? ceilDiv(H - 2*overlap, tileH - 2*overlap) : 1;
+    tileCountW = (W > tileW) ? ceilDiv(W - 2*overlap, tileW - 2*overlap) : 1;
+
+    if (device->isVerbose(2))
+    {
+      std::cout << "Tile size : " << tileW << "x" << tileH << std::endl;
+      std::cout << "Tile count: " << tileCountW << "x" << tileCountH << std::endl;
+    }
+  }
+
+  template<int K>
+  std::shared_ptr<Executable> AutoencoderFilter::buildNet()
+  {
+    H = color.height;
+    W = color.width;
+
+    // Configure the network
+    int inputC;
+    void* weightPtr;
+
+    if (srgb && hdr)
+      throw Exception(Error::InvalidOperation, "srgb and hdr modes cannot be enabled at the same time");
+
+    if (color && !albedo && !normal && weightData.hdr)
+    {
+      inputC = 3;
+      weightPtr = hdr ? weightData.hdr : weightData.ldr;
+    }
+    else if (color && albedo && !normal && weightData.hdr_alb)
+    {
+      inputC = 6;
+      weightPtr = hdr ? weightData.hdr_alb : weightData.ldr_alb;
+    }
+    else if (color && albedo && normal && weightData.hdr_alb_nrm)
+    {
+      inputC = 9;
+      weightPtr = hdr ? weightData.hdr_alb_nrm : weightData.ldr_alb_nrm;
+    }
+    else
+    {
+      throw Exception(Error::InvalidOperation, "unsupported combination of input features");
+    }
+
+    if (!output)
+      throw Exception(Error::InvalidOperation, "output image not specified");
+
+    if ((color.format != Format::Float3)
+        || (albedo && albedo.format != Format::Float3)
+        || (normal && normal.format != Format::Float3)
+        || (output.format != Format::Float3))
+      throw Exception(Error::InvalidOperation, "unsupported image format");
+
+    if ((albedo && (albedo.width != W || albedo.height != H))
+        || (normal && (normal.width != W || normal.height != H))
+        || (output.width != W || output.height != H))
+      throw Exception(Error::InvalidOperation, "image size mismatch");
+
+    // Compute the tile size
+    computeTileSize();
+
+    // If the image size is zero, there is nothing else to do
+    if (H <= 0 || W <= 0)
+      return nullptr;
+
+    // Parse the weights
+    const auto weightMap = parseTensors(weightPtr);
+
+    // Create the network
+    std::shared_ptr<Network<K>> net = std::make_shared<Network<K>>(device, weightMap);
+
+    // Compute the tensor sizes
+    const auto inputDims        = memory::dims({1, inputC, tileH, tileW});
+    const auto inputReorderDims = net->getInputReorderDims(inputDims, alignment);   //-> concat0
+
+    const auto conv1Dims     = net->getConvDims("conv1", inputReorderDims);         //-> temp0
+    const auto conv1bDims    = net->getConvDims("conv1b", conv1Dims);               //-> temp1
+    const auto pool1Dims     = net->getPoolDims(conv1bDims);                        //-> concat1
+    const auto conv2Dims     = net->getConvDims("conv2", pool1Dims);                //-> temp0
+    const auto pool2Dims     = net->getPoolDims(conv2Dims);                         //-> concat2
+    const auto conv3Dims     = net->getConvDims("conv3", pool2Dims);                //-> temp0
+    const auto pool3Dims     = net->getPoolDims(conv3Dims);                         //-> concat3
+    const auto conv4Dims     = net->getConvDims("conv4", pool3Dims);                //-> temp0
+    const auto pool4Dims     = net->getPoolDims(conv4Dims);                         //-> concat4
+    const auto conv5Dims     = net->getConvDims("conv5", pool4Dims);                //-> temp0
+    const auto pool5Dims     = net->getPoolDims(conv5Dims);                         //-> temp1
+    const auto upsample4Dims = net->getUpsampleDims(pool5Dims);                     //-> concat4
+    const auto concat4Dims   = net->getConcatDims(upsample4Dims, pool4Dims);
+    const auto conv6Dims     = net->getConvDims("conv6", concat4Dims);              //-> temp0
+    const auto conv6bDims    = net->getConvDims("conv6b", conv6Dims);               //-> temp1
+    const auto upsample3Dims = net->getUpsampleDims(conv6bDims);                    //-> concat3
+    const auto concat3Dims   = net->getConcatDims(upsample3Dims, pool3Dims);
+    const auto conv7Dims     = net->getConvDims("conv7", concat3Dims);              //-> temp0
+    const auto conv7bDims    = net->getConvDims("conv7b", conv7Dims);               //-> temp1
+    const auto upsample2Dims = net->getUpsampleDims(conv7bDims);                    //-> concat2
+    const auto concat2Dims   = net->getConcatDims(upsample2Dims, pool2Dims);
+    const auto conv8Dims     = net->getConvDims("conv8", concat2Dims);              //-> temp0
+    const auto conv8bDims    = net->getConvDims("conv8b", conv8Dims);               //-> temp1
+    const auto upsample1Dims = net->getUpsampleDims(conv8bDims);                    //-> concat1
+    const auto concat1Dims   = net->getConcatDims(upsample1Dims, pool1Dims);
+    const auto conv9Dims     = net->getConvDims("conv9", concat1Dims);              //-> temp0
+    const auto conv9bDims    = net->getConvDims("conv9b", conv9Dims);               //-> temp1
+    const auto upsample0Dims = net->getUpsampleDims(conv9bDims);                    //-> concat0
+    const auto concat0Dims   = net->getConcatDims(upsample0Dims, inputReorderDims);
+    const auto conv10Dims    = net->getConvDims("conv10", concat0Dims);             //-> temp0
+    const auto conv10bDims   = net->getConvDims("conv10b", conv10Dims);             //-> temp1
+    const auto conv11Dims    = net->getConvDims("conv11", conv10bDims);             //-> temp0
+
+    const auto outputDims = memory::dims({1, 3, tileH, tileW});
+
+    // Allocate two temporary ping-pong buffers to decrease memory usage
+    const auto temp0Dims = getMaxTensorDims({
+      conv1Dims,
+      conv2Dims,
+      conv3Dims,
+      conv4Dims,
+      conv5Dims,
+      conv6Dims,
+      conv7Dims,
+      conv8Dims,
+      conv9Dims,
+      conv10Dims,
+      conv11Dims
+    });
+
+    const auto temp1Dims = getMaxTensorDims({
+      conv1bDims,
+      pool5Dims,
+      conv6bDims,
+      conv7bDims,
+      conv8bDims,
+      conv9bDims,
+      conv10bDims,
+    });
+
+    auto temp0 = net->allocTensor(temp0Dims);
+    auto temp1 = net->allocTensor(temp1Dims);
+
+    // Allocate enough memory to hold the concat outputs. Then use the first
+    // half to hold the previous conv output and the second half to hold the
+    // pool/orig image output. This works because everything is C dimension
+    // outermost, padded to K floats, and all the concats are on the C dimension.
+    auto concat0Dst = net->allocTensor(concat0Dims);
+    auto concat1Dst = net->allocTensor(concat1Dims);
+    auto concat2Dst = net->allocTensor(concat2Dims);
+    auto concat3Dst = net->allocTensor(concat3Dims);
+    auto concat4Dst = net->allocTensor(concat4Dims);
+
+    // Transfer function
+    std::shared_ptr<TransferFunction> transferFunc = makeTransferFunc();
+
+    // Autoexposure
+    if (auto tf = std::dynamic_pointer_cast<HDRTransferFunction>(transferFunc))
+    {
+      if (isnan(hdrScale))
+        net->addAutoexposure(color, tf);
+      else
+        tf->setExposure(hdrScale);
+    }
+
+    // Input reorder
+    auto inputReorderDst = net->castTensor(inputReorderDims, concat0Dst, upsample0Dims);
+    inputReorder = net->addInputReorder(color, albedo, normal,
+                                        transferFunc,
+                                        alignment, inputReorderDst);
+
+    // conv1
+    auto conv1 = net->addConv("conv1", inputReorder->getDst(), temp0);
+
+    // conv1b
+    auto conv1b = net->addConv("conv1b", conv1->getDst(), temp1);
+
+    // pool1
+    // Adjust pointer for pool1 to eliminate concat1
+    auto pool1Dst = net->castTensor(pool1Dims, concat1Dst, upsample1Dims);
+    auto pool1 = net->addPool(conv1b->getDst(), pool1Dst);
+
+    // conv2
+    auto conv2 = net->addConv("conv2", pool1->getDst(), temp0);
+
+    // pool2
+    // Adjust pointer for pool2 to eliminate concat2
+    auto pool2Dst = net->castTensor(pool2Dims, concat2Dst, upsample2Dims);
+    auto pool2 = net->addPool(conv2->getDst(), pool2Dst);
+
+    // conv3
+    auto conv3 = net->addConv("conv3", pool2->getDst(), temp0);
+
+    // pool3
+    // Adjust pointer for pool3 to eliminate concat3
+    auto pool3Dst = net->castTensor(pool3Dims, concat3Dst, upsample3Dims);
+    auto pool3 = net->addPool(conv3->getDst(), pool3Dst);
+
+    // conv4
+    auto conv4 = net->addConv("conv4", pool3->getDst(), temp0);
+
+    // pool4
+    // Adjust pointer for pool4 to eliminate concat4
+    auto pool4Dst = net->castTensor(pool4Dims, concat4Dst, upsample4Dims);
+    auto pool4 = net->addPool(conv4->getDst(), pool4Dst);
+
+    // conv5
+    auto conv5 = net->addConv("conv5", pool4->getDst(), temp0);
+
+    // pool5
+    auto pool5 = net->addPool(conv5->getDst(), temp1);
+
+    // upsample4
+    auto upsample4Dst = net->castTensor(upsample4Dims, concat4Dst);
+    auto upsample4 = net->addUpsample(pool5->getDst(), upsample4Dst);
+
+    // conv6
+    auto conv6 = net->addConv("conv6", concat4Dst, temp0);
+
+    // conv6b
+    auto conv6b = net->addConv("conv6b", conv6->getDst(), temp1);
+
+    // upsample3
+    auto upsample3Dst = net->castTensor(upsample3Dims, concat3Dst);
+    auto upsample3 = net->addUpsample(conv6b->getDst(), upsample3Dst);
+
+    // conv7
+    auto conv7 = net->addConv("conv7", concat3Dst, temp0);
+
+    // conv7b
+    auto conv7b = net->addConv("conv7b", conv7->getDst(), temp1);
+
+    // upsample2
+    auto upsample2Dst = net->castTensor(upsample2Dims, concat2Dst);
+    auto upsample2 = net->addUpsample(conv7b->getDst(), upsample2Dst);
+
+    // conv8
+    auto conv8 = net->addConv("conv8", concat2Dst, temp0);
+
+    // conv8b
+    auto conv8b = net->addConv("conv8b", conv8->getDst(), temp1);
+
+    // upsample1
+    auto upsample1Dst = net->castTensor(upsample1Dims, concat1Dst);
+    auto upsample1 = net->addUpsample(conv8b->getDst(), upsample1Dst);
+
+    // conv9
+    auto conv9 = net->addConv("conv9", concat1Dst, temp0);
+
+    // conv9b
+    auto conv9b = net->addConv("conv9b", conv9->getDst(), temp1);
+
+    // upsample0
+    auto upsample0Dst = net->castTensor(upsample0Dims, concat0Dst);
+    auto upsample0 = net->addUpsample(conv9b->getDst(), upsample0Dst);
+
+    // conv10
+    auto conv10 = net->addConv("conv10", concat0Dst, temp0);
+
+    // conv10b
+    auto conv10b = net->addConv("conv10b", conv10->getDst(), temp1);
+
+    // conv11
+    auto conv11 = net->addConv("conv11", conv10b->getDst(), temp0, false /* no relu */);
+
+    // Output reorder
+    outputReorder = net->addOutputReorder(conv11->getDst(), transferFunc, output);
+
+    net->finalize();
+    return net;
+  }
+
+  std::shared_ptr<TransferFunction> AutoencoderFilter::makeTransferFunc()
+  {
+    if (hdr)
+      return std::make_shared<PQXTransferFunction>();
+    else if (srgb)
+      return std::make_shared<LinearTransferFunction>();
+    else
+      return std::make_shared<GammaTransferFunction>();
+  }
+
+// Godot doesn't need Raytracing filters. Removing them saves space in the weights files.
+#if 0
+  // --------------------------------------------------------------------------
+  // RTFilter
+  // --------------------------------------------------------------------------
+
+  namespace weights
+  {
+    // LDR
+    extern unsigned char rt_ldr[];         // color
+    extern unsigned char rt_ldr_alb[];     // color, albedo
+    extern unsigned char rt_ldr_alb_nrm[]; // color, albedo, normal
+
+    // HDR
+    extern unsigned char rt_hdr[];         // color
+    extern unsigned char rt_hdr_alb[];     // color, albedo
+    extern unsigned char rt_hdr_alb_nrm[]; // color, albedo, normal
+  }
+
+  RTFilter::RTFilter(const Ref<Device>& device)
+    : AutoencoderFilter(device)
+  {
+    weightData.ldr         = weights::rt_ldr;
+    weightData.ldr_alb     = weights::rt_ldr_alb;
+    weightData.ldr_alb_nrm = weights::rt_ldr_alb_nrm;
+    weightData.hdr         = weights::rt_hdr;
+    weightData.hdr_alb     = weights::rt_hdr_alb;
+    weightData.hdr_alb_nrm = weights::rt_hdr_alb_nrm;
+  }
+#endif
+
+  // --------------------------------------------------------------------------
+  // RTLightmapFilter
+  // --------------------------------------------------------------------------
+
+  namespace weights
+  {
+    // HDR
+    extern unsigned char rtlightmap_hdr[]; // color
+  }
+
+  RTLightmapFilter::RTLightmapFilter(const Ref<Device>& device)
+    : AutoencoderFilter(device)
+  {
+    weightData.hdr = weights::rtlightmap_hdr;
+
+    hdr = true;
+  }
+
+  std::shared_ptr<TransferFunction> RTLightmapFilter::makeTransferFunc()
+  {
+    return std::make_shared<LogTransferFunction>();
+  }
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/autoencoder.h b/thirdparty/oidn/core/autoencoder.h
new file mode 100644
index 0000000000..97432f2bbd
--- /dev/null
+++ b/thirdparty/oidn/core/autoencoder.h
@@ -0,0 +1,116 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "filter.h"
+#include "network.h"
+#include "transfer_function.h"
+
+namespace oidn {
+
+  // --------------------------------------------------------------------------
+  // AutoencoderFilter - Direct-predicting autoencoder
+  // --------------------------------------------------------------------------
+
+  class AutoencoderFilter : public Filter
+  {
+  protected:
+    static constexpr int alignment       = 32;  // required spatial alignment in pixels (padding may be necessary)
+    static constexpr int receptiveField  = 222; // receptive field in pixels
+    static constexpr int overlap         = roundUp(receptiveField / 2, alignment); // required spatial overlap between tiles in pixels
+
+    static constexpr int estimatedBytesBase       = 16*1024*1024; // estimated base memory usage
+    static constexpr int estimatedBytesPerPixel8  = 889;          // estimated memory usage per pixel for K=8
+    static constexpr int estimatedBytesPerPixel16 = 2185;         // estimated memory usage per pixel for K=16
+
+    Image color;
+    Image albedo;
+    Image normal;
+    Image output;
+    bool hdr = false;
+    float hdrScale = std::numeric_limits<float>::quiet_NaN();
+    bool srgb = false;
+    int maxMemoryMB = 6000; // approximate maximum memory usage in MBs
+
+    int H = 0;          // image height
+    int W = 0;          // image width
+    int tileH = 0;      // tile height
+    int tileW = 0;      // tile width
+    int tileCountH = 1; // number of tiles in H dimension
+    int tileCountW = 1; // number of tiles in W dimension
+
+    std::shared_ptr<Executable> net;
+    std::shared_ptr<Node> inputReorder;
+    std::shared_ptr<Node> outputReorder;
+
+    struct
+    {
+      void* ldr         = nullptr;
+      void* ldr_alb     = nullptr;
+      void* ldr_alb_nrm = nullptr;
+      void* hdr         = nullptr;
+      void* hdr_alb     = nullptr;
+      void* hdr_alb_nrm = nullptr;
+    } weightData;
+
+    explicit AutoencoderFilter(const Ref<Device>& device);
+    virtual std::shared_ptr<TransferFunction> makeTransferFunc();
+
+  public:
+    void setImage(const std::string& name, const Image& data) override;
+    void set1i(const std::string& name, int value) override;
+    int get1i(const std::string& name) override;
+    void set1f(const std::string& name, float value) override;
+    float get1f(const std::string& name) override;
+
+    void commit() override;
+    void execute() override;
+
+  private:
+    void computeTileSize();
+
+    template<int K>
+    std::shared_ptr<Executable> buildNet();
+
+    bool isCommitted() const { return bool(net); }
+  };
+
+  // --------------------------------------------------------------------------
+  // RTFilter - Generic ray tracing denoiser
+  // --------------------------------------------------------------------------
+
+// Godot doesn't need Raytracing filters. Removing them saves space in the weights files.
+#if 0
+  class RTFilter : public AutoencoderFilter
+  {
+  public:
+    explicit RTFilter(const Ref<Device>& device);
+  };
+#endif
+
+  // --------------------------------------------------------------------------
+  // RTLightmapFilter - Ray traced lightmap denoiser
+  // --------------------------------------------------------------------------
+
+  class RTLightmapFilter : public AutoencoderFilter
+  {
+  public:
+    explicit RTLightmapFilter(const Ref<Device>& device);
+    std::shared_ptr<TransferFunction> makeTransferFunc() override;
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/buffer.h b/thirdparty/oidn/core/buffer.h
new file mode 100644
index 0000000000..b95109152e
--- /dev/null
+++ b/thirdparty/oidn/core/buffer.h
@@ -0,0 +1,75 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "common.h"
+#include "device.h"
+
+namespace oidn {
+
+  class Device;
+
+  // Buffer which may or may not own its data
+  class Buffer : public RefCount
+  {
+  private:
+    char* ptr;
+    size_t byteSize;
+    bool shared;
+    Ref<Device> device;
+
+  public:
+    __forceinline Buffer(const Ref<Device>& device, size_t size)
+      : ptr((char*)alignedMalloc(size, 64)),
+        byteSize(size),
+        shared(false),
+        device(device) {}
+
+    __forceinline Buffer(const Ref<Device>& device, void* data, size_t size)
+      : ptr((char*)data),
+        byteSize(size),
+        shared(true),
+        device(device)
+    {
+      if (data == nullptr)
+        throw Exception(Error::InvalidArgument, "buffer pointer null");
+    }
+
+    __forceinline ~Buffer()
+    {
+      if (!shared)
+        alignedFree(ptr);
+    }
+
+    __forceinline char* data() { return ptr; }
+    __forceinline const char* data() const { return ptr; }
+    __forceinline size_t size() const { return byteSize; }
+
+    void* map(size_t offset, size_t size)
+    {
+      if (offset + size > byteSize)
+        throw Exception(Error::InvalidArgument, "buffer region out of range");
+
+      return ptr + offset;
+    }
+
+    void unmap(void* mappedPtr) {}
+
+    Device* getDevice() { return device.get(); }
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/common.h b/thirdparty/oidn/core/common.h
new file mode 100644
index 0000000000..6c87f377bc
--- /dev/null
+++ b/thirdparty/oidn/core/common.h
@@ -0,0 +1,133 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "common/platform.h"
+
+#include "mkl-dnn/include/mkldnn.hpp"
+#include "mkl-dnn/include/mkldnn_debug.h"
+#include "mkl-dnn/src/common/mkldnn_thread.hpp"
+#include "mkl-dnn/src/common/type_helpers.hpp"
+#include "mkl-dnn/src/cpu/jit_generator.hpp"
+
+#include "common/ref.h"
+#include "common/exception.h"
+#include "common/thread.h"
+#include "math.h"
+
+namespace oidn {
+
+  using namespace mkldnn;
+  using namespace mkldnn::impl::cpu;
+  using mkldnn::impl::parallel_nd;
+  using mkldnn::impl::memory_desc_matches_tag;
+
+
+  inline size_t getFormatBytes(Format format)
+  {
+    switch (format)
+    {
+    case Format::Undefined: return 1;
+    case Format::Float:     return sizeof(float);
+    case Format::Float2:    return sizeof(float)*2;
+    case Format::Float3:    return sizeof(float)*3;
+    case Format::Float4:    return sizeof(float)*4;
+    }
+    assert(0);
+    return 0;
+  }
+
+
+  inline memory::dims getTensorDims(const std::shared_ptr<memory>& mem)
+  {
+    const mkldnn_memory_desc_t& desc = mem->get_desc().data;
+    return memory::dims(&desc.dims[0], &desc.dims[desc.ndims]);
+  }
+
+  inline memory::data_type getTensorType(const std::shared_ptr<memory>& mem)
+  {
+    const mkldnn_memory_desc_t& desc = mem->get_desc().data;
+    return memory::data_type(desc.data_type);
+  }
+
+  // Returns the number of values in a tensor
+  inline size_t getTensorSize(const memory::dims& dims)
+  {
+    size_t res = 1;
+    for (int i = 0; i < (int)dims.size(); ++i)
+      res *= dims[i];
+    return res;
+  }
+
+  inline memory::dims getMaxTensorDims(const std::vector<memory::dims>& dims)
+  {
+    memory::dims result;
+    size_t maxSize = 0;
+
+    for (const auto& d : dims)
+    {
+      const size_t size = getTensorSize(d);
+      if (size > maxSize)
+      {
+        result = d;
+        maxSize = size;
+      }
+    }
+
+    return result;
+  }
+
+  inline size_t getTensorSize(const std::shared_ptr<memory>& mem)
+  {
+    return getTensorSize(getTensorDims(mem));
+  }
+
+
+  template<int K>
+  inline int getPadded(int dim)
+  {
+    return (dim + (K-1)) & ~(K-1);
+  }
+
+  template<int K>
+  inline memory::dims getPadded_nchw(const memory::dims& dims)
+  {
+    assert(dims.size() == 4);
+    memory::dims padDims = dims;
+    padDims[1] = getPadded<K>(dims[1]); // pad C
+    return padDims;
+  }
+
+
+  template<int K>
+  struct BlockedFormat;
+
+  template<>
+  struct BlockedFormat<8>
+  {
+    static constexpr memory::format_tag nChwKc   = memory::format_tag::nChw8c;
+    static constexpr memory::format_tag OIhwKiKo = memory::format_tag::OIhw8i8o;
+  };
+
+  template<>
+  struct BlockedFormat<16>
+  {
+    static constexpr memory::format_tag nChwKc   = memory::format_tag::nChw16c;
+    static constexpr memory::format_tag OIhwKiKo = memory::format_tag::OIhw16i16o;
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/device.cpp b/thirdparty/oidn/core/device.cpp
new file mode 100644
index 0000000000..0812624bb5
--- /dev/null
+++ b/thirdparty/oidn/core/device.cpp
@@ -0,0 +1,205 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#include "device.h"
+#include "autoencoder.h"
+
+namespace oidn {
+
+  thread_local Device::ErrorState Device::globalError;
+
+  Device::Device()
+  {
+    if (!mayiuse(sse41))
+      throw Exception(Error::UnsupportedHardware, "SSE4.1 support is required at minimum");
+  }
+
+  Device::~Device()
+  {
+  }
+
+  void Device::setError(Device* device, Error code, const std::string& message)
+  {
+    // Update the stored error only if the previous error was queried
+    if (device)
+    {
+      ErrorState& curError = device->error.get();
+
+      if (curError.code == Error::None)
+      {
+        curError.code = code;
+        curError.message = message;
+      }
+
+      // Print the error message in verbose mode
+      if (device->isVerbose())
+        std::cerr << "Error: " << message << std::endl;
+
+      // Call the error callback function
+      ErrorFunction errorFunc;
+      void* errorUserPtr;
+
+      {
+        std::lock_guard<std::mutex> lock(device->mutex);
+        errorFunc = device->errorFunc;
+        errorUserPtr = device->errorUserPtr;
+      }
+
+      if (errorFunc)
+        errorFunc(errorUserPtr, code, (code == Error::None) ? nullptr : message.c_str());
+    }
+    else
+    {
+      if (globalError.code == Error::None)
+      {
+        globalError.code = code;
+        globalError.message = message;
+      }
+    }
+  }
+
+  Error Device::getError(Device* device, const char** outMessage)
+  {
+    // Return and clear the stored error code, but keep the error message so pointers to it will
+    // remain valid until the next getError call
+    if (device)
+    {
+      ErrorState& curError = device->error.get();
+      const Error code = curError.code;
+      if (outMessage)
+        *outMessage = (code == Error::None) ? nullptr : curError.message.c_str();
+      curError.code = Error::None;
+      return code;
+    }
+    else
+    {
+      const Error code = globalError.code;
+      if (outMessage)
+        *outMessage = (code == Error::None) ? nullptr : globalError.message.c_str();
+      globalError.code = Error::None;
+      return code;
+    }
+  }
+
+  void Device::setErrorFunction(ErrorFunction func, void* userPtr)
+  {
+    errorFunc = func;
+    errorUserPtr = userPtr;
+  }
+
+  int Device::get1i(const std::string& name)
+  {
+    if (name == "numThreads")
+      return numThreads;
+    else if (name == "setAffinity")
+      return setAffinity;
+    else if (name == "verbose")
+      return verbose;
+    else if (name == "version")
+      return OIDN_VERSION;
+    else if (name == "versionMajor")
+      return OIDN_VERSION_MAJOR;
+    else if (name == "versionMinor")
+      return OIDN_VERSION_MINOR;
+    else if (name == "versionPatch")
+      return OIDN_VERSION_PATCH;
+    else
+      throw Exception(Error::InvalidArgument, "invalid parameter");
+  }
+
+  void Device::set1i(const std::string& name, int value)
+  {
+    if (name == "numThreads")
+      numThreads = value;
+    else if (name == "setAffinity")
+      setAffinity = value;
+    else if (name == "verbose")
+    {
+      verbose = value;
+      error.verbose = value;
+    }
+
+    dirty = true;
+  }
+
+  void Device::commit()
+  {
+    if (isCommitted())
+      throw Exception(Error::InvalidOperation, "device can be committed only once");
+
+    // Create the task arena
+    const int maxNumThreads = 1; //affinity ? affinity->getNumThreads() : tbb::this_task_arena::max_concurrency();
+    numThreads = (numThreads > 0) ? min(numThreads, maxNumThreads) : maxNumThreads;
+
+    dirty = false;
+
+    if (isVerbose())
+      print();
+  }
+
+  void Device::checkCommitted()
+  {
+    if (dirty)
+      throw Exception(Error::InvalidOperation, "changes to the device are not committed");
+  }
+
+  Ref<Buffer> Device::newBuffer(size_t byteSize)
+  {
+    checkCommitted();
+    return makeRef<Buffer>(Ref<Device>(this), byteSize);
+  }
+
+  Ref<Buffer> Device::newBuffer(void* ptr, size_t byteSize)
+  {
+    checkCommitted();
+    return makeRef<Buffer>(Ref<Device>(this), ptr, byteSize);
+  }
+
+  Ref<Filter> Device::newFilter(const std::string& type)
+  {
+    checkCommitted();
+
+    if (isVerbose())
+      std::cout << "Filter: " << type << std::endl;
+
+    Ref<Filter> filter;
+
+// Godot doesn't need Raytracing filters. Removing them saves space in the weights files.
+#if 0
+    if (type == "RT")
+      filter = makeRef<RTFilter>(Ref<Device>(this));
+#endif
+	if (type == "RTLightmap")
+      filter = makeRef<RTLightmapFilter>(Ref<Device>(this));
+    else
+      throw Exception(Error::InvalidArgument, "unknown filter type");
+
+    return filter;
+  }
+
+  void Device::print()
+  {
+    std::cout << std::endl;
+
+    std::cout << "Intel(R) Open Image Denoise " << OIDN_VERSION_STRING << std::endl;
+    std::cout << "  Compiler: " << getCompilerName() << std::endl;
+    std::cout << "  Build   : " << getBuildName() << std::endl;
+    std::cout << "  Platform: " << getPlatformName() << std::endl;
+
+    std::cout << std::endl;
+  }
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/device.h b/thirdparty/oidn/core/device.h
new file mode 100644
index 0000000000..93a83eb731
--- /dev/null
+++ b/thirdparty/oidn/core/device.h
@@ -0,0 +1,78 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "common.h"
+
+namespace oidn {
+
+  class Buffer;
+  class Filter;
+
+  class Device : public RefCount, public Verbose
+  {
+  private:
+    // Thread-safety
+    std::mutex mutex;
+
+    // Error handling
+    struct ErrorState
+    {
+      Error code = Error::None;
+      std::string message;
+    };
+
+    static thread_local ErrorState globalError;
+    ThreadLocal<ErrorState> error;
+    ErrorFunction errorFunc = nullptr;
+    void* errorUserPtr = nullptr;
+
+    // Parameters
+    int numThreads = 0; // autodetect by default
+    bool setAffinity = true;
+
+    bool dirty = true;
+
+  public:
+    Device();
+    ~Device();
+
+    static void setError(Device* device, Error code, const std::string& message);
+    static Error getError(Device* device, const char** outMessage);
+
+    void setErrorFunction(ErrorFunction func, void* userPtr);
+
+    int get1i(const std::string& name);
+    void set1i(const std::string& name, int value);
+
+    void commit();
+
+    Ref<Buffer> newBuffer(size_t byteSize);
+    Ref<Buffer> newBuffer(void* ptr, size_t byteSize);
+    Ref<Filter> newFilter(const std::string& type);
+
+    __forceinline Device* getDevice() { return this; }
+    __forceinline std::mutex& getMutex() { return mutex; }
+
+  private:
+    bool isCommitted() const { return false; }
+    void checkCommitted();
+
+    void print();
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/filter.cpp b/thirdparty/oidn/core/filter.cpp
new file mode 100644
index 0000000000..ec1f10af87
--- /dev/null
+++ b/thirdparty/oidn/core/filter.cpp
@@ -0,0 +1,27 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#include "filter.h"
+
+namespace oidn {
+
+  void Filter::setProgressMonitorFunction(ProgressMonitorFunction func, void* userPtr)
+  {
+    progressFunc = func;
+    progressUserPtr = userPtr;
+  }
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/filter.h b/thirdparty/oidn/core/filter.h
new file mode 100644
index 0000000000..935fa202f4
--- /dev/null
+++ b/thirdparty/oidn/core/filter.h
@@ -0,0 +1,52 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "common.h"
+#include "device.h"
+#include "image.h"
+
+namespace oidn {
+
+  class Filter : public RefCount
+  {
+  protected:
+    Ref<Device> device;
+
+    ProgressMonitorFunction progressFunc = nullptr;
+    void* progressUserPtr = nullptr;
+
+    bool dirty = true;
+
+  public:
+    explicit Filter(const Ref<Device>& device) : device(device) {}
+
+    virtual void setImage(const std::string& name, const Image& data) = 0;
+    virtual void set1i(const std::string& name, int value) = 0;
+    virtual int get1i(const std::string& name) = 0;
+    virtual void set1f(const std::string& name, float value) = 0;
+    virtual float get1f(const std::string& name) = 0;
+
+    void setProgressMonitorFunction(ProgressMonitorFunction func, void* userPtr);
+
+    virtual void commit() = 0;
+    virtual void execute() = 0;
+
+    Device* getDevice() { return device.get(); }
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/image.h b/thirdparty/oidn/core/image.h
new file mode 100644
index 0000000000..748f49c4e5
--- /dev/null
+++ b/thirdparty/oidn/core/image.h
@@ -0,0 +1,111 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "common.h"
+#include "buffer.h"
+
+namespace oidn {
+
+  struct Image
+  {
+    static constexpr int maxSize = 65536;
+
+    char* ptr;              // pointer to the first pixel
+    int width;              // width in number of pixels
+    int height;             // height in number of pixels
+    size_t bytePixelStride; // pixel stride in number of *bytes*
+    size_t rowStride;       // row stride in number of *pixel strides*
+    Format format;          // pixel format
+    Ref<Buffer> buffer;     // buffer containing the image data
+
+    Image() : ptr(nullptr), width(0), height(0), bytePixelStride(0), rowStride(0), format(Format::Undefined) {}
+
+    Image(void* ptr, Format format, int width, int height, size_t byteOffset, size_t inBytePixelStride, size_t inByteRowStride)
+    {
+      if (ptr == nullptr)
+        throw Exception(Error::InvalidArgument, "buffer pointer null");
+
+      init((char*)ptr + byteOffset, format, width, height, inBytePixelStride, inByteRowStride);
+    }
+
+    Image(const Ref<Buffer>& buffer, Format format, int width, int height, size_t byteOffset, size_t inBytePixelStride, size_t inByteRowStride)
+    {
+      init(buffer->data() + byteOffset, format, width, height, inBytePixelStride, inByteRowStride);
+
+      if (byteOffset + height * rowStride * bytePixelStride > buffer->size())
+        throw Exception(Error::InvalidArgument, "buffer region out of range");
+    }
+
+    void init(char* ptr, Format format, int width, int height, size_t inBytePixelStride, size_t inByteRowStride)
+    {
+      assert(width >= 0);
+      assert(height >= 0);
+      if (width > maxSize || height > maxSize)
+        throw Exception(Error::InvalidArgument, "image size too large");
+
+      this->ptr = ptr;
+      this->width = width;
+      this->height = height;
+
+      const size_t pixelSize = getFormatBytes(format);
+      if (inBytePixelStride != 0)
+      {
+        if (inBytePixelStride < pixelSize)
+          throw Exception(Error::InvalidArgument, "pixel stride smaller than pixel size");
+
+        this->bytePixelStride = inBytePixelStride;
+      }
+      else
+      {
+        this->bytePixelStride = pixelSize;
+      }
+
+      if (inByteRowStride != 0)
+      {
+        if (inByteRowStride < width * this->bytePixelStride)
+          throw Exception(Error::InvalidArgument, "row stride smaller than width * pixel stride");
+        if (inByteRowStride % this->bytePixelStride != 0)
+          throw Exception(Error::InvalidArgument, "row stride not integer multiple of pixel stride");
+
+        this->rowStride = inByteRowStride / this->bytePixelStride;
+      }
+      else
+      {
+        this->rowStride = width;
+      }
+
+      this->format = format;
+    }
+
+    __forceinline char* get(int y, int x)
+    {
+      return ptr + ((size_t(y) * rowStride + size_t(x)) * bytePixelStride);
+    }
+
+    __forceinline const char* get(int y, int x) const
+    {
+      return ptr + ((size_t(y) * rowStride + size_t(x)) * bytePixelStride);
+    }
+
+    operator bool() const
+    {
+      return ptr != nullptr;
+    }
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/input_reorder.h b/thirdparty/oidn/core/input_reorder.h
new file mode 100644
index 0000000000..966856afe9
--- /dev/null
+++ b/thirdparty/oidn/core/input_reorder.h
@@ -0,0 +1,232 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "node.h"
+#include "image.h"
+
+namespace oidn {
+
+  // Input reorder node
+  template<int K, class TransferFunction>
+  class InputReorderNode : public Node
+  {
+  private:
+    // Source
+    Image color;
+    Image albedo;
+    Image normal;
+
+    // Destination
+    std::shared_ptr<memory> dst;
+    float* dstPtr;
+    int C2;
+    int H2;
+    int W2;
+
+    // Tile
+    int h1Begin;
+    int w1Begin;
+    int h2Begin;
+    int w2Begin;
+    int H;
+    int W;
+
+    std::shared_ptr<TransferFunction> transferFunc;
+
+  public:
+    InputReorderNode(const Image& color,
+                     const Image& albedo,
+                     const Image& normal,
+                     const std::shared_ptr<memory>& dst,
+                     const std::shared_ptr<TransferFunction>& transferFunc)
+      : color(color), albedo(albedo), normal(normal),
+        dst(dst),
+        h1Begin(0), w1Begin(0),
+        H(color.height), W(color.width),
+        transferFunc(transferFunc)
+    {
+      const mkldnn_memory_desc_t& dstDesc = dst->get_desc().data;
+      assert(memory_desc_matches_tag(dstDesc, mkldnn_format_tag_t(BlockedFormat<K>::nChwKc)));
+      assert(dstDesc.ndims == 4);
+      assert(dstDesc.data_type == memory::data_type::f32);
+      assert(dstDesc.dims[0] == 1);
+      //assert(dstDesc.dims[1] >= getPadded<K>(C1));
+
+      dstPtr = (float*)dst->get_data_handle();
+      C2 = dstDesc.dims[1];
+      H2 = dstDesc.dims[2];
+      W2 = dstDesc.dims[3];
+    }
+
+    void setTile(int h1, int w1, int h2, int w2, int H, int W) override
+    {
+      h1Begin = h1;
+      w1Begin = w1;
+      h2Begin = h2;
+      w2Begin = w2;
+      this->H = H;
+      this->W = W;
+    }
+
+    void execute(stream& sm) override
+    {
+      assert(H + h1Begin <= color.height);
+      assert(W + w1Begin <= color.width);
+      assert(H + h2Begin <= H2);
+      assert(W + w2Begin <= W2);
+
+      parallel_nd(H2, [&](int h2)
+      {
+        const int h = h2 - h2Begin;
+
+        if (h >= 0 && h < H)
+        {
+          const int h1 = h + h1Begin;
+
+          // Zero pad
+          for (int w2 = 0; w2 < w2Begin; ++w2)
+          {
+            int c = 0;
+            while (c < C2)
+              store(h2, w2, c, 0.f);
+          }
+
+          // Reorder
+          for (int w = 0; w < W; ++w)
+          {
+            const int w1 = w + w1Begin;
+            const int w2 = w + w2Begin;
+
+            int c = 0;
+            storeColor(h2, w2, c, (float*)color.get(h1, w1));
+            if (albedo)
+              storeAlbedo(h2, w2, c, (float*)albedo.get(h1, w1));
+            if (normal)
+              storeNormal(h2, w2, c, (float*)normal.get(h1, w1));
+            while (c < C2)
+              store(h2, w2, c, 0.f);
+          }
+
+          // Zero pad
+          for (int w2 = W + w2Begin; w2 < W2; ++w2)
+          {
+            int c = 0;
+            while (c < C2)
+              store(h2, w2, c, 0.f);
+          }
+        }
+        else
+        {
+          // Zero pad
+          for (int w2 = 0; w2 < W2; ++w2)
+          {
+            int c = 0;
+            while (c < C2)
+              store(h2, w2, c, 0.f);
+          }
+        }
+      });
+    }
+
+    std::shared_ptr<memory> getDst() const override { return dst; }
+
+  private:
+    // Stores a single value
+    __forceinline void store(int h, int w, int& c, float value)
+    {
+      // Destination is in nChwKc format
+      float* dst_c = dstPtr + (H2*W2*K*(c/K)) + h*W2*K + w*K + (c%K);
+      *dst_c = value;
+      c++;
+    }
+
+    // Stores a color
+    __forceinline void storeColor(int h, int w, int& c, const float* values)
+    {
+      #pragma unroll
+      for (int i = 0; i < 3; ++i)
+      {
+        // Load the value
+        float x = values[i];
+
+        // Sanitize the value
+        x = maxSafe(x, 0.f);
+
+        // Apply the transfer function
+        x = transferFunc->forward(x);
+
+        // Store the value
+        store(h, w, c, x);
+      }
+    }
+
+    // Stores an albedo
+    __forceinline void storeAlbedo(int h, int w, int& c, const float* values)
+    {
+      #pragma unroll
+      for (int i = 0; i < 3; ++i)
+      {
+        // Load the value
+        float x = values[i];
+
+        // Sanitize the value
+        x = clampSafe(x, 0.f, 1.f);
+
+        // Store the value
+        store(h, w, c, x);
+      }
+    }
+
+    // Stores a normal
+    __forceinline void storeNormal(int h, int w, int& c, const float* values)
+    {
+      // Load the normal
+      float x = values[0];
+      float y = values[1];
+      float z = values[2];
+
+      // Compute the length of the normal
+      const float lengthSqr = sqr(x) + sqr(y) + sqr(z);
+
+      // Normalize the normal and transform it to [0..1]
+      if (isfinite(lengthSqr))
+      {
+        const float invLength = (lengthSqr > minVectorLengthSqr) ? rsqrt(lengthSqr) : 1.f;
+
+        const float scale  = invLength * 0.5f;
+        const float offset = 0.5f;
+
+        x = x * scale + offset;
+        y = y * scale + offset;
+        z = z * scale + offset;
+      }
+      else
+      {
+        x = 0.f;
+        y = 0.f;
+        z = 0.f;
+      }
+
+      // Store the normal
+      store(h, w, c, x);
+      store(h, w, c, y);
+      store(h, w, c, z);
+    }
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/math.h b/thirdparty/oidn/core/math.h
new file mode 100644
index 0000000000..a844ef0d1d
--- /dev/null
+++ b/thirdparty/oidn/core/math.h
@@ -0,0 +1,78 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "common/platform.h"
+
+namespace oidn {
+
+  constexpr float minVectorLength    = 1e-10f;
+  constexpr float minVectorLengthSqr = minVectorLength * minVectorLength;
+
+  using std::log;
+  using std::log2;
+  using std::exp;
+  using std::exp2;
+  using std::pow;
+  using std::isfinite;
+  using std::isnan;
+
+  __forceinline float sqr(float x)
+  {
+    return x * x;
+  }
+
+  __forceinline float rcp(float x)
+  {
+    __m128 r = _mm_rcp_ss(_mm_set_ss(x));
+    return _mm_cvtss_f32(_mm_sub_ss(_mm_add_ss(r, r), _mm_mul_ss(_mm_mul_ss(r, r), _mm_set_ss(x))));
+  }
+
+  __forceinline float rsqrt(float x)
+  {
+    __m128 r = _mm_rsqrt_ss(_mm_set_ss(x));
+    return _mm_cvtss_f32(_mm_add_ss(_mm_mul_ss(_mm_set_ss(1.5f), r),
+             _mm_mul_ss(_mm_mul_ss(_mm_mul_ss(_mm_set_ss(x), _mm_set_ss(-0.5f)), r), _mm_mul_ss(r, r))));
+  }
+
+  __forceinline float maxSafe(float value, float minValue)
+  {
+    return isfinite(value) ? max(value, minValue) : minValue;
+  }
+
+  __forceinline float clampSafe(float value, float minValue, float maxValue)
+  {
+    return isfinite(value) ? clamp(value, minValue, maxValue) : minValue;
+  }
+
+  // Returns ceil(a / b) for non-negative integers
+  template<class Int>
+  __forceinline constexpr Int ceilDiv(Int a, Int b)
+  {
+    //assert(a >= 0);
+    //assert(b > 0);
+    return (a + b - 1) / b;
+  }
+
+  // Returns a rounded up to multiple of b
+  template<class Int>
+  __forceinline constexpr Int roundUp(Int a, Int b)
+  {
+    return ceilDiv(a, b) * b;
+  }
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/network.cpp b/thirdparty/oidn/core/network.cpp
new file mode 100644
index 0000000000..4da32073cd
--- /dev/null
+++ b/thirdparty/oidn/core/network.cpp
@@ -0,0 +1,434 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#include "network.h"
+#include "upsample.h"
+#include "weights_reorder.h"
+#include <cstring>
+
+namespace oidn {
+
+  template<int K>
+  Network<K>::Network(const Ref<Device>& device, const std::map<std::string, Tensor>& weightMap)
+    : device(device),
+      eng(engine::cpu, 0),
+      sm(eng),
+      weightMap(weightMap)
+  {
+  }
+
+  template<int K>
+  void Network<K>::execute(const Progress& progress, int taskIndex)
+  {
+    if (progress.func)
+    {
+      const double value = double(taskIndex) / double(progress.taskCount);
+      if (!progress.func(progress.userPtr, value))
+        throw Exception(Error::Cancelled, "execution was cancelled");
+    }
+
+    for (size_t i = 0; i < nodes.size(); ++i)
+    {
+      nodes[i]->execute(sm);
+
+      if (progress.func)
+      {
+        const double value = (double(taskIndex) + double(i+1) / double(nodes.size())) / double(progress.taskCount);
+        if (!progress.func(progress.userPtr, value))
+          throw Exception(Error::Cancelled, "execution was cancelled");
+      }
+    }
+  }
+
+  template<int K>
+  std::shared_ptr<memory> Network<K>::allocTensor(const memory::dims& dims,
+                                                  memory::format_tag format,
+                                                  void* data)
+  {
+    if (format == memory::format_tag::any)
+    {
+      if (dims.size() == 4)
+        format = BlockedFormat<K>::nChwKc;
+      else if (dims.size() == 1)
+        format = memory::format_tag::x;
+      else
+        assert(0);
+    }
+    memory::desc desc(dims, memory::data_type::f32, format);
+    if (data == nullptr)
+    {
+      const size_t bytes = getTensorSize(dims) * sizeof(float);
+      if (format == BlockedFormat<K>::nChwKc)
+        activationAllocBytes += bytes;
+      totalAllocBytes += bytes;
+
+      return std::make_shared<memory>(desc, eng);
+    }
+    else
+    {
+      return std::make_shared<memory>(desc, eng, data);
+    }
+  }
+
+  template<int K>
+  std::shared_ptr<memory> Network<K>::castTensor(const memory::dims& dims,
+                                                 const std::shared_ptr<memory>& src,
+                                                 size_t srcOffset,
+                                                 memory::format_tag format)
+  {
+    const mkldnn_memory_desc_t& srcDesc = src->get_desc().data;
+    MAYBE_UNUSED(srcDesc);
+    assert(srcDesc.data_type == memory::data_type::f32);
+    assert(getTensorSize(src) >= srcOffset + getTensorSize(dims));
+
+    if (format == memory::format_tag::any)
+    {
+      if (dims.size() == 4)
+        format = BlockedFormat<K>::nChwKc;
+      else if (dims.size() == 1)
+        format = memory::format_tag::x;
+      else
+        assert(0);
+    }
+    memory::desc desc(dims, memory::data_type::f32, format);
+    float* srcPtr = (float*)src->get_data_handle() + srcOffset;
+    return std::make_shared<memory>(desc, eng, srcPtr);
+  }
+
+  template<int K>
+  std::shared_ptr<memory> Network<K>::castTensor(const memory::dims& dims,
+                                                 const std::shared_ptr<memory>& src,
+                                                 const memory::dims& srcOffset)
+  {
+    return castTensor(dims, src, getTensorSize(srcOffset));
+  }
+
+  template<int K>
+  void Network<K>::zeroTensor(const std::shared_ptr<memory>& dst)
+  {
+    assert(getTensorType(dst) == memory::data_type::f32);
+    memset(dst->get_data_handle(), 0, getTensorSize(dst)*sizeof(float));
+  }
+
+  template<int K>
+  memory::dims Network<K>::getInputReorderDims(const memory::dims& srcDims, int alignment)
+  {
+    memory::dims dstDims = srcDims;
+    dstDims[1] = getPadded<K>(srcDims[1]); // round up C
+    dstDims[2] = roundUp(srcDims[2], memory::dim(alignment)); // round up H
+    dstDims[3] = roundUp(srcDims[3], memory::dim(alignment)); // round up W
+    return dstDims;
+  }
+
+  template<int K>
+  std::shared_ptr<Node> Network<K>::addInputReorder(const Image& color,
+                                                    const Image& albedo,
+                                                    const Image& normal,
+                                                    const std::shared_ptr<TransferFunction>& transferFunc,
+                                                    int alignment,
+                                                    const std::shared_ptr<memory>& userDst)
+  {
+    assert(color);
+    int inputC = 3;
+    if (albedo) inputC += 3;
+    if (normal) inputC += 3;
+
+    memory::dims srcDims = {1, inputC, color.height, color.width};
+    memory::dims dstDims = getInputReorderDims(srcDims, alignment);
+
+    // Allocate padded memory
+    auto dst = userDst;
+    if (!dst)
+      dst = allocTensor(dstDims);
+
+    // Push node
+    std::shared_ptr<Node> node;
+
+    if (auto tf = std::dynamic_pointer_cast<LinearTransferFunction>(transferFunc))
+      node = std::make_shared<InputReorderNode<K, LinearTransferFunction>>(color, albedo, normal, dst, tf);
+    else if (auto tf = std::dynamic_pointer_cast<GammaTransferFunction>(transferFunc))
+      node = std::make_shared<InputReorderNode<K, GammaTransferFunction>>(color, albedo, normal, dst, tf);
+    else if (auto tf = std::dynamic_pointer_cast<LogTransferFunction>(transferFunc))
+      node = std::make_shared<InputReorderNode<K, LogTransferFunction>>(color, albedo, normal, dst, tf);
+    else if (auto tf = std::dynamic_pointer_cast<PQXTransferFunction>(transferFunc))
+      node = std::make_shared<InputReorderNode<K, PQXTransferFunction>>(color, albedo, normal, dst, tf);
+    else
+      assert(0);
+
+    nodes.push_back(node);
+    return node;
+  }
+
+  template<int K>
+  std::shared_ptr<Node> Network<K>::addOutputReorder(const std::shared_ptr<memory>& src,
+                                                     const std::shared_ptr<TransferFunction>& transferFunc,
+                                                     const Image& output)
+  {
+    memory::dims srcDims = getTensorDims(src);
+    assert(srcDims[1] == K);
+
+    // Push node
+    std::shared_ptr<Node> node;
+
+    if (auto tf = std::dynamic_pointer_cast<LinearTransferFunction>(transferFunc))
+      node = std::make_shared<OutputReorderNode<K, LinearTransferFunction>>(src, output, tf);
+    else if (auto tf = std::dynamic_pointer_cast<GammaTransferFunction>(transferFunc))
+      node = std::make_shared<OutputReorderNode<K, GammaTransferFunction>>(src, output, tf);
+    else if (auto tf = std::dynamic_pointer_cast<LogTransferFunction>(transferFunc))
+      node = std::make_shared<OutputReorderNode<K, LogTransferFunction>>(src, output, tf);
+    else if (auto tf = std::dynamic_pointer_cast<PQXTransferFunction>(transferFunc))
+      node = std::make_shared<OutputReorderNode<K, PQXTransferFunction>>(src, output, tf);
+    else
+      assert(0);
+
+    nodes.push_back(node);
+    return node;
+  }
+
+  template<int K>
+  memory::dims Network<K>::getConvDims(const std::string& name, const memory::dims& srcDims)
+  {
+    auto b = weightMap[name + "/b"];
+    memory::dims dstDims = srcDims;
+    dstDims[1] = getPadded<K>(b.dims[0]); // dstDims[C] = getPadded(OC)
+    return dstDims;
+  }
+
+  template<int K>
+  std::shared_ptr<Node> Network<K>::addConv(const std::string& name,
+                                            const std::shared_ptr<memory>& src,
+                                            const std::shared_ptr<memory>& userDst,
+                                            bool relu)
+  {
+    const memory::dims strides = {1, 1};
+    const memory::dims padding = {1, 1};
+
+    memory::dims srcDims = getTensorDims(src);
+
+    // Get the weights
+    const auto& W = weightMap[name + "/W"];
+    if (W.ndims() != 4 || W.format != "oihw")
+      throw Exception(Error::InvalidOperation, "invalid convolution weights");
+    memory::dims weightsDims = W.dims;
+    auto userWeights = allocTensor(weightsDims, memory::format_tag::oihw, W.data);
+
+    // Pad the weights
+    memory::dims weightsPadDims = weightsDims;
+    weightsPadDims[1] = getPadded<K>(weightsDims[1]); // IC
+    weightsPadDims[0] = getPadded<K>(weightsDims[0]); // OC
+    assert(srcDims[1] == weightsPadDims[1]); // srcDims[C] == weightsPadDims[IC]
+    auto weightsPad = allocTensor(weightsPadDims, memory::format_tag::oihw);
+    WeightsReorderNode<K>(userWeights, weightsPad).execute(sm);
+
+    // Get the biases
+    const auto& b = weightMap[name + "/b"];
+    if (b.ndims() != 1)
+      throw Exception(Error::InvalidOperation, "invalid convolution biases");
+    memory::dims biasDims = b.dims;
+
+    // Copy/pad the biases
+    memory::dims biasPadDims = {getPadded<K>(biasDims[0])};
+    auto bias = allocTensor(biasPadDims);
+    if (biasDims[0] != biasPadDims[0])
+      memset(bias->get_data_handle(), 0, biasPadDims[0]*sizeof(float));
+    memcpy(bias->get_data_handle(), b.data, biasDims[0]*sizeof(float));
+
+    // Allocate memory for destination
+    memory::dims dstDims = srcDims;
+    dstDims[1] = weightsPadDims[0]; // dstDims[C] = weightsPadDims[OC]
+
+    std::shared_ptr<memory> dst;
+    if (!userDst)
+      dst = allocTensor(dstDims);
+    else if (getTensorDims(userDst) == dstDims)
+      dst = userDst;
+    else
+      dst = castTensor(dstDims, userDst);
+
+    // Create a convolution
+    // Let the convolution primitive choose the weights format
+    auto weightsDesc = memory::desc({ weightsPadDims }, memory::data_type::f32, memory::format_tag::any);
+
+    auto convAlgo = (K == 16) ? convolution_winograd : convolution_direct;
+    auto convDesc = convolution_forward::desc(
+      prop_kind::forward_inference, convAlgo,
+      src->get_desc(),
+      weightsDesc,
+      bias->get_desc(),
+      dst->get_desc(),
+      strides, padding, padding, padding_kind::zero);
+
+    // Incorporate relu
+    mkldnn::primitive_attr convAttr;
+    if (relu)
+    {
+      mkldnn::post_ops ops;
+      ops.append_eltwise(
+        1.f,   // scale factor, not used
+        algorithm::eltwise_relu,
+        0.f,   // max with
+        0.f    // unused
+      );
+      convAttr.set_post_ops(ops);
+    }
+    convAttr.set_scratchpad_mode(scratchpad_mode_user);
+
+    auto convPrimDesc = convolution_forward::primitive_desc(convDesc, convAttr, eng);
+
+    // Reorder the weights to the final format, if necessary
+    auto weights = weightsPad;
+    if (convPrimDesc.weights_desc() != weightsPad->get_desc())
+    {
+      weights = std::make_shared<memory>(convPrimDesc.weights_desc(), eng);
+      ReorderNode(weightsPad, weights).execute(sm);
+    }
+
+    // Create convolution node and add it to the net
+    auto node = std::make_shared<ConvNode>(convPrimDesc, src, weights, bias, dst);
+    nodes.push_back(node);
+    return node;
+  }
+
+  template<int K>
+  memory::dims Network<K>::getPoolDims(const memory::dims& srcDims)
+  {
+    memory::dims dstDims = srcDims;
+    dstDims[2] /= 2; // H/2
+    dstDims[3] /= 2; // W/2
+    return dstDims;
+  }
+
+  template<int K>
+  std::shared_ptr<Node> Network<K>::addPool(const std::shared_ptr<memory>& src,
+                                            const std::shared_ptr<memory>& userDst)
+  {
+    const memory::dims kernel  = {2, 2};
+    const memory::dims strides = {2, 2};
+    const memory::dims padding = {0, 0};
+
+    memory::dims srcDims = getTensorDims(src);
+    memory::dims dstDims = getPoolDims(srcDims);
+
+    std::shared_ptr<memory> dst;
+    if (!userDst)
+      dst = allocTensor(dstDims);
+    else if (getTensorDims(userDst) == dstDims)
+      dst = userDst;
+    else
+      dst = castTensor(dstDims, userDst);
+
+    auto poolDesc = pooling_forward::desc(
+      prop_kind::forward_inference, pooling_max,
+      src->get_desc(),
+      dst->get_desc(),
+      strides, kernel, padding, padding, padding_kind::zero);
+
+    mkldnn::primitive_attr poolAttr;
+    poolAttr.set_scratchpad_mode(scratchpad_mode_user);
+
+    auto poolPrimDesc = pooling_forward::primitive_desc(poolDesc, poolAttr, eng);
+
+    auto node = std::make_shared<PoolNode>(poolPrimDesc, src, dst);
+    nodes.push_back(node);
+    return node;
+  }
+
+  template<int K>
+  memory::dims Network<K>::getUpsampleDims(const memory::dims& srcDims)
+  {
+    memory::dims dstDims = srcDims;
+    dstDims[2] *= 2; // H*2
+    dstDims[3] *= 2; // W*2
+    return dstDims;
+  }
+
+  template<int K>
+  std::shared_ptr<Node> Network<K>::addUpsample(const std::shared_ptr<memory>& src,
+                                                const std::shared_ptr<memory>& userDst)
+  {
+    memory::dims srcDims = getTensorDims(src);
+    memory::dims dstDims = getUpsampleDims(srcDims);
+
+    std::shared_ptr<memory> dst;
+    if (!userDst)
+      dst = allocTensor(dstDims);
+    else if (getTensorDims(userDst) == dstDims)
+      dst = userDst;
+    else
+      dst = castTensor(dstDims, userDst);
+
+    // Create upsampling node and add it to net
+    auto node = std::make_shared<UpsampleNode<K>>(src, dst);
+    nodes.push_back(node);
+    return node;
+  }
+
+  template<int K>
+  memory::dims Network<K>::getConcatDims(const memory::dims& src1Dims, const memory::dims& src2Dims)
+  {
+    assert(src1Dims[0] == src2Dims[0]); // N
+    assert(src1Dims[2] == src2Dims[2]); // H
+    assert(src1Dims[3] == src2Dims[3]); // W
+
+    memory::dims dstDims = src1Dims;
+    dstDims[1] += src2Dims[1]; // C
+    return dstDims;
+  }
+
+  template<int K>
+  std::shared_ptr<Node> Network<K>::addAutoexposure(const Image& color,
+                                                    const std::shared_ptr<HDRTransferFunction>& transferFunc)
+  {
+    auto node = std::make_shared<AutoexposureNode>(color, transferFunc);
+    nodes.push_back(node);
+    return node;
+  }
+
+  template <int K>
+  void Network<K>::finalize()
+  {
+    // Compute the size of the scratchpad
+    size_t scratchpadSize = 0;
+    for (const auto& node : nodes)
+      scratchpadSize = max(scratchpadSize, node->getScratchpadSize());
+
+    // Allocate the scratchpad
+    memory::dims scratchpadDims = { memory::dim(scratchpadSize) };
+    memory::desc scratchpadDesc(scratchpadDims, memory::data_type::u8, memory::format_tag::x);
+    auto scratchpad = std::make_shared<memory>(scratchpadDesc, eng);
+    activationAllocBytes += scratchpadSize;
+    totalAllocBytes += scratchpadSize;
+
+    // Set the scratchpad for the nodes
+    for (auto& node : nodes)
+      node->setScratchpad(scratchpad);
+
+    // Free the weights
+    weightMap.clear();
+
+    // Print statistics
+    if (device->isVerbose(2))
+    {
+      std::cout << "Activation bytes: " << activationAllocBytes << std::endl;
+      std::cout << "Scratchpad bytes: " << scratchpadSize << std::endl;
+      std::cout << "Total bytes     : " << totalAllocBytes << std::endl;
+    }
+  }
+
+  template class Network<8>;
+  template class Network<16>;
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/network.h b/thirdparty/oidn/core/network.h
new file mode 100644
index 0000000000..7a696fd355
--- /dev/null
+++ b/thirdparty/oidn/core/network.h
@@ -0,0 +1,112 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#include "common/tensor.h"
+#include "image.h"
+#include "node.h"
+#include "input_reorder.h"
+#include "output_reorder.h"
+#include "transfer_function.h"
+
+#pragma once
+
+namespace oidn {
+
+  // Progress state
+  struct Progress
+  {
+    ProgressMonitorFunction func;
+    void* userPtr;
+    int taskCount;
+  };
+
+  class Executable
+  {
+  public:
+    virtual ~Executable() {}
+    virtual void execute(const Progress& progress, int taskIndex) = 0;
+  };
+
+  template<int K>
+  class Network : public Executable
+  {
+  public:
+    Network(const Ref<Device>& device, const std::map<std::string, Tensor>& weightMap);
+
+    void execute(const Progress& progress, int taskIndex) override;
+
+    std::shared_ptr<memory> allocTensor(const memory::dims& dims,
+                                        memory::format_tag format = memory::format_tag::any,
+                                        void* data = nullptr);
+
+    std::shared_ptr<memory> castTensor(const memory::dims& dims,
+                                       const std::shared_ptr<memory>& src,
+                                       size_t srcOffset = 0,
+                                       memory::format_tag format = memory::format_tag::any);
+
+    std::shared_ptr<memory> castTensor(const memory::dims& dims,
+                                       const std::shared_ptr<memory>& src,
+                                       const memory::dims& srcOffset);
+
+    void zeroTensor(const std::shared_ptr<memory>& dst);
+
+    memory::dims getInputReorderDims(const memory::dims& srcDims, int alignment);
+
+    std::shared_ptr<Node> addInputReorder(const Image& color,
+                                          const Image& albedo,
+                                          const Image& normal,
+                                          const std::shared_ptr<TransferFunction>& transferFunc,
+                                          int alignment,
+                                          const std::shared_ptr<memory>& userDst = nullptr);
+
+    std::shared_ptr<Node> addOutputReorder(const std::shared_ptr<memory>& src,
+                                           const std::shared_ptr<TransferFunction>& transferFunc,
+                                           const Image& output);
+
+    memory::dims getConvDims(const std::string& name, const memory::dims& srcDims);
+    std::shared_ptr<Node> addConv(const std::string& name,
+                                  const std::shared_ptr<memory>& src,
+                                  const std::shared_ptr<memory>& userDst = nullptr,
+                                  bool relu = true);
+
+    memory::dims getPoolDims(const memory::dims& srcDims);
+    std::shared_ptr<Node> addPool(const std::shared_ptr<memory>& src,
+                                  const std::shared_ptr<memory>& userDst = nullptr);
+
+    memory::dims getUpsampleDims(const memory::dims& srcDims);
+    std::shared_ptr<Node> addUpsample(const std::shared_ptr<memory>& src,
+                                      const std::shared_ptr<memory>& userDst = nullptr);
+
+    memory::dims getConcatDims(const memory::dims& src1Dims, const memory::dims& src2Dims);
+
+    std::shared_ptr<Node> addAutoexposure(const Image& color,
+                                          const std::shared_ptr<HDRTransferFunction>& transferFunc);
+
+    void finalize();
+
+  private:
+    Ref<Device> device;
+    engine eng;
+    stream sm;
+    std::vector<std::shared_ptr<Node>> nodes;
+    std::map<std::string, Tensor> weightMap;
+
+    // Memory allocation statistics
+    size_t activationAllocBytes = 0; // number of allocated activation bytes
+    size_t totalAllocBytes      = 0; // total number of allocated bytes
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/node.h b/thirdparty/oidn/core/node.h
new file mode 100644
index 0000000000..b9ffe906df
--- /dev/null
+++ b/thirdparty/oidn/core/node.h
@@ -0,0 +1,142 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "common.h"
+#include <vector>
+
+namespace oidn {
+
+  class Node
+  {
+  public:
+    virtual ~Node() = default;
+
+    virtual void execute(stream& sm) = 0;
+
+    virtual std::shared_ptr<memory> getDst() const { return nullptr; }
+
+    virtual size_t getScratchpadSize() const { return 0; }
+    virtual void setScratchpad(const std::shared_ptr<memory>& mem) {}
+
+    virtual void setTile(int h1, int w1, int h2, int w2, int H, int W)
+    {
+      assert(0); // not supported
+    }
+  };
+
+  // Node wrapping an MKL-DNN primitive
+  class MklNode : public Node
+  {
+  private:
+    primitive prim;
+    std::unordered_map<int, memory> args;
+    std::shared_ptr<memory> scratchpad;
+
+  public:
+    MklNode(const primitive& prim, const std::unordered_map<int, memory>& args)
+      : prim(prim),
+        args(args)
+    {}
+
+    size_t getScratchpadSize() const override
+    {
+      const auto primDesc = prim.get_primitive_desc();
+      const mkldnn_memory_desc_t* scratchpadDesc = mkldnn_primitive_desc_query_md(primDesc, mkldnn_query_scratchpad_md, 0);
+      if (scratchpadDesc == nullptr)
+        return 0;
+      return mkldnn_memory_desc_get_size(scratchpadDesc);
+    }
+
+    void setScratchpad(const std::shared_ptr<memory>& mem) override
+    {
+      scratchpad = mem;
+      args.insert(std::make_pair(MKLDNN_ARG_SCRATCHPAD, *scratchpad));
+    }
+
+    void execute(stream& sm) override
+    {
+      prim.execute(sm, args);
+    }
+  };
+
+  // Convolution node
+  class ConvNode : public MklNode
+  {
+  private:
+    std::shared_ptr<memory> src;
+    std::shared_ptr<memory> weights;
+    std::shared_ptr<memory> bias;
+    std::shared_ptr<memory> dst;
+
+  public:
+    ConvNode(const convolution_forward::primitive_desc& desc,
+             const std::shared_ptr<memory>& src,
+             const std::shared_ptr<memory>& weights,
+             const std::shared_ptr<memory>& bias,
+             const std::shared_ptr<memory>& dst)
+      : MklNode(convolution_forward(desc),
+                { { MKLDNN_ARG_SRC, *src },
+                  { MKLDNN_ARG_WEIGHTS, *weights },
+                  { MKLDNN_ARG_BIAS, *bias },
+                  { MKLDNN_ARG_DST, *dst } }),
+                src(src), weights(weights), bias(bias), dst(dst)
+    {}
+
+    std::shared_ptr<memory> getDst() const override { return dst; }
+  };
+
+  // Pooling node
+  class PoolNode : public MklNode
+  {
+  private:
+    std::shared_ptr<memory> src;
+    std::shared_ptr<memory> dst;
+
+  public:
+    PoolNode(const pooling_forward::primitive_desc& desc,
+             const std::shared_ptr<memory>& src,
+             const std::shared_ptr<memory>& dst)
+      : MklNode(pooling_forward(desc),
+                { { MKLDNN_ARG_SRC, *src },
+                  { MKLDNN_ARG_DST, *dst } }),
+                src(src), dst(dst)
+    {}
+
+    std::shared_ptr<memory> getDst() const override { return dst; }
+  };
+
+  // Reorder node
+  class ReorderNode : public MklNode
+  {
+  private:
+    std::shared_ptr<memory> src;
+    std::shared_ptr<memory> dst;
+
+  public:
+    ReorderNode(const std::shared_ptr<memory>& src,
+                const std::shared_ptr<memory>& dst)
+      : MklNode(reorder(reorder::primitive_desc(*src, *dst)),
+                { { MKLDNN_ARG_SRC, *src },
+                  { MKLDNN_ARG_DST, *dst } }),
+                src(src), dst(dst)
+    {}
+
+    std::shared_ptr<memory> getDst() const override { return dst; }
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/output_reorder.h b/thirdparty/oidn/core/output_reorder.h
new file mode 100644
index 0000000000..7918d48e15
--- /dev/null
+++ b/thirdparty/oidn/core/output_reorder.h
@@ -0,0 +1,126 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "node.h"
+#include "image.h"
+
+namespace oidn {
+
+  // Output reorder node
+  template<int K, class TransferFunction>
+  class OutputReorderNode : public Node
+  {
+  private:
+    // Source
+    std::shared_ptr<memory> src;
+    const float* srcPtr;
+    int H1;
+    int W1;
+
+    // Destination
+    Image output;
+
+    // Tile
+    int h1Begin;
+    int w1Begin;
+    int h2Begin;
+    int w2Begin;
+    int H;
+    int W;
+
+    std::shared_ptr<TransferFunction> transferFunc;
+
+  public:
+    OutputReorderNode(const std::shared_ptr<memory>& src,
+                      const Image& output,
+                      const std::shared_ptr<TransferFunction>& transferFunc)
+      : src(src),
+        output(output),
+        h1Begin(0), w1Begin(0),
+        h2Begin(0), w2Begin(0),
+        H(output.height), W(output.width),
+        transferFunc(transferFunc)
+    {
+      const mkldnn_memory_desc_t& srcDesc = src->get_desc().data;
+      MAYBE_UNUSED(srcDesc);
+      assert(memory_desc_matches_tag(srcDesc, mkldnn_format_tag_t(BlockedFormat<K>::nChwKc)));
+      assert(srcDesc.ndims == 4);
+      assert(srcDesc.data_type == memory::data_type::f32);
+      assert(srcDesc.dims[0] == 1);
+      // We assume output data is <= K OC
+      assert(srcDesc.dims[1] == K);
+
+      srcPtr = (float*)src->get_data_handle();
+      H1 = srcDesc.dims[2];
+      W1 = srcDesc.dims[3];
+    }
+
+    void setTile(int h1, int w1, int h2, int w2, int H, int W) override
+    {
+      h1Begin = h1;
+      w1Begin = w1;
+      h2Begin = h2;
+      w2Begin = w2;
+      this->H = H;
+      this->W = W;
+    }
+
+    void execute(stream& sm) override
+    {
+      assert(h1Begin + H <= H1);
+      assert(w1Begin + W <= W1);
+      assert(h2Begin + H <= output.height);
+      assert(w2Begin + W <= output.width);
+
+      const int C1 = K;
+
+      parallel_nd(H, [&](int h)
+      {
+        const int h1 = h + h1Begin;
+        const int h2 = h + h2Begin;
+
+        for (int w = 0; w < W; ++w)
+        {
+          const int w1 = w + w1Begin;
+          const int w2 = w + w2Begin;
+          float* dstPtr_C = (float*)output.get(h2, w2);
+
+          // Source is in nChwKc format. In this case C is 1 so this is really nhwc
+          const float* srcPtr_C = srcPtr + h1*W1*C1 + w1*C1;
+
+          #pragma unroll
+          for (int i = 0; i < 3; ++i)
+          {
+            // Load the value
+            float x = srcPtr_C[i];
+
+            // The CNN output may contain negative values or even NaNs, so it must be sanitized
+            x = maxSafe(x, 0.f);
+
+            // Apply the inverse transfer function
+            x = transferFunc->inverse(x);
+
+            // Sanitize and store the final value
+            dstPtr_C[i] = max(x, 0.f);
+          }
+        }
+      });
+    }
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/transfer_function.cpp b/thirdparty/oidn/core/transfer_function.cpp
new file mode 100644
index 0000000000..a33e3c84bc
--- /dev/null
+++ b/thirdparty/oidn/core/transfer_function.cpp
@@ -0,0 +1,95 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#include "transfer_function.h"
+
+namespace oidn {
+
+  const float LogTransferFunction::xScale = 1.f / log(LogTransferFunction::yMax + 1.f);
+  const float PQXTransferFunction::xScale = 1.f / PQXTransferFunction::pqxForward(PQXTransferFunction::yMax * PQXTransferFunction::yScale);
+
+  float AutoexposureNode::autoexposure(const Image& color)
+  {
+    assert(color.format == Format::Float3);
+    return 1.0f;
+
+    /*constexpr float key = 0.18f;
+    constexpr float eps = 1e-8f;
+    constexpr int K = 16; // downsampling amount
+
+    // Downsample the image to minimize sensitivity to noise
+    const int H  = color.height;  // original height
+    const int W  = color.width;   // original width
+    const int HK = (H + K/2) / K; // downsampled height
+    const int WK = (W + K/2) / K; // downsampled width
+
+    // Compute the average log luminance of the downsampled image
+    using Sum = std::pair<float, int>;
+
+    Sum sum =
+      tbb::parallel_reduce(
+        tbb::blocked_range2d<int>(0, HK, 0, WK),
+        Sum(0.f, 0),
+        [&](const tbb::blocked_range2d<int>& r, Sum sum) -> Sum
+        {
+          // Iterate over blocks
+          for (int i = r.rows().begin(); i != r.rows().end(); ++i)
+          {
+            for (int j = r.cols().begin(); j != r.cols().end(); ++j)
+            {
+              // Compute the average luminance in the current block
+              const int beginH = int(ptrdiff_t(i)   * H / HK);
+              const int beginW = int(ptrdiff_t(j)   * W / WK);
+              const int endH   = int(ptrdiff_t(i+1) * H / HK);
+              const int endW   = int(ptrdiff_t(j+1) * W / WK);
+
+              float L = 0.f;
+
+              for (int h = beginH; h < endH; ++h)
+              {
+                for (int w = beginW; w < endW; ++w)
+                {
+                  const float* rgb = (const float*)color.get(h, w);
+
+                  const float r = maxSafe(rgb[0], 0.f);
+                  const float g = maxSafe(rgb[1], 0.f);
+                  const float b = maxSafe(rgb[2], 0.f);
+
+                  L += luminance(r, g, b);
+                }
+              }
+
+              L /= (endH - beginH) * (endW - beginW);
+
+              // Accumulate the log luminance
+              if (L > eps)
+              {
+                sum.first += log2(L);
+                sum.second++;
+              }
+            }
+          }
+
+          return sum;
+        },
+        [](Sum a, Sum b) -> Sum { return Sum(a.first+b.first, a.second+b.second); },
+        tbb::static_partitioner()
+      );
+
+    return (sum.second > 0) ? (key / exp2(sum.first / float(sum.second))) : 1.f;*/
+  }
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/transfer_function.h b/thirdparty/oidn/core/transfer_function.h
new file mode 100644
index 0000000000..35f2833092
--- /dev/null
+++ b/thirdparty/oidn/core/transfer_function.h
@@ -0,0 +1,201 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "image.h"
+#include "node.h"
+
+namespace oidn {
+
+  __forceinline float luminance(float r, float g, float b)
+  {
+    return 0.212671f * r + 0.715160f * g + 0.072169f * b;
+  }
+
+  // Color transfer function base class
+  class TransferFunction
+  {
+  public:
+    virtual ~TransferFunction() = default;
+
+    virtual float forward(float y) const = 0;
+    virtual float inverse(float x) const = 0;
+  };
+
+  // HDR transfer function base class
+  class HDRTransferFunction : public TransferFunction
+  {
+  protected:
+    static constexpr float yMax = 65504.f;
+
+    float exposure;
+    float rcpExposure;
+
+  public:
+    HDRTransferFunction(float exposure = 1.f)
+    {
+      setExposure(exposure);
+    }
+
+    void setExposure(float exposure)
+    {
+      this->exposure = exposure;
+      this->rcpExposure = (exposure != 0.f) ? (1.f / exposure) : 0.f;
+    }
+  };
+
+  // Linear transfer function (LDR)
+  class LinearTransferFunction : public TransferFunction
+  {
+  public:
+    __forceinline float forward(float y) const override
+    {
+      return min(y, 1.f);
+    }
+
+    __forceinline float inverse(float x) const override
+    {
+      return min(x, 1.f);
+    }
+  };
+
+  // 2.2 gamma transfer function (LDR)
+  class GammaTransferFunction : public TransferFunction
+  {
+  public:
+    __forceinline float forward(float y) const override
+    {
+      return min(pow(y, 1.f/2.2f), 1.f);
+    }
+
+    __forceinline float inverse(float x) const override
+    {
+      return min(pow(x, 2.2f), 1.f);
+    }
+  };
+
+  // Logarithmic transfer function (HDR)
+  // Compresses [0..65504] to [0..1]
+  class LogTransferFunction : public HDRTransferFunction
+  {
+  private:
+    static const float xScale;
+
+  public:
+    LogTransferFunction(float exposure = 1.f)
+      : HDRTransferFunction(exposure)
+    {
+    }
+
+    __forceinline float forward(float y) const override
+    {
+      return log(y * exposure + 1.f) * xScale;
+    }
+
+    __forceinline float inverse(float x) const override
+    {
+      return (exp(x * (1.f/xScale)) - 1.f) * rcpExposure;
+    }
+  };
+
+  // PQX transfer function (HDR)
+  // Compresses [0..65504] to [0..1]
+  class PQXTransferFunction : public HDRTransferFunction
+  {
+  private:
+    static constexpr float m1 = 2610.f / 4096.f / 4.f;
+    static constexpr float m2 = 2523.f / 4096.f * 128.f;
+    static constexpr float c1 = 3424.f / 4096.f;
+    static constexpr float c2 = 2413.f / 4096.f * 32.f;
+    static constexpr float c3 = 2392.f / 4096.f * 32.f;
+    static constexpr float  a = 3711.f / 4096.f / 8.f;
+
+    static constexpr float yScale = 100.f / 10000.f;
+    static const float     xScale;
+
+  public:
+    PQXTransferFunction(float exposure = 1.f)
+      : HDRTransferFunction(exposure)
+    {
+    }
+
+    __forceinline float forward(float y) const override
+    {
+      return pqxForward(y * exposure * yScale) * xScale;
+    }
+
+    __forceinline float inverse(float x) const override
+    {
+      return pqxInverse(x * (1.f/xScale)) * (1.f/yScale) * rcpExposure;
+    }
+
+  private:
+    static __forceinline float pqForward(float y)
+    {
+      const float yp = pow(y, m1);
+      return pow((c1 + c2 * yp) * rcp(1.f + c3 * yp), m2);
+    }
+
+    static __forceinline float pqxForward(float y)
+    {
+      if (y <= 1.f)
+        return pqForward(y);
+      else
+        return a * log(y) + 1.f;
+    }
+
+    static __forceinline float pqInverse(float x)
+    {
+      const float xp = pow(x, 1.f/m2);
+      return pow(max((xp - c1) * rcp(c2 - c3 * xp), 0.f), 1.f/m1);
+    }
+
+    static __forceinline float pqxInverse(float x)
+    {
+      if (x <= 1.f)
+        return pqInverse(x);
+      else
+        return exp((x - 1.f) * (1.f/a));
+    }
+  };
+
+  // Autoexposure node
+  class AutoexposureNode : public Node
+  {
+  private:
+    Image color;
+    std::shared_ptr<HDRTransferFunction> transferFunc;
+
+  public:
+    AutoexposureNode(const Image& color,
+                     const std::shared_ptr<HDRTransferFunction>& transferFunc)
+      : color(color),
+        transferFunc(transferFunc)
+    {}
+
+    void execute(stream& sm) override
+    {
+      const float exposure = autoexposure(color);
+      //printf("exposure = %f\n", exposure);
+      transferFunc->setExposure(exposure);
+    }
+
+  private:
+    static float autoexposure(const Image& color);
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/upsample.h b/thirdparty/oidn/core/upsample.h
new file mode 100644
index 0000000000..f6cace44cd
--- /dev/null
+++ b/thirdparty/oidn/core/upsample.h
@@ -0,0 +1,92 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "node.h"
+
+namespace oidn {
+
+  // 2x2 nearest-neighbor upsampling node
+  template<int K>
+  class UpsampleNode : public Node
+  {
+  private:
+    std::shared_ptr<memory> src;
+    std::shared_ptr<memory> dst;
+
+  public:
+    UpsampleNode(const std::shared_ptr<memory>& src,
+                 const std::shared_ptr<memory>& dst)
+      : src(src),
+        dst(dst)
+    {
+      const mkldnn_memory_desc_t& srcDesc = src->get_desc().data;
+      const mkldnn_memory_desc_t& dstDesc = dst->get_desc().data;
+      MAYBE_UNUSED(srcDesc);
+      MAYBE_UNUSED(dstDesc);
+      assert(memory_desc_matches_tag(srcDesc, mkldnn_format_tag_t(BlockedFormat<K>::nChwKc)));
+      assert(memory_desc_matches_tag(dstDesc, mkldnn_format_tag_t(BlockedFormat<K>::nChwKc)));
+      assert(srcDesc.ndims == 4);
+      assert(dstDesc.ndims == 4);
+      assert(srcDesc.data_type == memory::data_type::f32);
+      assert(dstDesc.data_type == memory::data_type::f32);
+      assert(srcDesc.dims[0] == 1);
+      assert(dstDesc.dims[0] == 1);
+      // 2x2 upsampling
+      assert(dstDesc.dims[2] == srcDesc.dims[2] * 2);
+      assert(dstDesc.dims[3] == srcDesc.dims[3] * 2);
+    }
+
+    void execute(stream& sm) override
+    {
+      const mkldnn_memory_desc_t& srcDesc = src->get_desc().data;
+
+      const float* srcPtr = (float*)src->get_data_handle();
+      float* dstPtr = (float*)dst->get_data_handle();
+
+      const int C = srcDesc.dims[1];
+      const int H = srcDesc.dims[2];
+      const int W = srcDesc.dims[3];
+      const int CK = C / K;
+
+      parallel_nd(CK, H, [&](int ck, int h)
+      {
+        const size_t offset = ck*H*W*K + h*W*K;
+        const float* srcPtr_line = srcPtr + offset;
+        float* dstPtr_line0 = dstPtr + offset * 4;
+        float* dstPtr_line1 = dstPtr_line0 + W*2*K; // next line
+
+        for (int w = 0; w < W; ++w)
+        {
+          #pragma unroll
+          for (int k = 0; k < K; k += 4)
+          {
+            const __m128 m = _mm_load_ps(&srcPtr_line[w*K + k]);
+
+            _mm_stream_ps(&dstPtr_line0[w*2*K   + k], m);
+            _mm_stream_ps(&dstPtr_line0[w*2*K+K + k], m);
+            _mm_stream_ps(&dstPtr_line1[w*2*K   + k], m);
+            _mm_stream_ps(&dstPtr_line1[w*2*K+K + k], m);
+          }
+        }
+      });
+    }
+
+    std::shared_ptr<memory> getDst() const override { return dst; }
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/core/weights_reorder.h b/thirdparty/oidn/core/weights_reorder.h
new file mode 100644
index 0000000000..6c5dacb8aa
--- /dev/null
+++ b/thirdparty/oidn/core/weights_reorder.h
@@ -0,0 +1,99 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include "node.h"
+
+namespace oidn {
+
+  // Reorders weights from oihw to padded oihw format
+  template<int K>
+  class WeightsReorderNode : public Node
+  {
+  private:
+    std::shared_ptr<memory> src;
+    std::shared_ptr<memory> dst;
+
+  public:
+    WeightsReorderNode(const std::shared_ptr<memory>& src,
+                       const std::shared_ptr<memory>& dst)
+      : src(src),
+        dst(dst)
+    {
+      const mkldnn_memory_desc_t& srcDesc = src->get_desc().data;
+      const mkldnn_memory_desc_t& dstDesc = dst->get_desc().data;
+      MAYBE_UNUSED(srcDesc);
+      MAYBE_UNUSED(dstDesc);
+      assert(memory_desc_matches_tag(srcDesc, mkldnn_format_tag_t(memory::format_tag::oihw)));
+      assert(memory_desc_matches_tag(dstDesc, mkldnn_format_tag_t(memory::format_tag::oihw)));
+      assert(srcDesc.ndims == 4);
+      assert(dstDesc.ndims == 4);
+      assert(srcDesc.data_type == memory::data_type::f32);
+      assert(dstDesc.data_type == memory::data_type::f32);
+      assert(getPadded<K>(srcDesc.dims[0]) == dstDesc.dims[0]); // OC
+      assert(getPadded<K>(srcDesc.dims[1]) == dstDesc.dims[1]); // IC
+      assert(srcDesc.dims[2] == dstDesc.dims[2]);
+      assert(srcDesc.dims[3] == dstDesc.dims[3]);
+    }
+
+    void execute(stream& sm) override
+    {
+      const mkldnn_memory_desc_t& srcDesc = src->get_desc().data;
+      const mkldnn_memory_desc_t& dstDesc = dst->get_desc().data;
+
+      const float* srcPtr = (float*)src->get_data_handle();
+      float* dstPtr = (float*)dst->get_data_handle();
+
+      const int OC1 = srcDesc.dims[0];
+      const int OC2 = dstDesc.dims[0];
+      const int IC1 = srcDesc.dims[1];
+      const int IC2 = dstDesc.dims[1];
+      const int H   = dstDesc.dims[2];
+      const int W   = dstDesc.dims[3];
+
+      for (int oc = 0; oc < OC2; ++oc)
+      {
+        for (int ic = 0; ic < IC2; ++ic)
+        {
+          for (int h = 0; h < H; ++h)
+          {
+            for (int w = 0; w < W; ++w)
+            {
+              // Output is in oihw format
+              float* dstPtr_c = dstPtr + oc*IC2*H*W + ic*H*W + h*W + w;
+
+              if (oc < OC1 && ic < IC1)
+              {
+                // Input is in oihw format
+                const float* srcPtr_c = srcPtr + oc*IC1*H*W + ic*H*W + h*W + w;
+                *dstPtr_c = *srcPtr_c;
+              }
+              else
+              {
+                // padding
+                *dstPtr_c = 0;
+              }
+            }
+          }
+        }
+      }
+    }
+
+    std::shared_ptr<memory> getDst() const override { return dst; }
+  };
+
+} // namespace oidn
diff --git a/thirdparty/oidn/include/OpenImageDenoise/oidn.h b/thirdparty/oidn/include/OpenImageDenoise/oidn.h
new file mode 100644
index 0000000000..57ba6baa21
--- /dev/null
+++ b/thirdparty/oidn/include/OpenImageDenoise/oidn.h
@@ -0,0 +1,214 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include <stddef.h>
+#include <stdbool.h>
+#include <stdint.h>
+
+#include "version.h"
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#ifndef OIDN_API
+#if defined(_WIN32) && !defined(OIDN_STATIC_LIB)
+#  define OIDN_API __declspec(dllimport)
+#else
+#  define OIDN_API
+#endif
+#endif
+
+// ----------------------------------------------------------------------------
+// Device
+// ----------------------------------------------------------------------------
+
+// Device types
+typedef enum
+{
+  OIDN_DEVICE_TYPE_DEFAULT = 0, // select device automatically
+
+  OIDN_DEVICE_TYPE_CPU = 1, // CPU device
+} OIDNDeviceType;
+
+// Error codes
+typedef enum
+{
+  OIDN_ERROR_NONE                 = 0, // no error occurred
+  OIDN_ERROR_UNKNOWN              = 1, // an unknown error occurred
+  OIDN_ERROR_INVALID_ARGUMENT     = 2, // an invalid argument was specified
+  OIDN_ERROR_INVALID_OPERATION    = 3, // the operation is not allowed
+  OIDN_ERROR_OUT_OF_MEMORY        = 4, // not enough memory to execute the operation
+  OIDN_ERROR_UNSUPPORTED_HARDWARE = 5, // the hardware (e.g. CPU) is not supported
+  OIDN_ERROR_CANCELLED            = 6, // the operation was cancelled by the user
+} OIDNError;
+
+// Error callback function
+typedef void (*OIDNErrorFunction)(void* userPtr, OIDNError code, const char* message);
+
+// Device handle
+typedef struct OIDNDeviceImpl* OIDNDevice;
+
+// Creates a new device.
+OIDN_API OIDNDevice oidnNewDevice(OIDNDeviceType type);
+
+// Retains the device (increments the reference count).
+OIDN_API void oidnRetainDevice(OIDNDevice device);
+
+// Releases the device (decrements the reference count).
+OIDN_API void oidnReleaseDevice(OIDNDevice device);
+
+// Sets a boolean parameter of the device.
+OIDN_API void oidnSetDevice1b(OIDNDevice device, const char* name, bool value);
+
+// Sets an integer parameter of the device.
+OIDN_API void oidnSetDevice1i(OIDNDevice device, const char* name, int value);
+
+// Gets a boolean parameter of the device.
+OIDN_API bool oidnGetDevice1b(OIDNDevice device, const char* name);
+
+// Gets an integer parameter of the device (e.g. "version").
+OIDN_API int oidnGetDevice1i(OIDNDevice device, const char* name);
+
+// Sets the error callback function of the device.
+OIDN_API void oidnSetDeviceErrorFunction(OIDNDevice device, OIDNErrorFunction func, void* userPtr);
+
+// Returns the first unqueried error code stored in the device for the current
+// thread, optionally also returning a string message (if not NULL), and clears
+// the stored error. Can be called with a NULL device as well to check why a
+// device creation failed.
+OIDN_API OIDNError oidnGetDeviceError(OIDNDevice device, const char** outMessage);
+
+// Commits all previous changes to the device.
+// Must be called before first using the device (e.g. creating filters).
+OIDN_API void oidnCommitDevice(OIDNDevice device);
+
+// ----------------------------------------------------------------------------
+// Buffer
+// ----------------------------------------------------------------------------
+
+// Formats for images and other data stored in buffers
+typedef enum
+{
+  OIDN_FORMAT_UNDEFINED = 0,
+
+  // 32-bit single-precision floating point scalar and vector formats
+  OIDN_FORMAT_FLOAT  = 1,
+  OIDN_FORMAT_FLOAT2 = 2,
+  OIDN_FORMAT_FLOAT3 = 3,
+  OIDN_FORMAT_FLOAT4 = 4,
+} OIDNFormat;
+
+// Access modes for mapping buffers
+typedef enum
+{
+  OIDN_ACCESS_READ          = 0, // read-only access
+  OIDN_ACCESS_WRITE         = 1, // write-only access
+  OIDN_ACCESS_READ_WRITE    = 2, // read and write access
+  OIDN_ACCESS_WRITE_DISCARD = 3, // write-only access, previous contents discarded
+} OIDNAccess;
+
+// Buffer handle
+typedef struct OIDNBufferImpl* OIDNBuffer;
+
+// Creates a new buffer (data allocated and owned by the device).
+OIDN_API OIDNBuffer oidnNewBuffer(OIDNDevice device, size_t byteSize);
+
+// Creates a new shared buffer (data allocated and owned by the user).
+OIDN_API OIDNBuffer oidnNewSharedBuffer(OIDNDevice device, void* ptr, size_t byteSize);
+
+// Maps a region of the buffer to host memory.
+// If byteSize is 0, the maximum available amount of memory will be mapped.
+OIDN_API void* oidnMapBuffer(OIDNBuffer buffer, OIDNAccess access, size_t byteOffset, size_t byteSize);
+
+// Unmaps a region of the buffer.
+// mappedPtr must be a pointer returned by a previous call to oidnMapBuffer.
+OIDN_API void oidnUnmapBuffer(OIDNBuffer buffer, void* mappedPtr);
+
+// Retains the buffer (increments the reference count).
+OIDN_API void oidnRetainBuffer(OIDNBuffer buffer);
+
+// Releases the buffer (decrements the reference count).
+OIDN_API void oidnReleaseBuffer(OIDNBuffer buffer);
+
+// ----------------------------------------------------------------------------
+// Filter
+// ----------------------------------------------------------------------------
+
+// Progress monitor callback function
+typedef bool (*OIDNProgressMonitorFunction)(void* userPtr, double n);
+
+// Filter handle
+typedef struct OIDNFilterImpl* OIDNFilter;
+
+// Creates a new filter of the specified type (e.g. "RT").
+OIDN_API OIDNFilter oidnNewFilter(OIDNDevice device, const char* type);
+
+// Retains the filter (increments the reference count).
+OIDN_API void oidnRetainFilter(OIDNFilter filter);
+
+// Releases the filter (decrements the reference count).
+OIDN_API void oidnReleaseFilter(OIDNFilter filter);
+
+// Sets an image parameter of the filter (stored in a buffer).
+// If bytePixelStride and/or byteRowStride are zero, these will be computed automatically.
+OIDN_API void oidnSetFilterImage(OIDNFilter filter, const char* name,
+                                 OIDNBuffer buffer, OIDNFormat format,
+                                 size_t width, size_t height,
+                                 size_t byteOffset,
+                                 size_t bytePixelStride, size_t byteRowStride);
+
+// Sets an image parameter of the filter (owned by the user).
+// If bytePixelStride and/or byteRowStride are zero, these will be computed automatically.
+OIDN_API void oidnSetSharedFilterImage(OIDNFilter filter, const char* name,
+                                       void* ptr, OIDNFormat format,
+                                       size_t width, size_t height,
+                                       size_t byteOffset,
+                                       size_t bytePixelStride, size_t byteRowStride);
+
+// Sets a boolean parameter of the filter.
+OIDN_API void oidnSetFilter1b(OIDNFilter filter, const char* name, bool value);
+
+// Gets a boolean parameter of the filter.
+OIDN_API bool oidnGetFilter1b(OIDNFilter filter, const char* name);
+
+// Sets an integer parameter of the filter.
+OIDN_API void oidnSetFilter1i(OIDNFilter filter, const char* name, int value);
+
+// Gets an integer parameter of the filter.
+OIDN_API int oidnGetFilter1i(OIDNFilter filter, const char* name);
+
+// Sets a float parameter of the filter.
+OIDN_API void oidnSetFilter1f(OIDNFilter filter, const char* name, float value);
+
+// Gets a float parameter of the filter.
+OIDN_API float oidnGetFilter1f(OIDNFilter filter, const char* name);
+
+// Sets the progress monitor callback function of the filter.
+OIDN_API void oidnSetFilterProgressMonitorFunction(OIDNFilter filter, OIDNProgressMonitorFunction func, void* userPtr);
+
+// Commits all previous changes to the filter.
+// Must be called before first executing the filter.
+OIDN_API void oidnCommitFilter(OIDNFilter filter);
+
+// Executes the filter.
+OIDN_API void oidnExecuteFilter(OIDNFilter filter);
+
+#if defined(__cplusplus)
+}
+#endif
diff --git a/thirdparty/oidn/include/OpenImageDenoise/oidn.hpp b/thirdparty/oidn/include/OpenImageDenoise/oidn.hpp
new file mode 100644
index 0000000000..9f95a56fe1
--- /dev/null
+++ b/thirdparty/oidn/include/OpenImageDenoise/oidn.hpp
@@ -0,0 +1,468 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#include <algorithm>
+#include "oidn.h"
+
+namespace oidn {
+
+  // --------------------------------------------------------------------------
+  // Buffer
+  // --------------------------------------------------------------------------
+
+  // Formats for images and other data stored in buffers
+  enum class Format
+  {
+    Undefined = OIDN_FORMAT_UNDEFINED,
+
+    // 32-bit single-precision floating point scalar and vector formats
+    Float  = OIDN_FORMAT_FLOAT,
+    Float2 = OIDN_FORMAT_FLOAT2,
+    Float3 = OIDN_FORMAT_FLOAT3,
+    Float4 = OIDN_FORMAT_FLOAT4,
+  };
+
+  // Access modes for mapping buffers
+  enum class Access
+  {
+    Read         = OIDN_ACCESS_READ,          // read-only access
+    Write        = OIDN_ACCESS_WRITE,         // write-only access
+    ReadWrite    = OIDN_ACCESS_READ_WRITE,    // read and write access
+    WriteDiscard = OIDN_ACCESS_WRITE_DISCARD, // write-only access, previous contents discarded
+  };
+
+  // Buffer object with automatic reference counting
+  class BufferRef
+  {
+  private:
+    OIDNBuffer handle;
+
+  public:
+    BufferRef() : handle(nullptr) {}
+    BufferRef(OIDNBuffer handle) : handle(handle) {}
+
+    BufferRef(const BufferRef& other) : handle(other.handle)
+    {
+      if (handle)
+        oidnRetainBuffer(handle);
+    }
+
+    BufferRef(BufferRef&& other) : handle(other.handle)
+    {
+      other.handle = nullptr;
+    }
+
+    BufferRef& operator =(const BufferRef& other)
+    {
+      if (&other != this)
+      {
+        if (other.handle)
+          oidnRetainBuffer(other.handle);
+        if (handle)
+          oidnReleaseBuffer(handle);
+        handle = other.handle;
+      }
+      return *this;
+    }
+
+    BufferRef& operator =(BufferRef&& other)
+    {
+      std::swap(handle, other.handle);
+      return *this;
+    }
+
+    BufferRef& operator =(OIDNBuffer other)
+    {
+      if (other)
+        oidnRetainBuffer(other);
+      if (handle)
+        oidnReleaseBuffer(handle);
+      handle = other;
+      return *this;
+    }
+
+    ~BufferRef()
+    {
+      if (handle)
+        oidnReleaseBuffer(handle);
+    }
+
+    OIDNBuffer getHandle() const
+    {
+      return handle;
+    }
+
+    operator bool() const
+    {
+      return handle != nullptr;
+    }
+
+    // Maps a region of the buffer to host memory.
+    // If byteSize is 0, the maximum available amount of memory will be mapped.
+    void* map(Access access = Access::ReadWrite, size_t byteOffset = 0, size_t byteSize = 0)
+    {
+      return oidnMapBuffer(handle, (OIDNAccess)access, byteOffset, byteSize);
+    }
+
+    // Unmaps a region of the buffer.
+    // mappedPtr must be a pointer returned by a previous call to map.
+    void unmap(void* mappedPtr)
+    {
+      oidnUnmapBuffer(handle, mappedPtr);
+    }
+  };
+
+  // --------------------------------------------------------------------------
+  // Filter
+  // --------------------------------------------------------------------------
+
+  // Progress monitor callback function
+  typedef bool (*ProgressMonitorFunction)(void* userPtr, double n);
+
+  // Filter object with automatic reference counting
+  class FilterRef
+  {
+  private:
+    OIDNFilter handle;
+
+  public:
+    FilterRef() : handle(nullptr) {}
+    FilterRef(OIDNFilter handle) : handle(handle) {}
+
+    FilterRef(const FilterRef& other) : handle(other.handle)
+    {
+      if (handle)
+        oidnRetainFilter(handle);
+    }
+
+    FilterRef(FilterRef&& other) : handle(other.handle)
+    {
+      other.handle = nullptr;
+    }
+
+    FilterRef& operator =(const FilterRef& other)
+    {
+      if (&other != this)
+      {
+        if (other.handle)
+          oidnRetainFilter(other.handle);
+        if (handle)
+          oidnReleaseFilter(handle);
+        handle = other.handle;
+      }
+      return *this;
+    }
+
+    FilterRef& operator =(FilterRef&& other)
+    {
+      std::swap(handle, other.handle);
+      return *this;
+    }
+
+    FilterRef& operator =(OIDNFilter other)
+    {
+      if (other)
+        oidnRetainFilter(other);
+      if (handle)
+        oidnReleaseFilter(handle);
+      handle = other;
+      return *this;
+    }
+
+    ~FilterRef()
+    {
+      if (handle)
+        oidnReleaseFilter(handle);
+    }
+
+    OIDNFilter getHandle() const
+    {
+      return handle;
+    }
+
+    operator bool() const
+    {
+      return handle != nullptr;
+    }
+
+    // Sets an image parameter of the filter (stored in a buffer).
+    void setImage(const char* name,
+                  const BufferRef& buffer, Format format,
+                  size_t width, size_t height,
+                  size_t byteOffset = 0,
+                  size_t bytePixelStride = 0, size_t byteRowStride = 0)
+    {
+      oidnSetFilterImage(handle, name,
+                         buffer.getHandle(), (OIDNFormat)format,
+                         width, height,
+                         byteOffset,
+                         bytePixelStride, byteRowStride);
+    }
+
+    // Sets an image parameter of the filter (owned by the user).
+    void setImage(const char* name,
+                  void* ptr, Format format,
+                  size_t width, size_t height,
+                  size_t byteOffset = 0,
+                  size_t bytePixelStride = 0, size_t byteRowStride = 0)
+    {
+      oidnSetSharedFilterImage(handle, name,
+                               ptr, (OIDNFormat)format,
+                               width, height,
+                               byteOffset,
+                               bytePixelStride, byteRowStride);
+    }
+
+    // Sets a boolean parameter of the filter.
+    void set(const char* name, bool value)
+    {
+      oidnSetFilter1b(handle, name, value);
+    }
+
+    // Sets an integer parameter of the filter.
+    void set(const char* name, int value)
+    {
+      oidnSetFilter1i(handle, name, value);
+    }
+
+    // Sets a float parameter of the filter.
+    void set(const char* name, float value)
+    {
+      oidnSetFilter1f(handle, name, value);
+    }
+
+    // Gets a parameter of the filter.
+    template<typename T>
+    T get(const char* name);
+
+    // Sets the progress monitor callback function of the filter.
+    void setProgressMonitorFunction(ProgressMonitorFunction func, void* userPtr = nullptr)
+    {
+      oidnSetFilterProgressMonitorFunction(handle, (OIDNProgressMonitorFunction)func, userPtr);
+    }
+
+    // Commits all previous changes to the filter.
+    void commit()
+    {
+      oidnCommitFilter(handle);
+    }
+
+    // Executes the filter.
+    void execute()
+    {
+      oidnExecuteFilter(handle);
+    }
+  };
+
+  // Gets a boolean parameter of the filter.
+  template<>
+  inline bool FilterRef::get(const char* name)
+  {
+    return oidnGetFilter1b(handle, name);
+  }
+
+  // Gets an integer parameter of the filter.
+  template<>
+  inline int FilterRef::get(const char* name)
+  {
+    return oidnGetFilter1i(handle, name);
+  }
+
+  // Gets a float parameter of the filter.
+  template<>
+  inline float FilterRef::get(const char* name)
+  {
+    return oidnGetFilter1f(handle, name);
+  }
+
+  // --------------------------------------------------------------------------
+  // Device
+  // --------------------------------------------------------------------------
+
+  // Device types
+  enum class DeviceType
+  {
+    Default = OIDN_DEVICE_TYPE_DEFAULT, // select device automatically
+
+    CPU = OIDN_DEVICE_TYPE_CPU, // CPU device
+  };
+
+  // Error codes
+  enum class Error
+  {
+    None                = OIDN_ERROR_NONE,                 // no error occurred
+    Unknown             = OIDN_ERROR_UNKNOWN,              // an unknown error occurred
+    InvalidArgument     = OIDN_ERROR_INVALID_ARGUMENT,     // an invalid argument was specified
+    InvalidOperation    = OIDN_ERROR_INVALID_OPERATION,    // the operation is not allowed
+    OutOfMemory         = OIDN_ERROR_OUT_OF_MEMORY,        // not enough memory to execute the operation
+    UnsupportedHardware = OIDN_ERROR_UNSUPPORTED_HARDWARE, // the hardware (e.g. CPU) is not supported
+    Cancelled           = OIDN_ERROR_CANCELLED,            // the operation was cancelled by the user
+  };
+
+  // Error callback function
+  typedef void (*ErrorFunction)(void* userPtr, Error code, const char* message);
+
+  // Device object with automatic reference counting
+  class DeviceRef
+  {
+  private:
+    OIDNDevice handle;
+
+  public:
+    DeviceRef() : handle(nullptr) {}
+    DeviceRef(OIDNDevice handle) : handle(handle) {}
+
+    DeviceRef(const DeviceRef& other) : handle(other.handle)
+    {
+      if (handle)
+        oidnRetainDevice(handle);
+    }
+
+    DeviceRef(DeviceRef&& other) : handle(other.handle)
+    {
+      other.handle = nullptr;
+    }
+
+    DeviceRef& operator =(const DeviceRef& other)
+    {
+      if (&other != this)
+      {
+        if (other.handle)
+          oidnRetainDevice(other.handle);
+        if (handle)
+          oidnReleaseDevice(handle);
+        handle = other.handle;
+      }
+      return *this;
+    }
+
+    DeviceRef& operator =(DeviceRef&& other)
+    {
+      std::swap(handle, other.handle);
+      return *this;
+    }
+
+    DeviceRef& operator =(OIDNDevice other)
+    {
+      if (other)
+        oidnRetainDevice(other);
+      if (handle)
+        oidnReleaseDevice(handle);
+      handle = other;
+      return *this;
+    }
+
+    ~DeviceRef()
+    {
+      if (handle)
+        oidnReleaseDevice(handle);
+    }
+
+    OIDNDevice getHandle() const
+    {
+      return handle;
+    }
+
+    operator bool() const
+    {
+      return handle != nullptr;
+    }
+
+    // Sets a boolean parameter of the device.
+    void set(const char* name, bool value)
+    {
+      oidnSetDevice1b(handle, name, value);
+    }
+
+    // Sets an integer parameter of the device.
+    void set(const char* name, int value)
+    {
+      oidnSetDevice1i(handle, name, value);
+    }
+
+    // Gets a parameter of the device.
+    template<typename T>
+    T get(const char* name);
+
+    // Sets the error callback function of the device.
+    void setErrorFunction(ErrorFunction func, void* userPtr = nullptr)
+    {
+      oidnSetDeviceErrorFunction(handle, (OIDNErrorFunction)func, userPtr);
+    }
+
+    // Returns the first unqueried error code and clears the stored error.
+    // Can be called for a null device as well to check why a device creation failed.
+    Error getError()
+    {
+      return (Error)oidnGetDeviceError(handle, nullptr);
+    }
+
+    // Returns the first unqueried error code and string message, and clears the stored error.
+    // Can be called for a null device as well to check why a device creation failed.
+    Error getError(const char*& outMessage)
+    {
+      return (Error)oidnGetDeviceError(handle, &outMessage);
+    }
+
+    // Commits all previous changes to the device.
+    // Must be called before first using the device (e.g. creating filters).
+    void commit()
+    {
+      oidnCommitDevice(handle);
+    }
+
+    // Creates a new buffer (data allocated and owned by the device).
+    BufferRef newBuffer(size_t byteSize)
+    {
+      return oidnNewBuffer(handle, byteSize);
+    }
+
+    // Creates a new shared buffer (data allocated and owned by the user).
+    BufferRef newBuffer(void* ptr, size_t byteSize)
+    {
+      return oidnNewSharedBuffer(handle, ptr, byteSize);
+    }
+
+    // Creates a new filter of the specified type (e.g. "RT").
+    FilterRef newFilter(const char* type)
+    {
+      return oidnNewFilter(handle, type);
+    }
+  };
+
+  // Gets a boolean parameter of the device.
+  template<>
+  inline bool DeviceRef::get(const char* name)
+  {
+    return oidnGetDevice1b(handle, name);
+  }
+
+  // Gets an integer parameter of the device (e.g. "version").
+  template<>
+  inline int DeviceRef::get(const char* name)
+  {
+    return oidnGetDevice1i(handle, name);
+  }
+
+  // Creates a new device.
+  inline DeviceRef newDevice(DeviceType type = DeviceType::Default)
+  {
+    return DeviceRef(oidnNewDevice((OIDNDeviceType)type));
+  }
+
+} // namespace oidn
diff --git a/thirdparty/oidn/include/OpenImageDenoise/version.h b/thirdparty/oidn/include/OpenImageDenoise/version.h
new file mode 100644
index 0000000000..66b347c992
--- /dev/null
+++ b/thirdparty/oidn/include/OpenImageDenoise/version.h
@@ -0,0 +1,23 @@
+// ======================================================================== //
+// Copyright 2009-2019 Intel Corporation                                    //
+//                                                                          //
+// Licensed under the Apache License, Version 2.0 (the "License");          //
+// you may not use this file except in compliance with the License.         //
+// You may obtain a copy of the License at                                  //
+//                                                                          //
+//     http://www.apache.org/licenses/LICENSE-2.0                           //
+//                                                                          //
+// Unless required by applicable law or agreed to in writing, software      //
+// distributed under the License is distributed on an "AS IS" BASIS,        //
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. //
+// See the License for the specific language governing permissions and      //
+// limitations under the License.                                           //
+// ======================================================================== //
+
+#pragma once
+
+#define OIDN_VERSION_MAJOR 1
+#define OIDN_VERSION_MINOR 1
+#define OIDN_VERSION_PATCH 0
+#define OIDN_VERSION 10100
+#define OIDN_VERSION_STRING "1.1.0"
diff --git a/thirdparty/oidn/mkl-dnn/LICENSE b/thirdparty/oidn/mkl-dnn/LICENSE
new file mode 100644
index 0000000000..d13f7b7ca0
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/LICENSE
@@ -0,0 +1,214 @@
+                                 Apache License
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+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
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+       http://www.apache.org/licenses/LICENSE-2.0
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+   Unless required by applicable law or agreed to in writing, software
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+   ============================================================================
+
+   Intel MKL-DNN includes components with separate copyright
+   notices and license terms.
+
+   XByak, 3-clause BSD license
+   Copyright (c) 2007 MITSUNARI Shigeo
+   See full copyright notice and license text in src/cpu/xbyak/COPYRIGHT
+
+   gtest, 3-clause BSD license
+   Copyright 2008, Google Inc.
+   See full copyright notice and license text in tests/gtests/gtest/LICENSE
diff --git a/thirdparty/oidn/mkl-dnn/include/mkldnn.h b/thirdparty/oidn/mkl-dnn/include/mkldnn.h
new file mode 100644
index 0000000000..9b64994922
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/include/mkldnn.h
@@ -0,0 +1,1771 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MKLDNN_H
+#define MKLDNN_H
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+
+/* All symbols shall be internal unless marked as MKLDNN_API */
+#if defined _WIN32 || defined __CYGWIN__
+#   define MKLDNN_HELPER_DLL_IMPORT __declspec(dllimport)
+#   define MKLDNN_HELPER_DLL_EXPORT __declspec(dllexport)
+#else
+#   if __GNUC__ >= 4
+#       define MKLDNN_HELPER_DLL_IMPORT __attribute__ ((visibility ("default")))
+#       define MKLDNN_HELPER_DLL_EXPORT __attribute__ ((visibility ("default")))
+#   else
+#       define MKLDNN_HELPER_DLL_IMPORT
+#       define MKLDNN_HELPER_DLL_EXPORT
+#   endif
+#endif
+
+#ifdef MKLDNN_DLL
+#   ifdef MKLDNN_DLL_EXPORTS
+#       define MKLDNN_API MKLDNN_HELPER_DLL_EXPORT
+#   else
+#       define MKLDNN_API MKLDNN_HELPER_DLL_IMPORT
+#   endif
+#else
+#   define MKLDNN_API
+#endif
+
+#if defined (__GNUC__)
+#   define MKLDNN_DEPRECATED __attribute__((deprecated))
+#elif defined(_MSC_VER)
+#   define MKLDNN_DEPRECATED __declspec(deprecated)
+#else
+#   define MKLDNN_DEPRECATED
+#endif
+
+#include "mkldnn_types.h"
+#include "mkldnn_version.h"
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/** @addtogroup c_api C API
+ * @{ */
+
+/** @addtogroup c_api_primitive Primitive operations
+ * @{ */
+
+/** @addtogroup c_api_primitive_common Common primitive operations
+ * @{ */
+
+/** Creates a primitive descriptor @p iterator for given @p op_desc, @p attr,
+ * @p engine, and optionally a hint primitive descriptor from forward
+ * propagation (required for backward propagation). Pass @c NULL for forward
+ * propagation.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_iterator_create(
+        mkldnn_primitive_desc_iterator_t *iterator,
+        const_mkldnn_op_desc_t op_desc, const_mkldnn_primitive_attr_t attr,
+        mkldnn_engine_t engine,
+        const_mkldnn_primitive_desc_t hint_forward_primitive_desc);
+
+/** Iterates over primitive descriptors. Returns #mkldnn_iterator_ends if no
+ * more primitive descriptors are available. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_iterator_next(
+        mkldnn_primitive_desc_iterator_t iterator);
+
+/** Fetches the current primitive descriptor.
+ *
+ * @note
+ *     The user should delete the fetched primitive descriptor using
+ *     mkldnn_primitive_desc_destroy() once it is no longer needed. */
+mkldnn_primitive_desc_t MKLDNN_API mkldnn_primitive_desc_iterator_fetch(
+        const_mkldnn_primitive_desc_iterator_t iterator);
+
+/** Deletes a primitive descriptor @p iterator */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_iterator_destroy(
+        mkldnn_primitive_desc_iterator_t iterator);
+
+/** Creates a @p primitive_desc using @p op_desc, @p attr, @p engine, and
+ * optionally a hint primitive descriptor from forward propagation. The call is
+ * equivalent to creating a primitive descriptor iterator, immediately fetching
+ * a primitive descriptor, and then destroying the iterator. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_create(
+        mkldnn_primitive_desc_t *primitive_desc,
+        const_mkldnn_op_desc_t op_desc, const_mkldnn_primitive_attr_t attr,
+        mkldnn_engine_t engine,
+        const_mkldnn_primitive_desc_t hint_forward_primitive_desc);
+
+/** Makes a copy of a @p primitive_desc. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_clone(
+        mkldnn_primitive_desc_t *primitive_desc,
+        const_mkldnn_primitive_desc_t existing_primitive_desc);
+
+/** Returns a constant reference to the attribute of a @p primitive_desc.
+ *
+ * @warning
+ *      The user should not destroy the obtained @p attr.
+ *
+ * @warning
+ *      The lifetime of an @p attr is the same as that of a @p primitive_desc,
+ *      so it is illegal to use the @p attr once @p primitive_desc has been
+ *      destroyed. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_get_attr(
+        const_mkldnn_primitive_desc_t primitive_desc,
+        const_mkldnn_primitive_attr_t *attr);
+
+/** Deletes a @p primitive_desc. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_destroy(
+        mkldnn_primitive_desc_t primitive_desc);
+
+/** Queries primitive descriptor
+ *
+ * One of the most typical use cases is to query a convolution primitive
+ * descriptor created with source, weights, and destination formats equal
+ * to #mkldnn_format_tag_any about the corresponding memory descriptors
+ * (@p what equals #mkldnn_query_src_md, #mkldnn_query_weights_md, and
+ * #mkldnn_query_dst_md respectively) to be able to prepare memory and
+ * create reorders if required.
+ *
+ * Another quite typical use case is to query an operation primitive
+ * descriptor for a workspace (@p what equals #mkldnn_query_workspace_md).
+ * The returned status #mkldnn_not_required indicates that a workspace is
+ * not required.
+ *
+ * A few other possibilities:
+ *  - query an operation primitive descriptor for the underlying operation
+ *    descriptor (#mkldnn_query_convolution_d, #mkldnn_query_eltwise_d,
+ *    #mkldnn_query_rnn_d, etc.)
+ *  - query an operation primitive descriptor for the implementation
+ *    information string (#mkldnn_query_impl_info_str)
+ *  - query an operation primitive descriptor for the number of inputs and
+ *    outputs (#mkldnn_query_num_of_inputs_s32 and
+ *    #mkldnn_query_num_of_outputs_s32 respectively)
+ *
+ * @sa mkldnn_query_t for more options
+ */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_desc_query(
+        const_mkldnn_primitive_desc_t primitive_desc, mkldnn_query_t what,
+        int index, void *result);
+
+/** Queries primitive descriptor for memory descriptor
+ *
+ * @returns NULL in case of any error.
+ *
+ * This is just a specialized version of mkldnn_primitive_desc_query
+ * used for convenience.
+ */
+const mkldnn_memory_desc_t MKLDNN_API *mkldnn_primitive_desc_query_md(
+        const_mkldnn_primitive_desc_t primitive_desc, mkldnn_query_t what,
+        int index);
+
+/** Queries primitive descriptor for signed 32bit int
+ *
+ * @returns 0 in case of any error (in particular if the queried entity is
+ * not of type int32_t). Note that 0 might also be the actual returned
+ * value.
+ *
+ * This is just a specialized version of mkldnn_primitive_desc_query
+ * used for convenience.
+ */
+int MKLDNN_API mkldnn_primitive_desc_query_s32(
+        const_mkldnn_primitive_desc_t primitive_desc, mkldnn_query_t what,
+        int index);
+
+/** Creates a @p primitive using a @p primitive_desc descriptor. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_create(
+        mkldnn_primitive_t *primitive,
+        const_mkldnn_primitive_desc_t primitive_desc);
+
+/** Executes a @p primitive using a @p stream, and @p nargs arguments
+ * @p args. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_execute(
+        const_mkldnn_primitive_t primitive, mkldnn_stream_t stream,
+        int nargs, const mkldnn_exec_arg_t *args);
+
+/** Retrieves a reference to the @p primitive_desc descriptor of given @p
+ * primitive.
+ *
+ * @warning
+ *     The returned object must not be destroyed by the user. The @c const
+ *     qualifier of the returned object prevents such attempts. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_get_primitive_desc(
+        const_mkldnn_primitive_t primitive,
+        const_mkldnn_primitive_desc_t *primitive_desc);
+
+/** Deletes a @p primitive. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_destroy(
+        mkldnn_primitive_t primitive);
+
+/** @} */
+
+/** @addtogroup c_api_attributes Attributes
+ * An extension for controlling primitive behavior.
+ * @{ */
+
+/** Creates an empty (default) @p attr attribute. All the parameters are set to
+ * default values.
+ *
+ * An empty attribute is used in primitive descriptor creation whenever it
+ * is not passed explicitly, e.g. in mkldnn_primitive_desc_create.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_create(
+        mkldnn_primitive_attr_t *attr);
+
+/** Makes a copy of an @p existing_attr. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_clone(
+        mkldnn_primitive_attr_t *attr,
+        const_mkldnn_primitive_attr_t existing_attr);
+
+/** Deletes an @p attr. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_destroy(
+        mkldnn_primitive_attr_t attr);
+
+/** Returns the scratchpad @p mode set in the attribute @p attr */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_get_scratchpad_mode(
+        const_mkldnn_primitive_attr_t attr, mkldnn_scratchpad_mode_t *mode);
+
+/** Sets scratchpad @p mode.
+ *
+ * The possible values are: #mkldnn_scratchpad_mode_library (default) and
+ * #mkldnn_scratchpad_mode_user. */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_set_scratchpad_mode(
+        mkldnn_primitive_attr_t attr, mkldnn_scratchpad_mode_t mode);
+
+/** Returns @p count, correspondence scale @p mask, and a pointer to a constant
+ * floating point array of output @p scales for given @p attr, previously set
+ * by mkldnn_primitive_attr_set_output_scales.
+ *
+ * @warning
+ *      The @p scales array points to the internal @p attr field, so the user
+ *      should not modify or destroy @p scales.
+ *
+ * @warning
+ *      The lifetime of @p scales is the same as that of the @p attr to which it
+ *      belongs, so it is illegal to use @p scales after @p attr is destroyed.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_get_output_scales(
+        const_mkldnn_primitive_attr_t attr, mkldnn_dim_t *count, int *mask,
+        const float **scales);
+
+/** Sets output @p scales for primitive operations. The number of elements @p
+ * count and correspondence scale @p mask are stored for future use.
+ *
+ * The @p mask argument defines the correspondence between the output tensor
+ * dimensions and the @p scales array. Set the i-th bit of @p mask to 1 to use a
+ * dedicated scaling factor for each slice of the output tensor over the i-th
+ * dimension. Set @p mask to 0 to use a common scaling factor for the whole
+ * output tensor.
+ *
+ * @note
+ *      The dimension order is always native and does not depend on the actual
+ *      layout used. Examples:
+ *       - 2D dimensional data the order of dimensions is always: (n, c)
+ *       - 4D dimensional data the order is always: (n, c, h, w)
+ *       - 5D dimensional weights the order is always: (g, oc, ic, kh, kw)
+ *
+ * Example usage:
+ * @code
+ *      int mb = 32, oc = 32, oh = 14, ow = 14; // convolution output params
+ *      float scales[oc] = { ... }; // unique output scales per output channel
+ *      int oc_dim = 1; // mb_dim = 0, channel_dim = 1, height_dim = 2, ...
+ *
+ *      mkldnn_convolution_desc_t cd; // create & configure convolution op_desc
+ *
+ *      mkldnn_primitive_attr_t attr;
+ *      mkldnn_primitive_attr_create(&attr);  // create default attributes
+ *      mkldnn_primitive_attr_set_output_scales(attr, oc, 1 << oc_dim, scales);
+ *
+ *      mkldnn_primitive_desc_t cpd;
+ *      mkldnn_primitive_desc_create(&cpd, &cd, attr, NULL);
+ * @endcode
+ *
+ * @note
+ *      There is no way to check that @p count corresponds to @p mask until an
+ *      actual primitive descriptor is created, so it is the user's
+ *      responsibility to set proper values. The following formula must hold:
+ *
+ *      \f[count = \prod\limits_{d \in mask} output.dims[d]\f]
+ */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_set_output_scales(
+        mkldnn_primitive_attr_t attr, mkldnn_dim_t count, int mask,
+        const float *scales);
+
+/** Returns @p post_ops for given @p attr.
+ *
+ * @warning
+ *      @p post_ops points to the internal @p attr field, so the user should not
+ *      modify or destroy @p post_ops. Also, the lifetime of @p post_ops is the
+ *      same as that of the @p attr it belongs to, so it is illegal to use @p
+ *      post_ops after @p attr has been destroyed.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_get_post_ops(
+        const_mkldnn_primitive_attr_t attr, const_mkldnn_post_ops_t *post_ops);
+
+/** Sets configured @p post_ops to an attribute @p attr for future use (when
+ * primitive descriptor is being created).
+ *
+ * @note
+ *      At this point in time, there is no way to check whether the primitive
+ *      descriptor does or does not support a given sequence of post operations.
+ *      Therefore the user should handle an error that might occur at the
+ *      mkldnn_primitive_desc_create call.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_set_post_ops(
+        mkldnn_primitive_attr_t attr, const_mkldnn_post_ops_t post_ops);
+
+/** @addtogroup c_api_attributes_post_ops Sequence of post operations
+ * An extension for performing extra operations after a base operation.
+ * @{ */
+
+/** Creates an empty sequence of post operations @p post_ops. */
+mkldnn_status_t MKLDNN_API mkldnn_post_ops_create(mkldnn_post_ops_t *post_ops);
+
+/** Deletes a @p post_ops sequence. */
+mkldnn_status_t MKLDNN_API mkldnn_post_ops_destroy(mkldnn_post_ops_t post_ops);
+
+/** Returns the @p length of post operations for given @p post_ops. */
+int MKLDNN_API mkldnn_post_ops_len(const_mkldnn_post_ops_t post_ops);
+
+/** Returns the type of post operation with index @p index in given
+ * @p post_ops. In case of error, returns #mkldnn_undefined_primitive. */
+mkldnn_primitive_kind_t MKLDNN_API mkldnn_post_ops_get_kind(
+        const_mkldnn_post_ops_t post_ops, int index);
+
+/** Appends accumulation (sum) post operation to the @p post_ops. Prior to
+ * accumulating the result, the previous value would be multiplied by @p scale.
+ *
+ * The kind of this post operation is #mkldnn_sum.
+ *
+ * This feature might improve performance for cases like residual learning
+ * blocks, where the result of convolution is accumulated to the previously
+ * computed activations. The parameter @p scale might be extreme for the
+ * integer-based computations when the result and previous activations have
+ * different logical scaling factors.
+ *
+ * In the simplest case when the accumulation is the only post operation, the
+ * computations would be:
+ * dst[] <- scale * dst[] + op(...) // instead of dst[] <- op(...)
+ *
+ * @note
+ *      This post operation (as well as all the others) disregards the original
+ *      layout of the destination; that is, the layout of the original
+ *      destination is expected to be the same as the layout of the stored
+ *      destination.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_post_ops_append_sum(
+        mkldnn_post_ops_t post_ops, float scale);
+
+/** Gets the parameters of the accumulation (sum) post operation with index
+ * @p index in the sequence of @p post_ops.
+ *
+ * @note
+ *      If index @p index would not correspond to the accumulation post
+ *      operation, the function returns #mkldnn_invalid_arguments.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_post_ops_get_params_sum(
+        const_mkldnn_post_ops_t post_ops, int index, float *scale);
+
+/** Appends eltwise post operation to the @p post_ops with given parameters
+ * @p kind, @p alpha, and @p beta (@sa mkldnn_eltwise_forward_desc_init and
+ * mkldnn_eltwise_desc_t).
+ *
+ * The kind of this post operation is #mkldnn_eltwise.
+ *
+ * In the simplest case when the eltwise is the only post operation, the
+ * computations would be:
+ * dst[] <- scale * eltwise_op ( op(...) ) // instead of dst[] <- op(...)
+ * where eltwise_op is configured with the given parameters.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_post_ops_append_eltwise(
+        mkldnn_post_ops_t post_ops, float scale, mkldnn_alg_kind_t alg,
+        float alpha, float beta);
+
+/** Gets the eltwise parameters of the post operation with index @p index in
+ * the sequence of @p post_ops.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_post_ops_get_params_eltwise(
+        const_mkldnn_post_ops_t post_ops, int index, float *scale,
+        mkldnn_alg_kind_t *alg, float *alpha, float *beta);
+
+/** @} */
+
+/** @} */
+
+/** @addtogroup c_api_memory Memory
+ * A primitive to describe and store data.
+ *
+ * The library supports various data types and formats. Memory hierarchy
+ * consists of three levels of abstraction:
+ * 1. **Memory descriptor** -- engine agnostic logical description of data
+ *      (number of dimensions, dimensions themselves, and data type), and
+ *      optionally the format/layout that describes the physical representation
+ *      of data in memory. If the format is not known yet, one can pass
+ *      #mkldnn_format_tag_any. This approach is used to allow compute-intensive
+ *      primitives to specify the most appropriate format on their own with
+ *      users required to reorder the data if the incoming format doesn't match
+ *      the primitive's selection. Memory descriptor can be initialized with
+ *      mkldnn_memory_desc_init_by_tag() or mkldnn_memory_desc_init_by_strides()
+ *      functions, or by directly filling the mkldnn_memory_desc_t structure.
+ *      The latter requires deep knowledge of how the physical data
+ *      representation is mapped to the structure.
+ *      The @ref understanding_memory_formats topic should shed some light on
+ *      that.
+ *      For the fully defined memory descriptors (i.e. where the format kind is
+ *      not equal to #mkldnn_format_kind_any) a user can the size, using the
+ *      mkldnn_memory_desc_get_size() function. As described in
+ *      @ref understanding_memory_formats, the size of data sometimes cannot
+ *      be computed as the product of dimensions times the size of the data
+ *      type. So users are encouraged to use this function for better code
+ *      portability.
+ *      Two memory descriptors can be compared with mkldnn_memory_desc_equal().
+ *      The comparison is especially useful when checking whether a primitive
+ *      requires reorder from the user's data format to the primitive's format.
+ * 2. **Memory** -- an engine-specific object that handles the data and its
+ *      description (a memory descriptor). For CPU enigne, the data handle is
+ *      simply a pointer to @c void. The data handle can be queried using
+ *      mkldnn_memory_get_data_handle() and set using
+ *      mkldnn_memory_set_data_handle(). The latter function always sets the
+ *      memory in the padding region to zero, which is the invariant maintained
+ *      by all the primitives in Intel MKL-DNN.
+ *      See @ref understanding_memory_formats for more details.
+ *      A memory can be created using mkldnn_memory_create() function.
+ *      A memory can also be queried for the underlying memory descriptor and
+ *      engine using mkldnn_memory_get_memory_desc() and
+ *      mkldnn_memory_get_engine() functions.
+ *
+ * Along with ordinary memory with all dimensions being positive, Intel
+ * MKL-DNN supports *zero-volume* memory with one or more dimensions set to
+ * zero. This is to support the NumPy\* convention.
+ * If a *zero-volume* memory is passed to a primitive, the primitive does
+ * not perform any computations on this memory. For example:
+ *  - Convolution with `(0 batch, 3 input channels, 13 height, 13 width)`
+ *    source and `(16 output channels, 3 inputs, channel, 3 height, 3 width)`
+ *    weights would produce `(0 batch, 16 output channels, 11 height, 11 width)`
+ *    destination (assuming strides are `1` and paddings are zero) and perform
+ *    zero multiply-add operations.
+ *  - Concatenation of three memories of shapes `(3, 4, 13, 13)`,
+ *    `(3, 0, 13, 13)`, and `(3, 1, 13, 13)` along the second axis would produce
+ *    the output of the shape `(3, 5, 13, 13)`, effectively ignoring the second
+ *    input (however, if the user created a concatenation primitive descriptor
+ *    with three inputs they should also provide all three memories to the
+ *    concatenation primitive, including the one with zero second dimension).
+ *  - However, Intel MKL-DNN would return an error when attempting to create a
+ *    convolution with *zero-volume* memory passed for weights because such a
+ *    convolution is not well-defined:
+ *    ~~~
+ *    dst(1, 16, 11, 11) <-- src(1, 0, 13, 13) (*) wei(16, 0, 3, 3)
+ *    ~~~
+ *    Should the values in the destination be zeroes or just not accessed at
+ *    all? Moreover, backward pass w.r.t. weights in such cases is also not
+ *    well-defined.
+ *
+ *  Data handle of *zero-volume* memory is never accessed and hence can be
+ *  unset (NULL in case of CPU engine).
+ *
+ * @sa @ref understanding_memory_formats
+ * @{ */
+
+/** Initializes a @p memory_desc memory descriptor using @p ndims, @p dims, @p
+ * data_type, and @p strides.
+ *
+ * The @p strides might be NULL, which means the order of physical dimensions
+ * is the same as the order of logical ones.
+ *
+ * @note The logical order of dimensions is defined by a primitive that
+ *       consumes the memory.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_memory_desc_init_by_strides(
+        mkldnn_memory_desc_t *memory_desc, int ndims, const mkldnn_dims_t dims,
+        mkldnn_data_type_t data_type, const mkldnn_dims_t strides);
+
+/** Initializes a @p memory_desc memory descriptor using @p ndims, @p dims, @p
+ * data_type, and format @p tag.
+ *
+ * @p tag can be #mkldnn_format_tag_any, which allows a primitive to define
+ * the appropriate memory format. In this case, the @p format_kind would be set
+ * to #mkldnn_format_kind_any */
+mkldnn_status_t MKLDNN_API mkldnn_memory_desc_init_by_tag(
+        mkldnn_memory_desc_t *memory_desc, int ndims, const mkldnn_dims_t dims,
+        mkldnn_data_type_t data_type, mkldnn_format_tag_t tag);
+
+/** Initializes a @p memory_desc for a given @p parent_memory_desc, with
+ * @p dims sizes and @p offsets. May fail if layout used does not allow
+ * obtain desired submemory. In this case consider using `extract` or `insert`
+ * primitive */
+mkldnn_status_t MKLDNN_API mkldnn_memory_desc_init_submemory(
+        mkldnn_memory_desc_t *memory_desc,
+        const mkldnn_memory_desc_t *parent_memory_desc,
+        const mkldnn_dims_t dims, const mkldnn_dims_t offsets);
+
+/** Compares two memory descriptors.
+ * @return 1 if the descriptors are the same.
+ * @return 0 if the descriptors are different.
+ *
+ * Use this function to identify whether a reorder is required between the
+ * two memories */
+int MKLDNN_API mkldnn_memory_desc_equal(
+        const mkldnn_memory_desc_t *lhs,
+        const mkldnn_memory_desc_t *rhs);
+
+/** Returns the size (in bytes) that is required for given @p memory_desc */
+size_t MKLDNN_API mkldnn_memory_desc_get_size(
+        const mkldnn_memory_desc_t *memory_desc);
+
+/** Creates a memory for given @p memory_desc and @p engine. Also sets handle
+ * to @p native_handle.
+ * The @p native_handle can:
+ * - point to the user allocated memory, i.e. valid handle. In this case the
+ *   library doesn't own allocated memory.
+ * - be MKLDNN_NATIVE_HANDLE_ALLOCATE to ask the library to allocate and
+ *   attach memory. In this case the library owns allocated memory.
+ * - be MKLDNN_NATIVE_HANDLE_NONE to create mkldnn_memory w/o attached memory.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_memory_create(mkldnn_memory_t *memory,
+        const mkldnn_memory_desc_t *memory_desc, mkldnn_engine_t engine,
+        void *native_handle);
+
+/** Returns a @p memory_desc associated with @p memory. */
+mkldnn_status_t MKLDNN_API mkldnn_memory_get_memory_desc(
+        const_mkldnn_memory_t memory,
+        const mkldnn_memory_desc_t **memory_desc);
+
+/** Returns an @p engine associated with @p memory. */
+mkldnn_status_t MKLDNN_API mkldnn_memory_get_engine(
+        const_mkldnn_memory_t memory, mkldnn_engine_t *engine);
+
+/** For a @p memory, returns the data @p handle.
+ *
+ * For the CPU engine, the data handle is a pointer to the actual data. */
+mkldnn_status_t MKLDNN_API mkldnn_memory_get_data_handle(
+        const_mkldnn_memory_t memory, void **handle);
+
+/** For a @p memory, sets the data @p handle. */
+mkldnn_status_t MKLDNN_API mkldnn_memory_set_data_handle(
+        mkldnn_memory_t memory, void *handle);
+
+/** Deletes a @p memory. */
+mkldnn_status_t MKLDNN_API mkldnn_memory_destroy(mkldnn_memory_t memory);
+
+/** @} */
+
+/** @addtogroup c_api_reorder Reorder
+ * A primitive to copy data between memory formats.
+ * @{ */
+
+/** Initializes a @p reorder_primitive_desc using the description of the source
+ * (@p src_engine and @p src_md) and destination (@p dst_engine and @p dst_md)
+ * memory, and an @p attr attribute.
+ *
+ * Inputs:
+ *  - input (#mkldnn_query_src_md, 0)
+ *
+ * Outputs:
+ *  - output (#mkldnn_query_dst_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_reorder_primitive_desc_create(
+        mkldnn_primitive_desc_t *reorder_primitive_desc,
+        mkldnn_engine_t src_engine, const mkldnn_memory_desc_t *src_md,
+        mkldnn_engine_t dst_engine, const mkldnn_memory_desc_t *dst_md,
+        const_mkldnn_primitive_attr_t attr);
+
+/** @} */
+
+/** @addtogroup c_api_concat Concat
+ * A primitive to concatenate data by arbitrary dimension.
+ * @{ */
+
+/** Creates out-of-place @p concat_primitive_desc for concatenation of @p n
+ * inputs by @p concat_dimension with resulting @p output_desc memory
+ * descriptor. @p output_desc can be NULL or specified with the
+ * #mkldnn_format_kind_any format kind -- in this case, the appropriate memory
+ * format would be chosen automatically.
+ *
+ * Inputs:
+ *  - input 0 (#mkldnn_query_src_md, 0)
+ *  - input 1 (#mkldnn_query_src_md, 1)
+ *  - ...
+ *  - input @p n - 1 (#mkldnn_query_src_md, @p n - 1)
+ *
+ * Outputs:
+ *  - output (#mkldnn_query_dst_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_concat_primitive_desc_create(
+        mkldnn_primitive_desc_t *concat_primitive_desc,
+        const mkldnn_memory_desc_t *dst_md,
+        int n, int concat_dimension,
+        const mkldnn_memory_desc_t *src_mds,
+        const_mkldnn_primitive_attr_t attr,
+        mkldnn_engine_t engine);
+
+/** @} */
+
+/** @addtogroup c_api_sum Sum
+ * A primitive to sum data.
+ * @{ */
+
+/** Creates out-of-place @p sum_primitive_desc for sum of @p n
+ * inputs multiplied by scale with resulting @p output_desc memory
+ * descriptor. @p output_desc can be NULL or specified with the
+ * #mkldnn_format_kind_any format kind -- in this case, the appropriate memory
+ * format would be chosen automatically.
+ *
+ * Inputs:
+ *  - src 0 (#mkldnn_query_src_md, 0)
+ *  - src 1 (#mkldnn_query_src_md, 1)
+ *  - ...
+ *  - src @p n - 1 (#mkldnn_query_src_md, @p n - 1)
+ *
+ * Outputs:
+ *  - output (#mkldnn_query_dst_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_sum_primitive_desc_create(
+        mkldnn_primitive_desc_t *sum_primitive_desc,
+        const mkldnn_memory_desc_t *dst_mds,
+        int n, const float *scales,
+        const mkldnn_memory_desc_t *src_mds,
+        const_mkldnn_primitive_attr_t attr,
+        mkldnn_engine_t engine);
+
+/** @} */
+
+/** @addtogroup c_api_convolution Convolution
+ * A primitive to compute convolution using different algorithms.
+ *
+ * \f[dst[n][oc][oh][ow]  =
+ *     \sum_{kw=0}^{KW}\sum_{kh=0}^{KH}\sum_{ic=0}^{IC}
+ *     src[n][ic][oh \cdot s_h - p_l[0] + kh][ow \cdot s_w - p_r[1] + kw]
+ *     \cdot weights[g][oc][ic][kh][kw]
+ *     + bias[g][oc],\f]
+ *
+ * where size of output spatial domain is given by
+ * \f$ OH = \left\lfloor{\frac{IH - KH + p_l[0] + p_r[0]}{s_h}}
+ *          \right\rfloor + 1\f$,
+ * \f$ OW = \left\lfloor{\frac{IW - KW + p_l[1] + p_r[1]}{s_w}}
+ *          \right\rfloor + 1\f$,
+ *
+ * and summation is carried over input channels \f$ic\f$ in
+ * group \f$g\f$, and \f$s_h, s_w\f$ are @p strides and
+ * \f$p_l, p_r\f$ are @p padding_l and @p padding_r.
+ * @{ */
+
+/** Initializes a convolution descriptor @p conv_desc for forward propagation
+ * using @p prop_kind (possible values are #mkldnn_forward_training and
+ * #mkldnn_forward_inference), @p alg_kind, memory descriptors, @p strides, @p
+ * padding_l, @p padding_r, and @p padding_kind. In order to create a
+ * convolution without bias, @p bias_desc should either be @c NULL or point to
+ * a descriptor with memory format kind equal to #mkldnn_format_kind_undef.
+ *
+ * @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - weights (#mkldnn_query_weights_md, 0)
+ *  - bias (#mkldnn_query_weights_md, 1), if created with bias
+ *
+ * Outputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_convolution_forward_desc_init(
+        mkldnn_convolution_desc_t *conv_desc, mkldnn_prop_kind_t prop_kind,
+        mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *src_desc,
+        const mkldnn_memory_desc_t *weights_desc,
+        const mkldnn_memory_desc_t *bias_desc,
+        const mkldnn_memory_desc_t *dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
+        mkldnn_padding_kind_t padding_kind);
+
+/** Initializes a dilated convolution descriptor @p conv_desc for forward
+ * propagation using @p prop_kind (possible values are #mkldnn_forward_training
+ * and #mkldnn_forward_inference), @p alg_kind, memory descriptors, @p strides,
+ * @p dilates, @p padding_l, @p padding_r, and @p padding_kind.
+ * In order to create a dilated convolution without bias, @p bias_desc
+ * should either be @c NULL or point to a descriptor with memory format kind
+ * equals #mkldnn_format_kind_undef.
+ *
+ * @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - weights (#mkldnn_query_weights_md, 0)
+ *  - bias (#mkldnn_query_weights_md, 1), if created with bias
+ *
+ * Outputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_dilated_convolution_forward_desc_init(
+        mkldnn_convolution_desc_t *conv_desc, mkldnn_prop_kind_t prop_kind,
+        mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *src_desc,
+        const mkldnn_memory_desc_t *weights_desc,
+        const mkldnn_memory_desc_t *bias_desc,
+        const mkldnn_memory_desc_t *dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
+        const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
+
+/** Initializes a convolution descriptor @p conv_desc for backward propagation
+ * with respect to data using @p alg_kind, memory descriptors, @p strides, @p
+ * padding_l, @p padding_r, and @p padding_kind.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *  - weights (#mkldnn_query_weights_md, 0)
+ *
+ * Outputs:
+ *  - diff_src (#mkldnn_query_diff_src_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_convolution_backward_data_desc_init(
+        mkldnn_convolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
+        const mkldnn_memory_desc_t *diff_src_desc,
+        const mkldnn_memory_desc_t *weights_desc,
+        const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
+        mkldnn_padding_kind_t padding_kind);
+
+/** Initializes a dilated convolution descriptor @p conv_desc for backward
+ * propagation with respect to data using @p alg_kind, memory descriptors, @p
+ * strides, @p dilates @p padding_l, @p padding_r, and @p padding_kind.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *  - weights (#mkldnn_query_weights_md, 0)
+ *
+ * Outputs:
+ *  - diff_src (#mkldnn_query_diff_src_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_dilated_convolution_backward_data_desc_init(
+        mkldnn_convolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
+        const mkldnn_memory_desc_t *diff_src_desc,
+        const mkldnn_memory_desc_t *weights_desc,
+        const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
+        const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
+
+/** Initializes a convolution descriptor @p conv_desc for backward propagation
+ * with respect to weights using @p alg_kind, memory descriptors, @p strides,
+ * @p padding_l, @p padding_r, and @p padding_kind.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *
+ * Outputs:
+ *  - diff_weights (#mkldnn_query_diff_weights_md, 0)
+ *  - diff_bias (#mkldnn_query_diff_weights_md, 1), if created with bias
+ */
+mkldnn_status_t MKLDNN_API mkldnn_convolution_backward_weights_desc_init(
+        mkldnn_convolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
+        const mkldnn_memory_desc_t *src_desc,
+        const mkldnn_memory_desc_t *diff_weights_desc,
+        const mkldnn_memory_desc_t *diff_bias_desc,
+        const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
+        mkldnn_padding_kind_t padding_kind);
+
+/** Initializes a convolution descriptor @p conv_desc for backward propagation
+ * with respect to weights using @p alg_kind, memory descriptors, @p strides,
+ * @p dilates @p padding_l, @p padding_r, and @p padding_kind.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *
+ * Outputs:
+ *  - diff_weights (#mkldnn_query_diff_weights_md, 0)
+ *  - diff_bias (#mkldnn_query_diff_weights_md, 1), if created with bias
+ */
+mkldnn_status_t MKLDNN_API
+mkldnn_dilated_convolution_backward_weights_desc_init(
+        mkldnn_convolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
+        const mkldnn_memory_desc_t *src_desc,
+        const mkldnn_memory_desc_t *diff_weights_desc,
+        const mkldnn_memory_desc_t *diff_bias_desc,
+        const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
+        const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
+
+/** @} */
+
+/** @addtogroup c_api_deconvolution Deconvolution
+ * A primitive to compute deconvolution using different algorithms.
+ *
+ * @{ */
+
+
+/** Initializes a deconvolution descriptor @p deconv_desc for forward
+ * propagation using @p prop_kind (possible values are #mkldnn_forward_training
+ * and #mkldnn_forward_inference), @p alg_kind, memory descriptors, @p strides,
+ * @p padding_l, @p padding_r, and @p padding_kind. In order to create a
+ * deconvolution without bias, @p bias_desc should either be @c NULL or point to
+ * a descriptor with memory format kind equals #mkldnn_format_kind_undef.
+ *
+ * @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - weights (#mkldnn_query_weights_md, 0)
+ *  - bias (#mkldnn_query_weights_md, 1), if created with bias
+ *
+ * Outputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_deconvolution_forward_desc_init(
+        mkldnn_deconvolution_desc_t *conv_desc, mkldnn_prop_kind_t prop_kind,
+        mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *src_desc,
+        const mkldnn_memory_desc_t *weights_desc,
+        const mkldnn_memory_desc_t *bias_desc,
+        const mkldnn_memory_desc_t *dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
+        mkldnn_padding_kind_t padding_kind);
+
+/** Initializes a dilated deconvolution descriptor @p deconv_desc for forward
+ * propagation using @p prop_kind (possible values are #mkldnn_forward_training
+ * and #mkldnn_forward_inference), @p alg_kind, memory descriptors, @p strides,
+ * @p dilates, @p padding_l, @p padding_r, and @p padding_kind. In order to
+ * create a dilated deconvolution without bias, @p bias_desc should either be
+ * @c NULL or point to a descriptor with memory format kind equal
+ * #mkldnn_format_kind_undef.
+ *
+ * @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - weights (#mkldnn_query_weights_md, 0)
+ *  - bias (#mkldnn_query_weights_md, 1), if created with bias
+ *
+ * Outputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_dilated_deconvolution_forward_desc_init(
+        mkldnn_deconvolution_desc_t *conv_desc, mkldnn_prop_kind_t prop_kind,
+        mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *src_desc,
+        const mkldnn_memory_desc_t *weights_desc,
+        const mkldnn_memory_desc_t *bias_desc,
+        const mkldnn_memory_desc_t *dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
+        const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
+
+/** Initializes a deconvolution descriptor @p conv_desc for backward propagation
+ * with respect to data using @p alg_kind, memory descriptors, @p strides, @p
+ * padding_l, @p padding_r, and @p padding_kind.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *  - weights (#mkldnn_query_weights_md, 0)
+ *
+ * Outputs:
+ *  - diff_src (#mkldnn_query_diff_src_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_deconvolution_backward_data_desc_init(
+        mkldnn_deconvolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
+        const mkldnn_memory_desc_t *diff_src_desc,
+        const mkldnn_memory_desc_t *weights_desc,
+        const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
+        mkldnn_padding_kind_t padding_kind);
+
+/** Initializes a dilated deconvolution descriptor @p conv_desc for backward
+ * propagation with respect to data using @p alg_kind, memory descriptors, @p
+ * strides, @p dilates, @p padding_l, @p padding_r, and @p padding_kind.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *  - weights (#mkldnn_query_weights_md, 0)
+ *
+ * Outputs:
+ *  - diff_src (#mkldnn_query_diff_src_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_dilated_deconvolution_backward_data_desc_init(
+        mkldnn_deconvolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
+        const mkldnn_memory_desc_t *diff_src_desc,
+        const mkldnn_memory_desc_t *weights_desc,
+        const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
+        const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
+
+/** Initializes a deconvolution descriptor @p conv_desc for backward propagation
+ * with respect to weights using @p alg_kind, memory descriptors, @p strides,
+ * @p padding_l, @p padding_r, and @p padding_kind.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *
+ * Outputs:
+ *  - diff_weights (#mkldnn_query_diff_weights_md, 0)
+ *  - diff_bias (#mkldnn_query_diff_weights_md, 1), if created with bias
+ */
+mkldnn_status_t MKLDNN_API mkldnn_deconvolution_backward_weights_desc_init(
+        mkldnn_deconvolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
+        const mkldnn_memory_desc_t *src_desc,
+        const mkldnn_memory_desc_t *diff_weights_desc,
+        const mkldnn_memory_desc_t *diff_bias_desc,
+        const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t padding_l, const mkldnn_dims_t padding_r,
+        mkldnn_padding_kind_t padding_kind);
+
+/** Initializes a dilated deconvolution descriptor @p conv_desc for backward
+ * propagation with respect to weights using @p alg_kind, memory descriptors,
+ * @p strides, @p dilates, @p padding_l, @p padding_r, and @p padding_kind.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *
+ * Outputs:
+ *  - diff_weights (#mkldnn_query_diff_weights_md, 0)
+ *  - diff_bias (#mkldnn_query_diff_weights_md, 1), if created with bias
+ */
+mkldnn_status_t MKLDNN_API mkldnn_dilated_deconvolution_backward_weights_desc_init(
+        mkldnn_deconvolution_desc_t *conv_desc, mkldnn_alg_kind_t alg_kind,
+        const mkldnn_memory_desc_t *src_desc,
+        const mkldnn_memory_desc_t *diff_weights_desc,
+        const mkldnn_memory_desc_t *diff_bias_desc,
+        const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t dilates, const mkldnn_dims_t padding_l,
+        const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
+
+/** @} */
+
+/** @addtogroup c_api_shuffle Shuffle
+ * A primitive to shuffle data along the axis.
+ * @{ */
+
+/** Initializes a @p shuffle_desc for forward propagation using @p prop_kind,
+ * memory descriptor @p data_desc, @p axis, and @p group_size.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *
+ * Outputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ *
+ */
+mkldnn_status_t MKLDNN_API mkldnn_shuffle_forward_desc_init(
+        mkldnn_shuffle_desc_t *shuffle_desc, mkldnn_prop_kind_t prop_kind,
+        const mkldnn_memory_desc_t *data_desc, int axis,
+        mkldnn_dim_t group_size);
+
+/** Initializes a @p shuffle_desc for backward propagation using memory
+ * descriptor @p diff_data_desc, @p axis, and @p group_size.
+ *
+ *
+ * Inputs:
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *
+ * Outputs:
+ *  - diff_src (#mkldnn_query_diff_src_md, 0)
+ *
+ */
+mkldnn_status_t MKLDNN_API mkldnn_shuffle_backward_desc_init(
+        mkldnn_shuffle_desc_t *shuffle_desc,
+        const mkldnn_memory_desc_t *diff_data_desc, int axis,
+        mkldnn_dim_t group_size);
+
+/** @} */
+
+/** @addtogroup c_api_eltwise Eltwise
+ * A primitive to compute element-wise operations like parametric rectifier
+ * linear unit (ReLU).
+ *
+ * Both forward and backward passes support in-place operation; that is, src
+ * and dst point to the same memory for forward pass, and diff_dst and diff_src
+ * point to the same memory for backward pass.
+ *
+ * @warning Because the original src is required for backward pass, in-place
+ * forward pass in general cannot be applied during training. However, for some
+ * kinds of element-wise operations (namely ReLU with alpha parameter equals 0),
+ * dst and src can be interchangeable for the backward pass, which enables
+ * performing in-place forward even for training.
+ *
+ * @{ */
+
+/** Initializes an @p eltwise_desc for forward propagation using @p prop_kind
+ * (possible values are #mkldnn_forward_training and #mkldnn_forward_inference),
+ * @p alg_kind algorithm, memory descriptor @p data_desc, @p alpha, and
+ * @p beta parameters.
+ *
+ * @sa mkldnn_eltwise_desc_t for details.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *
+ * Outputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_eltwise_forward_desc_init(
+        mkldnn_eltwise_desc_t *eltwise_desc, mkldnn_prop_kind_t prop_kind,
+        mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *data_desc,
+        float alpha, float beta);
+
+/** Initializes an @p eltwise_desc for backward propagation using @p alg_kind
+ * algorithm memory descriptors @p diff_data_desc and @p data_desc, and the
+ * @p alpha and @p beta parameters.
+ *
+ * @sa mkldnn_eltwise_desc_t for details.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *
+ * Outputs:
+ *  - diff_src (#mkldnn_query_diff_src_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_eltwise_backward_desc_init(
+        mkldnn_eltwise_desc_t *eltwise_desc, mkldnn_alg_kind_t alg_kind,
+        const mkldnn_memory_desc_t *diff_data_desc,
+        const mkldnn_memory_desc_t *data_desc, float alpha, float beta);
+
+/** @} */
+
+/** @addtogroup c_api_softmax Softmax
+ * A primitive to perform softmax.
+ *
+ * \f[dst[u][c][in] =
+ *    \frac{\exp(src[ou][c][in]) - \max\limits_{c}(src[ou][c][in])}
+ *    {\sum\limits_{c}\{\exp(src[ou][c][in])
+ *    - \max\limits_{c}(src[ou][c][in])\}},\f]
+ *
+ * where \f$ou, iu\f$ are outer and inner sizes repectively, defined
+ * by @p data_desc.dims and @p softmax_axis.
+ * @{ */
+
+/** Initializes a @p softmax_desc for forward propagation using @p prop_kind
+ * (possible values are #mkldnn_forward_training and #mkldnn_forward_inference)
+ * and memory descriptor @p data_desc.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *
+ * Outputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_softmax_forward_desc_init(
+        mkldnn_softmax_desc_t *softmax_desc, mkldnn_prop_kind_t prop_kind,
+        const mkldnn_memory_desc_t *data_desc, int softmax_axis);
+
+/** Initializes a @p softmax_desc for backward propagation using memory
+ * descriptors @p diff_desc and @p data_desc.
+ *
+ * Inputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *
+ * Outputs:
+ *  - diff_src (#mkldnn_query_diff_src_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_softmax_backward_desc_init(
+        mkldnn_softmax_desc_t *softmax_desc,
+        const mkldnn_memory_desc_t *diff_desc,
+        const mkldnn_memory_desc_t *data_desc, int softmax_axis);
+
+/** @} */
+
+/** @addtogroup c_api_pooling Pooling
+ * A primitive to perform max or average pooling.
+ *
+ * Max pooling:
+ * \f[dst[n][oc][oh][ow] =
+ *     \max\limits_{kw,kh}
+ *     (src[n][ic][oh \cdot s_h - p_l[0] + kh][ow \cdot s_w - p_r[1] + kw]),\f]
+ *
+ * Average pooling:
+ * \f[dst[n][oc][oh][ow] =
+ *     \frac{1}{KW \cdot KH}\sum\limits_{kw,kh}
+ *     src[n][ic][oh \cdot s_h - p_l[0] + kh][ow \cdot s_w - p_r[1] + kw],\f]
+ *
+ * where \f$p_l, p_r\f$ are @p padding_l and @p padding_r respectively, and
+ * output spatial dimensions are calculated similarly to how they are done in
+ * convolution.
+ *
+ * During training, max pooling requires a workspace on forward
+ * (#mkldnn_forward_training) and backward (#mkldnn_backward) passes to
+ * save indices where maximum was found. The workspace layout is opaque, and
+ * the indices cannot be restored from it. However, one can use backward
+ * pooling to perform up-sampling (used in some detection topologies).
+ *
+ * @{ */
+
+/** Initializes a pooling descriptor @p pool_desc for forward propagation using
+ * @p prop_kind (possible values are #mkldnn_forward_training and
+ * #mkldnn_forward_inference), @p alg_kind, memory descriptors, and pooling
+ * parameters in the spatial domain: @p strides, @p kernel sizes, @p padding_l,
+ * @p padding_r, and @p padding_kind.
+ *
+ * @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *
+ * Outputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ *  - workspace (#mkldnn_query_workspace_md, 0),
+ *      if @p alg_kind = #mkldnn_pooling_max and
+ *      @p prop_kind = #mkldnn_forward_training
+ */
+mkldnn_status_t MKLDNN_API mkldnn_pooling_forward_desc_init(
+        mkldnn_pooling_desc_t *pool_desc, mkldnn_prop_kind_t prop_kind,
+        mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *src_desc,
+        const mkldnn_memory_desc_t *dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t kernel, const mkldnn_dims_t padding_l,
+        const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
+
+/** Initializes a pooling descriptor @p pool_desc for backward propagation
+ * using @p alg_kind, memory descriptors, and pooling parameters in the spatial
+ * domain: @p strides, @p kernel sizes, @p padding_l, @p padding_r, and @p
+ * padding_kind.
+ *
+ * @note If @p padding_r is @c NULL, the padding is supposed to be symmetric.
+ *
+ * Inputs:
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *  - workspace (#mkldnn_query_workspace_md, 0),
+ *      if @p alg_kind = #mkldnn_pooling_max
+ *
+ * Outputs:
+ *  - diff_src (#mkldnn_query_diff_src_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_pooling_backward_desc_init(
+        mkldnn_pooling_desc_t *pool_desc, mkldnn_alg_kind_t alg_kind,
+        const mkldnn_memory_desc_t *diff_src_desc,
+        const mkldnn_memory_desc_t *diff_dst_desc, const mkldnn_dims_t strides,
+        const mkldnn_dims_t kernel, const mkldnn_dims_t padding_l,
+        const mkldnn_dims_t padding_r, mkldnn_padding_kind_t padding_kind);
+
+/** @} */
+
+/** @addtogroup c_api_lrn LRN
+ * A primitive to perform local response normalization (LRN) across or within
+ * channels.
+ *
+ * LRN accross channels:
+ * \f[dst[n][c][h][w] = \left\{k + \frac{\alpha}{n_{l}}
+ *                      \sum\limits_{i=-(n_{l}-1)/2}^{(n_{l}+1)/2}
+ *                      (src[n][c+i][h][w])^2\right\}^{-\beta}
+ *                      src[n][c][h][w],\f]
+ *
+ * LRN within channels:
+ * \f[dst[n][c][h][w] = \left\{k + \frac{\alpha}{n_{l}}
+ *                      \sum\limits_{i=-(n_{l}-1)/2}^{(n_{l}+1)/2}
+ *                      (src[n][c][h+i][w+i])^2\right\}^{-\beta}
+ *                      src[n][c][h][w],\f]
+ *
+ * where \f$n_{l}\f$ is the @p local_size.
+ *
+ * During training, LRN might or might not require a workspace on forward
+ * (#mkldnn_forward_training) and backward (#mkldnn_backward) passes. The
+ * behavior is implementation specific. Optimized implementations typically
+ * require a workspace and use it to save some intermediate results from the
+ * forward pass that accelerate computations on the backward pass.
+ *
+ * To check whether a workspace is required, query the LRN primitive descriptor
+ * for the workspace (#mkldnn_query_workspace_md). Success indicates that the
+ * workspace is required and its description will be returned.
+ * @sa mkldnn_primitive_desc_query and mkldnn_primitive_desc_query_pd
+ *
+ * @{ */
+
+/** Initializes an @p lrn_desc for forward propagation using @p prop_kind
+ * (possible values are #mkldnn_forward_training and #mkldnn_forward_inference),
+ * @p alg_kind, memory descriptor @p data_desc, and regularization
+ * parameters @p local_size, @p alpha, @p beta, and @p k.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *
+ * Outputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ *  - workspace (#mkldnn_query_workspace_md, 0),
+ *      if the underlying implementation requires
+ */
+mkldnn_status_t MKLDNN_API mkldnn_lrn_forward_desc_init(
+        mkldnn_lrn_desc_t *lrn_desc, mkldnn_prop_kind_t prop_kind,
+        mkldnn_alg_kind_t alg_kind, const mkldnn_memory_desc_t *data_desc,
+        mkldnn_dim_t local_size, float alpha, float beta, float k);
+
+/** Initializes an @p lrn_desc for backward propagation using @p alg_kind,
+ * memory descriptors @p data_desc and @p diff_data_desc, and regularization
+ * parameters @p local_size, @p alpha, @p beta, and @p k.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *  - workspace (#mkldnn_query_workspace_md, 0),
+ *      if the underlying implementation requires
+ *
+ * Outputs:
+ *  - diff_src (#mkldnn_query_diff_src_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_lrn_backward_desc_init(
+        mkldnn_lrn_desc_t *lrn_desc, mkldnn_alg_kind_t alg_kind,
+        const mkldnn_memory_desc_t *diff_data_desc,
+        const mkldnn_memory_desc_t *data_desc, mkldnn_dim_t local_size,
+        float alpha, float beta, float k);
+
+/** @} */
+
+/** @addtogroup c_api_batch_normalization Batch Normalization
+ * A primitive to perform batch normalization.
+ *
+ * \f[dst[n][c][h][w] = \gamma[c] \frac{src[n][c][h][w] - \mu[c]}
+ *                      {\sqrt{\sigma[c] + eps}} + \beta[c],\f]
+ *
+ * where \f$\gamma[c], \beta[c]\f$ are weights and bias for a channel and,
+ *
+ * \f$\mu[c] = \frac{1}{NHW} \sum\limits_{whn} src[n][c][h][w]\f$,
+ * \f$\sigma[c] = \frac{1}{NHW} \sum\limits_{whn}
+ *                              (src[n][c][h][w] - \mu[c])^2\f$,
+ *
+ * and @c eps is a constant to improve numerical stability.
+ *
+ * Both forward and backward passes support in-place operation; that is, src
+ * and dst point to the same memory for forward pass, and diff_dst and diff_src
+ * point to the same memory for backward pass.
+ *
+ * Batch normalization supports different flavors controlled by
+ * mkldnn_batch_normalization_desc_t. For example, batch normalization can
+ * compute the mean and variance on its own or take them as inputs. It can
+ * either perform scaling and shifting using gamma and beta parameters or not.
+ * Optionally it can also perform a fused ReLU, which in case of training would
+ * also require a workspace.
+ *
+ * @sa mkldnn_batch_normalization_desc_t
+ * @{ */
+
+/** Initializes a batch normalization descriptor @p bnrm_desc for forward
+ * propagation using @p prop_kind (possible values are
+ * #mkldnn_forward_training and #mkldnn_forward_inference), memory descriptor
+ * @p data_desc, normalization parameter @p epsilon, and @p flags set using bit
+ * flags of type mkldnn_batch_normalization_desc_t.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - mean (#mkldnn_query_src_md, 1),
+ *      if #mkldnn_use_global_stats bit-flags is set in @p flags
+ *  - variance (#mkldnn_query_src_md, 2),
+ *      if #mkldnn_use_global_stats bit-flags is set in @p flags
+ *  - scale_and_shift (#mkldnn_query_weights_md, 0),
+ *      if #mkldnn_use_scaleshift bit-flags is set in @p flags
+ *
+ * Outputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ *  - mean (#mkldnn_query_dst_md, 1),
+ *      if #mkldnn_use_global_stats bit-flags is not set in @p flags
+ *      @p prop_kind = #mkldnn_forward_training
+ *  - variance (#mkldnn_query_dst_md, 2),
+ *      if #mkldnn_use_global_stats bit-flags is not set in @p flags
+ *      and @p prop_kind = #mkldnn_forward_training
+ *  - workspace (#mkldnn_query_workspace_md, 0),
+ *      if #mkldnn_fuse_bn_relu bit-flags is set in @p flags
+ *      and @p prop_kind = #mkldnn_forward_training
+ *
+ * @note In-place operation is supported; that is, dst points to the same memory
+ *       as src.
+ *
+ * @sa mkldnn_batch_normalization_desc_t
+ */
+mkldnn_status_t MKLDNN_API mkldnn_batch_normalization_forward_desc_init(
+        mkldnn_batch_normalization_desc_t *bnrm_desc,
+        mkldnn_prop_kind_t prop_kind, const mkldnn_memory_desc_t *data_desc,
+        float epsilon, unsigned flags);
+
+/** Initializes a batch normalization descriptor @p bnrm_desc for backward
+ * propagation with respect to data and scale-shift parameters using memory
+ * descriptors @p data_desc and @p diff_data_desc, normalization parameter
+ * @p epsilon, and @p flags set using bit flags of type
+ * mkldnn_batch_normalization_desc_t.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - mean (#mkldnn_query_src_md, 1)
+ *  - variance (#mkldnn_query_src_md, 2)
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *  - scale_and_shift (#mkldnn_query_weights_md, 0),
+ *      if #mkldnn_use_scaleshift bit-flags is set in @p flags
+ *  - workspace (#mkldnn_query_workspace_md, 0),
+ *      if #mkldnn_fuse_bn_relu bit-flags is set in @p flags
+ *
+ * Outputs:
+ *  - diff_src (#mkldnn_query_diff_src_md, 0)
+ *  - diff_scale_and_shift (#mkldnn_query_diff_weights_md, 0),
+ *      if #mkldnn_use_scaleshift bit-flags is set in @p flags
+ *      and @p prop_kind = #mkldnn_backward
+ *
+ * @note in-place operation is supported,
+ *       i.e. diff_src points to the same memory as diff_dst.
+ *
+ * @sa mkldnn_batch_normalization_desc_t
+ */
+mkldnn_status_t MKLDNN_API mkldnn_batch_normalization_backward_desc_init(
+        mkldnn_batch_normalization_desc_t *bnrm_desc,
+        mkldnn_prop_kind_t prop_kind,
+        const mkldnn_memory_desc_t *diff_data_desc,
+        const mkldnn_memory_desc_t *data_desc,
+        float epsilon, unsigned flags);
+
+/** @} */
+
+/** @addtogroup c_api_inner_product Inner product
+ * A primitive to compute an inner product.
+ *
+ * Inner product layer is also known as fully connected layer.
+ * With spatial dimension:
+ *
+ * \f[dst[n][oc] = \sum\limits_{ic, kh, kw}
+ *                 src[n][ic][kh][kw] \cdot weights[oc][ic][kh][kw]
+ *                 + bias[oc]\f]
+ * @{ */
+
+/** Initializes an inner product descriptor @p ip_desc for forward propagation
+ * using @p prop_kind (possible values are #mkldnn_forward_training and
+ * #mkldnn_forward_inference) and memory descriptors. In order to create an
+ * inner product without bias, @p bias_desc should be either @c NULL or a
+ * pointer to a descriptor with memory format kind equals
+ * #mkldnn_format_kind_undef.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - weights (#mkldnn_query_weights_md, 0)
+ *  - bias (#mkldnn_query_weights_md, 1), if created with bias
+ *
+ * Outputs:
+ *  - dst (#mkldnn_query_dst_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_inner_product_forward_desc_init(
+        mkldnn_inner_product_desc_t *ip_desc, mkldnn_prop_kind_t prop_kind,
+        const mkldnn_memory_desc_t *src_desc,
+        const mkldnn_memory_desc_t *weights_desc,
+        const mkldnn_memory_desc_t *bias_desc,
+        const mkldnn_memory_desc_t *dst_desc);
+
+/** Initializes an inner product descriptor @p ip_desc for backward propagation
+ * with respect to data using memory descriptors.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *  - weights (#mkldnn_query_weights_md, 0)
+ *
+ * Outputs:
+ *  - diff_src (#mkldnn_query_diff_src_md, 0)
+ */
+mkldnn_status_t MKLDNN_API mkldnn_inner_product_backward_data_desc_init(
+        mkldnn_inner_product_desc_t *ip_desc,
+        const mkldnn_memory_desc_t *diff_src_desc,
+        const mkldnn_memory_desc_t *weights_desc,
+        const mkldnn_memory_desc_t *diff_dst_desc);
+
+/** Initializes an inner product descriptor @p ip_desc for backward propagation
+ * with respect to weights using memory descriptors.
+ *
+ * @note Memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - src (#mkldnn_query_src_md, 0)
+ *  - diff_dst (#mkldnn_query_diff_dst_md, 0)
+ *
+ * Outputs:
+ *  - diff_weights (#mkldnn_query_diff_weights_md, 0)
+ *  - diff_bias (#mkldnn_query_diff_weights_md, 1), if created with bias
+ */
+mkldnn_status_t MKLDNN_API mkldnn_inner_product_backward_weights_desc_init(
+        mkldnn_inner_product_desc_t *ip_desc,
+        const mkldnn_memory_desc_t *src_desc,
+        const mkldnn_memory_desc_t *diff_weights_desc,
+        const mkldnn_memory_desc_t *diff_bias_desc,
+        const mkldnn_memory_desc_t *diff_dst_desc);
+
+/** @} */
+
+/** @addtogroup c_api_rnn RNN
+ * A primitive to compute the common recurrent layer.
+ * @todo add additional description for the group
+ * @{ */
+
+/**
+ * Initializes a recurrent cell descriptor @p rnn_cell_desc
+ * using @p rnn_cell_desc, @p kind (possible values are
+ *  #mkldnn_vanilla_rnn, #mkldnn_vanilla_lstm, #mkldnn_vanilla_gru, and
+ *  #mkldnn_gru_linear_before_reset),
+ *  @p f (possible values are #mkldnn_eltwise_relu and
+ *   #mkldnn_eltwise_tanh), @p flags, @p alpha, and @p clipping.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_rnn_cell_desc_init(
+        mkldnn_rnn_cell_desc_t *rnn_cell_desc,
+        mkldnn_alg_kind_t kind, mkldnn_alg_kind_t f,
+        unsigned int flags, float alpha, float clipping);
+
+/** Returns the number of gates of a particular @p rnn_cell_desc. */
+int MKLDNN_API mkldnn_rnn_cell_get_gates_count(
+        const mkldnn_rnn_cell_desc_t *rnn_cell_desc);
+
+/** Returns the number of states of a particular @p rnn_cell_desc. */
+int MKLDNN_API mkldnn_rnn_cell_get_states_count(
+        const mkldnn_rnn_cell_desc_t *rnn_cell_desc);
+
+/** Sets quantization @p scale and @p shift for RNN data tensors.
+ *  For performance reasons, low precision configuration of RNN primitive
+ *  expects input activations to have unsigned int8 data type. Scale and shift
+ *  used to quantize floating point data to unsigned integer must be passed to
+ *  RNN primitive using attributes.
+ *  Example usage:
+ * @code
+ *      // rnn parameters
+ *      int l = 2, t = 2, mb = 32, sic = 32, slc = 32, dic = 32, dlc = 32;
+ *      // activations quantization parameters
+ *      float scale = ..., shift = ..;
+ *
+ *      mkldnn_primitive_attr_t rnn_attr;
+ *      // create default attributes
+ *      mkldnn_primitive_attr_create(&rnn_attr);
+ *
+ *      // set scale and shift for int8 quantization of activation
+ *      mkldnn_primitive_attr_set_rnn_data_qparams(rnn_attr, scale, shift);
+ *
+ *      // create & configure rnn op_desc
+ *      mkldnn_rnn_desc_t rnn_d;
+ *      mkldnn_primitive_desc_t rnn_pd;
+ *      mkldnn_primitive_desc_create(&rnn_pd, &rnn_d, attr, engine, NULL);
+ * @endcode
+ * @note
+ *      Quantization scale and shift are common for src_layer, src_iter,
+ *      dst_iter and dst_layer.
+ */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_set_rnn_data_qparams(
+        mkldnn_primitive_attr_t attr, const float scale, const float shift);
+
+/** Sets quantization scales @p weights_scales for RNN weights tensors.
+ * Low precision configuration of RNN primitive expects input weights to have
+ * signed int8 data type. Scales used to quantize floating point data
+ * to signed integer must be passed to RNN primitive using attributes.
+ * The @p mask argument defines correspondence between output tensor dimensions
+ * and the @p weights_scales array. Set i-th bit of @p mask to 1 to use
+ * dedicated scaling factor for each slice of the output tensor over i-th
+ * dimension. Set @p mask to 0 to use common scaling factor for the whole output
+ * tensor. Example usage:
+ * @code
+ *      // rnn parameters
+ *      int l = 2, t = 2, mb = 32, sic = 32, slc = 32, dic = 32, dlc = 32;
+ *      // unique output scales per output channel
+ *      float weights_scales[dic * n_gates] = { ... };
+ *      // mask that specifies last two dimensions of ldigo format
+ *      int mask = 0x3;
+ *
+ *      mkldnn_primitive_attr_t attr;
+ *      // create default attributes
+ *      mkldnn_primitive_attr_create(&attr);
+ *
+ *      // set output channel-wise weights scales
+ *      mkldnn_primitive_attr_set_rnn_weights_qparams(attr, dic * n_gates, mask,
+ *              weights_scales);
+ *
+ *      // create & configure rnn op_desc
+ *      mkldnn_rnn_desc_t rnn_d;
+ *      mkldnn_primitive_desc_t rnn_pd;
+ *      mkldnn_primitive_desc_create(&rnn_pd, &rnn_d, attr, engine, NULL);
+ * @endcode
+ * @note
+ *      The dimension order is always native and does not depend on the actual
+ *      layout used. For example, 5 dimensional weights always have
+ *      (l, d, i, g, o) logical dimension ordering.
+ * @note
+ *      Quantization sales are common for weights_layer and weights_iteration
+ * @note
+ *      There is no way to check that @p count corresponds to @p mask until an
+ *      actual primitive descriptor is created, so it is user's responsibility
+ *      to set proper values. The following formula must be held:
+ *
+ *      \f[count = \prod\limits_{d \in mask} output.dims[d]\f]
+ */
+mkldnn_status_t MKLDNN_API mkldnn_primitive_attr_set_rnn_weights_qparams (
+        mkldnn_primitive_attr_t attr, mkldnn_dim_t count, int mask,
+                const float *weights_scales);
+
+/** Initializes a rnn descriptor @p rnn_desc for forward propagation
+ * using @p prop_kind, @p rnn_cell_desc, @p direction, and memory descriptors.
+ * @note If @p prop_kind equals #mkldnn_forward_training, you must query a
+ * workspace memory descriptor before creating the primitive.
+ *
+ * @p src_iter_desc, @p bias_desc, and @p dst_iter_desc are allowed to either be
+ * @c NULL or point to a zero memory descriptor, which would indicate that the
+ * RNN primitive should not use them.
+ *
+ * @note All memory descriptors except @p src_iter_desc are allowed to be
+ *       initialized with #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * Inputs:
+ *  - src_layer (#mkldnn_query_src_md, 0)
+ *  - src_iter (#mkldnn_query_src_md, 1), if used
+ *  - weights_layer (#mkldnn_query_weights_md, 0)
+ *  - weights_iter (#mkldnn_query_weights_md, 1)
+ *  - bias (#mkldnn_query_weights_md, 2), if used
+ *
+ * Outputs:
+ *  - dst_layer (#mkldnn_query_dst_md, 0)
+ *  - dst_iter (#mkldnn_query_dst_md, 1), if used
+ *  - workspace (#mkldnn_query_workspace_md, 0),
+ *      if @p prop_kind equals #mkldnn_forward_training
+ */
+mkldnn_status_t MKLDNN_API mkldnn_rnn_forward_desc_init(
+        mkldnn_rnn_desc_t *rnn_desc, mkldnn_prop_kind_t prop_kind,
+        const mkldnn_rnn_cell_desc_t *rnn_cell_desc,
+        const mkldnn_rnn_direction_t direction,
+        const mkldnn_memory_desc_t *src_layer_desc,
+        const mkldnn_memory_desc_t *src_iter_desc,
+        const mkldnn_memory_desc_t *weights_layer_desc,
+        const mkldnn_memory_desc_t *weights_iter_desc,
+        const mkldnn_memory_desc_t *bias_desc,
+        const mkldnn_memory_desc_t *dst_layer_desc,
+        const mkldnn_memory_desc_t *dst_iter_desc);
+
+/** Initializes a rnn descriptor @p rnn_desc for backward propagation
+ * using @p prop_kind, @p rnn_cell_desc, @p direction, and memory descriptors.
+ *
+ * @note All memory descriptors are allowed to be initialized with
+ *       #mkldnn_format_kind_any value of @p format_kind.
+ *
+ * @p src_iter_desc (simultaneously with @p diff_src_iter_desc),
+ * @p bias_desc (simultaneously with @p diff_bias_desc), and
+ * @p dst_iter_desc (simultaneously with @p diff_src_iter_desc) are allowed to
+ * either be @c NULL or point to a zero memory descriptor, which would indicate
+ * that the RNN primitive should not use them.
+ *
+ * Inputs:
+ *  - src_layer (#mkldnn_query_src_md, 0)
+ *  - src_iter (#mkldnn_query_src_md, 1), if used
+ *  - weights_layer (#mkldnn_query_weights_md, 0)
+ *  - weights_iter (#mkldnn_query_weights_md, 1)
+ *  - bias (#mkldnn_query_weights_md, 2), if used
+ *  - dst_layer (#mkldnn_query_dst_md, 0)
+ *  - dst_iter (#mkldnn_query_dst_md, 1), if used
+ *  - diff_dst_layer (#mkldnn_query_diff_dst_md, 0)
+ *  - diff_dst_iter (#mkldnn_query_diff_dst_md, 1), if used
+ *  - workspace (#mkldnn_query_workspace_md, 0)
+ *
+ * Outputs:
+ *  - diff_src_layer (#mkldnn_query_diff_src_md, 0)
+ *  - diff_src_iter (#mkldnn_query_diff_src_md, 1), if used
+ *  - diff_weights_layer (#mkldnn_query_diff_weights_md, 0)
+ *  - diff_weights_iter (#mkldnn_query_diff_weights_md, 1)
+ *  - diff_bias (#mkldnn_query_diff_weights_md, 2), if used
+ */
+mkldnn_status_t MKLDNN_API mkldnn_rnn_backward_desc_init(
+        mkldnn_rnn_desc_t *rnn_desc, mkldnn_prop_kind_t prop_kind,
+        const mkldnn_rnn_cell_desc_t *rnn_cell_desc,
+        const mkldnn_rnn_direction_t direction,
+        const mkldnn_memory_desc_t *src_layer_desc,
+        const mkldnn_memory_desc_t *src_iter_desc,
+        const mkldnn_memory_desc_t *weights_layer_desc,
+        const mkldnn_memory_desc_t *weights_iter_desc,
+        const mkldnn_memory_desc_t *bias_desc,
+        const mkldnn_memory_desc_t *dst_layer_desc,
+        const mkldnn_memory_desc_t *dst_iter_desc,
+        const mkldnn_memory_desc_t *diff_src_layer_desc,
+        const mkldnn_memory_desc_t *diff_src_iter_desc,
+        const mkldnn_memory_desc_t *diff_weights_layer_desc,
+        const mkldnn_memory_desc_t *diff_weights_iter_desc,
+        const mkldnn_memory_desc_t *diff_bias_desc,
+        const mkldnn_memory_desc_t *diff_dst_layer,
+        const mkldnn_memory_desc_t *diff_dst_iter_desc);
+
+/** @} */
+
+/** @} */
+
+/** @addtogroup c_api_engine Engine operations
+ * @{ */
+
+/** Returns the number of engines of a particular @p kind. */
+size_t MKLDNN_API mkldnn_engine_get_count(mkldnn_engine_kind_t kind);
+
+/** Creates an @p engine of particular @p kind and @p index. */
+mkldnn_status_t MKLDNN_API mkldnn_engine_create(mkldnn_engine_t *engine,
+        mkldnn_engine_kind_t kind, size_t index);
+
+/** Returns the kind of an @p engine. */
+mkldnn_status_t MKLDNN_API mkldnn_engine_get_kind(mkldnn_engine_t engine,
+        mkldnn_engine_kind_t *kind);
+
+/** Destroys an @p engine. */
+mkldnn_status_t MKLDNN_API mkldnn_engine_destroy(mkldnn_engine_t engine);
+
+/** @} */
+
+/** @addtogroup c_api_stream Execution stream operations
+ * @{ */
+
+/** Creates an execution @p stream for @p engine and with @p flags. */
+mkldnn_status_t MKLDNN_API mkldnn_stream_create(mkldnn_stream_t *stream,
+        mkldnn_engine_t engine, unsigned flags);
+
+/** Destroys an execution @p stream. */
+mkldnn_status_t MKLDNN_API mkldnn_stream_destroy(mkldnn_stream_t stream);
+
+/** @} */
+
+/** @addtogroup c_api_service Service functions
+ * @{ */
+
+/** Sets verbosity level (print information to stdout).
+ * Possible levels are:
+ *  - 0 -- no verbose output (default)
+ *  - 1 -- primitive information at execution
+ *  - 2 -- primitive information at creation and execution
+ *
+ * @note
+ *     Dumping information might affect performance.
+ *     This setting overrides the MKLDNN_VERBOSE environment variable. */
+mkldnn_status_t MKLDNN_API mkldnn_set_verbose(int level);
+
+/** Enables or disables dumping of JIT-generated code.
+ * The enable parameter can be:
+ *  - 0 -- disable
+ *  - any other value -- enable
+ *
+ * @note
+ *     This setting overrides the MKLDNN_JIT_DUMP environment variable. */
+mkldnn_status_t MKLDNN_API mkldnn_set_jit_dump(int enable);
+
+/** Gets library version information.
+ * Version information includes:
+ *  - major -- major version number
+ *  - minor -- minor version number
+ *  - patch -- patch release number
+ *  - hash -- git commit hash */
+const mkldnn_version_t MKLDNN_API *mkldnn_version();
+
+/** @} */
+
+/** @addtogroup c_api_blas BLAS functions
+ * A subset of Basic Linear ALgebra (BLAS) functions to perform
+ * matrix-matrix multiplication.
+ * @{ */
+
+/** SGEMM performs a matrix-matrix multiplication operation defined as
+ *
+ * C := alpha*op( A )*op( B ) + beta*C
+ *
+ * where
+ *  - op( X ) is one of op( X ) = X or op( X ) = X**T,
+ *  - alpha and beta are scalars,
+ *  - A, B and C are matrices, with op( A ) an m by k matrix, op( B ) a k by n matrix
+ *    and C an m by n matrix.
+ *
+ * The matrices are assumed to be stored in column-major order (the elements
+ * in a matrix columns are contiguous in memory).
+ *
+ * @note
+ *      The API is different from the standard BLAS routine
+ *      because it returns mkldnn_status_t for error handling.
+ *      XERBLA is not supported: no error message will be printed
+ *      in case of incorrect parameters. */
+mkldnn_status_t MKLDNN_API mkldnn_sgemm(
+        const char *transa, const char *transb,
+        const mkldnn_dim_t *M, const mkldnn_dim_t *N, const mkldnn_dim_t *K,
+        const float *alpha, const float *A, const mkldnn_dim_t *lda,
+        const float *B, const mkldnn_dim_t *ldb,
+        const float *beta, float *C, const mkldnn_dim_t *ldc);
+
+/** gemm_s8u8s32 and gemm_s8s8s32 perform a matrix-matrix multiplication
+ * operation and add the result to a scalar-matrix product. For the final
+ * result, a vector is added to each row or column of the output matrix.
+ * The operation is defined as:
+ *
+ * C := alpha*(op(A) + A_offset) * (op(B) + B_offset) + beta*C + C_offset
+ *
+ * where
+ *  - op( X ) = X or op( X ) = X**T,
+ *  - A_offset is an m-by-k matrix with every element equal to the value oa,
+ *  - B_offset is an k-by-n matrix with every element equal to the value ob,
+ *  - C_offset is an m-by-n matrix defined by the oc array, size len:
+ *    - if offsetc = F: len must be at least 1
+ *    - if offsetc = C: len must be at least max(1, m)
+ *    - if offsetc = R: len must be at least max(1, n)
+ *  - alpha and beta are scalars, and A, B and C are matrices, with op( A )
+ *    an m-by-k matrix, op( B ) a k-by-n matrix and C an m-by-n matrix.
+ *
+ * The matrices are assumed to be stored in column-major order (the elements
+ * in a matrix columns are contiguous in memory).
+ *
+ * @note
+ *      The API is different compared with the standard BLAS routine
+ *      because it returns mkldnn_status_t for error handling.
+ *      XERBLA is not supported: no error message will be printed
+ *      in case of incorrect parameters. */
+mkldnn_status_t MKLDNN_API mkldnn_gemm_s8u8s32(
+        const char *transa, const char *transb, const char *offsetc,
+        const mkldnn_dim_t *M, const mkldnn_dim_t *N, const mkldnn_dim_t *K,
+        const float *alpha,
+        const int8_t *A, const mkldnn_dim_t *lda, const int8_t *ao,
+        const uint8_t *B, const mkldnn_dim_t *ldb, const int8_t *bo,
+        const float *beta,
+        int32_t *c, const mkldnn_dim_t *ldc, const int32_t *co);
+
+mkldnn_status_t MKLDNN_API mkldnn_gemm_s8s8s32(
+        const char *transa, const char *transb, const char *offsetc,
+        const mkldnn_dim_t *M, const mkldnn_dim_t *N, const mkldnn_dim_t *K,
+        const float *alpha,
+        const int8_t *A, const mkldnn_dim_t *lda, const int8_t *ao,
+        const int8_t *B, const mkldnn_dim_t *ldb, const int8_t *bo,
+        const float *beta,
+        int32_t *c, const mkldnn_dim_t *ldc, const int32_t *co);
+/** @} */
+
+/** @} */
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/include/mkldnn.hpp b/thirdparty/oidn/mkl-dnn/include/mkldnn.hpp
new file mode 100644
index 0000000000..581400a013
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/include/mkldnn.hpp
@@ -0,0 +1,2615 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MKLDNN_HPP
+#define MKLDNN_HPP
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+#include <stdlib.h>
+#include <memory>
+#include <vector>
+#include <unordered_map>
+#include <algorithm>
+#include <iterator>
+
+#include "mkldnn.h"
+#endif
+
+namespace mkldnn {
+
+/// @addtogroup cpp_api C++ API
+/// @{
+
+/// @addtogroup cpp_api_utils Utils
+/// @{
+
+/// A class that provides the destructor for an Intel(R) MKL-DNN C handle
+template <typename T> class handle_traits {};
+
+/// A class for wrapping an Intel(R) MKL-DNN handle. It is used as the base
+/// class for primitive (#mkldnn_primitive_t), engine (#mkldnn_engine_t), and
+/// stream (#mkldnn_stream_t) handles. An object of the #mkldnn::handle class
+/// can be passed by value. This class enables wrapping:
+///  - Newly constructed handles.
+///    @n In this case, the constructed handle uses reference counting provided
+///    by @p std::shared_ptr with a proper deleter function specified through
+///    the @p handle_traits class.
+///  - Pre-existing handles returned by the Intel(R) MKL-DNN C API (for
+///    example, through mkldnn_primitive_get_primitive_desc()).
+///    @n In this case, an Intel(R) MKL-DNN C API handle is wrapped without a
+///    deleter because it is assumed that the handle wrapper for the original
+///    object deletes the handle (this model is similar to @p std::weak_ptr).
+template <typename T, typename traits=handle_traits<T>> class handle {
+private:
+    std::shared_ptr<typename std::remove_pointer<T>::type> _data;
+    handle(const handle &&) = delete;
+    handle &operator=(const handle &&other) = delete;
+protected:
+    bool operator==(const T other) const { return other == _data.get(); }
+    bool operator!=(const T other) const { return !(*this == other); }
+public:
+    /// Constructs a C handle wrapper.
+    /// @param t The C handle to wrap.
+    /// @param weak A flag to specify whether to construct a weak wrapper.
+    handle(T t = 0, bool weak = false): _data(0) {
+        reset(t, weak);
+    }
+
+    handle(const handle &other): _data(other._data) {}
+    handle &operator=(const handle &other) {
+        _data = other._data;
+        return *this;
+    }
+    /// Resets the value of a C handle.
+    /// @param t The new value of the C handle.
+    /// @param weak A flag to specify whether the wrapper should be weak.
+    void reset(T t, bool weak = false) {
+        auto dummy_destructor = [](T) { return decltype(traits::destructor(0))(0); };
+        _data.reset(t, weak ? dummy_destructor : traits::destructor);
+    }
+
+    /// Returns the value of the underlying C handle.
+    T get() const { return _data.get(); }
+
+    bool operator==(const handle &other) const { return other._data.get() == _data.get(); }
+    bool operator!=(const handle &other) const { return !(*this == other); }
+};
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+template <> struct handle_traits<mkldnn_memory_t> {
+    static constexpr auto destructor = &mkldnn_memory_destroy;
+};
+
+template <> struct handle_traits<mkldnn_primitive_desc_t> {
+    static constexpr auto destructor = &mkldnn_primitive_desc_destroy;
+};
+
+template <> struct handle_traits<mkldnn_primitive_t> {
+    static constexpr auto destructor = &mkldnn_primitive_destroy;
+};
+
+template <> struct handle_traits<mkldnn_primitive_desc_iterator_t> {
+    static constexpr auto destructor = &mkldnn_primitive_desc_iterator_destroy;
+};
+#endif
+
+struct memory;
+struct primitive_desc;
+
+/// Base class for all computational primitives.
+class primitive: public handle<mkldnn_primitive_t> {
+    friend struct error;
+    friend struct stream;
+    using handle::handle;
+public:
+    /// A proxy to C primitive kind enum
+    enum class kind {
+        undefined_primitive = mkldnn_undefined_primitive,
+        reorder = mkldnn_reorder,
+        concat = mkldnn_concat,
+        sum = mkldnn_sum,
+        convolution = mkldnn_convolution,
+        deconvolution = mkldnn_deconvolution,
+        shuffle = mkldnn_shuffle,
+        eltwise = mkldnn_eltwise,
+        softmax = mkldnn_softmax,
+        pooling = mkldnn_pooling,
+        lrn = mkldnn_lrn,
+        batch_normalization = mkldnn_batch_normalization,
+        inner_product = mkldnn_inner_product,
+        rnn = mkldnn_rnn,
+    };
+
+    primitive(const_mkldnn_primitive_desc_t c_pd);
+    primitive(const primitive_desc &pd);
+
+    /// Returns the descriptor of the underlying C API primitive.
+    inline const_mkldnn_primitive_desc_t get_primitive_desc() const;
+    // TODO: use the C++ API wrapper structure.
+
+    void execute(struct stream &astream,
+            const std::unordered_map<int, memory> &args) const;
+};
+
+inline mkldnn_primitive_kind_t convert_to_c(primitive::kind akind) {
+    return static_cast<mkldnn_primitive_kind_t>(akind);
+}
+/// Intel(R) MKL-DNN exception class.
+///
+/// This class captures the status returned by the failed C API function, error
+/// message, and, optionally, handle of the primitive that caused the error.
+struct error: public std::exception {
+    mkldnn_status_t status;
+    const char *message;
+
+    /// Constructs an error instance.
+    ///
+    /// @param astatus The error status returned by the C API.
+    /// @param amessage The error message.
+    error(mkldnn_status_t astatus, const char *amessage)
+        : status(astatus), message(amessage) {}
+
+    /// A convenience function for wrapping calls to the C API. Checks the
+    /// return status and throws an #error in case of failure.
+    ///
+    /// @param status The error status returned by the C API.
+    /// @param message The error message.
+    static void wrap_c_api(mkldnn_status_t status, const char *message) {
+        if (status != mkldnn_success)
+            throw error(status, message);
+    }
+};
+
+const_mkldnn_primitive_desc_t primitive::get_primitive_desc() const {
+    const_mkldnn_primitive_desc_t pd;
+    error::wrap_c_api(mkldnn_primitive_get_primitive_desc(get(), &pd),
+            "could not get primitive descriptor by primitive");
+    return pd;
+}
+/// @}
+
+/// @addtogroup cpp_api_enums Common data types and enumerations
+/// A proxy to @ref c_api_types in @ref c_api.
+///
+/// @{
+
+enum scratchpad_mode {
+    scratchpad_mode_library = mkldnn_scratchpad_mode_library,
+    scratchpad_mode_user = mkldnn_scratchpad_mode_user,
+};
+
+inline mkldnn_scratchpad_mode_t convert_to_c(scratchpad_mode mode) {
+    return static_cast<mkldnn_scratchpad_mode_t>(mode);
+}
+
+enum padding_kind {
+    zero = mkldnn_padding_zero
+};
+
+inline mkldnn_padding_kind_t convert_to_c(padding_kind kind) {
+    return static_cast<mkldnn_padding_kind_t>(kind);
+}
+
+enum prop_kind {
+    forward_training = mkldnn_forward_training,
+    forward_scoring = mkldnn_forward_scoring,
+    forward_inference = mkldnn_forward_inference,
+    forward = mkldnn_forward,
+    backward = mkldnn_backward,
+    backward_data = mkldnn_backward_data,
+    backward_weights = mkldnn_backward_weights,
+    backward_bias = mkldnn_backward_bias
+};
+
+inline mkldnn_prop_kind_t convert_to_c(prop_kind kind) {
+    return static_cast<mkldnn_prop_kind_t>(kind);
+}
+
+enum algorithm {
+    algorithm_undef = mkldnn_alg_kind_undef,
+    convolution_auto = mkldnn_convolution_auto,
+    convolution_direct = mkldnn_convolution_direct,
+    convolution_winograd = mkldnn_convolution_winograd,
+    deconvolution_direct = mkldnn_deconvolution_direct,
+    deconvolution_winograd = mkldnn_deconvolution_winograd,
+    eltwise_relu = mkldnn_eltwise_relu,
+    eltwise_tanh = mkldnn_eltwise_tanh,
+    eltwise_elu = mkldnn_eltwise_elu,
+    eltwise_square = mkldnn_eltwise_square,
+    eltwise_abs = mkldnn_eltwise_abs,
+    eltwise_sqrt = mkldnn_eltwise_sqrt,
+    eltwise_linear = mkldnn_eltwise_linear,
+    eltwise_bounded_relu = mkldnn_eltwise_bounded_relu,
+    eltwise_soft_relu = mkldnn_eltwise_soft_relu,
+    eltwise_logistic = mkldnn_eltwise_logistic,
+    lrn_across_channels = mkldnn_lrn_across_channels,
+    lrn_within_channel  = mkldnn_lrn_within_channel,
+    pooling_max = mkldnn_pooling_max,
+    pooling_avg = mkldnn_pooling_avg,
+    pooling_avg_include_padding = mkldnn_pooling_avg_include_padding,
+    pooling_avg_exclude_padding = mkldnn_pooling_avg_exclude_padding,
+    vanilla_rnn = mkldnn_vanilla_rnn,
+    vanilla_lstm = mkldnn_vanilla_lstm,
+    vanilla_gru = mkldnn_vanilla_gru,
+    gru_linear_before_reset = mkldnn_gru_linear_before_reset
+};
+
+inline mkldnn_alg_kind_t convert_to_c(algorithm aalgorithm) {
+    return static_cast<mkldnn_alg_kind_t>(aalgorithm);
+}
+
+enum batch_normalization_flag {
+    use_global_stats = mkldnn_use_global_stats,
+    use_scale_shift = mkldnn_use_scaleshift,
+    fuse_bn_relu = mkldnn_fuse_bn_relu
+};
+
+inline mkldnn_batch_normalization_flag_t convert_to_c(
+        batch_normalization_flag aflag) {
+    return static_cast<mkldnn_batch_normalization_flag_t>(aflag);
+}
+
+enum rnn_direction {
+    unidirectional_left2right = mkldnn_unidirectional_left2right,
+    unidirectional_right2left = mkldnn_unidirectional_right2left,
+    unidirectional = mkldnn_unidirectional,
+    bidirectional_concat = mkldnn_bidirectional_concat,
+    bidirectional_sum = mkldnn_bidirectional_sum,
+};
+
+inline mkldnn_rnn_direction_t convert_to_c(rnn_direction adir) {
+    return static_cast<mkldnn_rnn_direction_t>(adir);
+}
+
+enum query {
+    undef = mkldnn_query_undef,
+
+    query_engine = mkldnn_query_engine,
+    primitive_kind = mkldnn_query_primitive_kind,
+
+    num_of_inputs_s32 = mkldnn_query_num_of_inputs_s32,
+    num_of_outputs_s32 = mkldnn_query_num_of_outputs_s32,
+
+    time_estimate_f64 = mkldnn_query_time_estimate_f64,
+    memory_consumption_s64 = mkldnn_query_memory_consumption_s64,
+
+    query_scratchpad_engine = mkldnn_query_scratchpad_engine,
+
+    impl_info_str = mkldnn_query_impl_info_str,
+
+    op_d = mkldnn_query_op_d,
+    convolution_d = mkldnn_query_convolution_d,
+    deconvolution_d = mkldnn_query_deconvolution_d,
+    shuffle_d = mkldnn_query_shuffle_d,
+    eltwise_d = mkldnn_query_eltwise_d,
+    softmax_d = mkldnn_query_softmax_d,
+    pooling_d = mkldnn_query_pooling_d,
+    lrn_d = mkldnn_query_lrn_d,
+    batch_normalization_d = mkldnn_query_batch_normalization_d,
+    inner_product_d = mkldnn_query_inner_product_d,
+    rnn_d = mkldnn_query_rnn_d,
+
+    src_md = mkldnn_query_src_md,
+    diff_src_md = mkldnn_query_diff_src_md,
+    weights_md = mkldnn_query_weights_md,
+    diff_weights_md = mkldnn_query_diff_weights_md,
+    dst_md = mkldnn_query_dst_md,
+    diff_dst_md = mkldnn_query_diff_dst_md,
+    workspace_md = mkldnn_query_workspace_md,
+    scratchpad_md = mkldnn_query_scratchpad_md,
+};
+
+inline mkldnn_query_t convert_to_c(query aquery) {
+    return static_cast<mkldnn_query_t>(aquery);
+}
+
+/// @}
+
+/// @addtogroup cpp_api_attr Attributes
+/// An extension for controlling primitive behavior.
+///
+/// @sa @ref c_api_attributes in @ref c_api
+/// @{
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+template <> struct handle_traits<mkldnn_post_ops_t> {
+    static constexpr auto destructor = &mkldnn_post_ops_destroy;
+};
+#endif
+
+struct post_ops: public handle<mkldnn_post_ops_t> {
+    post_ops() {
+        mkldnn_post_ops_t result;
+        error::wrap_c_api(mkldnn_post_ops_create(&result),
+                "could not create post operation sequence");
+        reset(result);
+    }
+
+    int len() const { return mkldnn_post_ops_len(get()); }
+
+    primitive::kind kind(int index) const {
+        error::wrap_c_api(
+                index < len() ? mkldnn_success : mkldnn_invalid_arguments,
+                "post_ops index is out of range");
+        return static_cast<primitive::kind>(mkldnn_post_ops_get_kind(get(),
+                    index));
+    }
+
+    void append_sum(float scale = 1.) {
+        error::wrap_c_api(mkldnn_post_ops_append_sum(get(), scale),
+                "could not append sum");
+    }
+
+    void get_params_sum(int index, float &scale) const {
+        error::wrap_c_api(mkldnn_post_ops_get_params_sum(get(), index, &scale),
+                "could not get sum params");
+    }
+
+    void append_eltwise(float scale, algorithm alg, float alpha,
+            float beta) {
+        error::wrap_c_api(mkldnn_post_ops_append_eltwise(get(), scale,
+                    convert_to_c(alg), alpha, beta),
+                "could not append eltwise");
+    }
+
+    void get_params_eltwise(int index, float &scale, algorithm &alg,
+            float &alpha, float &beta) const {
+        mkldnn_alg_kind_t c_alg;
+        error::wrap_c_api(mkldnn_post_ops_get_params_eltwise(get(), index,
+                    &scale, &c_alg, &alpha, &beta),
+                "could not get eltwise params");
+        alg = static_cast<algorithm>(c_alg);
+    }
+};
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+template <> struct handle_traits<mkldnn_primitive_attr_t> {
+    static constexpr auto destructor = &mkldnn_primitive_attr_destroy;
+};
+#endif
+
+struct primitive_attr: public handle<mkldnn_primitive_attr_t> {
+    primitive_attr() {
+        mkldnn_primitive_attr_t result;
+        error::wrap_c_api(mkldnn_primitive_attr_create(&result),
+                "could not create a primitive attr");
+        reset(result);
+    }
+
+    scratchpad_mode get_scratchpad_mode() const {
+        mkldnn_scratchpad_mode_t result;
+        error::wrap_c_api(mkldnn_primitive_attr_get_scratchpad_mode(
+                    get(), &result), "could not get scratchpad mode");
+        return scratchpad_mode(result);
+    }
+
+    void set_scratchpad_mode(scratchpad_mode mode) {
+        error::wrap_c_api(mkldnn_primitive_attr_set_scratchpad_mode(
+                    get(), mkldnn::convert_to_c(mode)),
+                "could not set scratchpad mode");
+    }
+
+    void get_output_scales(int &mask, std::vector<float> &scales) const
+    {
+        mkldnn_dim_t count;
+        int c_mask;
+        const float *c_scales;
+        error::wrap_c_api(mkldnn_primitive_attr_get_output_scales(get(),
+                    &count, &c_mask, &c_scales),
+                "could not get int output scales");
+        scales.resize(count);
+
+        mask = c_mask;
+        for (mkldnn_dim_t c = 0; c < count; ++c)
+            scales[c] = c_scales[c];
+    }
+
+    void set_output_scales(int mask, const std::vector<float> &scales)
+    {
+        error::wrap_c_api(mkldnn_primitive_attr_set_output_scales(get(),
+                    (mkldnn_dim_t)scales.size(), mask, &scales[0]),
+                "could not set int output scales");
+    }
+
+    const post_ops get_post_ops() const {
+        post_ops result;
+        const_mkldnn_post_ops_t c_result;
+        error::wrap_c_api(mkldnn_primitive_attr_get_post_ops(get(), &c_result),
+                "could not get post operation sequence");
+        result.reset(const_cast<mkldnn_post_ops_t>(c_result), true);
+        return result;
+    }
+
+    void set_post_ops(post_ops ops) {
+        error::wrap_c_api(mkldnn_primitive_attr_set_post_ops(get(), ops.get()),
+                "could not set post operation sequence");
+    }
+
+    void set_rnn_data_qparams(const float scale, const float shift)
+    {
+        error::wrap_c_api(mkldnn_primitive_attr_set_rnn_data_qparams(get(),
+                    scale, shift), "could not set rnn data int scale/shift");
+    }
+
+    void set_rnn_weights_qparams(int mask, const std::vector<float> &scales)
+    {
+        error::wrap_c_api(mkldnn_primitive_attr_set_rnn_weights_qparams(get(),
+                    (int)scales.size(), mask, &scales[0]),
+                "could not set rnn weights int scales");
+    }
+};
+
+/// @}
+
+/// @addtogroup cpp_api_engine Engine
+/// Engine operations.
+///
+/// @sa @ref c_api_engine in @ref c_api
+/// @{
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+template <> struct handle_traits<mkldnn_engine_t> {
+    static constexpr auto destructor = &mkldnn_engine_destroy;
+};
+#endif
+
+/// An execution engine.
+struct engine: public handle<mkldnn_engine_t> {
+    friend class primitive;
+    // gcc bug??? using handle::handle;
+
+    /// Kinds of engines.
+    enum kind {
+        /// An unspecified engine
+        any = mkldnn_any_engine,
+        /// CPU engine
+        cpu = mkldnn_cpu,
+    };
+
+    /// Returns the number of engines of a certain kind.
+    ///
+    /// @param akind The kind of engines to count.
+
+    static size_t get_count(kind akind) {
+        return mkldnn_engine_get_count(convert_to_c(akind));
+    }
+
+    /// Constructs an engine.
+    ///
+    /// @param akind The kind of engine to construct.
+    /// @param index The index of the engine. Must be less than the value
+    ///              returned by #get_count() for this particular kind of engine.
+
+    engine(kind akind, size_t index) {
+        mkldnn_engine_t aengine;
+        error::wrap_c_api(
+                mkldnn_engine_create(&aengine,
+                    convert_to_c(akind), index),
+                "could not create an engine");
+        reset(aengine);
+    }
+
+    explicit engine(const mkldnn_engine_t& aengine)
+        : handle(aengine, true) {}
+
+    engine(const handle<mkldnn_primitive_desc_t> &pd) {
+        mkldnn_engine_t engine_q;
+        error::wrap_c_api(
+                mkldnn_primitive_desc_query(pd.get(),
+                    mkldnn::convert_to_c(query_engine), 0, &engine_q),
+                "could not get engine from primitive_desc");
+        reset(engine_q, true);
+    }
+
+    template <class primitive_desc>
+    static engine query(const primitive_desc &pd) {
+        mkldnn_engine_t engine_q;
+        error::wrap_c_api(
+                mkldnn_primitive_desc_query(pd.get(),
+                    mkldnn::convert_to_c(query_engine), 0, &engine_q),
+                "could not get engine from primitive_desc");
+
+        return engine(engine_q);
+    }
+
+private:
+    static mkldnn_engine_kind_t convert_to_c(kind akind) {
+        return static_cast<mkldnn_engine_kind_t>(akind);
+    }
+};
+
+/// @}
+
+/// @addtogroup cpp_api_stream Stream
+/// Execution stream operations
+///
+/// @sa @ref c_api_stream in @ref c_api
+/// @{
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+template <> struct handle_traits<mkldnn_stream_t> {
+    static constexpr auto destructor = &mkldnn_stream_destroy;
+};
+#endif
+
+struct stream: public handle<mkldnn_stream_t> {
+    using handle::handle;
+
+    enum: unsigned {
+        default_flags = mkldnn_stream_default_flags,
+    };
+
+    /// Constructs a stream.
+    stream(const engine &aengine,
+            unsigned flags = static_cast<unsigned>(default_flags)) {
+        mkldnn_stream_t astream;
+        error::wrap_c_api(mkldnn_stream_create(&astream, aengine.get(), flags),
+                "could not create a stream");
+        reset(astream);
+    }
+};
+
+/// @}
+
+/// @addtogroup cpp_api_memory_related Memory and memory related operations
+/// @{
+
+/// @addtogroup cpp_api_memory Memory
+/// A primitive to describe and store data.
+///
+/// For more information, refer to @ref c_api_memory in @ref c_api.
+/// @{
+
+/// Memory that describes the data.
+struct memory: public handle<mkldnn_memory_t> {
+    public:
+    typedef mkldnn_dim_t dim;
+    typedef std::vector<dim> dims;
+
+    template <typename T> static void validate_dims(const std::vector<T> &v) {
+        if (v.size() > MKLDNN_MAX_NDIMS)
+            throw error(mkldnn_invalid_arguments, "invalid dimensions");
+    }
+
+    /// Data type specification. See #mkldnn_data_type_t for a detailed
+    /// description.
+    enum data_type {
+        data_undef = mkldnn_data_type_undef,
+        f32 = mkldnn_f32,
+        s32 = mkldnn_s32,
+        s8 = mkldnn_s8,
+        u8 = mkldnn_u8,
+    };
+
+    /// Memory format tag specification. See #mkldnn_format_tag_t
+    /// for a detailed description.
+    enum format_tag {
+        format_tag_undef = mkldnn_format_tag_undef,
+        any = mkldnn_format_tag_any,
+        a = mkldnn_a,
+        ab = mkldnn_ab,
+        abc = mkldnn_abc,
+        abcd = mkldnn_abcd,
+        abcde = mkldnn_abcde,
+        abcdef = mkldnn_abcdef,
+        abdec = mkldnn_abdec,
+        acb = mkldnn_acb,
+        acbde = mkldnn_acbde,
+        acdb = mkldnn_acdb,
+        acdeb = mkldnn_acdeb,
+        ba = mkldnn_ba,
+        bac = mkldnn_bac,
+        bacd = mkldnn_bacd,
+        bcda = mkldnn_bcda,
+        cba = mkldnn_cba,
+        cdba = mkldnn_cdba,
+        cdeba = mkldnn_cdeba,
+        decab = mkldnn_decab,
+        Abc16a = mkldnn_Abc16a,
+        ABc16a16b = mkldnn_ABc16a16b,
+        aBc16b = mkldnn_aBc16b,
+        ABc16b16a = mkldnn_ABc16b16a,
+        Abc4a = mkldnn_Abc4a,
+        aBc4b = mkldnn_aBc4b,
+        ABc4b16a4b = mkldnn_ABc4b16a4b,
+        ABc4b4a = mkldnn_ABc4b4a,
+        ABc8a16b2a = mkldnn_ABc8a16b2a,
+        ABc8a8b = mkldnn_ABc8a8b,
+        aBc8b = mkldnn_aBc8b,
+        ABc8b16a2b = mkldnn_ABc8b16a2b,
+        ABc8b8a = mkldnn_ABc8b8a,
+        Abcd16a = mkldnn_Abcd16a,
+        ABcd16a16b = mkldnn_ABcd16a16b,
+        aBcd16b = mkldnn_aBcd16b,
+        ABcd16b16a = mkldnn_ABcd16b16a,
+        aBCd16b16c = mkldnn_aBCd16b16c,
+        aBCd16c16b = mkldnn_aBCd16c16b,
+        Abcd4a = mkldnn_Abcd4a,
+        aBcd4b = mkldnn_aBcd4b,
+        ABcd4b16a4b = mkldnn_ABcd4b16a4b,
+        ABcd4b4a = mkldnn_ABcd4b4a,
+        aBCd4c16b4c = mkldnn_aBCd4c16b4c,
+        aBCd4c4b = mkldnn_aBCd4c4b,
+        ABcd8a16b2a = mkldnn_ABcd8a16b2a,
+        ABcd8a8b = mkldnn_ABcd8a8b,
+        aBcd8b = mkldnn_aBcd8b,
+        ABcd8b16a2b = mkldnn_ABcd8b16a2b,
+        aBCd8b16c2b = mkldnn_aBCd8b16c2b,
+        ABcd8b8a = mkldnn_ABcd8b8a,
+        aBCd8b8c = mkldnn_aBCd8b8c,
+        aBCd8c16b2c = mkldnn_aBCd8c16b2c,
+        aBCd8c8b = mkldnn_aBCd8c8b,
+        Abcde16a = mkldnn_Abcde16a,
+        ABcde16a16b = mkldnn_ABcde16a16b,
+        aBcde16b = mkldnn_aBcde16b,
+        ABcde16b16a = mkldnn_ABcde16b16a,
+        aBCde16b16c = mkldnn_aBCde16b16c,
+        aBCde16c16b = mkldnn_aBCde16c16b,
+        aBCde2c8b4c = mkldnn_aBCde2c8b4c,
+        Abcde4a = mkldnn_Abcde4a,
+        aBcde4b = mkldnn_aBcde4b,
+        ABcde4b4a = mkldnn_ABcde4b4a,
+        aBCde4b4c = mkldnn_aBCde4b4c,
+        aBCde4c16b4c = mkldnn_aBCde4c16b4c,
+        aBCde4c4b = mkldnn_aBCde4c4b,
+        Abcde8a = mkldnn_Abcde8a,
+        ABcde8a8b = mkldnn_ABcde8a8b,
+        aBcde8b = mkldnn_aBcde8b,
+        ABcde8b16a2b = mkldnn_ABcde8b16a2b,
+        aBCde8b16c2b = mkldnn_aBCde8b16c2b,
+        ABcde8b8a = mkldnn_ABcde8b8a,
+        aBCde8b8c = mkldnn_aBCde8b8c,
+        aBCde8c16b2c = mkldnn_aBCde8c16b2c,
+        aBCde8c8b = mkldnn_aBCde8c8b,
+        aBcdef16b = mkldnn_aBcdef16b,
+        aBCdef16b16c = mkldnn_aBCdef16b16c,
+        aBCdef16c16b = mkldnn_aBCdef16c16b,
+        aBcdef4b = mkldnn_aBcdef4b,
+        aBCdef4c4b = mkldnn_aBCdef4c4b,
+        aBCdef8b8c = mkldnn_aBCdef8b8c,
+        aBCdef8c16b2c = mkldnn_aBCdef8c16b2c,
+        aBCdef8c8b = mkldnn_aBCdef8c8b,
+        aBdc16b = mkldnn_aBdc16b,
+        aBdc4b = mkldnn_aBdc4b,
+        aBdc8b = mkldnn_aBdc8b,
+        aBdec16b = mkldnn_aBdec16b,
+        aBdec4b = mkldnn_aBdec4b,
+        aBdec8b = mkldnn_aBdec8b,
+        aBdefc16b = mkldnn_aBdefc16b,
+        aBdefc4b = mkldnn_aBdefc4b,
+        aBdefc8b = mkldnn_aBdefc8b,
+        Acb16a = mkldnn_Acb16a,
+        Acb4a = mkldnn_Acb4a,
+        Acb8a = mkldnn_Acb8a,
+        aCBd16b16c = mkldnn_aCBd16b16c,
+        aCBde16b16c = mkldnn_aCBde16b16c,
+        Acdb16a = mkldnn_Acdb16a,
+        Acdb4a = mkldnn_Acdb4a,
+        Acdb8a = mkldnn_Acdb8a,
+        Acdeb16a = mkldnn_Acdeb16a,
+        Acdeb4a = mkldnn_Acdeb4a,
+        Acdeb8a = mkldnn_Acdeb8a,
+        BAc16a16b = mkldnn_BAc16a16b,
+        BAcd16a16b = mkldnn_BAcd16a16b,
+        format_tag_last = mkldnn_format_tag_last,
+
+        x = mkldnn_x,
+        nc = mkldnn_nc,
+        cn = mkldnn_cn,
+        ncw = mkldnn_ncw,
+        nwc = mkldnn_nwc,
+        nchw = mkldnn_nchw,
+        nhwc = mkldnn_nhwc,
+        chwn = mkldnn_chwn,
+        ncdhw = mkldnn_ncdhw,
+        ndhwc = mkldnn_ndhwc,
+        oi = mkldnn_oi,
+        io = mkldnn_io,
+        oiw = mkldnn_oiw,
+        wio = mkldnn_wio,
+        oihw = mkldnn_oihw,
+        hwio = mkldnn_hwio,
+        ihwo = mkldnn_ihwo,
+        iohw = mkldnn_iohw,
+        oidhw = mkldnn_oidhw,
+        dhwio = mkldnn_dhwio,
+        goiw = mkldnn_goiw,
+        goihw = mkldnn_goihw,
+        hwigo = mkldnn_hwigo,
+        giohw = mkldnn_giohw,
+        goidhw = mkldnn_goidhw,
+        tnc = mkldnn_tnc,
+        ntc = mkldnn_ntc,
+        ldsnc = mkldnn_ldsnc,
+        ldigo = mkldnn_ldigo,
+        ldgoi = mkldnn_ldgoi,
+        ldgo = mkldnn_ldgo,
+        nCdhw16c = mkldnn_nCdhw16c,
+        nCdhw4c = mkldnn_nCdhw4c,
+        nCdhw8c = mkldnn_nCdhw8c,
+        nChw16c = mkldnn_nChw16c,
+        nChw4c = mkldnn_nChw4c,
+        nChw8c = mkldnn_nChw8c,
+        nCw16c = mkldnn_nCw16c,
+        nCw4c = mkldnn_nCw4c,
+        nCw8c = mkldnn_nCw8c,
+        IOw16o16i = mkldnn_IOw16o16i,
+        OIw16i16o = mkldnn_OIw16i16o,
+        OIw16o16i = mkldnn_OIw16o16i,
+        Oiw16o = mkldnn_Oiw16o,
+        OIw4i16o4i = mkldnn_OIw4i16o4i,
+        OIw4i4o = mkldnn_OIw4i4o,
+        Oiw4o = mkldnn_Oiw4o,
+        OIw8i16o2i = mkldnn_OIw8i16o2i,
+        OIw8i8o = mkldnn_OIw8i8o,
+        OIw8o16i2o = mkldnn_OIw8o16i2o,
+        OIw8o8i = mkldnn_OIw8o8i,
+        Owi16o = mkldnn_Owi16o,
+        Owi4o = mkldnn_Owi4o,
+        Owi8o = mkldnn_Owi8o,
+        IOhw16o16i = mkldnn_IOhw16o16i,
+        Ohwi16o = mkldnn_Ohwi16o,
+        Ohwi4o = mkldnn_Ohwi4o,
+        Ohwi8o = mkldnn_Ohwi8o,
+        OIhw16i16o = mkldnn_OIhw16i16o,
+        OIhw16o16i = mkldnn_OIhw16o16i,
+        Oihw16o = mkldnn_Oihw16o,
+        OIhw4i16o4i = mkldnn_OIhw4i16o4i,
+        OIhw4i4o = mkldnn_OIhw4i4o,
+        Oihw4o = mkldnn_Oihw4o,
+        OIhw8i16o2i = mkldnn_OIhw8i16o2i,
+        OIhw8i8o = mkldnn_OIhw8i8o,
+        OIhw8o16i2o = mkldnn_OIhw8o16i2o,
+        OIhw8o8i = mkldnn_OIhw8o8i,
+        Odhwi16o = mkldnn_Odhwi16o,
+        Odhwi4o = mkldnn_Odhwi4o,
+        Odhwi8o = mkldnn_Odhwi8o,
+        OIdhw16i16o = mkldnn_OIdhw16i16o,
+        OIdhw16o16i = mkldnn_OIdhw16o16i,
+        Oidhw16o = mkldnn_Oidhw16o,
+        OIdhw4i4o = mkldnn_OIdhw4i4o,
+        Oidhw4o = mkldnn_Oidhw4o,
+        OIdhw8i16o2i = mkldnn_OIdhw8i16o2i,
+        OIdhw8i8o = mkldnn_OIdhw8i8o,
+        OIdhw8o8i = mkldnn_OIdhw8o8i,
+        gIOw16o16i = mkldnn_gIOw16o16i,
+        gOIw16i16o = mkldnn_gOIw16i16o,
+        gOIw16o16i = mkldnn_gOIw16o16i,
+        gOiw16o = mkldnn_gOiw16o,
+        gOIw4i16o4i = mkldnn_gOIw4i16o4i,
+        gOIw4i4o = mkldnn_gOIw4i4o,
+        gOiw4o = mkldnn_gOiw4o,
+        gOIw8i16o2i = mkldnn_gOIw8i16o2i,
+        gOIw8i8o = mkldnn_gOIw8i8o,
+        gOIw8o16i2o = mkldnn_gOIw8o16i2o,
+        gOIw8o8i = mkldnn_gOIw8o8i,
+        gOwi16o = mkldnn_gOwi16o,
+        gOwi4o = mkldnn_gOwi4o,
+        gOwi8o = mkldnn_gOwi8o,
+        gIOhw16o16i = mkldnn_gIOhw16o16i,
+        gOhwi16o = mkldnn_gOhwi16o,
+        gOhwi4o = mkldnn_gOhwi4o,
+        gOhwi8o = mkldnn_gOhwi8o,
+        Goihw16g = mkldnn_Goihw16g,
+        gOIhw16i16o = mkldnn_gOIhw16i16o,
+        gOIhw16o16i = mkldnn_gOIhw16o16i,
+        gOihw16o = mkldnn_gOihw16o,
+        gOIhw2i8o4i = mkldnn_gOIhw2i8o4i,
+        gOIhw4i16o4i = mkldnn_gOIhw4i16o4i,
+        gOIhw4i4o = mkldnn_gOIhw4i4o,
+        gOIhw4o4i = mkldnn_gOIhw4o4i,
+        gOihw4o = mkldnn_gOihw4o,
+        Goihw8g = mkldnn_Goihw8g,
+        gOIhw8i16o2i = mkldnn_gOIhw8i16o2i,
+        gOIhw8i8o = mkldnn_gOIhw8i8o,
+        gOIhw8o16i2o = mkldnn_gOIhw8o16i2o,
+        gOIhw8o8i = mkldnn_gOIhw8o8i,
+        gOdhwi16o = mkldnn_gOdhwi16o,
+        gOdhwi4o = mkldnn_gOdhwi4o,
+        gOdhwi8o = mkldnn_gOdhwi8o,
+        gOIdhw16i16o = mkldnn_gOIdhw16i16o,
+        gOIdhw16o16i = mkldnn_gOIdhw16o16i,
+        gOidhw16o = mkldnn_gOidhw16o,
+        gOIdhw4i4o = mkldnn_gOIdhw4i4o,
+        gOidhw4o = mkldnn_gOidhw4o,
+        gOIdhw8i16o2i = mkldnn_gOIdhw8i16o2i,
+        gOIdhw8i8o = mkldnn_gOIdhw8i8o,
+        gOIdhw8o8i = mkldnn_gOIdhw8o8i,
+    };
+
+    /// A memory descriptor.
+    struct desc {
+        friend struct memory;
+        /// The underlying C API data structure.
+        mkldnn_memory_desc_t data;
+
+        /// Constructs a zero memory descriptor
+        desc(): data() {}
+
+        /// Constructs a memory descriptor.
+        ///
+        /// @param adims Data dimensions
+        /// @param adata_type Data precision/type.
+        /// @param aformat Data layout format tag.
+        desc(const dims &adims, data_type adata_type,
+                format_tag aformat) {
+            validate_dims(adims);
+            error::wrap_c_api(mkldnn_memory_desc_init_by_tag(&data, (int)adims.size(),
+                        adims.size() == 0 ? nullptr : &adims[0],
+                        convert_to_c(adata_type), convert_to_c(aformat)),
+                    "could not initialize a memory descriptor");
+        }
+
+        /// Constructs a memory descriptor from a C API data structure.
+        ///
+        /// @param adata A C API #mkldnn_memory_desc_t structure.
+        desc(const mkldnn_memory_desc_t &adata): data(adata) {}
+
+        /// Constructs a sub-memory descriptor
+        //
+        /// @param adims Sizes of a sub-memory
+        /// @param offsets Offsets of a sub-memory
+        desc submemory_desc(const dims &adims, const dims &offsets) {
+            mkldnn_memory_desc_t sub_md;
+            error::wrap_c_api(mkldnn_memory_desc_init_submemory(&sub_md,
+                        &data, &adims[0], &offsets[0]),
+                    "could not initialize a sub-memory");
+            return desc(sub_md);
+        }
+
+        /// Returns the number of bytes required to allocate the memory described
+        /// including the padding area.
+        size_t get_size() const { return mkldnn_memory_desc_get_size(&data); }
+
+        bool operator==(const desc &other) const {
+            return mkldnn_memory_desc_equal(&data, &other.data) != 0;
+        }
+
+        bool operator!=(const desc &other) const { return !operator==(other); }
+    };
+
+    /// Constructs a memory.
+    ///
+    /// @param md Memory descriptor.
+    /// @param aengine Engine.
+    /// @param ahandle Native handle.
+    memory(const desc &md, const engine &aengine, void *ahandle) {
+        mkldnn_memory_t result;
+        error::wrap_c_api(mkldnn_memory_create(&result, &md.data,
+                    aengine.get(), ahandle), "could not create a memory");
+        reset(result);
+    }
+
+    /// Constructs a memory.
+    ///
+    /// @param md Memory descriptor.
+    /// @param aengine Engine.
+    memory(const desc &md, const engine &aengine)
+        : memory(md, aengine, MKLDNN_NATIVE_HANDLE_ALLOCATE) {}
+
+    /// Returns the descriptor of the memory.
+    desc get_desc() const {
+        const mkldnn_memory_desc_t *cdesc;
+        error::wrap_c_api(mkldnn_memory_get_memory_desc(get(), &cdesc),
+                "could not get memory descriptor from a memory");
+        return desc(*cdesc);
+    }
+
+    /// Returns the engine of the memory.
+    engine get_engine() const {
+        mkldnn_engine_t engine_q;
+        error::wrap_c_api(mkldnn_memory_get_engine(get(), &engine_q),
+                "could not get engine from a memory");
+        return engine(engine_q);
+    }
+
+    /// Returns a handle of the data contained in the memory.
+    ///
+    /// On the CPU engine, this is a pointer to the allocated memory.
+    void *get_data_handle() const {
+        void *handle;
+        error::wrap_c_api(mkldnn_memory_get_data_handle(get(), &handle),
+                "could not get native handle");
+        return handle;
+    }
+
+    void set_data_handle(void *handle) const {
+        error::wrap_c_api(mkldnn_memory_set_data_handle(get(), handle),
+                "could not set native handle");
+    }
+
+    // Must go away or be private:
+    static mkldnn_data_type_t convert_to_c(data_type adata_type) {
+        return static_cast<mkldnn_data_type_t>(adata_type);
+    }
+    static mkldnn_format_tag_t convert_to_c(format_tag aformat) {
+        return static_cast<mkldnn_format_tag_t>(aformat);
+    }
+};
+
+inline bool operator==(mkldnn_data_type_t a, memory::data_type b) {
+    return a == memory::convert_to_c(b);
+}
+inline bool operator!=(mkldnn_data_type_t a, memory::data_type b) {
+    return !(a == b);
+}
+inline bool operator==(memory::data_type a, mkldnn_data_type_t b) {
+    return b == a;
+}
+inline bool operator!=(memory::data_type a, mkldnn_data_type_t b) {
+    return !(a == b);
+}
+
+inline bool operator==(mkldnn_format_tag_t a, memory::format_tag b) {
+    return a == memory::convert_to_c(b);
+}
+inline bool operator!=(mkldnn_format_tag_t a, memory::format_tag b) {
+    return !(a == b);
+}
+inline bool operator==(memory::format_tag a, mkldnn_format_tag_t b) {
+    return b == a;
+}
+inline bool operator!=(memory::format_tag a, mkldnn_format_tag_t b) {
+    return !(a == b);
+}
+
+/// @}
+
+/// @addtogroup cpp_api_reorder Reorder
+/// A primitive to copy data between memory formats.
+///
+/// @sa @ref c_api_reorder in @ref c_api
+/// @{
+
+struct reorder : public primitive {
+    struct primitive_desc : public handle<mkldnn_primitive_desc_t> {
+        primitive_desc(const engine &src_engine, const memory::desc &src_md,
+                const engine &dst_engine, const memory::desc &dst_md,
+                const primitive_attr &aattr) {
+            mkldnn_primitive_desc_t result;
+            error::wrap_c_api(mkldnn_reorder_primitive_desc_create(&result,
+                        src_engine.get(), &src_md.data,
+                        dst_engine.get(), &dst_md.data, aattr.get()),
+                    "could not create a reorder primitive descriptor");
+            reset(result);
+        }
+
+        primitive_desc(const engine &src_engine, const memory::desc &src_md,
+                const engine &dst_engine, const memory::desc &dst_md) {
+            mkldnn_primitive_desc_t result;
+            error::wrap_c_api(mkldnn_reorder_primitive_desc_create(&result,
+                        src_engine.get(), &src_md.data,
+                        dst_engine.get(), &dst_md.data, nullptr),
+                    "could not create a reorder primitive descriptor");
+            reset(result);
+        }
+
+        primitive_desc(const memory &src, const memory &dst,
+                const primitive_attr &aattr) {
+            mkldnn_primitive_desc_t result;
+            auto src_md = src.get_desc();
+            auto dst_md = dst.get_desc();
+            error::wrap_c_api(mkldnn_reorder_primitive_desc_create(&result,
+                        src.get_engine().get(), &src_md.data,
+                        dst.get_engine().get(), &dst_md.data, aattr.get()),
+                    "could not create a reorder primitive descriptor");
+            reset(result);
+        }
+
+        primitive_desc(const memory &src, const memory &dst) {
+            mkldnn_primitive_desc_t result;
+            auto src_md = src.get_desc();
+            auto dst_md = dst.get_desc();
+            error::wrap_c_api(mkldnn_reorder_primitive_desc_create(&result,
+                        src.get_engine().get(), &src_md.data,
+                        dst.get_engine().get(), &dst_md.data, nullptr),
+                    "could not create a reorder primitive descriptor");
+            reset(result);
+        }
+
+        memory::desc scratchpad_desc() const {
+            const mkldnn_memory_desc_t *cdesc = mkldnn_primitive_desc_query_md(
+                    get(), mkldnn::convert_to_c(scratchpad_md), 0);
+            if (cdesc == nullptr)
+                return memory::desc();
+            return memory::desc(*cdesc);
+        }
+
+        engine scratchpad_engine() {
+            mkldnn_engine_t engine_q;
+            error::wrap_c_api(
+                mkldnn_primitive_desc_query(get(),
+                    mkldnn::convert_to_c(query_scratchpad_engine), 0, &engine_q),
+                "could not get scratchpad engine from reorder primitive_desc");
+
+            return engine(engine_q);
+        }
+
+        engine get_engine() { return engine::query(*this); }
+    };
+
+    reorder(const primitive_desc &pd): primitive(pd.get()) {}
+
+    reorder(const memory &src, const memory &dst):
+        primitive(primitive_desc(src, dst).get()) {}
+
+    void execute(stream astream, memory &src, memory &dst) {
+        primitive::execute(astream,
+                {{MKLDNN_ARG_FROM, src}, {MKLDNN_ARG_TO, dst}});
+    }
+};
+
+/// @}
+
+/// @addtogroup cpp_api_concat Concat
+/// A primitive to concatenate data by arbitrary dimension.
+///
+/// @sa @ref c_api_concat in @ref c_api
+/// @{
+
+struct concat : public primitive {
+    struct primitive_desc : public handle<mkldnn_primitive_desc_t> {
+        std::vector<mkldnn_memory_desc_t> cpp_to_c(
+                const std::vector<memory::desc> &srcs) {
+            std::vector<mkldnn_memory_desc_t> c_api_srcs;
+            c_api_srcs.reserve(srcs.size());
+            for (const auto &s : srcs) c_api_srcs.push_back(s.data);
+            return c_api_srcs;
+        }
+
+        primitive_desc(const memory::desc &dst, int concat_dimension,
+                const std::vector<memory::desc> &srcs, const engine &aengine) {
+            auto c_api_srcs = cpp_to_c(srcs);
+
+            mkldnn_primitive_desc_t result;
+            error::wrap_c_api(mkldnn_concat_primitive_desc_create(
+                    &result, &dst.data, (int)c_api_srcs.size(),
+                    concat_dimension, &c_api_srcs[0], nullptr, aengine.get()),
+                "could not create a concat primitive descriptor");
+            reset(result);
+        }
+
+        primitive_desc(int concat_dimension,
+                const std::vector<memory::desc> &srcs, const engine &aengine) {
+            auto c_api_srcs = cpp_to_c(srcs);
+
+            mkldnn_primitive_desc_t result;
+            error::wrap_c_api(mkldnn_concat_primitive_desc_create(
+                    &result, nullptr, (int)c_api_srcs.size(),
+                    concat_dimension, &c_api_srcs[0], nullptr, aengine.get()),
+                "could not create a concat primitive descriptor");
+            reset(result);
+        }
+
+        memory::desc dst_desc() const {
+            const mkldnn_memory_desc_t *cdesc = mkldnn_primitive_desc_query_md(
+                    get(), mkldnn::convert_to_c(dst_md), 0);
+            error::wrap_c_api(
+                    cdesc == nullptr ? mkldnn_runtime_error : mkldnn_success,
+                    "could not get a dst memory descriptor");
+            return memory::desc(*cdesc);
+        }
+
+        memory::desc scratchpad_desc() const {
+            const mkldnn_memory_desc_t *cdesc = mkldnn_primitive_desc_query_md(
+                    get(), mkldnn::convert_to_c(scratchpad_md), 0);
+            if (cdesc == nullptr)
+                return memory::desc();
+            return memory::desc(*cdesc);
+        }
+
+        engine get_engine() { return engine::query(*this); }
+    };
+
+    concat(const primitive_desc &pd): primitive(pd.get()) {}
+};
+
+/// @}
+
+/// @addtogroup cpp_api_sum Sum
+/// A primitive to sum data.
+///
+/// @sa @ref c_api_sum in @ref c_api
+/// @{
+
+struct sum : public primitive {
+    struct primitive_desc : public handle<mkldnn_primitive_desc_t> {
+        std::vector<mkldnn_memory_desc_t> cpp_to_c(
+                const std::vector<memory::desc> &srcs) {
+            std::vector<mkldnn_memory_desc_t> c_api_srcs;
+            c_api_srcs.reserve(srcs.size());
+            for (const auto &s : srcs) c_api_srcs.push_back(s.data);
+            return c_api_srcs;
+        }
+
+        primitive_desc(const memory::desc &dst,
+                const std::vector<float> &scales,
+                const std::vector<memory::desc> &srcs, const engine &aengine) {
+            error::wrap_c_api(scales.size() == srcs.size()
+                    ? mkldnn_success : mkldnn_invalid_arguments,
+                "number of scales not equal to number of srcs");
+
+            auto c_api_srcs = cpp_to_c(srcs);
+
+            mkldnn_primitive_desc_t result;
+            error::wrap_c_api(mkldnn_sum_primitive_desc_create(
+                    &result, &dst.data, (int)c_api_srcs.size(),
+                    &scales[0], &c_api_srcs[0], nullptr, aengine.get()),
+                "could not create a sum primitive descriptor");
+            reset(result);
+        }
+
+        primitive_desc(const std::vector<float> &scales,
+                const std::vector<memory::desc> &srcs, const engine &aengine) {
+            error::wrap_c_api(scales.size() == srcs.size()
+                    ? mkldnn_success : mkldnn_invalid_arguments,
+                "number of scales not equal to number of srcs");
+
+            auto c_api_srcs = cpp_to_c(srcs);
+            mkldnn_primitive_desc_t result;
+            error::wrap_c_api(mkldnn_sum_primitive_desc_create(&result,
+                        nullptr, (int)c_api_srcs.size(), &scales[0],
+                        &c_api_srcs[0], nullptr, aengine.get()),
+                    "could not create a sum primitive descriptor");
+            reset(result);
+        }
+
+        memory::desc dst_desc() const {
+            const mkldnn_memory_desc_t *cdesc = mkldnn_primitive_desc_query_md(
+                    get(), mkldnn::convert_to_c(dst_md), 0);
+            error::wrap_c_api(
+                    cdesc == nullptr ? mkldnn_runtime_error : mkldnn_success,
+                    "could not get a dst memory descriptor");
+            return memory::desc(*cdesc);
+        }
+
+        memory::desc scratchpad_desc() const {
+            const mkldnn_memory_desc_t *cdesc = mkldnn_primitive_desc_query_md(
+                    get(), mkldnn::convert_to_c(scratchpad_md), 0);
+            if (cdesc == nullptr)
+                return memory::desc();
+            return memory::desc(*cdesc);
+        }
+
+        engine get_engine() { return engine::query(*this); }
+    };
+
+    sum(const primitive_desc &pd): primitive(pd.get()) {}
+};
+
+/// @}
+
+/// @}
+
+/// @addtogroup cpp_api_primitives Primitives
+/// @{
+
+/// @addtogroup cpp_api_primitive_descriptors Primitive descriptors
+/// @{
+
+/// A base class for all primitive descriptors.
+struct primitive_desc : public handle<mkldnn_primitive_desc_t> {
+    primitive_desc(const_mkldnn_op_desc_t desc, const primitive_attr *attr,
+            const engine &e, const_mkldnn_primitive_desc_t hint_fwd_pd) {
+        mkldnn_primitive_desc_iterator_t iterator = nullptr;
+        mkldnn_status_t status = mkldnn_primitive_desc_iterator_create(
+                &iterator, desc, attr ? attr->get() : nullptr, e.get(),
+                hint_fwd_pd);
+        error::wrap_c_api(status,
+                "could not create a primitive descriptor iterator");
+        pd_iterator.reset(iterator);
+        fetch_impl();
+    }
+
+    engine get_engine() { return engine::query(*this); }
+
+    primitive_attr get_primitive_attr() const {
+        const_mkldnn_primitive_attr_t const_cattr;
+        error::wrap_c_api(mkldnn_primitive_desc_get_attr(get(), &const_cattr),
+                "could not get attributes");
+        mkldnn_primitive_attr_t cattr;
+        error::wrap_c_api(mkldnn_primitive_attr_clone(&cattr, const_cattr),
+                "could not clone attributes");
+
+        primitive_attr attr;
+        attr.reset(cattr);
+        return attr;
+    }
+
+    /// Returns implementation name
+    const char *impl_info_str() const {
+        const char *res;
+        error::wrap_c_api(mkldnn_primitive_desc_query(get(),
+                    mkldnn_query_impl_info_str, 0, &res),
+                "could not query implementation info string");
+        return res;
+    }
+
+    /// Queries the memory::dim value (same as int64_t)
+    memory::dim query_s64(query q) const {
+        memory::dim res;
+        mkldnn_status_t status = mkldnn_primitive_desc_query(get(),
+                mkldnn::convert_to_c(q), 0, &res);
+        return status == mkldnn_success ? res : 0;
+    }
+
+    /// Advances the next implementation for the given op descriptor.
+    ///
+    /// Returns:
+    /// - @c true on success
+    /// - @c false if the last implementation reached, and
+    ///   the primitive descriptor itself is kept unchanged
+    bool next_impl() {
+        mkldnn_status_t status = mkldnn_primitive_desc_iterator_next(
+                pd_iterator.get());
+        if (status == mkldnn_iterator_ends) return false;
+        error::wrap_c_api(status, "primitive descriptor iterator next failed");
+
+        fetch_impl();
+        return true;
+    }
+
+    /// Queries and returns requested memory descriptor.
+    memory::desc query_md(query what, int idx = 0) const {
+        std::vector<query> valid_q{src_md, diff_src_md, weights_md,
+            diff_weights_md, dst_md, diff_dst_md, workspace_md, scratchpad_md};
+        if (!std::any_of(valid_q.cbegin(), valid_q.cend(),
+                    [=](query q) { return what == q; }))
+            throw error(mkldnn_invalid_arguments, "invalid memory query");
+
+        const mkldnn_memory_desc_t *cdesc = mkldnn_primitive_desc_query_md(
+                get(), mkldnn::convert_to_c(what), idx);
+        if (cdesc == nullptr) return memory::desc();
+
+        return memory::desc(*cdesc);
+    }
+
+    // register specialized queries, e.g. src_desc()
+#   define REG_QUERY_MD(name, what, idx) \
+    memory::desc name ## _desc() const { return query_md(what ## _md, idx); }
+
+  private:
+    handle<mkldnn_primitive_desc_iterator_t> pd_iterator;
+    void fetch_impl() {
+        mkldnn_primitive_desc_t pd = mkldnn_primitive_desc_iterator_fetch(
+                pd_iterator.get());
+        error::wrap_c_api(pd != nullptr ? mkldnn_success : mkldnn_runtime_error,
+                "could not fetch a primitive descriptor from the iterator");
+        reset(pd);
+    }
+};
+
+/// @}
+
+/// @addtogroup cpp_api_convolution Convolution
+/// A primitive to compute convolution using different algorithms.
+///
+/// @sa @ref c_api_convolution in @ref c_api
+/// @{
+
+struct convolution_forward: public primitive {
+    struct desc {
+        mkldnn_convolution_desc_t data;
+        desc(prop_kind aprop_kind, algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &bias_desc,
+                const memory::desc &dst_desc,
+                const memory::dims strides,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_convolution_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), convert_to_c(aalgorithm),
+                        &src_desc.data, &weights_desc.data, &bias_desc.data,
+                        &dst_desc.data, &strides[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a convolution forward descriptor");
+        }
+        desc(prop_kind aprop_kind, algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &dst_desc,
+                const memory::dims strides,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_convolution_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), convert_to_c(aalgorithm),
+                        &src_desc.data, &weights_desc.data, nullptr,
+                        &dst_desc.data, &strides[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a convolution forward descriptor");
+        }
+        desc(prop_kind aprop_kind, algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &bias_desc,
+                const memory::desc &dst_desc,
+                const memory::dims strides,
+                const memory::dims dilates,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(dilates);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(
+                mkldnn_dilated_convolution_forward_desc_init(&data,
+                    mkldnn::convert_to_c(aprop_kind), convert_to_c(aalgorithm),
+                        &src_desc.data, &weights_desc.data, &bias_desc.data,
+                        &dst_desc.data, &strides[0], &dilates[0],
+                        &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a dilated convolution forward descriptor");
+        }
+        desc(prop_kind aprop_kind, algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &dst_desc,
+                const memory::dims strides,
+                const memory::dims dilates,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(dilates);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(
+                mkldnn_dilated_convolution_forward_desc_init(&data,
+                    mkldnn::convert_to_c(aprop_kind), convert_to_c(aalgorithm),
+                        &src_desc.data, &weights_desc.data, nullptr,
+                        &dst_desc.data, &strides[0], &dilates[0],
+                        &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a dilated convolution forward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, nullptr) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, nullptr) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(weights, weights, 0);
+        REG_QUERY_MD(bias, weights, 1);
+        REG_QUERY_MD(dst, dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    convolution_forward(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct convolution_backward_data : public primitive {
+    struct desc {
+        mkldnn_convolution_desc_t data;
+        desc(algorithm aalgorithm,
+                const memory::desc &diff_src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_convolution_backward_data_desc_init(
+                        &data, convert_to_c(aalgorithm), &diff_src_desc.data,
+                        &weights_desc.data, &diff_dst_desc.data,
+                        &strides[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a convolution backward data descriptor");
+        }
+        desc(algorithm aalgorithm,
+                const memory::desc &diff_src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims dilates,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(dilates);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(
+                mkldnn_dilated_convolution_backward_data_desc_init(
+                    &data, convert_to_c(aalgorithm), &diff_src_desc.data,
+                    &weights_desc.data, &diff_dst_desc.data,
+                    &strides[0], &dilates[0], &padding_l[0], &padding_r[0],
+                    mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a convolution backward data descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const convolution_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const convolution_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(diff_src, diff_src, 0);
+        REG_QUERY_MD(weights, weights, 0);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    convolution_backward_data(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct convolution_backward_weights : public primitive {
+    struct desc {
+        mkldnn_convolution_desc_t data;
+        desc(algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &diff_weights_desc,
+                const memory::desc &diff_bias_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_convolution_backward_weights_desc_init(
+                        &data, convert_to_c(aalgorithm), &src_desc.data,
+                        &diff_weights_desc.data, &diff_bias_desc.data,
+                        &diff_dst_desc.data,
+                        &strides[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a convolution backward weights descriptor");
+        }
+        desc(algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &diff_weights_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_convolution_backward_weights_desc_init(
+                        &data, convert_to_c(aalgorithm), &src_desc.data,
+                        &diff_weights_desc.data, nullptr, &diff_dst_desc.data,
+                        &strides[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a convolution backward weights descriptor");
+        }
+        desc(algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &diff_weights_desc,
+                const memory::desc &diff_bias_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims dilates,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(dilates);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_dilated_convolution_backward_weights_desc_init(
+                        &data, convert_to_c(aalgorithm), &src_desc.data,
+                        &diff_weights_desc.data, &diff_bias_desc.data,
+                        &diff_dst_desc.data,
+                        &strides[0], &dilates[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a convolution backward weights descriptor");
+        }
+        desc(algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &diff_weights_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims dilates,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(dilates);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_dilated_convolution_backward_weights_desc_init(
+                        &data, convert_to_c(aalgorithm), &src_desc.data,
+                        &diff_weights_desc.data, nullptr, &diff_dst_desc.data,
+                        &strides[0], &dilates[0],  &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a convolution backward weights descriptor");
+        }
+
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const convolution_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const convolution_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(diff_weights, diff_weights, 0);
+        REG_QUERY_MD(diff_bias, diff_weights, 1);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    convolution_backward_weights(const primitive_desc &pd): primitive(pd) {}
+};
+
+/// @}
+//
+/// @addtogroup cpp_api_deconvolution Deconvolution
+/// A primitive to compute deconvolution using different algorithms.
+///
+/// @sa @ref c_api_deconvolution in @ref c_api
+/// @{
+
+struct deconvolution_forward: public primitive {
+    struct desc {
+        mkldnn_deconvolution_desc_t data;
+        desc(prop_kind aprop_kind, algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &bias_desc,
+                const memory::desc &dst_desc,
+                const memory::dims strides,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_deconvolution_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), convert_to_c(aalgorithm),
+                        &src_desc.data, &weights_desc.data, &bias_desc.data,
+                        &dst_desc.data, &strides[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a deconvolution forward descriptor");
+        }
+        desc(prop_kind aprop_kind, algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &dst_desc,
+                const memory::dims strides,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_deconvolution_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), convert_to_c(aalgorithm),
+                        &src_desc.data, &weights_desc.data, nullptr,
+                        &dst_desc.data, &strides[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a deconvolution forward descriptor");
+        }
+        desc(prop_kind aprop_kind, algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &bias_desc,
+                const memory::desc &dst_desc,
+                const memory::dims strides,
+                const memory::dims dilates,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(dilates);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_dilated_deconvolution_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), convert_to_c(aalgorithm),
+                        &src_desc.data, &weights_desc.data, &bias_desc.data,
+                        &dst_desc.data, &strides[0], &dilates[0], &padding_l[0],
+                        &padding_r[0], mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a dilated deconvolution forward descriptor");
+        }
+        desc(prop_kind aprop_kind, algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &dst_desc,
+                const memory::dims strides,
+                const memory::dims dilates,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(dilates);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_dilated_deconvolution_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), convert_to_c(aalgorithm),
+                        &src_desc.data, &weights_desc.data, nullptr,
+                        &dst_desc.data, &strides[0], &dilates[0], &padding_l[0],
+                        &padding_r[0], mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a dilated deconvolution forward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, nullptr) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, nullptr) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(weights, weights, 0);
+        REG_QUERY_MD(bias, weights, 1);
+        REG_QUERY_MD(dst, dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    deconvolution_forward(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct deconvolution_backward_data : public primitive {
+    struct desc {
+        mkldnn_deconvolution_desc_t data;
+        desc(algorithm aalgorithm,
+                const memory::desc &diff_src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_deconvolution_backward_data_desc_init(
+                        &data, convert_to_c(aalgorithm), &diff_src_desc.data,
+                        &weights_desc.data, &diff_dst_desc.data,
+                        &strides[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a deconvolution backward data descriptor");
+        }
+        desc(algorithm aalgorithm,
+                const memory::desc &diff_src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims dilates,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(dilates);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_dilated_deconvolution_backward_data_desc_init(
+                        &data, convert_to_c(aalgorithm), &diff_src_desc.data,
+                        &weights_desc.data, &diff_dst_desc.data,
+                        &strides[0], &dilates[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a dilated deconvolution backward data descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const deconvolution_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const deconvolution_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(diff_src, diff_src, 0);
+        REG_QUERY_MD(weights, weights, 0);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    deconvolution_backward_data(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct deconvolution_backward_weights : public primitive {
+    struct desc {
+        mkldnn_deconvolution_desc_t data;
+        desc(algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &diff_weights_desc,
+                const memory::desc &diff_bias_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_deconvolution_backward_weights_desc_init(
+                        &data, convert_to_c(aalgorithm), &src_desc.data,
+                        &diff_weights_desc.data, &diff_bias_desc.data,
+                        &diff_dst_desc.data,
+                        &strides[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a deconvolution backward weights descriptor");
+        }
+        desc(algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &diff_weights_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_deconvolution_backward_weights_desc_init(
+                        &data, convert_to_c(aalgorithm), &src_desc.data,
+                        &diff_weights_desc.data, nullptr, &diff_dst_desc.data,
+                        &strides[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a deconvolution backward weights descriptor");
+        }
+        desc(algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &diff_weights_desc,
+                const memory::desc &diff_bias_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims dilates,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(dilates);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_dilated_deconvolution_backward_weights_desc_init(
+                        &data, convert_to_c(aalgorithm), &src_desc.data,
+                        &diff_weights_desc.data, &diff_bias_desc.data,
+                        &diff_dst_desc.data,
+                        &strides[0], &dilates[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a dilated  deconvolution backward weights descriptor");
+        }
+        desc(algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &diff_weights_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims strides,
+                const memory::dims dilates,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(dilates);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_dilated_deconvolution_backward_weights_desc_init(
+                        &data, convert_to_c(aalgorithm), &src_desc.data,
+                        &diff_weights_desc.data, nullptr, &diff_dst_desc.data,
+                        &strides[0], &dilates[0], &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not create a dilated deconvolution backward weights descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const deconvolution_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const deconvolution_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(diff_weights, diff_weights, 0);
+        REG_QUERY_MD(diff_bias, diff_weights, 1);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    deconvolution_backward_weights(const primitive_desc &pd): primitive(pd) {}
+};
+
+/// @}
+
+/// @addtogroup cpp_api_lrn LRN
+/// A primitive to perform local response normalization (LRN) across or within
+/// channels.
+///
+/// @sa @ref c_api_lrn in @ref c_api
+/// @{
+
+struct lrn_forward : public primitive {
+    struct desc {
+        mkldnn_lrn_desc_t data;
+
+        desc(prop_kind aprop_kind, algorithm aalgorithm,
+                const memory::desc &src_desc, memory::dim local_size,
+                float alpha, float beta, float k = 1.f) {
+            error::wrap_c_api(mkldnn_lrn_forward_desc_init(&data,
+                mkldnn::convert_to_c(aprop_kind), convert_to_c(aalgorithm),
+                &src_desc.data, local_size, alpha, beta, k),
+                "could not create a lrn forward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, nullptr) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, nullptr) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(dst, dst, 0);
+        REG_QUERY_MD(workspace, workspace, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    lrn_forward(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct lrn_backward : public primitive {
+    struct desc {
+        mkldnn_lrn_desc_t data;
+
+        desc(algorithm aalgorithm, const memory::desc &data_desc,
+                const memory::desc &diff_data_desc, memory::dim local_size,
+                float alpha, float beta, float k = 1.f) {
+            error::wrap_c_api(mkldnn_lrn_backward_desc_init(&data,
+                convert_to_c(aalgorithm), &diff_data_desc.data,
+                &data_desc.data, local_size, alpha, beta, k),
+                "could not create a lrn backward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const lrn_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const lrn_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(diff_src, diff_src, 0);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(workspace, workspace, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    lrn_backward(const primitive_desc &pd): primitive(pd) {}
+};
+
+/// @}
+
+/// @addtogroup cpp_api_pooling Pooling
+/// A primitive to perform max or average pooling.
+///
+/// @sa @ref c_api_pooling in @ref c_api
+/// @{
+
+struct pooling_forward : public primitive {
+    struct desc {
+        mkldnn_pooling_desc_t data;
+        desc(prop_kind aprop_kind, algorithm aalgorithm,
+                const memory::desc &src_desc,
+                const memory::desc &dst_desc,
+                const memory::dims strides,
+                const memory::dims kernel,
+                const memory::dims padding_l,
+                const memory::dims padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(kernel);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_pooling_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind),
+                        convert_to_c(aalgorithm),
+                        &src_desc.data, &dst_desc.data,
+                        &strides[0], &kernel[0],
+                        &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not init a forward pooling descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, nullptr) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, nullptr) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(dst, dst, 0);
+        REG_QUERY_MD(workspace, workspace, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    pooling_forward(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct pooling_backward : public primitive {
+    struct desc {
+        mkldnn_pooling_desc_t data;
+        desc(algorithm aalgorithm,
+                const memory::desc &diff_src_desc,
+                const memory::desc &diff_dst_desc,
+                const memory::dims &strides,
+                const memory::dims &kernel,
+                const memory::dims &padding_l,
+                const memory::dims &padding_r,
+                const padding_kind apadding_kind) {
+            memory::validate_dims(strides);
+            memory::validate_dims(kernel);
+            memory::validate_dims(padding_l);
+            memory::validate_dims(padding_r);
+            error::wrap_c_api(mkldnn_pooling_backward_desc_init(&data,
+                        convert_to_c(aalgorithm),
+                        &diff_src_desc.data, &diff_dst_desc.data,
+                        &strides[0], &kernel[0],
+                        &padding_l[0], &padding_r[0],
+                        mkldnn::convert_to_c(apadding_kind)),
+                    "could not init a backward pooling descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const pooling_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const pooling_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(diff_src, diff_src, 0);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(workspace, workspace, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    pooling_backward(const primitive_desc &pd): primitive(pd) {}
+};
+
+/// @}
+
+/// @addtogroup cpp_api_eltwise Eltwise
+/// A primitive to compute element-wise operations like parametric rectifier
+/// linear unit (ReLU).
+///
+/// @sa @ref c_api_eltwise in @ref c_api
+/// @{
+
+struct eltwise_forward : public primitive {
+    struct desc {
+        mkldnn_eltwise_desc_t data;
+        template <typename T>
+        desc(prop_kind aprop_kind, algorithm alg_kind,
+                const memory::desc &src_desc, T alpha = 0, T beta = 0) {
+            error::wrap_c_api(mkldnn_eltwise_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind),
+                        mkldnn::convert_to_c(alg_kind), &src_desc.data,
+                        static_cast<float>(alpha), static_cast<float>(beta)),
+                    "could not create a eltwise forward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, nullptr) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, nullptr) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(dst, dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    eltwise_forward(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct eltwise_backward : public primitive {
+    struct desc {
+        mkldnn_eltwise_desc_t data;
+
+        template <typename T>
+        desc(algorithm alg_kind, const memory::desc &diff_data_desc,
+                const memory::desc &data_desc, T alpha = 0, T beta = 0) {
+            error::wrap_c_api(mkldnn_eltwise_backward_desc_init(&data,
+                        mkldnn::convert_to_c(alg_kind), &diff_data_desc.data,
+                        &data_desc.data, static_cast<float>(alpha),
+                        static_cast<float>(beta)),
+                    "could not create a eltwise backward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const eltwise_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const eltwise_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(diff_src, diff_src, 0);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    eltwise_backward(const primitive_desc &pd): primitive(pd) {}
+};
+
+/// @}
+
+/// @addtogroup cpp_api_softmax Softmax
+/// A primitive to perform softmax.
+///
+/// @sa @ref c_api_softmax in @ref c_api
+/// @{
+
+struct softmax_forward : public primitive {
+    struct desc {
+        mkldnn_softmax_desc_t data;
+        desc(prop_kind aprop_kind, const memory::desc &data_desc,
+             int softmax_axis) {
+            error::wrap_c_api(mkldnn_softmax_forward_desc_init(&data,
+                    mkldnn::convert_to_c(aprop_kind), &data_desc.data,
+                    softmax_axis),
+                "could not create a softmax forward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, nullptr) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, nullptr) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(dst, dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    softmax_forward(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct softmax_backward : public primitive {
+    struct desc {
+        mkldnn_softmax_desc_t data;
+        desc(const memory::desc &diff_desc, const memory::desc &data_desc,
+                int softmax_axis) {
+            error::wrap_c_api(mkldnn_softmax_backward_desc_init(&data,
+                        &diff_desc.data, &data_desc.data, softmax_axis),
+                    "could not init a backward softmax descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const softmax_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const softmax_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(dst, dst, 0);
+        REG_QUERY_MD(diff_src, diff_src, 0);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(workspace, workspace, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    softmax_backward(const primitive_desc &pd): primitive(pd) {}
+};
+
+/// @}
+
+/// @addtogroup cpp_api_batch_norm Batch normalization
+/// A primitive to perform batch normalization.
+///
+/// @sa @ref c_api_batch_normalization in @ref c_api
+/// @{
+
+struct batch_normalization_forward : public primitive {
+    struct desc {
+        mkldnn_batch_normalization_desc_t data;
+        template <typename T>
+        desc(prop_kind aprop_kind, const memory::desc &src_desc, T epsilon,
+                unsigned flags) {
+            error::wrap_c_api(
+                    mkldnn_batch_normalization_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), &src_desc.data,
+                        static_cast<float>(epsilon), flags),
+                "could not create a batch normalization forward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, nullptr) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, nullptr) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(weights, weights, 0);
+        REG_QUERY_MD(dst, dst, 0);
+        REG_QUERY_MD(workspace, workspace, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+
+        memory::desc mean_desc() const { return stat_desc(mean); }
+        memory::desc variance_desc() const { return stat_desc(var); }
+
+    private:
+        enum { mean = 1, var = 2, };
+        memory::desc stat_desc(int kind) const {
+            mkldnn_batch_normalization_desc_t *p;
+            error::wrap_c_api(mkldnn_primitive_desc_query(
+                    get(), mkldnn::convert_to_c(batch_normalization_d), 0, &p),
+                    "could not get a batch-normalization descriptor");
+            return query_md(p->flags & use_global_stats ? src_md : dst_md, kind);
+        }
+    };
+
+    batch_normalization_forward(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct batch_normalization_backward : public primitive {
+    struct desc {
+        mkldnn_batch_normalization_desc_t data;
+        template <typename T>
+        desc(prop_kind aprop_kind, const memory::desc &diff_data_desc,
+                const memory::desc &data_desc, T epsilon, unsigned flags) {
+            error::wrap_c_api(
+                    mkldnn_batch_normalization_backward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind),
+                        &diff_data_desc.data, &data_desc.data,
+                        static_cast<float>(epsilon), flags),
+                "could not create a batch normalization backward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const batch_normalization_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const batch_normalization_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(mean, src, 1);
+        REG_QUERY_MD(variance, src, 2);
+        REG_QUERY_MD(weights, weights, 0);
+        REG_QUERY_MD(dst, dst, 0);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(workspace, workspace, 0);
+
+        REG_QUERY_MD(diff_src, diff_src, 0);
+        REG_QUERY_MD(diff_weights, diff_weights, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    batch_normalization_backward(const primitive_desc &pd): primitive(pd) {}
+};
+
+/// @}
+
+/// @addtogroup cpp_api_inner_product Inner Product
+/// A primitive to compute an inner product.
+///
+/// @sa @ref c_api_inner_product in @ref c_api
+/// @{
+
+struct inner_product_forward: public primitive {
+    struct desc {
+        mkldnn_inner_product_desc_t data;
+        desc(prop_kind aprop_kind, const memory::desc &src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &bias_desc,
+                const memory::desc &dst_desc) {
+            error::wrap_c_api(
+                    mkldnn_inner_product_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), &src_desc.data,
+                        &weights_desc.data, &bias_desc.data, &dst_desc.data),
+                    "could not create a inner product forward descriptor");
+        }
+
+        desc(prop_kind aprop_kind, const memory::desc &src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &dst_desc) {
+            error::wrap_c_api(
+                    mkldnn_inner_product_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), &src_desc.data,
+                        &weights_desc.data, nullptr, &dst_desc.data),
+                    "could not create a inner product forward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, nullptr) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, nullptr) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(weights, weights, 0);
+        REG_QUERY_MD(bias, weights, 1);
+        REG_QUERY_MD(dst, dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    inner_product_forward(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct inner_product_backward_data: public primitive {
+    struct desc {
+        mkldnn_inner_product_desc_t data;
+        desc(const memory::desc &diff_src_desc,
+                const memory::desc &weights_desc,
+                const memory::desc &diff_dst_desc) {
+            error::wrap_c_api(
+                    mkldnn_inner_product_backward_data_desc_init(&data,
+                        &diff_src_desc.data, &weights_desc.data,
+                        &diff_dst_desc.data),
+                "could not create a inner product backward data descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const inner_product_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const inner_product_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(diff_src, diff_src, 0);
+        REG_QUERY_MD(weights, weights, 0);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    inner_product_backward_data(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct inner_product_backward_weights: public primitive {
+    struct desc {
+        mkldnn_inner_product_desc_t data;
+        desc(const memory::desc &src_desc,
+                const memory::desc &diff_weights_desc,
+                const memory::desc &diff_bias_desc,
+                const memory::desc &diff_dst_desc) {
+            error::wrap_c_api(
+                    mkldnn_inner_product_backward_weights_desc_init(
+                        &data, &src_desc.data, &diff_weights_desc.data,
+                        &diff_bias_desc.data, &diff_dst_desc.data),
+                "could not create a inner product backward weights descriptor");
+        }
+        desc(const memory::desc &src_desc,
+                const memory::desc &diff_weights_desc,
+                const memory::desc &diff_dst_desc) {
+            error::wrap_c_api(
+                    mkldnn_inner_product_backward_weights_desc_init(
+                        &data, &src_desc.data, &diff_weights_desc.data,
+                        nullptr, &diff_dst_desc.data),
+                "could not create a inner product backward weights descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const inner_product_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const inner_product_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(diff_weights, diff_weights, 0);
+        REG_QUERY_MD(diff_bias, diff_weights, 1);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    inner_product_backward_weights(const primitive_desc &pd): primitive(pd) {}
+};
+
+/// @}
+
+/// @addtogroup cpp_api_rnn RNN
+/// A primitive to compute common recurrent layer.
+///
+/// @sa @ref c_api_rnn in @ref c_api
+/// @{
+
+struct rnn_cell {
+    struct desc {
+        mkldnn_rnn_cell_desc_t c_rnn_cell_;
+
+        desc(algorithm kind, algorithm activation_f) {
+            error::wrap_c_api(mkldnn_rnn_cell_desc_init(&c_rnn_cell_,
+                        mkldnn::convert_to_c(kind),
+                        mkldnn::convert_to_c(activation_f), 0U, 0, 0),
+                    "could not init an rnn cell descriptor");
+        }
+        desc(algorithm kind): desc(kind, algorithm::algorithm_undef) {}
+
+        operator const mkldnn_rnn_cell_desc_t*() const { return &c_rnn_cell_; }
+
+        algorithm get_cell_kind() const
+        { return algorithm(c_rnn_cell_.cell_kind); }
+        algorithm get_activation() const
+        { return algorithm(c_rnn_cell_.activation_kind); }
+
+        float get_alpha() const { return c_rnn_cell_.alpha; }
+        void set_alpha(float alpha) {
+            c_rnn_cell_.flags |= mkldnn_rnn_cell_with_relu;
+            c_rnn_cell_.alpha = alpha;
+        }
+
+        float get_clipping() const { return c_rnn_cell_.clipping; }
+        void set_clipping(float clipping) {
+            c_rnn_cell_.flags |= mkldnn_rnn_cell_with_clipping;
+            c_rnn_cell_.clipping = clipping;
+        }
+
+        int get_gates_count() const {
+            return mkldnn_rnn_cell_get_gates_count(&c_rnn_cell_);
+        }
+        int get_state_count() const {
+            return mkldnn_rnn_cell_get_states_count(&c_rnn_cell_);
+        }
+    };
+};
+
+struct rnn_forward : public primitive {
+    struct desc {
+        mkldnn_rnn_desc_t data;
+        desc(prop_kind aprop_kind, rnn_cell::desc cell,
+                const rnn_direction direction,
+                const memory::desc &src_layer_desc,
+                const memory::desc &src_iter_desc,
+                const memory::desc &weights_layer_desc,
+                const memory::desc &weights_iter_desc,
+                const memory::desc &bias_desc,
+                const memory::desc &dst_layer_desc,
+                const memory::desc &dst_iter_desc
+            ) {
+            error::wrap_c_api(mkldnn_rnn_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), cell,
+                        mkldnn::convert_to_c(direction),
+                        &src_layer_desc.data, &src_iter_desc.data,
+                        &weights_layer_desc.data, &weights_iter_desc.data,
+                        &bias_desc.data,
+                        &dst_layer_desc.data, &dst_iter_desc.data),
+                    "could not create an RNN forward descriptor");
+        }
+
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, nullptr) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, nullptr) {}
+
+        REG_QUERY_MD(src_layer, src, 0);
+        REG_QUERY_MD(src_iter, src, 1);
+        REG_QUERY_MD(weights_layer, weights, 0);
+        REG_QUERY_MD(weights_iter, weights, 1);
+        REG_QUERY_MD(bias, weights, 2);
+        REG_QUERY_MD(dst_layer, dst, 0);
+        REG_QUERY_MD(dst_iter, dst, 1);
+        REG_QUERY_MD(workspace, workspace, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    rnn_forward(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct rnn_backward : public primitive {
+    struct desc {
+        mkldnn_rnn_desc_t data;
+        desc(prop_kind aprop_kind, rnn_cell::desc cell,
+                const rnn_direction direction,
+                const memory::desc &src_layer_desc,
+                const memory::desc &src_iter_desc,
+                const memory::desc &weights_layer_desc,
+                const memory::desc &weights_iter_desc,
+                const memory::desc &bias_desc,
+                const memory::desc &dst_layer_desc,
+                const memory::desc &dst_iter_desc,
+                const memory::desc &diff_src_layer_desc,
+                const memory::desc &diff_src_iter_desc,
+                const memory::desc &diff_weights_layer_desc,
+                const memory::desc &diff_weights_iter_desc,
+                const memory::desc &diff_bias_desc,
+                const memory::desc &diff_dst_layer_desc,
+                const memory::desc &diff_dst_iter_desc) {
+            error::wrap_c_api(mkldnn_rnn_backward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), cell,
+                        mkldnn::convert_to_c(direction),
+                        &src_layer_desc.data, &src_iter_desc.data,
+                        &weights_layer_desc.data, &weights_iter_desc.data,
+                        &bias_desc.data,
+                        &dst_layer_desc.data, &dst_iter_desc.data,
+                        &diff_src_layer_desc.data, &diff_src_iter_desc.data,
+                        &diff_weights_layer_desc.data,
+                        &diff_weights_iter_desc.data, &diff_bias_desc.data,
+                        &diff_dst_layer_desc.data, &diff_dst_iter_desc.data),
+                    "could not create an RNN backward descriptor");
+        }
+
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const rnn_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        primitive_desc(const desc &desc, const primitive_attr &attr, const engine &e,
+                const rnn_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, &attr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(src_layer, src, 0);
+        REG_QUERY_MD(src_iter, src, 1);
+        REG_QUERY_MD(weights_layer, weights, 0);
+        REG_QUERY_MD(weights_iter, weights, 1);
+        REG_QUERY_MD(bias, weights, 2);
+        REG_QUERY_MD(dst_layer, dst, 0);
+        REG_QUERY_MD(dst_iter, dst, 1);
+        REG_QUERY_MD(workspace, workspace, 0);
+
+        REG_QUERY_MD(diff_src_layer, diff_src, 0);
+        REG_QUERY_MD(diff_src_iter, diff_src, 1);
+        REG_QUERY_MD(diff_weights_layer, diff_weights, 0);
+        REG_QUERY_MD(diff_weights_iter, diff_weights, 1);
+        REG_QUERY_MD(diff_bias, diff_weights, 2);
+        REG_QUERY_MD(diff_dst_layer, diff_dst, 0);
+        REG_QUERY_MD(diff_dst_iter, diff_dst, 1);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    // With last iteration (with and without input src_iter)
+    rnn_backward(const primitive_desc &pd): primitive(pd) {}
+};
+
+/// @}
+
+/// @addtogroup cpp_api_shuffle Shuffle
+/// A primitive to shuffle data along the axis.
+///
+/// @sa @ref c_api_shuffle in @ref c_api
+/// @{
+
+struct shuffle_forward : public primitive {
+    struct desc {
+        mkldnn_shuffle_desc_t data;
+        desc(prop_kind aprop_kind, const memory::desc &data_desc,
+                int axis, int group_size) {
+            error::wrap_c_api(mkldnn_shuffle_forward_desc_init(&data,
+                        mkldnn::convert_to_c(aprop_kind), &data_desc.data,
+                        axis, group_size),
+                    "could not create a shuffle forward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, nullptr) {}
+
+        REG_QUERY_MD(src, src, 0);
+        REG_QUERY_MD(dst, dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    shuffle_forward(const primitive_desc &pd): primitive(pd) {}
+};
+
+struct shuffle_backward : public primitive {
+    struct desc {
+        mkldnn_shuffle_desc_t data;
+        desc(const memory::desc &diff_data_desc, int axis, int group_size) {
+            error::wrap_c_api(mkldnn_shuffle_backward_desc_init(&data,
+                        &diff_data_desc.data, axis, group_size),
+                    "could not create a shuffle backward descriptor");
+        }
+    };
+
+    struct primitive_desc : public mkldnn::primitive_desc {
+        primitive_desc(const desc &desc, const engine &e,
+                const shuffle_forward::primitive_desc &hint_fwd_pd)
+            : mkldnn::primitive_desc(&desc.data, nullptr, e, hint_fwd_pd.get()) {}
+
+        REG_QUERY_MD(diff_src, diff_src, 0);
+        REG_QUERY_MD(diff_dst, diff_dst, 0);
+        REG_QUERY_MD(scratchpad, scratchpad, 0);
+    };
+
+    shuffle_backward(const primitive_desc &pd): primitive(pd) {}
+};
+
+/// @}
+
+/// @} Primitives
+
+/// @} C++ API
+
+#undef REG_QUERY_MD
+
+// implementation section
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+
+inline primitive::primitive(const_mkldnn_primitive_desc_t c_pd) {
+    mkldnn_primitive_t result;
+    error::wrap_c_api(mkldnn_primitive_create(&result, c_pd),
+            "could not create a primitive");
+    reset(result);
+}
+
+inline primitive::primitive(const primitive_desc &pd): primitive(pd.get()) {}
+
+inline void primitive::execute(stream &astream,
+        const std::unordered_map<int, memory> &args) const {
+    std::vector<mkldnn_exec_arg_t> c_args;
+    c_args.reserve(args.size());
+    for (const auto &a: args)
+        c_args.push_back({a.first, a.second.get()});
+
+    error::wrap_c_api(mkldnn_primitive_execute(get(), astream.get(),
+                (int)c_args.size(), c_args.data()),
+            "primitive execution fail");
+}
+#endif // DOXYGEN_SHOULD_SKIP_THIS
+
+} // namespace mkldnn
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/include/mkldnn_debug.h b/thirdparty/oidn/mkl-dnn/include/mkldnn_debug.h
new file mode 100644
index 0000000000..f4dc2fdfa6
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/include/mkldnn_debug.h
@@ -0,0 +1,98 @@
+/*******************************************************************************
+* Copyright 2018-2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/* DO NOT EDIT, AUTO-GENERATED */
+
+#ifndef MKLDNN_DEBUG_H
+#define MKLDNN_DEBUG_H
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+
+/* All symbols shall be internal unless marked as MKLDNN_API */
+#if defined _WIN32 || defined __CYGWIN__
+#   define MKLDNN_HELPER_DLL_IMPORT __declspec(dllimport)
+#   define MKLDNN_HELPER_DLL_EXPORT __declspec(dllexport)
+#else
+#   if __GNUC__ >= 4
+#       define MKLDNN_HELPER_DLL_IMPORT __attribute__ ((visibility ("default")))
+#       define MKLDNN_HELPER_DLL_EXPORT __attribute__ ((visibility ("default")))
+#   else
+#       define MKLDNN_HELPER_DLL_IMPORT
+#       define MKLDNN_HELPER_DLL_EXPORT
+#   endif
+#endif
+
+#ifdef MKLDNN_DLL
+#   ifdef MKLDNN_DLL_EXPORTS
+#       define MKLDNN_API MKLDNN_HELPER_DLL_EXPORT
+#   else
+#       define MKLDNN_API MKLDNN_HELPER_DLL_IMPORT
+#   endif
+#else
+#   define MKLDNN_API
+#endif
+
+#if defined (__GNUC__)
+#   define MKLDNN_DEPRECATED __attribute__((deprecated))
+#elif defined(_MSC_VER)
+#   define MKLDNN_DEPRECATED __declspec(deprecated)
+#else
+#   define MKLDNN_DEPRECATED
+#endif
+
+#include "mkldnn_types.h"
+#endif /* DOXYGEN_SHOULD_SKIP_THIS */
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+const char MKLDNN_API *mkldnn_status2str(mkldnn_status_t v);
+const char MKLDNN_API *mkldnn_dt2str(mkldnn_data_type_t v);
+const char MKLDNN_API *mkldnn_fmt_kind2str(mkldnn_format_kind_t v);
+const char MKLDNN_API *mkldnn_fmt_tag2str(mkldnn_format_tag_t v);
+const char MKLDNN_API *mkldnn_prop_kind2str(mkldnn_prop_kind_t v);
+const char MKLDNN_API *mkldnn_prim_kind2str(mkldnn_primitive_kind_t v);
+const char MKLDNN_API *mkldnn_alg_kind2str(mkldnn_alg_kind_t v);
+const char MKLDNN_API *mkldnn_rnn_direction2str(mkldnn_rnn_direction_t v);
+
+/** Forms a format string for a given memory descriptor.
+ *
+ * The format is defined as: 'dt:[p|o|0]:fmt_kind:fmt:extra'.
+ * Here:
+ *  - dt       -- data type
+ *  - p        -- indicates there is non-trivial padding
+ *  - o        -- indicates there is non-trivial padding offset
+ *  - 0        -- indicates there is non-trivial offset0
+ *  - fmt_kind -- format kind (blocked, wino, etc...)
+ *  - fmt      -- extended format string (format_kind specific)
+ *  - extra    -- shows extra fields (underspecified)
+ */
+int MKLDNN_API mkldnn_md2fmt_str(char *fmt_str, size_t fmt_str_len,
+        const mkldnn_memory_desc_t *md);
+
+/** Forms a dimension string for a given memory descriptor.
+ *
+ * The format is defined as: 'dim0xdim1x...xdimN
+ */
+int MKLDNN_API mkldnn_md2dim_str(char *dim_str, size_t dim_str_len,
+        const mkldnn_memory_desc_t *md);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/include/mkldnn_types.h b/thirdparty/oidn/mkl-dnn/include/mkldnn_types.h
new file mode 100644
index 0000000000..1b6c356982
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/include/mkldnn_types.h
@@ -0,0 +1,1415 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MKLDNN_TYPES_H
+#define MKLDNN_TYPES_H
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#ifndef DOXYGEN_SHOULD_SKIP_THIS
+#include <stddef.h>
+#include <stdint.h>
+#endif
+
+/** @addtogroup c_api C API
+ *  @{
+ *
+ *  @addtogroup c_api_types Types
+ *  @{
+ *
+ *  @addtogroup c_api_types_generic Generic
+ *  @{ */
+
+/** Intel(R) MKL-DNN Version type */
+typedef struct {
+    int    major;
+    int    minor;
+    int    patch;
+    const char *hash;
+} mkldnn_version_t;
+
+/** Status values returned by Intel(R) MKL-DNN functions. */
+typedef enum {
+    /** The operation was successful */
+    mkldnn_success = 0,
+    /** The operation failed due to an out-of-memory condition */
+    mkldnn_out_of_memory = 1,
+    /** The operation failed and should be retried */
+    mkldnn_try_again = 2,
+    /** The operation failed because of incorrect function arguments  */
+    mkldnn_invalid_arguments = 3,
+    /** The operation failed because a primitive was not ready for execution */
+    mkldnn_not_ready = 4,
+    /** The operation failed because requested functionality is not implemented
+     */
+    mkldnn_unimplemented = 5,
+    /** Primitive iterator passed over last primitive descriptor */
+    mkldnn_iterator_ends = 6,
+    /** Primitive or engine failed on execution */
+    mkldnn_runtime_error = 7,
+    /** Queried element is not required for given primitive */
+    mkldnn_not_required = 8,
+} mkldnn_status_t;
+
+/** Data type specification */
+typedef enum {
+    /** Undefined data type, used for empty memory descriptors. */
+    mkldnn_data_type_undef = 0,
+    /** 32-bit/single-precision floating point. */
+    mkldnn_f32 = 1,
+    /** 32-bit signed integer. */
+    mkldnn_s32 = 2,
+    /** 8-bit signed integer. */
+    mkldnn_s8 = 3,
+    /** 8-bit unsigned integer. */
+    mkldnn_u8 = 4,
+} mkldnn_data_type_t;
+
+/** Memory format kind */
+typedef enum {
+    /** Undefined memory format, used for empty memory descriptors. */
+    mkldnn_format_kind_undef = 0,
+    /** Unspecified format. The primitive selects a format automatically. */
+    mkldnn_format_kind_any,
+    /** A tensor in a generic format described by the stride and blocking
+     * values in each dimension. See #mkldnn_blocking_desc_t for more
+     * information. */
+    mkldnn_blocked,
+    /** Weights format used in 8bit Winograd convolution */
+    mkldnn_format_kind_wino,
+    /** Packed weights format used in RNN */
+    mkldnn_format_kind_rnn_packed,
+} mkldnn_format_kind_t;
+
+/** Memory format tag specification.
+ *
+ * Intel MKL-DNN formats describe physical data layout. The physical layout
+ * is described as a sequence of the dimensions as they are laid out in the
+ * memory (from the outer-most to the inner-most). Note that this order
+ * doesn't affect the logical order of the dimensions that is kept in the
+ * `dims` field of the mkldnn_memory_desc_t structure. The logical order of the
+ * dimensions is specified by the type of tensor.
+ *
+ * For example, CNN 5D tensor always has its logical dimensions in the order
+ * `(batch, channels, depth, height, width)`, while the physical layout might be
+ * #mkldnn_ncdhw or #mkldnn_ndhwc:
+ *
+ * ~~~cpp
+ * int batch = 2, channels = 16, depth = 13, height = 13, width = 13;
+ *
+ * int ndims = 5; // 5D tensor
+ * mkldnn_dims_t dims = {batch, channels, depth, height, width};
+ * mkldnn_memory_desc_t data_in_ncdhw;
+ * mkldnn_memory_desc_init_by_tag(
+ *      &data_in_ncdhw, 5, dims, mkldnn_f32, mkldnn_ncdhw);
+ *
+ * // note that in both cases dims passed are the same
+ * mkldnn_memory_desc_t data_in_ndhwc;
+ * mkldnn_memory_desc_init_by_tag(
+ *      &data_in_ndhwc, 5, dims, mkldnn_f32, mkldnn_ndhwc);
+ * ~~~
+ *
+ * The following notation applies to memory format names:
+ *  - @c 'n' denotes the mini-batch dimension
+ *  - @c 'c' denotes a channels dimension
+ *  - When there are multiple channel dimensions (for example, in convolution
+ *    weights tensor), @c 'i' and @c 'o' denote dimensions of input and output
+ *    channels
+ *  - @c 'd', @c 'h', and @c 'w' denote spatial depth, height, and width
+ *    respectively
+ *  - Upper-case letters indicate that the data is laid out in blocks
+ *    for a particular dimension. In such cases, the format name contains both
+ *    upper- and lower-case letters for that dimension with a lower-case letter
+ *    preceded by the block size. For example: @c 'mkldnn_nChw8c' describes a
+ *    format where the outermost dimension is mini-batch, followed by the
+ *    channel block number, followed by the spatial height and width, and
+ *    finally followed by 8-element channel blocks.
+ *
+ * @note
+ *    Channel designations can be different. For example, both the @c
+ *    'mkldnn_nc' and @c 'mkldnn_io' formats can be used to describe a 2D
+ *    tensor.
+ *
+ * @sa @ref understanding_memory_formats
+ */
+typedef enum {
+    /** Undefined memory format tag */
+    mkldnn_format_tag_undef = 0,
+    /** Undefined memory format tag.
+     * The primitive selects a format automatically. */
+    mkldnn_format_tag_any,
+
+    /* Semantic agnostic section */
+    /* The physical order of dimensions is defined by the permutation of the
+     * characters, assuming that ab..z defines the natural order.
+     */
+
+    /* Plain formats */
+
+    mkldnn_a,
+    mkldnn_ab,
+    mkldnn_abc,
+    mkldnn_abcd,
+    mkldnn_abcde,
+    mkldnn_abcdef,
+    mkldnn_abdec,
+    mkldnn_acb,
+    mkldnn_acbde,
+    mkldnn_acdb,
+    mkldnn_acdeb,
+    mkldnn_ba,
+    mkldnn_bac,
+    mkldnn_bacd,
+    mkldnn_bcda,
+    mkldnn_cba,
+    mkldnn_cdba,
+    mkldnn_cdeba,
+    mkldnn_decab,
+
+    /* Opaque blocked formats */
+
+    mkldnn_Abc16a,
+    mkldnn_ABc16a16b,
+    mkldnn_aBc16b,
+    mkldnn_ABc16b16a,
+    mkldnn_Abc4a,
+    mkldnn_aBc4b,
+    mkldnn_ABc4b16a4b,
+    mkldnn_ABc4b4a,
+    mkldnn_ABc8a16b2a,
+    mkldnn_ABc8a8b,
+    mkldnn_aBc8b,
+    mkldnn_ABc8b16a2b,
+    mkldnn_ABc8b8a,
+    mkldnn_Abcd16a,
+    mkldnn_ABcd16a16b,
+    mkldnn_aBcd16b,
+    mkldnn_ABcd16b16a,
+    mkldnn_aBCd16b16c,
+    mkldnn_aBCd16c16b,
+    mkldnn_Abcd4a,
+    mkldnn_aBcd4b,
+    mkldnn_ABcd4b16a4b,
+    mkldnn_ABcd4b4a,
+    mkldnn_aBCd4c16b4c,
+    mkldnn_aBCd4c4b,
+    mkldnn_ABcd8a16b2a,
+    mkldnn_ABcd8a8b,
+    mkldnn_aBcd8b,
+    mkldnn_ABcd8b16a2b,
+    mkldnn_aBCd8b16c2b,
+    mkldnn_ABcd8b8a,
+    mkldnn_aBCd8b8c,
+    mkldnn_aBCd8c16b2c,
+    mkldnn_aBCd8c8b,
+    mkldnn_Abcde16a,
+    mkldnn_ABcde16a16b,
+    mkldnn_aBcde16b,
+    mkldnn_ABcde16b16a,
+    mkldnn_aBCde16b16c,
+    mkldnn_aBCde16c16b,
+    mkldnn_aBCde2c8b4c,
+    mkldnn_Abcde4a,
+    mkldnn_aBcde4b,
+    mkldnn_ABcde4b4a,
+    mkldnn_aBCde4b4c,
+    mkldnn_aBCde4c16b4c,
+    mkldnn_aBCde4c4b,
+    mkldnn_Abcde8a,
+    mkldnn_ABcde8a8b,
+    mkldnn_aBcde8b,
+    mkldnn_ABcde8b16a2b,
+    mkldnn_aBCde8b16c2b,
+    mkldnn_ABcde8b8a,
+    mkldnn_aBCde8b8c,
+    mkldnn_aBCde8c16b2c,
+    mkldnn_aBCde8c8b,
+    mkldnn_aBcdef16b,
+    mkldnn_aBCdef16b16c,
+    mkldnn_aBCdef16c16b,
+    mkldnn_aBcdef4b,
+    mkldnn_aBCdef4c4b,
+    mkldnn_aBCdef8b8c,
+    mkldnn_aBCdef8c16b2c,
+    mkldnn_aBCdef8c8b,
+    mkldnn_aBdc16b,
+    mkldnn_aBdc4b,
+    mkldnn_aBdc8b,
+    mkldnn_aBdec16b,
+    mkldnn_aBdec4b,
+    mkldnn_aBdec8b,
+    mkldnn_aBdefc16b,
+    mkldnn_aBdefc4b,
+    mkldnn_aBdefc8b,
+    mkldnn_Acb16a,
+    mkldnn_Acb4a,
+    mkldnn_Acb8a,
+    mkldnn_aCBd16b16c,
+    mkldnn_aCBde16b16c,
+    mkldnn_Acdb16a,
+    mkldnn_Acdb4a,
+    mkldnn_Acdb8a,
+    mkldnn_Acdeb16a,
+    mkldnn_Acdeb4a,
+    mkldnn_Acdeb8a,
+    mkldnn_BAc16a16b,
+    mkldnn_BAcd16a16b,
+
+    /** Just a sentinel, not real memory format tag. Must be changed after new
+     * format tag is added. */
+    mkldnn_format_tag_last,
+
+    /* Aliases */
+
+    mkldnn_x = mkldnn_a,
+    mkldnn_nc = mkldnn_ab,
+    mkldnn_cn = mkldnn_ba,
+    mkldnn_ncw = mkldnn_abc,
+    mkldnn_nwc = mkldnn_acb,
+    mkldnn_nchw = mkldnn_abcd,
+    mkldnn_nhwc = mkldnn_acdb,
+    mkldnn_chwn = mkldnn_bcda,
+    mkldnn_ncdhw = mkldnn_abcde,
+    mkldnn_ndhwc = mkldnn_acdeb,
+
+    mkldnn_oi = mkldnn_ab,
+    mkldnn_io = mkldnn_ba,
+    mkldnn_oiw = mkldnn_abc,
+    mkldnn_wio = mkldnn_cba,
+    mkldnn_oihw = mkldnn_abcd,
+    mkldnn_hwio = mkldnn_cdba,
+    mkldnn_ihwo = mkldnn_bcda,
+    mkldnn_iohw = mkldnn_bacd,
+    mkldnn_oidhw = mkldnn_abcde,
+    mkldnn_dhwio = mkldnn_cdeba,
+    mkldnn_goiw = mkldnn_abcd,
+    mkldnn_goihw = mkldnn_abcde,
+    mkldnn_hwigo = mkldnn_decab,
+    mkldnn_giohw = mkldnn_acbde,
+    mkldnn_goidhw = mkldnn_abcdef,
+
+    /** 3D RNN data tensor in the format (seq_length, batch, input channels). */
+    mkldnn_tnc = mkldnn_abc,
+    /** 3D RNN data tensor in the format (batch, seq_length, input channels). */
+    mkldnn_ntc = mkldnn_bac,
+    /** 5D RNN states tensor in the format (num_layers, num_directions,
+     * num_states, batch, state channels). */
+    mkldnn_ldsnc = mkldnn_abcde,
+    /** 5D RNN weights tensor in the format (num_layers, num_directions,
+     *  input_channels, num_gates, output_channels).
+     *
+     *  - For LSTM cells, the gates order is input, forget, candidate
+     *    and output gate.
+     *  - For GRU cells, the gates order is update, reset and output gate. */
+    mkldnn_ldigo = mkldnn_abcde,
+    /** 5D RNN weights tensor in the format (num_layers, num_directions,
+     * num_gates, output_channels, input_channels).
+     *
+     *  - For LSTM cells, the gates order is input, forget, candidate
+     *    and output gate.
+     *  - For GRU cells, the gates order is update, reset and output gate. */
+    mkldnn_ldgoi = mkldnn_abdec,
+    /** 4D RNN bias tensor in the format (num_layers, num_directions,
+     * num_gates, output_channels).
+     *
+     *  - For LSTM cells, the gates order is input, forget, candidate
+     *    and output gate.
+     *  - For GRU cells, the gates order is update, reset and output gate. */
+    mkldnn_ldgo = mkldnn_abcd,
+
+    /* Opaque data types, are not to be used explicitly */
+
+    /* data */
+    mkldnn_nCdhw16c = mkldnn_aBcde16b,
+    mkldnn_nCdhw4c = mkldnn_aBcde4b,
+    mkldnn_nCdhw8c = mkldnn_aBcde8b,
+    mkldnn_nChw16c = mkldnn_aBcd16b,
+    mkldnn_nChw4c = mkldnn_aBcd4b,
+    mkldnn_nChw8c = mkldnn_aBcd8b,
+    mkldnn_nCw16c = mkldnn_aBc16b,
+    mkldnn_nCw4c = mkldnn_aBc4b,
+    mkldnn_nCw8c = mkldnn_aBc8b,
+
+    /* weights, 3D */
+    mkldnn_IOw16o16i = mkldnn_BAc16a16b,
+    mkldnn_OIw16i16o = mkldnn_ABc16b16a,
+    mkldnn_OIw16o16i = mkldnn_ABc16a16b,
+    mkldnn_Oiw16o = mkldnn_Abc16a,
+    mkldnn_OIw4i16o4i = mkldnn_ABc4b16a4b,
+    mkldnn_OIw4i4o = mkldnn_ABc4b4a,
+    mkldnn_Oiw4o = mkldnn_Abc4a,
+    mkldnn_OIw8i16o2i = mkldnn_ABc8b16a2b,
+    mkldnn_OIw8i8o = mkldnn_ABc8b8a,
+    mkldnn_OIw8o16i2o = mkldnn_ABc8a16b2a,
+    mkldnn_OIw8o8i = mkldnn_ABc8a8b,
+    mkldnn_Owi16o = mkldnn_Acb16a,
+    mkldnn_Owi4o = mkldnn_Acb4a,
+    mkldnn_Owi8o = mkldnn_Acb8a,
+
+    /* weights, 4D */
+    mkldnn_IOhw16o16i = mkldnn_BAcd16a16b,
+    mkldnn_Ohwi16o = mkldnn_Acdb16a,
+    mkldnn_Ohwi4o = mkldnn_Acdb4a,
+    mkldnn_Ohwi8o = mkldnn_Acdb8a,
+    mkldnn_OIhw16i16o = mkldnn_ABcd16b16a,
+    mkldnn_OIhw16o16i = mkldnn_ABcd16a16b,
+    mkldnn_Oihw16o = mkldnn_Abcd16a,
+    mkldnn_OIhw4i16o4i = mkldnn_ABcd4b16a4b,
+    mkldnn_OIhw4i4o = mkldnn_ABcd4b4a,
+    mkldnn_Oihw4o = mkldnn_Abcd4a,
+    mkldnn_OIhw8i16o2i = mkldnn_ABcd8b16a2b,
+    mkldnn_OIhw8i8o = mkldnn_ABcd8b8a,
+    mkldnn_OIhw8o16i2o = mkldnn_ABcd8a16b2a,
+    mkldnn_OIhw8o8i = mkldnn_ABcd8a8b,
+
+    /* weights, 5D */
+    mkldnn_Odhwi16o = mkldnn_Acdeb16a,
+    mkldnn_Odhwi4o = mkldnn_Acdeb4a,
+    mkldnn_Odhwi8o = mkldnn_Acdeb8a,
+    mkldnn_OIdhw16i16o = mkldnn_ABcde16b16a,
+    mkldnn_OIdhw16o16i = mkldnn_ABcde16a16b,
+    mkldnn_Oidhw16o = mkldnn_Abcde16a,
+    mkldnn_OIdhw4i4o = mkldnn_ABcde4b4a,
+    mkldnn_Oidhw4o = mkldnn_Abcde4a,
+    mkldnn_OIdhw8i16o2i = mkldnn_ABcde8b16a2b,
+    mkldnn_OIdhw8i8o = mkldnn_ABcde8b8a,
+    mkldnn_OIdhw8o8i = mkldnn_ABcde8a8b,
+
+    /* weights w/ groups, 3D */
+    mkldnn_Goiw16g = mkldnn_Abcd16a,
+    mkldnn_gIOw16o16i = mkldnn_aCBd16b16c,
+    mkldnn_gOIw16i16o = mkldnn_aBCd16c16b,
+    mkldnn_gOIw16o16i = mkldnn_aBCd16b16c,
+    mkldnn_gOiw16o = mkldnn_aBcd16b,
+    mkldnn_gOIw4i16o4i = mkldnn_aBCd4c16b4c,
+    mkldnn_gOIw4i4o = mkldnn_aBCd4c4b,
+    mkldnn_gOiw4o = mkldnn_aBcd4b,
+    mkldnn_gOIw8i16o2i = mkldnn_aBCd8c16b2c,
+    mkldnn_gOIw8i8o = mkldnn_aBCd8c8b,
+    mkldnn_gOIw8o16i2o = mkldnn_aBCd8b16c2b,
+    mkldnn_gOIw8o8i = mkldnn_aBCd8b8c,
+    mkldnn_gOwi16o = mkldnn_aBdc16b,
+    mkldnn_gOwi4o = mkldnn_aBdc4b,
+    mkldnn_gOwi8o = mkldnn_aBdc8b,
+
+    /* weights w/ groups, 4D */
+    mkldnn_gIOhw16o16i = mkldnn_aCBde16b16c,
+    mkldnn_gOhwi16o = mkldnn_aBdec16b,
+    mkldnn_gOhwi4o = mkldnn_aBdec4b,
+    mkldnn_gOhwi8o = mkldnn_aBdec8b,
+    mkldnn_Goihw16g = mkldnn_Abcde16a,
+    mkldnn_gOIhw16i16o = mkldnn_aBCde16c16b,
+    mkldnn_gOIhw16o16i = mkldnn_aBCde16b16c,
+    mkldnn_gOihw16o = mkldnn_aBcde16b,
+    mkldnn_gOIhw2i8o4i = mkldnn_aBCde2c8b4c,
+    mkldnn_gOIhw4i16o4i = mkldnn_aBCde4c16b4c,
+    mkldnn_gOIhw4i4o = mkldnn_aBCde4c4b,
+    mkldnn_gOIhw4o4i = mkldnn_aBCde4b4c,
+    mkldnn_gOihw4o = mkldnn_aBcde4b,
+    mkldnn_Goihw8g = mkldnn_Abcde8a,
+    mkldnn_gOIhw8i16o2i = mkldnn_aBCde8c16b2c,
+    mkldnn_gOIhw8i8o = mkldnn_aBCde8c8b,
+    mkldnn_gOIhw8o16i2o = mkldnn_aBCde8b16c2b,
+    mkldnn_gOIhw8o8i = mkldnn_aBCde8b8c,
+
+    /* weights w/ groups, 6D */
+    mkldnn_gOdhwi16o = mkldnn_aBdefc16b,
+    mkldnn_gOdhwi4o = mkldnn_aBdefc4b,
+    mkldnn_gOdhwi8o = mkldnn_aBdefc8b,
+    mkldnn_gOIdhw16i16o = mkldnn_aBCdef16c16b,
+    mkldnn_gOIdhw16o16i = mkldnn_aBCdef16b16c,
+    mkldnn_gOidhw16o = mkldnn_aBcdef16b,
+    mkldnn_gOIdhw4i4o = mkldnn_aBCdef4c4b,
+    mkldnn_gOidhw4o = mkldnn_aBcdef4b,
+    mkldnn_gOIdhw8i16o2i = mkldnn_aBCdef8c16b2c,
+    mkldnn_gOIdhw8i8o = mkldnn_aBCdef8c8b,
+    mkldnn_gOIdhw8o8i = mkldnn_aBCdef8b8c,
+} mkldnn_format_tag_t;
+
+/** Kinds of padding. Define how to interpret the data in padding regions. */
+typedef enum {
+    /** The data in padding regions is zero. */
+    mkldnn_padding_zero,
+} mkldnn_padding_kind_t;
+
+/** Kinds of propagation. */
+typedef enum {
+    /* TODO: suggest renames */
+    /** Undefined propagation type. */
+    mkldnn_prop_kind_undef = 0,
+    /** Forward data propagation (training mode). In this mode primitives
+     * perform computations necessary for subsequent backward propagation. */
+    mkldnn_forward_training = 64,
+    /** Forward data propagation (inference mode). In this mode primitives
+     * perform only computations that are necessary for inference and omit
+     * computations that are necessary only for backward propagation. */
+    mkldnn_forward_inference = 96,
+    /** Forward data propagation (alias for @c mkldnn_forward_inference) */
+    mkldnn_forward_scoring = mkldnn_forward_inference,
+   /** Forward data propagation (alias for @c mkldnn_forward_training) */
+    mkldnn_forward = mkldnn_forward_training,
+    /** Backward propagation (with respect to all parameters */
+    mkldnn_backward = 128,
+    /** Backward data propagation */
+    mkldnn_backward_data = 160,
+    /** Backward weights propagation */
+    mkldnn_backward_weights = 192,
+    /** Backward bias propagation */
+    mkldnn_backward_bias = 193,
+} mkldnn_prop_kind_t;
+
+/** Kinds of primitives. Used to implement a way to extend the library with new
+ * primitives without changing the ABI. */
+typedef enum {
+    /** Undefined primitive (XXX: why do we have it?). */
+    mkldnn_undefined_primitive,
+    /** A reorder primitive.*/
+    mkldnn_reorder,
+    /** A shuffle primitive.*/
+    mkldnn_shuffle,
+    /** A (out-of-place) concat primitive. */
+    mkldnn_concat,
+    /** A sum primitive. */
+    mkldnn_sum,
+    /** A convolution primitive. */
+    mkldnn_convolution,
+    /** A deconvolution primitive. */
+    mkldnn_deconvolution,
+    /** An element-wise primitive. */
+    mkldnn_eltwise,
+    /** A Softmax primitive. */
+    mkldnn_softmax,
+    /** A pooling primitive. */
+    mkldnn_pooling,
+    /** An LRN primitive. */
+    mkldnn_lrn,
+    /** An batch normalization primitive. */
+    mkldnn_batch_normalization,
+    /** An inner product primitive. */
+    mkldnn_inner_product,
+    /** A rnn primitive. */
+    mkldnn_rnn,
+} mkldnn_primitive_kind_t;
+
+/** Kinds of algorithms. */
+typedef enum {
+    mkldnn_alg_kind_undef,
+    /** Direct convolution */
+    mkldnn_convolution_direct = 0x1,
+    /** Winograd convolution */
+    mkldnn_convolution_winograd = 0x2,
+    /** Convolution algorithm(either direct or Winograd) is chosen just in time **/
+    mkldnn_convolution_auto = 0x3,
+    /** Direct deconvolution */
+    mkldnn_deconvolution_direct = 0xa,
+    /** Winograd deconvolution */
+    mkldnn_deconvolution_winograd = 0xb,
+    /** Eltwise: ReLU */
+    mkldnn_eltwise_relu = 0x1f,
+    /** Eltwise: hyperbolic tangent non-linearity (tanh) */
+    mkldnn_eltwise_tanh = 0x2f,
+    /** Eltwise: parametric exponential linear unit (elu) */
+    mkldnn_eltwise_elu = 0x3f,
+    /** Eltwise: square */
+    mkldnn_eltwise_square = 0x4f,
+    /** Eltwise: abs */
+    mkldnn_eltwise_abs = 0x5f,
+    /** Eltwise: square root */
+    mkldnn_eltwise_sqrt = 0x6f,
+    /** Eltwise: linear */
+    mkldnn_eltwise_linear = 0x7f,
+    /** Eltwise: bounded_relu */
+    mkldnn_eltwise_bounded_relu = 0x8f,
+    /** Eltwise: soft_relu */
+    mkldnn_eltwise_soft_relu = 0x9f,
+    /** Eltwise: logistic */
+    mkldnn_eltwise_logistic = 0xaf,
+    /** Max pooling */
+    mkldnn_pooling_max = 0x1ff,
+    /** Average pooling include padding */
+    mkldnn_pooling_avg_include_padding = 0x2ff,
+    /** Average pooling exclude padding */
+    mkldnn_pooling_avg_exclude_padding = 0x3ff,
+    mkldnn_pooling_avg = mkldnn_pooling_avg_exclude_padding,
+    /** Local response normalization (LRN) across multiple channels */
+    mkldnn_lrn_across_channels = 0xaff,
+    /** LRN within a single channel */
+    mkldnn_lrn_within_channel = 0xbff,
+    /** RNN cell */
+    mkldnn_vanilla_rnn = 0x1fff,
+    /** LSTM cell */
+    mkldnn_vanilla_lstm = 0x2fff,
+    /** GRU cell */
+    mkldnn_vanilla_gru = 0x3fff,
+    /** GRU cell with linear before reset
+     *
+     * Modification of original GRU cell. Differs from #mkldnn_vanilla_gru
+     * in how the new memory gate is calculated:
+     * \f[ c_t = tanh(W_c*x_t + b_{c_x} + r_t*(U_c*h_{t-1}+b_{c_h})) \f]
+     * Primitive expects 4 biases on input:
+     * \f$[b_{u}, b_{r}, b_{c_x}, b_{c_h}]\f$
+     * */
+    mkldnn_gru_linear_before_reset = 0x4fff,
+} mkldnn_alg_kind_t;
+
+/** Flags for batch-normalization primititve. */
+typedef enum {
+    /** Use global statistics
+     *
+     * If specified
+     *  - on forward propagation use mean and variance provided by user (input)
+     *  - on backward propagation reduces the amount of computations, since
+     *    mean and variance are considered as constants
+     *
+     *  If not specified:
+     *   - on forward propagation mean and variance are computed and stored in
+     *     output
+     *   - on backward propagation compute full derivative wrt to data
+     */
+    mkldnn_use_global_stats = 0x1U,
+    /** Use scale and shift parameters
+     *
+     * If specified:
+     *  - on forward propagation use scale and shift (aka scale and bias) for
+     *    the batch normalization results
+     *  - on backward propagation (for prop_kind == #mkldnn_backward) compute
+     *    diff wrt to scale and shift (hence one extra output used)
+     *
+     * If no specified:
+     *  - on backward propagation prop_kind == #mkldnn_backward_data has the
+     *    same behavior as prop_kind == #mkldnn_backward
+     */
+    mkldnn_use_scaleshift = 0x2U,
+    /** Fuse with ReLU
+     *
+     * If specified:
+     *  - on inference this option behaves the same as if the primitive were
+     *    fused with ReLU via post ops API
+     *  - on training primitive requires workspace (required to be able to
+     *    perform backward pass)
+     */
+    mkldnn_fuse_bn_relu = 0x4U,
+} mkldnn_batch_normalization_flag_t;
+
+/** @} */
+
+/** @addtogroup c_api_types_memory Memory
+ *  @{ */
+
+/** Maximum number of dimensions a tensor can have. Only restricts the amount
+ * of space used for the tensor description. Individual computational
+ * primitives may support only tensors of certain dimensions. */
+#define MKLDNN_MAX_NDIMS 12
+
+/** A type to describe tensor dimension. */
+typedef int64_t mkldnn_dim_t;
+
+/** A type to describe tensor dimensions. */
+typedef mkldnn_dim_t mkldnn_dims_t[MKLDNN_MAX_NDIMS];
+
+/** A type to describe strides within a tensor. */
+typedef mkldnn_dim_t mkldnn_strides_t[MKLDNN_MAX_NDIMS];
+
+/** Generic description of blocked data layout for most memory formats.
+ *
+ * @sa @ref understanding_memory_formats */
+typedef struct {
+    /** The strides between the outermost blocks.
+     * In case of plain (non-blocked) formats the strides between dimensions. */
+    mkldnn_dims_t strides;
+    /* Innermost section
+     * ASSUMPTION: the innermost blocks are always dense */
+    /** The number of innermost blocks, e.g. 3 in case of `OIhw_4i16o4i_` */
+    int inner_nblks;
+    /** The size of the blocks, e.g. `{4, 16, 4}` in case of `OIhw_4i16o4i` */
+    mkldnn_dims_t inner_blks;
+    /** The logical indices of the blocks, e.g. `{1, 0, 1}` in case of
+     * `4i16o4i`, because `i` is the 1st dim and `o` is the 0st dim */
+    mkldnn_dims_t inner_idxs;
+} mkldnn_blocking_desc_t;
+
+typedef enum {
+    /** Undefined memory format, used for empty memory descriptors. */
+    mkldnn_wino_undef = 0,
+    /** Tensors of weights for 2x3 winograd convolutions. */
+    mkldnn_wino_wei_aaOIoi,
+    mkldnn_wino_wei_aaOio,
+    mkldnn_wino_wei_aaOBiOo,
+    /** Tensor of weights for 4x3 convolution. */
+    mkldnn_wino_wei_OBaaIBOIio
+} mkldnn_wino_memory_format_t;
+
+/** Description of tensor of weights for winograd 2x3 convolution. */
+typedef struct {
+    mkldnn_wino_memory_format_t wino_format;
+    int r;
+    int alpha;
+    int ic;
+    int oc;
+    int ic_block;
+    int oc_block;
+    int ic2_block;
+    int oc2_block;
+    float adj_scale;
+    size_t size;
+} mkldnn_wino_desc_t;
+
+typedef enum {
+    mkldnn_packed_format_undef = 0,
+    mkldnn_ldigo_p,
+    mkldnn_ldgoi_p
+} mkldnn_rnn_packed_memory_format_t;
+
+/* Maximum number of parts of RNN weights tensor that require separate
+ * computation. */
+#define MKLDNN_RNN_MAX_N_PARTS 4
+
+/** Description of tensor of packed weights for rnn. */
+typedef struct {
+    mkldnn_rnn_packed_memory_format_t format;
+    int n_parts;
+    int n;
+    int parts[MKLDNN_RNN_MAX_N_PARTS];
+    size_t part_pack_size[MKLDNN_RNN_MAX_N_PARTS];
+    size_t offset_compensation;
+    size_t size;
+} mkldnn_rnn_packed_desc_t;
+
+typedef enum {
+    mkldnn_memory_extra_flag_none = 0x0U,
+    /** Indicates the weights have an additional buffer, that depends on the
+     * @p compensation_mask.
+     *
+     * For instance, in 4D case with the compensation mask equals (1 << 0)
+     * the additional buffer would consist of OC values:
+     * O[oc : 0,OC] =
+     *  -128 * SUM(ic : 0,IC; kh : 0,KH; kw : 0,KW){ weights(oc, ic, kh, kw) }
+     */
+    mkldnn_memory_extra_flag_compensation_conv_s8s8 = 0x1U,
+    mkldnn_memory_extra_flag_scale_adjust = 0x2U,
+} mkldnn_memory_extra_flags_t;
+
+/** Description of extra information stored in memory */
+typedef struct {
+    /** The flags contain arbitrary extra information, such as compensation.
+     * @sa mkldnn_memory_extra_flags_t */
+    uint64_t flags;
+    /** Compensation mask */
+    int compensation_mask;
+    /** Scale applied to the data */
+    float scale_adjust;
+    /** For future backwards compatibility */
+    char reserved[64];
+} mkldnn_memory_extra_desc_t;
+
+/** Memory descriptor. The description is based on a number of dimensions,
+ * dimensions themselves, plus information about elements type and memory
+ * format. Additionally, contains format-specific descriptions of the data
+ * layout. */
+typedef struct {
+    /** Number of dimensions */
+    int ndims;
+    /** Dimensions in the following order:
+     * - CNN data tensors: mini-batch, channel, spatial
+     *   (<code>{N, C, [[D,] H,] W}</code>)
+     * - CNN weight tensors: group (optional), output channel, input channel,
+     *   spatial (<code>{[G,] O, I, [[D,] H,] W}</code>)
+     * - RNN data tensors: time, mini-batch, channels (<code>{T, N, C}</code>)
+     *   or layers, directions, states, mini-batch, channels (<code>{L, D, S, N, C}</code>)
+     * - RNN weight tensor: layers, directions, input channel, gates, output channels
+     *   (<code>{L, D, I, G, O}</code>).
+     *
+     * @note
+     *    The order of dimensions does not depend on the memory format, so
+     *    whether the data is laid out in #mkldnn_nchw or #mkldnn_nhwc
+     *    the dims for 4D CN data tensor would be <code>{N, C, H, W}</code>.
+     */
+    mkldnn_dims_t dims;
+    /** Data type of the tensor elements. */
+    mkldnn_data_type_t data_type;
+
+    /** Size of the data including padding in each dimension. */
+    mkldnn_dims_t padded_dims;
+    /** Per-dimension offset from the padding to actual data, the top-level
+     * tensor with offsets applied must lie within the padding area. */
+    mkldnn_dims_t padded_offsets;
+
+    /** Offset from memory origin to the current block, non-zero only in
+     * a description of a memory sub-block. */
+    mkldnn_dim_t offset0;
+
+    /** Memory format kind. */
+    mkldnn_format_kind_t format_kind;
+    union {
+        /** Description of the data layout for memory formats that use
+         * blocking. */
+        mkldnn_blocking_desc_t blocking;
+        /** Tensor of weights for integer 8bit winograd convolution. */
+        mkldnn_wino_desc_t wino_desc;
+        /** Tensor of packed weights for RNN. */
+        mkldnn_rnn_packed_desc_t rnn_packed_desc;
+        /* ... other descriptions possible */
+    } format_desc;
+
+    mkldnn_memory_extra_desc_t extra;
+} mkldnn_memory_desc_t;
+
+/** @struct mkldnn_memory
+ * An opaque structure to describe a memory. */
+struct mkldnn_memory;
+
+/** A memory handle. */
+typedef struct mkldnn_memory *mkldnn_memory_t;
+
+/** A constant memory handle. */
+typedef const struct mkldnn_memory *const_mkldnn_memory_t;
+
+#define MKLDNN_NATIVE_HANDLE_NONE (NULL)
+#define MKLDNN_NATIVE_HANDLE_ALLOCATE ((void *)(size_t)-1)
+
+/** @} */
+
+/** @addtogroup c_api_types_op_descs Operation descriptors
+ *  @{*/
+
+/** A pointer to any of the operation descriptors. */
+typedef void *mkldnn_op_desc_t;
+/** A pointer to any of the operation descriptors (constant variant). */
+typedef const void *const_mkldnn_op_desc_t;
+
+/** A descriptor of a convolution operation. */
+typedef struct {
+    /** The kind of primitive. Used for self-identifying the primitive
+     * descriptor. Must be #mkldnn_convolution. */
+    mkldnn_primitive_kind_t primitive_kind;
+    /** The kind of propagation. Possible values: #mkldnn_forward_training,
+     * #mkldnn_forward_inference, #mkldnn_backward_data,
+     * #mkldnn_backward_weights, and #mkldnn_backward_bias. */
+    mkldnn_prop_kind_t prop_kind;
+    /** The kind of the convolution algorithm. Possible values:
+     * #mkldnn_convolution_direct. */
+    mkldnn_alg_kind_t alg_kind;
+    /** Source memory descriptor. */
+    mkldnn_memory_desc_t src_desc;
+    /** Source gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_src_desc;
+    /** Weights memory descriptor. */
+    mkldnn_memory_desc_t weights_desc;
+    /** Weights gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_weights_desc;
+    /** Bias memory descriptor. */
+    mkldnn_memory_desc_t bias_desc;
+    /** Bias gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_bias_desc;
+    /** Destination memory descriptor. */
+    mkldnn_memory_desc_t dst_desc;
+    /** Destination gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_dst_desc;
+    /** Convolution strides in each spatial dimension. */
+    mkldnn_dims_t strides;
+    /** Convolution dilates in each spatial dimension. */
+    mkldnn_dims_t dilates;
+    /** Padding in each spatial dimension. padding[0] is a padding in the
+     * beginning (@p padding_l), padding[1] is a padding in the end (@p
+     * padding_r). */
+    mkldnn_dims_t padding[2];
+    /** The kind of padding to use. */
+    mkldnn_padding_kind_t padding_kind;
+    /** The accumulator data type. Initialized automatically. */
+    mkldnn_data_type_t accum_data_type;
+} mkldnn_convolution_desc_t;
+
+/** A descriptor of a deconvolution operation. */
+typedef mkldnn_convolution_desc_t mkldnn_deconvolution_desc_t;
+
+/** A descriptor of a shuffle operation. */
+typedef struct {
+    /** The kind of primitive. Used for self-identifying the primitive
+     * descriptor. Must be #mkldnn_convolution. */
+    mkldnn_primitive_kind_t primitive_kind;
+    /** The kind of propagation. Possible values: #mkldnn_forward_training,
+     * #mkldnn_forward_inference, and #mkldnn_backward_data. */
+    mkldnn_prop_kind_t prop_kind;
+    /** Source and destination memory descriptor,
+     *  and source and destination gradient memory descriptor. */
+    mkldnn_memory_desc_t data_desc;
+    /** axis for shuffling. */
+    int axis;
+    /** number of groups in group convolution */
+    mkldnn_dim_t group_size;
+} mkldnn_shuffle_desc_t;
+
+/** A descriptor of a element-wise operation. */
+typedef struct {
+    /** The kind of primitive. Used for self-identifying the primitive
+     * descriptor. Must be #mkldnn_eltwise. */
+    mkldnn_primitive_kind_t primitive_kind;
+    /** The kind of propagation. Possible values: #mkldnn_forward_training,
+     * #mkldnn_forward_inference, #mkldnn_backward, and #mkldnn_backward_data.
+     */
+    mkldnn_prop_kind_t prop_kind;
+    /** The kind of eltwise algorithm. Possible values: #mkldnn_eltwise_relu,
+     * #mkldnn_eltwise_tanh, #mkldnn_eltwise_elu, #mkldnn_eltwise_square,
+     * #mkldnn_eltwise_abs, #mkldnn_eltwise_sqrt, #mkldnn_eltwise_linear,
+     * #mkldnn_eltwise_bounded_relu, #mkldnn_eltwise_soft_relu, and
+     * #mkldnn_eltwise_logistic. */
+    mkldnn_alg_kind_t alg_kind;
+    /** Source and destination memory descriptor. */
+    mkldnn_memory_desc_t data_desc;
+    /** Source and destination gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_data_desc;
+    /** Algorithm specific parameter.
+     * Accordance table:
+     *  - #mkldnn_eltwise_relu: @p alpha -- negative slope, @p beta ignored
+     *  - #mkldnn_eltwise_tanh: @p alpha and @p beta ignored
+     *  - #mkldnn_eltwise_elu: @p alpha -- negative slope, @p beta ignored
+     *  - #mkldnn_eltwise_square: @p alpha and @p beta ignored
+     *  - #mkldnn_eltwise_abs: @p alpha and @p beta ignored
+     *  - #mkldnn_eltwise_sqrt: @p alpha and @p beta ignored
+     *  - #mkldnn_eltwise_linear: @p alpha -- scale, @p beta -- shift
+     *  - #mkldnn_eltwise_bounded_relu: @p alpha -- upper bound, @p beta ignored
+     *  - #mkldnn_eltwise_soft_relu: @p alpha and @p beta ignored
+     *  - #mkldnn_eltwise_logistic: @p alpha and @p beta ignored
+     */
+    float alpha, beta;
+} mkldnn_eltwise_desc_t;
+
+/** A descriptor of a Softmax operation. */
+typedef struct {
+    /** The kind of primitive. Used for self-identifying the primitive
+    * descriptor. Must be #mkldnn_softmax. */
+    mkldnn_primitive_kind_t primitive_kind;
+    /** The kind of propagation. Possible values: #mkldnn_forward_training and
+     * #mkldnn_forward_inference. */
+    mkldnn_prop_kind_t prop_kind;
+    /** Source and destination memory descriptor. */
+    mkldnn_memory_desc_t data_desc;
+    /** Source and Destination of gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_desc;
+    /** The axis along which to perform the softmax. */
+    int softmax_axis;
+} mkldnn_softmax_desc_t;
+
+/** A descriptor of a pooling operation. */
+typedef struct {
+    /** The kind of primitive. Used for self-identifying the primitive
+     * descriptor. Must be #mkldnn_pooling. */
+    mkldnn_primitive_kind_t primitive_kind;
+    /** The kind of propagation. Possible values: #mkldnn_forward_training,
+     * #mkldnn_forward_inference, #mkldnn_backward, and #mkldnn_backward_data.
+     */
+    mkldnn_prop_kind_t prop_kind;
+    /** The kind of pooling algorithm. Possible values: #mkldnn_pooling_max and
+     * #mkldnn_pooling_avg. */
+    mkldnn_alg_kind_t alg_kind;
+    /** Source memory descriptor. */
+    mkldnn_memory_desc_t src_desc;
+    /** Source gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_src_desc;
+    /** Destination memory descriptor. */
+    mkldnn_memory_desc_t dst_desc;
+    /** Destination gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_dst_desc;
+    /** Pooling kernel strides for spatial dimensions. */
+    mkldnn_dims_t strides;
+    /** Pooling kernel spatial dimensions. */
+    mkldnn_dims_t kernel;
+    /** Padding in each spatial dimension. padding[0] is a padding in the
+     * beginning (@p padding_l), padding[1] is a padding in the end (@p
+     * padding_r). */
+    mkldnn_dims_t padding[2];
+    /** The kind of padding to use. */
+    mkldnn_padding_kind_t padding_kind;
+    /** The accumulator data type. Initialized automatically. */
+    mkldnn_data_type_t accum_data_type;
+} mkldnn_pooling_desc_t;
+
+/** A descriptor of a Local Response Normalization (LRN) operation. */
+typedef struct {
+    /** The kind of primitive. Used for self-identifying the primitive
+     * descriptor. Must be #mkldnn_lrn. */
+    mkldnn_primitive_kind_t primitive_kind;
+    /** The kind of propagation. Possible values: #mkldnn_forward_training,
+     * #mkldnn_forward_inference, #mkldnn_backward, and #mkldnn_backward_data.
+     */
+    mkldnn_prop_kind_t prop_kind;
+    /** LRN algorithm. Possible values: #mkldnn_lrn_within_channel and
+     * #mkldnn_lrn_across_channels. */
+    mkldnn_alg_kind_t alg_kind;
+    /** Source and destination memory descriptor. */
+    mkldnn_memory_desc_t data_desc;
+    /** Source and destination gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_data_desc;
+    /** The number of channels to sum over (for cross-channel LRN) or the side
+     * length of the square region to sum over (for within-channel LRN). */
+    mkldnn_dim_t local_size;
+    /** LRN alpha parameter. */
+    float lrn_alpha;
+    /** LRN beta parameter. */
+    float lrn_beta;
+    /** LRN k parameter. */
+    float lrn_k;
+} mkldnn_lrn_desc_t;
+
+/** A descriptor of a Batch Normalization operation. */
+typedef struct {
+    /** The kind of primitive. Used for self-identifying the primitive
+     * descriptor. Must be #mkldnn_batch_normalization. */
+    mkldnn_primitive_kind_t primitive_kind;
+    /** The kind of propagation. Possible values: #mkldnn_forward_training,
+     * #mkldnn_forward_inference, #mkldnn_backward, and #mkldnn_backward_data.
+     */
+    mkldnn_prop_kind_t prop_kind;
+    /** Source and destination memory descriptor. */
+    mkldnn_memory_desc_t data_desc;
+    /** Source and destination gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_data_desc;
+    /** Scale and shift data and gradient memory descriptors.
+     *
+     * Scaleshift memory descriptor uses 2D #mkldnn_nc format[2,Channels]. 1-st
+     * dimension contains gamma parameter, 2-nd dimension contains beta
+     * parameter. */
+    mkldnn_memory_desc_t data_scaleshift_desc;
+    mkldnn_memory_desc_t diff_data_scaleshift_desc;
+    /** Mean and variance data memory descriptors.
+     *
+     * Mean and variance memory descriptors use 1D #mkldnn_x format[Channels].
+     */
+    mkldnn_memory_desc_t mean_desc;
+    mkldnn_memory_desc_t variance_desc;
+    /** Batch normalization epsilon parameter. */
+    float batch_norm_epsilon;
+    unsigned flags;
+} mkldnn_batch_normalization_desc_t;
+
+/** A descriptor of an inner product operation. */
+typedef struct {
+    /** The kind of primitive. Used for self-identifying the primitive
+     * descriptor. Must be #mkldnn_inner_product. */
+    mkldnn_primitive_kind_t primitive_kind;
+    /** The kind of propagation. Possible values: #mkldnn_forward_training,
+     * #mkldnn_forward_inference, #mkldnn_backward_data,
+     * #mkldnn_backward_weights, and #mkldnn_backward_bias. */
+    mkldnn_prop_kind_t prop_kind;
+    /** Source memory descriptor. */
+    mkldnn_memory_desc_t src_desc;
+    /** Source gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_src_desc;
+    /** Weights memory descriptor. */
+    mkldnn_memory_desc_t weights_desc;
+    /** Weights gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_weights_desc;
+    /** Bias memory descriptor. */
+    mkldnn_memory_desc_t bias_desc;
+    /** Bias gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_bias_desc;
+    /** Destination memory descriptor. */
+    mkldnn_memory_desc_t dst_desc;
+    /** Destination gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_dst_desc;
+    /** The accumulator data type. Initialized automatically. */
+    mkldnn_data_type_t accum_data_type;
+} mkldnn_inner_product_desc_t;
+
+/** Flags for RNN cell. */
+typedef enum {
+    mkldnn_rnn_cell_with_relu = 0x1U,
+    mkldnn_rnn_cell_with_clipping = 0x2U,
+} mkldnn_rnn_cell_flags_t;
+
+typedef struct {
+    /** RNN cell kind. Must be one of #mkldnn_vanilla_rnn,
+     * #mkldnn_vanilla_lstm, #mkldnn_vanilla_gru,
+     * or #mkldnn_gru_linear_before_reset. */
+    mkldnn_alg_kind_t cell_kind;
+    /** Activation function used. Must be either #mkldnn_eltwise_relu or
+     * #mkldnn_eltwise_tanh. */
+    mkldnn_alg_kind_t activation_kind;
+    /** RNN cell flags */
+    unsigned int flags;
+    /** @c alpha is a negative slope parameter (used only if
+     * `(flags & #mkldnn_rnn_cell_with_relu) != 0`) */
+    float alpha;
+    /** clipping parameter (used only if
+     * `(flags & #mkldnn_rnn_cell_with_clipping) != 0`) */
+    float clipping;
+} mkldnn_rnn_cell_desc_t;
+
+/** A direction of RNN primitive execution. */
+typedef enum {
+    /* Unidirectional execution of RNN primitive from left to right. */
+    mkldnn_unidirectional_left2right,
+    /* Unidirectional execution of RNN primitive from right to left. */
+    mkldnn_unidirectional_right2left,
+    /* Bidirectional execution of RNN primitive with concatenation of the
+     * results. */
+    mkldnn_bidirectional_concat,
+    /* Bidirectional execution of RNN primitive with summation of the
+     * results. */
+    mkldnn_bidirectional_sum,
+    mkldnn_unidirectional = mkldnn_unidirectional_left2right,
+} mkldnn_rnn_direction_t;
+
+/** A descriptor for an RNN operation. */
+typedef struct {
+    /** The kind of primitive. Used for self-identifying the primitive
+     * descriptor. Must be #mkldnn_rnn. */
+    mkldnn_primitive_kind_t primitive_kind;
+    /** The kind of propagation. Possible values: #mkldnn_forward_training,
+     * #mkldnn_forward_inference, and #mkldnn_backward. */
+    mkldnn_prop_kind_t prop_kind;
+    /** The RNN cell desc. */
+    mkldnn_rnn_cell_desc_t cell_desc;
+    /** The direction of RNN primitive execution. */
+    mkldnn_rnn_direction_t direction;
+    /** Source layer memory descriptor. */
+    mkldnn_memory_desc_t src_layer_desc;
+    /** Source iteration memory descriptor. */
+    mkldnn_memory_desc_t src_iter_desc;
+    /** Weights layer memory descriptor. */
+    mkldnn_memory_desc_t weights_layer_desc;
+    /** Weights iteration memory descriptor. */
+    mkldnn_memory_desc_t weights_iter_desc;
+    /** Bias memory descriptor. */
+    mkldnn_memory_desc_t bias_desc;
+    /** Destination layer memory descriptor. */
+    mkldnn_memory_desc_t dst_layer_desc;
+    /** Destination iter memory descriptor. */
+    mkldnn_memory_desc_t dst_iter_desc;
+    /** Source gradient layer memory descriptor. */
+    mkldnn_memory_desc_t diff_src_layer_desc;
+    /** Source gradient iter memory descriptor. */
+    mkldnn_memory_desc_t diff_src_iter_desc;
+    /** Weights gradient layer memory descriptor. */
+    mkldnn_memory_desc_t diff_weights_layer_desc;
+    /** Weights gradient iter memory descriptor. */
+    mkldnn_memory_desc_t diff_weights_iter_desc;
+    /** Bias gradient memory descriptor. */
+    mkldnn_memory_desc_t diff_bias_desc;
+    /** Destination gradient layer memory descriptor. */
+    mkldnn_memory_desc_t diff_dst_layer_desc;
+    /** Destination gradient iteration memory descriptor. */
+    mkldnn_memory_desc_t diff_dst_iter_desc;
+} mkldnn_rnn_desc_t;
+
+/** @} */
+
+/** @addtogroup c_api_engine_types Engine
+ * @{ */
+
+/** @brief Kinds of engines. */
+typedef enum {
+    /** An unspecified engine. */
+    mkldnn_any_engine,
+    /** CPU engine. */
+    mkldnn_cpu,
+} mkldnn_engine_kind_t;
+
+/** @struct mkldnn_engine
+ * @brief An opaque structure to describe an engine. */
+struct mkldnn_engine;
+/** @brief An engine handle. */
+typedef struct mkldnn_engine *mkldnn_engine_t;
+#if 0
+/* FIXME: looks like this never happens */
+/** @brief A constant engine handle. */
+typedef const struct mkldnn_engine *const_mkldnn_engine_t;
+#endif
+
+/** @} */
+
+/** @addtogroup c_api_primitive_desc_iterators Primitive descriptor iterators
+ * @{ */
+
+/** @struct mkldnn_primitive_desc_iterator
+ * @brief An opaque structure to describe a primitive descriptor iterator. */
+struct mkldnn_primitive_desc_iterator;
+
+/** @brief A primitive descriptor iterator handle. */
+typedef struct mkldnn_primitive_desc_iterator
+    *mkldnn_primitive_desc_iterator_t;
+
+/** @brief A constant primitive descriptor iterator handle. */
+typedef const struct mkldnn_primitive_desc_iterator
+    *const_mkldnn_primitive_desc_iterator_t;
+
+/** @} */
+
+/** @addtogroup c_api_primitive_descs Primitive descriptors
+ * @{ */
+
+/** @struct mkldnn_primitive_desc
+ * @brief An opaque structure to describe a primitive descriptor. */
+struct mkldnn_primitive_desc;
+
+/** @brief A primitive descriptor handle. */
+typedef struct mkldnn_primitive_desc *mkldnn_primitive_desc_t;
+
+/** @brief A constant primitive descriptor handle. */
+typedef const struct mkldnn_primitive_desc *const_mkldnn_primitive_desc_t;
+
+/** @} */
+
+/** @addtogroup c_api_primitive_attr Primitive descriptor attributes
+ * @{ */
+
+/** Scratchpad mode */
+typedef enum {
+    /** The library manages scratchpad (default) */
+    mkldnn_scratchpad_mode_library,
+    /** A user shall query and provide the scratchpad memory to primitives */
+    mkldnn_scratchpad_mode_user,
+} mkldnn_scratchpad_mode_t;
+
+/** @struct mkldnn_primitive_attr
+ * @brief An opaque structure for primitive descriptor attributes.
+ *
+ * Attributes may contain:
+ *  - output scales (to scale the result prior to storing it to the memory)
+ */
+struct mkldnn_primitive_attr;
+
+/** @brief A primitive descriptor attributes handle that controls primitive
+ * behavior. */
+typedef struct mkldnn_primitive_attr *mkldnn_primitive_attr_t;
+
+/** @brief A constant primitive descriptor attributes handle. */
+typedef const struct mkldnn_primitive_attr *const_mkldnn_primitive_attr_t;
+
+/** @struct mkldnn_post_ops
+ * @brief An opaque structure for a chain of post operations.
+ *
+ * mkldnn_post_ops can be used to perform some (trivial) operations like
+ * accumulation or eltwise after certain primitives like convolution.
+ *
+ * Post operations might be combined together, making a chain of post
+ * operations. For instance one can configure convolution followed by
+ * accumulation followed by eltwise. This might be especially beneficial
+ * for residual learning blocks.
+ *
+ * @warning
+ *      Of course not all combinations are supported, so the user should handle
+ *      errors accordingly.
+ *
+ * Supported post operations:
+ *  - accumulation (base primitive: convolution)
+ *  - eltwise (base primitive: convolution)
+ */
+struct mkldnn_post_ops;
+
+/** @brief A post operation chain handle. */
+typedef struct mkldnn_post_ops *mkldnn_post_ops_t;
+
+/** @brief A constant post operation chain handle. */
+typedef const struct mkldnn_post_ops *const_mkldnn_post_ops_t;
+
+/** @} */
+
+/** @addtogroup c_api_types_primitive Primitive
+ * @{ */
+
+/** @struct mkldnn_primitive
+ * An opaque structure to describe a primitive. */
+struct mkldnn_primitive;
+/** A primitive handle. */
+typedef struct mkldnn_primitive *mkldnn_primitive_t;
+/** A constant primitive handle. */
+typedef const struct mkldnn_primitive *const_mkldnn_primitive_t;
+
+/** @addtogroup c_api_types_arguments Argument indices
+ * @{ */
+
+#define MKLDNN_ARG_SRC_0                1
+#define MKLDNN_ARG_SRC                  MKLDNN_ARG_SRC_0
+#define MKLDNN_ARG_SRC_LAYER            MKLDNN_ARG_SRC_0
+#define MKLDNN_ARG_FROM                 MKLDNN_ARG_SRC_0
+
+#define MKLDNN_ARG_SRC_1                2
+#define MKLDNN_ARG_SRC_ITER             MKLDNN_ARG_SRC_1
+
+#define MKLDNN_ARG_DST_0                17
+#define MKLDNN_ARG_DST                  MKLDNN_ARG_DST_0
+#define MKLDNN_ARG_TO                   MKLDNN_ARG_DST_0
+#define MKLDNN_ARG_DST_LAYER            MKLDNN_ARG_DST_0
+
+#define MKLDNN_ARG_DST_1                18
+#define MKLDNN_ARG_DST_ITER             MKLDNN_ARG_DST_1
+
+#define MKLDNN_ARG_WEIGHTS_0            33
+#define MKLDNN_ARG_WEIGHTS              MKLDNN_ARG_WEIGHTS_0
+#define MKLDNN_ARG_SCALE_SHIFT          MKLDNN_ARG_WEIGHTS_0
+#define MKLDNN_ARG_WEIGHTS_LAYER        MKLDNN_ARG_WEIGHTS_0
+
+#define MKLDNN_ARG_WEIGHTS_1            34
+#define MKLDNN_ARG_WEIGHTS_ITER         MKLDNN_ARG_WEIGHTS_1
+
+#define MKLDNN_ARG_BIAS                 41
+
+#define MKLDNN_ARG_MEAN                 49
+#define MKLDNN_ARG_VARIANCE             50
+
+#define MKLDNN_ARG_WORKSPACE            64
+#define MKLDNN_ARG_SCRATCHPAD           80
+
+#define MKLDNN_ARG_DIFF_SRC_0           129
+#define MKLDNN_ARG_DIFF_SRC             MKLDNN_ARG_DIFF_SRC_0
+#define MKLDNN_ARG_DIFF_SRC_LAYER       MKLDNN_ARG_DIFF_SRC_0
+
+#define MKLDNN_ARG_DIFF_SRC_1           130
+#define MKLDNN_ARG_DIFF_SRC_ITER        MKLDNN_ARG_DIFF_SRC_1
+
+#define MKLDNN_ARG_DIFF_DST_0           145
+#define MKLDNN_ARG_DIFF_DST             MKLDNN_ARG_DIFF_DST_0
+#define MKLDNN_ARG_DIFF_DST_LAYER       MKLDNN_ARG_DIFF_DST_0
+
+#define MKLDNN_ARG_DIFF_DST_1           146
+#define MKLDNN_ARG_DIFF_DST_ITER        MKLDNN_ARG_DIFF_DST_1
+
+#define MKLDNN_ARG_DIFF_WEIGHTS_0       161
+#define MKLDNN_ARG_DIFF_WEIGHTS         MKLDNN_ARG_DIFF_WEIGHTS_0
+#define MKLDNN_ARG_DIFF_SCALE_SHIFT     MKLDNN_ARG_DIFF_WEIGHTS_0
+#define MKLDNN_ARG_DIFF_WEIGHTS_LAYER   MKLDNN_ARG_DIFF_WEIGHTS_0
+
+#define MKLDNN_ARG_DIFF_WEIGHTS_1       162
+#define MKLDNN_ARG_DIFF_WEIGHTS_ITER    MKLDNN_ARG_DIFF_WEIGHTS_1
+
+#define MKLDNN_ARG_DIFF_BIAS            169
+
+#define MKLDNN_ARG_MULTIPLE_SRC         1024
+#define MKLDNN_ARG_MULTIPLE_DST         2048
+
+/** @} */
+
+/** An auxiliary structure to specify primitive's inputs/outputs at execution
+ *
+ * @warning
+ *      With this API it's impossible to preserve constness of memory, so all
+ *      memories are passed w/o const qualifier. However only memories with
+ *      output semantics might be changed during the execution */
+typedef struct {
+    int arg; /**< An argument index, e.g. MKLDNN_ARG_SRC */
+    mkldnn_memory_t memory; /**< Input/output memory */
+} mkldnn_exec_arg_t;
+
+/** @} */
+
+/** @addtogroup c_api_types_query Queries
+ * @{ */
+
+/** Primitive descriptor query specification
+ *
+ * For generic function mkldnn_primitive_desc_query(), the type of result must
+ * agree with the queried argument. The correspondence table:
+ *      Query                           | type of result
+ *      --------------------------------------------------------------
+ *      #mkldnn_query_engine            | mkldnn_engine_t *
+ *      #mkldnn_query_scratchpad_engine | mkldnn_engine_t *
+ *      #mkldnn_query_primitive_kind    | mkldnn_primitive_kind_t *
+ *      *_s32                           | int *
+ *      *_s64                           | mkldnn_dim_t * (same as int64_t *)
+ *      *_f64                           | double *
+ *      *_str                           | const char **
+ *      #mkldnn_query_op_d              | const_mkldnn_op_desc_t *
+ *      *_md                            | const mkldnn_memory_desc_t **
+ *      *_${op}_d                       | const mkldnn_${op}_desc_t **
+ *      *_pd                            | const_mkldnn_primitive_desc_t *
+ *
+ * @note
+ *     Rule of thumb: all opaque types and structures are returned by
+ *     reference. All numbers are returned by value.
+ *
+ * @warning
+ *     All returned references point to constant objects and are valid only
+ *     during the lifetime of the queried primitive descriptor. Returned objects
+ *     must not be destroyed by the user. If you need to keep the object longer
+ *     than the lifetime of the queried primitive descriptor, use
+ *     mkldnn_primitive_desc_clone() to make a copy. */
+typedef enum {
+    mkldnn_query_undef = 0,  /**< no query */
+
+    mkldnn_query_engine, /**< execution engine */
+    mkldnn_query_primitive_kind, /**< primitive kind */
+
+    mkldnn_query_num_of_inputs_s32, /**< number of inputs expected */
+    mkldnn_query_num_of_outputs_s32, /**< number of outputs expected */
+
+    mkldnn_query_time_estimate_f64, /**< runtime estimation (seconds) */
+    mkldnn_query_memory_consumption_s64, /**< memory consumption -- extra
+                                           (scratch) memory, additional to all
+                                           inputs and outputs memory (bytes) */
+
+    mkldnn_query_scratchpad_engine, /**< scratchpad engine -- engine to be used
+                                       for creating scratchpad memory */
+
+    mkldnn_query_impl_info_str, /**< implementation name */
+
+    /* memory and op descriptor section */
+    mkldnn_query_some_d = 64, /**< stub */
+    mkldnn_query_op_d, /**< op descriptor */
+    mkldnn_query_convolution_d, /**< convolution descriptor */
+    mkldnn_query_deconvolution_d, /**< deconvolution descriptor */
+    mkldnn_query_shuffle_d, /**< shuffle descriptor */
+    mkldnn_query_eltwise_d, /**< eltwise descriptor */
+    mkldnn_query_softmax_d, /**< softmax descriptor */
+    mkldnn_query_pooling_d, /**< pooling descriptor */
+    mkldnn_query_lrn_d, /**< lrn descriptor */
+    mkldnn_query_batch_normalization_d, /**< batch normalization descriptor */
+    mkldnn_query_inner_product_d, /**< inner product descriptor */
+    mkldnn_query_rnn_d, /**< rnn descriptor */
+
+    /* memory descriptor section */
+    mkldnn_query_some_md = 128, /**< stub */
+    mkldnn_query_src_md, /**< source memory desc */
+    mkldnn_query_diff_src_md, /**< source gradient memory desc */
+    mkldnn_query_weights_md, /**< weights memory descriptor desc */
+    mkldnn_query_diff_weights_md, /**< weights grad. memory desc */
+    mkldnn_query_dst_md, /**< destination memory desc */
+    mkldnn_query_diff_dst_md, /**< destination grad. memory desc */
+    mkldnn_query_workspace_md, /**< workspace memory desc */
+    mkldnn_query_scratchpad_md, /**< scratchpad memory desc */
+} mkldnn_query_t;
+
+/** @} */
+
+/** @addtogroup c_api_types_stream Execution stream
+ * @{ */
+
+/** @brief Stream flags. */
+typedef enum {
+    /** A default stream configuration. */
+    mkldnn_stream_default_flags = 0x0U,
+} mkldnn_stream_flags_t;
+
+/** @struct mkldnn_stream
+ * An opaque structure to describe an execution stream. */
+struct mkldnn_stream;
+/** An execution stream handle. */
+typedef struct mkldnn_stream *mkldnn_stream_t;
+/** A constant execution stream handle. */
+typedef const struct mkldnn_stream *const_mkldnn_stream_t;
+
+/** @} */
+/** @} */
+/** @} */
+
+#ifdef __cplusplus
+}
+#endif
+
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/include/mkldnn_version.h b/thirdparty/oidn/mkl-dnn/include/mkldnn_version.h
new file mode 100644
index 0000000000..a2713deccb
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/include/mkldnn_version.h
@@ -0,0 +1,32 @@
+/*******************************************************************************
+* Copyright 2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MKLDNN_VERSION_H
+#define MKLDNN_VERSION_H
+
+/* Major version of MKL-DNN */
+#define MKLDNN_VERSION_MAJOR 0
+
+/* Minor version of MKL-DNN */
+#define MKLDNN_VERSION_MINOR 90
+
+/* Patch version of MKL-DNN */
+#define MKLDNN_VERSION_PATCH 0
+
+/* Git Commit Hash of MKL-DNN */
+#define MKLDNN_VERSION_HASH  "096bda1ca23324879f2df5a129e610e4405f775c"
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/include/mkldnn_version.h.in b/thirdparty/oidn/mkl-dnn/include/mkldnn_version.h.in
new file mode 100644
index 0000000000..5ee0126188
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/include/mkldnn_version.h.in
@@ -0,0 +1,32 @@
+/*******************************************************************************
+* Copyright 2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MKLDNN_VERSION_H
+#define MKLDNN_VERSION_H
+
+/* Major version of MKL-DNN */
+#define MKLDNN_VERSION_MAJOR @MKLDNN_VERSION_MAJOR@
+
+/* Minor version of MKL-DNN */
+#define MKLDNN_VERSION_MINOR @MKLDNN_VERSION_MINOR@
+
+/* Patch version of MKL-DNN */
+#define MKLDNN_VERSION_PATCH @MKLDNN_VERSION_PATCH@
+
+/* Git Commit Hash of MKL-DNN */
+#define MKLDNN_VERSION_HASH  "@MKLDNN_VERSION_HASH@"
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/batch_normalization.cpp b/thirdparty/oidn/mkl-dnn/src/common/batch_normalization.cpp
new file mode 100644
index 0000000000..1a51d8562b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/batch_normalization.cpp
@@ -0,0 +1,104 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::alg_kind;
+using namespace mkldnn::impl::types;
+
+namespace {
+status_t bnrm_desc_init(batch_normalization_desc_t *bnrm_desc,
+        prop_kind_t prop_kind, const memory_desc_t *data_desc,
+        const memory_desc_t *diff_data_desc, float epsilon, unsigned flags) {
+    bool args_ok = true
+        && !any_null(bnrm_desc, data_desc)
+        && one_of(prop_kind, forward_training, forward_inference,
+                backward_data, backward)
+        && IMPLICATION(prop_kind & backward, diff_data_desc != nullptr);
+    if (!args_ok) return invalid_arguments;
+
+    auto bd = batch_normalization_desc_t();
+    bd.primitive_kind = primitive_kind::batch_normalization;
+    bd.prop_kind = prop_kind;
+
+    bd.data_desc = *data_desc;
+    bd.diff_data_desc = zero_md();
+    if ( one_of(bd.prop_kind,backward_data, backward) )
+        bd.diff_data_desc = *diff_data_desc;
+
+    dims_t scaleshift_dims = { 2, data_desc->dims[1] };
+    mkldnn_memory_desc_init_by_tag(&bd.data_scaleshift_desc, 2,
+            scaleshift_dims, data_type::f32, mkldnn_nc);
+    bd.diff_data_scaleshift_desc = zero_md();
+    if (bd.prop_kind == backward) {
+        bd.diff_data_scaleshift_desc = bd.data_scaleshift_desc;
+    }
+
+    dims_t stats_dims = { data_desc->dims[1] };
+    mkldnn_memory_desc_init_by_tag(&bd.mean_desc, 1, stats_dims,
+            data_type::f32, mkldnn_x);
+    bd.variance_desc = bd.mean_desc;
+    bd.batch_norm_epsilon = epsilon;
+
+    unsigned bnorm_flags =
+        mkldnn_use_global_stats | mkldnn_use_scaleshift | mkldnn_fuse_bn_relu;
+    if ((~bnorm_flags & flags) != 0) return invalid_arguments;
+
+    bd.flags = flags;
+
+    bool consistency = true
+        && utils::one_of(bd.data_desc.ndims, 2, 4, 5);
+    if (bd.prop_kind == backward_data)
+        consistency = consistency
+            && utils::one_of(bd.diff_data_desc.ndims, 2, 4, 5)
+            && array_cmp(bd.diff_data_desc.dims, bd.data_desc.dims,
+                    bd.diff_data_desc.ndims);
+    if (!consistency) return invalid_arguments;
+
+    *bnrm_desc = bd;
+    return success;
+}
+}
+
+status_t mkldnn_batch_normalization_forward_desc_init(
+        batch_normalization_desc_t *bnrm_desc, prop_kind_t prop_kind,
+        const memory_desc_t *data_desc, float epsilon, unsigned flags) {
+    if (!one_of(prop_kind, forward_training, forward_inference))
+        return invalid_arguments;
+    return bnrm_desc_init(bnrm_desc, prop_kind, data_desc, nullptr,
+            epsilon, flags);
+}
+
+status_t mkldnn_batch_normalization_backward_desc_init(
+        batch_normalization_desc_t *bnrm_desc, prop_kind_t prop_kind,
+        const memory_desc_t *diff_data_desc, const memory_desc_t *data_desc,
+        float epsilon, unsigned flags) {
+    if (!one_of(prop_kind, backward, backward_data))
+        return invalid_arguments;
+    return bnrm_desc_init(bnrm_desc, prop_kind, data_desc, diff_data_desc,
+            epsilon, flags);
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/batch_normalization_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/batch_normalization_pd.hpp
new file mode 100644
index 0000000000..f61410b33c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/batch_normalization_pd.hpp
@@ -0,0 +1,240 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef BATCH_NORMALIZATION_PD_HPP
+#define BATCH_NORMALIZATION_PD_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "primitive_desc.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct batch_normalization_fwd_pd_t;
+
+struct batch_normalization_pd_t: public primitive_desc_t {
+    static constexpr auto base_pkind = primitive_kind::batch_normalization;
+
+    batch_normalization_pd_t(engine_t *engine,
+            const batch_normalization_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const batch_normalization_fwd_pd_t *hint_fwd_pd)
+        : primitive_desc_t(engine, attr, base_pkind)
+        , desc_(*adesc)
+        , hint_fwd_pd_(hint_fwd_pd)
+        , data_md_(desc_.data_desc)
+        , stat_md_(desc_.mean_desc)
+        , scaleshift_md_(desc_.data_scaleshift_desc)
+        , ws_md_()
+    {}
+
+    const batch_normalization_desc_t *desc() const { return &desc_; }
+    virtual const op_desc_t *op_desc() const override
+    { return reinterpret_cast<const op_desc_t *>(this->desc()); }
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual status_t query(query_t what, int idx, void *result) const override {
+        switch (what) {
+        case query::batch_normalization_d:
+            *(const batch_normalization_desc_t**)result = desc(); break;
+        default: return primitive_desc_t::query(what, idx, result);
+        }
+        return status::success;
+    }
+
+    /* common batch_normalization aux functions */
+
+    dim_t MB() const { return data_desc().dims[0]; }
+    dim_t C() const { return data_desc().dims[1]; }
+    dim_t D() const { return ndims() >= 5 ? data_desc().dims[ndims() - 3] : 1; }
+    dim_t H() const { return ndims() >= 4 ? data_desc().dims[ndims() - 2] : 1; }
+    dim_t W() const { return ndims() >= 3 ? data_desc().dims[ndims() - 1] : 1; }
+
+    int ndims() const { return desc_.data_desc.ndims; }
+
+    bool stats_is_src() const { return desc_.flags & mkldnn_use_global_stats; }
+    bool use_scaleshift() const { return desc_.flags & mkldnn_use_scaleshift; }
+    bool use_global_stats() const
+    { return desc_.flags & mkldnn_use_global_stats; }
+    bool fuse_bn_relu() const { return desc_.flags & mkldnn_fuse_bn_relu; }
+    bool with_relu_post_op() const {
+        const auto &p = this->attr()->post_ops_;
+        return p.len_ == 1 && p.entry_[0].is_relu(true, true);
+    }
+
+    bool is_fwd() const {
+        return utils::one_of(desc_.prop_kind, prop_kind::forward_training,
+                prop_kind::forward_inference);
+    }
+    bool is_bwd() const { return !this->is_fwd(); }
+    bool is_training() const
+    { return desc_.prop_kind == prop_kind::forward_training; }
+
+    bool has_zero_dim_memory() const
+    { return memory_desc_wrapper(desc_.data_desc).has_zero_dim(); }
+
+protected:
+    batch_normalization_desc_t desc_;
+    const batch_normalization_fwd_pd_t *hint_fwd_pd_;
+
+    memory_desc_t data_md_;
+    memory_desc_t stat_md_;
+    memory_desc_t scaleshift_md_;
+
+    memory_desc_t ws_md_;
+
+    void init_default_ws(size_t bits_per_element) {
+        const auto data_mdw = memory_desc_wrapper(data_md_);
+
+        const dim_t data_nelems = data_mdw.nelems(true);
+        const dim_t bits_per_byte = 8;
+        const dims_t ws_sz = { (dim_t)utils::div_up(
+                data_nelems * bits_per_element, bits_per_byte) };
+        mkldnn_memory_desc_init_by_tag(&ws_md_, 1, ws_sz, impl::data_type::u8,
+                format_tag::x);
+    }
+
+private:
+    const memory_desc_t &data_desc() const { return desc_.data_desc; }
+};
+
+struct batch_normalization_fwd_pd_t: public batch_normalization_pd_t {
+    typedef batch_normalization_fwd_pd_t base_class;
+    typedef batch_normalization_fwd_pd_t hint_class;
+
+    batch_normalization_fwd_pd_t(engine_t *engine,
+            const batch_normalization_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const batch_normalization_fwd_pd_t *hint_fwd_pd)
+        : batch_normalization_pd_t(engine, adesc, attr, hint_fwd_pd)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (arg == MKLDNN_ARG_SRC) return arg_usage_t::input;
+        if (arg == MKLDNN_ARG_DST) return arg_usage_t::output;
+
+        if (utils::one_of(arg, MKLDNN_ARG_MEAN, MKLDNN_ARG_VARIANCE)) {
+            if (stats_is_src()) return arg_usage_t::input;
+            if (!stats_is_src() && is_training()) return arg_usage_t::output;
+            return arg_usage_t::unused;
+        }
+
+        if (arg == MKLDNN_ARG_SCALE_SHIFT && use_scaleshift())
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_WORKSPACE && is_training() && fuse_bn_relu())
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override {
+        if (index == 0) return &data_md_;
+        if (stats_is_src() && (index == 1 || index == 2)) return &stat_md_;
+        return nullptr;
+    }
+
+    virtual const memory_desc_t *dst_md(int index = 0) const override {
+        if (index == 0) return &data_md_;
+        if (!stats_is_src() && is_training() && (index == 1 || index == 2))
+            return &stat_md_;
+        return nullptr;
+    }
+
+    virtual const memory_desc_t *weights_md(int index = 0) const override
+    { return index == 0 ? &scaleshift_md_ : nullptr; }
+
+    virtual const memory_desc_t *workspace_md(int index = 0) const override
+    { return index == 0 && is_training() && fuse_bn_relu() ? &ws_md_ : nullptr; }
+
+    const memory_desc_t *stat_md() const
+    { return stats_is_src() ? src_md(1) : dst_md(1); }
+
+    virtual int n_inputs() const override
+    { return 1 + 2 * stats_is_src() + use_scaleshift(); }
+    virtual int n_outputs() const override
+    { return 1 + (fuse_bn_relu() + 2 * (!stats_is_src())) * is_training(); }
+};
+
+struct batch_normalization_bwd_pd_t: public batch_normalization_pd_t {
+    typedef batch_normalization_bwd_pd_t base_class;
+    typedef batch_normalization_fwd_pd_t hint_class;
+
+    batch_normalization_bwd_pd_t(engine_t *engine,
+            const batch_normalization_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const batch_normalization_fwd_pd_t *hint_fwd_pd)
+        : batch_normalization_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , diff_data_md_(desc_.diff_data_desc)
+        , diff_scaleshift_md_(desc_.diff_data_scaleshift_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_SRC, MKLDNN_ARG_MEAN,
+                    MKLDNN_ARG_VARIANCE, MKLDNN_ARG_DIFF_DST))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_SCALE_SHIFT && use_scaleshift())
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_WORKSPACE && fuse_bn_relu())
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DIFF_SRC)
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_DIFF_SCALE_SHIFT && use_scaleshift())
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &data_md_ : index <= 2 ? &stat_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 ? &diff_data_md_ : nullptr; }
+    virtual const memory_desc_t *diff_src_md(int index = 0) const override
+    { return index == 0 ? &diff_data_md_ : nullptr; }
+
+    virtual const memory_desc_t *weights_md(int index = 0) const override
+    { return index == 0 ? &scaleshift_md_ : nullptr; }
+    virtual const memory_desc_t *diff_weights_md(int index = 0) const override
+    { return index == 0 ? &diff_scaleshift_md_ : nullptr; }
+
+    virtual const memory_desc_t *workspace_md(int index = 0) const override
+    { return index == 0 && fuse_bn_relu() ? &ws_md_ : nullptr; }
+
+    const memory_desc_t *stat_md() const { return src_md(1); }
+
+    virtual int n_inputs() const override
+    { return 4 + use_scaleshift() + fuse_bn_relu(); }
+    virtual int n_outputs() const override
+    { return 1 + (desc_.prop_kind == prop_kind::backward); }
+
+protected:
+    memory_desc_t diff_data_md_;
+    memory_desc_t diff_scaleshift_md_;
+};
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/c_types_map.hpp b/thirdparty/oidn/mkl-dnn/src/common/c_types_map.hpp
new file mode 100644
index 0000000000..3d43a0fbee
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/c_types_map.hpp
@@ -0,0 +1,550 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef TYPE_MAPPING_HPP
+#define TYPE_MAPPING_HPP
+
+#include "mkldnn_types.h"
+
+namespace mkldnn {
+namespace impl {
+
+// TODO: autogenerate this
+
+using dim_t = mkldnn_dim_t;
+using dims_t = mkldnn_dims_t;
+using stride_t = mkldnn_dim_t;
+using strides_t = mkldnn_strides_t;
+
+using status_t = mkldnn_status_t;
+namespace status {
+    const status_t success = mkldnn_success;
+    const status_t out_of_memory = mkldnn_out_of_memory;
+    const status_t try_again = mkldnn_try_again;
+    const status_t invalid_arguments = mkldnn_invalid_arguments;
+    const status_t not_ready = mkldnn_not_ready;
+    const status_t unimplemented = mkldnn_unimplemented;
+    const status_t iterator_ends = mkldnn_iterator_ends;
+    const status_t runtime_error = mkldnn_runtime_error;
+    const status_t not_required = mkldnn_not_required;
+}
+
+using prop_kind_t = mkldnn_prop_kind_t;
+namespace prop_kind {
+    const prop_kind_t undef = mkldnn_prop_kind_undef;
+    const prop_kind_t forward_training = mkldnn_forward_training;
+    const prop_kind_t forward_inference = mkldnn_forward_inference;
+    const prop_kind_t forward_scoring = mkldnn_forward_scoring;
+    const prop_kind_t forward = mkldnn_forward;
+    const prop_kind_t backward = mkldnn_backward;
+    const prop_kind_t backward_data = mkldnn_backward_data;
+    const prop_kind_t backward_weights = mkldnn_backward_weights;
+    const prop_kind_t backward_bias = mkldnn_backward_bias;
+}
+
+using alg_kind_t = mkldnn_alg_kind_t;
+namespace alg_kind {
+    const alg_kind_t undef = mkldnn_alg_kind_undef;
+    const alg_kind_t convolution_auto = mkldnn_convolution_auto;
+    const alg_kind_t convolution_direct = mkldnn_convolution_direct;
+    const alg_kind_t convolution_winograd = mkldnn_convolution_winograd;
+    const alg_kind_t deconvolution_direct = mkldnn_deconvolution_direct;
+    const alg_kind_t deconvolution_winograd = mkldnn_deconvolution_winograd;
+    const alg_kind_t eltwise_relu = mkldnn_eltwise_relu;
+    const alg_kind_t eltwise_tanh = mkldnn_eltwise_tanh;
+    const alg_kind_t eltwise_elu = mkldnn_eltwise_elu;
+    const alg_kind_t eltwise_square = mkldnn_eltwise_square;
+    const alg_kind_t eltwise_abs = mkldnn_eltwise_abs;
+    const alg_kind_t eltwise_sqrt = mkldnn_eltwise_sqrt;
+    const alg_kind_t eltwise_linear = mkldnn_eltwise_linear;
+    const alg_kind_t eltwise_bounded_relu = mkldnn_eltwise_bounded_relu;
+    const alg_kind_t eltwise_soft_relu = mkldnn_eltwise_soft_relu;
+    const alg_kind_t eltwise_logistic = mkldnn_eltwise_logistic;
+    const alg_kind_t pooling_max = mkldnn_pooling_max;
+    const alg_kind_t pooling_avg = mkldnn_pooling_avg;
+    const alg_kind_t pooling_avg_include_padding = mkldnn_pooling_avg_include_padding;
+    const alg_kind_t pooling_avg_exclude_padding = mkldnn_pooling_avg_exclude_padding;
+    const alg_kind_t lrn_across_channels = mkldnn_lrn_across_channels;
+    const alg_kind_t lrn_within_channel = mkldnn_lrn_within_channel;
+    const alg_kind_t vanilla_rnn = mkldnn_vanilla_rnn;
+    const alg_kind_t vanilla_lstm = mkldnn_vanilla_lstm;
+    const alg_kind_t vanilla_gru = mkldnn_vanilla_gru;
+    const alg_kind_t gru_linear_before_reset = mkldnn_gru_linear_before_reset;
+}
+
+using data_type_t = mkldnn_data_type_t;
+namespace data_type {
+    const data_type_t undef = mkldnn_data_type_undef;
+    const data_type_t f32 = mkldnn_f32;
+    const data_type_t s32 = mkldnn_s32;
+    const data_type_t s8 = mkldnn_s8;
+    const data_type_t u8 = mkldnn_u8;
+}
+
+using scratchpad_mode_t = mkldnn_scratchpad_mode_t;
+namespace scratchpad_mode {
+    const scratchpad_mode_t library = mkldnn_scratchpad_mode_library;
+    const scratchpad_mode_t user = mkldnn_scratchpad_mode_user;
+}
+
+using rnn_packed_format_t = mkldnn_rnn_packed_memory_format_t;
+namespace rnn_packed_format {
+    const rnn_packed_format_t undef = mkldnn_packed_format_undef;
+    const rnn_packed_format_t ldigo_p = mkldnn_ldigo_p;
+    const rnn_packed_format_t ldgoi_p = mkldnn_ldgoi_p;
+}
+
+using format_kind_t = mkldnn_format_kind_t;
+namespace format_kind {
+    const format_kind_t undef = mkldnn_format_kind_undef;
+    const format_kind_t any = mkldnn_format_kind_any;
+    const format_kind_t blocked = mkldnn_blocked;
+    const format_kind_t wino = mkldnn_format_kind_wino;
+    const format_kind_t rnn_packed = mkldnn_format_kind_rnn_packed;
+}
+
+using format_tag_t = mkldnn_format_tag_t;
+namespace format_tag {
+    const format_tag_t undef = mkldnn_format_tag_undef;
+    const format_tag_t any = mkldnn_format_tag_any;
+    const format_tag_t a = mkldnn_a;
+    const format_tag_t ab = mkldnn_ab;
+    const format_tag_t abc = mkldnn_abc;
+    const format_tag_t abcd = mkldnn_abcd;
+    const format_tag_t abcde = mkldnn_abcde;
+    const format_tag_t abcdef = mkldnn_abcdef;
+    const format_tag_t abdec = mkldnn_abdec;
+    const format_tag_t acb = mkldnn_acb;
+    const format_tag_t acbde = mkldnn_acbde;
+    const format_tag_t acdb = mkldnn_acdb;
+    const format_tag_t acdeb = mkldnn_acdeb;
+    const format_tag_t ba = mkldnn_ba;
+    const format_tag_t bac = mkldnn_bac;
+    const format_tag_t bacd = mkldnn_bacd;
+    const format_tag_t bcda = mkldnn_bcda;
+    const format_tag_t cba = mkldnn_cba;
+    const format_tag_t cdba = mkldnn_cdba;
+    const format_tag_t cdeba = mkldnn_cdeba;
+    const format_tag_t decab = mkldnn_decab;
+    const format_tag_t Abc16a = mkldnn_Abc16a;
+    const format_tag_t ABc16a16b = mkldnn_ABc16a16b;
+    const format_tag_t aBc16b = mkldnn_aBc16b;
+    const format_tag_t ABc16b16a = mkldnn_ABc16b16a;
+    const format_tag_t Abc4a = mkldnn_Abc4a;
+    const format_tag_t aBc4b = mkldnn_aBc4b;
+    const format_tag_t ABc4b16a4b = mkldnn_ABc4b16a4b;
+    const format_tag_t ABc4b4a = mkldnn_ABc4b4a;
+    const format_tag_t ABc8a16b2a = mkldnn_ABc8a16b2a;
+    const format_tag_t ABc8a8b = mkldnn_ABc8a8b;
+    const format_tag_t aBc8b = mkldnn_aBc8b;
+    const format_tag_t ABc8b16a2b = mkldnn_ABc8b16a2b;
+    const format_tag_t ABc8b8a = mkldnn_ABc8b8a;
+    const format_tag_t Abcd16a = mkldnn_Abcd16a;
+    const format_tag_t ABcd16a16b = mkldnn_ABcd16a16b;
+    const format_tag_t aBcd16b = mkldnn_aBcd16b;
+    const format_tag_t ABcd16b16a = mkldnn_ABcd16b16a;
+    const format_tag_t aBCd16b16c = mkldnn_aBCd16b16c;
+    const format_tag_t aBCd16c16b = mkldnn_aBCd16c16b;
+    const format_tag_t Abcd4a = mkldnn_Abcd4a;
+    const format_tag_t aBcd4b = mkldnn_aBcd4b;
+    const format_tag_t ABcd4b16a4b = mkldnn_ABcd4b16a4b;
+    const format_tag_t ABcd4b4a = mkldnn_ABcd4b4a;
+    const format_tag_t aBCd4c16b4c = mkldnn_aBCd4c16b4c;
+    const format_tag_t aBCd4c4b = mkldnn_aBCd4c4b;
+    const format_tag_t ABcd8a16b2a = mkldnn_ABcd8a16b2a;
+    const format_tag_t ABcd8a8b = mkldnn_ABcd8a8b;
+    const format_tag_t aBcd8b = mkldnn_aBcd8b;
+    const format_tag_t ABcd8b16a2b = mkldnn_ABcd8b16a2b;
+    const format_tag_t aBCd8b16c2b = mkldnn_aBCd8b16c2b;
+    const format_tag_t ABcd8b8a = mkldnn_ABcd8b8a;
+    const format_tag_t aBCd8b8c = mkldnn_aBCd8b8c;
+    const format_tag_t aBCd8c16b2c = mkldnn_aBCd8c16b2c;
+    const format_tag_t aBCd8c8b = mkldnn_aBCd8c8b;
+    const format_tag_t Abcde16a = mkldnn_Abcde16a;
+    const format_tag_t ABcde16a16b = mkldnn_ABcde16a16b;
+    const format_tag_t aBcde16b = mkldnn_aBcde16b;
+    const format_tag_t ABcde16b16a = mkldnn_ABcde16b16a;
+    const format_tag_t aBCde16b16c = mkldnn_aBCde16b16c;
+    const format_tag_t aBCde16c16b = mkldnn_aBCde16c16b;
+    const format_tag_t aBCde2c8b4c = mkldnn_aBCde2c8b4c;
+    const format_tag_t Abcde4a = mkldnn_Abcde4a;
+    const format_tag_t aBcde4b = mkldnn_aBcde4b;
+    const format_tag_t ABcde4b4a = mkldnn_ABcde4b4a;
+    const format_tag_t aBCde4b4c = mkldnn_aBCde4b4c;
+    const format_tag_t aBCde4c16b4c = mkldnn_aBCde4c16b4c;
+    const format_tag_t aBCde4c4b = mkldnn_aBCde4c4b;
+    const format_tag_t Abcde8a = mkldnn_Abcde8a;
+    const format_tag_t ABcde8a8b = mkldnn_ABcde8a8b;
+    const format_tag_t aBcde8b = mkldnn_aBcde8b;
+    const format_tag_t ABcde8b16a2b = mkldnn_ABcde8b16a2b;
+    const format_tag_t aBCde8b16c2b = mkldnn_aBCde8b16c2b;
+    const format_tag_t ABcde8b8a = mkldnn_ABcde8b8a;
+    const format_tag_t aBCde8b8c = mkldnn_aBCde8b8c;
+    const format_tag_t aBCde8c16b2c = mkldnn_aBCde8c16b2c;
+    const format_tag_t aBCde8c8b = mkldnn_aBCde8c8b;
+    const format_tag_t aBcdef16b = mkldnn_aBcdef16b;
+    const format_tag_t aBCdef16b16c = mkldnn_aBCdef16b16c;
+    const format_tag_t aBCdef16c16b = mkldnn_aBCdef16c16b;
+    const format_tag_t aBcdef4b = mkldnn_aBcdef4b;
+    const format_tag_t aBCdef4c4b = mkldnn_aBCdef4c4b;
+    const format_tag_t aBCdef8b8c = mkldnn_aBCdef8b8c;
+    const format_tag_t aBCdef8c16b2c = mkldnn_aBCdef8c16b2c;
+    const format_tag_t aBCdef8c8b = mkldnn_aBCdef8c8b;
+    const format_tag_t aBdc16b = mkldnn_aBdc16b;
+    const format_tag_t aBdc4b = mkldnn_aBdc4b;
+    const format_tag_t aBdc8b = mkldnn_aBdc8b;
+    const format_tag_t aBdec16b = mkldnn_aBdec16b;
+    const format_tag_t aBdec4b = mkldnn_aBdec4b;
+    const format_tag_t aBdec8b = mkldnn_aBdec8b;
+    const format_tag_t aBdefc16b = mkldnn_aBdefc16b;
+    const format_tag_t aBdefc4b = mkldnn_aBdefc4b;
+    const format_tag_t aBdefc8b = mkldnn_aBdefc8b;
+    const format_tag_t Acb16a = mkldnn_Acb16a;
+    const format_tag_t Acb4a = mkldnn_Acb4a;
+    const format_tag_t Acb8a = mkldnn_Acb8a;
+    const format_tag_t aCBd16b16c = mkldnn_aCBd16b16c;
+    const format_tag_t aCBde16b16c = mkldnn_aCBde16b16c;
+    const format_tag_t Acdb16a = mkldnn_Acdb16a;
+    const format_tag_t Acdb4a = mkldnn_Acdb4a;
+    const format_tag_t Acdb8a = mkldnn_Acdb8a;
+    const format_tag_t Acdeb16a = mkldnn_Acdeb16a;
+    const format_tag_t Acdeb4a = mkldnn_Acdeb4a;
+    const format_tag_t Acdeb8a = mkldnn_Acdeb8a;
+    const format_tag_t BAc16a16b = mkldnn_BAc16a16b;
+    const format_tag_t BAcd16a16b = mkldnn_BAcd16a16b;
+    const format_tag_t last = mkldnn_format_tag_last;
+
+    const format_tag_t x = mkldnn_x;
+    const format_tag_t nc = mkldnn_nc;
+    const format_tag_t cn = mkldnn_cn;
+    const format_tag_t ncw = mkldnn_ncw;
+    const format_tag_t nwc = mkldnn_nwc;
+    const format_tag_t nchw = mkldnn_nchw;
+    const format_tag_t nhwc = mkldnn_nhwc;
+    const format_tag_t chwn = mkldnn_chwn;
+    const format_tag_t ncdhw = mkldnn_ncdhw;
+    const format_tag_t ndhwc = mkldnn_ndhwc;
+    const format_tag_t oi = mkldnn_oi;
+    const format_tag_t io = mkldnn_io;
+    const format_tag_t oiw = mkldnn_oiw;
+    const format_tag_t wio = mkldnn_wio;
+    const format_tag_t oihw = mkldnn_oihw;
+    const format_tag_t hwio = mkldnn_hwio;
+    const format_tag_t ihwo = mkldnn_ihwo;
+    const format_tag_t iohw = mkldnn_iohw;
+    const format_tag_t oidhw = mkldnn_oidhw;
+    const format_tag_t dhwio = mkldnn_dhwio;
+    const format_tag_t goiw = mkldnn_goiw;
+    const format_tag_t goihw = mkldnn_goihw;
+    const format_tag_t hwigo = mkldnn_hwigo;
+    const format_tag_t giohw = mkldnn_giohw;
+    const format_tag_t goidhw = mkldnn_goidhw;
+    const format_tag_t tnc = mkldnn_tnc;
+    const format_tag_t ntc = mkldnn_ntc;
+    const format_tag_t ldsnc = mkldnn_ldsnc;
+    const format_tag_t ldigo = mkldnn_ldigo;
+    const format_tag_t ldgoi = mkldnn_ldgoi;
+    const format_tag_t ldgo = mkldnn_ldgo;
+    const format_tag_t nCdhw16c = mkldnn_nCdhw16c;
+    const format_tag_t nCdhw4c = mkldnn_nCdhw4c;
+    const format_tag_t nCdhw8c = mkldnn_nCdhw8c;
+    const format_tag_t nChw16c = mkldnn_nChw16c;
+    const format_tag_t nChw4c = mkldnn_nChw4c;
+    const format_tag_t nChw8c = mkldnn_nChw8c;
+    const format_tag_t nCw16c = mkldnn_nCw16c;
+    const format_tag_t nCw4c = mkldnn_nCw4c;
+    const format_tag_t nCw8c = mkldnn_nCw8c;
+    const format_tag_t IOw16o16i = mkldnn_IOw16o16i;
+    const format_tag_t OIw16i16o = mkldnn_OIw16i16o;
+    const format_tag_t OIw16o16i = mkldnn_OIw16o16i;
+    const format_tag_t Oiw16o = mkldnn_Oiw16o;
+    const format_tag_t OIw4i16o4i = mkldnn_OIw4i16o4i;
+    const format_tag_t OIw4i4o = mkldnn_OIw4i4o;
+    const format_tag_t Oiw4o = mkldnn_Oiw4o;
+    const format_tag_t OIw8i16o2i = mkldnn_OIw8i16o2i;
+    const format_tag_t OIw8i8o = mkldnn_OIw8i8o;
+    const format_tag_t OIw8o16i2o = mkldnn_OIw8o16i2o;
+    const format_tag_t OIw8o8i = mkldnn_OIw8o8i;
+    const format_tag_t Owi16o = mkldnn_Owi16o;
+    const format_tag_t Owi4o = mkldnn_Owi4o;
+    const format_tag_t Owi8o = mkldnn_Owi8o;
+    const format_tag_t IOhw16o16i = mkldnn_IOhw16o16i;
+    const format_tag_t Ohwi16o = mkldnn_Ohwi16o;
+    const format_tag_t Ohwi4o = mkldnn_Ohwi4o;
+    const format_tag_t Ohwi8o = mkldnn_Ohwi8o;
+    const format_tag_t OIhw16i16o = mkldnn_OIhw16i16o;
+    const format_tag_t OIhw16o16i = mkldnn_OIhw16o16i;
+    const format_tag_t Oihw16o = mkldnn_Oihw16o;
+    const format_tag_t OIhw4i16o4i = mkldnn_OIhw4i16o4i;
+    const format_tag_t OIhw4i4o = mkldnn_OIhw4i4o;
+    const format_tag_t Oihw4o = mkldnn_Oihw4o;
+    const format_tag_t OIhw8i16o2i = mkldnn_OIhw8i16o2i;
+    const format_tag_t OIhw8i8o = mkldnn_OIhw8i8o;
+    const format_tag_t OIhw8o16i2o = mkldnn_OIhw8o16i2o;
+    const format_tag_t OIhw8o8i = mkldnn_OIhw8o8i;
+    const format_tag_t Odhwi16o = mkldnn_Odhwi16o;
+    const format_tag_t Odhwi4o = mkldnn_Odhwi4o;
+    const format_tag_t Odhwi8o = mkldnn_Odhwi8o;
+    const format_tag_t OIdhw16i16o = mkldnn_OIdhw16i16o;
+    const format_tag_t OIdhw16o16i = mkldnn_OIdhw16o16i;
+    const format_tag_t Oidhw16o = mkldnn_Oidhw16o;
+    const format_tag_t OIdhw4i4o = mkldnn_OIdhw4i4o;
+    const format_tag_t Oidhw4o = mkldnn_Oidhw4o;
+    const format_tag_t OIdhw8i16o2i = mkldnn_OIdhw8i16o2i;
+    const format_tag_t OIdhw8i8o = mkldnn_OIdhw8i8o;
+    const format_tag_t OIdhw8o8i = mkldnn_OIdhw8o8i;
+    const format_tag_t gIOw16o16i = mkldnn_gIOw16o16i;
+    const format_tag_t Goiw16g = mkldnn_Goiw16g;
+    const format_tag_t gOIw16i16o = mkldnn_gOIw16i16o;
+    const format_tag_t gOIw16o16i = mkldnn_gOIw16o16i;
+    const format_tag_t gOiw16o = mkldnn_gOiw16o;
+    const format_tag_t gOIw4i16o4i = mkldnn_gOIw4i16o4i;
+    const format_tag_t gOIw4i4o = mkldnn_gOIw4i4o;
+    const format_tag_t gOiw4o = mkldnn_gOiw4o;
+    const format_tag_t gOIw8i16o2i = mkldnn_gOIw8i16o2i;
+    const format_tag_t gOIw8i8o = mkldnn_gOIw8i8o;
+    const format_tag_t gOIw8o16i2o = mkldnn_gOIw8o16i2o;
+    const format_tag_t gOIw8o8i = mkldnn_gOIw8o8i;
+    const format_tag_t gOwi16o = mkldnn_gOwi16o;
+    const format_tag_t gOwi4o = mkldnn_gOwi4o;
+    const format_tag_t gOwi8o = mkldnn_gOwi8o;
+    const format_tag_t gIOhw16o16i = mkldnn_gIOhw16o16i;
+    const format_tag_t gOhwi16o = mkldnn_gOhwi16o;
+    const format_tag_t gOhwi4o = mkldnn_gOhwi4o;
+    const format_tag_t gOhwi8o = mkldnn_gOhwi8o;
+    const format_tag_t Goihw16g = mkldnn_Goihw16g;
+    const format_tag_t gOIhw16i16o = mkldnn_gOIhw16i16o;
+    const format_tag_t gOIhw16o16i = mkldnn_gOIhw16o16i;
+    const format_tag_t gOihw16o = mkldnn_gOihw16o;
+    const format_tag_t gOIhw2i8o4i = mkldnn_gOIhw2i8o4i;
+    const format_tag_t gOIhw4i16o4i = mkldnn_gOIhw4i16o4i;
+    const format_tag_t gOIhw4i4o = mkldnn_gOIhw4i4o;
+    const format_tag_t gOIhw4o4i = mkldnn_gOIhw4o4i;
+    const format_tag_t gOihw4o = mkldnn_gOihw4o;
+    const format_tag_t Goihw8g = mkldnn_Goihw8g;
+    const format_tag_t gOIhw8i16o2i = mkldnn_gOIhw8i16o2i;
+    const format_tag_t gOIhw8i8o = mkldnn_gOIhw8i8o;
+    const format_tag_t gOIhw8o16i2o = mkldnn_gOIhw8o16i2o;
+    const format_tag_t gOIhw8o8i = mkldnn_gOIhw8o8i;
+    const format_tag_t gOdhwi16o = mkldnn_gOdhwi16o;
+    const format_tag_t gOdhwi4o = mkldnn_gOdhwi4o;
+    const format_tag_t gOdhwi8o = mkldnn_gOdhwi8o;
+    const format_tag_t gOIdhw16i16o = mkldnn_gOIdhw16i16o;
+    const format_tag_t gOIdhw16o16i = mkldnn_gOIdhw16o16i;
+    const format_tag_t gOidhw16o = mkldnn_gOidhw16o;
+    const format_tag_t gOIdhw4i4o = mkldnn_gOIdhw4i4o;
+    const format_tag_t gOidhw4o = mkldnn_gOidhw4o;
+    const format_tag_t gOIdhw8i16o2i = mkldnn_gOIdhw8i16o2i;
+    const format_tag_t gOIdhw8i8o = mkldnn_gOIdhw8i8o;
+    const format_tag_t gOIdhw8o8i = mkldnn_gOIdhw8o8i;
+}
+
+using memory_extra_flags_t = mkldnn_memory_extra_flags_t;
+namespace memory_extra_flags {
+    const memory_extra_flags_t none = mkldnn_memory_extra_flag_none;
+    const memory_extra_flags_t compensation_conv_s8s8 = mkldnn_memory_extra_flag_compensation_conv_s8s8;
+    const memory_extra_flags_t scale_adjust = mkldnn_memory_extra_flag_scale_adjust;
+}
+
+using padding_kind_t = mkldnn_padding_kind_t;
+namespace padding_kind {
+    const padding_kind_t padding_zero = mkldnn_padding_zero;
+}
+
+using engine_kind_t = mkldnn_engine_kind_t;
+namespace engine_kind {
+    const engine_kind_t any_engine = mkldnn_any_engine;
+    const engine_kind_t cpu = mkldnn_cpu;
+}
+
+using primitive_kind_t = mkldnn_primitive_kind_t;
+namespace primitive_kind {
+    const primitive_kind_t undefined = mkldnn_undefined_primitive;
+    const primitive_kind_t reorder = mkldnn_reorder;
+    const primitive_kind_t concat = mkldnn_concat;
+    const primitive_kind_t sum = mkldnn_sum;
+    const primitive_kind_t convolution = mkldnn_convolution;
+    const primitive_kind_t deconvolution = mkldnn_deconvolution;
+    const primitive_kind_t shuffle = mkldnn_shuffle;
+    const primitive_kind_t eltwise = mkldnn_eltwise;
+    const primitive_kind_t softmax = mkldnn_softmax;
+    const primitive_kind_t pooling = mkldnn_pooling;
+    const primitive_kind_t lrn = mkldnn_lrn;
+    const primitive_kind_t batch_normalization = mkldnn_batch_normalization;
+    const primitive_kind_t inner_product = mkldnn_inner_product;
+    const primitive_kind_t rnn = mkldnn_rnn;
+}
+
+using query_t = mkldnn_query_t;
+namespace query {
+    const query_t undef = mkldnn_query_undef;
+
+    const query_t engine = mkldnn_query_engine;
+    const query_t primitive_kind = mkldnn_query_primitive_kind;
+
+    const query_t num_of_inputs_s32 = mkldnn_query_num_of_inputs_s32;
+    const query_t num_of_outputs_s32 = mkldnn_query_num_of_outputs_s32;
+
+    const query_t time_estimate_f64 = mkldnn_query_time_estimate_f64;
+    const query_t memory_consumption_s64 = mkldnn_query_memory_consumption_s64;
+
+    const query_t scratchpad_engine = mkldnn_query_scratchpad_engine;
+
+    const query_t impl_info_str = mkldnn_query_impl_info_str;
+
+    const query_t some_d = mkldnn_query_some_d;
+    const query_t op_d = mkldnn_query_op_d;
+    const query_t convolution_d = mkldnn_query_convolution_d;
+    const query_t deconvolution_d = mkldnn_query_deconvolution_d;
+    const query_t shuffle_d = mkldnn_query_shuffle_d;
+    const query_t eltwise_d = mkldnn_query_eltwise_d;
+    const query_t softmax_d = mkldnn_query_softmax_d;
+    const query_t pooling_d = mkldnn_query_pooling_d;
+    const query_t lrn_d = mkldnn_query_lrn_d;
+    const query_t batch_normalization_d = mkldnn_query_batch_normalization_d;
+    const query_t inner_product_d = mkldnn_query_inner_product_d;
+    const query_t rnn_d = mkldnn_query_rnn_d;
+
+    const query_t some_md = mkldnn_query_some_md;
+    const query_t src_md = mkldnn_query_src_md;
+    const query_t diff_src_md = mkldnn_query_diff_src_md;
+    const query_t weights_md = mkldnn_query_weights_md;
+    const query_t diff_weights_md = mkldnn_query_diff_weights_md;
+    const query_t dst_md = mkldnn_query_dst_md;
+    const query_t diff_dst_md = mkldnn_query_diff_dst_md;
+
+    const query_t workspace_md = mkldnn_query_workspace_md;
+    const query_t scratchpad_md = mkldnn_query_scratchpad_md;
+}
+
+using blocking_desc_t = mkldnn_blocking_desc_t;
+using rnn_packed_desc_t = mkldnn_rnn_packed_desc_t;
+using wino_desc_t = mkldnn_wino_desc_t;
+using memory_extra_desc_t = mkldnn_memory_extra_desc_t;
+using memory_desc_t = mkldnn_memory_desc_t;
+using convolution_desc_t = mkldnn_convolution_desc_t;
+using deconvolution_desc_t = mkldnn_deconvolution_desc_t;
+using shuffle_desc_t = mkldnn_shuffle_desc_t;
+using pooling_desc_t = mkldnn_pooling_desc_t;
+using eltwise_desc_t = mkldnn_eltwise_desc_t;
+using softmax_desc_t = mkldnn_softmax_desc_t;
+using lrn_desc_t = mkldnn_lrn_desc_t;
+using batch_normalization_desc_t = mkldnn_batch_normalization_desc_t;
+using inner_product_desc_t = mkldnn_inner_product_desc_t;
+
+using rnn_direction_t = mkldnn_rnn_direction_t;
+using rnn_cell_desc_t = mkldnn_rnn_cell_desc_t;
+using rnn_desc_t = mkldnn_rnn_desc_t;
+
+/* C op_desc_t, which eventually are just (void*) */
+using c_op_desc_t = mkldnn_op_desc_t;
+using const_c_op_desc_t = const_mkldnn_op_desc_t;
+
+struct op_desc_t {
+    union {
+        primitive_kind_t kind;
+        convolution_desc_t convolution;
+        deconvolution_desc_t deconvolution;
+        shuffle_desc_t shuffle;
+        pooling_desc_t pooling;
+        eltwise_desc_t eltwise;
+        softmax_desc_t softmax;
+        lrn_desc_t lrn;
+        batch_normalization_desc_t batch_normalization;
+        inner_product_desc_t inner_product;
+        rnn_desc_t rnn;
+    };
+
+    op_desc_t(const primitive_kind_t &_): kind(_) {}
+
+#   define DECL_CTOR_AND_CONVERTERS(c_type, name) \
+    op_desc_t(const c_type &_): name(_) {} \
+    static op_desc_t *convert_from_c(c_type *_) \
+    { return reinterpret_cast<op_desc_t*>(_); } \
+    static const op_desc_t *convert_from_c(const c_type *_) \
+    { return reinterpret_cast<const op_desc_t*>(_); }
+
+    DECL_CTOR_AND_CONVERTERS(convolution_desc_t, convolution);
+    DECL_CTOR_AND_CONVERTERS(shuffle_desc_t, shuffle);
+    DECL_CTOR_AND_CONVERTERS(pooling_desc_t, pooling);
+    DECL_CTOR_AND_CONVERTERS(eltwise_desc_t, eltwise);
+    DECL_CTOR_AND_CONVERTERS(softmax_desc_t, softmax);
+    DECL_CTOR_AND_CONVERTERS(lrn_desc_t, lrn);
+    DECL_CTOR_AND_CONVERTERS(batch_normalization_desc_t, batch_normalization);
+    DECL_CTOR_AND_CONVERTERS(inner_product_desc_t, inner_product);
+    DECL_CTOR_AND_CONVERTERS(rnn_desc_t, rnn);
+
+#   undef DECL_CTOR_AND_CONVERTERS
+};
+
+using engine_t = mkldnn_engine;
+using primitive_desc_iterator_t = mkldnn_primitive_desc_iterator;
+using primitive_desc_t = mkldnn_primitive_desc;
+using primitive_attr_t = mkldnn_primitive_attr;
+using post_ops_t = mkldnn_post_ops;
+using memory_t = mkldnn_memory;
+using primitive_t = mkldnn_primitive;
+
+using primitive_arg_index_t = int;
+
+using stream_flags_t = mkldnn_stream_flags_t;
+namespace stream_flags {
+    const stream_flags_t default_flags = mkldnn_stream_default_flags;
+}
+using stream_t = mkldnn_stream;
+
+/* forward declaration of the internal primitive_desc types */
+struct batch_normalization_bwd_pd_t;
+struct batch_normalization_fwd_pd_t;
+struct batch_normalization_pd_t;
+struct concat_pd_t;
+struct convolution_bwd_data_pd_t;
+struct convolution_bwd_weights_pd_t;
+struct convolution_fwd_pd_t;
+struct convolution_pd_t;
+struct deconvolution_bwd_data_pd_t;
+struct deconvolution_bwd_weights_pd_t;
+struct deconvolution_fwd_pd_t;
+struct deconvolution_pd_t;
+struct eltwise_bwd_pd_t;
+struct eltwise_fwd_pd_t;
+struct eltwise_pd_t;
+struct inner_product_bwd_data_pd_t;
+struct inner_product_bwd_weights_pd_t;
+struct inner_product_fwd_pd_t;
+struct inner_product_pd_t;
+struct lrn_bwd_pd_t;
+struct lrn_fwd_pd_t;
+struct lrn_pd_t;
+struct pooling_bwd_pd_t;
+struct pooling_fwd_pd_t;
+struct pooling_pd_t;
+struct reorder_pd_t;
+struct rnn_bwd_pd_t;
+struct rnn_fwd_pd_t;
+struct rnn_pd_t;
+struct shuffle_pd_t;
+struct softmax_bwd_pd_t;
+struct softmax_fwd_pd_t;
+struct softmax_pd_t;
+struct sum_pd_t;
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/concat.cpp b/thirdparty/oidn/mkl-dnn/src/common/concat.cpp
new file mode 100644
index 0000000000..ed4c35c6e9
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/concat.cpp
@@ -0,0 +1,86 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "engine.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "concat_pd.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+
+status_t mkldnn_concat_primitive_desc_create(primitive_desc_t **concat_pd,
+        const memory_desc_t *dst_md, int n, int concat_dim,
+        const memory_desc_t *src_mds,
+        const primitive_attr_t *attr,
+        engine_t *engine) {
+    bool args_ok = !any_null(concat_pd, src_mds) && n > 0;
+    if (!args_ok) return invalid_arguments;
+
+    const primitive_attr_t dummy_attr;
+    if (attr == NULL)
+        attr = &dummy_attr;
+
+    const int ndims = src_mds[0].ndims;
+    const dims_t &dims = src_mds[0].dims;
+    const data_type_t dt = src_mds[0].data_type;
+
+    int concat_dim_sz = dims[concat_dim];
+    for (int i = 1; i < n; ++i) {
+        if (src_mds[i].ndims != ndims) return invalid_arguments;
+        for (int d = 0; d < ndims; ++d) {
+            if (d == concat_dim) continue;
+            if (src_mds[i].dims[d] != dims[d])
+                return invalid_arguments;
+        }
+        if (src_mds[i].data_type != dt) return invalid_arguments;
+        concat_dim_sz += src_mds[i].dims[concat_dim];
+    }
+
+    memory_desc_t dummy_dst_md;
+    if (dst_md) {
+        if (dst_md->ndims != ndims) return invalid_arguments;
+        for (int d = 0; d < ndims; ++d) {
+            if (dst_md->dims[d] !=
+                    (d == concat_dim ? concat_dim_sz : dims[d]))
+                return invalid_arguments;
+        }
+    } else {
+        dummy_dst_md = src_mds[0];
+        dummy_dst_md.dims[concat_dim] = concat_dim_sz;
+        dummy_dst_md.format_kind = format_kind::any;
+        dst_md = &dummy_dst_md;
+    }
+
+    auto c_pd = reinterpret_cast<concat_pd_t **>(concat_pd);
+
+    for (auto c = engine->get_concat_implementation_list(); *c; ++c) {
+        if ((*c)(c_pd, engine, attr, dst_md, n, concat_dim, src_mds)
+                == success) {
+            (*c_pd)->init_info();
+            (*c_pd)->init_scratchpad_md();
+            return success;
+        }
+    }
+    return unimplemented;
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/concat_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/concat_pd.hpp
new file mode 100644
index 0000000000..29311927e2
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/concat_pd.hpp
@@ -0,0 +1,211 @@
+/*******************************************************************************
+* Copyright 2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CONCAT_PD_HPP
+#define CONCAT_PD_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "primitive_desc.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct concat_pd_t: public primitive_desc_t {
+    concat_pd_t(engine_t *engine, const primitive_attr_t *attr,
+            const memory_desc_t *dst_md, int n, int concat_dim,
+            const memory_desc_t *src_mds)
+        : primitive_desc_t(engine, attr, primitive_kind::concat)
+        , n_(n), concat_dim_(concat_dim), dst_md_(*dst_md)
+    {
+        src_mds_.reserve(n_);
+        for (int i = 0; i < n_; ++i) src_mds_.push_back(src_mds[i]);
+    }
+
+    concat_pd_t(const concat_pd_t &rhs) = default;
+
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (arg >= MKLDNN_ARG_MULTIPLE_SRC
+                && arg < MKLDNN_ARG_MULTIPLE_SRC + n_inputs())
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DST)
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index < n_inputs() ? &src_mds_[index] : nullptr; }
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 ? &dst_md_ : nullptr; }
+
+    virtual int n_inputs() const override { return n_; }
+    virtual int n_outputs() const override { return 1; }
+
+    int concat_dim() const { return concat_dim_; }
+
+    const memory_desc_t *src_image_md(int index = 0) const
+    { return index < n_inputs() ? &src_image_mds_[index] : nullptr; }
+
+protected:
+    int n_, concat_dim_;
+    memory_desc_t dst_md_;
+    nstl::vector<memory_desc_t> src_mds_;
+
+    /* contains images of srcs in the dst memory (if possible)
+     * Lives here to simplify some implementations. An implementation might
+     * use this auxiliary array iff init() returned success */
+    nstl::vector<memory_desc_t> src_image_mds_;
+
+protected:
+    /* inits src_image_mds_ and dst_md_ in simple cases. The call may fail */
+    status_t init() {
+        bool ok = true
+            && set_default_params() == status::success
+            && attr()->has_default_values();
+        if (!ok) return status::unimplemented;
+
+        for (int i = 0; i < n_; ++i) {
+            const memory_desc_wrapper i_d(&src_mds_[i]);
+            if (!i_d.is_blocking_desc() || i_d.is_additional_buffer())
+                return status::unimplemented;
+        }
+
+        const int ndims = dst_md_.ndims;
+        int current_concat_dim_offset = 0;
+        for (int i = 0; i < n_; ++i) {
+            const int dim = src_mds_[i].dims[concat_dim_];
+            dims_t dims, offsets = {};
+            utils::array_copy(dims, dst_md_.dims, ndims);
+            dims[concat_dim_] = dim;
+            offsets[concat_dim_] = current_concat_dim_offset;
+
+            memory_desc_t src_img_d;
+            status_t status = mkldnn_memory_desc_init_submemory(&src_img_d,
+                    &dst_md_, dims, offsets);
+            if (status != status::success) return status;
+            src_image_mds_.push_back(src_img_d);
+            current_concat_dim_offset += dim;
+        }
+
+        return status::success;
+    }
+
+    status_t set_default_params() {
+        if (dst_md_.format_kind != format_kind::any)
+            return status::success;
+
+        const int ndims = dst_md_.ndims;
+
+        /* The stupidest ever heuristics (but not the same as we had before):
+         *  - Pick the first non-plain format;
+         *  - If all formats are plain or it is not possible to create a
+         *    blocked format for the output, pick the format of the plain input
+         *  - If this fails as well, use plain layout (abcd...)
+         */
+        status_t status = status::unimplemented;
+        for (int i = 0; i < n_; ++i) {
+            const memory_desc_wrapper src_d(src_mds_[i]);
+            if (src_d.is_blocking_desc() && !src_d.is_plain()) {
+                status = memory_desc_init_by_blocking_desc(dst_md_,
+                        src_d.blocking_desc());
+                if (status == status::success) break;
+            }
+        }
+
+        if (status == status::success) {
+            /* check if we can create a sub-memory for the dst */
+            bool desired_format_ok = true;
+            int current_concat_dim_offset = 0;
+            for (int i = 0; i < n_; ++i) {
+                const int dim = src_mds_[i].dims[concat_dim_];
+                dims_t dims, offsets = {};
+                utils::array_copy(dims, dst_md_.dims, ndims);
+                dims[concat_dim_] = dim;
+                offsets[concat_dim_] = current_concat_dim_offset;
+
+                memory_desc_t src_img_d;
+                status_t status = mkldnn_memory_desc_init_submemory(&src_img_d,
+                        &dst_md_, dims, offsets);
+                if (status != status::success) {
+                    desired_format_ok = false;
+                    break;
+                }
+                current_concat_dim_offset += dim;
+            }
+
+            if (!desired_format_ok)
+                status = status::unimplemented;
+        }
+
+        /* if no success so far, try using the format of the first plain input */
+        if (status != status::success) {
+            for (int i = 0; i < n_; ++i) {
+                const memory_desc_wrapper src_d(src_mds_[i]);
+                if (src_d.is_blocking_desc() && src_d.is_plain()) {
+                    status = memory_desc_init_by_blocking_desc(dst_md_,
+                            memory_desc_wrapper(src_mds_[0]).blocking_desc());
+                    if (status == status::success) return status;
+                }
+            }
+        }
+
+        /* the last line of defense: use plain abcd... format */
+        if (status != status::success)
+            status = memory_desc_init_by_strides(dst_md_, nullptr);
+
+        return status;
+    }
+};
+
+#define DECLARE_CONCAT_PD_t(impl_name, ...) \
+    static status_t create(concat_pd_t **concat_pd, \
+            engine_t *engine, const primitive_attr_t *attr, \
+            const memory_desc_t *dst_md, int n, int concat_dim, \
+            const memory_desc_t *src_mds) { \
+        using namespace status; \
+        auto _pd = new pd_t(engine, attr, dst_md, n, concat_dim, src_mds); \
+        if (_pd == nullptr) return out_of_memory; \
+        if (_pd->init() != success) { delete _pd; return unimplemented; } \
+        return safe_ptr_assign<concat_pd_t>(*concat_pd, _pd); \
+    } \
+    virtual status_t create_primitive(primitive_t **p) const override { \
+        double ms = get_msec(); \
+        auto ret = safe_ptr_assign<primitive_t>(*p, new (__VA_ARGS__)(this)); \
+        ms = get_msec() - ms; \
+        if (mkldnn_verbose()->level >= 2) { \
+            printf("mkldnn_verbose,create,%s,%g\n", this->info(), ms); \
+            fflush(0); \
+        } \
+        return ret; \
+    } \
+    virtual pd_t *clone() const override { return new pd_t(*this); } \
+    virtual const char *name() const override { return impl_name; } \
+
+#define DECLARE_CONCAT_PD_T(impl_name, ...) \
+    DECLARE_CONCAT_PD_t(impl_name, __VA_ARGS__)
+
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/convolution.cpp b/thirdparty/oidn/mkl-dnn/src/common/convolution.cpp
new file mode 100644
index 0000000000..0c5c02bcd1
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/convolution.cpp
@@ -0,0 +1,200 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::alg_kind;
+using namespace mkldnn::impl::types;
+
+namespace mkldnn {
+namespace impl {
+status_t conv_desc_init(convolution_desc_t *conv_desc,
+        prop_kind_t prop_kind, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *weights_desc,
+        const memory_desc_t *bias_desc, const memory_desc_t *dst_desc,
+        const dims_t strides, const dims_t dilates,
+        const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    bool args_ok = true
+        && !any_null(conv_desc, src_desc, weights_desc, dst_desc, strides,
+                padding_l)
+        && one_of(alg_kind, convolution_auto, convolution_direct, convolution_winograd)
+        && one_of(padding_kind, padding_kind::padding_zero);
+    if (!args_ok) return invalid_arguments;
+
+    if (padding_r == nullptr) padding_r = padding_l;
+
+    auto cd = convolution_desc_t();
+    cd.primitive_kind = primitive_kind::convolution;
+    cd.prop_kind = prop_kind;
+    cd.alg_kind = alg_kind;
+
+    cd.diff_src_desc = cd.src_desc = zero_md();
+    cd.diff_dst_desc = cd.dst_desc = zero_md();
+    cd.diff_weights_desc = cd.weights_desc = zero_md();
+    cd.diff_bias_desc = cd.bias_desc = zero_md();
+
+    const bool is_fwd = one_of(prop_kind, forward_training, forward_inference);
+    const bool with_bias =
+        bias_desc && bias_desc->format_kind != format_kind::undef;
+    const bool with_groups = weights_desc->ndims == src_desc->ndims + 1;
+
+    (prop_kind == backward_data ? cd.diff_src_desc : cd.src_desc) = *src_desc;
+    (is_fwd ? cd.dst_desc : cd.diff_dst_desc)  = *dst_desc;
+    (prop_kind == backward_weights ? cd.diff_weights_desc : cd.weights_desc) =
+        *weights_desc;
+    if (with_bias)
+        (prop_kind == backward_weights ? cd.diff_bias_desc : cd.bias_desc) =
+            *bias_desc;
+
+    int sp_dims = src_desc->ndims - 2;
+    utils::array_copy(cd.strides, strides, sp_dims);
+    utils::array_copy(cd.padding[0], padding_l, sp_dims);
+    utils::array_copy(cd.padding[1], padding_r, sp_dims);
+    if (dilates)
+        utils::array_copy(cd.dilates, dilates, sp_dims);
+    else
+        utils::array_set(cd.dilates, 0, sp_dims);
+
+    cd.padding_kind = padding_kind;
+    cd.accum_data_type = types::default_accum_data_type(src_desc->data_type,
+            weights_desc->data_type, dst_desc->data_type, prop_kind);
+
+    const int g = with_groups ? weights_desc->dims[0] : 1;
+    const int bias_dim = prop_kind == backward_data
+        ? src_desc->dims[1]
+        : dst_desc->dims[1];
+
+    bool consistency = true
+        && memory_desc_wrapper(weights_desc).nelems()
+        && src_desc->ndims == dst_desc->ndims
+        && utils::one_of(src_desc->ndims, 3, 4, 5)
+        && utils::one_of(weights_desc->ndims, src_desc->ndims,
+                src_desc->ndims + 1)
+        && (with_bias ? bias_desc->ndims == 1 : true)
+        && (with_bias ? bias_desc->dims[0] == bias_dim : true)
+        && src_desc->dims[0] == dst_desc->dims[0]
+        && src_desc->dims[1] == g * weights_desc->dims[with_groups + 1]
+        && dst_desc->dims[1] == g * weights_desc->dims[with_groups + 0];
+    for (int i = 2; i < src_desc->ndims; ++i)
+    {
+        int src = src_desc->dims[i];
+        int ker = weights_desc->dims[with_groups + i];
+        int dil = cd.dilates[i - 2];
+        int pad_l = padding_l[i - 2];
+        int pad_r = padding_r[i - 2];
+        int str = strides[i - 2];
+        int dst = dst_desc->dims[i];
+        int ker_range = 1 + (ker - 1) * (dil + 1);
+
+        if (str < 1) return invalid_arguments;
+        consistency = consistency
+            && dil >= 0
+            && pad_l >= 0
+            && pad_r + str > 0
+            && (src - ker_range + pad_l + pad_r) / str + 1 == dst;
+    }
+    if (!consistency) return invalid_arguments;
+
+    *conv_desc = cd;
+    return success;
+}
+}
+}
+
+status_t mkldnn_convolution_forward_desc_init(convolution_desc_t *conv_desc,
+        prop_kind_t prop_kind, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *weights_desc,
+        const memory_desc_t *bias_desc, const memory_desc_t *dst_desc,
+        const dims_t strides, const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    if (!one_of(prop_kind, forward_training, forward_inference))
+        return invalid_arguments;
+    return mkldnn::impl::conv_desc_init(conv_desc, prop_kind, alg_kind, src_desc,
+            weights_desc, bias_desc, dst_desc, strides, nullptr,
+            padding_l, padding_r, padding_kind);
+}
+
+status_t mkldnn_dilated_convolution_forward_desc_init(
+        convolution_desc_t *conv_desc, prop_kind_t prop_kind,
+        alg_kind_t alg_kind, const memory_desc_t *src_desc,
+        const memory_desc_t *weights_desc, const memory_desc_t *bias_desc,
+        const memory_desc_t *dst_desc, const dims_t strides,
+        const dims_t dilates, const dims_t padding_l,
+        const dims_t padding_r, padding_kind_t padding_kind) {
+    if (!one_of(prop_kind, forward_training, forward_inference))
+        return invalid_arguments;
+    return mkldnn::impl::conv_desc_init(conv_desc, prop_kind, alg_kind, src_desc,
+            weights_desc, bias_desc, dst_desc, strides, dilates,
+            padding_l, padding_r, padding_kind);
+}
+
+status_t mkldnn_convolution_backward_data_desc_init(
+        convolution_desc_t *conv_desc, alg_kind_t alg_kind,
+        const memory_desc_t *diff_src_desc, const memory_desc_t *weights_desc,
+        const memory_desc_t *diff_dst_desc, const dims_t strides,
+        const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    return mkldnn::impl::conv_desc_init(conv_desc, backward_data, alg_kind, diff_src_desc,
+            weights_desc, nullptr, diff_dst_desc, strides, nullptr,
+            padding_l, padding_r, padding_kind);
+}
+
+status_t mkldnn_dilated_convolution_backward_data_desc_init(
+        convolution_desc_t *conv_desc, alg_kind_t alg_kind,
+        const memory_desc_t *diff_src_desc, const memory_desc_t *weights_desc,
+        const memory_desc_t *diff_dst_desc, const dims_t strides,
+        const dims_t dilates, const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    return mkldnn::impl::conv_desc_init(conv_desc, backward_data, alg_kind, diff_src_desc,
+            weights_desc, nullptr, diff_dst_desc, strides, dilates,
+            padding_l, padding_r, padding_kind);
+}
+
+status_t mkldnn_convolution_backward_weights_desc_init(
+        convolution_desc_t *conv_desc, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *diff_weights_desc,
+        const memory_desc_t *diff_bias_desc,
+        const memory_desc_t *diff_dst_desc, const dims_t strides,
+        const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    return mkldnn::impl::conv_desc_init(conv_desc, backward_weights, alg_kind, src_desc,
+            diff_weights_desc, diff_bias_desc, diff_dst_desc, strides,
+            nullptr, padding_l, padding_r, padding_kind);
+}
+
+status_t mkldnn_dilated_convolution_backward_weights_desc_init(
+        convolution_desc_t *conv_desc, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *diff_weights_desc,
+        const memory_desc_t *diff_bias_desc,
+        const memory_desc_t *diff_dst_desc, const dims_t strides,
+        const dims_t dilates, const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    return mkldnn::impl::conv_desc_init(conv_desc, backward_weights, alg_kind, src_desc,
+            diff_weights_desc, diff_bias_desc, diff_dst_desc, strides,
+            dilates, padding_l, padding_r, padding_kind);
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/convolution_pd.cpp b/thirdparty/oidn/mkl-dnn/src/common/convolution_pd.cpp
new file mode 100644
index 0000000000..9604e0acf5
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/convolution_pd.cpp
@@ -0,0 +1,56 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "utils.hpp"
+
+#include "convolution_pd.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+using namespace prop_kind;
+
+memory_desc_t *conv_prop_invariant_src_d(convolution_desc_t *desc) {
+    return desc->prop_kind == backward_data
+        ? &desc->diff_src_desc : &desc->src_desc;
+}
+
+memory_desc_t *conv_prop_invariant_wei_d(convolution_desc_t *desc) {
+    return desc->prop_kind == backward_weights
+        ? &desc->diff_weights_desc : &desc->weights_desc;
+}
+
+memory_desc_t *conv_prop_invariant_bia_d(convolution_desc_t *desc) {
+    return desc->prop_kind == backward_weights
+        ? &desc->diff_bias_desc : &desc->bias_desc;
+}
+
+memory_desc_t *conv_prop_invariant_dst_d(convolution_desc_t *desc) {
+    return utils::one_of(desc->prop_kind, forward_inference, forward_training)
+        ? &desc->dst_desc : &desc->diff_dst_desc;
+}
+
+const memory_desc_t *conv_prop_invariant_src_d(const convolution_desc_t *desc)
+{ return conv_prop_invariant_src_d(const_cast<convolution_desc_t *>(desc)); }
+const memory_desc_t *conv_prop_invariant_wei_d(const convolution_desc_t *desc)
+{ return conv_prop_invariant_wei_d(const_cast<convolution_desc_t *>(desc)); }
+const memory_desc_t *conv_prop_invariant_bia_d(const convolution_desc_t *desc)
+{ return conv_prop_invariant_bia_d(const_cast<convolution_desc_t *>(desc)); }
+const memory_desc_t *conv_prop_invariant_dst_d(const convolution_desc_t *desc)
+{ return conv_prop_invariant_dst_d(const_cast<convolution_desc_t *>(desc)); }
+
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/convolution_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/convolution_pd.hpp
new file mode 100644
index 0000000000..b10c36db49
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/convolution_pd.hpp
@@ -0,0 +1,348 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CONVOLUTION_PD_HPP
+#define CONVOLUTION_PD_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "primitive_desc.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+status_t conv_desc_init(convolution_desc_t *conv_desc,
+        prop_kind_t prop_kind, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *weights_desc,
+        const memory_desc_t *bias_desc, const memory_desc_t *dst_desc,
+        const dims_t strides, const dims_t dilates,
+        const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind);
+
+memory_desc_t *conv_prop_invariant_src_d(convolution_desc_t *desc);
+memory_desc_t *conv_prop_invariant_wei_d(convolution_desc_t *desc);
+memory_desc_t *conv_prop_invariant_bia_d(convolution_desc_t *desc);
+memory_desc_t *conv_prop_invariant_dst_d(convolution_desc_t *desc);
+const memory_desc_t *conv_prop_invariant_src_d(const convolution_desc_t *desc);
+const memory_desc_t *conv_prop_invariant_wei_d(const convolution_desc_t *desc);
+const memory_desc_t *conv_prop_invariant_bia_d(const convolution_desc_t *desc);
+const memory_desc_t *conv_prop_invariant_dst_d(const convolution_desc_t *desc);
+
+struct convolution_fwd_pd_t;
+
+struct convolution_pd_t: public primitive_desc_t {
+    static constexpr auto base_pkind = primitive_kind::convolution;
+
+    convolution_pd_t(engine_t *engine,
+            const convolution_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const convolution_fwd_pd_t *hint_fwd_pd)
+        : primitive_desc_t(engine, attr, base_pkind)
+        , desc_(*adesc)
+        , hint_fwd_pd_(hint_fwd_pd)
+    {}
+
+    const convolution_desc_t *desc() const { return &desc_; }
+    virtual const op_desc_t *op_desc() const override
+    { return reinterpret_cast<const op_desc_t *>(this->desc()); }
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual status_t query(query_t what, int idx, void *result) const override {
+        switch (what) {
+        case pkind_traits<base_pkind>::query_d:
+            *(const convolution_desc_t**)result = desc(); break;
+        default: return primitive_desc_t::query(what, idx, result);
+        }
+        return status::success;
+    }
+
+    /* common conv aux functions */
+
+    dim_t MB() const { return _src_md()->dims[0]; }
+
+    dim_t IC() const { return _src_md()->dims[1]; }
+    dim_t OC() const { return _dst_md()->dims[1]; }
+    dim_t G() const { return with_groups() ? _wei_md()->dims[0] : 1; }
+
+    dim_t ID() const { return ndims() >= 5 ? _src_md()->dims[ndims() - 3] : 1; }
+    dim_t IH() const { return ndims() >= 4 ? _src_md()->dims[ndims() - 2] : 1; }
+    dim_t IW() const { return _src_md()->dims[ndims() - 1]; }
+
+    dim_t OD() const { return ndims() >= 5 ? _dst_md()->dims[ndims() - 3] : 1; }
+    dim_t OH() const { return ndims() >= 4 ? _dst_md()->dims[ndims() - 2] : 1; }
+    dim_t OW() const { return _dst_md()->dims[ndims() - 1]; }
+
+    dim_t KD() const { return ndims() >= 5 ? _wei_md()->dims[ndims() + with_groups() - 3] : 1; }
+    dim_t KH() const { return ndims() >= 4 ? _wei_md()->dims[ndims() + with_groups() - 2] : 1; }
+    dim_t KW() const { return _wei_md()->dims[ndims() + with_groups() - 1]; }
+
+    dim_t KSD() const { return ndims() >= 5 ? desc_.strides[ndims() - 5] : 1; }
+    dim_t KSH() const { return ndims() >= 4 ? desc_.strides[ndims() - 4] : 1; }
+    dim_t KSW() const { return desc_.strides[ndims() - 3]; }
+
+    dim_t KDD() const { return ndims() >= 5 ? desc_.dilates[ndims() - 5] : 0; }
+    dim_t KDH() const { return ndims() >= 4 ? desc_.dilates[ndims() - 4] : 1; }
+    dim_t KDW() const { return desc_.dilates[ndims() - 3]; }
+
+    dim_t padFront() const { return ndims() >= 5 ? desc_.padding[0][ndims() - 5] : 0; }
+    dim_t padBack() const { return ndims() >= 5 ? desc_.padding[1][ndims() - 5] : 0; }
+    dim_t padT() const { return ndims() >= 4 ? desc_.padding[0][ndims() - 4] : 0; }
+    dim_t padB() const { return ndims() >= 4 ? desc_.padding[1][ndims() - 4] : 0; }
+    dim_t padL() const { return desc_.padding[0][ndims() - 3]; }
+    dim_t padR() const { return desc_.padding[1][ndims() - 3]; }
+
+    int ndims() const { return _src_md()->ndims; }
+
+    bool with_bias() const { return !memory_desc_wrapper(*_bia_md()).is_zero(); }
+    bool with_groups() const { return _wei_md()->ndims == ndims() + 1; }
+
+    bool is_fwd() const {
+        return utils::one_of(desc_.prop_kind, prop_kind::forward_training,
+                prop_kind::forward_inference);
+    }
+
+    bool has_zero_dim_memory() const {
+        const auto s_d = memory_desc_wrapper(*_src_md());
+        const auto d_d = memory_desc_wrapper(*_dst_md());
+        return s_d.has_zero_dim() || d_d.has_zero_dim();
+    }
+
+protected:
+    convolution_desc_t desc_;
+    const convolution_fwd_pd_t *hint_fwd_pd_;
+
+    bool set_default_formats_common_template(
+            memory_desc_t &src_md, format_tag_t src_tag,
+            memory_desc_t &wei_md, format_tag_t wei_tag,
+            memory_desc_t &dst_md, format_tag_t dst_tag,
+            memory_desc_t &bia_md) {
+        using namespace format_tag;
+
+#       define IS_OK(f) \
+        do { if ((f) != status::success) return false; } while(0)
+        if (src_md.format_kind == format_kind::any
+                && !utils::one_of(src_tag, any, undef))
+            IS_OK(memory_desc_init_by_tag(src_md, src_tag));
+        if (dst_md.format_kind == format_kind::any
+                && !utils::one_of(dst_tag, any, undef))
+            IS_OK(memory_desc_init_by_tag(dst_md, dst_tag));
+        if (wei_md.format_kind == format_kind::any
+                && !utils::one_of(wei_tag, any, undef))
+            IS_OK(memory_desc_init_by_tag(wei_md, wei_tag));
+        if (with_bias() && bia_md.format_kind == format_kind::any)
+            IS_OK(memory_desc_init_by_tag(bia_md, x));
+#       undef IS_OK
+
+        return true;
+    }
+
+    bool set_default_alg_kind(alg_kind_t alg_kind) {
+        assert(utils::one_of(alg_kind, alg_kind::convolution_direct,
+                    alg_kind::convolution_winograd));
+        if (desc_.alg_kind == alg_kind::convolution_auto)
+            desc_.alg_kind = alg_kind;
+        return desc_.alg_kind == alg_kind;
+    }
+
+    bool expect_data_types(data_type_t src_dt, data_type_t wei_dt,
+            data_type_t bia_dt, data_type_t dst_dt, data_type_t acc_dt) const {
+        bool ok = true
+            && (src_dt == data_type::undef || _src_md()->data_type == src_dt)
+            && (wei_dt == data_type::undef || _wei_md()->data_type == wei_dt)
+            && (dst_dt == data_type::undef || _dst_md()->data_type == dst_dt)
+            && (acc_dt == data_type::undef || desc_.accum_data_type == acc_dt);
+        if (with_bias() && bia_dt != data_type::undef)
+            ok = ok && _bia_md()->data_type == bia_dt;
+        return ok;
+    }
+
+private:
+    const memory_desc_t *_src_md() const { return conv_prop_invariant_src_d(&desc_); }
+    const memory_desc_t *_wei_md() const { return conv_prop_invariant_wei_d(&desc_); }
+    const memory_desc_t *_bia_md() const { return conv_prop_invariant_bia_d(&desc_); }
+    const memory_desc_t *_dst_md() const { return conv_prop_invariant_dst_d(&desc_); }
+};
+
+struct convolution_fwd_pd_t: public convolution_pd_t {
+    typedef convolution_fwd_pd_t base_class;
+    typedef convolution_fwd_pd_t hint_class;
+
+    convolution_fwd_pd_t(engine_t *engine,
+            const convolution_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const convolution_fwd_pd_t *hint_fwd_pd)
+        : convolution_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , src_md_(desc_.src_desc)
+        , weights_md_(desc_.weights_desc)
+        , bias_md_(desc_.bias_desc)
+        , dst_md_(desc_.dst_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_SRC, MKLDNN_ARG_WEIGHTS))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_BIAS && with_bias())
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DST)
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &src_md_ : nullptr; }
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 ? &dst_md_ : nullptr; }
+    virtual const memory_desc_t *weights_md(int index = 0) const override {
+        if (index == 0) return &weights_md_;
+        if (index == 1 && with_bias()) return &bias_md_;
+        return nullptr;
+    }
+
+    virtual int n_inputs() const override { return 2 + with_bias(); }
+    virtual int n_outputs() const override { return 1; }
+
+protected:
+    memory_desc_t src_md_;
+    memory_desc_t weights_md_;
+    memory_desc_t bias_md_;
+    memory_desc_t dst_md_;
+
+    bool set_default_formats_common(format_tag_t src_tag,
+            format_tag_t wei_tag, format_tag_t dst_tag) {
+        return set_default_formats_common_template(src_md_, src_tag,
+                weights_md_, wei_tag, dst_md_, dst_tag, bias_md_);
+    }
+};
+
+struct convolution_bwd_data_pd_t: public convolution_pd_t {
+    typedef convolution_bwd_data_pd_t base_class;
+    typedef convolution_fwd_pd_t hint_class;
+
+    convolution_bwd_data_pd_t(engine_t *engine,
+            const convolution_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const convolution_fwd_pd_t *hint_fwd_pd)
+        : convolution_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , diff_src_md_(desc_.diff_src_desc)
+        , weights_md_(desc_.weights_desc)
+        , bias_md_(desc_.bias_desc)
+        , diff_dst_md_(desc_.diff_dst_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_WEIGHTS, MKLDNN_ARG_DIFF_DST))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DIFF_SRC)
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *diff_src_md(int index = 0) const override
+    { return index == 0 ? &diff_src_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 ? &diff_dst_md_ : nullptr; }
+    virtual const memory_desc_t *weights_md(int index = 0) const override {
+        if (index == 0) return &weights_md_;
+        if (index == 1 && with_bias()) return &bias_md_;
+        return nullptr;
+    }
+
+    virtual int n_inputs() const override { return 2 + with_bias(); }
+    virtual int n_outputs() const override { return 1; }
+
+    virtual bool support_bias() const { return false; }
+
+protected:
+    memory_desc_t diff_src_md_;
+    memory_desc_t weights_md_;
+    memory_desc_t bias_md_;
+    memory_desc_t diff_dst_md_;
+
+    bool set_default_formats_common(format_tag_t diff_src_tag,
+            format_tag_t wei_tag, format_tag_t diff_dst_tag) {
+        return set_default_formats_common_template(diff_src_md_, diff_src_tag,
+                weights_md_, wei_tag, diff_dst_md_, diff_dst_tag, bias_md_);
+    }
+};
+
+struct convolution_bwd_weights_pd_t: public convolution_pd_t {
+    typedef convolution_bwd_weights_pd_t base_class;
+    typedef convolution_fwd_pd_t hint_class;
+
+    convolution_bwd_weights_pd_t(engine_t *engine,
+            const convolution_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const convolution_fwd_pd_t *hint_fwd_pd)
+        : convolution_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , src_md_(desc_.src_desc)
+        , diff_weights_md_(desc_.diff_weights_desc)
+        , diff_bias_md_(desc_.diff_bias_desc)
+        , diff_dst_md_(desc_.diff_dst_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_SRC, MKLDNN_ARG_DIFF_DST))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DIFF_WEIGHTS)
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_DIFF_BIAS && with_bias())
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &src_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 ? &diff_dst_md_ : nullptr; }
+    virtual const memory_desc_t *diff_weights_md(int index = 0) const override {
+        if (index == 0) return &diff_weights_md_;
+        if (index == 1 && with_bias()) return &diff_bias_md_;
+        return nullptr;
+    }
+
+    virtual int n_inputs() const override { return 2; }
+    virtual int n_outputs() const override { return 1 + with_bias(); }
+
+protected:
+    memory_desc_t src_md_;
+    memory_desc_t diff_weights_md_;
+    memory_desc_t diff_bias_md_;
+    memory_desc_t diff_dst_md_;
+
+    bool set_default_formats_common(format_tag_t src_tag,
+            format_tag_t diff_wei_tag, format_tag_t diff_dst_tag) {
+        return set_default_formats_common_template(src_md_, src_tag,
+                diff_weights_md_, diff_wei_tag, diff_dst_md_, diff_dst_tag,
+                diff_bias_md_);
+    }
+};
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/deconvolution.cpp b/thirdparty/oidn/mkl-dnn/src/common/deconvolution.cpp
new file mode 100644
index 0000000000..98063c1c37
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/deconvolution.cpp
@@ -0,0 +1,188 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn.h"
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::alg_kind;
+using namespace mkldnn::impl::types;
+
+namespace {
+status_t deconv_desc_init(deconvolution_desc_t *deconv_desc,
+        prop_kind_t prop_kind, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *weights_desc,
+        const memory_desc_t *bias_desc, const memory_desc_t *dst_desc,
+        const dims_t strides, const dims_t dilates, const dims_t padding_l,
+        const dims_t padding_r, padding_kind_t padding_kind) {
+    bool args_ok = true
+            && !any_null(deconv_desc, src_desc, weights_desc, dst_desc, strides,
+                           padding_l)
+            && one_of(alg_kind, deconvolution_direct, deconvolution_winograd)
+            && one_of(padding_kind, padding_kind::padding_zero);
+    if (!args_ok)
+        return invalid_arguments;
+
+    if (padding_r == nullptr)
+        padding_r = padding_l;
+
+    auto dd = deconvolution_desc_t();
+    dd.primitive_kind = primitive_kind::deconvolution;
+    dd.prop_kind = prop_kind;
+    dd.alg_kind = alg_kind;
+
+    dd.diff_src_desc = dd.src_desc = zero_md();
+    dd.diff_dst_desc = dd.dst_desc = zero_md();
+    dd.diff_weights_desc = dd.weights_desc = zero_md();
+    dd.diff_bias_desc = dd.bias_desc = zero_md();
+
+    const bool is_fwd = one_of(prop_kind, forward_training, forward_inference);
+    const bool with_bias
+            = bias_desc && bias_desc->format_kind != format_kind::undef;
+    const bool with_groups = weights_desc->ndims == src_desc->ndims + 1;
+
+    (prop_kind == backward_data ? dd.diff_src_desc : dd.src_desc) = *src_desc;
+    (is_fwd ? dd.dst_desc : dd.diff_dst_desc) = *dst_desc;
+    (prop_kind == backward_weights ? dd.diff_weights_desc : dd.weights_desc)
+            = *weights_desc;
+    if (with_bias)
+        (prop_kind == backward_weights ? dd.diff_bias_desc : dd.bias_desc)
+                = *bias_desc;
+
+    int sp_dims = src_desc->ndims - 2;
+    utils::array_copy(dd.strides, strides, sp_dims);
+    utils::array_copy(dd.padding[0], padding_l, sp_dims);
+    utils::array_copy(dd.padding[1], padding_r, sp_dims);
+    if (dilates)
+        utils::array_copy(dd.dilates, dilates, sp_dims);
+    else
+        utils::array_set(dd.dilates, 0, sp_dims);
+
+    dd.padding_kind = padding_kind;
+    dd.accum_data_type = types::default_accum_data_type(src_desc->data_type,
+            weights_desc->data_type, dst_desc->data_type, prop_kind);
+
+    const int g = with_groups ? weights_desc->dims[0] : 1;
+    bool consistency = true
+            && src_desc->ndims == dst_desc->ndims
+            && utils::one_of(src_desc->ndims, 3, 4, 5)
+            && utils::one_of(weights_desc->ndims, src_desc->ndims,
+                    src_desc->ndims + 1)
+            && (with_bias ? bias_desc->ndims == 1 : true)
+            && (with_bias ? bias_desc->dims[0] == dst_desc->dims[1] : true)
+            && src_desc->dims[0] == dst_desc->dims[0]
+            && src_desc->dims[1] == g * weights_desc->dims[with_groups + 1]
+            && dst_desc->dims[1] == g * weights_desc->dims[with_groups + 0];
+    for (int i = 2; i < src_desc->ndims; ++i) {
+        int src = src_desc->dims[i];
+        int ker = weights_desc->dims[with_groups + i];
+        int dil = dd.dilates[i - 2];
+        int pad = padding_l[i - 2] + padding_r[i - 2];
+        int str = strides[i - 2];
+        int dst = dst_desc->dims[i];
+        int ker_range = 1 + (ker - 1) * (dil + 1);
+
+        consistency
+                = consistency && (dst - ker_range + pad) / str + 1 == src;
+    }
+    if (!consistency)
+        return invalid_arguments;
+
+    *deconv_desc = dd;
+    return success;
+}
+}
+
+status_t mkldnn_deconvolution_forward_desc_init(
+        deconvolution_desc_t *deconv_desc, prop_kind_t prop_kind,
+        alg_kind_t alg_kind, const memory_desc_t *src_desc,
+        const memory_desc_t *weights_desc, const memory_desc_t *bias_desc,
+        const memory_desc_t *dst_desc, const dims_t strides,
+        const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    if (!one_of(prop_kind, forward_training, forward_inference))
+        return invalid_arguments;
+    return deconv_desc_init(deconv_desc, prop_kind, alg_kind, src_desc,
+            weights_desc, bias_desc, dst_desc, strides, nullptr, padding_l,
+            padding_r, padding_kind);
+}
+
+status_t mkldnn_dilated_deconvolution_forward_desc_init(
+        deconvolution_desc_t *deconv_desc, prop_kind_t prop_kind,
+        alg_kind_t alg_kind, const memory_desc_t *src_desc,
+        const memory_desc_t *weights_desc, const memory_desc_t *bias_desc,
+        const memory_desc_t *dst_desc, const dims_t strides,
+        const dims_t dilates, const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    if (!one_of(prop_kind, forward_training, forward_inference))
+        return invalid_arguments;
+    return deconv_desc_init(deconv_desc, prop_kind, alg_kind, src_desc,
+            weights_desc, bias_desc, dst_desc, strides, dilates, padding_l,
+            padding_r, padding_kind);
+}
+
+status_t mkldnn_deconvolution_backward_data_desc_init(
+        deconvolution_desc_t *deconv_desc, alg_kind_t alg_kind,
+        const memory_desc_t *diff_src_desc, const memory_desc_t *weights_desc,
+        const memory_desc_t *diff_dst_desc, const dims_t strides,
+        const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    return deconv_desc_init(deconv_desc, backward_data, alg_kind, diff_src_desc,
+            weights_desc, nullptr, diff_dst_desc, strides, nullptr, padding_l,
+            padding_r, padding_kind);
+}
+
+status_t mkldnn_dilated_deconvolution_backward_data_desc_init(
+        deconvolution_desc_t *deconv_desc, alg_kind_t alg_kind,
+        const memory_desc_t *diff_src_desc, const memory_desc_t *weights_desc,
+        const memory_desc_t *diff_dst_desc, const dims_t strides,
+        const dims_t dilates, const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    return deconv_desc_init(deconv_desc, backward_data, alg_kind, diff_src_desc,
+            weights_desc, nullptr, diff_dst_desc, strides,dilates, padding_l,
+            padding_r, padding_kind);
+}
+
+status_t mkldnn_deconvolution_backward_weights_desc_init(
+        deconvolution_desc_t *deconv_desc, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *diff_weights_desc,
+        const memory_desc_t *diff_bias_desc, const memory_desc_t *diff_dst_desc,
+        const dims_t strides, const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    return deconv_desc_init(deconv_desc, backward_weights, alg_kind, src_desc,
+            diff_weights_desc, diff_bias_desc, diff_dst_desc, strides, nullptr,
+            padding_l, padding_r, padding_kind);
+}
+
+status_t mkldnn_dilated_deconvolution_backward_weights_desc_init(
+        deconvolution_desc_t *deconv_desc, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *diff_weights_desc,
+        const memory_desc_t *diff_bias_desc, const memory_desc_t *diff_dst_desc,
+        const dims_t strides, const dims_t dilates, const dims_t padding_l,
+        const dims_t padding_r, padding_kind_t padding_kind) {
+    return deconv_desc_init(deconv_desc, backward_weights, alg_kind, src_desc,
+            diff_weights_desc, diff_bias_desc, diff_dst_desc, strides, dilates,
+            padding_l, padding_r, padding_kind);
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/deconvolution_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/deconvolution_pd.hpp
new file mode 100644
index 0000000000..539e44bd9b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/deconvolution_pd.hpp
@@ -0,0 +1,293 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef DECONVOLUTION_PD_HPP
+#define DECONVOLUTION_PD_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "convolution_pd.hpp"
+#include "primitive_desc.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct deconvolution_fwd_pd_t;
+
+struct deconvolution_pd_t: public primitive_desc_t {
+    static constexpr auto base_pkind = primitive_kind::deconvolution;
+
+    deconvolution_pd_t(engine_t *engine,
+            const deconvolution_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const deconvolution_fwd_pd_t *hint_fwd_pd)
+        : primitive_desc_t(engine, attr, base_pkind)
+        , desc_(*adesc)
+        , hint_fwd_pd_(hint_fwd_pd)
+    {}
+
+    const deconvolution_desc_t *desc() const { return &desc_; }
+    virtual const op_desc_t *op_desc() const override
+    { return reinterpret_cast<const op_desc_t *>(this->desc()); }
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual status_t query(query_t what, int idx, void *result) const override {
+        switch (what) {
+        case pkind_traits<base_pkind>::query_d:
+            *(const deconvolution_desc_t **)result = desc();
+            break;
+        default: return primitive_desc_t::query(what, idx, result);
+        }
+        return status::success;
+    }
+
+    /* common deconv aux functions (note that conv_desc_t == deconv_desc_t) */
+
+    dim_t MB() const { return conv_prop_invariant_src_d(&desc_)->dims[0]; }
+
+    dim_t IC() const { return conv_prop_invariant_src_d(&desc_)->dims[1]; }
+    dim_t OC() const { return conv_prop_invariant_dst_d(&desc_)->dims[1]; }
+    dim_t G() const
+    { return with_groups() ? conv_prop_invariant_wei_d(&desc_)->dims[0] : 1; }
+
+    dim_t ID() const {
+        return ndims() >= 5
+            ? conv_prop_invariant_src_d(&desc_)->dims[ndims() - 3] : 1;
+    }
+    dim_t IH() const {
+        return ndims() >= 4
+            ? conv_prop_invariant_src_d(&desc_)->dims[ndims() - 2] : 1;
+    }
+    dim_t IW() const {
+        return conv_prop_invariant_src_d(&desc_)->dims[ndims() - 1];
+    }
+
+    dim_t OD() const {
+        return ndims() >= 5
+            ? conv_prop_invariant_dst_d(&desc_)->dims[ndims() - 3] : 1;
+    }
+    dim_t OH() const {
+        return ndims() >= 4
+            ? conv_prop_invariant_dst_d(&desc_)->dims[ndims() - 2] : 1;
+    }
+    dim_t OW() const {
+        return conv_prop_invariant_dst_d(&desc_)->dims[ndims() - 1];
+    }
+
+    dim_t KD() const {
+        const int w_ndims = ndims() + with_groups();
+        return ndims() >= 5
+            ? conv_prop_invariant_wei_d(&desc_)->dims[w_ndims - 3] : 1;
+    }
+    dim_t KH() const {
+        const int w_ndims = ndims() + with_groups();
+        return ndims() >= 4
+            ? conv_prop_invariant_wei_d(&desc_)->dims[w_ndims - 2] : 1;
+    }
+    dim_t KW() const {
+        const int w_ndims = ndims() + with_groups();
+        return conv_prop_invariant_wei_d(&desc_)->dims[w_ndims - 1];
+    }
+
+    dim_t KSD() const { return ndims() >= 5 ? desc_.strides[ndims() - 5] : 1; }
+    dim_t KSH() const { return ndims() >= 4 ? desc_.strides[ndims() - 4] : 1; }
+    dim_t KSW() const { return desc_.strides[ndims() - 3]; }
+
+    dim_t KDD() const { return ndims() >= 5 ? desc_.dilates[ndims() - 5] : 0; }
+    dim_t KDH() const { return ndims() >= 4 ? desc_.dilates[ndims() - 4] : 1; }
+    dim_t KDW() const { return desc_.dilates[ndims() - 3]; }
+
+    dim_t padFront() const
+    { return ndims() >= 5 ? desc_.padding[0][ndims() - 5] : 0; }
+    dim_t padBack() const
+    { return ndims() >= 5 ? desc_.padding[1][ndims() - 5] : 0; }
+    dim_t padT() const
+    { return ndims() >= 4 ? desc_.padding[0][ndims() - 4] : 0; }
+    dim_t padB() const
+    { return ndims() >= 4 ? desc_.padding[1][ndims() - 4] : 0; }
+    dim_t padL() const { return desc_.padding[0][ndims() - 3]; }
+    dim_t padR() const { return desc_.padding[1][ndims() - 3]; }
+
+    bool with_bias() const {
+        return
+            !memory_desc_wrapper(*conv_prop_invariant_bia_d(&desc_)).is_zero();
+    }
+
+    bool with_groups() const
+    { return conv_prop_invariant_wei_d(&desc_)->ndims == ndims() + 1; }
+
+    int ndims() const { return conv_prop_invariant_src_d(&desc_)->ndims; }
+
+    bool is_fwd() const {
+        return utils::one_of(desc_.prop_kind, prop_kind::forward_training,
+                prop_kind::forward_inference);
+    }
+
+    bool has_zero_dim_memory() const {
+        const auto s_d = memory_desc_wrapper(*conv_prop_invariant_src_d(&desc_));
+        const auto d_d = memory_desc_wrapper(*conv_prop_invariant_dst_d(&desc_));
+        return s_d.has_zero_dim() || d_d.has_zero_dim();
+    }
+
+protected:
+    deconvolution_desc_t desc_;
+    const deconvolution_fwd_pd_t *hint_fwd_pd_;
+};
+
+struct deconvolution_fwd_pd_t: public deconvolution_pd_t {
+    typedef deconvolution_fwd_pd_t base_class;
+    typedef deconvolution_fwd_pd_t hint_class;
+
+    deconvolution_fwd_pd_t(engine_t *engine,
+            const deconvolution_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const deconvolution_fwd_pd_t *hint_fwd_pd)
+        : deconvolution_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , src_md_(desc_.src_desc)
+        , weights_md_(desc_.weights_desc)
+        , bias_md_(desc_.bias_desc)
+        , dst_md_(desc_.dst_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_SRC, MKLDNN_ARG_WEIGHTS))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_BIAS && with_bias())
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DST)
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &src_md_ : nullptr; }
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 ? &dst_md_ : nullptr; }
+    virtual const memory_desc_t *weights_md(int index = 0) const override {
+        if (index == 0) return &weights_md_;
+        if (index == 1 && with_bias()) return &bias_md_;
+        return nullptr;
+    }
+
+    virtual int n_inputs() const override { return 2 + with_bias(); }
+    virtual int n_outputs() const override { return 1; }
+
+protected:
+    memory_desc_t src_md_;
+    memory_desc_t weights_md_;
+    memory_desc_t bias_md_;
+    memory_desc_t dst_md_;
+};
+
+struct deconvolution_bwd_data_pd_t: public deconvolution_pd_t {
+    typedef deconvolution_bwd_data_pd_t base_class;
+    typedef deconvolution_fwd_pd_t hint_class;
+
+    deconvolution_bwd_data_pd_t(engine_t *engine,
+            const deconvolution_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const deconvolution_fwd_pd_t *hint_fwd_pd)
+        : deconvolution_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , diff_src_md_(desc_.diff_src_desc)
+        , weights_md_(desc_.weights_desc)
+        , diff_dst_md_(desc_.diff_dst_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_WEIGHTS, MKLDNN_ARG_DIFF_DST))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DIFF_SRC)
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *diff_src_md(int index = 0) const override
+    { return index == 0 ? &diff_src_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 ? &diff_dst_md_ : nullptr; }
+    virtual const memory_desc_t *weights_md(int index = 0) const override
+    { return index == 0 ? &weights_md_ : nullptr; }
+
+    virtual int n_inputs() const override { return 2; }
+    virtual int n_outputs() const override { return 1; }
+
+protected:
+    memory_desc_t diff_src_md_;
+    memory_desc_t weights_md_;
+    memory_desc_t diff_dst_md_;
+};
+
+struct deconvolution_bwd_weights_pd_t: public deconvolution_pd_t {
+    typedef deconvolution_bwd_weights_pd_t base_class;
+    typedef deconvolution_fwd_pd_t hint_class;
+
+    deconvolution_bwd_weights_pd_t(engine_t *engine,
+            const deconvolution_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const deconvolution_fwd_pd_t *hint_fwd_pd)
+        : deconvolution_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , src_md_(desc_.src_desc)
+        , diff_weights_md_(desc_.diff_weights_desc)
+        , diff_bias_md_(desc_.diff_bias_desc)
+        , diff_dst_md_(desc_.diff_dst_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_SRC, MKLDNN_ARG_DIFF_DST))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DIFF_WEIGHTS)
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_DIFF_BIAS && with_bias())
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &src_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 ? &diff_dst_md_ : nullptr; }
+    virtual const memory_desc_t *diff_weights_md(int index = 0) const override {
+        if (index == 0) return &diff_weights_md_;
+        if (index == 1 && with_bias()) return &diff_bias_md_;
+        return nullptr;
+    }
+
+    virtual int n_inputs() const override { return 2; }
+    virtual int n_outputs() const override { return 1 + with_bias(); }
+
+protected:
+    memory_desc_t src_md_;
+    memory_desc_t diff_weights_md_;
+    memory_desc_t diff_bias_md_;
+    memory_desc_t diff_dst_md_;
+};
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/eltwise.cpp b/thirdparty/oidn/mkl-dnn/src/common/eltwise.cpp
new file mode 100644
index 0000000000..f1708fca52
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/eltwise.cpp
@@ -0,0 +1,84 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::alg_kind;
+using namespace mkldnn::impl::types;
+
+namespace {
+status_t eltwise_desc_init(eltwise_desc_t *eltwise_desc, prop_kind_t prop_kind,
+        alg_kind_t alg_kind, const memory_desc_t *data_desc,
+        const memory_desc_t *diff_data_desc, float alpha, float beta) {
+    bool args_ok = true
+        && !any_null(eltwise_desc, data_desc)
+        && one_of(prop_kind, forward_training, forward_inference,
+                backward_data)
+        && one_of(alg_kind, eltwise_relu, eltwise_tanh, eltwise_elu,
+                  eltwise_square, eltwise_abs, eltwise_sqrt, eltwise_linear,
+                  eltwise_bounded_relu, eltwise_soft_relu, eltwise_logistic)
+        && IMPLICATION(prop_kind == backward_data, diff_data_desc != nullptr);
+    if (!args_ok) return invalid_arguments;
+
+    auto ed = eltwise_desc_t();
+    ed.primitive_kind = primitive_kind::eltwise;
+    ed.prop_kind = prop_kind;
+    ed.alg_kind = alg_kind;
+
+    ed.data_desc = *data_desc;
+    ed.diff_data_desc =
+        (ed.prop_kind == backward_data) ? *diff_data_desc : zero_md();
+
+    ed.alpha = alpha;
+    ed.beta = beta;
+
+    bool consistency = true
+        && IMPLICATION(ed.prop_kind == backward_data,
+                array_cmp(ed.diff_data_desc.dims, ed.data_desc.dims,
+                    ed.diff_data_desc.ndims));
+    if (!consistency) return invalid_arguments;
+
+    *eltwise_desc = ed;
+    return success;
+}
+}
+
+status_t mkldnn_eltwise_forward_desc_init(eltwise_desc_t *eltwise_desc,
+        prop_kind_t prop_kind, alg_kind_t alg_kind,
+        const memory_desc_t *data_desc, float alpha, float beta) {
+    if (!one_of(prop_kind, forward_training, forward_inference))
+        return invalid_arguments;
+    return eltwise_desc_init(eltwise_desc, prop_kind, alg_kind, data_desc,
+            nullptr, alpha, beta);
+}
+
+status_t mkldnn_eltwise_backward_desc_init(eltwise_desc_t *eltwise_desc,
+        alg_kind_t alg_kind, const memory_desc_t *diff_data_desc,
+        const memory_desc_t *data_desc, float alpha, float beta) {
+    return eltwise_desc_init(eltwise_desc, backward_data, alg_kind, data_desc,
+            diff_data_desc, alpha, beta);
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/eltwise_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/eltwise_pd.hpp
new file mode 100644
index 0000000000..9fd260fcee
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/eltwise_pd.hpp
@@ -0,0 +1,161 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef ELTWISE_PD_HPP
+#define ELTWISE_PD_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "primitive_desc.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct eltwise_fwd_pd_t;
+
+struct eltwise_pd_t: public primitive_desc_t {
+    static constexpr auto base_pkind = primitive_kind::eltwise;
+
+    eltwise_pd_t(mkldnn::impl::engine_t *engine,
+            const eltwise_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const eltwise_fwd_pd_t *hint_fwd_pd)
+        : primitive_desc_t(engine, attr, base_pkind)
+        , desc_(*adesc)
+        , hint_fwd_pd_(hint_fwd_pd)
+        , data_md_(desc_.data_desc)
+    {}
+
+    const eltwise_desc_t *desc() const { return &desc_; }
+    virtual const op_desc_t *op_desc() const override
+    { return reinterpret_cast<const op_desc_t *>(this->desc()); }
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual status_t query(query_t what, int idx, void *result) const override {
+        switch (what) {
+        case query::eltwise_d:
+            *(const eltwise_desc_t**)result = desc(); break;
+        default: return primitive_desc_t::query(what, idx, result);
+        }
+        return status::success;
+    }
+
+    /* common eltwise aux functions */
+
+    dim_t MB() const { return data_desc().dims[0]; }
+    dim_t C() const { return data_desc().dims[1]; }
+    dim_t D() const { return ndims() >= 5 ? data_desc().dims[ndims() - 3] : 1; }
+    dim_t H() const { return ndims() >= 4 ? data_desc().dims[ndims() - 2] : 1; }
+    dim_t W() const { return ndims() >= 3 ? data_desc().dims[ndims() - 1] : 1; }
+
+    int ndims() const { return data_desc().ndims; }
+
+    bool is_fwd() const {
+        return utils::one_of(desc_.prop_kind, prop_kind::forward_training,
+                prop_kind::forward_inference);
+    }
+
+    bool has_zero_dim_memory() const
+    { return memory_desc_wrapper(desc_.data_desc).has_zero_dim(); }
+
+protected:
+    eltwise_desc_t desc_;
+    const eltwise_fwd_pd_t *hint_fwd_pd_;
+
+    memory_desc_t data_md_;
+
+private:
+    const memory_desc_t &data_desc() const { return desc_.data_desc; }
+};
+
+struct eltwise_fwd_pd_t: public eltwise_pd_t {
+    typedef eltwise_fwd_pd_t base_class;
+    typedef eltwise_fwd_pd_t hint_class;
+
+    eltwise_fwd_pd_t(mkldnn::impl::engine_t *engine,
+            const eltwise_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const eltwise_fwd_pd_t *hint_fwd_pd)
+        : eltwise_pd_t(engine, adesc, attr, hint_fwd_pd)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (arg == MKLDNN_ARG_SRC)
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DST)
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &data_md_ : nullptr; }
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 ? &data_md_ : nullptr; }
+
+    virtual int n_inputs() const override { return 1; }
+    virtual int n_outputs() const override { return 1; }
+
+    bool is_zero_preserved() const
+    { return math::eltwise_fwd_preserves_zero(desc_.alg_kind); }
+};
+
+struct eltwise_bwd_pd_t: public eltwise_pd_t {
+    typedef eltwise_bwd_pd_t base_class;
+    typedef eltwise_fwd_pd_t hint_class;
+
+    eltwise_bwd_pd_t(engine_t *engine,
+            const eltwise_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const eltwise_fwd_pd_t *hint_fwd_pd)
+        : eltwise_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , diff_data_md_(desc_.diff_data_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_SRC, MKLDNN_ARG_DIFF_DST))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DIFF_SRC)
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &data_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 ? &diff_data_md_ : nullptr; }
+    virtual const memory_desc_t *diff_src_md(int index = 0) const override
+    { return index == 0 ? &diff_data_md_ : nullptr; }
+
+    virtual int n_inputs() const override { return 2; }
+    virtual int n_outputs() const override { return 1; }
+
+    bool is_zero_preserved() const { return true; }
+
+protected:
+    memory_desc_t diff_data_md_;
+};
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/engine.cpp b/thirdparty/oidn/mkl-dnn/src/common/engine.cpp
new file mode 100644
index 0000000000..3b3e25456d
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/engine.cpp
@@ -0,0 +1,75 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn.h"
+#include "engine.hpp"
+#include "nstl.hpp"
+
+#include "c_types_map.hpp"
+#include "../cpu/cpu_engine.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+engine_factory_t *engine_factories[] = {
+    &cpu::engine_factory,
+    nullptr,
+};
+
+static inline engine_factory_t *get_engine_factory(engine_kind_t kind) {
+    for (engine_factory_t **ef = engine_factories; *ef; ef++)
+        if ((*ef)->kind() == kind)
+            return *ef;
+    return nullptr;
+}
+
+}
+}
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::status;
+
+size_t mkldnn_engine_get_count(engine_kind_t kind) {
+    engine_factory_t *ef = get_engine_factory(kind);
+    return ef != nullptr ? ef->count() : 0;
+}
+
+status_t mkldnn_engine_create(engine_t **engine,
+        engine_kind_t kind, size_t index) {
+    if (engine == nullptr)
+        return invalid_arguments;
+
+    engine_factory_t *ef = get_engine_factory(kind);
+    if (ef == nullptr || index >= ef->count())
+        return invalid_arguments;
+
+    return ef->engine_create(engine, index);
+}
+
+status_t mkldnn_engine_get_kind(engine_t *engine, engine_kind_t *kind) {
+    if (engine == nullptr)
+        return invalid_arguments;
+    *kind = engine->kind();
+    return success;
+}
+
+status_t mkldnn_engine_destroy(engine_t *engine) {
+    /* TODO: engine->dec_ref_count(); */
+    delete engine;
+    return success;
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/engine.hpp b/thirdparty/oidn/mkl-dnn/src/common/engine.hpp
new file mode 100644
index 0000000000..8ac8a29de5
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/engine.hpp
@@ -0,0 +1,119 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef ENGINE_HPP
+#define ENGINE_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "primitive.hpp"
+#include "utils.hpp"
+
+/** \brief An abstraction of an execution unit with shared resources
+ *
+ * Responsibilities:
+ *   - Provide engine specific memory allocation
+ *   - Provide engine specific primitive_desc_t creators
+ */
+struct mkldnn_engine: public mkldnn::impl::c_compatible {
+    mkldnn_engine(mkldnn::impl::engine_kind_t kind)
+        : kind_(kind)
+    {}
+    virtual ~mkldnn_engine() {}
+
+    /** get kind of the current engine */
+    virtual mkldnn::impl::engine_kind_t kind() const { return kind_; }
+
+    /** allocate memory */
+    virtual mkldnn::impl::status_t memory_create(
+            mkldnn::impl::memory_t **memory,
+            const mkldnn::impl::memory_desc_t *md,
+            void *handle) = 0;
+
+    /** implementation section (typedefs) */
+
+    // TODO: remove engine?
+    typedef mkldnn::impl::status_t (*reorder_primitive_desc_create_f)(
+            mkldnn::impl::reorder_pd_t **reorder_pd,
+            mkldnn::impl::engine_t *engine,
+            const mkldnn::impl::primitive_attr_t *attr,
+            mkldnn::impl::engine_t *src_engine,
+            const mkldnn::impl::memory_desc_t *src_md,
+            mkldnn::impl::engine_t *dst_engine,
+            const mkldnn::impl::memory_desc_t *dst_md);
+
+    typedef mkldnn::impl::status_t (*concat_primitive_desc_create_f)(
+            mkldnn::impl::concat_pd_t **concat_pd,
+            mkldnn::impl::engine_t *engine,
+            const mkldnn::impl::primitive_attr_t *attr,
+            const mkldnn::impl::memory_desc_t *dst_md,
+            int n, int concat_dim,
+            const mkldnn::impl::memory_desc_t *src_mds);
+
+    typedef mkldnn::impl::status_t (*sum_primitive_desc_create_f)(
+            mkldnn::impl::sum_pd_t **sum_pd,
+            mkldnn::impl::engine_t *engine,
+            const mkldnn::impl::primitive_attr_t *attr,
+            const mkldnn::impl::memory_desc_t *dst_md,
+            int n, const float *scales,
+            const mkldnn::impl::memory_desc_t *src_mds);
+
+    typedef mkldnn::impl::status_t (*primitive_desc_create_f)(
+            mkldnn::impl::primitive_desc_t **, const mkldnn::impl::op_desc_t *,
+            const mkldnn::impl::primitive_attr_t *attr,
+            mkldnn::impl::engine_t *, const mkldnn::impl::primitive_desc_t *);
+
+    /* implementation section */
+
+    /** return the list of reorder implementations. engine guarantees to return
+     * a NULL-terminated list */
+    virtual const reorder_primitive_desc_create_f*
+        get_reorder_implementation_list() const = 0;
+
+    /** return the list of concat implementations. engine guarantees to return
+     * a NULL-terminated list */
+    virtual const concat_primitive_desc_create_f*
+        get_concat_implementation_list() const = 0;
+
+    /** return the list of sum implementations. engine guarantees to return
+     * a NULL-terminated list */
+    virtual const sum_primitive_desc_create_f*
+        get_sum_implementation_list() const = 0;
+
+    /** return the list of implementations. engine guarantees to return a
+     * NULL-terminated list */
+    virtual const primitive_desc_create_f* get_implementation_list() const = 0;
+
+protected:
+    mkldnn::impl::engine_kind_t kind_;
+};
+
+namespace mkldnn {
+namespace impl {
+
+struct engine_factory_t: public c_compatible {
+    virtual size_t count() const = 0;
+    virtual engine_kind_t kind() const = 0;
+    virtual status_t engine_create(engine_t **engine, size_t index) const = 0;
+};
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/inner_product.cpp b/thirdparty/oidn/mkl-dnn/src/common/inner_product.cpp
new file mode 100644
index 0000000000..5a9f58cb1e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/inner_product.cpp
@@ -0,0 +1,106 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::types;
+
+namespace {
+status_t ip_desc_init(inner_product_desc_t *ip_desc, prop_kind_t prop_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *weights_desc,
+        const memory_desc_t *bias_desc, const memory_desc_t *dst_desc) {
+    bool args_ok = !any_null(ip_desc, src_desc, weights_desc, dst_desc);
+    if (!args_ok) return invalid_arguments;
+
+    auto id = inner_product_desc_t();
+    id.primitive_kind = primitive_kind::inner_product;
+    id.prop_kind = prop_kind;
+
+    id.diff_src_desc = id.src_desc = zero_md();
+    id.diff_dst_desc = id.dst_desc = zero_md();
+    id.diff_weights_desc = id.weights_desc = zero_md();
+    id.diff_bias_desc = id.bias_desc = zero_md();
+
+    const bool is_fwd = one_of(prop_kind, forward_training, forward_inference);
+    const bool with_bias =
+        bias_desc && bias_desc->format_kind != format_kind::undef;
+
+    (prop_kind == backward_data ? id.diff_src_desc : id.src_desc) = *src_desc;
+    (is_fwd ? id.dst_desc : id.diff_dst_desc)  = *dst_desc;
+    (prop_kind == backward_weights ? id.diff_weights_desc : id.weights_desc) =
+        *weights_desc;
+    if (with_bias)
+        (prop_kind == backward_weights ? id.diff_bias_desc : id.bias_desc) =
+            *bias_desc;
+
+    id.accum_data_type = types::default_accum_data_type(src_desc->data_type,
+            weights_desc->data_type, dst_desc->data_type, prop_kind);
+
+    bool consistency = true
+        && memory_desc_wrapper(weights_desc).nelems()
+        && one_of(src_desc->ndims, 2, 3, 4, 5)
+        && dst_desc->ndims == 2
+        && weights_desc->ndims == src_desc->ndims
+        && (with_bias ? bias_desc->ndims == 1 : true)
+        && (with_bias ? bias_desc->dims[0] == dst_desc->dims[1] : true)
+        && src_desc->dims[0] == dst_desc->dims[0]
+        && array_cmp(&src_desc->dims[1], &weights_desc->dims[1],
+                src_desc->ndims - 1)
+        && dst_desc->dims[1] == weights_desc->dims[0];
+    if (!consistency) return invalid_arguments;
+
+    *ip_desc = id;
+    return success;
+}
+}
+
+status_t mkldnn_inner_product_forward_desc_init(inner_product_desc_t *ip_desc,
+        prop_kind_t prop_kind, const memory_desc_t *src_desc,
+        const memory_desc_t *weights_desc, const memory_desc_t *bias_desc,
+        const memory_desc_t *dst_desc) {
+    if (!one_of(prop_kind, forward_training, forward_inference))
+        return invalid_arguments;
+    return ip_desc_init(ip_desc, prop_kind, src_desc, weights_desc, bias_desc,
+            dst_desc);
+}
+
+status_t mkldnn_inner_product_backward_data_desc_init(
+        inner_product_desc_t *ip_desc, const memory_desc_t *diff_src_desc,
+        const memory_desc_t *weights_desc, const memory_desc_t *diff_dst_desc)
+{
+    return ip_desc_init(ip_desc, backward_data, diff_src_desc, weights_desc,
+            nullptr, diff_dst_desc);
+}
+
+status_t mkldnn_inner_product_backward_weights_desc_init(
+        inner_product_desc_t *ip_desc, const memory_desc_t *src_desc,
+        const memory_desc_t *diff_weights_desc,
+        const memory_desc_t *diff_bias_desc,
+        const memory_desc_t *diff_dst_desc) {
+    return ip_desc_init(ip_desc, backward_weights, src_desc, diff_weights_desc,
+            diff_bias_desc, diff_dst_desc);
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/inner_product_pd.cpp b/thirdparty/oidn/mkl-dnn/src/common/inner_product_pd.cpp
new file mode 100644
index 0000000000..091cf0f5d6
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/inner_product_pd.cpp
@@ -0,0 +1,56 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "utils.hpp"
+
+#include "inner_product_pd.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+using namespace prop_kind;
+
+memory_desc_t *ip_prop_invariant_src_d(inner_product_desc_t *desc) {
+    return desc->prop_kind == backward_data
+        ? &desc->diff_src_desc : &desc->src_desc;
+}
+
+memory_desc_t *ip_prop_invariant_wei_d(inner_product_desc_t *desc) {
+    return desc->prop_kind == backward_weights
+        ? &desc->diff_weights_desc : &desc->weights_desc;
+}
+
+memory_desc_t *ip_prop_invariant_bia_d(inner_product_desc_t *desc) {
+    return desc->prop_kind == backward_weights
+        ? &desc->diff_bias_desc : &desc->bias_desc;
+}
+
+memory_desc_t *ip_prop_invariant_dst_d(inner_product_desc_t *desc) {
+    return utils::one_of(desc->prop_kind, forward_inference, forward_training)
+        ? &desc->dst_desc : &desc->diff_dst_desc;
+}
+
+const memory_desc_t *ip_prop_invariant_src_d(const inner_product_desc_t *desc)
+{ return ip_prop_invariant_src_d(const_cast<inner_product_desc_t *>(desc)); }
+const memory_desc_t *ip_prop_invariant_wei_d(const inner_product_desc_t *desc)
+{ return ip_prop_invariant_wei_d(const_cast<inner_product_desc_t *>(desc)); }
+const memory_desc_t *ip_prop_invariant_bia_d(const inner_product_desc_t *desc)
+{ return ip_prop_invariant_bia_d(const_cast<inner_product_desc_t *>(desc)); }
+const memory_desc_t *ip_prop_invariant_dst_d(const inner_product_desc_t *desc)
+{ return ip_prop_invariant_dst_d(const_cast<inner_product_desc_t *>(desc)); }
+
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/inner_product_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/inner_product_pd.hpp
new file mode 100644
index 0000000000..c426de632c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/inner_product_pd.hpp
@@ -0,0 +1,321 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef INNER_PRODUCT_PD_HPP
+#define INNER_PRODUCT_PD_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "primitive_desc.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+memory_desc_t *ip_prop_invariant_src_d(inner_product_desc_t *desc);
+memory_desc_t *ip_prop_invariant_wei_d(inner_product_desc_t *desc);
+memory_desc_t *ip_prop_invariant_bia_d(inner_product_desc_t *desc);
+memory_desc_t *ip_prop_invariant_dst_d(inner_product_desc_t *desc);
+const memory_desc_t *ip_prop_invariant_src_d(const inner_product_desc_t *desc);
+const memory_desc_t *ip_prop_invariant_wei_d(const inner_product_desc_t *desc);
+const memory_desc_t *ip_prop_invariant_bia_d(const inner_product_desc_t *desc);
+const memory_desc_t *ip_prop_invariant_dst_d(const inner_product_desc_t *desc);
+
+struct inner_product_fwd_pd_t;
+
+struct inner_product_pd_t: public primitive_desc_t {
+    static constexpr auto base_pkind = primitive_kind::inner_product;
+
+    inner_product_pd_t(engine_t *engine,
+            const inner_product_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const inner_product_fwd_pd_t *hint_fwd_pd)
+        : primitive_desc_t(engine, attr, base_pkind)
+        , desc_(*adesc)
+        , hint_fwd_pd_(hint_fwd_pd)
+    {}
+
+    const inner_product_desc_t *desc() const { return &desc_; }
+    virtual const op_desc_t *op_desc() const override
+    { return reinterpret_cast<const op_desc_t *>(this->desc()); }
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual status_t query(query_t what, int idx, void *result) const override {
+        switch (what) {
+        case query::inner_product_d:
+            *(const inner_product_desc_t**)result = desc(); break;
+        default: return primitive_desc_t::query(what, idx, result);
+        }
+        return status::success;
+    }
+
+    /* common inner_product aux functions */
+
+    dim_t MB() const { return ip_prop_invariant_src_d(&desc_)->dims[0]; }
+    dim_t IC() const { return ip_prop_invariant_src_d(&desc_)->dims[1]; }
+    dim_t OC() const { return ip_prop_invariant_dst_d(&desc_)->dims[1]; }
+
+    dim_t ID() const {
+        return ndims() >= 5
+            ? ip_prop_invariant_src_d(&desc_)->dims[ndims() - 3] : 1;
+    }
+    dim_t IH() const {
+        return ndims() >= 4
+            ? ip_prop_invariant_src_d(&desc_)->dims[ndims() - 2] : 1;
+    }
+    dim_t IW() const {
+        return ndims() >= 3
+            ? ip_prop_invariant_src_d(&desc_)->dims[ndims() - 1] : 1;
+    }
+
+    dim_t OD() const {
+        return ndims() >= 5
+            ? ip_prop_invariant_dst_d(&desc_)->dims[ndims() - 3] : 1;
+    }
+    dim_t OH() const {
+        return ndims() >= 4
+            ? ip_prop_invariant_dst_d(&desc_)->dims[ndims() - 2] : 1;
+    }
+    dim_t OW() const {
+        return ndims() >= 3
+            ? ip_prop_invariant_dst_d(&desc_)->dims[ndims() - 1] : 1;
+    }
+
+    dim_t KD() const {
+        return ndims() >= 5
+            ? ip_prop_invariant_wei_d(&desc_)->dims[ndims() - 3] : 1;
+    }
+    dim_t KH() const {
+        return ndims() >= 4
+            ? ip_prop_invariant_wei_d(&desc_)->dims[ndims() - 2] : 1;
+    }
+    dim_t KW() const {
+        return ndims() >= 3
+            ? ip_prop_invariant_wei_d(&desc_)->dims[ndims() - 1] : 1;
+    }
+
+    dim_t IC_total() const {
+        return utils::array_product(&ip_prop_invariant_src_d(&desc_)->dims[1],
+                ndims() - 1);
+    }
+
+    dim_t IC_total_padded() const {
+        auto src_d = desc()->prop_kind == prop_kind::backward_data
+            ? memory_desc_wrapper(diff_src_md())
+            : memory_desc_wrapper(src_md());
+        assert(src_d.is_blocking_desc());
+        if (!src_d.is_blocking_desc()) return -1;
+        return utils::array_product(src_d.padded_dims() + 1, ndims() - 1);
+    }
+
+    int ndims() const { return ip_prop_invariant_src_d(&desc_)->ndims; }
+
+    bool with_bias() const
+    { return !memory_desc_wrapper(*ip_prop_invariant_bia_d(&desc_)).is_zero(); }
+
+    bool has_zero_dim_memory() const {
+        const auto s_d = memory_desc_wrapper(*ip_prop_invariant_src_d(&desc_));
+        const auto d_d = memory_desc_wrapper(*ip_prop_invariant_dst_d(&desc_));
+        return s_d.has_zero_dim() || d_d.has_zero_dim();
+    }
+
+    bool is_fwd() const {
+        return utils::one_of(desc_.prop_kind, prop_kind::forward_training,
+                prop_kind::forward_inference);
+    }
+
+protected:
+    inner_product_desc_t desc_;
+    const inner_product_fwd_pd_t *hint_fwd_pd_;
+
+    status_t template_set_default_params(memory_desc_t &src_md,
+            memory_desc_t &weights_md, memory_desc_t &dst_md,
+            memory_desc_t *bias_md) {
+        using namespace format_tag;
+        if (src_md.format_kind == format_kind::any) {
+            CHECK(memory_desc_init_by_tag(src_md,
+                        utils::pick(ndims() - 2, nc, ncw, nchw, ncdhw)));
+        }
+        if (dst_md.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(dst_md, nc));
+        if (weights_md.format_kind == format_kind::any) {
+            CHECK(memory_desc_init_by_tag(weights_md,
+                        utils::pick(ndims() - 2, oi, oiw, oihw, oidhw)));
+        }
+        if (bias_md && bias_md->format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(*bias_md, x));
+        return status::success;
+    }
+};
+
+struct inner_product_fwd_pd_t: public inner_product_pd_t {
+    typedef inner_product_fwd_pd_t base_class;
+    typedef inner_product_fwd_pd_t hint_class;
+
+    inner_product_fwd_pd_t(engine_t *engine,
+            const inner_product_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const inner_product_fwd_pd_t *hint_fwd_pd)
+        : inner_product_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , src_md_(desc_.src_desc)
+        , weights_md_(desc_.weights_desc)
+        , bias_md_(desc_.bias_desc)
+        , dst_md_(desc_.dst_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_SRC, MKLDNN_ARG_WEIGHTS))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_BIAS && with_bias())
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DST)
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &src_md_ : nullptr; }
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 ? &dst_md_ : nullptr; }
+    virtual const memory_desc_t *weights_md(int index = 0) const override {
+        if (index == 0) return &weights_md_;
+        if (index == 1 && with_bias()) return &bias_md_;
+        return nullptr;
+    }
+
+    virtual int n_inputs() const override { return 2 + with_bias(); }
+    virtual int n_outputs() const override { return 1; }
+
+protected:
+    memory_desc_t src_md_;
+    memory_desc_t weights_md_;
+    memory_desc_t bias_md_;
+    memory_desc_t dst_md_;
+
+    status_t set_default_params() {
+        return template_set_default_params(src_md_, weights_md_, dst_md_,
+                &bias_md_);
+    }
+};
+
+struct inner_product_bwd_data_pd_t: public inner_product_pd_t {
+    typedef inner_product_bwd_data_pd_t base_class;
+    typedef inner_product_fwd_pd_t hint_class;
+
+    inner_product_bwd_data_pd_t(engine_t *engine,
+            const inner_product_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const inner_product_fwd_pd_t *hint_fwd_pd)
+        : inner_product_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , diff_src_md_(desc_.diff_src_desc)
+        , weights_md_(desc_.weights_desc)
+        , diff_dst_md_(desc_.diff_dst_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_WEIGHTS, MKLDNN_ARG_DIFF_DST))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DIFF_SRC)
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *diff_src_md(int index = 0) const override
+    { return index == 0 ? &diff_src_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 ? &diff_dst_md_ : nullptr; }
+    virtual const memory_desc_t *weights_md(int index = 0) const override
+    { return index == 0 ? &weights_md_ : nullptr; }
+
+    virtual int n_inputs() const override { return 2; }
+    virtual int n_outputs() const override { return 1; }
+
+protected:
+    memory_desc_t diff_src_md_;
+    memory_desc_t weights_md_;
+    memory_desc_t diff_dst_md_;
+
+    status_t set_default_params() {
+        return template_set_default_params(diff_src_md_, weights_md_,
+                diff_dst_md_, nullptr);
+    }
+};
+
+struct inner_product_bwd_weights_pd_t: public inner_product_pd_t {
+    typedef inner_product_bwd_weights_pd_t base_class;
+    typedef inner_product_fwd_pd_t hint_class;
+
+    inner_product_bwd_weights_pd_t(engine_t *engine,
+            const inner_product_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const inner_product_fwd_pd_t *hint_fwd_pd)
+        : inner_product_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , src_md_(desc_.src_desc)
+        , diff_weights_md_(desc_.diff_weights_desc)
+        , diff_bias_md_(desc_.diff_bias_desc)
+        , diff_dst_md_(desc_.diff_dst_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_SRC, MKLDNN_ARG_DIFF_DST))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DIFF_WEIGHTS)
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_DIFF_BIAS && with_bias())
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &src_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 ? &diff_dst_md_ : nullptr; }
+    virtual const memory_desc_t *diff_weights_md(int index = 0) const override {
+        if (index == 0) return &diff_weights_md_;
+        if (index == 1 && with_bias()) return &diff_bias_md_;
+        return nullptr;
+    }
+
+    virtual int n_inputs() const override { return 2; }
+    virtual int n_outputs() const override { return 1 + with_bias(); }
+
+protected:
+    memory_desc_t src_md_;
+    memory_desc_t diff_weights_md_;
+    memory_desc_t diff_bias_md_;
+    memory_desc_t diff_dst_md_;
+
+    status_t set_default_params() {
+        return template_set_default_params(src_md_, diff_weights_md_,
+                diff_dst_md_, &diff_bias_md_);
+    }
+};
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/lrn.cpp b/thirdparty/oidn/mkl-dnn/src/common/lrn.cpp
new file mode 100644
index 0000000000..fcf18b556f
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/lrn.cpp
@@ -0,0 +1,91 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::alg_kind;
+using namespace mkldnn::impl::types;
+
+namespace {
+status_t lrn_desc_init(lrn_desc_t *lrn_desc,
+        prop_kind_t prop_kind, alg_kind_t alg_kind,
+        const memory_desc_t *data_desc, const memory_desc_t *diff_data_desc,
+        dim_t local_size, float alpha, float beta, float k) {
+    bool args_ok = true
+        && !any_null(lrn_desc, data_desc)
+        && one_of(alg_kind, lrn_within_channel, lrn_across_channels)
+        && one_of(prop_kind, forward_training, forward_inference, backward_data)
+        && IMPLICATION(prop_kind == backward_data, diff_data_desc != nullptr);
+    if (!args_ok) return invalid_arguments;
+
+    auto ld = lrn_desc_t();
+    ld.primitive_kind = primitive_kind::lrn;
+    ld.prop_kind = prop_kind;
+    ld.alg_kind = alg_kind;
+
+    const bool is_fwd = one_of(prop_kind, forward_training, forward_inference);
+
+    ld.data_desc = *data_desc;
+    if (!is_fwd)
+        ld.diff_data_desc = *diff_data_desc;
+    else
+        ld.diff_data_desc = zero_md();
+    ld.local_size = local_size;
+    ld.lrn_alpha = alpha;
+    ld.lrn_beta = beta;
+    ld.lrn_k = k;
+
+    bool consistency = true
+        && ld.data_desc.ndims == 4;
+    if (ld.prop_kind == backward_data)
+        consistency = consistency
+            && ld.diff_data_desc.ndims == 4
+            && array_cmp(ld.diff_data_desc.dims, ld.data_desc.dims, 4);
+    if (!consistency) return invalid_arguments;
+
+    *lrn_desc = ld;
+    return success;
+}
+}
+
+status_t mkldnn_lrn_forward_desc_init(lrn_desc_t *lrn_desc,
+        prop_kind_t prop_kind, alg_kind_t alg_kind,
+        const memory_desc_t *data_desc, dim_t local_size, float alpha,
+        float beta, float k) {
+    if (!one_of(prop_kind, forward_training, forward_inference))
+        return invalid_arguments;
+    return lrn_desc_init(lrn_desc, prop_kind, alg_kind, data_desc, nullptr,
+            local_size, alpha, beta, k);
+}
+
+status_t mkldnn_lrn_backward_desc_init(lrn_desc_t *lrn_desc,
+        alg_kind_t alg_kind, const memory_desc_t *data_desc,
+        const memory_desc_t *diff_data_desc, dim_t local_size, float alpha,
+        float beta, float k) {
+    return lrn_desc_init(lrn_desc, backward_data, alg_kind, data_desc,
+            diff_data_desc, local_size, alpha, beta, k);
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/lrn_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/lrn_pd.hpp
new file mode 100644
index 0000000000..90886e9656
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/lrn_pd.hpp
@@ -0,0 +1,170 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef LRN_PD_HPP
+#define LRN_PD_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "primitive_desc.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct lrn_fwd_pd_t;
+
+struct lrn_pd_t: public primitive_desc_t {
+    static constexpr auto base_pkind = primitive_kind::lrn;
+
+    lrn_pd_t(engine_t *engine,
+            const lrn_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const lrn_fwd_pd_t *hint_fwd_pd)
+        : primitive_desc_t(engine, attr, base_pkind)
+        , desc_(*adesc)
+        , hint_fwd_pd_(hint_fwd_pd)
+        , data_md_(desc_.data_desc)
+        , ws_md_()
+    {}
+
+    const lrn_desc_t *desc() const { return &desc_; }
+    virtual const op_desc_t *op_desc() const override
+    { return reinterpret_cast<const op_desc_t *>(this->desc()); }
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual status_t query(query_t what, int idx, void *result) const override {
+        switch (what) {
+        case query::lrn_d:
+            *(const lrn_desc_t**)result = desc(); break;
+        default: return primitive_desc_t::query(what, idx, result);
+        }
+        return status::success;
+    }
+
+    /* common lrn aux functions */
+
+    dim_t MB() const { return data_desc().dims[0]; }
+    dim_t C() const { return data_desc().dims[1]; }
+    dim_t D() const { return ndims() >= 5 ? data_desc().dims[ndims() - 3] : 1; }
+    dim_t H() const { return ndims() >= 4 ? data_desc().dims[ndims() - 2] : 1; }
+    dim_t W() const { return ndims() >= 3 ? data_desc().dims[ndims() - 1] : 1; }
+
+    int ndims() const { return data_desc().ndims; }
+
+    bool has_zero_dim_memory() const
+    { return memory_desc_wrapper(desc_.data_desc).has_zero_dim(); }
+
+    bool is_fwd() const {
+        return utils::one_of(desc_.prop_kind, prop_kind::forward_training,
+                prop_kind::forward_inference);
+    }
+
+protected:
+    lrn_desc_t desc_;
+    const lrn_fwd_pd_t *hint_fwd_pd_;
+
+    memory_desc_t data_md_;
+    memory_desc_t ws_md_;
+
+private:
+    const memory_desc_t &data_desc() const { return desc_.data_desc; }
+};
+
+struct lrn_fwd_pd_t: public lrn_pd_t {
+    typedef lrn_fwd_pd_t base_class;
+    typedef lrn_fwd_pd_t hint_class;
+
+    lrn_fwd_pd_t(engine_t *engine,
+            const lrn_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const lrn_fwd_pd_t *hint_fwd_pd)
+        : lrn_pd_t(engine, adesc, attr, hint_fwd_pd)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (arg == MKLDNN_ARG_SRC)
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DST)
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_WORKSPACE && (workspace_md() != nullptr))
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &data_md_ : nullptr; }
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 ? &data_md_ : nullptr; }
+    virtual const memory_desc_t *workspace_md(int index = 0) const override
+    { return index == 0 && !types::is_zero_md(&ws_md_) ? &ws_md_ : nullptr; }
+
+    virtual int n_inputs() const override { return 1; }
+    virtual int n_outputs() const override
+    { return 1 + (workspace_md() != nullptr); }
+};
+
+struct lrn_bwd_pd_t: public lrn_pd_t {
+    typedef lrn_bwd_pd_t base_class;
+    typedef lrn_fwd_pd_t hint_class;
+
+    lrn_bwd_pd_t(engine_t *engine,
+            const lrn_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const lrn_fwd_pd_t *hint_fwd_pd)
+        : lrn_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , diff_data_md_(desc_.diff_data_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_SRC, MKLDNN_ARG_DIFF_DST))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DIFF_SRC)
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_WORKSPACE && (workspace_md() != nullptr))
+            return arg_usage_t::input;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &data_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 ? &diff_data_md_ : nullptr; }
+    virtual const memory_desc_t *diff_src_md(int index = 0) const override
+    { return index == 0 ? &diff_data_md_ : nullptr; }
+    virtual const memory_desc_t *workspace_md(int index = 0) const override
+    { return index == 0 && !types::is_zero_md(&ws_md_) ? &ws_md_ : nullptr; }
+
+    virtual int n_inputs() const override
+    { return 2 + (workspace_md() != nullptr); }
+    virtual int n_outputs() const override { return 1; }
+
+protected:
+    memory_desc_t diff_data_md_;
+};
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/math_utils.hpp b/thirdparty/oidn/mkl-dnn/src/common/math_utils.hpp
new file mode 100644
index 0000000000..3fddc0bd45
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/math_utils.hpp
@@ -0,0 +1,280 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MATH_UTILS_HPP
+#define MATH_UTILS_HPP
+
+#include <stdint.h>
+#include <math.h>
+
+#include "utils.hpp"
+#include "nstl.hpp"
+#include "mkldnn_traits.hpp"
+
+#if defined(MKLDNN_X86_64)
+#include "immintrin.h"
+#endif
+
+namespace mkldnn {
+namespace impl {
+namespace math {
+
+/** rounds @p f to an integer according to the mxcsr register */
+inline int mxcsr_round(float f) {
+#if defined(MKLDNN_X86_64)
+    return _mm_cvtss_si32(_mm_load_ss(&f));
+#else
+    return (int)nearbyintf(f); // optimism
+#endif
+}
+
+template <typename data_t, typename acc_t>
+inline typename utils::enable_if<!nstl::is_integral<data_t>::value,
+       typename utils::remove_reference<data_t>::type>::type
+saturate(const acc_t &x) {
+    return (typename utils::remove_reference<data_t>::type)x;
+}
+
+template <typename data_t, typename acc_t>
+inline typename utils::enable_if<nstl::is_integral<data_t>::value,
+       typename utils::remove_reference<data_t>::type>::type
+saturate(const acc_t &x) {
+    acc_t v = x;
+    if (v < (acc_t)nstl::numeric_limits<data_t>::lowest())
+        v = (acc_t)nstl::numeric_limits<data_t>::lowest();
+    if (v > (acc_t)nstl::numeric_limits<data_t>::max())
+        v = (acc_t)nstl::numeric_limits<data_t>::max();
+    return (typename utils::remove_reference<data_t>::type)v;
+}
+
+template <typename data_t>
+double saturate(const double &x) {
+    double v = x;
+    if (v < (double)nstl::numeric_limits<data_t>::lowest())
+        v = (double)nstl::numeric_limits<data_t>::lowest();
+    if (v > (double)nstl::numeric_limits<data_t>::max())
+        v = (double)nstl::numeric_limits<data_t>::max();
+    return v;
+}
+
+template <> inline int8_t saturate<int8_t, uint8_t>(const uint8_t &x) {
+    return x <= 127u ? x : 127;
+}
+
+template <> inline uint8_t saturate<uint8_t, int8_t>(const int8_t &x) {
+    return x >= 0 ? x : 0;
+}
+
+template <typename out_t>
+typename utils::enable_if<nstl::is_integral<out_t>::value, out_t>::type
+out_round(float v) { return (out_t)mxcsr_round(v); }
+
+template <typename out_t>
+typename utils::enable_if<nstl::is_integral<out_t>::value, out_t>::type
+out_round(double v) { return (out_t)mxcsr_round((float)v); }
+
+template <typename out_t>
+typename utils::enable_if<!nstl::is_integral<out_t>::value, out_t>::type
+out_round(float v) { return v; }
+
+inline int gcd(int a, int b) {
+    a = impl::nstl::abs(a);
+    b = impl::nstl::abs(b);
+    if (a < b) { int x = a; a = b; b = x; }
+
+    if (b == 0) return a;
+
+    int r;
+    while ((r = a % b) != 0) { a = b; b = r; }
+
+    return b;
+}
+
+template <typename T>
+inline bool is_pow2(const T& v) { return (v & (v - 1)) == 0; }
+
+/** returns floor(log2(v)), aka the position of the leftmost non-0 bit */
+inline int ilog2q(size_t v) {
+    if (v == 0)
+        return -1;
+
+    int p = 0;
+#   define CP(pw) do { if (v >= (1ull << pw)) { v >>= pw; p += pw; } } while(0)
+    CP(32); CP(16); CP(8); CP(4); CP(2); CP(1);
+#   undef CP
+    return p;
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U one_m_square(T x) {
+    return (U)(1 - x) * (1 + x);
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U x_m_square(T x) {
+    return (U)(1 - x) * x;
+}
+
+/* activation */
+template <typename T, typename A,
+         typename U = typename utils::remove_reference<T>::type>
+inline U relu_fwd(T s, A alpha) {
+    return s > 0 ? s : (U)(s * alpha);
+}
+template <typename T, typename A,
+         typename U = typename utils::remove_reference<T>::type>
+inline U relu_bwd(T dd, T s, A alpha) {
+    return s > 0 ? dd : (U)(dd * alpha);
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U tanh_fwd(T s) {
+    const float e = tanhf((float) s);
+    return (U)e;
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U tanh_bwd(T dd, T s) {
+    const float e = tanh_fwd<float>((float) s);
+    return (U)(dd * (1 - e) * (1 + e));
+}
+
+template <typename T, typename A,
+         typename U = typename utils::remove_reference<T>::type>
+inline U elu_fwd(T s, A alpha) {
+    return s > 0 ? s : (U)(alpha * (::expm1f((float)s)));
+}
+template <typename T, typename A,
+         typename U = typename utils::remove_reference<T>::type>
+ inline U elu_bwd(T dd, T s, A alpha) {
+    return (U)(dd * (s > 0 ? 1 : alpha * ::expf((float)s)));
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U square_fwd(T s) {
+    return s * s;
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U square_bwd(T dd, T s) {
+    return dd * 2 * s;
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U abs_fwd(T s) {
+    return s > 0 ? s : -s;
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U abs_bwd(T dd, T s) {
+    return s > 0 ? dd : s < 0 ? -dd : 0;
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U sqrt_fwd(T s) {
+    return s > 0 ? (U)(::sqrtf((float)(s))) : 0;
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U sqrt_bwd(T dd, T s) {
+    return s > 0
+        ? (U)(dd / (2 * ::sqrtf((float)(s))))
+        : 0;
+}
+
+template <typename T, typename A,
+         typename U = typename utils::remove_reference<T>::type>
+inline U linear_fwd(T s, A alpha, A beta) {
+    return (U)(alpha * s + beta);
+}
+
+template <typename T, typename A,
+         typename U = typename utils::remove_reference<T>::type>
+inline U linear_bwd(T dd, T s, A alpha, A beta) {
+    (void) s;
+    (void) beta;
+    return (U)(dd * alpha);
+}
+
+template <typename T, typename A,
+         typename U = typename utils::remove_reference<T>::type>
+inline U bounded_relu_fwd(T s, A alpha) {
+    s = s > 0 ? s : 0;
+    return s > alpha ? (U)(alpha) : s;
+}
+
+template <typename T, typename A,
+         typename U = typename utils::remove_reference<T>::type>
+inline U bounded_relu_bwd(T dd, T s, A alpha) {
+    return dd * (0 < s && s < alpha ? 1 : 0);
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U soft_relu_fwd(T s) {
+    float max_logf = 8.872284e+01; //::logf(FLT_MAX)
+    return s < max_logf ? (U)(::log1pf(::expf((float)s))) : s;
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U soft_relu_bwd(T dd, T s) {
+    return (U)(dd / (1 + ::expf((float)(-s))));
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U logistic_fwd(T s) {
+    U v = (U)(::expf((float) -s));
+    return 1 / (1 + v);
+}
+
+template <typename T, typename U = typename utils::remove_reference<T>::type>
+inline U logistic_bwd(T dd, T s) {
+    U v = logistic_fwd<T, U>(s);
+    return dd * v * (1 - v);
+}
+
+inline bool eltwise_fwd_preserves_zero(alg_kind_t alg, bool jit_impl = false) {
+    using namespace alg_kind;
+    using namespace utils;
+    const bool preserves_zero = true
+        && !one_of(alg, eltwise_linear, eltwise_soft_relu, eltwise_logistic)
+        && IMPLICATION(jit_impl, !one_of(alg, eltwise_elu, eltwise_tanh));
+    return preserves_zero;
+}
+
+inline float get_bias(const char *bias, size_t offset, data_type_t data_type)
+{
+    if (!bias)
+        return 0.0f;
+
+#define CASE(dt) \
+    case dt: return (float)((const prec_traits<dt>::type *)bias)[offset]
+
+    switch (data_type) {
+    CASE(data_type::s8);
+    CASE(data_type::u8);
+    CASE(data_type::s32);
+    CASE(data_type::f32);
+    default: assert(!"unimplemented");
+    }
+    return 0; // never happens (should probably be a NaN)
+#undef CASE
+}
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/memory.cpp b/thirdparty/oidn/mkl-dnn/src/common/memory.cpp
new file mode 100644
index 0000000000..cea849c96e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/memory.cpp
@@ -0,0 +1,238 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "engine.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::data_type;
+
+namespace {
+bool memory_desc_sanity_check(int ndims,const dims_t dims,
+        data_type_t data_type, format_kind_t format_kind) {
+    if (ndims == 0) return true;
+
+    bool ok = true
+        && dims != nullptr
+        && 0 < ndims && ndims <= MKLDNN_MAX_NDIMS
+        && one_of(data_type, f32, s32, s8, u8)
+        && format_kind != format_kind::undef;
+    if (!ok) return false;
+    for (int d = 0; d < ndims; ++d)
+        if (dims[d] < 0) return false;
+
+    return true;
+}
+
+bool memory_desc_sanity_check(const memory_desc_t *md) {
+    if (md == nullptr) return false;
+    return memory_desc_sanity_check(md->ndims, md->dims, md->data_type,
+            format_kind::any);
+}
+}
+
+status_t mkldnn_memory_desc_init_by_tag(memory_desc_t *memory_desc, int ndims,
+        const dims_t dims, data_type_t data_type, format_tag_t tag) {
+    if (any_null(memory_desc)) return invalid_arguments;
+    if (ndims == 0 || tag == format_tag::undef) {
+        *memory_desc = types::zero_md();
+        return success;
+    }
+
+    format_kind_t format_kind = types::format_tag_to_kind(tag);
+
+    /* memory_desc != 0 */
+    bool args_ok = !any_null(memory_desc)
+        && memory_desc_sanity_check(ndims, dims, data_type, format_kind);
+    if (!args_ok) return invalid_arguments;
+
+    auto md = memory_desc_t();
+    md.ndims = ndims;
+    array_copy(md.dims, dims, ndims);
+    md.data_type = data_type;
+    array_copy(md.padded_dims, dims, ndims);
+    md.format_kind = format_kind;
+
+    status_t status = success;
+    if (tag == format_tag::undef) {
+        status = invalid_arguments;
+    } else if (tag == format_tag::any) {
+        // nop
+    } else if (format_kind == format_kind::blocked) {
+        status = memory_desc_wrapper::compute_blocking(md, tag);
+    } else {
+        assert(!"unreachable");
+        status = invalid_arguments;
+    }
+
+    if (status == success)
+        *memory_desc = md;
+
+    return status;
+}
+
+status_t mkldnn_memory_desc_init_by_strides(memory_desc_t *memory_desc,
+        int ndims, const dims_t dims, data_type_t data_type,
+        const dims_t strides) {
+    if (any_null(memory_desc)) return invalid_arguments;
+    if (ndims == 0) {
+        *memory_desc = types::zero_md();
+        return success;
+    }
+
+    /* memory_desc != 0 */
+    bool args_ok = !any_null(memory_desc)
+        && memory_desc_sanity_check(ndims, dims, data_type, format_kind::any);
+    if (!args_ok) return invalid_arguments;
+
+    auto md = memory_desc_t();
+    md.ndims = ndims;
+    array_copy(md.dims, dims, ndims);
+    md.data_type = data_type;
+    array_copy(md.padded_dims, dims, ndims);
+    md.format_kind = format_kind::blocked;
+
+    dims_t default_strides = {0};
+    if (strides == nullptr) {
+        default_strides[md.ndims - 1] = 1;
+        for (int d = md.ndims - 2; d >= 0; --d)
+            default_strides[d] = default_strides[d + 1] * md.padded_dims[d + 1];
+        strides = default_strides;
+    } else {
+        /* TODO: add sanity check for the provided strides */
+    }
+
+    array_copy(md.format_desc.blocking.strides, strides, md.ndims);
+
+    *memory_desc = md;
+
+    return status::success;
+}
+
+status_t mkldnn_memory_desc_init_submemory(memory_desc_t *md,
+        const memory_desc_t *parent_md, const dims_t dims,
+        const dims_t offsets) {
+    if (any_null(md, parent_md) || !memory_desc_sanity_check(parent_md))
+        return invalid_arguments;
+
+    const memory_desc_wrapper src_d(parent_md);
+
+    for (int d = 0; d < src_d.ndims(); ++d) {
+        if (dims[d] < 0 || offsets[d] < 0
+                || (offsets[d] + dims[d] > src_d.dims()[d]))
+            return invalid_arguments;
+    }
+
+    if (src_d.format_kind() != format_kind::blocked)
+        return unimplemented;
+
+    dims_t blocks;
+    src_d.compute_blocks(blocks);
+
+    memory_desc_t dst_d = *parent_md;
+    auto &dst_d_blk = dst_d.format_desc.blocking;
+
+    /* TODO: put this into memory_desc_wrapper */
+    for (int d = 0; d < src_d.ndims(); ++d) {
+        /* very limited functionality for now */
+        const bool ok = true
+            && offsets[d] % blocks[d] == 0 /* [r1] */
+            && src_d.padded_offsets()[d] == 0
+            && (false
+                    || dims[d] % blocks[d] == 0
+                    || dims[d] < blocks[d]);
+        if (!ok)
+            return unimplemented;
+
+        const bool is_right_border = offsets[d] + dims[d] == src_d.dims()[d];
+
+        dst_d.dims[d] = dims[d];
+        dst_d.padded_dims[d] = is_right_border
+            ? src_d.padded_dims()[d] - offsets[d] : dst_d.dims[d];
+        dst_d.padded_offsets[d] = src_d.padded_offsets()[d];
+        dst_d.offset0 += /* [r1] */
+            offsets[d] / blocks[d] * dst_d_blk.strides[d];
+    }
+
+    *md = dst_d;
+
+    return success;
+}
+
+int mkldnn_memory_desc_equal(const memory_desc_t *lhs,
+        const memory_desc_t *rhs) {
+    if (lhs == rhs) return 1;
+    if (any_null(lhs, rhs)) return 0;
+    return memory_desc_wrapper(*lhs) == memory_desc_wrapper(*rhs);
+}
+
+size_t mkldnn_memory_desc_get_size(const memory_desc_t *md) {
+    if (md == nullptr) return 0;
+    return memory_desc_wrapper(*md).size();
+}
+
+status_t mkldnn_memory_create(memory_t **memory, const memory_desc_t *md,
+        engine_t *engine, void *handle) {
+    if (any_null(memory, engine)) return invalid_arguments;
+    memory_desc_t z_md = types::zero_md();
+    return engine->memory_create(memory, md ? md : &z_md, handle);
+}
+
+status_t mkldnn_memory_get_memory_desc(const memory_t *memory,
+        const memory_desc_t **md) {
+    if (any_null(memory, md)) return invalid_arguments;
+    *md = memory->md();
+    return success;
+}
+
+status_t mkldnn_memory_get_engine(const memory_t *memory, engine_t **engine) {
+    if (any_null(memory, engine)) return invalid_arguments;
+    *engine = memory->engine();
+    return success;
+}
+
+status_t mkldnn_memory_get_data_handle(const memory_t *memory,
+        void **handle) {
+    if (any_null(handle))
+        return invalid_arguments;
+    if (memory == nullptr) {
+        *handle = nullptr;
+        return success;
+    }
+    return memory->get_data_handle(handle);
+}
+
+status_t mkldnn_memory_set_data_handle(memory_t *memory, void *handle) {
+    if (any_null(memory)) return invalid_arguments;
+    return memory->set_data_handle(handle);
+}
+
+status_t mkldnn_memory_destroy(memory_t *memory) {
+    delete memory;
+    return success;
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/memory.hpp b/thirdparty/oidn/mkl-dnn/src/common/memory.hpp
new file mode 100644
index 0000000000..03dfee01ff
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/memory.hpp
@@ -0,0 +1,63 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MEMORY_HPP
+#define MEMORY_HPP
+
+#include <assert.h>
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+
+struct mkldnn_memory: public mkldnn::impl::c_compatible {
+    mkldnn_memory(mkldnn::impl::engine_t *engine,
+            const mkldnn::impl::memory_desc_t *md)
+        : engine_(engine), md_(*md) {}
+    virtual ~mkldnn_memory() {}
+
+    /** allocates/initializes memory */
+    virtual mkldnn::impl::status_t init() = 0;
+
+    /** returns memory's engine */
+    mkldnn::impl::engine_t *engine() const { return engine_; }
+    /** returns memory's description */
+    const mkldnn::impl::memory_desc_t *md() const { return &md_; }
+
+    /** returns data handle */
+    virtual mkldnn::impl::status_t get_data_handle(void **handle) const = 0;
+
+    /** sets data handle */
+    virtual mkldnn::impl::status_t set_data_handle(void *handle) = 0;
+
+    /** zeros padding */
+    virtual mkldnn::impl::status_t zero_pad() const
+    { return mkldnn::impl::status::success; }
+
+protected:
+    mkldnn::impl::engine_t *engine_;
+    const mkldnn::impl::memory_desc_t md_;
+
+private:
+    mkldnn_memory() = delete;
+    mkldnn_memory(const mkldnn_memory &) = delete;
+    mkldnn_memory(mkldnn_memory &&) = delete;
+    mkldnn_memory &operator=(const mkldnn_memory &) = delete;
+    mkldnn_memory &operator=(mkldnn_memory &&) = delete;
+};
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/memory_desc_wrapper.cpp b/thirdparty/oidn/mkl-dnn/src/common/memory_desc_wrapper.cpp
new file mode 100644
index 0000000000..8a99be33f3
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/memory_desc_wrapper.cpp
@@ -0,0 +1,212 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include <initializer_list>
+
+#include "c_types_map.hpp"
+#include "memory_desc_wrapper.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+status_t fill_blocked(memory_desc_t &md,
+        std::initializer_list<int> perm,
+        std::initializer_list<int> inner_blks,
+        std::initializer_list<int> inner_idxs) {
+    const bool ok = true
+        && perm.size() == (size_t)md.ndims
+        && inner_blks.size() == inner_idxs.size();
+    if (!ok) return status::invalid_arguments;
+
+    md.offset0 = 0;
+
+    blocking_desc_t &blk = md.format_desc.blocking;
+
+    dim_t block_size = 1;
+    dims_t blocks = {0};
+    utils::array_set(blocks, 1, md.ndims);
+
+    blk.inner_nblks = (int)inner_blks.size();
+
+    int iblk = 0;
+    for (const auto &b: inner_idxs)
+        blk.inner_idxs[iblk++] = b;
+
+    iblk = 0;
+    for (const auto &b: inner_blks) {
+        int dim = blk.inner_idxs[iblk];
+        block_size *= b;
+        blocks[dim] *= b;
+        blk.inner_blks[iblk++] = b;
+    }
+
+    utils::array_set(md.padded_offsets, 0, md.ndims);
+    for (int d = 0; d < md.ndims; ++d)
+        md.padded_dims[d] = utils::rnd_up(md.dims[d], blocks[d]);
+
+    dim_t stride = block_size;
+    // if only we use C++14, the initializer_list would have rbegin()/rend()...
+    for (int d = 0; d < md.ndims; ++d)
+        stride *= md.padded_dims[d] == 0 ? 1 : md.padded_dims[d] / blocks[d];
+
+    for (const auto &d: perm) {
+        if (md.padded_dims[d] == 0) {
+             blk.strides[d] = 1;
+             continue;
+        }
+        stride /= md.padded_dims[d] / blocks[d];
+        blk.strides[d] = stride;
+    }
+
+    assert(stride == block_size);
+
+    return status::success;
+}
+
+status_t memory_desc_wrapper::compute_blocking(memory_desc_t &memory_desc,
+        format_tag_t tag)
+{
+    using namespace format_tag;
+
+    if (memory_desc.ndims == 0) return status::invalid_arguments;
+
+#   define C(tag, ... /* perm, inner_blks, inner_idxs */) \
+    case tag: return fill_blocked(memory_desc, __VA_ARGS__)
+
+    switch (tag) {
+    C(a, {0}, {}, {});
+    C(ab, {0, 1}, {}, {});
+    C(abc, {0, 1, 2}, {}, {});
+    C(abcd, {0, 1, 2, 3}, {}, {});
+    C(abcde, {0, 1, 2, 3, 4}, {}, {});
+    C(abcdef, {0, 1, 2, 3, 4, 5}, {}, {});
+    C(abdec, {0, 1, 3, 4, 2}, {}, {});
+    C(acb, {0, 2, 1}, {}, {});
+    C(acbde, {0, 2, 1, 3, 4}, {}, {});
+    C(acdb, {0, 2, 3, 1}, {}, {});
+    C(acdeb, {0, 2, 3, 4, 1}, {}, {});
+    C(ba, {1, 0}, {}, {});
+    C(bac, {1, 0, 2}, {}, {});
+    C(bacd, {1, 0, 2, 3}, {}, {});
+    C(bcda, {1, 2, 3, 0}, {}, {});
+    C(cba, {2, 1, 0}, {}, {});
+    C(cdba, {2, 3, 1, 0}, {}, {});
+    C(cdeba, {2, 3, 4, 1, 0}, {}, {});
+    C(decab, {3, 4, 2, 0, 1}, {}, {});
+
+    C(Abc4a, {0, 1, 2}, {4}, {0});
+    C(aBc4b, {0, 1, 2}, {4}, {1});
+    C(ABc4b16a4b, {0, 1, 2}, {4, 16, 4}, {1, 0, 1});
+    C(ABc4b4a, {0, 1, 2}, {4, 4}, {1, 0});
+    C(Abcd4a, {0, 1, 2, 3}, {4}, {0});
+    C(aBcd4b, {0, 1, 2, 3}, {4}, {1});
+    C(ABcd4b4a, {0, 1, 2, 3}, {4, 4}, {1, 0});
+    C(aBCd4c16b4c, {0, 1, 2, 3}, {4, 16, 4}, {2, 1, 2});
+    C(aBCd4c4b, {0, 1, 2, 3, 4}, {4, 4}, {2, 1});
+    C(Abcde4a, {0, 1, 2, 3, 4}, {4}, {0});
+    C(aBcde4b, {0, 1, 2, 3, 4}, {4}, {1});
+    C(ABcde4b4a, {0, 1, 2, 3, 4}, {4, 4}, {1, 0});
+    C(aBCde4c4b, {0, 1, 2, 3, 4}, {4, 4}, {2, 1});
+    C(aBcdef4b, {0, 1, 2, 3, 4, 5}, {4}, {1});
+    C(aBCdef4c4b, {0, 1, 2, 3, 4, 5}, {4, 4}, {2, 1});
+    C(aBdc4b, {0, 1, 3, 2}, {4}, {1});
+    C(aBdec4b, {0, 1, 3, 4, 2}, {4}, {1});
+    C(aBdefc4b, {0, 1, 3, 4, 5, 2}, {4}, {1});
+    C(Acb4a, {0, 2, 1}, {4}, {0});
+    C(Acdb4a, {0, 2, 3, 1}, {4}, {0});
+    C(Acdeb4a, {0, 2, 3, 4, 1}, {4}, {0});
+
+    C(Abc16a, {0, 1, 2}, {16}, {0});
+    C(ABc16a16b, {0, 1, 2}, {16, 16}, {0, 1});
+    C(aBc16b, {0, 1, 2}, {16}, {1});
+    C(ABc16b16a, {0, 1, 2}, {16, 16}, {1, 0});
+    C(ABc8a16b2a, {0, 1, 2}, {8, 16, 2}, {0, 1, 0});
+    C(ABc8a8b, {0, 1, 2}, {8, 8}, {0, 1});
+    C(aBc8b, {0, 1, 2}, {8}, {1});
+    C(ABc8b16a2b, {0, 1, 2}, {8, 16, 2}, {1, 0, 1});
+    C(ABc8b8a, {0, 1, 2}, {8, 8}, {1, 0});
+    C(Abcd16a, {0, 1, 2, 3}, {16}, {0});
+    C(ABcd16a16b, {0, 1, 2, 3}, {16, 16}, {0, 1});
+    C(aBcd16b, {0, 1, 2, 3}, {16}, {1});
+    C(ABcd16b16a, {0, 1, 2, 3}, {16, 16}, {1, 0});
+    C(aBCd16b16c, {0, 1, 2, 3}, {16, 16}, {1, 2});
+    C(aBCd16c16b, {0, 1, 2, 3}, {16, 16}, {2, 1});
+    C(ABcd4b16a4b, {0, 1, 2, 3}, {4, 16, 4}, {1, 0, 1});
+    C(ABcd8a16b2a, {0, 1, 2, 3}, {8, 16, 2}, {0, 1, 0});
+    C(ABcd8a8b, {0, 1, 2, 3}, {8, 8}, {0, 1});
+    C(aBcd8b, {0, 1, 2, 3}, {8}, {1});
+    C(ABcd8b16a2b, {0, 1, 2, 3}, {8, 16, 2}, {1, 0, 1});
+    C(aBCd8b16c2b, {0, 1, 2, 3}, {8, 16, 2}, {1, 2, 1});
+    C(ABcd8b8a, {0, 1, 2, 3}, {8, 8}, {1, 0});
+    C(aBCd8b8c, {0, 1, 2, 3}, {8, 8}, {1, 2});
+    C(aBCd8c16b2c, {0, 1, 2, 3}, {8, 16, 2}, {2, 1, 2});
+    C(aBCd8c8b, {0, 1, 2, 3}, {8, 8}, {2, 1});
+    C(Abcde16a, {0, 1, 2, 3, 4}, {16}, {0});
+    C(ABcde16a16b, {0, 1, 2, 3, 4}, {16, 16}, {0, 1});
+    C(aBcde16b, {0, 1, 2, 3, 4}, {16}, {1});
+    C(ABcde16b16a, {0, 1, 2, 3, 4}, {16, 16}, {1, 0});
+    C(aBCde16b16c, {0, 1, 2, 3, 4}, {16, 16}, {1, 2});
+    C(aBCde16c16b, {0, 1, 2, 3, 4}, {16, 16}, {2, 1});
+    C(aBCde2c8b4c, {0, 1, 2, 3, 4}, {2, 8, 4}, {2, 1, 2});
+    C(aBCde4b4c, {0, 1, 2, 3, 4}, {4, 4}, {1, 2});
+    C(aBCde4c16b4c, {0, 1, 2, 3, 4}, {4, 16, 4}, {2, 1, 2});
+    C(Abcde8a, {0, 1, 2, 3, 4}, {8}, {0});
+    C(ABcde8a8b, {0, 1, 2, 3, 4}, {8, 8}, {0, 1});
+    C(aBcde8b, {0, 1, 2, 3, 4}, {8}, {1});
+    C(ABcde8b16a2b, {0, 1, 2, 3, 4}, {8, 16, 2}, {1, 0, 1});
+    C(aBCde8b16c2b, {0, 1, 2, 3, 4}, {8, 16, 2}, {1, 2, 1});
+    C(ABcde8b8a, {0, 1, 2, 3, 4}, {8, 8}, {1, 0});
+    C(aBCde8b8c, {0, 1, 2, 3, 4}, {8, 8}, {1, 2});
+    C(aBCde8c16b2c, {0, 1, 2, 3, 4}, {8, 16, 2}, {2, 1, 2});
+    C(aBCde8c8b, {0, 1, 2, 3, 4}, {8, 8}, {2, 1});
+    C(aBcdef16b, {0, 1, 2, 3, 4, 5}, {16}, {1});
+    C(aBCdef16b16c, {0, 1, 2, 3, 4, 5}, {16, 16}, {1, 2});
+    C(aBCdef16c16b, {0, 1, 2, 3, 4, 5}, {16, 16}, {2, 1});
+    C(aBCdef8b8c, {0, 1, 2, 3, 4, 5}, {8, 8}, {1, 2});
+    C(aBCdef8c16b2c, {0, 1, 2, 3, 4, 5}, {8, 16, 2}, {2, 1, 2});
+    C(aBCdef8c8b, {0, 1, 2, 3, 4, 5}, {8, 8}, {2, 1});
+    C(aBdc16b, {0, 1, 3, 2}, {16}, {1});
+    C(aBdc8b, {0, 1, 3, 2}, {8}, {1});
+    C(aBdec16b, {0, 1, 3, 4, 2}, {16}, {1});
+    C(aBdec8b, {0, 1, 3, 4, 2}, {8}, {1});
+    C(aBdefc16b, {0, 1, 3, 4, 5, 2}, {16}, {1});
+    C(aBdefc8b, {0, 1, 3, 4, 5, 2}, {8}, {1});
+    C(Acb16a, {0, 2, 1}, {16}, {0});
+    C(Acb8a, {0, 2, 1}, {8}, {0});
+    C(aCBd16b16c, {0, 2, 1, 3}, {16, 16}, {1, 2});
+    C(aCBde16b16c, {0, 2, 1, 3, 4}, {16, 16}, {1, 2});
+    C(Acdb16a, {0, 2, 3, 1}, {16}, {0});
+    C(Acdb8a, {0, 2, 3, 1}, {8}, {0});
+    C(Acdeb16a, {0, 2, 3, 4, 1}, {16}, {0});
+    C(Acdeb8a, {0, 2, 3, 4, 1}, {8}, {0});
+    C(BAc16a16b, {1, 0, 2}, {16, 16}, {0, 1});
+    C(BAcd16a16b, {1, 0, 2, 3}, {16, 16}, {0, 1});
+    default: break;
+    }
+
+#undef C
+
+    return status::invalid_arguments;
+}
+
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/memory_desc_wrapper.hpp b/thirdparty/oidn/mkl-dnn/src/common/memory_desc_wrapper.hpp
new file mode 100644
index 0000000000..1758f9078a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/memory_desc_wrapper.hpp
@@ -0,0 +1,400 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MEMORY_DESC_WRAPPER_HPP
+#define MEMORY_DESC_WRAPPER_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "utils.hpp"
+
+#include "type_helpers.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+/** thin wrapper class over \struct memory_desc_t which allows easy
+ * manipulations with underlying C structure, which is taken by reference */
+struct memory_desc_wrapper: public c_compatible {
+    const memory_desc_t *md_;
+
+    /** constructor which takes a reference to a constant underlying C memory
+     * descriptor \param md */
+    memory_desc_wrapper(const memory_desc_t *md): md_(md) {}
+    memory_desc_wrapper(const memory_desc_t &md): memory_desc_wrapper(&md) {}
+
+    /* implementing attributes */
+    int ndims() const { return md_->ndims; }
+    const dims_t &dims() const { return md_->dims; }
+    data_type_t data_type() const { return md_->data_type; }
+
+    const dims_t &padded_dims() const { return md_->padded_dims; }
+    const dims_t &padded_offsets() const { return md_->padded_offsets; }
+    dim_t offset0() const { return md_->offset0; }
+
+    format_kind_t format_kind() const { return md_->format_kind; }
+
+    bool is_blocking_desc() const
+    { return format_kind() == format_kind::blocked; }
+    bool is_wino_desc() const
+    { return format_kind() == format_kind::wino; }
+    bool is_rnn_packed_desc() const
+    { return format_kind() == format_kind::rnn_packed; }
+
+    const blocking_desc_t &blocking_desc() const {
+        assert(is_blocking_desc());
+        return md_->format_desc.blocking;
+    }
+    const wino_desc_t &wino_desc() const {
+        assert(is_wino_desc());
+        return md_->format_desc.wino_desc;
+    }
+    const rnn_packed_desc_t &rnn_packed_desc() const {
+        assert(is_rnn_packed_desc());
+        return md_->format_desc.rnn_packed_desc;
+    }
+
+    const memory_extra_desc_t &extra() const { return md_->extra; }
+
+    /* some useful function */
+
+    /** returns the number of elements including padding if \param with_padding
+     * is true, and the number of data elements otherwise */
+    dim_t nelems(bool with_padding = false) const {
+        if (is_zero()) return 0;
+        return utils::array_product(
+                with_padding ? padded_dims() : dims(), ndims());
+    }
+
+    /** returns true if memory descriptor is zero */
+    bool is_zero() const { return ndims() == 0; }
+
+    /** returns true if memory descriptor contains zero as one of its dim */
+    bool has_zero_dim() const { return nelems() == 0; }
+
+    /** return the size of data type (a shortcut) */
+    size_t data_type_size() const
+    { return types::data_type_size(data_type()); }
+
+    /** return the size of data type of additional buffer */
+    size_t additional_buffer_data_size() const {
+        if (extra().flags & memory_extra_flags::compensation_conv_s8s8)
+            return sizeof(int32_t);
+        return 0;
+    }
+
+    /** return true if memory format has additional buffer */
+    bool is_additional_buffer() const {
+        return (extra().flags & memory_extra_flags::compensation_conv_s8s8);
+    }
+
+    /** returns the size of additional buffer */
+    size_t additional_buffer_size() const {
+        if (extra().flags & memory_extra_flags::compensation_conv_s8s8) {
+            int cmask = extra().compensation_mask;
+            assert(cmask == 1 || cmask == 3);
+            dim_t prod = 1;
+            for (int d = 0; d < ndims(); ++d)
+                if (cmask & (1<<d)) prod *= padded_dims()[d];
+            return prod * additional_buffer_data_size();
+        }
+
+        return 0;
+    }
+
+    /** returns the size required to store described memory
+     * note: if offset0 != 0 returns 0 (need to specify the behavior) */
+    size_t size() const {
+        if (is_zero() || has_zero_dim() || format_kind() == format_kind::any)
+            return 0;
+
+        if (format_kind() == format_kind::wino) {
+            return wino_desc().size;
+        } else if (format_kind() == format_kind::rnn_packed) {
+            return rnn_packed_desc().size;
+        } else {
+            if (offset0() != 0) return 0;
+
+            dims_t blocks = {0};
+            compute_blocks(blocks);
+
+            const auto &bd = blocking_desc();
+
+            size_t max_size = 0;
+            for (int d = 0; d < ndims(); ++d)
+                max_size = nstl::max<size_t>(max_size,
+                        padded_dims()[d] / blocks[d] * bd.strides[d]);
+
+            if (max_size == 1 && bd.inner_nblks != 0) {
+                max_size = utils::array_product(bd.inner_blks, bd.inner_nblks);
+            }
+
+            return max_size * data_type_size() + additional_buffer_size();
+        }
+    }
+
+    /** returns true if data is dense in memory */
+    bool is_dense(bool with_padding = false) const {
+        if (utils::one_of(format_kind(), format_kind::undef, format_kind::any))
+            return false;
+        return nelems(with_padding) * data_type_size() == size();
+    }
+
+    /** returns true if memory desc is fully defined */
+    bool is_defined() const { return format_kind() != format_kind::any; }
+
+    /** returns true if the only (potentially) padded dim is \param dim */
+    bool only_padded_dim(int dim) const {
+        for (int d = 0; d < ndims(); ++d)
+            if (d != dim && dims()[d] != padded_dims()[d])
+                return false;
+        return true;
+    }
+
+    /** returns true if memory desc has blocked layout and block dims are 1s */
+    bool is_plain() const {
+        if (!is_blocking_desc()) return false;
+        return blocking_desc().inner_nblks == 0;
+    }
+
+    /** returns overall block sizes */
+    void compute_blocks(dims_t blocks) const {
+        if (!is_blocking_desc()) {
+            utils::array_set(blocks, 0, ndims());
+            return;
+        }
+
+        utils::array_set(blocks, 1, ndims());
+
+        const auto &bd = blocking_desc();
+        for (int iblk = 0; iblk < bd.inner_nblks; ++iblk)
+            blocks[bd.inner_idxs[iblk]] *= bd.inner_blks[iblk];
+    }
+
+    /* comparison section */
+
+    bool operator==(const memory_desc_wrapper &rhs) const
+    { return *this->md_ == *rhs.md_; }
+    bool operator!=(const memory_desc_wrapper &rhs) const
+    { return !operator==(rhs); }
+    bool operator==(const memory_desc_t &rhs) const
+    { return operator==(memory_desc_wrapper(rhs)); }
+    bool operator!=(const memory_desc_t &rhs) const
+    { return !operator==(rhs); }
+
+    /** returns true if data (w/o padding if with_padding == false and w/
+     * padding otherwise) have the same physical structure, i.e. dimensions,
+     * strides, and blocked structure. Depending on with_data_type flag
+     * data_type is taken or not taken into account. dim_start allows to check
+     * similarity for the logical part of data [dim_start .. ndims()].
+     * CAUTION: format kind any and undef are not similar to whatever, hence the
+     * following statement might be true: lhs == rhs && !lhs.similar_to(rhs) */
+    /* TODO: revise */
+    bool similar_to(const memory_desc_wrapper &rhs,
+            bool with_padding = true, bool with_data_type = true,
+            int dim_start = 0) const;
+
+    /** returns true if one memory can be reordered to another */
+    bool consistent_with(const memory_desc_wrapper &rhs) const;
+
+    /** returns true if the memory desc corresponds to the given format tag and
+     * strides.
+     * @sa memory_desc_matches_tag */
+    bool matches_tag(format_tag_t tag, const dims_t strides = nullptr) const {
+        return memory_desc_matches_tag(*md_, tag, strides);
+    }
+
+    /** returns matching tag (or undef if match is not found)
+     * XXX: This is a workaround that eventually should go away! */
+    template <typename... Tags>
+    format_tag_t matches_one_of_tag(Tags ...tags) const {
+        for (const auto tag: {tags...}) {
+            if (memory_desc_matches_tag(*md_, tag))
+                return tag;
+        }
+        return format_tag::undef;
+    }
+
+    /* offset section */
+
+    /** returns physical offset by logical one. logical offset is represented by
+     * an array \param pos. if \param is_pos_padded is true \param pos
+     * represents the position in already padded area */
+    dim_t off_v(const dims_t pos, bool is_pos_padded = false) const {
+        assert(is_blocking_desc());
+        const blocking_desc_t &blk = blocking_desc();
+
+        dims_t pos_copy = {0};
+        for (int d = 0; d < ndims(); ++d)
+            pos_copy[d] = pos[d] + (is_pos_padded ? 0 : padded_offsets()[d]);
+
+        dim_t phys_offset = offset0();
+
+        if (blk.inner_nblks > 0) {
+            dim_t blk_stride = 1;
+            for (int iblk = blk.inner_nblks - 1; iblk >= 0; --iblk) {
+                const int d = blk.inner_idxs[iblk];
+                const dim_t p = pos_copy[d] % blk.inner_blks[iblk];
+
+                phys_offset += p * blk_stride;
+
+                pos_copy[d] /= blk.inner_blks[iblk];
+
+                blk_stride *= blk.inner_blks[iblk];
+            }
+        }
+
+        for (int d = 0; d < ndims(); ++d) {
+            const dim_t p = pos_copy[d];
+            phys_offset += p * blk.strides[d];
+        }
+
+        return phys_offset;
+    }
+
+    /** returns physical offset by logical one. logical offset is represented by
+     * a scalar \param l_offset. if \param is_pos_padded is true, \param
+     * l_offset represents logical offset in already padded area */
+    dim_t off_l(dim_t l_offset, bool is_pos_padded = false) const {
+        assert(is_blocking_desc());
+        dims_t pos;
+        for (int rd = 0; rd < ndims(); ++rd) {
+            const int d = ndims() - 1 - rd;
+            const dim_t cur_dim = is_pos_padded ? padded_dims()[d] : dims()[d];
+            pos[d] = l_offset % cur_dim;
+            l_offset /= cur_dim;
+        }
+        return off_v(pos, is_pos_padded);
+    }
+
+    /** returns physical offset by logical one. logical offset is represented by
+     * a tuple of indices (\param xn, ..., \param x1, \param x0) */
+    template<typename... Args>
+    dim_t off(Args... args) const {
+        assert(sizeof...(args) == ndims());
+        dims_t pos = { args... };
+        return off_v(pos, false);
+    }
+
+    /** returns physical offset by logical one. logical offset is represented by
+     * a tuple of indices (\param xn, ..., \param x1, \param x0) in already
+     * padded area */
+    template<typename... Args>
+    dim_t off_padding(Args... args) const {
+        assert(sizeof...(args) == ndims());
+        dims_t pos = { args... };
+        return off_v(pos, true);
+    }
+
+    /** returns physical offset by logical one. Logical offset is represented by
+     * a tuple of block indices (\param bn, ..., \param b1, \param b0). It is a
+     * user responsibility to adjust the result to get offset within blocks */
+    template<typename ...Args>
+    dim_t blk_off(Args... args) const {
+        return _blk_off<sizeof...(args), Args...>(args...);
+    }
+
+    template<bool skip_first, typename T, typename ...Args>
+    dim_t blk_off(T xn, Args... args) const {
+        return skip_first
+            ? blk_off<Args...>(args...)
+            : blk_off<T, Args...>(xn, args...);
+    }
+
+    /* static functions section */
+    /* TODO: replace with non-static, once md_ becomes non-const ref */
+
+    static status_t compute_blocking(memory_desc_t &memory_desc,
+            format_tag_t tag);
+
+private:
+    /* TODO: put logical_offset in utils */
+    template<typename T>
+    dim_t logical_offset(T x0) const { return x0; }
+
+    template<typename T, typename... Args>
+    dim_t logical_offset(T xn, Args... args) const {
+        const size_t n_args = sizeof...(args);
+        return xn * utils::array_product<n_args>(
+                &dims()[ndims() - n_args]) + logical_offset(args...);
+    }
+
+    template<int ORIG_LEN, typename ...Void>
+    dim_t _blk_off() const { return offset0(); }
+
+    template<int ORIG_LEN, typename T, typename ...Args>
+    dim_t _blk_off(T xc, Args ...args) const {
+        assert(is_blocking_desc());
+        constexpr int dc = ORIG_LEN - sizeof...(args) - 1;
+        return xc * blocking_desc().strides[dc]
+            + _blk_off<ORIG_LEN, Args...>(args...);
+    }
+};
+
+inline bool memory_desc_wrapper::similar_to(const memory_desc_wrapper &rhs,
+        bool with_padding, bool with_data_type, int dim_start) const {
+    using namespace utils;
+
+    if (one_of(format_kind(), format_kind::undef, format_kind::any))
+        return false;
+    if (is_wino_desc() || is_rnn_packed_desc())
+        return false;
+
+    const int ds = dim_start;
+    const auto &blk = blocking_desc();
+    const auto &r_blk = rhs.blocking_desc();
+
+    return ndims() == rhs.ndims()
+        && dim_start <= ndims() /* guard */
+        && format_kind() == rhs.format_kind()
+        && IMPLICATION(with_data_type, data_type() == rhs.data_type())
+        && array_cmp(dims() + ds, rhs.dims() + ds, ndims() - ds)
+        && array_cmp(blk.strides + ds, r_blk.strides + ds, ndims() - ds)
+        && blk.inner_nblks == r_blk.inner_nblks
+        && array_cmp(blk.inner_blks, r_blk.inner_blks, blk.inner_nblks)
+        && array_cmp(blk.inner_idxs, r_blk.inner_idxs, blk.inner_nblks)
+        && IMPLICATION(with_padding, true
+                && array_cmp(padded_dims() + ds, rhs.padded_dims() + ds,
+                    ndims() - ds)
+                && array_cmp(padded_offsets() + ds, rhs.padded_offsets() + ds,
+                    ndims() - ds));
+}
+
+inline bool memory_desc_wrapper::consistent_with(
+        const memory_desc_wrapper &rhs) const {
+    if (ndims() == rhs.ndims()) {
+        for (int d = 0; d < ndims(); ++d) {
+            if (dims()[d] != rhs.dims()[d]) return false;
+        }
+        return true;
+    } else {
+        /* TODO: revise.
+         * is the following possible?
+         * [1, a, b] <--reorder--> [a, b]
+         * [a, 1, b] <--reorder--> [a, b]
+         * not, at least for now */
+        return false;
+    }
+}
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/memory_tracking.hpp b/thirdparty/oidn/mkl-dnn/src/common/memory_tracking.hpp
new file mode 100644
index 0000000000..ec077b308c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/memory_tracking.hpp
@@ -0,0 +1,295 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MEMORY_TRACKING_HPP
+#define MEMORY_TRACKING_HPP
+
+#include <assert.h>
+#include <unordered_map>
+
+#include "nstl.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace memory_tracking {
+
+/* Memory tracking capabilities
+ *
+ * The main purpose of this header file is to provide uniform way to register
+ * required memory for a scratchpad at a primitive descriptor creation time
+ * and then easily access it having only the base address of the scratchpad.
+ *
+ * Primitives might contain multiple disjoint parts that require temporary
+ * buffers (known as scratchpad) during their execution. A primitive descriptor
+ * should summarize all the needs into one single number -- the buffer size
+ * that would be requested from a user. At execution time, the corresponding
+ * primitive will receive a base pointer to a scratchpad. It then needs to
+ * provide each part of algorithm the corresponding piece of memory. Three main
+ * challenges here are:
+ * 1. Track correct offset (from the base scratchpad address) for each piece
+ * 2. Algorithm might require that different memory pieces to be aligned, so
+ *    the scratchpad size is no more just a sum of size of the corresponding
+ *    subparts.
+ * 3. While a primitive is responsible for its scratchpad, the implementation
+ *    might use some other basic blocks (e.g. cpu_reducer) that also require
+ *    scratchpad memory. So there should be a simple way of passing the
+ *    information back and force between the main algorithm (a primitive) and
+ *    auxiliary stuff that lives completely separately from it (e.g. reducer).
+ *
+ * To address these challenges this header file provides 3 structures:
+ * 1. registry_t  -- the class the stores the information about requested
+ *                   memory. The information includes required size and desired
+ *                   alignment for each piece. This class is also responsible
+ *                   for computing the right offset to a given piece using the
+ *                   base pointer.
+ *                   This class is basically a ledger with all entries.
+ *                   Lives in primitive descriptors.
+ *
+ * 2. registrar_t -- the interface to a registry_t to book memory. Used at
+ *                   primitive descriptor creation time only. Contains a
+ *                   reference to the corresponding *mutable* registry.
+ *                   Always modifiable.
+ *                   Allows chaining (using prefixes).
+ *
+ * 3. grantor_t   -- the interface to a registry_t to access memory. Used at
+ *                   primitive execution time only. Contains a reference to
+ *                   the corresponding *constant* registry and base pointer.
+ *                   Always constant.
+ *                   Allows chaining (using prefixes).
+ *
+ * Both registrar_t and grantor_t allow chaining with extra prefix provided.
+ * The feature is useful when a primitive offload a part of computations to
+ * some other primitives which require their own scratchpad space
+ * (e.g. reducer). Prefixes are used to avoid key collision in cases when
+ * multiple sub-primitive (e.g. multiple reducers) are used.
+ *
+ * A short example below demonstrates how to use aforementioned classes. In it
+ * the main primitive is convolution that uses scratchpad for keeping padded
+ * bias. It also needs a reducer, that needs its own space as well.
+ *
+ *  ``` c++
+ *  struct reducer_t {
+ *      static void init(registrar_t &scratchpad) {
+ *          // preserve space for the reduction (one page aligned)
+ *          scratchpad.book(key_space, sizeof(float) * 980 * 1024, 4096);
+ *      }
+ *
+ *      void exec(const grantor_t &scratchpad) {
+ *          // get the pointer to preserved space. scratchpad came from
+ *          // upper primitive (convolution in this example)
+ *          auto space = scratchpad.get<float>(key_reducer_space);
+ *
+ *          space[:] += ...;
+ *      }
+ *  };
+ *
+ *  struct conv_t {
+ *      struct pd_t {
+ *          void init() {
+ *              registrar_t scratchpad(scratchpad_registry_);
+ *
+ *              // preserve a space for padded bias (using default alignment)
+ *              scratchpad.book(key_conv_padded_bias, 128);
+ *
+ *              // create a proxy registrar for the reducer All entries made
+ *              // by reducer would live in convolution's registry, but would
+ *              // have their own `prefix`, so no interference with conv's
+ *              // buffers.
+ *              registrar_t reducer_scratchpad(scratchpad, prefix_reducer);
+ *
+ *              reducer_t::init(reducer_scratchpad);
+ *          }
+ *
+ *          registry_t scratchpad_registry_;
+ *      }
+ *
+ *      void exec() {
+ *          // get the base pointer to a scratchpad memory from a user
+ *          void *scratchpad_ptr = this->input(MKLDNN_MEM_SCRATCHPAD);
+ *
+ *          // create a grantor to the scratchpad (and provide the base
+ *          // pointer).
+ *          grantor_t scratchpad(pd()->scratchpad_registry_, scratchpad_ptr);
+ *
+ *          // access the padded_bias (need only key name and the grantor)
+ *          auto padded_bias = scratchpad.get<float>(key_conv_padded_bias);
+ *
+ *          // to give the `right` grantor to reducer we need to add the
+ *          // corresponding prefix, so that reducer would be able to access
+ *          // its keys. The call is very similar to the one in pd_t::init
+ *          // with only difference in types: grantor_t vs registrar_t.
+ *          grantor_t reducer_scratchpad(scratchpad, prefix_reducer);
+ *          reducer->exec(reducer_scratchpad);
+ *      }
+ *  };
+ *  ```
+ */
+
+
+/* namespace with common keys and prefixes */
+namespace names {
+enum {
+    key_none = 0,
+    key_bnorm_tmp_mean,
+    key_bnorm_tmp_var,
+    key_bnorm_tmp_diff_ss,
+    key_bnorm_tmp_stats,
+    key_bnorm_reduction,
+    key_concat_iptrs,
+    key_concat_istrides,
+    key_concat_nelems,
+    key_concat_optrs,
+    key_conv_adjusted_scales,
+    key_conv_bia_reduction,
+    key_conv_gemm_col,
+    key_conv_gemm_imtr,
+    key_conv_int_dat_in_acc_dt,
+    key_conv_padded_bias,
+    key_conv_rtus_space,
+    key_conv_tr_diff_dst,
+    key_conv_tr_diff_dst_bctx,
+    key_conv_tr_src,
+    key_conv_tr_src_bctx,
+    key_conv_wei_reduction,
+    key_conv_wei_bia_reduction,
+    key_conv_wei_bia_reduction_bctx,
+    key_iprod_int_dat_in_acc_dt,
+    key_reducer_space,
+    key_reducer_space_bctx,
+    key_reorder_wino_plain,
+    key_reorder_wino_transform_space,
+    key_reorder_rnn_weights_quantization,
+    key_reorder_rnn_weights_reduction,
+    key_rnn_space,
+    key_rnn_ptrs_bia,
+    key_rnn_ptrs_wei_layer,
+    key_rnn_ptrs_wei_iter,
+    key_softmax_reduction,
+    key_wino_U,
+    key_wino_V,
+    key_wino_M,
+    key_barrier,
+};
+
+enum {
+    prefix_none = 0,
+    prefix_reducer_bia,
+    prefix_reducer_wei,
+};
+}
+
+// level 0: 00 00 00 xxx
+// level 1: 00 00 aa xxx
+// level 2: 00 aa bb xxx
+// level 3: aa bb cc xxx
+// max # of levels: 3 + 1 (base_level)
+// here:
+//      xxx        : [1 ..    MAX_KEY) : key
+//      aa, bb, cc : [1 .. MAX_PREFIX) : prefixes for levels 1, 2, and 3
+
+using key_t = uint32_t;
+enum { MAX_KEY = (1u << 10), MAX_PREFIX = (1u << 7), };
+
+/// generates global key based on a prefix and a local key
+inline key_t make_key(key_t prefix, key_t key) { return prefix + key; }
+
+/// generates global prefix based on the global parent and the local ones
+inline key_t make_prefix(key_t parent_prefix, key_t prefix)
+{ return MAX_PREFIX * parent_prefix + MAX_KEY * prefix; }
+
+struct registrar_t;
+struct grantor_t;
+
+struct registry_t {
+    void book(const key_t &key, size_t size, size_t alignment) {
+        if (size == 0) return;
+        assert(offset_map_.count(key) == 0);
+
+        size = utils::rnd_up(size, minimal_alignment);
+        alignment = nstl::max<size_t>(alignment, minimal_alignment);
+        offset_map_[key] = entry_t{size_, size, alignment};
+
+        size_ += size + alignment - minimal_alignment;
+    }
+
+    void *get(const key_t &key, void *base_ptr) const {
+        if (base_ptr == nullptr) { assert(size() == 0); return nullptr; }
+        if (offset_map_.count(key) != 1) return nullptr;
+
+        const auto &e = offset_map_.at(key);
+        base_ptr = utils::align_ptr<void>(base_ptr, minimal_alignment);
+        char *ptr = (char *)base_ptr + e.offset;
+        return utils::align_ptr<void>(ptr, e.alignment);
+    }
+
+    size_t size() const
+    { return size_ > 0 ? size_ + minimal_alignment - 1 : 0; }
+
+    registrar_t registrar();
+    grantor_t grantor(void *base_ptr) const;
+
+protected:
+    enum { minimal_alignment = 64 };
+    struct entry_t { size_t offset, size, alignment; };
+
+    std::unordered_map<key_t, entry_t> offset_map_;
+    size_t size_ = 0;
+};
+
+struct registrar_t {
+    enum { default_alignment = 64 };
+
+    registrar_t(registry_t &registry): registry_(registry), prefix_(0) {}
+    registrar_t(registrar_t &parent, const key_t &prefix)
+        : registry_(parent.registry_)
+        , prefix_(make_prefix(parent.prefix_, prefix)) {}
+
+    void book(const key_t &key, size_t size,
+            size_t alignment = default_alignment)
+    { registry_.book(make_key(prefix_, key), size, alignment); }
+
+protected:
+    registry_t &registry_;
+    const key_t prefix_;
+};
+
+struct grantor_t {
+    grantor_t(const registry_t &registry, void *base_ptr)
+        : registry_(registry), prefix_(0), base_ptr_(base_ptr) {}
+    grantor_t(const grantor_t &parent, const key_t &prefix)
+        : registry_(parent.registry_)
+        , prefix_(make_prefix(parent.prefix_, prefix))
+        , base_ptr_(parent.base_ptr_) {}
+
+    template <typename T = void> T *get(const key_t &key) const
+    { return (T *)registry_.get(make_key(prefix_, key), base_ptr_); }
+
+protected:
+    const registry_t &registry_;
+    const key_t prefix_;
+    void *base_ptr_;
+};
+
+inline registrar_t registry_t::registrar() { return registrar_t(*this); }
+inline grantor_t registry_t::grantor(void *base_ptr) const
+{ return grantor_t(*this, base_ptr); }
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/mkldnn_debug.cpp b/thirdparty/oidn/mkl-dnn/src/common/mkldnn_debug.cpp
new file mode 100644
index 0000000000..2ef4a8fddc
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/mkldnn_debug.cpp
@@ -0,0 +1,131 @@
+/*******************************************************************************
+* Copyright 2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <stdio.h>
+#include <cinttypes>
+
+#include "mkldnn_debug.h"
+#include "mkldnn_types.h"
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#define DPRINT(...) do { \
+    int l = snprintf(str + written_len, str_len, __VA_ARGS__); \
+    if (l < 0) return l; \
+    if ((size_t)l >= str_len) return -1; \
+    written_len += l; str_len -= l; \
+} while(0)
+
+int mkldnn_md2fmt_str(char *str, size_t str_len,
+        const mkldnn_memory_desc_t *mdesc) {
+    using namespace mkldnn::impl;
+
+    if (str == nullptr || str_len <= 1u)
+        return -1;
+
+    int written_len = 0;
+
+    if (mdesc == nullptr) {
+        DPRINT("%s::%s::",
+                mkldnn_dt2str(data_type::undef),
+                mkldnn_fmt_kind2str(format_kind::undef));
+        return written_len;
+    }
+
+    memory_desc_wrapper md(mdesc);
+
+    DPRINT("%s:", mkldnn_dt2str(md.data_type()));
+
+    bool padded_dims = false, padded_offsets = false;
+    for (int d = 0; d < md.ndims(); ++d) {
+        if (md.dims()[d] != md.padded_dims()[d]) padded_dims = true;
+        if (md.padded_offsets()[d] != 0) padded_offsets = true;
+    }
+    bool offset0 = md.offset0();
+    DPRINT("%s%s%s:",
+            padded_dims ? "p" : "",
+            padded_offsets ? "o" : "",
+            offset0 ? "0" : "");
+
+    DPRINT("%s:", mkldnn_fmt_kind2str(md.format_kind()));
+
+    if (!md.is_blocking_desc()) {
+        /* TODO: extend */
+        DPRINT("%s:", "");
+    } else {
+        const auto &blk = md.blocking_desc();
+
+        dims_t blocks;
+        md.compute_blocks(blocks);
+
+        char dim_chars[MKLDNN_MAX_NDIMS + 1];
+
+        bool plain = true;
+        for (int d = 0; d < md.ndims(); ++d) {
+            dim_chars[d] = (blocks[d] == 1 ? 'a' : 'A') + (char)d;
+            if (blocks[d] != 1) plain = false;
+        }
+
+        dims_t strides;
+        utils::array_copy(strides, blk.strides, md.ndims());
+        utils::simultaneous_sort(strides, dim_chars, md.ndims(),
+                [](dim_t a, dim_t b) { return b - a; });
+
+        dim_chars[md.ndims()] = '\0';
+        DPRINT("%s", dim_chars);
+
+        if (!plain) {
+            for (int iblk = 0; iblk < blk.inner_nblks; ++iblk) {
+                DPRINT("%d%c", (int)blk.inner_blks[iblk],
+                        'a' + (char)blk.inner_idxs[iblk]);
+            }
+        }
+
+        DPRINT("%s", ":");
+    }
+
+    DPRINT("f%lx", (long)md.extra().flags);
+
+    return written_len;
+}
+
+int mkldnn_md2dim_str(char *str, size_t str_len,
+        const mkldnn_memory_desc_t *mdesc) {
+    using namespace mkldnn::impl;
+
+    if (str == nullptr || str_len <= 1)
+        return -1;
+
+    int written_len = 0;
+
+    if (mdesc == nullptr || mdesc->ndims == 0) {
+        DPRINT("%s", "");
+        return written_len;
+    }
+
+    memory_desc_wrapper md(mdesc);
+
+    for (int d = 0; d < md.ndims() - 1; ++d)
+        DPRINT("%" PRId64 "x", md.dims()[d]);
+    DPRINT("%" PRId64, md.dims()[md.ndims() - 1]);
+
+    return written_len;
+}
+
+#undef  DPRINT
diff --git a/thirdparty/oidn/mkl-dnn/src/common/mkldnn_debug_autogenerated.cpp b/thirdparty/oidn/mkl-dnn/src/common/mkldnn_debug_autogenerated.cpp
new file mode 100644
index 0000000000..16a8f7ea5e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/mkldnn_debug_autogenerated.cpp
@@ -0,0 +1,365 @@
+/*******************************************************************************
+* Copyright 2018-2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/* DO NOT EDIT, AUTO-GENERATED */
+
+#include <assert.h>
+
+#include "mkldnn_debug.h"
+#include "mkldnn_types.h"
+
+const char *mkldnn_status2str(mkldnn_status_t v) {
+    if (v == mkldnn_success) return "success";
+    if (v == mkldnn_out_of_memory) return "out_of_memory";
+    if (v == mkldnn_try_again) return "try_again";
+    if (v == mkldnn_invalid_arguments) return "invalid_arguments";
+    if (v == mkldnn_not_ready) return "not_ready";
+    if (v == mkldnn_unimplemented) return "unimplemented";
+    if (v == mkldnn_iterator_ends) return "iterator_ends";
+    if (v == mkldnn_runtime_error) return "runtime_error";
+    if (v == mkldnn_not_required) return "not_required";
+    assert(!"unknown status");
+    return "unknown status";
+}
+
+const char *mkldnn_dt2str(mkldnn_data_type_t v) {
+    if (v == mkldnn_data_type_undef) return "undef";
+    if (v == mkldnn_f32) return "f32";
+    if (v == mkldnn_s32) return "s32";
+    if (v == mkldnn_s8) return "s8";
+    if (v == mkldnn_u8) return "u8";
+    assert(!"unknown dt");
+    return "unknown dt";
+}
+
+const char *mkldnn_fmt_kind2str(mkldnn_format_kind_t v) {
+    if (v == mkldnn_format_kind_undef) return "undef";
+    if (v == mkldnn_format_kind_any) return "any";
+    if (v == mkldnn_blocked) return "blocked";
+    if (v == mkldnn_format_kind_wino) return "wino";
+    if (v == mkldnn_format_kind_rnn_packed) return "rnn_packed";
+    assert(!"unknown fmt_kind");
+    return "unknown fmt_kind";
+}
+
+const char *mkldnn_fmt_tag2str(mkldnn_format_tag_t v) {
+    if (v == mkldnn_format_tag_undef) return "undef";
+    if (v == mkldnn_format_tag_any) return "format_tag_any";
+    if (v == mkldnn_a) return "a";
+    if (v == mkldnn_ab) return "ab";
+    if (v == mkldnn_abc) return "abc";
+    if (v == mkldnn_abcd) return "abcd";
+    if (v == mkldnn_abcde) return "abcde";
+    if (v == mkldnn_abcdef) return "abcdef";
+    if (v == mkldnn_abdec) return "abdec";
+    if (v == mkldnn_acb) return "acb";
+    if (v == mkldnn_acbde) return "acbde";
+    if (v == mkldnn_acdb) return "acdb";
+    if (v == mkldnn_acdeb) return "acdeb";
+    if (v == mkldnn_ba) return "ba";
+    if (v == mkldnn_bac) return "bac";
+    if (v == mkldnn_bacd) return "bacd";
+    if (v == mkldnn_bcda) return "bcda";
+    if (v == mkldnn_cba) return "cba";
+    if (v == mkldnn_cdba) return "cdba";
+    if (v == mkldnn_cdeba) return "cdeba";
+    if (v == mkldnn_decab) return "decab";
+    if (v == mkldnn_Abc16a) return "Abc16a";
+    if (v == mkldnn_ABc16a16b) return "ABc16a16b";
+    if (v == mkldnn_aBc16b) return "aBc16b";
+    if (v == mkldnn_ABc16b16a) return "ABc16b16a";
+    if (v == mkldnn_Abc4a) return "Abc4a";
+    if (v == mkldnn_aBc4b) return "aBc4b";
+    if (v == mkldnn_ABc4b16a4b) return "ABc4b16a4b";
+    if (v == mkldnn_ABc4b4a) return "ABc4b4a";
+    if (v == mkldnn_ABc8a16b2a) return "ABc8a16b2a";
+    if (v == mkldnn_ABc8a8b) return "ABc8a8b";
+    if (v == mkldnn_aBc8b) return "aBc8b";
+    if (v == mkldnn_ABc8b16a2b) return "ABc8b16a2b";
+    if (v == mkldnn_ABc8b8a) return "ABc8b8a";
+    if (v == mkldnn_Abcd16a) return "Abcd16a";
+    if (v == mkldnn_ABcd16a16b) return "ABcd16a16b";
+    if (v == mkldnn_aBcd16b) return "aBcd16b";
+    if (v == mkldnn_ABcd16b16a) return "ABcd16b16a";
+    if (v == mkldnn_aBCd16b16c) return "aBCd16b16c";
+    if (v == mkldnn_aBCd16c16b) return "aBCd16c16b";
+    if (v == mkldnn_Abcd4a) return "Abcd4a";
+    if (v == mkldnn_aBcd4b) return "aBcd4b";
+    if (v == mkldnn_ABcd4b16a4b) return "ABcd4b16a4b";
+    if (v == mkldnn_ABcd4b4a) return "ABcd4b4a";
+    if (v == mkldnn_aBCd4c16b4c) return "aBCd4c16b4c";
+    if (v == mkldnn_aBCd4c4b) return "aBCd4c4b";
+    if (v == mkldnn_ABcd8a16b2a) return "ABcd8a16b2a";
+    if (v == mkldnn_ABcd8a8b) return "ABcd8a8b";
+    if (v == mkldnn_aBcd8b) return "aBcd8b";
+    if (v == mkldnn_ABcd8b16a2b) return "ABcd8b16a2b";
+    if (v == mkldnn_aBCd8b16c2b) return "aBCd8b16c2b";
+    if (v == mkldnn_ABcd8b8a) return "ABcd8b8a";
+    if (v == mkldnn_aBCd8b8c) return "aBCd8b8c";
+    if (v == mkldnn_aBCd8c16b2c) return "aBCd8c16b2c";
+    if (v == mkldnn_aBCd8c8b) return "aBCd8c8b";
+    if (v == mkldnn_Abcde16a) return "Abcde16a";
+    if (v == mkldnn_ABcde16a16b) return "ABcde16a16b";
+    if (v == mkldnn_aBcde16b) return "aBcde16b";
+    if (v == mkldnn_ABcde16b16a) return "ABcde16b16a";
+    if (v == mkldnn_aBCde16b16c) return "aBCde16b16c";
+    if (v == mkldnn_aBCde16c16b) return "aBCde16c16b";
+    if (v == mkldnn_aBCde2c8b4c) return "aBCde2c8b4c";
+    if (v == mkldnn_Abcde4a) return "Abcde4a";
+    if (v == mkldnn_aBcde4b) return "aBcde4b";
+    if (v == mkldnn_ABcde4b4a) return "ABcde4b4a";
+    if (v == mkldnn_aBCde4b4c) return "aBCde4b4c";
+    if (v == mkldnn_aBCde4c16b4c) return "aBCde4c16b4c";
+    if (v == mkldnn_aBCde4c4b) return "aBCde4c4b";
+    if (v == mkldnn_Abcde8a) return "Abcde8a";
+    if (v == mkldnn_ABcde8a8b) return "ABcde8a8b";
+    if (v == mkldnn_ABcde8b16a2b) return "ABcde8b16a2b";
+    if (v == mkldnn_aBCde8b16c2b) return "aBCde8b16c2b";
+    if (v == mkldnn_ABcde8b8a) return "ABcde8b8a";
+    if (v == mkldnn_aBCde8b8c) return "aBCde8b8c";
+    if (v == mkldnn_aBCde8c16b2c) return "aBCde8c16b2c";
+    if (v == mkldnn_aBCde8c8b) return "aBCde8c8b";
+    if (v == mkldnn_aBcdef16b) return "aBcdef16b";
+    if (v == mkldnn_aBCdef16b16c) return "aBCdef16b16c";
+    if (v == mkldnn_aBCdef16c16b) return "aBCdef16c16b";
+    if (v == mkldnn_aBcdef4b) return "aBcdef4b";
+    if (v == mkldnn_aBCdef4c4b) return "aBCdef4c4b";
+    if (v == mkldnn_aBCdef8b8c) return "aBCdef8b8c";
+    if (v == mkldnn_aBCdef8c16b2c) return "aBCdef8c16b2c";
+    if (v == mkldnn_aBCdef8c8b) return "aBCdef8c8b";
+    if (v == mkldnn_aBdc16b) return "aBdc16b";
+    if (v == mkldnn_aBdc4b) return "aBdc4b";
+    if (v == mkldnn_aBdc8b) return "aBdc8b";
+    if (v == mkldnn_aBdec16b) return "aBdec16b";
+    if (v == mkldnn_aBdec4b) return "aBdec4b";
+    if (v == mkldnn_aBdec8b) return "aBdec8b";
+    if (v == mkldnn_aBdefc16b) return "aBdefc16b";
+    if (v == mkldnn_aBdefc4b) return "aBdefc4b";
+    if (v == mkldnn_aBdefc8b) return "aBdefc8b";
+    if (v == mkldnn_Acb16a) return "Acb16a";
+    if (v == mkldnn_Acb4a) return "Acb4a";
+    if (v == mkldnn_Acb8a) return "Acb8a";
+    if (v == mkldnn_aCBd16b16c) return "aCBd16b16c";
+    if (v == mkldnn_aCBde16b16c) return "aCBde16b16c";
+    if (v == mkldnn_Acdb16a) return "Acdb16a";
+    if (v == mkldnn_Acdb4a) return "Acdb4a";
+    if (v == mkldnn_Acdb8a) return "Acdb8a";
+    if (v == mkldnn_Acdeb16a) return "Acdeb16a";
+    if (v == mkldnn_Acdeb4a) return "Acdeb4a";
+    if (v == mkldnn_Acdeb8a) return "Acdeb8a";
+    if (v == mkldnn_BAc16a16b) return "BAc16a16b";
+    if (v == mkldnn_BAcd16a16b) return "BAcd16a16b";
+    if (v == mkldnn_format_tag_last) return "format_tag_last";
+    if (v == mkldnn_x) return "x";
+    if (v == mkldnn_nc) return "nc";
+    if (v == mkldnn_cn) return "cn";
+    if (v == mkldnn_ncw) return "ncw";
+    if (v == mkldnn_nwc) return "nwc";
+    if (v == mkldnn_nchw) return "nchw";
+    if (v == mkldnn_nhwc) return "nhwc";
+    if (v == mkldnn_chwn) return "chwn";
+    if (v == mkldnn_ncdhw) return "ncdhw";
+    if (v == mkldnn_ndhwc) return "ndhwc";
+    if (v == mkldnn_oi) return "oi";
+    if (v == mkldnn_io) return "io";
+    if (v == mkldnn_oiw) return "oiw";
+    if (v == mkldnn_wio) return "wio";
+    if (v == mkldnn_oihw) return "oihw";
+    if (v == mkldnn_hwio) return "hwio";
+    if (v == mkldnn_ihwo) return "ihwo";
+    if (v == mkldnn_iohw) return "iohw";
+    if (v == mkldnn_oidhw) return "oidhw";
+    if (v == mkldnn_dhwio) return "dhwio";
+    if (v == mkldnn_goiw) return "goiw";
+    if (v == mkldnn_goihw) return "goihw";
+    if (v == mkldnn_hwigo) return "hwigo";
+    if (v == mkldnn_giohw) return "giohw";
+    if (v == mkldnn_goidhw) return "goidhw";
+    if (v == mkldnn_tnc) return "tnc";
+    if (v == mkldnn_ntc) return "ntc";
+    if (v == mkldnn_ldsnc) return "ldsnc";
+    if (v == mkldnn_ldigo) return "ldigo";
+    if (v == mkldnn_ldgoi) return "ldgoi";
+    if (v == mkldnn_ldgo) return "ldgo";
+    if (v == mkldnn_nCdhw16c) return "nCdhw16c";
+    if (v == mkldnn_nCdhw4c) return "nCdhw4c";
+    if (v == mkldnn_nCdhw8c) return "nCdhw8c";
+    if (v == mkldnn_nChw16c) return "nChw16c";
+    if (v == mkldnn_nChw4c) return "nChw4c";
+    if (v == mkldnn_nChw8c) return "nChw8c";
+    if (v == mkldnn_nCw16c) return "nCw16c";
+    if (v == mkldnn_nCw4c) return "nCw4c";
+    if (v == mkldnn_nCw8c) return "nCw8c";
+    if (v == mkldnn_IOw16o16i) return "IOw16o16i";
+    if (v == mkldnn_OIw16i16o) return "OIw16i16o";
+    if (v == mkldnn_OIw16o16i) return "OIw16o16i";
+    if (v == mkldnn_Oiw16o) return "Oiw16o";
+    if (v == mkldnn_OIw4i16o4i) return "OIw4i16o4i";
+    if (v == mkldnn_OIw4i4o) return "OIw4i4o";
+    if (v == mkldnn_Oiw4o) return "Oiw4o";
+    if (v == mkldnn_OIw8i16o2i) return "OIw8i16o2i";
+    if (v == mkldnn_OIw8i8o) return "OIw8i8o";
+    if (v == mkldnn_OIw8o16i2o) return "OIw8o16i2o";
+    if (v == mkldnn_OIw8o8i) return "OIw8o8i";
+    if (v == mkldnn_Owi16o) return "Owi16o";
+    if (v == mkldnn_Owi4o) return "Owi4o";
+    if (v == mkldnn_Owi8o) return "Owi8o";
+    if (v == mkldnn_IOhw16o16i) return "IOhw16o16i";
+    if (v == mkldnn_Ohwi16o) return "Ohwi16o";
+    if (v == mkldnn_Ohwi4o) return "Ohwi4o";
+    if (v == mkldnn_Ohwi8o) return "Ohwi8o";
+    if (v == mkldnn_OIhw16i16o) return "OIhw16i16o";
+    if (v == mkldnn_OIhw16o16i) return "OIhw16o16i";
+    if (v == mkldnn_Oihw16o) return "Oihw16o";
+    if (v == mkldnn_OIhw4i16o4i) return "OIhw4i16o4i";
+    if (v == mkldnn_OIhw4i4o) return "OIhw4i4o";
+    if (v == mkldnn_Oihw4o) return "Oihw4o";
+    if (v == mkldnn_OIhw8i16o2i) return "OIhw8i16o2i";
+    if (v == mkldnn_OIhw8i8o) return "OIhw8i8o";
+    if (v == mkldnn_OIhw8o16i2o) return "OIhw8o16i2o";
+    if (v == mkldnn_OIhw8o8i) return "OIhw8o8i";
+    if (v == mkldnn_Odhwi16o) return "Odhwi16o";
+    if (v == mkldnn_Odhwi4o) return "Odhwi4o";
+    if (v == mkldnn_Odhwi8o) return "Odhwi8o";
+    if (v == mkldnn_OIdhw16i16o) return "OIdhw16i16o";
+    if (v == mkldnn_OIdhw16o16i) return "OIdhw16o16i";
+    if (v == mkldnn_Oidhw16o) return "Oidhw16o";
+    if (v == mkldnn_OIdhw4i4o) return "OIdhw4i4o";
+    if (v == mkldnn_Oidhw4o) return "Oidhw4o";
+    if (v == mkldnn_OIdhw8i16o2i) return "OIdhw8i16o2i";
+    if (v == mkldnn_OIdhw8i8o) return "OIdhw8i8o";
+    if (v == mkldnn_OIdhw8o8i) return "OIdhw8o8i";
+    if (v == mkldnn_Goiw16g) return "Goiw16g";
+    if (v == mkldnn_gIOw16o16i) return "gIOw16o16i";
+    if (v == mkldnn_gOIw16i16o) return "gOIw16i16o";
+    if (v == mkldnn_gOIw16o16i) return "gOIw16o16i";
+    if (v == mkldnn_gOiw16o) return "gOiw16o";
+    if (v == mkldnn_gOIw4i16o4i) return "gOIw4i16o4i";
+    if (v == mkldnn_gOIw4i4o) return "gOIw4i4o";
+    if (v == mkldnn_gOiw4o) return "gOiw4o";
+    if (v == mkldnn_gOIw8i16o2i) return "gOIw8i16o2i";
+    if (v == mkldnn_gOIw8i8o) return "gOIw8i8o";
+    if (v == mkldnn_gOIw8o16i2o) return "gOIw8o16i2o";
+    if (v == mkldnn_gOIw8o8i) return "gOIw8o8i";
+    if (v == mkldnn_gOwi16o) return "gOwi16o";
+    if (v == mkldnn_gOwi4o) return "gOwi4o";
+    if (v == mkldnn_gOwi8o) return "gOwi8o";
+    if (v == mkldnn_gIOhw16o16i) return "gIOhw16o16i";
+    if (v == mkldnn_gOhwi16o) return "gOhwi16o";
+    if (v == mkldnn_gOhwi4o) return "gOhwi4o";
+    if (v == mkldnn_gOhwi8o) return "gOhwi8o";
+    if (v == mkldnn_Goihw16g) return "Goihw16g";
+    if (v == mkldnn_gOIhw16i16o) return "gOIhw16i16o";
+    if (v == mkldnn_gOIhw16o16i) return "gOIhw16o16i";
+    if (v == mkldnn_gOihw16o) return "gOihw16o";
+    if (v == mkldnn_gOIhw2i8o4i) return "gOIhw2i8o4i";
+    if (v == mkldnn_gOIhw4i16o4i) return "gOIhw4i16o4i";
+    if (v == mkldnn_gOIhw4i4o) return "gOIhw4i4o";
+    if (v == mkldnn_gOIhw4o4i) return "gOIhw4o4i";
+    if (v == mkldnn_gOihw4o) return "gOihw4o";
+    if (v == mkldnn_Goihw8g) return "Goihw8g";
+    if (v == mkldnn_gOIhw8i16o2i) return "gOIhw8i16o2i";
+    if (v == mkldnn_gOIhw8i8o) return "gOIhw8i8o";
+    if (v == mkldnn_gOIhw8o16i2o) return "gOIhw8o16i2o";
+    if (v == mkldnn_gOIhw8o8i) return "gOIhw8o8i";
+    if (v == mkldnn_gOdhwi16o) return "gOdhwi16o";
+    if (v == mkldnn_gOdhwi4o) return "gOdhwi4o";
+    if (v == mkldnn_gOdhwi8o) return "gOdhwi8o";
+    if (v == mkldnn_gOIdhw16i16o) return "gOIdhw16i16o";
+    if (v == mkldnn_gOIdhw16o16i) return "gOIdhw16o16i";
+    if (v == mkldnn_gOidhw16o) return "gOidhw16o";
+    if (v == mkldnn_gOIdhw4i4o) return "gOIdhw4i4o";
+    if (v == mkldnn_gOidhw4o) return "gOidhw4o";
+    if (v == mkldnn_gOIdhw8i16o2i) return "gOIdhw8i16o2i";
+    if (v == mkldnn_gOIdhw8i8o) return "gOIdhw8i8o";
+    if (v == mkldnn_gOIdhw8o8i) return "gOIdhw8o8i";
+    assert(!"unknown fmt_tag");
+    return "unknown fmt_tag";
+}
+
+const char *mkldnn_prop_kind2str(mkldnn_prop_kind_t v) {
+    if (v == mkldnn_prop_kind_undef) return "undef";
+    if (v == mkldnn_forward_training) return "forward_training";
+    if (v == mkldnn_forward_inference) return "forward_inference";
+    if (v == mkldnn_forward_scoring) return "forward_scoring";
+    if (v == mkldnn_forward) return "forward";
+    if (v == mkldnn_backward) return "backward";
+    if (v == mkldnn_backward_data) return "backward_data";
+    if (v == mkldnn_backward_weights) return "backward_weights";
+    if (v == mkldnn_backward_bias) return "backward_bias";
+    assert(!"unknown prop_kind");
+    return "unknown prop_kind";
+}
+
+const char *mkldnn_prim_kind2str(mkldnn_primitive_kind_t v) {
+    if (v == mkldnn_undefined_primitive) return "undef";
+    if (v == mkldnn_reorder) return "reorder";
+    if (v == mkldnn_shuffle) return "shuffle";
+    if (v == mkldnn_concat) return "concat";
+    if (v == mkldnn_sum) return "sum";
+    if (v == mkldnn_convolution) return "convolution";
+    if (v == mkldnn_deconvolution) return "deconvolution";
+    if (v == mkldnn_eltwise) return "eltwise";
+    if (v == mkldnn_softmax) return "softmax";
+    if (v == mkldnn_pooling) return "pooling";
+    if (v == mkldnn_lrn) return "lrn";
+    if (v == mkldnn_batch_normalization) return "batch_normalization";
+    if (v == mkldnn_inner_product) return "inner_product";
+    if (v == mkldnn_rnn) return "rnn";
+    assert(!"unknown prim_kind");
+    return "unknown prim_kind";
+}
+
+const char *mkldnn_alg_kind2str(mkldnn_alg_kind_t v) {
+    if (v == mkldnn_alg_kind_undef) return "undef";
+    if (v == mkldnn_convolution_direct) return "convolution_direct";
+    if (v == mkldnn_convolution_winograd) return "convolution_winograd";
+    if (v == mkldnn_convolution_auto) return "convolution_auto";
+    if (v == mkldnn_deconvolution_direct) return "deconvolution_direct";
+    if (v == mkldnn_deconvolution_winograd) return "deconvolution_winograd";
+    if (v == mkldnn_eltwise_relu) return "eltwise_relu";
+    if (v == mkldnn_eltwise_tanh) return "eltwise_tanh";
+    if (v == mkldnn_eltwise_elu) return "eltwise_elu";
+    if (v == mkldnn_eltwise_square) return "eltwise_square";
+    if (v == mkldnn_eltwise_abs) return "eltwise_abs";
+    if (v == mkldnn_eltwise_sqrt) return "eltwise_sqrt";
+    if (v == mkldnn_eltwise_linear) return "eltwise_linear";
+    if (v == mkldnn_eltwise_bounded_relu) return "eltwise_bounded_relu";
+    if (v == mkldnn_eltwise_soft_relu) return "eltwise_soft_relu";
+    if (v == mkldnn_eltwise_logistic) return "eltwise_logistic";
+    if (v == mkldnn_pooling_max) return "pooling_max";
+    if (v == mkldnn_pooling_avg_include_padding) return "pooling_avg_include_padding";
+    if (v == mkldnn_pooling_avg_exclude_padding) return "pooling_avg_exclude_padding";
+    if (v == mkldnn_pooling_avg) return "pooling_avg";
+    if (v == mkldnn_lrn_across_channels) return "lrn_across_channels";
+    if (v == mkldnn_lrn_within_channel) return "lrn_within_channel";
+    if (v == mkldnn_vanilla_rnn) return "vanilla_rnn";
+    if (v == mkldnn_vanilla_lstm) return "vanilla_lstm";
+    if (v == mkldnn_vanilla_gru) return "vanilla_gru";
+    if (v == mkldnn_gru_linear_before_reset) return "gru_linear_before_reset";
+    assert(!"unknown alg_kind");
+    return "unknown alg_kind";
+}
+
+const char *mkldnn_rnn_direction2str(mkldnn_rnn_direction_t v) {
+    if (v == mkldnn_unidirectional_left2right) return "unidirectional_left2right";
+    if (v == mkldnn_unidirectional_right2left) return "unidirectional_right2left";
+    if (v == mkldnn_bidirectional_concat) return "bidirectional_concat";
+    if (v == mkldnn_bidirectional_sum) return "bidirectional_sum";
+    if (v == mkldnn_unidirectional) return "unidirectional";
+    assert(!"unknown rnn_direction");
+    return "unknown rnn_direction";
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/mkldnn_thread.hpp b/thirdparty/oidn/mkl-dnn/src/common/mkldnn_thread.hpp
new file mode 100644
index 0000000000..7e5789e2c3
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/mkldnn_thread.hpp
@@ -0,0 +1,115 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MKLDNN_THREAD_HPP
+#define MKLDNN_THREAD_HPP
+
+#include "utils.hpp"
+#include "z_magic.hpp"
+
+#define MKLDNN_THR_SEQ 0
+#define MKLDNN_THR_OMP 1
+#define MKLDNN_THR_TBB 2
+
+/* Ideally this condition below should never happen (if the library is built
+ * using regular cmake). For the 3rd-party projects that build the library
+ * from the sources on their own try to guess the right threading... */
+#if !defined(MKLDNN_THR)
+#   define MKLDNN_THR MKLDNN_THR_TBB
+#endif
+
+#if MKLDNN_THR == MKLDNN_THR_SEQ
+#define MKLDNN_THR_SYNC 1
+inline int mkldnn_get_max_threads() { return 1; }
+inline int mkldnn_get_num_threads() { return 1; }
+inline int mkldnn_get_thread_num() { return 0; }
+inline int mkldnn_in_parallel() { return 0; }
+inline void mkldnn_thr_barrier() {}
+
+#define PRAGMA_OMP(...)
+
+#elif MKLDNN_THR == MKLDNN_THR_OMP
+#include <omp.h>
+#define MKLDNN_THR_SYNC 1
+
+inline int mkldnn_get_max_threads() { return omp_get_max_threads(); }
+inline int mkldnn_get_num_threads() { return omp_get_num_threads(); }
+inline int mkldnn_get_thread_num() { return omp_get_thread_num(); }
+inline int mkldnn_in_parallel() { return omp_in_parallel(); }
+inline void mkldnn_thr_barrier() {
+#   pragma omp barrier
+}
+
+#define PRAGMA_OMP(...) PRAGMA_MACRO(CHAIN2(omp, __VA_ARGS__))
+
+#elif MKLDNN_THR == MKLDNN_THR_TBB
+#include "tbb/task_arena.h"
+#include "tbb/parallel_for.h"
+#define MKLDNN_THR_SYNC 0
+
+inline int mkldnn_get_max_threads()
+{ return tbb::this_task_arena::max_concurrency(); }
+inline int mkldnn_get_num_threads() { return mkldnn_get_max_threads(); }
+inline int mkldnn_get_thread_num()
+{ return tbb::this_task_arena::current_thread_index(); }
+inline int mkldnn_in_parallel() { return 0; }
+inline void mkldnn_thr_barrier() { assert(!"no barrier in TBB"); }
+
+#define PRAGMA_OMP(...)
+
+#endif
+
+/* MSVC still supports omp 2.0 only */
+#if defined(_MSC_VER) && !defined(__clang__) && !defined(__INTEL_COMPILER)
+#   define collapse(x)
+#   define PRAGMA_OMP_SIMD(...)
+#else
+#   define PRAGMA_OMP_SIMD(...) PRAGMA_MACRO(CHAIN2(omp, simd __VA_ARGS__))
+#endif // defined(_MSC_VER) && !defined(__INTEL_COMPILER)
+
+namespace mkldnn {
+namespace impl {
+
+inline bool mkldnn_thr_syncable() { return MKLDNN_THR_SYNC == 1; }
+
+template <typename T, typename U>
+inline void balance211(T n, U team, U tid, T &n_start, T &n_end) {
+    T n_min = 1;
+    T &n_my = n_end;
+    if (team <= 1 || n == 0) {
+        n_start = 0;
+        n_my = n;
+    } else if (n_min == 1) {
+        // team = T1 + T2
+        // n = T1*n1 + T2*n2  (n1 - n2 = 1)
+        T n1 = utils::div_up(n, (T)team);
+        T n2 = n1 - 1;
+        T T1 = n - n2 * (T)team;
+        n_my = (T)tid < T1 ? n1 : n2;
+        n_start = (T)tid <= T1 ? tid * n1 : T1 * n1 + ((T)tid - T1) * n2;
+    }
+
+    n_end += n_start;
+}
+
+} // namespace impl
+} // namespace mkldnn
+
+#include "mkldnn_thread_parallel_nd.hpp"
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/mkldnn_thread_parallel_nd.hpp b/thirdparty/oidn/mkl-dnn/src/common/mkldnn_thread_parallel_nd.hpp
new file mode 100644
index 0000000000..50f9b29622
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/mkldnn_thread_parallel_nd.hpp
@@ -0,0 +1,277 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MKLDNN_THREAD_PARALLEL_ND_HPP
+#define MKLDNN_THREAD_PARALLEL_ND_HPP
+
+/* This header must be included by mkldnn_thread.hpp only */
+
+/* Functions:
+ *  - parallel(nthr, f)              - executes f in parallel using at most
+ *                                     nthr threads. If nthr equals 0
+ *                                     mkldnn_get_max_threads() threads is
+ *                                     used
+ *  - for_nd(ithr, nthr, dims..., f) - multidimensional for loop for already
+ *                                     created threads
+ *  - parallel_nd(dims..., f)        - creates a parallel section and then
+ *                                     calls for_nd
+ *  - parallel_nd_in_omp(dims..., f) - queries current nthr and ithr and then
+ *                                     calls for_nd (mostly for convenience)
+ */
+
+namespace mkldnn {
+namespace impl {
+
+/* general parallelization */
+template <typename F>
+void parallel(int nthr, F f) {
+    if (nthr == 0) nthr = mkldnn_get_max_threads();
+#if MKLDNN_THR == MKLDNN_THR_SEQ
+    assert(nthr == 1);
+    f(0, 1);
+#elif MKLDNN_THR == MKLDNN_THR_OMP
+    if (nthr == 1) { f(0, 1); return; }
+#   pragma omp parallel num_threads(nthr)
+    f(mkldnn_get_thread_num(), mkldnn_get_num_threads());
+#elif MKLDNN_THR == MKLDNN_THR_TBB
+    if (nthr == 1) { f(0, 1); return; }
+    tbb::parallel_for(0, nthr, [&](int ithr) { f(ithr, nthr); }, tbb::static_partitioner());
+#endif
+}
+
+/* for_nd section */
+
+template <typename T0, typename F>
+void for_nd(const int ithr, const int nthr, const T0 &D0, F f) {
+    T0 start{0}, end{0};
+    balance211(D0, nthr, ithr, start, end);
+    for (T0 d0 = start; d0 < end; ++d0) f(d0);
+}
+
+template <typename T0, typename T1, typename F>
+void for_nd(const int ithr, const int nthr, const T0 &D0, const T1 &D1, F f) {
+    const size_t work_amount = (size_t)D0 * D1;
+    if (work_amount == 0) return;
+    size_t start{0}, end{0};
+    balance211(work_amount, nthr, ithr, start, end);
+
+    T0 d0{0}; T1 d1{0};
+    utils::nd_iterator_init(start, d0, D0, d1, D1);
+    for (size_t iwork = start; iwork < end; ++iwork) {
+        f(d0, d1);
+        utils::nd_iterator_step(d0, D0, d1, D1);
+    }
+}
+
+template <typename T0, typename T1, typename T2, typename F>
+void for_nd(const int ithr, const int nthr, const T0 &D0, const T1 &D1,
+        const T2 &D2, F f) {
+    const size_t work_amount = (size_t)D0 * D1 * D2;
+    if (work_amount == 0) return;
+    size_t start{0}, end{0};
+    balance211(work_amount, nthr, ithr, start, end);
+
+    T0 d0{0}; T1 d1{0}; T2 d2{0};
+    utils::nd_iterator_init(start, d0, D0, d1, D1, d2, D2);
+    for (size_t iwork = start; iwork < end; ++iwork) {
+        f(d0, d1, d2);
+        utils::nd_iterator_step(d0, D0, d1, D1, d2, D2);
+    }
+}
+
+template <typename T0, typename T1, typename T2, typename T3, typename F>
+void for_nd(const int ithr, const int nthr, const T0 &D0, const T1 &D1,
+        const T2 &D2, const T3 &D3, F f) {
+    const size_t work_amount = (size_t)D0 * D1 * D2 * D3;
+    if (work_amount == 0) return;
+    size_t start{0}, end{0};
+    balance211(work_amount, nthr, ithr, start, end);
+
+    T0 d0{0}; T1 d1{0}; T2 d2{0}; T3 d3{0};
+    utils::nd_iterator_init(start, d0, D0, d1, D1, d2, D2, d3, D3);
+    for (size_t iwork = start; iwork < end; ++iwork) {
+        f(d0, d1, d2, d3);
+        utils::nd_iterator_step(d0, D0, d1, D1, d2, D2, d3, D3);
+    }
+}
+
+template <typename T0, typename T1, typename T2, typename T3, typename T4,
+         typename F>
+void for_nd(const int ithr, const int nthr, const T0 &D0, const T1 &D1,
+        const T2 &D2, const T3 &D3, const T4 &D4, F f) {
+    const size_t work_amount = (size_t)D0 * D1 * D2 * D3 * D4;
+    if (work_amount == 0) return;
+    size_t start{0}, end{0};
+    balance211(work_amount, nthr, ithr, start, end);
+
+    T0 d0{0}; T1 d1{0}; T2 d2{0}; T3 d3{0}; T4 d4{0};
+    utils::nd_iterator_init(start, d0, D0, d1, D1, d2, D2, d3, D3, d4, D4);
+    for (size_t iwork = start; iwork < end; ++iwork) {
+        f(d0, d1, d2, d3, d4);
+        utils::nd_iterator_step(d0, D0, d1, D1, d2, D2, d3, D3, d4, D4);
+    }
+}
+
+template <typename T0, typename T1, typename T2, typename T3, typename T4,
+         typename T5, typename F>
+void for_nd(const int ithr, const int nthr, const T0 &D0, const T1 &D1,
+        const T2 &D2, const T3 &D3, const T4 &D4, const T5 &D5, F f) {
+    const size_t work_amount = (size_t)D0 * D1 * D2 * D3 * D4 * D5;
+    if (work_amount == 0) return;
+    size_t start{0}, end{0};
+    balance211(work_amount, nthr, ithr, start, end);
+
+    T0 d0{0}; T1 d1{0}; T2 d2{0}; T3 d3{0}; T4 d4{0}; T5 d5{0};
+    utils::nd_iterator_init(start, d0, D0, d1, D1, d2, D2, d3, D3, d4, D4,
+            d5, D5);
+    for (size_t iwork = start; iwork < end; ++iwork) {
+        f(d0, d1, d2, d3, d4, d5);
+        utils::nd_iterator_step(d0, D0, d1, D1, d2, D2, d3, D3, d4, D4, d5, D5);
+    }
+}
+
+// Skip a lambda function in the parameter pack.
+template <typename T>
+constexpr size_t get_work_amount(const T &v) { return 1; }
+template <typename T, typename ...Args>
+constexpr size_t get_work_amount(const T &v, Args &&...args)
+{ return (size_t)v * get_work_amount(utils::forward<Args>(args)...); }
+
+/* parallel_nd and parallel_nd_in_omp section */
+
+#if MKLDNN_THR != MKLDNN_THR_TBB
+template <typename ...Args>
+void parallel_nd(Args &&...args) {
+#if MKLDNN_THR == MKLDNN_THR_SEQ
+    for_nd(0, 1, utils::forward<Args>(args)...);
+#elif MKLDNN_THR == MKLDNN_THR_OMP
+    const bool do_parallel = get_work_amount(utils::forward<Args>(args)...) > 1;
+#   pragma omp parallel if (do_parallel)
+    {
+        const int nthr = !do_parallel ? 1 : mkldnn_get_num_threads();
+        const int ithr = !do_parallel ? 0 : mkldnn_get_thread_num();
+        for_nd(ithr, nthr, utils::forward<Args>(args)...);
+    }
+#endif
+}
+#else // MKLDNN_THR != MKLDNN_THR_TBB
+
+// gcc 4.8 has a bug with passing parameter pack to lambdas.
+// So have to explicitly instantiate all the cases.
+
+template <typename T0, typename F>
+void parallel_nd(const T0 &D0, F f) {
+    const size_t work_amount = (size_t)D0;
+    if (work_amount == 0) return;
+    tbb::parallel_for(tbb::blocked_range<size_t>(0, work_amount), [&](const tbb::blocked_range<size_t>& r) {
+        for (size_t iwork = r.begin(); iwork != r.end(); ++iwork) {
+            f(T0(iwork));
+        }
+    }, tbb::static_partitioner());
+}
+
+template <typename T0, typename T1, typename F>
+void parallel_nd(const T0 &D0, const T1 &D1, F f) {
+    const size_t work_amount = (size_t)D0 * D1;
+    if (work_amount == 0) return;
+    tbb::parallel_for(tbb::blocked_range<size_t>(0, work_amount), [&](const tbb::blocked_range<size_t>& r) {
+        T0 d0{0}; T1 d1{0};
+        utils::nd_iterator_init(r.begin(), d0, D0, d1, D1);
+        for (size_t iwork = r.begin(); iwork != r.end(); ++iwork) {
+            f(d0, d1);
+            utils::nd_iterator_step(d0, D0, d1, D1);
+        }
+    }, tbb::static_partitioner());
+}
+
+template <typename T0, typename T1, typename T2, typename F>
+void parallel_nd(const T0 &D0, const T1 &D1, const T2 &D2, F f) {
+    const size_t work_amount = (size_t)D0 * D1 * D2;
+    if (work_amount == 0) return;
+    tbb::parallel_for(tbb::blocked_range<size_t>(0, work_amount), [&](const tbb::blocked_range<size_t>& r) {
+        T0 d0{0}; T1 d1{0}; T2 d2{0};
+        utils::nd_iterator_init(r.begin(), d0, D0, d1, D1, d2, D2);
+        for (size_t iwork = r.begin(); iwork != r.end(); ++iwork) {
+            f(d0, d1, d2);
+            utils::nd_iterator_step(d0, D0, d1, D1, d2, D2);
+        }
+    }, tbb::static_partitioner());
+}
+
+template <typename T0, typename T1, typename T2, typename T3, typename F>
+void parallel_nd(const T0 &D0, const T1 &D1, const T2 &D2, const T3 &D3, F f) {
+    const size_t work_amount = (size_t)D0 * D1 * D2 * D3;
+    if (work_amount == 0) return;
+    tbb::parallel_for(tbb::blocked_range<size_t>(0, work_amount), [&](const tbb::blocked_range<size_t>& r) {
+        T0 d0{0}; T1 d1{0}; T2 d2{0}; T3 d3{0};
+        utils::nd_iterator_init(r.begin(), d0, D0, d1, D1, d2, D2, d3, D3);
+        for (size_t iwork = r.begin(); iwork != r.end(); ++iwork) {
+            f(d0, d1, d2, d3);
+            utils::nd_iterator_step(d0, D0, d1, D1, d2, D2, d3, D3);
+        }
+    }, tbb::static_partitioner());
+}
+
+template <typename T0, typename T1, typename T2, typename T3, typename T4,
+         typename F>
+void parallel_nd(const T0 &D0, const T1 &D1, const T2 &D2, const T3 &D3,
+        const T4 &D4, F f) {
+    const size_t work_amount = (size_t)D0 * D1 * D2 * D3 * D4;
+    if (work_amount == 0) return;
+    tbb::parallel_for(tbb::blocked_range<size_t>(0, work_amount), [&](const tbb::blocked_range<size_t>& r) {
+        T0 d0{0}; T1 d1{0}; T2 d2{0}; T3 d3{0}; T4 d4{0};
+        utils::nd_iterator_init(r.begin(), d0, D0, d1, D1, d2, D2, d3, D3, d4, D4);
+        for (size_t iwork = r.begin(); iwork != r.end(); ++iwork) {
+            f(d0, d1, d2, d3, d4);
+            utils::nd_iterator_step(d0, D0, d1, D1, d2, D2, d3, D3, d4, D4);
+        }
+    }, tbb::static_partitioner());
+}
+
+template <typename T0, typename T1, typename T2, typename T3, typename T4,
+         typename T5, typename F>
+void parallel_nd(const T0 &D0, const T1 &D1, const T2 &D2, const T3 &D3,
+        const T4 &D4, const T5 &D5, F f) {
+    const size_t work_amount = (size_t)D0 * D1 * D2 * D3 * D4 * D5;
+    if (work_amount == 0) return;
+    tbb::parallel_for(tbb::blocked_range<size_t>(0, work_amount), [&](const tbb::blocked_range<size_t>& r) {
+        T0 d0{0}; T1 d1{0}; T2 d2{0}; T3 d3{0}; T4 d4{0}; T5 d5{0};
+        utils::nd_iterator_init(r.begin(), d0, D0, d1, D1, d2, D2, d3, D3, d4, D4,
+                d5, D5);
+        for (size_t iwork = r.begin(); iwork != r.end(); ++iwork) {
+            f(d0, d1, d2, d3, d4, d5);
+            utils::nd_iterator_step(d0, D0, d1, D1, d2, D2, d3, D3, d4, D4, d5, D5);
+        }
+    }, tbb::static_partitioner());
+}
+#endif
+
+template <typename ...Args>
+void parallel_nd_in_omp(Args &&...args) {
+#if MKLDNN_THR == MKLDNN_THR_SEQ
+    for_nd(0, 1, utils::forward<Args>(args)...);
+#elif MKLDNN_THR == MKLDNN_THR_OMP
+    for_nd(mkldnn_get_thread_num(), mkldnn_get_num_threads(),
+            utils::forward<Args>(args)...);
+#elif MKLDNN_THR == MKLDNN_THR_TBB
+    assert(!"unsupported parallel_nd_in_omp()");
+#endif
+}
+
+} // namespace impl
+} // namespace mkldnn
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/mkldnn_traits.hpp b/thirdparty/oidn/mkl-dnn/src/common/mkldnn_traits.hpp
new file mode 100644
index 0000000000..aa671a0b6e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/mkldnn_traits.hpp
@@ -0,0 +1,77 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef MKLDNN_TRAITS_HPP
+#define MKLDNN_TRAITS_HPP
+
+#include <assert.h>
+#include <stdint.h>
+
+#include "mkldnn.h"
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "utils.hpp"
+#include "z_magic.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+template <data_type_t> struct prec_traits {}; /* ::type -> float */
+template <typename> struct data_traits {}; /* ::data_type -> f32 */
+template <int> struct typesize_traits {}; /* ::data_type_size -> f32 */
+template <primitive_kind_t> struct pkind_traits {}; /* ::desc_type, ::query_d */
+
+template <> struct prec_traits<data_type::f32> { typedef float type; };
+template <> struct prec_traits<data_type::s32> { typedef int32_t type; };
+template <> struct prec_traits<data_type::s8> { typedef int8_t type; };
+template <> struct prec_traits<data_type::u8> { typedef uint8_t type; };
+
+template <> struct data_traits<float>
+{ static constexpr data_type_t data_type = data_type::f32; };
+template <> struct data_traits<int32_t>
+{ static constexpr data_type_t data_type = data_type::s32; };
+template <> struct data_traits<int8_t>
+{ static constexpr data_type_t data_type = data_type::s8; };
+template <> struct data_traits<uint8_t>
+{ static constexpr data_type_t data_type = data_type::u8; };
+
+template <> struct typesize_traits<4> { typedef float type; };
+template <> struct typesize_traits<2> { typedef int16_t type; };
+template <> struct typesize_traits<1> { typedef uint8_t type; };
+
+#define PKIND_TRAITS_INST(op) \
+template <> struct pkind_traits<primitive_kind::op> { \
+    typedef CONCAT2(op, _desc_t) desc_type; \
+    static constexpr query_t query_d = query::CONCAT2(op, _d); \
+}
+PKIND_TRAITS_INST(convolution);
+PKIND_TRAITS_INST(deconvolution);
+PKIND_TRAITS_INST(shuffle);
+PKIND_TRAITS_INST(eltwise);
+PKIND_TRAITS_INST(softmax);
+PKIND_TRAITS_INST(pooling);
+PKIND_TRAITS_INST(lrn);
+PKIND_TRAITS_INST(batch_normalization);
+PKIND_TRAITS_INST(inner_product);
+PKIND_TRAITS_INST(rnn);
+#undef PKIND_TRAITS_INST
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/nstl.hpp b/thirdparty/oidn/mkl-dnn/src/common/nstl.hpp
new file mode 100644
index 0000000000..f89ea999e2
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/nstl.hpp
@@ -0,0 +1,193 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef NSTL_HPP
+#define NSTL_HPP
+
+#include <stdint.h>
+#include <limits.h>
+#include <float.h>
+
+#include <vector>
+#include <map>
+
+#include "z_magic.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+void *malloc(size_t size, int alignment);
+void free(void *p);
+
+struct c_compatible {
+    enum { default_alignment = 64 };
+    static void *operator new(size_t sz) {
+        return malloc(sz, default_alignment);
+    }
+    static void *operator new(size_t sz, void *p) { UNUSED(sz); return p; }
+    static void *operator new[](size_t sz) {
+        return malloc(sz, default_alignment);
+    }
+    static void operator delete(void *p) { free(p); }
+    static void operator delete[](void *p) { free(p); }
+};
+
+namespace nstl {
+
+template<typename T>
+inline const T abs(const T& a) {
+    return a >= 0 ? a : -a;
+}
+
+template<typename T>
+inline const T& max(const T& a, const T& b) {
+    return a > b ? a : b;
+}
+
+template<typename T>
+inline const T& min(const T& a, const T& b) {
+    return a < b ? a : b;
+}
+
+template<typename T> void swap(T& t1, T& t2) {
+    T tmp(t1);
+    t1 = t2;
+    t2 = tmp;
+}
+
+// Rationale: MKL-DNN needs numeric limits implementation that does not
+// generate dependencies on C++ run-time libraries.
+
+template<typename T> struct numeric_limits;
+
+template<> struct numeric_limits<float> {
+    static constexpr float lowest() { return -FLT_MAX; }
+    static constexpr float max() { return FLT_MAX; }
+};
+
+template<> struct numeric_limits<int32_t> {
+    static constexpr int lowest() { return INT32_MIN; }
+    static constexpr int max() { return INT32_MAX; }
+};
+
+template<> struct numeric_limits<int16_t> {
+    static constexpr int16_t lowest() { return INT16_MIN; }
+    static constexpr int16_t max() { return INT16_MAX; }
+};
+
+template<> struct numeric_limits<int8_t> {
+    static constexpr int8_t lowest() { return INT8_MIN; }
+    static constexpr int8_t max() { return INT8_MAX; }
+};
+
+template<> struct numeric_limits<uint8_t> {
+    static constexpr uint8_t lowest() { return 0; }
+    static constexpr uint8_t max() { return UINT8_MAX; }
+};
+
+template<typename T> struct is_integral
+{ static constexpr bool value = false; };
+template<> struct is_integral<int32_t> { static constexpr bool value = true; };
+template<> struct is_integral<int16_t> { static constexpr bool value = true; };
+template<> struct is_integral<int8_t> { static constexpr bool value = true; };
+template<> struct is_integral<uint8_t> { static constexpr bool value = true; };
+
+template <typename T, typename U> struct is_same
+{ static constexpr bool value = false; };
+template <typename T> struct is_same<T, T>
+{ static constexpr bool value = true; };
+
+// Rationale: MKL-DNN needs container implementations that do not generate
+// dependencies on C++ run-time libraries.
+//
+// Implementation philosophy: caller is responsible to check if the operation
+// is valid. The only functions that have to return status are those that
+// depend on memory allocation or similar operations.
+//
+// This means that e.g. an operator [] does not have to check for boundaries.
+// The caller should have checked the boundaries. If it did not we crash and
+// burn: this is a bug in MKL-DNN and throwing an exception would not have been
+// recoverable.
+//
+// On the other hand, insert() or resize() or a similar operation needs to
+// return a status because the outcome depends on factors external to the
+// caller. The situation is probably also not recoverable also, but MKL-DNN
+// needs to be nice and report "out of memory" to the users.
+
+enum nstl_status_t {
+    success = 0,
+    out_of_memory
+};
+
+template <typename T> class vector: public c_compatible {
+private:
+    std::vector<T> _impl;
+public:
+    typedef typename std::vector<T>::iterator iterator;
+    typedef typename std::vector<T>::const_iterator const_iterator;
+    typedef typename std::vector<T>::size_type size_type;
+    vector() {}
+    vector(size_type n): _impl(n) {}
+    vector(size_type n, const T &value): _impl(n, value) {}
+    template <typename input_iterator>
+    vector(input_iterator first, input_iterator last): _impl(first, last) {}
+    ~vector() {}
+    size_type size() const { return _impl.size(); }
+    T& operator[] (size_type i) { return _impl[i]; }
+    const T& operator[] (size_type i) const { return _impl[i]; }
+    iterator begin() { return _impl.begin(); }
+    const_iterator begin() const { return _impl.begin(); }
+    iterator end() { return _impl.end(); }
+    const_iterator end() const { return _impl.end(); }
+    template <typename input_iterator>
+    nstl_status_t insert(iterator pos, input_iterator begin, input_iterator end)
+    {
+        _impl.insert(pos, begin, end);
+        return success;
+    }
+    void clear() { _impl.clear(); }
+    void push_back(const T& t) { _impl.push_back(t); }
+    void resize(size_type count) { _impl.resize(count); }
+    void reserve(size_type count) { _impl.reserve(count); }
+};
+
+template <typename Key, typename T> class map: public c_compatible {
+private:
+    std::map<Key, T> _impl;
+public:
+    typedef typename std::map<Key, T>::iterator iterator;
+    typedef typename std::map<Key, T>::const_iterator const_iterator;
+    typedef typename std::map<Key, T>::size_type size_type;
+    map() {}
+    ~map() {}
+    size_type size() const { return _impl.size(); }
+    T& operator[](const Key &k) { return _impl[k]; }
+    const T& operator[](const Key &k) const { return _impl[k]; }
+    iterator begin() { return _impl.begin(); }
+    const_iterator begin() const { return _impl.begin(); }
+    iterator end() { return _impl.end(); }
+    const_iterator end() const { return _impl.end(); }
+    template <typename input_iterator>
+    void clear() { _impl.clear(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/pooling.cpp b/thirdparty/oidn/mkl-dnn/src/common/pooling.cpp
new file mode 100644
index 0000000000..be96e654ff
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/pooling.cpp
@@ -0,0 +1,114 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::alg_kind;
+using namespace mkldnn::impl::types;
+
+namespace {
+status_t pooling_desc_init(pooling_desc_t *pool_desc,
+        prop_kind_t prop_kind, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *dst_desc,
+        const dims_t strides, const dims_t kernel, const dims_t padding_l,
+        const dims_t padding_r, padding_kind_t padding_kind) {
+    bool args_ok = true
+        && !any_null(pool_desc, src_desc, dst_desc, strides, kernel, padding_l)
+        && one_of(alg_kind, pooling_max,
+                pooling_avg_include_padding,
+                pooling_avg_exclude_padding)
+        && one_of(padding_kind, padding_kind::padding_zero);
+    if (!args_ok) return invalid_arguments;
+
+    if (padding_r == nullptr) padding_r = padding_l;
+
+    auto pd = pooling_desc_t();
+    pd.primitive_kind = primitive_kind::pooling;
+    pd.prop_kind = prop_kind;
+    pd.alg_kind = alg_kind;
+    pd.src_desc.ndims = src_desc->ndims;
+
+    const bool is_fwd = one_of(prop_kind, forward_training, forward_inference);
+
+    pd.diff_src_desc = pd.src_desc = zero_md();
+    pd.diff_dst_desc = pd.dst_desc = zero_md();
+
+    (is_fwd ? pd.src_desc : pd.diff_src_desc) = *src_desc;
+    (is_fwd ? pd.dst_desc : pd.diff_dst_desc) = *dst_desc;
+
+    int sp_dims = src_desc->ndims - 2;
+    utils::array_copy(pd.strides, strides, sp_dims);
+    utils::array_copy(pd.kernel, kernel, sp_dims);
+    utils::array_copy(pd.padding[0], padding_l, sp_dims);
+    utils::array_copy(pd.padding[1], padding_r, sp_dims);
+
+    pd.padding_kind = padding_kind;
+    if (one_of(alg_kind, pooling_max, pooling_avg_include_padding,
+                pooling_avg_exclude_padding)) {
+        pd.accum_data_type = types::default_accum_data_type(
+                src_desc->data_type, dst_desc->data_type);
+    } else {
+        pd.accum_data_type = dst_desc->data_type;
+    }
+
+    bool consistency = true
+        && utils::one_of(src_desc->ndims, 4, 5)
+        && utils::one_of(dst_desc->ndims, 4, 5)
+        && src_desc->dims[0] == dst_desc->dims[0]
+        && src_desc->dims[1] == dst_desc->dims[1];
+    for (int i = 2; i < src_desc->ndims; ++i)
+        consistency = consistency && (
+                (src_desc->dims[i] - kernel[i - 2] + padding_l[i - 2]
+                 + padding_r[i - 2]) / strides[i - 2] + 1
+                == dst_desc->dims[i]);
+    if (!consistency) return invalid_arguments;
+
+    *pool_desc = pd;
+    return success;
+}
+}
+
+status_t mkldnn_pooling_forward_desc_init(pooling_desc_t *pool_desc,
+        prop_kind_t prop_kind, alg_kind_t alg_kind,
+        const memory_desc_t *src_desc, const memory_desc_t *dst_desc,
+        const dims_t strides, const dims_t kernel, const dims_t padding_l,
+        const dims_t padding_r, padding_kind_t padding_kind) {
+    if (!one_of(prop_kind, forward_training, forward_inference))
+        return invalid_arguments;
+    return pooling_desc_init(pool_desc, prop_kind, alg_kind, src_desc,
+            dst_desc, strides, kernel, padding_l, padding_r, padding_kind);
+}
+
+status_t mkldnn_pooling_backward_desc_init(pooling_desc_t *pool_desc,
+        alg_kind_t alg_kind, const memory_desc_t *diff_src_desc,
+        const memory_desc_t *diff_dst_desc, const dims_t strides,
+        const dims_t kernel, const dims_t padding_l, const dims_t padding_r,
+        padding_kind_t padding_kind) {
+    return pooling_desc_init(pool_desc, prop_kind::backward_data, alg_kind,
+            diff_src_desc, diff_dst_desc, strides, kernel, padding_l,
+            padding_r, padding_kind);
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/pooling_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/pooling_pd.hpp
new file mode 100644
index 0000000000..4c9c009412
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/pooling_pd.hpp
@@ -0,0 +1,238 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef POOLING_PD_HPP
+#define POOLING_PD_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "primitive_desc.hpp"
+#include "type_helpers.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct pooling_fwd_pd_t;
+
+struct pooling_pd_t: public primitive_desc_t {
+    static constexpr auto base_pkind = primitive_kind::pooling;
+
+    pooling_pd_t(engine_t *engine,
+            const pooling_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const pooling_fwd_pd_t *hint_fwd_pd)
+        : primitive_desc_t(engine, attr, base_pkind)
+        , desc_(*adesc)
+        , hint_fwd_pd_(hint_fwd_pd)
+        , ws_md_()
+    {}
+
+    const pooling_desc_t *desc() const { return &desc_; }
+    virtual const op_desc_t *op_desc() const override
+    { return reinterpret_cast<const op_desc_t *>(this->desc()); }
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual status_t query(query_t what, int idx, void *result) const override {
+        switch (what) {
+        case query::pooling_d:
+            *(const pooling_desc_t**)result = desc(); break;
+        default: return primitive_desc_t::query(what, idx, result);
+        }
+        return status::success;
+    }
+
+    /* common pooling aux functions */
+
+    dim_t MB() const { return src_desc().dims[0]; }
+    dim_t C() const { return src_desc().dims[1]; }
+
+    dim_t ID() const { return ndims() >= 5 ? src_desc().dims[ndims() - 3] : 1; }
+    dim_t IH() const { return ndims() >= 4 ? src_desc().dims[ndims() - 2] : 1; }
+    dim_t IW() const { return src_desc().dims[ndims() - 1]; }
+
+    dim_t OD() const { return ndims() >= 5 ? dst_desc().dims[ndims() - 3] : 1; }
+    dim_t OH() const { return ndims() >= 4 ? dst_desc().dims[ndims() - 2] : 1; }
+    dim_t OW() const { return dst_desc().dims[ndims() - 1]; }
+
+    dim_t KD() const { return ndims() >= 5 ? desc_.kernel[ndims() - 5] : 1; }
+    dim_t KH() const { return ndims() >= 4 ? desc_.kernel[ndims() - 4] : 1; }
+    dim_t KW() const { return desc_.kernel[ndims() - 3]; }
+
+    dim_t KSD() const { return ndims() >= 5 ? desc_.strides[ndims() - 5] : 1; }
+    dim_t KSH() const { return ndims() >= 4 ? desc_.strides[ndims() - 4] : 1; }
+    dim_t KSW() const { return desc_.strides[ndims() - 3]; }
+
+    dim_t padFront() const
+    { return ndims() >= 5 ? desc_.padding[0][ndims() - 5] : 0; }
+    dim_t padBack() const
+    { return ndims() >= 5 ? desc_.padding[1][ndims() - 5] : 0; }
+    dim_t padT() const
+    { return ndims() >= 4 ? desc_.padding[0][ndims() - 4] : 0; }
+    dim_t padB() const
+    { return ndims() >= 4 ? desc_.padding[1][ndims() - 4] : 0; }
+    dim_t padL() const { return desc_.padding[0][ndims() - 3]; }
+    dim_t padR() const { return desc_.padding[1][ndims() - 3]; }
+
+    int ndims() const { return src_desc().ndims; }
+    bool is_3d() const { return ndims() == 5; }
+
+    bool has_zero_dim_memory() const
+    { return memory_desc_wrapper(src_desc()).has_zero_dim(); }
+
+    bool is_fwd() const {
+        return utils::one_of(desc_.prop_kind, prop_kind::forward_training,
+                prop_kind::forward_inference);
+    }
+
+protected:
+    pooling_desc_t desc_;
+    const pooling_fwd_pd_t *hint_fwd_pd_;
+
+    memory_desc_t ws_md_;
+
+    void init_default_ws() {
+        ws_md_ = is_fwd() ? *dst_md() : *diff_dst_md();
+        ws_md_.data_type = indices_data_type();
+    }
+
+    data_type_t indices_data_type() const {
+        /* the simplest way to express 256... */
+        const int u8_max = nstl::numeric_limits<
+            typename prec_traits<data_type::u8>::type>::max();
+        return utils::array_product(desc()->kernel, ndims()) <= u8_max
+            ? data_type::u8 : data_type::s32;
+    }
+
+private:
+    const memory_desc_t &src_desc() const
+    { return is_fwd() ? desc_.src_desc : desc_.diff_src_desc; }
+    const memory_desc_t &dst_desc() const
+    { return is_fwd() ? desc_.dst_desc : desc_.diff_dst_desc; }
+};
+
+struct pooling_fwd_pd_t: public pooling_pd_t {
+    typedef pooling_fwd_pd_t base_class;
+    typedef pooling_fwd_pd_t hint_class;
+
+    pooling_fwd_pd_t(engine_t *engine,
+            const pooling_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const pooling_fwd_pd_t *hint_fwd_pd)
+        : pooling_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , src_md_(desc_.src_desc)
+        , dst_md_(desc_.dst_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (arg == MKLDNN_ARG_SRC)
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DST)
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_WORKSPACE && (workspace_md() != nullptr))
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &src_md_ : nullptr; }
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 ? &dst_md_ : nullptr; }
+    virtual const memory_desc_t *workspace_md(int index = 0) const override
+    { return index == 0 && !types::is_zero_md(&ws_md_) ? &ws_md_ : nullptr; }
+
+    virtual int n_inputs() const override { return 1; }
+    virtual int n_outputs() const override
+    { return 1 + (workspace_md() != nullptr); }
+
+protected:
+    memory_desc_t src_md_;
+    memory_desc_t dst_md_;
+
+    virtual status_t set_default_params() {
+        if (dst_md()->format_kind != format_kind::any)
+            return status::success;
+
+        if (src_md()->format_kind != format_kind::blocked)
+            return status::unimplemented;
+
+        return memory_desc_init_by_blocking_desc(dst_md_,
+                src_md_.format_desc.blocking);
+    }
+};
+
+struct pooling_bwd_pd_t: public pooling_pd_t {
+    typedef pooling_bwd_pd_t base_class;
+    typedef pooling_fwd_pd_t hint_class;
+
+    pooling_bwd_pd_t(engine_t *engine,
+            const pooling_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const pooling_fwd_pd_t *hint_fwd_pd)
+        : pooling_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , diff_src_md_(desc_.diff_src_desc)
+        , diff_dst_md_(desc_.diff_dst_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (arg == MKLDNN_ARG_DIFF_DST)
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DIFF_SRC)
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_WORKSPACE && (workspace_md() != nullptr))
+            return arg_usage_t::input;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *diff_src_md(int index = 0) const override
+    { return index == 0 ? &diff_src_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 ? &diff_dst_md_ : nullptr; }
+    virtual const memory_desc_t *workspace_md(int index = 0) const override
+    { return index == 0 && !types::is_zero_md(&ws_md_) ? &ws_md_ : nullptr; }
+
+    virtual int n_inputs() const override
+    { return 1 + (workspace_md() != nullptr); }
+    virtual int n_outputs() const override { return 1; }
+
+protected:
+    memory_desc_t diff_src_md_;
+    memory_desc_t diff_dst_md_;
+
+    virtual status_t set_default_params() {
+        if (diff_src_md()->format_kind != format_kind::any)
+            return status::success;
+
+        if (diff_dst_md()->format_kind != format_kind::blocked)
+            return status::unimplemented;
+
+        return memory_desc_init_by_blocking_desc(diff_src_md_,
+                diff_dst_md_.format_desc.blocking);
+    }
+};
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/primitive.cpp b/thirdparty/oidn/mkl-dnn/src/common/primitive.cpp
new file mode 100644
index 0000000000..fdf6522f62
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/primitive.cpp
@@ -0,0 +1,103 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "engine.hpp"
+#include "primitive_desc.hpp"
+#include "primitive.hpp"
+#include "type_helpers.hpp"
+#include "stream.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::primitive_kind;
+
+namespace {
+// XXX: this is a huge hammer. This disables all and any msan checks on
+// primitives outputs.
+//
+// A proper approach would be an implementation-specific unpoisoning.
+void unpoison_outputs(const exec_args_t &args) {
+    for(const auto &arg: args) {
+        if (arg.second.is_const) continue;
+        auto *mem = arg.second.mem;
+        void *p;
+        mem->get_data_handle(&p);
+        size_t s = memory_desc_wrapper(*mem->md()).size();
+        msan_unpoison(p, s);
+    }
+}
+}
+
+status_t mkldnn_primitive_desc_destroy(primitive_desc_t *primitive_desc) {
+    if (primitive_desc) delete primitive_desc;
+    return success;
+}
+
+status_t mkldnn_primitive_create(primitive_t **primitive,
+        const primitive_desc_t *primitive_desc) {
+    if (utils::any_null(primitive, primitive_desc))
+        return invalid_arguments;
+    return primitive_desc->create_primitive(primitive);
+}
+
+status_t mkldnn_primitive_execute(const primitive_t *primitive,
+        stream_t *stream, int nargs, const mkldnn_exec_arg_t *c_args) {
+    bool ok = true
+        && !utils::any_null(primitive, stream)
+        && primitive->engine() == stream->engine()
+        && IMPLICATION(nargs > 0, c_args != nullptr);
+    if (!ok) return invalid_arguments;
+
+    exec_args_t args;
+    status_t status = cvt_primtive_args(primitive->pd(), nargs, c_args, args);
+    if (status != status::success) return status;
+
+    exec_ctx_t ctx(stream, std::move(args));
+
+    if (mkldnn_verbose()->level) {
+        double ms = get_msec();
+        status = primitive->execute(ctx);
+        ms = get_msec() - ms;
+        printf("mkldnn_verbose,exec,%s,%g\n", primitive->pd()->info(), ms);
+        fflush(0);
+    } else {
+        status = primitive->execute(ctx);
+    }
+
+    if (msan_enabled) unpoison_outputs(ctx.args());
+
+    return status;
+}
+
+status_t mkldnn_primitive_get_primitive_desc(const primitive_t *primitive,
+        const primitive_desc_t **primitive_desc) {
+    if (utils::any_null(primitive, primitive_desc))
+        return invalid_arguments;
+    return safe_ptr_assign<const primitive_desc_t>(*primitive_desc,
+            primitive->pd());
+}
+
+status_t mkldnn_primitive_destroy(primitive_t *primitive) {
+    if (primitive != nullptr)
+        delete primitive;
+    return success;
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/primitive.hpp b/thirdparty/oidn/mkl-dnn/src/common/primitive.hpp
new file mode 100644
index 0000000000..3b506d6d1f
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/primitive.hpp
@@ -0,0 +1,76 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef PRIMITIVE_HPP
+#define PRIMITIVE_HPP
+
+#include <assert.h>
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "primitive_desc.hpp"
+#include "primitive_exec_types.hpp"
+
+/** \brief A pure virtual primitive class
+ *
+ * Primitive contains links to its inputs & outputs, though it does not track
+ * their readiness on execution step.
+ *
+ * @remark @b Rational.
+ *   Dependencies are essential through-out the whole MKL-DNN library, so it
+ *   makes sense to include them on the very low level. On the other hand,
+ *   tracking them should be a task for corresponding essence, like scheduler,
+ *   stream or whatever. Primitive itself should know nothing about the
+ *   environment it is running in.
+ *
+ * @note
+ *   To make user experience better we should provide API which allows
+ *   achieving the best (or good enough) performance when creating primitives
+ *   in natural order: i.e. from bottom to top for forward pass and from top to
+ *   bottom for backward pass. Please consider restriction [1] in Level 0.
+ */
+struct mkldnn_primitive: public mkldnn::impl::c_compatible {
+    mkldnn_primitive(const mkldnn::impl::primitive_desc_t *pd)
+        : pd_(pd->clone()) {}
+    virtual ~mkldnn_primitive() { delete pd_; }
+
+    /** returns primitive's engine */
+    mkldnn::impl::engine_t *engine() const { return pd_->engine(); }
+    /** returns primitive's inputs */
+    const mkldnn::impl::primitive_desc_t *pd() const { return pd_; }
+    /** returns primitive's kind */
+    mkldnn::impl::primitive_kind_t kind() const { return pd_->kind(); }
+
+    /** executes primitive with execution context @p ctx */
+    virtual mkldnn::impl::status_t execute(const mkldnn::impl::exec_ctx_t &ctx)
+        const = 0;
+
+protected:
+    const mkldnn::impl::primitive_desc_t *pd_;
+
+private:
+    mkldnn_primitive() = delete;
+    mkldnn_primitive(const mkldnn_primitive &) = delete;
+    mkldnn_primitive(mkldnn_primitive &&) = delete;
+    mkldnn_primitive &operator=(const mkldnn_primitive &) = delete;
+    mkldnn_primitive &operator=(mkldnn_primitive &&) = delete;
+};
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/primitive_attr.cpp b/thirdparty/oidn/mkl-dnn/src/common/primitive_attr.cpp
new file mode 100644
index 0000000000..9fd638842c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/primitive_attr.cpp
@@ -0,0 +1,290 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "primitive_attr.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::utils;
+
+namespace mkldnn {
+namespace impl {
+
+status_t scales_t::set(dim_t count, int mask, const float *scales) {
+    cleanup();
+
+    count_ = count;
+    mask_ = mask;
+
+    if (count_ == 1) {
+        scales_ = scales_buf_;
+        utils::array_set(scales_, scales[0], scales_buf_size);
+    } else {
+        scales_ = (float *)impl::malloc(count_ * sizeof(*scales_), 64);
+        if (scales_ == nullptr)
+            return status::out_of_memory;
+
+        for (dim_t c = 0; c < count_; ++c)
+            scales_[c] = scales[c];
+    }
+
+    return status::success;
+}
+
+}
+}
+
+status_t post_ops_t::append_sum(float scale) {
+    if (len_ == capacity)
+        return out_of_memory;
+
+    entry_[len_].kind = primitive_kind::sum;
+    entry_[len_].sum.scale = scale;
+
+    len_++;
+
+    return success;
+}
+
+status_t post_ops_t::append_eltwise(float scale, alg_kind_t alg, float alpha,
+        float beta) {
+    using namespace mkldnn::impl::alg_kind;
+    bool known_alg = one_of(alg, eltwise_relu, eltwise_tanh, eltwise_elu,
+            eltwise_square, eltwise_abs, eltwise_sqrt, eltwise_linear,
+            eltwise_bounded_relu, eltwise_soft_relu, eltwise_logistic);
+    if (!known_alg)
+        return invalid_arguments;
+
+    if (len_ == capacity)
+        return out_of_memory;
+
+    entry_[len_].kind = primitive_kind::eltwise;
+    entry_[len_].eltwise.scale = scale;
+    entry_[len_].eltwise.alg = alg;
+    entry_[len_].eltwise.alpha = alpha;
+    entry_[len_].eltwise.beta = beta;
+
+    len_++;
+
+    return success;
+}
+
+status_t primitive_attr_t::set_scratchpad_mode(
+        scratchpad_mode_t scratchpad_mode) {
+    using namespace mkldnn::impl::scratchpad_mode;
+
+    const bool ok = one_of(scratchpad_mode, library, user);
+    if (!ok)
+        return invalid_arguments;
+
+    scratchpad_mode_ = scratchpad_mode;
+    return success;
+}
+
+status_t primitive_attr_t::set_post_ops(const post_ops_t &post_ops) {
+    this->post_ops_ = post_ops;
+    return success;
+}
+
+/* Public C API */
+
+status_t mkldnn_primitive_attr_create(primitive_attr_t **attr) {
+    if (attr == nullptr)
+        return invalid_arguments;
+
+    return safe_ptr_assign<mkldnn_primitive_attr>(*attr,
+            new mkldnn_primitive_attr);
+}
+
+status_t mkldnn_primitive_attr_clone(primitive_attr_t **attr,
+        const primitive_attr_t *existing_attr) {
+    if (any_null(attr, existing_attr))
+        return invalid_arguments;
+
+    return safe_ptr_assign<mkldnn_primitive_attr>(*attr,
+            existing_attr->clone());
+}
+
+status_t mkldnn_primitive_attr_destroy(primitive_attr_t *attr) {
+    if (attr)
+        delete attr;
+
+    return success;
+}
+
+status_t mkldnn_primitive_attr_get_scratchpad_mode(
+        const primitive_attr_t *attr, scratchpad_mode_t *scratchpad_mode) {
+    if (any_null(attr, scratchpad_mode))
+        return invalid_arguments;
+
+    *scratchpad_mode = attr->scratchpad_mode_;
+
+    return success;
+}
+
+status_t mkldnn_primitive_attr_set_scratchpad_mode(
+        primitive_attr_t *attr, scratchpad_mode_t scratchpad_mode) {
+    if (any_null(attr))
+        return invalid_arguments;
+
+    return attr->set_scratchpad_mode(scratchpad_mode);
+}
+
+status_t mkldnn_primitive_attr_get_output_scales(const primitive_attr_t *attr,
+        dim_t *count, int *mask, const float **scales) {
+    if (any_null(attr, count, mask, scales))
+        return invalid_arguments;
+
+    *count = attr->output_scales_.count_;
+    *mask = attr->output_scales_.mask_;
+    *scales = attr->output_scales_.scales_;
+
+    return success;
+}
+
+status_t mkldnn_primitive_attr_set_output_scales(primitive_attr_t *attr,
+        dim_t count, int mask, const float *scales) {
+    bool ok = !any_null(attr, scales) && count > 0 && mask >= 0;
+    if (!ok)
+        return invalid_arguments;
+
+    return attr->output_scales_.set(count, mask, scales);
+}
+
+status_t mkldnn_primitive_attr_get_post_ops(const primitive_attr_t *attr,
+        const post_ops_t **post_ops) {
+    if (any_null(attr, post_ops))
+        return invalid_arguments;
+
+    *post_ops = &attr->post_ops_;
+    return success;
+}
+
+status_t mkldnn_primitive_attr_set_post_ops(primitive_attr_t *attr,
+        const post_ops_t *post_ops) {
+    if (any_null(attr, post_ops))
+        return invalid_arguments;
+
+    return attr->set_post_ops(*post_ops);
+}
+
+status_t mkldnn_post_ops_create(post_ops_t **post_ops) {
+    if (post_ops == nullptr)
+        return invalid_arguments;
+
+    return safe_ptr_assign<mkldnn_post_ops>(*post_ops, new mkldnn_post_ops);
+}
+
+status_t mkldnn_post_ops_destroy(post_ops_t *post_ops) {
+    if (post_ops)
+        delete post_ops;
+
+    return success;
+}
+
+int mkldnn_post_ops_len(const post_ops_t *post_ops) {
+    if (post_ops)
+        return post_ops->len_;
+
+    return 0;
+}
+
+primitive_kind_t mkldnn_post_ops_get_kind(const post_ops_t *post_ops,
+        int index) {
+    bool ok = post_ops && 0 <= index && index < post_ops->len_;
+    if (!ok)
+        return primitive_kind::undefined;
+
+    return post_ops->entry_[index].kind;
+}
+
+status_t mkldnn_post_ops_append_sum(post_ops_t *post_ops, float scale) {
+    if (post_ops == nullptr)
+        return invalid_arguments;
+
+    return post_ops->append_sum(scale);
+}
+
+namespace {
+bool simple_get_params_check(const post_ops_t *post_ops, int index,
+        primitive_kind_t kind) {
+    bool ok = true
+        && post_ops != nullptr
+        && 0 <= index
+        && index < post_ops->len_
+        && post_ops->entry_[index].kind == kind;
+   return ok;
+}
+}
+
+status_t mkldnn_post_ops_get_params_sum(const post_ops_t *post_ops, int index,
+        float *scale) {
+    bool ok = true
+        && simple_get_params_check(post_ops, index, primitive_kind::sum)
+        && !any_null(scale);
+    if (!ok)
+        return invalid_arguments;
+
+    *scale = post_ops->entry_[index].sum.scale;
+    return success;
+}
+
+status_t mkldnn_post_ops_append_eltwise(post_ops_t *post_ops, float scale,
+        alg_kind_t kind, float alpha, float beta) {
+    if (post_ops == nullptr)
+        return invalid_arguments;
+
+    return post_ops->append_eltwise(scale, kind, alpha, beta);
+}
+
+status_t mkldnn_post_ops_get_params_eltwise(const post_ops_t *post_ops,
+        int index, float *scale, alg_kind_t *alg, float *alpha, float *beta) {
+    bool ok = true
+        && simple_get_params_check(post_ops, index, primitive_kind::eltwise)
+        && !any_null(scale, alpha, beta);
+    if (!ok)
+        return invalid_arguments;
+
+    const auto &e = post_ops->entry_[index].eltwise;
+    *scale = e.scale;
+    *alg = e.alg;
+    *alpha = e.alpha;
+    *beta = e.beta;
+
+    return success;
+}
+
+status_t mkldnn_primitive_attr_set_rnn_data_qparams(
+        primitive_attr_t *attr, const float scale, const float shift) {
+    if (attr == nullptr)
+        return invalid_arguments;
+
+    return attr->rnn_data_qparams_.set(scale, shift);
+}
+
+status_t mkldnn_primitive_attr_set_rnn_weights_qparams(
+        primitive_attr_t *attr, dim_t count, int mask, const float *scales) {
+    bool ok = !any_null(attr, scales) && count > 0 && mask >= 0;
+    if (!ok)
+        return invalid_arguments;
+
+    return attr->rnn_weights_qparams_.set(count, mask, scales);
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/primitive_attr.hpp b/thirdparty/oidn/mkl-dnn/src/common/primitive_attr.hpp
new file mode 100644
index 0000000000..e2130c7ab1
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/primitive_attr.hpp
@@ -0,0 +1,183 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef PRIMITIVE_ATTR_HPP
+#define PRIMITIVE_ATTR_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct rnn_data_qparams_t : public c_compatible {
+    rnn_data_qparams_t() : scale_(1.), shift_(0.) {}
+    bool has_default_values() const { return (scale_ == 1. && shift_ == 0.); }
+
+    status_t set(float scale, float shift) {
+        scale_ = scale;
+        shift_ = shift;
+        return status::success;
+    }
+
+    float scale_;
+    float shift_;
+};
+
+struct scales_t: public c_compatible {
+    scales_t(): count_(1), mask_(0), scales_(scales_buf_)
+    { set(1.); }
+
+    scales_t(const scales_t &rhs): scales_t()
+    { set(rhs.count_, rhs.mask_, rhs.scales_); }
+
+    ~scales_t() { cleanup(); }
+
+    scales_t &operator=(const scales_t &rhs) {
+        if (&rhs == this)
+            return *this;
+        status_t status = set(rhs.count_, rhs.mask_, rhs.scales_);
+        assert(status == status::success);
+        (void)status;
+        return *this;
+    }
+
+    bool has_default_values() const {
+        for (dim_t c = 0; c < count_; ++c) {
+            if(scales_[c] != 1.) return false;
+        }
+        return true;
+    }
+
+    status_t set(dim_t count, int mask, const float *scales);
+    status_t set(float single_scale) { return this->set(1, 0, &single_scale); }
+
+    dim_t count_;
+    int mask_;
+    float *scales_;
+
+private:
+    enum { scales_buf_size = 16 };
+    float scales_buf_[scales_buf_size];
+
+    void cleanup() {
+        if (scales_ != scales_buf_ && scales_ != nullptr)
+            impl::free(scales_);
+
+        count_ = 1;
+        mask_ = 0;
+        scales_ = scales_buf_;
+    }
+};
+
+}
+}
+
+struct mkldnn_post_ops: public mkldnn::impl::c_compatible {
+    struct entry_t {
+        struct eltwise_t {
+            mkldnn::impl::alg_kind_t alg;
+            float scale, alpha, beta;
+        };
+
+        mkldnn::impl::primitive_kind_t kind;
+        union {
+            struct { float scale; } sum;
+            eltwise_t eltwise;
+        };
+
+        bool is_eltwise(bool require_scale_one = true) const {
+            using namespace mkldnn::impl;
+            return kind == primitive_kind::eltwise
+                && IMPLICATION(require_scale_one, eltwise.scale == 1.f);
+        }
+
+        bool is_relu(bool require_scale_one = true,
+                bool require_nslope_zero = true) const {
+            using namespace mkldnn::impl;
+            return is_eltwise(require_scale_one)
+                && eltwise.alg == alg_kind::eltwise_relu
+                && IMPLICATION(require_nslope_zero, eltwise.alpha == 0.f);
+        }
+
+        bool is_sum(bool require_scale_one = true) const {
+            using namespace mkldnn::impl;
+            return kind == primitive_kind::sum
+                && IMPLICATION(require_scale_one, sum.scale == 1.f);
+        }
+    };
+
+    mkldnn_post_ops(): len_(0) {}
+
+    mkldnn::impl::status_t append_sum(float scale);
+    mkldnn::impl::status_t append_eltwise(float scale,
+            mkldnn::impl::alg_kind_t alg, float alpha, float beta);
+
+    int find(mkldnn::impl::primitive_kind_t kind, int start = 0,
+            int stop = -1) const {
+        if (stop == -1) stop = len_;
+        stop = mkldnn::impl::nstl::min(stop, len_);
+        for (int idx = start; idx < stop; ++idx)
+            if (entry_[idx].kind == kind) return idx;
+        return -1;
+    }
+
+    bool has_default_values() const { return len_ == 0; }
+
+    bool contain(mkldnn::impl::primitive_kind_t kind, int index) const
+    { return find(kind, index, index + 1) == index; }
+
+    enum { capacity = 4 };
+
+    int len_;
+    entry_t entry_[capacity];
+};
+
+struct mkldnn_primitive_attr: public mkldnn::impl::c_compatible {
+    mkldnn_primitive_attr()
+        : scratchpad_mode_(mkldnn::impl::scratchpad_mode::library)
+    {}
+
+    mkldnn_primitive_attr *clone() const
+    { return new mkldnn_primitive_attr(*this); }
+
+    /** Returns true if the attributes have default values.
+     *
+     * @note The scratchpad_mode_ is not take into account */
+    bool has_default_values() const {
+       return true
+            && output_scales_.has_default_values()
+            && post_ops_.has_default_values()
+            && rnn_data_qparams_.has_default_values()
+            && rnn_weights_qparams_.has_default_values();
+    }
+
+    mkldnn::impl::status_t set_scratchpad_mode(
+            mkldnn::impl::scratchpad_mode_t scratchpad_mode);
+    mkldnn::impl::status_t set_post_ops(
+            const mkldnn::impl::post_ops_t &post_ops);
+
+    mkldnn::impl::scratchpad_mode_t scratchpad_mode_;
+    mkldnn::impl::scales_t output_scales_;
+    mkldnn::impl::post_ops_t post_ops_;
+    mkldnn::impl::rnn_data_qparams_t rnn_data_qparams_;
+    mkldnn::impl::scales_t rnn_weights_qparams_;
+};
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/primitive_desc.cpp b/thirdparty/oidn/mkl-dnn/src/common/primitive_desc.cpp
new file mode 100644
index 0000000000..723c41e05a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/primitive_desc.cpp
@@ -0,0 +1,78 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "primitive_desc.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::status;
+
+status_t primitive_desc_t::query(query_t what, int idx, void *result) const {
+    auto safe_ret_md = [&](const memory_desc_t *_) {
+        if (_ == nullptr) return not_required;
+        *(const memory_desc_t **)result = _;
+        return success;
+    };
+
+    switch (what) {
+        case query::engine: *(engine_t**)result = engine(); break;
+        case query::primitive_kind: *(primitive_kind_t*)result = kind(); break;
+
+        case query::scratchpad_engine:
+            *(engine_t**)result = scratchpad_engine(); break;
+
+        case query::memory_consumption_s64:
+            *(dim_t *)result = scratchpad_size(scratchpad_mode::library); break;
+
+        case query::op_d:
+            if (idx != 0 || op_desc() == nullptr) return invalid_arguments;
+            *(const_c_op_desc_t *)result
+                = static_cast<const_c_op_desc_t>(op_desc()); break;
+
+        case query::src_md: return safe_ret_md(src_md(idx));
+        case query::diff_src_md: return safe_ret_md(diff_src_md(idx));
+        case query::dst_md: return safe_ret_md(dst_md(idx));
+        case query::diff_dst_md: return safe_ret_md(diff_dst_md(idx));
+        case query::weights_md: return safe_ret_md(weights_md(idx));
+        case query::diff_weights_md: return safe_ret_md(diff_weights_md(idx));
+        case query::workspace_md:
+            if (idx != 0) return status::invalid_arguments;
+            return safe_ret_md(workspace_md(idx));
+        case query::scratchpad_md:
+            if (idx != 0) return status::invalid_arguments;
+            return safe_ret_md(scratchpad_md(idx));
+
+        case query::num_of_inputs_s32: *(int*)result = n_inputs(); break;
+        case query::num_of_outputs_s32: *(int*)result = n_outputs(); break;
+
+        case query::impl_info_str: *(const char **)result = name(); break;
+
+        default: return unimplemented;
+    }
+    return success;
+}
+
+status_t mkldnn_primitive_desc_get_attr(const primitive_desc_t *primitive_desc,
+        const primitive_attr_t **attr) {
+    if (utils::any_null(primitive_desc, attr))
+        return invalid_arguments;
+
+    *attr = primitive_desc->attr();
+    return success;
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/primitive_desc.hpp b/thirdparty/oidn/mkl-dnn/src/common/primitive_desc.hpp
new file mode 100644
index 0000000000..536dcfa1d0
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/primitive_desc.hpp
@@ -0,0 +1,174 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef PRIMITIVE_DESC_HPP
+#define PRIMITIVE_DESC_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "primitive_attr.hpp"
+#include "verbose.hpp"
+
+struct mkldnn_primitive_desc: public mkldnn::impl::c_compatible {
+    using md_t = mkldnn::impl::memory_desc_t;
+
+    mkldnn_primitive_desc(mkldnn::impl::engine_t *engine,
+            const mkldnn::impl::primitive_attr_t *attr,
+            mkldnn::impl::primitive_kind_t kind)
+        : engine_(engine), attr_(*attr), kind_(kind) { info_[0] = '\0'; }
+
+    mkldnn_primitive_desc(mkldnn::impl::engine_t *engine,
+            mkldnn::impl::primitive_kind_t kind)
+        : engine_(engine), kind_(kind) { info_[0] = '\0'; }
+
+    virtual mkldnn_primitive_desc *clone() const = 0;
+    virtual ~mkldnn_primitive_desc() {}
+
+    const mkldnn::impl::primitive_attr_t *attr() const { return &attr_; }
+    mkldnn::impl::engine_t *engine() const { return engine_; }
+    mkldnn::impl::primitive_kind_t kind() const { return kind_; }
+
+    virtual void init_info() {}
+    const char *info() const { return info_; }
+
+    mkldnn::impl::memory_tracking::registry_t &scratchpad_registry()
+    { return scratchpad_registry_; }
+    const mkldnn::impl::memory_tracking::registry_t &scratchpad_registry() const
+    { return scratchpad_registry_; }
+    virtual mkldnn::impl::engine_t *scratchpad_engine() const
+    { return engine_; }
+
+    virtual const mkldnn::impl::op_desc_t *op_desc() const { return nullptr; }
+
+    enum class arg_usage_t { unused, input, output };
+    virtual arg_usage_t arg_usage(
+            mkldnn::impl::primitive_arg_index_t arg) const {
+        using mkldnn::impl::types::is_zero_md;
+        if (arg == MKLDNN_ARG_SCRATCHPAD && !is_zero_md(scratchpad_md()))
+            return arg_usage_t::output;
+        return arg_usage_t::unused;
+    }
+
+#   define DECLARE_MD_STUB(stub) \
+    virtual const mkldnn::impl::memory_desc_t *stub(int idx = 0) const \
+    { return nullptr; }
+
+    DECLARE_MD_STUB(input_md); DECLARE_MD_STUB(output_md);
+    DECLARE_MD_STUB(src_md); DECLARE_MD_STUB(diff_src_md);
+    DECLARE_MD_STUB(dst_md); DECLARE_MD_STUB(diff_dst_md);
+    DECLARE_MD_STUB(weights_md); DECLARE_MD_STUB(diff_weights_md);
+    DECLARE_MD_STUB(workspace_md);
+#   undef DECLARE_MD_STUB
+
+    const mkldnn::impl::memory_desc_t *scratchpad_md(int idx = 0) const {
+        return idx == 0 ? &scratchpad_md_ : nullptr;
+    }
+
+    virtual void init_scratchpad_md() {
+        auto size = scratchpad_size(mkldnn::impl::scratchpad_mode::user);
+        mkldnn::impl::dims_t dims = { size };
+        mkldnn_memory_desc_init_by_tag(&scratchpad_md_, size ? 1 : 0, dims,
+                mkldnn::impl::data_type::u8, mkldnn_x);
+    }
+
+    /** returns the scratchpad size for the given scratchpad mode. */
+    mkldnn::impl::dim_t scratchpad_size(
+            mkldnn::impl::scratchpad_mode_t mode) const {
+        if (mode != attr_.scratchpad_mode_) return 0;
+        return scratchpad_registry().size();
+    }
+
+    virtual int n_inputs() const { return 0; }
+    virtual int n_outputs() const { return 0; }
+
+    virtual mkldnn::impl::status_t query(mkldnn::impl::query_t what, int idx,
+            void *result) const;
+
+    virtual mkldnn::impl::status_t create_primitive(
+            mkldnn::impl::primitive_t **primitive) const = 0;
+
+    virtual const char *name() const { return "mkldnn_primitive_desc"; }
+
+    /* static magic */
+
+    template<typename pd_t>
+    static mkldnn::impl::status_t create(mkldnn::impl::primitive_desc_t **pd,
+            const mkldnn::impl::op_desc_t *adesc,
+            const mkldnn::impl::primitive_attr_t *attr,
+            mkldnn::impl::engine_t *engine,
+            const mkldnn::impl::primitive_desc_t *hint_fwd) {
+        using namespace mkldnn::impl;
+        using namespace mkldnn::impl::status;
+        using pd_op_desc_t = typename pkind_traits<pd_t::base_pkind>::desc_type;
+        if (adesc->kind != pd_t::base_pkind) return invalid_arguments;
+        assert(hint_fwd ? hint_fwd->kind() == pd_t::base_pkind : true);
+        auto hint =
+            reinterpret_cast<const typename pd_t::hint_class *>(hint_fwd);
+        auto _pd = new pd_t(engine, (const pd_op_desc_t *)adesc, attr, hint);
+        if (_pd == nullptr) return out_of_memory;
+        if (_pd->init() != success) { delete _pd; return unimplemented; }
+        _pd->init_info();
+        _pd->init_scratchpad_md();
+        *pd = _pd;
+        return success;
+    }
+
+protected:
+    mkldnn::impl::engine_t *engine_;
+    mkldnn::impl::primitive_attr_t attr_;
+    mkldnn::impl::primitive_kind_t kind_;
+
+    mkldnn::impl::memory_desc_t scratchpad_md_;
+
+    char info_[MKLDNN_VERBOSE_BUF_LEN];
+
+    mkldnn::impl::memory_tracking::registry_t scratchpad_registry_;
+
+protected:
+    /** compares ws between fwd_pd and this (make sense to use for bwd_pd)
+     * Expectation: this already set workspace, and this workspace should
+     *              exactly match the one from fwd_pd */
+    bool compare_ws(const mkldnn_primitive_desc *fwd_pd) const {
+        using namespace mkldnn::impl;
+        if (!workspace_md()) return true; // the impl lives fine w/o workspace
+        return fwd_pd && fwd_pd->workspace_md()
+            && *fwd_pd->workspace_md() == *workspace_md();
+    }
+};
+
+#define DECLARE_COMMON_PD_t(impl_name, ...) \
+    virtual pd_t *clone() const override { return new pd_t(*this); } \
+    virtual status_t create_primitive(primitive_t **p) const override { \
+        double ms = get_msec(); \
+        auto ret = safe_ptr_assign<primitive_t>(*p, new (__VA_ARGS__)(this)); \
+        ms = get_msec() - ms; \
+        if (mkldnn_verbose()->level >= 2) { \
+            printf("mkldnn_verbose,create,%s,%g\n", this->info(), ms); \
+            fflush(0); \
+        } \
+        return ret; \
+    } \
+    virtual const char *name() const override { return impl_name; }
+#define DECLARE_COMMON_PD_T(impl_name, ...) \
+    DECLARE_COMMON_PD_t(impl_name, __VA_ARGS__)
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/primitive_exec_types.cpp b/thirdparty/oidn/mkl-dnn/src/common/primitive_exec_types.cpp
new file mode 100644
index 0000000000..43e5a31ef3
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/primitive_exec_types.cpp
@@ -0,0 +1,90 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "memory.hpp"
+#include "primitive.hpp"
+#include "primitive_exec_types.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+status_t cvt_primtive_args(const primitive_desc_t *pd, int nargs,
+        const mkldnn_exec_arg_t *c_args, exec_args_t &args) {
+    using namespace status;
+
+    if (!IMPLICATION(nargs > 0, c_args != nullptr)) return invalid_arguments;
+
+    int n_inputs = 0;
+    int n_outputs = 0;
+
+    for (int i = 0; i < nargs; ++i) {
+        primitive_arg_index_t arg = c_args[i].arg;
+        auto *mem = c_args[i].memory;
+
+        switch (pd->arg_usage(arg)) {
+        case primitive_desc_t::arg_usage_t::input:
+            if (args.count(arg) != 0) return invalid_arguments;
+            args[arg] = {mem, true};
+            n_inputs++;
+            break;
+        case primitive_desc_t::arg_usage_t::output:
+            if (args.count(arg) != 0) return invalid_arguments;
+            args[arg] = {mem, false};
+            n_outputs++;
+            break;
+        case primitive_desc_t::arg_usage_t::unused:
+            break;
+        }
+    }
+
+    bool scratchpad_required = !types::is_zero_md(pd->scratchpad_md());
+
+    if (n_inputs != pd->n_inputs()) return invalid_arguments;
+    if (n_outputs != pd->n_outputs() + (scratchpad_required ? 1 : 0))
+        return invalid_arguments;
+
+    return success;
+}
+
+const void *exec_ctx_t::input(primitive_arg_index_t arg) const {
+    if (args_.count(arg) != 1) return nullptr;
+    const auto ma = args_.at(arg);
+    assert(ma.is_const);
+    void *ptr;
+    status_t status = ma.mem->get_data_handle(&ptr);
+    assert(status == status::success); MAYBE_UNUSED(status);
+    return ptr;
+}
+
+void *exec_ctx_t::output(primitive_arg_index_t arg) const {
+    if (args_.count(arg) != 1) return nullptr;
+    const auto ma = args_.at(arg);
+    assert(!ma.is_const);
+    void *ptr;
+    status_t status = ma.mem->get_data_handle(&ptr);
+    assert(status == status::success); MAYBE_UNUSED(status);
+    return ptr;
+}
+
+const memory_t *exec_ctx_t::memory(primitive_arg_index_t arg) const {
+    assert(args_.count(arg) == 1);
+    const auto ma = args_.at(arg);
+    assert(!ma.is_const);
+    return ma.mem;
+}
+
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/primitive_exec_types.hpp b/thirdparty/oidn/mkl-dnn/src/common/primitive_exec_types.hpp
new file mode 100644
index 0000000000..0645891da7
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/primitive_exec_types.hpp
@@ -0,0 +1,68 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef PRIMITIVE_EXEC_TYPES_HPP
+#define PRIMITIVE_EXEC_TYPES_HPP
+
+#include <unordered_map>
+
+#include "mkldnn_types.h"
+
+#include "c_types_map.hpp"
+#include "memory.hpp"
+#include "primitive_desc.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct memory_arg_t {
+    memory_t *mem;
+    bool is_const;
+};
+
+using exec_args_t = std::unordered_map<primitive_arg_index_t, memory_arg_t>;
+
+status_t cvt_primtive_args(const primitive_desc_t *pd, int nargs,
+        const mkldnn_exec_arg_t *c_args, exec_args_t &args);
+
+/** Primitive execution context (helps passing stream, memories, and events. */
+struct exec_ctx_t {
+    exec_ctx_t(const exec_ctx_t &) = default;
+    exec_ctx_t(exec_ctx_t &&) = default;
+
+    exec_ctx_t(stream_t *stream): stream_(stream) {}
+    exec_ctx_t(stream_t *stream, exec_args_t &&args)
+        : stream_(stream)
+        , args_(std::move(args)) {}
+
+    stream_t *stream() const { return stream_; }
+    const exec_args_t &args() const { return args_; }
+
+    /* tentative solution... TODO: replace with functions return memory_t */
+    const void *input(primitive_arg_index_t arg) const;
+    void *output(primitive_arg_index_t arg) const;
+
+    const memory_t *memory(primitive_arg_index_t arg) const;
+
+private:
+    stream_t *stream_;
+    exec_args_t args_;
+};
+
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/primitive_iterator.cpp b/thirdparty/oidn/mkl-dnn/src/common/primitive_iterator.cpp
new file mode 100644
index 0000000000..5a1cd7d379
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/primitive_iterator.cpp
@@ -0,0 +1,89 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "engine.hpp"
+#include "primitive_desc.hpp"
+#include "type_helpers.hpp"
+#include "primitive_iterator.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::status;
+
+status_t mkldnn_primitive_desc_iterator_create(
+        primitive_desc_iterator_t **iterator, const_c_op_desc_t c_op_desc,
+        const primitive_attr_t *attr, engine_t *engine,
+        const primitive_desc_t *hint_fwd_pd) {
+    const op_desc_t *op_desc = (const op_desc_t *)c_op_desc;
+
+    auto it = new primitive_desc_iterator_t(engine, op_desc, attr, hint_fwd_pd);
+    if (it == nullptr) return out_of_memory;
+
+    ++(*it);
+    if (*it == it->end()) {
+        delete it;
+        return unimplemented;
+    }
+
+    *iterator = it;
+    return success;
+}
+
+status_t mkldnn_primitive_desc_iterator_next(
+        primitive_desc_iterator_t *iterator) {
+    if (iterator == nullptr) return invalid_arguments;
+    ++(*iterator);
+    return *iterator == iterator->end() ? iterator_ends : success;
+}
+
+primitive_desc_t *mkldnn_primitive_desc_iterator_fetch(
+        const primitive_desc_iterator_t *iterator) {
+    if (iterator == nullptr) return nullptr;
+    return *(*iterator);
+}
+
+status_t mkldnn_primitive_desc_clone(primitive_desc_t **primitive_desc,
+        const primitive_desc_t *existing_primitive_desc) {
+    if (utils::any_null(primitive_desc, existing_primitive_desc))
+        return invalid_arguments;
+    return safe_ptr_assign<primitive_desc_t>(*primitive_desc,
+            existing_primitive_desc->clone());
+}
+
+status_t mkldnn_primitive_desc_iterator_destroy(
+        primitive_desc_iterator_t *iterator) {
+    if (iterator != nullptr)
+        delete iterator;
+    return success;
+}
+
+status_t mkldnn_primitive_desc_create(primitive_desc_t **primitive_desc,
+        const_c_op_desc_t c_op_desc, const primitive_attr_t *attr,
+        engine_t *engine, const primitive_desc_t *hint_fwd_pd) {
+    const op_desc_t *op_desc = (const op_desc_t *)c_op_desc;
+
+    mkldnn_primitive_desc_iterator it(engine, op_desc, attr, hint_fwd_pd);
+    ++it;
+    if (it == it.end()) return unimplemented;
+
+    return safe_ptr_assign<primitive_desc_t>(*primitive_desc, *it);
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/primitive_iterator.hpp b/thirdparty/oidn/mkl-dnn/src/common/primitive_iterator.hpp
new file mode 100644
index 0000000000..4e88ab3aa5
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/primitive_iterator.hpp
@@ -0,0 +1,79 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+#ifndef PRIMITIVE_ITERATOR_HPP
+#define PRIMITIVE_ITERATOR_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "engine.hpp"
+#include "primitive_desc.hpp"
+#include "type_helpers.hpp"
+
+struct mkldnn_primitive_desc_iterator: public mkldnn::impl::c_compatible {
+    using pd_create_f = mkldnn::impl::engine_t::primitive_desc_create_f;
+
+    mkldnn_primitive_desc_iterator(mkldnn::impl::engine_t *engine, const mkldnn::impl::op_desc_t *op_desc,
+            const mkldnn::impl::primitive_attr_t *attr, const mkldnn::impl::primitive_desc_t *hint_fwd_pd)
+        : idx_(-1), engine_(engine), pd_(nullptr), op_desc_(op_desc)
+        , attr_(attr ? *attr : mkldnn::impl::primitive_attr_t()), hint_fwd_pd_(hint_fwd_pd)
+        , impl_list_(engine_->get_implementation_list()), last_idx_(0)
+    {
+        while (impl_list_[last_idx_] != nullptr) ++last_idx_;
+    }
+    ~mkldnn_primitive_desc_iterator() { if (pd_) delete pd_; }
+
+    bool operator==(const mkldnn::impl::primitive_desc_iterator_t& rhs) const
+    { return idx_ == rhs.idx_ && engine_ == rhs.engine_; }
+    bool operator!=(const mkldnn::impl::primitive_desc_iterator_t& rhs) const
+    { return !operator==(rhs); }
+
+    mkldnn::impl::primitive_desc_iterator_t end() const
+    { return mkldnn_primitive_desc_iterator(engine_, last_idx_); }
+
+    mkldnn::impl::primitive_desc_iterator_t &operator++() {
+        if (pd_) { delete pd_; pd_ = nullptr; }
+        while (++idx_ != last_idx_) {
+            auto s = impl_list_[idx_](&pd_, op_desc_, &attr_, engine_,
+                    hint_fwd_pd_);
+            if (s ==  mkldnn::impl::status::success) break;
+        }
+        return *this;
+    }
+
+    mkldnn::impl::primitive_desc_t *operator*() const {
+        if (*this == end() || pd_ == nullptr) return nullptr;
+        return pd_->clone();
+    }
+
+protected:
+    int idx_;
+    mkldnn::impl::engine_t *engine_;
+    mkldnn::impl::primitive_desc_t *pd_;
+    const mkldnn::impl::op_desc_t *op_desc_;
+    const mkldnn::impl::primitive_attr_t attr_;
+    const mkldnn::impl::primitive_desc_t *hint_fwd_pd_;
+    const pd_create_f *impl_list_;
+    int last_idx_;
+
+private:
+    mkldnn_primitive_desc_iterator(mkldnn::impl::engine_t *engine, int last_idx)
+        : idx_(last_idx), engine_(engine), pd_(nullptr)
+        , op_desc_(nullptr), hint_fwd_pd_(nullptr)
+        , impl_list_(nullptr), last_idx_(last_idx) {}
+};
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/query.cpp b/thirdparty/oidn/mkl-dnn/src/common/query.cpp
new file mode 100644
index 0000000000..835cd73581
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/query.cpp
@@ -0,0 +1,59 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "engine.hpp"
+#include "primitive_desc.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+
+status_t mkldnn_primitive_desc_query(const primitive_desc_t *primitive_desc,
+        query_t what, int index, void *result) {
+    if (any_null(primitive_desc, result))
+        return invalid_arguments;
+
+    return primitive_desc->query(what, index, result);
+}
+
+const memory_desc_t *mkldnn_primitive_desc_query_md(
+        const primitive_desc_t *primitive_desc, query_t what, int index) {
+    const memory_desc_t *res_md = nullptr;
+    bool args_ok = true
+        && primitive_desc != nullptr
+        && (what & query::some_md) == query::some_md
+        && what != query::some_md
+        && mkldnn_primitive_desc_query(primitive_desc,
+                what, index, &res_md) == success;
+    return args_ok ? res_md : nullptr;
+}
+
+int mkldnn_primitive_desc_query_s32(const primitive_desc_t *primitive_desc,
+        query_t what, int index) {
+    int res_s32;
+    bool args_ok = primitive_desc != nullptr
+        && one_of(what, query::num_of_inputs_s32, query::num_of_outputs_s32)
+        && mkldnn_primitive_desc_query(primitive_desc, what, index, &res_s32)
+                == success;
+    return args_ok ? res_s32 : 0;
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/reorder.cpp b/thirdparty/oidn/mkl-dnn/src/common/reorder.cpp
new file mode 100644
index 0000000000..d11f1a0361
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/reorder.cpp
@@ -0,0 +1,68 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "engine.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "reorder_pd.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+
+status_t mkldnn_reorder_primitive_desc_create(
+        primitive_desc_t **reorder_pd,
+        engine_t *src_engine, const memory_desc_t *src_md,
+        engine_t *dst_engine, const memory_desc_t *dst_md,
+        const primitive_attr_t *attr) {
+    if (any_null(reorder_pd, src_engine, src_md, dst_engine, dst_md))
+        return invalid_arguments;
+
+    auto s_ek = src_engine->kind();
+    auto d_ek = dst_engine->kind();
+    if (!IMPLICATION(s_ek != d_ek, one_of(engine_kind::cpu, s_ek, d_ek)))
+        return invalid_arguments;
+
+    auto r_pd = reinterpret_cast<reorder_pd_t **>(reorder_pd);
+    auto s_mdw = memory_desc_wrapper(*src_md);
+    auto d_mdw = memory_desc_wrapper(*dst_md);
+
+    if (!s_mdw.consistent_with(d_mdw))
+        return invalid_arguments;
+
+    auto e = (s_ek != engine_kind::cpu) ? src_engine : dst_engine;
+
+    const primitive_attr_t dummy_attr;
+    if (attr == NULL)
+        attr = &dummy_attr;
+
+    for (auto r = e->get_reorder_implementation_list(); *r; ++r) {
+        if ((*r)(r_pd, e, attr, src_engine, src_md, dst_engine, dst_md)
+                == success) {
+            (*r_pd)->init_info();
+            (*r_pd)->init_scratchpad_md();
+            return success;
+        }
+    }
+    return unimplemented;
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/reorder_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/reorder_pd.hpp
new file mode 100644
index 0000000000..963cb0f58a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/reorder_pd.hpp
@@ -0,0 +1,85 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef REORDER_PD_HPP
+#define REORDER_PD_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "primitive_attr.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct reorder_pd_t: public primitive_desc_t {
+    reorder_pd_t(engine_t *engine, const primitive_attr_t *attr,
+            engine_t *src_engine, const memory_desc_t *src_md,
+            engine_t *dst_engine, const memory_desc_t *dst_md)
+        : primitive_desc_t(engine, attr, primitive_kind::reorder)
+        , src_engine_(src_engine)
+        , dst_engine_(dst_engine)
+        , scratchpad_engine_(nullptr)
+        , src_md_(*src_md)
+        , dst_md_(*dst_md)
+    {}
+
+    virtual const op_desc_t *op_desc() const override { return nullptr; }
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (arg == MKLDNN_ARG_FROM)
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_TO)
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &src_md_ : nullptr; }
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 ? &dst_md_ : nullptr; }
+
+    virtual int n_inputs() const override { return 1; }
+    virtual int n_outputs() const override { return 1; }
+
+    float alpha() const { return attr()->output_scales_.scales_[0]; }
+    float beta() const {
+        const int sum_idx = attr()->post_ops_.find(primitive_kind::sum);
+        return sum_idx == -1 ? 0 : attr()->post_ops_.entry_[sum_idx].sum.scale;
+    }
+    virtual mkldnn::impl::engine_t *scratchpad_engine() const override
+    { return scratchpad_engine_; }
+
+protected:
+    engine_t *src_engine_;
+    engine_t *dst_engine_;
+    engine_t *scratchpad_engine_;
+
+    memory_desc_t src_md_;
+    memory_desc_t dst_md_;
+};
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/rnn.cpp b/thirdparty/oidn/mkl-dnn/src/common/rnn.cpp
new file mode 100644
index 0000000000..36967431a6
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/rnn.cpp
@@ -0,0 +1,400 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+#include "cpu/gemm/os_blas.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::types;
+using namespace mkldnn::impl::utils;
+
+namespace {
+memory_desc_t copy_maybe_null(const memory_desc_t *md) {
+    return md ? *md : zero_md();
+}
+
+rnn_desc_t zero_rnn_desc() {
+    auto rd = rnn_desc_t();
+    rd.src_layer_desc = zero_md();
+    rd.src_iter_desc = zero_md();
+    rd.weights_layer_desc = zero_md();
+    rd.weights_iter_desc = zero_md();
+    rd.bias_desc = zero_md();
+    rd.dst_layer_desc = zero_md();
+    rd.dst_iter_desc = zero_md();
+    rd.diff_src_layer_desc = zero_md();
+    rd.diff_src_iter_desc = zero_md();
+    rd.diff_weights_layer_desc = zero_md();
+    rd.diff_weights_iter_desc = zero_md();
+    rd.diff_bias_desc = zero_md();
+    rd.diff_dst_layer_desc = zero_md();
+    rd.diff_dst_iter_desc = zero_md();
+    return rd;
+}
+}
+
+/* Public C Api */
+
+status_t mkldnn_rnn_cell_desc_init(rnn_cell_desc_t *rnn_cell_desc,
+        mkldnn_alg_kind_t cell_kind, mkldnn_alg_kind_t act_f,
+        unsigned int flags, float alpha, float clipping) {
+    using namespace mkldnn::impl::alg_kind;
+
+    bool args_ok = true
+            && one_of(cell_kind, vanilla_rnn, vanilla_lstm, vanilla_gru,
+                    gru_linear_before_reset)
+            && IMPLICATION(cell_kind == vanilla_rnn,
+                    one_of(act_f, eltwise_relu, eltwise_tanh, eltwise_logistic));
+    if (!args_ok)
+        return invalid_arguments;
+
+    auto rcd = mkldnn_rnn_cell_desc_t();
+
+    rcd.cell_kind = cell_kind;
+    rcd.activation_kind = act_f;
+    rcd.flags = flags;
+    rcd.alpha = rcd.flags & mkldnn_rnn_cell_with_relu ? alpha : 0;
+    rcd.clipping = rcd.flags & mkldnn_rnn_cell_with_clipping ? clipping : 0;
+
+    *rnn_cell_desc = rcd;
+
+    return success;
+}
+
+int mkldnn_rnn_cell_get_gates_count(const rnn_cell_desc_t *rnn_cell_desc) {
+    switch (rnn_cell_desc->cell_kind) {
+    case mkldnn::impl::alg_kind::vanilla_rnn: return 1;
+    case mkldnn::impl::alg_kind::vanilla_gru: return 3;
+    case mkldnn::impl::alg_kind::gru_linear_before_reset: return 3;
+    case mkldnn::impl::alg_kind::vanilla_lstm: return 4;
+    default: assert(!"unknown cell kind"); return 0;
+    }
+    return 0;
+}
+
+int mkldnn_rnn_cell_get_states_count(const rnn_cell_desc_t *rnn_cell_desc) {
+    switch (rnn_cell_desc->cell_kind) {
+    case mkldnn::impl::alg_kind::vanilla_rnn: return 1;
+    case mkldnn::impl::alg_kind::vanilla_gru: return 1;
+    case mkldnn::impl::alg_kind::gru_linear_before_reset: return 1;
+    case mkldnn::impl::alg_kind::vanilla_lstm: return 2;
+    default: assert(!"unknown cell kind"); return 0;
+    }
+    return 0;
+}
+
+status_t check_data_type_consistency_fwd(const rnn_cell_desc_t *rnn_cell_desc,
+        prop_kind_t prop_kind, const memory_desc_t *src_layer_desc,
+        const memory_desc_t *src_iter_desc,
+        const memory_desc_t *weights_layer_desc,
+        const memory_desc_t *weights_iter_desc, const memory_desc_t *bias_desc,
+        const memory_desc_t *dst_layer_desc,
+        const memory_desc_t *dst_iter_desc) {
+    using namespace data_type;
+    data_type_t src_layer_dt = src_layer_desc->data_type;
+    data_type_t dst_layer_dt = dst_layer_desc->data_type;
+    data_type_t weights_iter_dt = weights_iter_desc->data_type;
+    data_type_t weights_layer_dt = weights_layer_desc->data_type;
+
+    bool is_f32 = everyone_is(f32, src_layer_dt, dst_layer_dt, weights_iter_dt,
+                          weights_layer_dt)
+            && IMPLICATION(!is_zero_md(src_iter_desc),
+                          src_iter_desc->data_type == f32)
+            && IMPLICATION(!is_zero_md(dst_iter_desc),
+                          dst_iter_desc->data_type == f32)
+            && IMPLICATION(!is_zero_md(bias_desc), bias_desc->data_type == f32);
+
+#if USE_MKL_PACKED_GEMM
+    bool is_u8u8u8 = src_layer_dt == u8
+            && IMPLICATION(!is_zero_md(src_iter_desc),
+                             src_iter_desc->data_type == u8)
+            && IMPLICATION(!is_zero_md(dst_iter_desc),
+                             dst_iter_desc->data_type == u8)
+            && one_of(dst_layer_dt, u8, f32)
+            && everyone_is(s8, weights_iter_dt, weights_layer_dt)
+            && IMPLICATION(!is_zero_md(bias_desc), bias_desc->data_type == f32);
+
+    bool is_f32u8f32 = src_layer_dt == u8
+            && IMPLICATION(!is_zero_md(src_iter_desc),
+                               src_iter_desc->data_type == f32)
+            && IMPLICATION(!is_zero_md(dst_iter_desc),
+                               dst_iter_desc->data_type == f32)
+            && one_of(dst_layer_dt, u8, f32)
+            && everyone_is(s8, weights_iter_dt, weights_layer_dt)
+            && IMPLICATION(!is_zero_md(bias_desc), bias_desc->data_type == f32);
+
+    bool is_inference = prop_kind == prop_kind::forward_inference;
+    bool is_lstm = rnn_cell_desc->cell_kind == mkldnn_vanilla_lstm;
+
+    return (is_f32 || ((is_u8u8u8 || is_f32u8f32) && is_lstm && is_inference))
+            ? success
+            : unimplemented;
+#else
+    return is_f32 ? success : unimplemented;
+#endif
+}
+
+status_t check_dim_consistency(const rnn_cell_desc_t *rnn_cell_desc,
+        rnn_direction_t direction, int L, int D, int T, int N, int S, int G,
+        int SLC, int SIC, int DLC, int DIC, const memory_desc_t *src_layer_desc,
+        const memory_desc_t *src_iter_desc,
+        const memory_desc_t *weights_layer_desc,
+        const memory_desc_t *weights_iter_desc, const memory_desc_t *bias_desc,
+        const memory_desc_t *dst_layer_desc,
+        const memory_desc_t *dst_iter_desc) {
+    bool args_ok;
+
+    // * algorithm specific
+    args_ok = true
+        && IMPLICATION(rnn_cell_desc->cell_kind == alg_kind::vanilla_gru,
+                       DIC == SIC);
+    if (!args_ok) return invalid_arguments;
+    int extra_bias =
+            rnn_cell_desc->cell_kind == alg_kind::gru_linear_before_reset;
+
+    // * on num layers
+    args_ok = true
+        && L == weights_layer_desc->dims[0]
+        && L == weights_iter_desc->dims[0]
+        && IMPLICATION(!is_zero_md(bias_desc), L == bias_desc->dims[0])
+        && IMPLICATION(!is_zero_md(src_iter_desc), L == src_iter_desc->dims[0])
+        && IMPLICATION(!is_zero_md(dst_iter_desc), L == dst_iter_desc->dims[0]);
+    if (!args_ok) return invalid_arguments;
+
+    // * on num directions
+    args_ok = true
+        && D == weights_layer_desc->dims[1]
+        && D == weights_iter_desc->dims[1]
+        && IMPLICATION(!is_zero_md(bias_desc), D == bias_desc->dims[1])
+        && IMPLICATION(!is_zero_md(src_iter_desc), D == src_iter_desc->dims[1])
+        && IMPLICATION(!is_zero_md(dst_iter_desc), D == dst_iter_desc->dims[1]);
+    if (!args_ok) return invalid_arguments;
+
+    // * on num iterations
+    args_ok = true
+        && T == src_layer_desc->dims[0]
+        && T == dst_layer_desc->dims[0];
+    if (!args_ok) return invalid_arguments;
+
+    // * on mb
+    args_ok = true
+        && N == src_layer_desc->dims[1]
+        && N == dst_layer_desc->dims[1]
+        && IMPLICATION(!is_zero_md(src_iter_desc), N == src_iter_desc->dims[3])
+        && IMPLICATION(!is_zero_md(dst_iter_desc), N == dst_iter_desc->dims[3]);
+    if (!args_ok) return invalid_arguments;
+
+    // * on num gates
+    args_ok = true
+        && G == mkldnn_rnn_cell_get_gates_count(rnn_cell_desc)
+        && G == weights_layer_desc->dims[3]
+        && G == weights_iter_desc->dims[3]
+        && IMPLICATION(!is_zero_md(bias_desc),
+                G + extra_bias == bias_desc->dims[2]);
+    if (!args_ok) return invalid_arguments;
+
+    // * on num states
+    args_ok = true
+        && S == mkldnn_rnn_cell_get_states_count(rnn_cell_desc)
+        && IMPLICATION(!is_zero_md(src_iter_desc), S == src_iter_desc->dims[2])
+        && IMPLICATION(!is_zero_md(dst_iter_desc), S == dst_iter_desc->dims[2]);
+    if (!args_ok) return invalid_arguments;
+
+    // * on slc
+    args_ok = true
+        && SLC == weights_layer_desc->dims[2]
+        && SLC == src_layer_desc->dims[2];
+    if (!args_ok) return invalid_arguments;
+
+    // * on sic
+    args_ok = true
+        && SIC == weights_iter_desc->dims[2]
+        && IMPLICATION(!is_zero_md(src_iter_desc),
+                SIC == src_iter_desc->dims[4]);
+    if (!args_ok) return invalid_arguments;
+
+    // * on dlc
+    int dlc_multiplier = (direction == mkldnn_bidirectional_concat) ? 2 : 1;
+    args_ok = true
+        && DLC == dlc_multiplier * DIC
+        && DLC == dst_layer_desc->dims[2];
+    if (!args_ok) return invalid_arguments;
+
+    // * on dic
+    args_ok = true
+        && DIC == weights_layer_desc->dims[4]
+        && DIC == weights_iter_desc->dims[4]
+        && IMPLICATION(!is_zero_md(bias_desc), DIC == bias_desc->dims[3])
+        && IMPLICATION(!is_zero_md(dst_iter_desc),
+                DIC == dst_iter_desc->dims[4]);
+    if (!args_ok) return invalid_arguments;
+
+    // * unrolling/fusion conditions
+    args_ok = true
+        && IMPLICATION(L > 1, (dlc_multiplier * SLC) == DLC)
+        && IMPLICATION(T > 1, SIC == DIC);
+    if (!args_ok) return invalid_arguments;
+
+    return success;
+}
+
+status_t MKLDNN_API mkldnn_rnn_forward_desc_init(mkldnn_rnn_desc_t *rnn_desc,
+        prop_kind_t prop_kind, const rnn_cell_desc_t *rnn_cell_desc,
+        const rnn_direction_t direction, const memory_desc_t *src_layer_desc,
+        const memory_desc_t *src_iter_desc,
+        const memory_desc_t *weights_layer_desc,
+        const memory_desc_t *weights_iter_desc, const memory_desc_t *bias_desc,
+        const memory_desc_t *dst_layer_desc,
+        const memory_desc_t *dst_iter_desc) {
+    bool args_ok = true && rnn_cell_desc != nullptr
+            && !any_null(src_layer_desc, weights_layer_desc, weights_iter_desc,
+                       dst_layer_desc);
+    if (!args_ok) return invalid_arguments;
+
+    //check dimensions consistency
+    int L = weights_layer_desc->dims[0];
+    int T = src_layer_desc->dims[0];
+    int N = src_layer_desc->dims[1];
+    const int D = one_of(direction, mkldnn_unidirectional_left2right,
+                          mkldnn_unidirectional_right2left) ?
+            1 :
+            2;
+    int G = mkldnn_rnn_cell_get_gates_count(rnn_cell_desc);
+    int S = mkldnn_rnn_cell_get_states_count(rnn_cell_desc);
+    int SLC = src_layer_desc->dims[2];
+    int SIC = weights_iter_desc->dims[2];
+    int DLC = dst_layer_desc->dims[2];
+    int DIC = weights_layer_desc->dims[4];
+
+    CHECK(check_dim_consistency(rnn_cell_desc, direction, L, D, T, N, S,
+            G, SLC, SIC, DLC, DIC, src_layer_desc, src_iter_desc,
+            weights_layer_desc, weights_iter_desc, bias_desc, dst_layer_desc,
+            dst_iter_desc));
+
+    CHECK(check_data_type_consistency_fwd(rnn_cell_desc, prop_kind,
+            src_layer_desc, src_iter_desc, weights_layer_desc,
+            weights_iter_desc, bias_desc, dst_layer_desc, dst_iter_desc));
+
+    // Create the descriptor
+    mkldnn_rnn_desc_t rd = zero_rnn_desc();
+
+    rd.primitive_kind = primitive_kind::rnn;
+    rd.prop_kind = prop_kind;
+    rd.cell_desc = *rnn_cell_desc;
+    rd.direction = direction;
+    rd.src_layer_desc = copy_maybe_null(src_layer_desc);
+    rd.src_iter_desc = copy_maybe_null(src_iter_desc);
+    rd.weights_layer_desc = copy_maybe_null(weights_layer_desc);
+    rd.weights_iter_desc = copy_maybe_null(weights_iter_desc);
+    rd.bias_desc = copy_maybe_null(bias_desc);
+    rd.dst_layer_desc = copy_maybe_null(dst_layer_desc);
+    rd.dst_iter_desc = copy_maybe_null(dst_iter_desc);
+
+    *rnn_desc = rd;
+
+    return success;
+}
+
+status_t MKLDNN_API mkldnn_rnn_backward_desc_init(mkldnn_rnn_desc_t *rnn_desc,
+        prop_kind_t prop_kind, const rnn_cell_desc_t *rnn_cell_desc,
+        const rnn_direction_t direction, const memory_desc_t *src_layer_desc,
+        const memory_desc_t *src_iter_desc,
+        const memory_desc_t *weights_layer_desc,
+        const memory_desc_t *weights_iter_desc, const memory_desc_t *bias_desc,
+        const memory_desc_t *dst_layer_desc, const memory_desc_t *dst_iter_desc,
+        const memory_desc_t *diff_src_layer_desc,
+        const memory_desc_t *diff_src_iter_desc,
+        const memory_desc_t *diff_weights_layer_desc,
+        const memory_desc_t *diff_weights_iter_desc,
+        const memory_desc_t *diff_bias_desc,
+        const memory_desc_t *diff_dst_layer_desc,
+        const memory_desc_t *diff_dst_iter_desc) {
+    bool args_ok = true
+            && !any_null(src_layer_desc, weights_layer_desc, weights_iter_desc,
+                       dst_layer_desc, diff_src_layer_desc,
+                       diff_weights_layer_desc, diff_weights_iter_desc,
+                       diff_dst_layer_desc);
+    if (!args_ok)
+        return invalid_arguments;
+
+    auto xnor_md = [=](const memory_desc_t *a_md, const memory_desc_t *b_md) {
+        return is_zero_md(a_md) == is_zero_md(b_md);
+    };
+
+    args_ok = args_ok && xnor_md(bias_desc, diff_bias_desc)
+            && xnor_md(dst_iter_desc, diff_dst_iter_desc)
+            && xnor_md(src_iter_desc, diff_src_iter_desc);
+    if (!args_ok)
+        return invalid_arguments;
+
+    //check dimensions consistency
+    int L = weights_layer_desc->dims[0];
+    int T = src_layer_desc->dims[0];
+    int N = src_layer_desc->dims[1];
+    const int D = one_of(direction, mkldnn_unidirectional_left2right,
+                          mkldnn_unidirectional_right2left) ?
+            1 :
+            2;
+    int G = mkldnn_rnn_cell_get_gates_count(rnn_cell_desc);
+    int S = mkldnn_rnn_cell_get_states_count(rnn_cell_desc);
+    int SLC = src_layer_desc->dims[2];
+    int SIC = weights_iter_desc->dims[2];
+    int DLC = dst_layer_desc->dims[2];
+    int DIC = weights_layer_desc->dims[4];
+
+    status_t st = check_dim_consistency(rnn_cell_desc, direction, L, D, T, N, S,
+            G, SLC, SIC, DLC, DIC, src_layer_desc, src_iter_desc,
+            weights_layer_desc, weights_iter_desc, bias_desc, dst_layer_desc,
+            dst_iter_desc);
+    if (st != success) return st;
+
+    st = check_dim_consistency(rnn_cell_desc, direction, L, D, T, N, S,
+            G, SLC, SIC, DLC, DIC, diff_src_layer_desc, diff_src_iter_desc,
+            diff_weights_layer_desc, diff_weights_iter_desc, diff_bias_desc,
+            diff_dst_layer_desc, diff_dst_iter_desc);
+    if (st != success) return st;
+
+    mkldnn_rnn_desc_t rd = zero_rnn_desc();
+
+    rd.primitive_kind = primitive_kind::rnn;
+    rd.prop_kind = prop_kind;
+    rd.cell_desc = *rnn_cell_desc;
+    rd.direction = direction;
+
+    rd.src_layer_desc = copy_maybe_null(src_layer_desc);
+    rd.src_iter_desc = copy_maybe_null(src_iter_desc);
+    rd.weights_layer_desc = copy_maybe_null(weights_layer_desc);
+    rd.weights_iter_desc = copy_maybe_null(weights_iter_desc);
+    rd.bias_desc = copy_maybe_null(bias_desc);
+    rd.dst_layer_desc = copy_maybe_null(dst_layer_desc);
+    rd.dst_iter_desc = copy_maybe_null(dst_iter_desc);
+    rd.diff_src_layer_desc = copy_maybe_null(diff_src_layer_desc);
+    rd.diff_src_iter_desc = copy_maybe_null(diff_src_iter_desc);
+    rd.diff_weights_layer_desc = copy_maybe_null(diff_weights_layer_desc);
+    rd.diff_weights_iter_desc = copy_maybe_null(diff_weights_iter_desc);
+    rd.diff_bias_desc = copy_maybe_null(diff_bias_desc);
+    rd.diff_dst_layer_desc = copy_maybe_null(diff_dst_layer_desc);
+    rd.diff_dst_iter_desc = copy_maybe_null(diff_dst_iter_desc);
+
+    *rnn_desc = rd;
+
+    return success;
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/rnn_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/rnn_pd.hpp
new file mode 100644
index 0000000000..1ee2ba1114
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/rnn_pd.hpp
@@ -0,0 +1,280 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef RNN_PD_HPP
+#define RNN_PD_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "primitive_desc.hpp"
+#include "type_helpers.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct rnn_fwd_pd_t;
+
+struct rnn_pd_t : public primitive_desc_t {
+    static constexpr auto base_pkind = primitive_kind::rnn;
+
+    rnn_pd_t(engine_t *engine,
+            const rnn_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const rnn_fwd_pd_t *hint_fwd_pd)
+        : primitive_desc_t(engine, attr, base_pkind)
+        , desc_(*adesc)
+        , hint_fwd_pd_(hint_fwd_pd)
+        , src_layer_md_(desc_.src_layer_desc)
+        , src_iter_md_(desc_.src_iter_desc)
+        , weights_layer_md_(desc_.weights_layer_desc)
+        , weights_iter_md_(desc_.weights_iter_desc)
+        , bias_md_(desc_.bias_desc)
+        , dst_layer_md_(desc_.dst_layer_desc)
+        , dst_iter_md_(desc_.dst_iter_desc)
+        , ws_md_()
+    {}
+
+    const rnn_desc_t *desc() const { return &desc_; }
+    virtual const op_desc_t *op_desc() const override
+    { return reinterpret_cast<const op_desc_t *>(this->desc()); }
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual status_t query(query_t what, int idx, void *result) const override {
+        switch (what) {
+        case query::rnn_d: *(const rnn_desc_t **)result = desc(); break;
+        default: return primitive_desc_t::query(what, idx, result);
+        }
+        return status::success;
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override {
+        if (index == 0) return &src_layer_md_;
+        if (index == 1 && with_src_iter()) return &src_iter_md_;
+        return nullptr;
+    }
+    virtual const memory_desc_t *weights_md(int index = 0) const override {
+        if (index == 0) return &weights_layer_md_;
+        if (index == 1) return &weights_iter_md_;
+        if (index == 2 && with_bias()) return &bias_md_;
+        return nullptr;
+    }
+    virtual const memory_desc_t *dst_md(int index = 0) const override {
+        if (index == 0) return &dst_layer_md_;
+        if (index == 1 && with_dst_iter()) return &dst_iter_md_;
+        return nullptr;
+    }
+    virtual const memory_desc_t *workspace_md(int index = 0) const override
+    { return index == 0 && !types::is_zero_md(&ws_md_) ? &ws_md_ : nullptr; }
+
+    /* common pooling aux functions */
+
+    bool is_training() const {
+        return utils::one_of(desc_.prop_kind, prop_kind::forward_training,
+                prop_kind::backward);
+    }
+
+    bool is_fwd() const {
+        return utils::one_of(desc_.prop_kind, prop_kind::forward_training,
+                prop_kind::forward_inference);
+    }
+
+    dim_t T() const { return desc_.src_layer_desc.dims[0]; }
+    dim_t MB() const { return desc_.src_layer_desc.dims[1]; }
+
+    dim_t L() const { return desc_.weights_layer_desc.dims[0]; }
+    dim_t D() const { return desc_.weights_layer_desc.dims[1]; }
+
+    dim_t SIC() const { return desc_.weights_iter_desc.dims[2]; }
+
+    dim_t SLC() const { return desc_.weights_layer_desc.dims[2]; }
+    dim_t G() const { return desc_.weights_layer_desc.dims[3]; }
+    dim_t DIC() const { return desc_.weights_layer_desc.dims[4]; }
+
+    dim_t DLC() const { return desc_.dst_layer_desc.dims[2]; }
+
+    bool with_bias() const
+    { return !memory_desc_wrapper(desc_.bias_desc).is_zero(); }
+
+    bool with_src_iter() const
+    { return !(memory_desc_wrapper(desc_.src_iter_desc).is_zero()); }
+
+    bool with_dst_iter() const
+    { return !memory_desc_wrapper(desc_.dst_iter_desc).is_zero(); }
+
+    mkldnn::impl::alg_kind_t cell_kind() const
+    { return desc_.cell_desc.cell_kind; }
+    mkldnn::impl::alg_kind_t activation_kind() const
+    { return desc_.cell_desc.activation_kind; }
+
+    bool is_lbr() const
+    { return cell_kind() == mkldnn_gru_linear_before_reset; }
+
+    mkldnn_rnn_direction_t direction() const { return desc_.direction; }
+
+protected:
+    rnn_desc_t desc_;
+    const rnn_fwd_pd_t *hint_fwd_pd_;
+
+    memory_desc_t src_layer_md_;
+    memory_desc_t src_iter_md_;
+    memory_desc_t weights_layer_md_;
+    memory_desc_t weights_iter_md_;
+    memory_desc_t bias_md_;
+    memory_desc_t dst_layer_md_;
+    memory_desc_t dst_iter_md_;
+
+    memory_desc_t ws_md_;
+};
+
+struct rnn_fwd_pd_t: public rnn_pd_t {
+    typedef rnn_fwd_pd_t base_class;
+    typedef rnn_fwd_pd_t hint_class;
+
+    rnn_fwd_pd_t(engine_t *engine,
+            const rnn_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const rnn_fwd_pd_t *hint_fwd_pd)
+        : rnn_pd_t(engine, adesc, attr, hint_fwd_pd)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (arg == MKLDNN_ARG_SRC_LAYER)
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_SRC_ITER && with_src_iter())
+            return arg_usage_t::input;
+
+        if (utils::one_of(arg, MKLDNN_ARG_WEIGHTS_LAYER,
+                    MKLDNN_ARG_WEIGHTS_ITER))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_BIAS && with_bias())
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DST_LAYER)
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_DST_ITER && with_dst_iter())
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_WORKSPACE && is_training())
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual int n_inputs() const override
+    { return 3 + with_bias() + with_src_iter(); }
+    virtual int n_outputs() const override
+    { return 1 + with_dst_iter() + is_training(); }
+};
+
+struct rnn_bwd_pd_t : public rnn_pd_t {
+    typedef rnn_bwd_pd_t base_class;
+    typedef rnn_fwd_pd_t hint_class;
+
+    rnn_bwd_pd_t(engine_t *engine,
+            const rnn_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const rnn_fwd_pd_t *hint_fwd_pd)
+        : rnn_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , diff_src_layer_md_(desc_.diff_src_layer_desc)
+        , diff_src_iter_md_(desc_.diff_src_iter_desc)
+        , diff_weights_layer_md_(desc_.diff_weights_layer_desc)
+        , diff_weights_iter_md_(desc_.diff_weights_iter_desc)
+        , diff_bias_md_(desc_.diff_bias_desc)
+        , diff_dst_layer_md_(desc_.diff_dst_layer_desc)
+        , diff_dst_iter_md_(desc_.diff_dst_iter_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_SRC_LAYER, MKLDNN_ARG_DST_LAYER,
+                    MKLDNN_ARG_DIFF_DST_LAYER))
+            return arg_usage_t::input;
+
+        if (with_src_iter()) {
+            if (arg == MKLDNN_ARG_SRC_ITER)
+                return arg_usage_t::input;
+
+            if (arg == MKLDNN_ARG_DIFF_SRC_ITER)
+                return arg_usage_t::output;
+        }
+
+        if (utils::one_of(arg, MKLDNN_ARG_WEIGHTS_LAYER,
+                    MKLDNN_ARG_WEIGHTS_ITER))
+            return arg_usage_t::input;
+
+        if (with_bias()) {
+            if (arg == MKLDNN_ARG_BIAS)
+                return arg_usage_t::input;
+
+            if (arg == MKLDNN_ARG_DIFF_BIAS)
+                return arg_usage_t::output;
+        }
+
+        if (utils::one_of(arg, MKLDNN_ARG_DST_ITER, MKLDNN_ARG_DIFF_DST_ITER)
+                && with_dst_iter())
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_WORKSPACE)
+            return arg_usage_t::input;
+
+        if (utils::one_of(arg, MKLDNN_ARG_DIFF_SRC_LAYER,
+                    MKLDNN_ARG_DIFF_WEIGHTS_LAYER,
+                    MKLDNN_ARG_DIFF_WEIGHTS_ITER))
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *diff_src_md(int index = 0) const override {
+        if (index == 0) return &diff_src_layer_md_;
+        if (index == 1 && with_src_iter()) return &diff_src_iter_md_;
+        return nullptr;
+    }
+    virtual const memory_desc_t *diff_weights_md(
+            int index = 0) const override {
+        if (index == 0) return &diff_weights_layer_md_;
+        if (index == 1) return &diff_weights_iter_md_;
+        if (index == 2 && with_bias()) return &diff_bias_md_;
+        return nullptr;
+    }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override {
+        if (index == 0) return &diff_dst_layer_md_;
+        if (index == 1 && with_dst_iter()) return &diff_dst_iter_md_;
+        return nullptr;
+    }
+
+    virtual int n_inputs() const override
+    { return 6 + with_src_iter() + with_bias() + 2 * with_dst_iter(); }
+    virtual int n_outputs() const override
+    { return 3 + with_src_iter() + with_bias(); }
+
+protected:
+    memory_desc_t diff_src_layer_md_;
+    memory_desc_t diff_src_iter_md_;
+    memory_desc_t diff_weights_layer_md_;
+    memory_desc_t diff_weights_iter_md_;
+    memory_desc_t diff_bias_md_;
+    memory_desc_t diff_dst_layer_md_;
+    memory_desc_t diff_dst_iter_md_;
+};
+
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/scratchpad.cpp b/thirdparty/oidn/mkl-dnn/src/common/scratchpad.cpp
new file mode 100644
index 0000000000..6bc14fc72a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/scratchpad.cpp
@@ -0,0 +1,112 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "scratchpad.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+/* Allocating memory buffers on a page boundary to reduce TLB/page misses */
+const size_t page_size = 2097152;
+
+/*
+  Implementation of the scratchpad_t interface that is compatible with
+  a concurrent execution
+*/
+struct concurent_scratchpad_t : public scratchpad_t {
+    concurent_scratchpad_t(size_t size) {
+        size_ = size;
+        scratchpad_ = (char *) malloc(size, page_size);
+        assert(scratchpad_ != nullptr);
+    }
+
+    ~concurent_scratchpad_t() {
+        free(scratchpad_);
+    }
+
+    virtual char *get() const {
+        return scratchpad_;
+    }
+
+private:
+    char *scratchpad_;
+    size_t size_;
+};
+
+/*
+  Implementation of the scratchpad_t interface that uses a global
+  scratchpad
+*/
+
+struct global_scratchpad_t : public scratchpad_t {
+    global_scratchpad_t(size_t size) {
+        if (size > size_) {
+            if (scratchpad_ != nullptr) free(scratchpad_);
+            size_ = size;
+            scratchpad_ = (char *) malloc(size, page_size);
+            assert(scratchpad_ != nullptr);
+        }
+        reference_count_++;
+    }
+
+    ~global_scratchpad_t() {
+        reference_count_--;
+        if (reference_count_ == 0) {
+            free(scratchpad_);
+            scratchpad_ = nullptr;
+            size_ = 0;
+        }
+    }
+
+    virtual char *get() const {
+        return scratchpad_;
+    }
+
+private:
+    /*
+      Using thread-local here is unnecessary and even buggy! All threads
+      actually share the same scratchpad, which is created and queried only
+      on the main thread. If the scratchpad is queried on some thread other
+      than the one it was created on (e.g. the application calls the API from
+      multiple threads), thread-local causes a segfault because the scratchpad
+      is uninitialized on the current thread.
+    */
+    /*thread_local*/ static char *scratchpad_;
+    /*thread_local*/ static size_t size_;
+    /*thread_local*/ static unsigned int reference_count_;
+};
+
+/*thread_local*/ char *global_scratchpad_t::scratchpad_ = nullptr;
+/*thread_local*/ size_t global_scratchpad_t::size_ = 0;
+/*thread_local*/ unsigned int global_scratchpad_t::reference_count_ = 0;
+
+
+/*
+   Scratchpad creation routine
+*/
+scratchpad_t *create_scratchpad(size_t size) {
+#ifndef MKLDNN_ENABLE_CONCURRENT_EXEC
+    return new global_scratchpad_t(size);
+#else
+    return new concurent_scratchpad_t(size);
+#endif
+}
+
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/scratchpad.hpp b/thirdparty/oidn/mkl-dnn/src/common/scratchpad.hpp
new file mode 100644
index 0000000000..f7a246bc99
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/scratchpad.hpp
@@ -0,0 +1,36 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef COMMON_SCRATCHPAD_HPP
+#define COMMON_SCRATCHPAD_HPP
+
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct scratchpad_t {
+    virtual ~scratchpad_t() {}
+    virtual char *get() const = 0;
+};
+
+scratchpad_t *create_scratchpad(size_t size);
+
+}
+}
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/shuffle.cpp b/thirdparty/oidn/mkl-dnn/src/common/shuffle.cpp
new file mode 100644
index 0000000000..e32e735224
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/shuffle.cpp
@@ -0,0 +1,72 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::types;
+
+namespace {
+status_t shuffle_desc_init(shuffle_desc_t *shuffle_desc, prop_kind_t prop_kind,
+        const memory_desc_t *data_desc, int axis, dim_t group_size) {
+    bool args_ok = true
+        && !any_null(shuffle_desc, data_desc)
+        && one_of(prop_kind, forward_training, forward_inference,
+                  backward, backward_data)
+        && axis >= 0 && axis < data_desc->ndims
+        && group_size > 0 && group_size <= data_desc->dims[axis];
+    if (!args_ok) return invalid_arguments;
+
+    auto sd = shuffle_desc_t();
+    sd.primitive_kind = primitive_kind::shuffle;
+    sd.prop_kind = prop_kind;
+    sd.data_desc = *data_desc;
+    sd.axis = axis;
+    sd.group_size = group_size;
+
+    bool consistency = true
+        && sd.data_desc.dims[axis] % sd.group_size == 0;
+    if (!consistency) return invalid_arguments;
+
+    *shuffle_desc = sd;
+    return success;
+}
+}
+
+status_t mkldnn_shuffle_forward_desc_init(shuffle_desc_t *shuffle_desc,
+        prop_kind_t prop_kind, const memory_desc_t *data_desc, int axis,
+        dim_t group_size) {
+    if (!one_of(prop_kind, forward_training, forward_inference))
+        return invalid_arguments;
+    return shuffle_desc_init(shuffle_desc, prop_kind, data_desc, axis,
+        group_size);
+}
+
+status_t mkldnn_shuffle_backward_desc_init(shuffle_desc_t *shuffle_desc,
+        const memory_desc_t *diff_data_desc, int axis, dim_t group_size) {
+    return shuffle_desc_init(shuffle_desc, backward_data, diff_data_desc, axis,
+        group_size);
+}
+
+// vim: et ts=5 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/shuffle_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/shuffle_pd.hpp
new file mode 100644
index 0000000000..cc5553fe7f
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/shuffle_pd.hpp
@@ -0,0 +1,121 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef SHUFFLE_PD_HPP
+#define SHUFFLE_PD_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "primitive_desc.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct shuffle_pd_t: public primitive_desc_t {
+    static constexpr auto base_pkind = primitive_kind::shuffle;
+
+    typedef shuffle_pd_t base_class;
+    typedef shuffle_pd_t hint_class;
+
+    shuffle_pd_t(engine_t *engine,
+            const shuffle_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const shuffle_pd_t *hint_fwd_pd)
+        : primitive_desc_t(engine, attr, base_pkind)
+        , desc_(*adesc)
+        , hint_fwd_pd_(hint_fwd_pd)
+        , data_md_(desc_.data_desc)
+    {}
+
+    const shuffle_desc_t *desc() const { return &desc_; }
+    virtual const op_desc_t *op_desc() const override
+    { return reinterpret_cast<const op_desc_t *>(this->desc()); }
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual status_t query(query_t what, int idx, void *result) const override {
+        switch (what) {
+        case query::shuffle_d:
+            *(const shuffle_desc_t**)result = desc(); break;
+        default: return primitive_desc_t::query(what, idx, result);
+        }
+        return status::success;
+    }
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (is_fwd()) {
+            if (arg == MKLDNN_ARG_SRC)
+                return arg_usage_t::input;
+
+            if (arg == MKLDNN_ARG_DST)
+                return arg_usage_t::output;
+        } else {
+            if (arg == MKLDNN_ARG_DIFF_DST)
+                return arg_usage_t::input;
+
+            if (arg == MKLDNN_ARG_DIFF_SRC)
+                return arg_usage_t::output;
+        }
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 && is_fwd() ? &data_md_ : nullptr; }
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 && is_fwd() ? &data_md_ : nullptr; }
+
+    virtual const memory_desc_t *diff_src_md(int index = 0) const override
+    { return index == 0 && !is_fwd() ? &data_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 && !is_fwd() ? &data_md_ : nullptr; }
+
+    virtual int n_inputs() const override { return 1; }
+    virtual int n_outputs() const override { return 1; }
+
+    /* shuffle aux functions */
+
+    dim_t MB() const { return data_md()->dims[0]; }
+    dim_t C() const { return ndims() >= 2 ? data_md()->dims[1] : 1; }
+    dim_t D() const { return ndims() >= 5 ? data_md()->dims[ndims() - 3] : 1; }
+    dim_t H() const { return ndims() >= 4 ? data_md()->dims[ndims() - 2] : 1; }
+    dim_t W() const { return ndims() >= 3 ? data_md()->dims[ndims() - 1] : 1; }
+
+    int ndims() const { return data_md()->ndims; }
+
+    int axis() const { return desc_.axis; }
+    dim_t group_size() const { return desc_.group_size; }
+    dim_t axis_size() const { return data_md()->dims[axis()]; }
+
+    bool is_fwd() const {
+        return utils::one_of(desc_.prop_kind, prop_kind::forward_training,
+                prop_kind::forward_inference);
+    }
+
+    const memory_desc_t *data_md() const { return &data_md_; }
+
+protected:
+    shuffle_desc_t desc_;
+    const shuffle_pd_t *hint_fwd_pd_;
+    memory_desc_t data_md_;
+};
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/softmax.cpp b/thirdparty/oidn/mkl-dnn/src/common/softmax.cpp
new file mode 100644
index 0000000000..82848e3d1f
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/softmax.cpp
@@ -0,0 +1,68 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "memory_desc_wrapper.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::alg_kind;
+using namespace mkldnn::impl::types;
+
+namespace {
+status_t softmax_desc_init(softmax_desc_t *softmax_desc, prop_kind_t prop_kind,
+        const memory_desc_t *data_desc, const memory_desc_t *diff_desc, int softmax_axis) {
+    bool args_ok = true
+        && !any_null(softmax_desc, data_desc)
+        && 0 <= softmax_axis
+        && softmax_axis < data_desc->ndims;
+    if (!args_ok) return invalid_arguments;
+
+    auto sd = softmax_desc_t();
+    sd.primitive_kind = primitive_kind::softmax;
+    sd.prop_kind = prop_kind;
+
+    bool is_bwd = (sd.prop_kind == backward_data);
+    sd.data_desc = *data_desc;
+    sd.diff_desc = is_bwd ? *diff_desc : zero_md();
+    sd.softmax_axis = softmax_axis;
+
+    *softmax_desc = sd;
+    return success;
+}
+}
+
+status_t mkldnn_softmax_forward_desc_init(softmax_desc_t *softmax_desc,
+        prop_kind_t prop_kind, const memory_desc_t *data_desc,
+        int softmax_axis) {
+    if (!one_of(prop_kind, forward_inference, forward_training))
+        return invalid_arguments;
+    return softmax_desc_init(softmax_desc, prop_kind, data_desc, nullptr, softmax_axis);
+}
+
+status_t mkldnn_softmax_backward_desc_init(softmax_desc_t *softmax_desc,
+        const memory_desc_t *diff_desc, const mkldnn_memory_desc_t *data_desc,
+        int softmax_axis) {
+    return softmax_desc_init(softmax_desc, prop_kind::backward_data,
+            data_desc, diff_desc, softmax_axis);
+}
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/softmax_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/softmax_pd.hpp
new file mode 100644
index 0000000000..8a16ce901c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/softmax_pd.hpp
@@ -0,0 +1,161 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef SOFTMAX_PD_HPP
+#define SOFTMAX_PD_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "primitive_desc.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct softmax_fwd_pd_t;
+
+struct softmax_pd_t: public primitive_desc_t {
+    static constexpr auto base_pkind = primitive_kind::softmax;
+
+    softmax_pd_t(engine_t *engine,
+            const softmax_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const softmax_fwd_pd_t *hint_fwd_pd)
+        : primitive_desc_t(engine, attr, base_pkind)
+        , desc_(*adesc)
+        , hint_fwd_pd_(hint_fwd_pd)
+        , data_md_(desc_.data_desc)
+    {}
+
+    const softmax_desc_t *desc() const { return &desc_; }
+    virtual const op_desc_t *op_desc() const override
+    { return reinterpret_cast<const op_desc_t *>(this->desc()); }
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual status_t query(query_t what, int idx, void *result) const override {
+        switch (what) {
+        case query::softmax_d:
+            *(const softmax_desc_t**)result = desc(); break;
+        default: return primitive_desc_t::query(what, idx, result);
+        }
+        return status::success;
+    }
+
+    /* common softmax aux functions */
+
+    dim_t MB() const { return data_desc().dims[0]; }
+    dim_t C() const { return data_desc().dims[1]; }
+    dim_t D() const { return ndims() >= 5 ? data_desc().dims[ndims() - 3] : 1; }
+    dim_t H() const { return ndims() >= 4 ? data_desc().dims[ndims() - 2] : 1; }
+    dim_t W() const { return ndims() >= 3 ? data_desc().dims[ndims() - 1] : 1; }
+
+    int ndims() const { return data_desc().ndims; }
+
+    bool is_fwd() const {
+        return utils::one_of(desc_.prop_kind, prop_kind::forward_training,
+                prop_kind::forward_inference);
+    }
+
+protected:
+    softmax_desc_t desc_;
+    const softmax_fwd_pd_t *hint_fwd_pd_;
+
+    memory_desc_t data_md_;
+
+private:
+    const memory_desc_t &data_desc() const { return desc_.data_desc; }
+};
+
+struct softmax_fwd_pd_t: public softmax_pd_t {
+    typedef softmax_fwd_pd_t base_class;
+    typedef softmax_fwd_pd_t hint_class;
+
+    softmax_fwd_pd_t(engine_t *engine,
+            const softmax_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const softmax_fwd_pd_t *hint_fwd_pd)
+        : softmax_pd_t(engine, adesc, attr, hint_fwd_pd)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (arg == MKLDNN_ARG_SRC)
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DST)
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_WORKSPACE && (workspace_md() != nullptr))
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index == 0 ? &data_md_ : nullptr; }
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 ? &data_md_ : nullptr; }
+
+    virtual int n_inputs() const override { return 1; }
+    virtual int n_outputs() const override
+    { return 1 + (workspace_md() != nullptr); }
+};
+
+struct softmax_bwd_pd_t: public softmax_pd_t {
+    typedef softmax_bwd_pd_t base_class;
+    typedef softmax_fwd_pd_t hint_class;
+
+    softmax_bwd_pd_t(engine_t *engine,
+            const softmax_desc_t *adesc,
+            const primitive_attr_t *attr,
+            const softmax_fwd_pd_t *hint_fwd_pd)
+        : softmax_pd_t(engine, adesc, attr, hint_fwd_pd)
+        , diff_data_md_(desc_.diff_desc)
+    {}
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (utils::one_of(arg, MKLDNN_ARG_DST, MKLDNN_ARG_DIFF_DST))
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DIFF_SRC)
+            return arg_usage_t::output;
+
+        if (arg == MKLDNN_ARG_WORKSPACE && (workspace_md() != nullptr))
+            return arg_usage_t::input;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 ? &data_md_ : nullptr; }
+    virtual const memory_desc_t *diff_dst_md(int index = 0) const override
+    { return index == 0 ? &diff_data_md_ : nullptr; }
+    virtual const memory_desc_t *diff_src_md(int index = 0) const override
+    { return index == 0 ? &diff_data_md_ : nullptr; }
+
+    virtual int n_inputs() const override
+    { return 2 + (workspace_md() != nullptr); }
+    virtual int n_outputs() const override { return 1; }
+
+protected:
+    memory_desc_t diff_data_md_;
+};
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/stream.cpp b/thirdparty/oidn/mkl-dnn/src/common/stream.cpp
new file mode 100644
index 0000000000..00af8935c0
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/stream.cpp
@@ -0,0 +1,46 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "engine.hpp"
+#include "stream.hpp"
+#include "utils.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::status;
+
+/* API */
+
+status_t mkldnn_stream_create(stream_t **stream, engine_t *engine,
+        unsigned flags) {
+    bool args_ok = true
+        && !utils::any_null(stream, engine)
+        && flags == stream_flags::default_flags;
+    if (!args_ok)
+        return invalid_arguments;
+
+    return safe_ptr_assign<stream_t>(*stream, new stream_t(engine, flags));
+}
+
+status_t mkldnn_stream_destroy(stream_t *stream) {
+    delete stream;
+    return success;
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/stream.hpp b/thirdparty/oidn/mkl-dnn/src/common/stream.hpp
new file mode 100644
index 0000000000..f010e5f6ed
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/stream.hpp
@@ -0,0 +1,44 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef STREAM_HPP
+#define STREAM_HPP
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "engine.hpp"
+
+struct mkldnn_stream: public mkldnn::impl::c_compatible {
+    mkldnn_stream(mkldnn::impl::engine_t *engine, unsigned flags)
+        : engine_(engine), flags_(flags) {}
+    virtual ~mkldnn_stream() {}
+
+    /** returns stream's engine */
+    mkldnn::impl::engine_t *engine() const { return engine_; }
+
+    /** returns stream's kind */
+    unsigned flags() const { return flags_; }
+
+protected:
+    mkldnn::impl::engine_t *engine_;
+    unsigned flags_;
+};
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/sum.cpp b/thirdparty/oidn/mkl-dnn/src/common/sum.cpp
new file mode 100644
index 0000000000..365663c0f8
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/sum.cpp
@@ -0,0 +1,79 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "engine.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "sum_pd.hpp"
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::status;
+
+status_t mkldnn_sum_primitive_desc_create(primitive_desc_t **sum_pd,
+        const memory_desc_t *dst_md, int n, const float *scales,
+        const memory_desc_t *src_mds, const primitive_attr_t *attr,
+        engine_t *engine) {
+    bool args_ok = !any_null(sum_pd, src_mds, scales) && n > 0;
+    if (!args_ok) return invalid_arguments;
+
+    const primitive_attr_t dummy_attr;
+    if (attr == NULL)
+        attr = &dummy_attr;
+
+    const int ndims = src_mds[0].ndims;
+    const dims_t &dims = src_mds[0].dims;
+    const data_type_t dt = src_mds[0].data_type;
+
+    for (int i = 1; i < n; ++i) {
+        if (src_mds[i].ndims != ndims) return invalid_arguments;
+        for (int d = 0; d < ndims; ++d) {
+            if (src_mds[i].dims[d] != dims[d])
+                return invalid_arguments;
+        }
+        if (src_mds[i].data_type != dt) return invalid_arguments;
+    }
+
+    memory_desc_t dummy_dst_md;
+    if (dst_md) {
+        if (dst_md->ndims != ndims) return invalid_arguments;
+        for (int d = 0; d < ndims; ++d) {
+            if (dst_md->dims[d] != dims[d])
+                return invalid_arguments;
+        }
+    } else {
+        dummy_dst_md = src_mds[0];
+        dummy_dst_md.format_kind = format_kind::any;
+        dst_md = &dummy_dst_md;
+    }
+
+    auto s_pd = reinterpret_cast<sum_pd_t **>(sum_pd);
+
+    for (auto s = engine->get_sum_implementation_list(); *s; ++s) {
+        if ((*s)(s_pd, engine, attr, dst_md, n, scales, src_mds) == success) {
+            (*s_pd)->init_info();
+            (*s_pd)->init_scratchpad_md();
+            return success;
+        }
+    }
+    return unimplemented;
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/sum_pd.hpp b/thirdparty/oidn/mkl-dnn/src/common/sum_pd.hpp
new file mode 100644
index 0000000000..80254667df
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/sum_pd.hpp
@@ -0,0 +1,143 @@
+/*******************************************************************************
+* Copyright 2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef SUM_PD_HPP
+#define SUM_PD_HPP
+
+#include <assert.h>
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "primitive_desc.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct sum_pd_t: public primitive_desc_t {
+    sum_pd_t(engine_t *engine, const primitive_attr_t *attr,
+            const memory_desc_t *dst_md, int n, const float *scales,
+            const memory_desc_t *src_mds)
+        : primitive_desc_t(engine, attr, primitive_kind::sum)
+        , n_(n), dst_md_(*dst_md)
+    {
+        scales_.reserve(n_);
+        for (int i = 0; i < n_; ++i) scales_.push_back(scales[i]);
+        src_mds_.reserve(n_);
+        for (int i = 0; i < n_; ++i) src_mds_.push_back(src_mds[i]);
+    }
+
+    virtual void init_info() override { impl::init_info(this, this->info_); }
+
+    virtual arg_usage_t arg_usage(primitive_arg_index_t arg) const override {
+        if (arg >= MKLDNN_ARG_MULTIPLE_SRC
+                && arg < MKLDNN_ARG_MULTIPLE_SRC + n_inputs())
+            return arg_usage_t::input;
+
+        if (arg == MKLDNN_ARG_DST)
+            return arg_usage_t::output;
+
+        return primitive_desc_t::arg_usage(arg);
+    }
+
+    virtual const memory_desc_t *src_md(int index = 0) const override
+    { return index < n_inputs() ? &src_mds_[index] : nullptr; }
+    virtual const memory_desc_t *dst_md(int index = 0) const override
+    { return index == 0 ? &dst_md_ : nullptr; }
+
+    virtual int n_inputs() const override { return n_; }
+    virtual int n_outputs() const override { return 1; }
+
+    const float *scales() const { return &scales_[0]; }
+
+protected:
+    int n_;
+    nstl::vector<float> scales_;
+    memory_desc_t dst_md_;
+    nstl::vector<memory_desc_t> src_mds_;
+
+protected:
+    /* inits dst_md_ in simple cases. The call may fail. */
+    status_t init() {
+        for (int i = 0; i < n_; ++i) {
+            const memory_desc_wrapper src_d(&src_mds_[i]);
+            if (!src_d.is_blocking_desc() || src_d.is_additional_buffer())
+                return status::unimplemented;
+        }
+        bool ok = true
+            && set_default_params() == status::success
+            && attr()->has_default_values();
+        return ok ? status::success : status::unimplemented;
+    }
+
+    status_t set_default_params() {
+        if (dst_md_.format_kind != format_kind::any)
+            return status::success;
+
+        /* The stupidest ever heuristics (but not the same as we had before):
+         *  - Pick the first non-plain format;
+         *  - If all formats are plain, pick the format of the first input
+         */
+        for (int i = 0; i < n_; ++i) {
+            const memory_desc_wrapper src_d(src_mds_[i]);
+            if (!src_d.is_plain() && src_d.is_blocking_desc()) {
+                return memory_desc_init_by_blocking_desc(dst_md_,
+                        src_d.blocking_desc());
+            }
+        }
+
+        if (src_mds_[0].format_kind != format_kind::blocked)
+            return status::unimplemented;
+
+        dst_md_ = src_mds_[0];
+
+        return status::success;
+    }
+};
+
+#define DECLARE_SUM_PD_t(impl_name, ...) \
+    static status_t create(sum_pd_t **sum_pd, \
+            engine_t *engine, const primitive_attr_t *attr, \
+            const memory_desc_t *dst_md, int n, const float *scales, \
+            const memory_desc_t *src_mds) { \
+        using namespace status; \
+        auto _pd = new pd_t(engine, attr, dst_md, n, scales, src_mds); \
+        if (_pd == nullptr) return out_of_memory; \
+        if (_pd->init() != success) { delete _pd; return unimplemented; } \
+        return safe_ptr_assign<sum_pd_t>(*sum_pd, _pd); \
+    } \
+    virtual status_t create_primitive(primitive_t **p) const override { \
+        double ms = get_msec(); \
+        auto ret = safe_ptr_assign<primitive_t>(*p, new (__VA_ARGS__)(this)); \
+        ms = get_msec() - ms; \
+        if (mkldnn_verbose()->level >= 2) { \
+            printf("mkldnn_verbose,create,%s,%g\n", this->info(), ms); \
+            fflush(0); \
+        } \
+        return ret; \
+    } \
+    virtual pd_t *clone() const override { return new pd_t(*this); } \
+    virtual const char *name() const override { return impl_name; } \
+
+#define DECLARE_SUM_PD_T(impl_name, ...) \
+    DECLARE_SUM_PD_t(impl_name, __VA_ARGS__)
+
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/tag_traits.hpp b/thirdparty/oidn/mkl-dnn/src/common/tag_traits.hpp
new file mode 100644
index 0000000000..a408f45980
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/tag_traits.hpp
@@ -0,0 +1,200 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef TAG_TRAITS_HPP
+#define TAG_TRAITS_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+enum class block_dim_t {
+    _,
+    _A, _B,
+    _AB, _BC,
+};
+
+enum class inner_blk_t {
+    _,
+    _4a, _4b,
+    _8a, _8b,
+    _16a, _16b,
+
+    _4b4a, _4b4c, _4c4b,
+    _8a8b, _8b8a, _8b8c, _8c8b,
+    _16a16b, _16a4b, _16b16a, _16b4c, _16b16c, _16c16b,
+
+    _2c8b4c, _8a16b2a, _4b16a4b, _8b16a2b, _8b16c2b, _4c16b4c, _8c16b2c,
+};
+
+/** returns the offset within the block for weights blocked over oc and ic */
+template <inner_blk_t f>
+constexpr int AB_or_BC_blk_off(int x0, int x1) {
+    using ib = inner_blk_t;
+    static_assert(utils::one_of(f, ib::_4b4a, ib::_4b4c, ib::_4c4b, ib::_8a8b,
+                ib::_8b8a, ib::_8b8c, ib::_8c8b, ib::_16a16b, ib::_16a4b,
+                ib::_16b16a, ib::_16b4c, ib::_16b16c, ib::_16c16b, ib::_2c8b4c,
+                ib::_8a16b2a, ib::_4b16a4b, ib::_8b16a2b, ib::_8b16c2b,
+                ib::_4c16b4c, ib::_8c16b2c),
+            "unexpected inner_blk format");
+    return false ? 0
+        : (f == ib::_4b4c) ? 4 * x0 + x1
+        : (f == ib::_4b4a || f == ib::_4c4b) ? 4 * x1 + x0
+        : (f == ib::_8a8b || f == ib::_8b8c) ? 8 * x0 + x1
+        : (f == ib::_8b8a || f == ib::_8c8b) ? 8 * x1 + x0
+        : (f == ib::_16a16b || f == ib::_16b16c) ? 16 * x0 + x1
+        : (f == ib::_16b16a || f == ib::_16c16b) ? 16 * x1 + x0
+        : (f == ib::_16a4b || f == ib::_16b4c) ? 4 * x0 + x1
+        : (f == ib::_8a16b2a || f == ib::_8b16c2b) ? (x0 / 2) * 32 + x1 * 2 + x0 % 2
+        : (f == ib::_4b16a4b || f == ib::_4c16b4c) ? (x1 / 4) * 64 + x0 * 4 + x1 % 4
+        : (f == ib::_8b16a2b || f == ib::_8c16b2c) ? (x1 / 2) * 32 + x0 * 2 + x1 % 2
+        : (f == ib::_2c8b4c) ? (x1 / 4) * 32 + x0 * 4 + x1 % 4
+        : INT_MIN;
+}
+
+template <inner_blk_t b> struct inner_blk_traits {
+    using ib = inner_blk_t;
+};
+
+template <format_tag_t> struct tag_traits {
+    // block_dim_t block_dims;
+    // inner_blk_t inner_blks;
+    // int ndims;
+};
+
+#define DECL_TRAITS(_tag, _blk_fmt, _inner_blk, _ndims) \
+template <> struct tag_traits<format_tag::_tag> { \
+    static constexpr block_dim_t block_dims = block_dim_t::_blk_fmt; \
+    static constexpr inner_blk_t inner_blks = inner_blk_t::_inner_blk; \
+    static constexpr int ndims = _ndims; \
+}
+
+DECL_TRAITS(a, _, _, 1);
+DECL_TRAITS(ab, _, _, 2);
+DECL_TRAITS(abc, _, _, 3);
+DECL_TRAITS(abcd, _, _, 4);
+DECL_TRAITS(abcde, _, _, 5);
+DECL_TRAITS(abcdef, _, _, 6);
+DECL_TRAITS(abdec, _, _, 5);
+DECL_TRAITS(acb, _, _, 3);
+DECL_TRAITS(acbde, _, _, 5);
+DECL_TRAITS(acdb, _, _, 4);
+DECL_TRAITS(acdeb, _, _, 5);
+DECL_TRAITS(ba, _, _, 2);
+DECL_TRAITS(bac, _, _, 3);
+DECL_TRAITS(bacd, _, _, 4);
+DECL_TRAITS(bcda, _, _, 4);
+DECL_TRAITS(cba, _, _, 3);
+DECL_TRAITS(cdba, _, _, 4);
+DECL_TRAITS(cdeba, _, _, 5);
+DECL_TRAITS(decab, _, _, 5);
+
+DECL_TRAITS(Abc4a, _A, _4a, 3);
+DECL_TRAITS(aBc4b, _B, _4b, 3);
+DECL_TRAITS(ABc4b16a4b, _AB, _4b16a4b, 3);
+DECL_TRAITS(ABc4b4a, _AB, _4b4a, 3);
+DECL_TRAITS(Abcd4a, _A, _4a, 4);
+DECL_TRAITS(aBcd4b, _B, _4b, 4);
+DECL_TRAITS(ABcd4b4a, _AB, _4b4a, 4);
+DECL_TRAITS(aBCd4c16b4c, _BC, _4c16b4c, 4);
+DECL_TRAITS(aBCd4c4b, _BC, _4c4b, 4);
+DECL_TRAITS(Abcde4a, _A, _4a, 5);
+DECL_TRAITS(aBcde4b, _B, _4b, 5);
+DECL_TRAITS(ABcde4b4a, _AB, _4b4a, 5);
+DECL_TRAITS(aBCde4c4b, _BC, _4c4b, 5);
+DECL_TRAITS(aBcdef4b, _B, _4b, 6);
+DECL_TRAITS(aBCdef4c4b, _BC, _4c4b, 6);
+DECL_TRAITS(aBdc4b, _B, _4b, 4);
+DECL_TRAITS(aBdec4b, _B, _4b, 5);
+DECL_TRAITS(aBdefc4b, _B, _4b, 6);
+DECL_TRAITS(Acb4a, _A, _4a, 3);
+DECL_TRAITS(Acdb4a, _A, _4a, 4);
+DECL_TRAITS(Acdeb4a, _A, _4a, 5);
+
+DECL_TRAITS(Abc16a, _A, _16a, 3);
+DECL_TRAITS(ABc16a16b, _AB, _16a16b, 3);
+DECL_TRAITS(aBc16b, _B, _16b, 3);
+DECL_TRAITS(ABc16b16a, _AB, _16b16a, 3);
+DECL_TRAITS(ABc8a16b2a, _AB, _8a16b2a, 3);
+DECL_TRAITS(ABc8a8b, _AB, _8a8b, 3);
+DECL_TRAITS(aBc8b, _B, _8b, 3);
+DECL_TRAITS(ABc8b16a2b, _AB, _8b16a2b, 3);
+DECL_TRAITS(ABc8b8a, _AB, _8b8a, 3);
+DECL_TRAITS(Abcd16a, _A, _16a, 4);
+DECL_TRAITS(ABcd16a16b, _AB, _16a16b, 4);
+DECL_TRAITS(aBcd16b, _B, _16b, 4);
+DECL_TRAITS(ABcd16b16a, _AB, _16b16a, 4);
+DECL_TRAITS(aBCd16b16c, _BC, _16b16c, 4);
+DECL_TRAITS(aBCd16c16b, _BC, _16c16b, 4);
+DECL_TRAITS(ABcd4b16a4b, _AB, _4b16a4b, 4);
+DECL_TRAITS(ABcd8a16b2a, _AB, _8a16b2a, 4);
+DECL_TRAITS(ABcd8a8b, _AB, _8a8b, 4);
+DECL_TRAITS(aBcd8b, _B, _8b, 4);
+DECL_TRAITS(ABcd8b16a2b, _AB, _8b16a2b, 4);
+DECL_TRAITS(aBCd8b16c2b, _BC, _8b16c2b, 4);
+DECL_TRAITS(ABcd8b8a, _AB, _8b8a, 4);
+DECL_TRAITS(aBCd8b8c, _BC, _8b8c, 4);
+DECL_TRAITS(aBCd8c16b2c, _BC, _8c16b2c, 4);
+DECL_TRAITS(aBCd8c8b, _BC, _8c8b, 4);
+DECL_TRAITS(Abcde16a, _A, _16a, 5);
+DECL_TRAITS(ABcde16a16b, _AB, _16a16b, 5);
+DECL_TRAITS(aBcde16b, _B, _16b, 5);
+DECL_TRAITS(ABcde16b16a, _AB, _16b16a, 5);
+DECL_TRAITS(aBCde16b16c, _BC, _16b16c, 5);
+DECL_TRAITS(aBCde16c16b, _BC, _16c16b, 5);
+DECL_TRAITS(aBCde4c16b4c, _BC, _4c16b4c, 5);
+DECL_TRAITS(Abcde8a, _A, _8a, 5);
+DECL_TRAITS(ABcde8a8b, _AB, _8a8b, 5);
+DECL_TRAITS(aBcde8b, _B, _8b, 5);
+DECL_TRAITS(ABcde8b16a2b, _AB, _8b16a2b, 5);
+DECL_TRAITS(aBCde8b16c2b, _BC, _8b16c2b, 5);
+DECL_TRAITS(ABcde8b8a, _AB, _8b8a, 5);
+DECL_TRAITS(aBCde8b8c, _BC, _8b8c, 5);
+DECL_TRAITS(aBCde2c8b4c, _BC, _2c8b4c, 5);
+DECL_TRAITS(aBCde8c16b2c, _BC, _8c16b2c, 5);
+DECL_TRAITS(aBCde4b4c, _BC, _4b4c, 5);
+DECL_TRAITS(aBCde8c8b, _BC, _8c8b, 5);
+DECL_TRAITS(aBcdef16b, _B, _16b, 6);
+DECL_TRAITS(aBCdef16b16c, _BC, _16b16c, 6);
+DECL_TRAITS(aBCdef16c16b, _BC, _16c16b, 6);
+DECL_TRAITS(aBCdef8b8c, _BC, _8b8c, 6);
+DECL_TRAITS(aBCdef8c16b2c, _BC, _8c16b2c, 6);
+DECL_TRAITS(aBCdef8c8b, _BC, _8c8b, 6);
+DECL_TRAITS(aBdc16b, _B, _16b, 4);
+DECL_TRAITS(aBdc8b, _B, _8b, 4);
+DECL_TRAITS(aBdec16b, _B, _16b, 5);
+DECL_TRAITS(aBdec8b, _B, _8b, 5);
+DECL_TRAITS(aBdefc16b, _B, _16b, 6);
+DECL_TRAITS(aBdefc8b, _B, _8b, 6);
+DECL_TRAITS(Acb16a, _A, _16a, 3);
+DECL_TRAITS(Acb8a, _A, _8a, 3);
+DECL_TRAITS(aCBd16b16c, _BC, _16b16c, 4);
+DECL_TRAITS(aCBde16b16c, _BC, _16b16c, 5);
+DECL_TRAITS(Acdb16a, _A, _16a, 4);
+DECL_TRAITS(Acdb8a, _A, _8a, 4);
+DECL_TRAITS(Acdeb16a, _A, _16a, 5);
+DECL_TRAITS(Acdeb8a, _A,  _8a, 5);
+DECL_TRAITS(BAc16a16b, _AB, _16a16b, 3);
+DECL_TRAITS(BAcd16a16b, _AB, _16a16b, 4);
+
+} // namespace impl
+} // namespace mkldnn
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/type_helpers.hpp b/thirdparty/oidn/mkl-dnn/src/common/type_helpers.hpp
new file mode 100644
index 0000000000..4f06368738
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/type_helpers.hpp
@@ -0,0 +1,348 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef TYPE_HELPERS_HPP
+#define TYPE_HELPERS_HPP
+
+#include <assert.h>
+#include <math.h>
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "mkldnn_traits.hpp"
+#include "nstl.hpp"
+#include "utils.hpp"
+#include "math_utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+template <typename T>
+status_t safe_ptr_assign(T * &lhs, T* rhs) {
+    if (rhs == nullptr) return status::out_of_memory;
+    lhs = rhs;
+    return status::success;
+}
+
+template <typename T, typename U> struct is_subset
+{ static constexpr bool value = false; };
+template <typename T> struct is_subset<T, T>
+{ static constexpr bool value = true; };
+template <typename T> struct is_subset<T,
+         typename utils::enable_if<nstl::is_integral<T>::value, float>::type>
+{ static constexpr bool value = true; };
+#define ISSPEC(t1, t2) template <> \
+    struct is_subset<t1, t2> { static constexpr bool value = true; }
+ISSPEC(int16_t, int32_t);
+ISSPEC(int8_t, int32_t);
+ISSPEC(uint8_t, int32_t);
+ISSPEC(int8_t, int16_t);
+ISSPEC(uint8_t, int16_t);
+#undef ISSPEC
+
+inline bool operator==(const memory_desc_t &lhs, const memory_desc_t &rhs);
+
+namespace types {
+
+inline size_t data_type_size(data_type_t data_type) {
+    using namespace data_type;
+    switch (data_type) {
+    case f32: return sizeof(prec_traits<f32>::type);
+    case s32: return sizeof(prec_traits<s32>::type);
+    case s8: return sizeof(prec_traits<s8>::type);
+    case u8: return sizeof(prec_traits<u8>::type);
+    case data_type::undef:
+    default: assert(!"unknown data_type");
+    }
+    return 0; /* not supposed to be reachable */
+}
+
+inline format_kind_t format_tag_to_kind(format_tag_t tag) {
+    switch (tag) {
+    case format_tag::undef: return format_kind::undef;
+    case format_tag::any: return format_kind::any;
+    case format_tag::last: return format_kind::undef;
+    default: return format_kind::blocked;
+    }
+
+    assert(!"unreachable");
+    return format_kind::undef;
+}
+
+inline bool memory_extra_desc_is_equal(const memory_extra_desc_t &lhs,
+        const memory_extra_desc_t &rhs) {
+    return true
+        && lhs.flags == rhs.flags
+        && IMPLICATION(lhs.flags & memory_extra_flags::compensation_conv_s8s8,
+                lhs.compensation_mask == rhs.compensation_mask)
+        && IMPLICATION(lhs.flags & memory_extra_flags::scale_adjust,
+                lhs.scale_adjust == rhs.scale_adjust);
+}
+
+inline bool blocking_desc_is_equal(const blocking_desc_t &lhs,
+        const blocking_desc_t &rhs, int ndims = MKLDNN_MAX_NDIMS) {
+    using mkldnn::impl::utils::array_cmp;
+    return true
+        && lhs.inner_nblks == rhs.inner_nblks
+        && array_cmp(lhs.strides, rhs.strides, ndims)
+        && array_cmp(lhs.inner_blks, rhs.inner_blks, lhs.inner_nblks)
+        && array_cmp(lhs.inner_idxs, rhs.inner_idxs, lhs.inner_nblks);
+}
+
+inline bool wino_desc_is_equal(const wino_desc_t &lhs,
+    const wino_desc_t &rhs) {
+    return lhs.wino_format == rhs.wino_format
+        && lhs.alpha == rhs.alpha
+        && lhs.ic == rhs.ic
+        && lhs.oc == rhs.oc
+        && lhs.ic_block == rhs.ic_block
+        && lhs.oc_block == rhs.oc_block
+        && lhs.ic2_block == rhs.ic2_block
+        && lhs.oc2_block == rhs.oc2_block
+        && lhs.r == rhs.r;
+}
+
+inline bool rnn_packed_desc_is_equal(
+        const rnn_packed_desc_t &lhs, const rnn_packed_desc_t &rhs) {
+    bool ok = true
+        && lhs.format == rhs.format
+        && lhs.n_parts == rhs.n_parts
+        && lhs.offset_compensation == rhs.offset_compensation
+        && lhs.size == rhs.size
+        && lhs.n == rhs.n;
+    if (!ok)
+        return false;
+
+    for (int i = 0; i < rhs.n_parts; i++)
+        ok = ok && lhs.parts[i] == rhs.parts[i];
+    for (int i = 0; i < rhs.n_parts; i++)
+        ok = ok && lhs.part_pack_size[i] == rhs.part_pack_size[i];
+    return ok;
+}
+
+inline memory_desc_t zero_md() {
+    auto zero = memory_desc_t();
+    return zero;
+}
+
+inline bool is_zero_md(const memory_desc_t *md) {
+    return md == nullptr || *md == zero_md();
+}
+
+inline data_type_t default_accum_data_type(data_type_t src_dt,
+        data_type_t dst_dt) {
+    using namespace utils;
+    using namespace data_type;
+
+    if (one_of(f32, src_dt, dst_dt)) return f32;
+    if (one_of(s32, src_dt, dst_dt)) return s32;
+
+    if (one_of(s8, src_dt, dst_dt) || one_of(u8, src_dt, dst_dt)) return s32;
+
+    assert(!"unimplemented use-case: no default parameters available");
+    return dst_dt;
+}
+
+inline data_type_t default_accum_data_type(data_type_t src_dt,
+        data_type_t wei_dt, data_type_t dst_dt, prop_kind_t prop_kind) {
+    using namespace utils;
+    using namespace data_type;
+    using namespace prop_kind;
+
+    /* prop_kind doesn't matter */
+    if (everyone_is(f32, src_dt, wei_dt, dst_dt)) return f32;
+
+    if (one_of(prop_kind, forward_training, forward_inference)) {
+        if ((src_dt == u8 || src_dt == s8)
+            && wei_dt == s8 && one_of(dst_dt, f32, s32, s8, u8))
+            return s32;
+    } else if (prop_kind == backward_data) {
+        if (one_of(src_dt, f32, s32, s8, u8) && wei_dt == s8 &&
+                one_of(dst_dt, s8, u8))
+            return s32;
+    }
+
+    assert(!"unimplemented use-case: no default parameters available");
+    return dst_dt;
+}
+
+}
+
+inline bool operator==(const memory_desc_t &lhs, const memory_desc_t &rhs) {
+    using namespace mkldnn::impl::utils;
+    bool base_equal = true
+        && lhs.ndims == rhs.ndims
+        && array_cmp(lhs.dims, rhs.dims, lhs.ndims)
+        && lhs.data_type == rhs.data_type
+        && array_cmp(lhs.padded_dims, rhs.padded_dims, lhs.ndims)
+        && array_cmp(lhs.padded_offsets, rhs.padded_offsets, lhs.ndims)
+        && lhs.offset0 == rhs.offset0
+        && lhs.format_kind == rhs.format_kind;
+    if (!base_equal) return false;
+    if (!types::memory_extra_desc_is_equal(lhs.extra, rhs.extra)) return false;
+    if (lhs.format_kind == format_kind::blocked)
+        return types::blocking_desc_is_equal(lhs.format_desc.blocking,
+                rhs.format_desc.blocking, lhs.ndims);
+    else if (lhs.format_kind == format_kind::wino)
+        return types::wino_desc_is_equal(lhs.format_desc.wino_desc,
+            rhs.format_desc.wino_desc);
+    else if (lhs.format_kind == format_kind::rnn_packed)
+        return types::rnn_packed_desc_is_equal(lhs.format_desc.rnn_packed_desc,
+                rhs.format_desc.rnn_packed_desc);
+    return true;
+}
+
+inline bool operator!=(const memory_desc_t &lhs, const memory_desc_t &rhs) {
+    return !operator==(lhs, rhs);
+}
+
+inline status_t memory_desc_init_by_strides(memory_desc_t &md,
+        const dims_t strides) {
+    return mkldnn_memory_desc_init_by_strides(
+            &md, md.ndims, md.dims, md.data_type, strides);
+}
+
+inline status_t memory_desc_init_by_tag(memory_desc_t &md, format_tag_t tag,
+        const dims_t strides = nullptr) {
+    status_t status = mkldnn_memory_desc_init_by_tag(
+            &md, md.ndims, md.dims, md.data_type, tag);
+    if (status != status::success || strides == nullptr)
+        return status;
+
+    /* TODO: add consistency check */
+
+    for (int d = 0; d < md.ndims; ++d)
+        md.format_desc.blocking.strides[d] = strides[d];
+
+    return status::success;
+}
+
+/** inits memory descriptor based on logical dimensions kept in @p md, and the
+ * blocking structure @p blk.
+ *
+ * @note blk.strides represent the order only (from smaller to bigger)
+ *
+ * TODO: move md related functions to one single place
+ */
+inline status_t memory_desc_init_by_blocking_desc(memory_desc_t &md,
+        const blocking_desc_t &blk) {
+    dims_t blocks = {0};
+    utils::array_set(blocks, 1, md.ndims);
+    dim_t block_size = 1;
+    for (int iblk = 0; iblk < blk.inner_nblks; ++iblk) {
+        blocks[blk.inner_idxs[iblk]] *= blk.inner_blks[iblk];
+        block_size *= blk.inner_blks[iblk];
+    }
+
+    for (int d = 0; d < md.ndims; ++d) {
+        md.padded_dims[d] = utils::rnd_up(md.dims[d], blocks[d]);
+        md.padded_offsets[d] = 0;
+    }
+    md.offset0 = 0;
+
+    md.format_kind = format_kind::blocked;
+    auto &mblk = md.format_desc.blocking;
+    mblk = blk;
+
+    const int ndims = nstl::min(MKLDNN_MAX_NDIMS, md.ndims); // make GCC 5 happy
+    utils::array_copy(mblk.strides, blk.strides, ndims);
+
+    int perm[MKLDNN_MAX_NDIMS];
+    for (int d = 0; d < ndims; ++d) perm[d] = d;
+
+    utils::simultaneous_sort(mblk.strides, perm, ndims,
+            [](stride_t a, stride_t b) { return b - a; });
+
+    dim_t stride = block_size;
+    for (int _d = ndims - 1; _d >= 0; --_d) {
+        const int d = perm[_d];
+        md.format_desc.blocking.strides[d] = stride;
+        stride *= md.padded_dims[d] / blocks[d];
+    }
+
+    md.extra = utils::zero<memory_extra_desc_t>();
+
+    return status::success;
+}
+
+/** returns true if memory desc @p md corresponds to the given format tag and
+ * strides.
+ * If strides are not passed (or passed as nullptr) the dense structure is
+ * assumed (i.e. the one that mkldnn_memory_desc_init_by_tag() returns).
+ * Strides might contain `0` value, indicating the stride must match the one
+ * that mkldnn_memory_desc_init_by_tag() returns.
+ * Strides might contain `-1` values, that would be ignored during the
+ * comparison. For instance, this can be used if a stride along minibatch
+ * doesn't matter. */
+inline bool memory_desc_matches_tag(const memory_desc_t &md, format_tag_t tag,
+        const dims_t strides = nullptr) {
+    if (md.format_kind != types::format_tag_to_kind(tag))
+        return false;
+
+    memory_desc_t md_gold;
+    status_t status = mkldnn_memory_desc_init_by_tag(
+            &md_gold, md.ndims, md.dims, md.data_type, tag);
+    if (status != status::success) return false;
+
+    if (md.format_kind != format_kind::blocked)
+        return false; // unimplemented yet
+
+    const auto &blk = md.format_desc.blocking;
+    const auto &blk_gold = md_gold.format_desc.blocking;
+
+    using utils::array_cmp;
+    bool same_blocks = true
+        && blk.inner_nblks == blk_gold.inner_nblks
+        && array_cmp(blk.inner_blks, blk_gold.inner_blks, blk.inner_nblks)
+        && array_cmp(blk.inner_idxs, blk_gold.inner_idxs, blk.inner_nblks);
+
+    if (!same_blocks)
+        return false;
+
+    if (strides == nullptr)
+        return array_cmp(blk.strides, blk_gold.strides, md.ndims);
+
+    for (int d = 0; d < md.ndims; ++d) {
+        dim_t stride = strides[d];
+        if (stride == -1) continue;
+        if (stride == 0) stride = blk_gold.strides[d];
+        if (blk.strides[d] != stride) return false;
+    }
+
+    return true;
+}
+
+/** returns matching tag (or undef if match is not found)
+ * XXX: This is a workaround that eventually should go away! */
+template <typename... Tags>
+format_tag_t memory_desc_matches_one_of_tag(const memory_desc_t &md,
+        Tags ...tags) {
+    for (const auto tag: {tags...}) {
+        if (memory_desc_matches_tag(md, tag))
+            return tag;
+    }
+    return format_tag::undef;
+}
+
+}
+}
+
+#include "memory_desc_wrapper.hpp"
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/utils.cpp b/thirdparty/oidn/mkl-dnn/src/common/utils.cpp
new file mode 100644
index 0000000000..d23f4682dc
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/utils.cpp
@@ -0,0 +1,135 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <string.h>
+#ifdef _WIN32
+#include <malloc.h>
+#include <windows.h>
+#endif
+#include <limits.h>
+#include <stdlib.h>
+#include <stdio.h>
+
+#include "mkldnn.h"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+int getenv(const char *name, char *buffer, int buffer_size) {
+    if (name == NULL || buffer_size < 0 || (buffer == NULL && buffer_size > 0))
+        return INT_MIN;
+
+    int result = 0;
+    int term_zero_idx = 0;
+    size_t value_length = 0;
+
+#ifdef _WIN32
+    value_length = GetEnvironmentVariable(name, buffer, buffer_size);
+#else
+    const char *value = ::getenv(name);
+    value_length = value == NULL ? 0 : strlen(value);
+#endif
+
+    if (value_length > INT_MAX)
+        result = INT_MIN;
+    else {
+        int int_value_length = (int)value_length;
+        if (int_value_length >= buffer_size) {
+            result = -int_value_length;
+        } else {
+            term_zero_idx = int_value_length;
+            result = int_value_length;
+#ifndef _WIN32
+            strncpy(buffer, value, value_length);
+#endif
+        }
+    }
+
+    if (buffer != NULL)
+        buffer[term_zero_idx] = '\0';
+    return result;
+}
+
+int getenv_int(const char *name, int default_value)
+{
+    int value = default_value;
+    // # of digits in the longest 32-bit signed int + sign + terminating null
+    const int len = 12;
+    char value_str[len];
+    if (getenv(name, value_str, len) > 0)
+        value = atoi(value_str);
+    return value;
+}
+
+FILE *fopen(const char *filename, const char *mode) {
+#ifdef _WIN32
+    FILE *fp = NULL;
+    return ::fopen_s(&fp, filename, mode) ? NULL : fp;
+#else
+    return ::fopen(filename, mode);
+#endif
+}
+
+void *malloc(size_t size, int alignment) {
+    void *ptr;
+
+#ifdef _WIN32
+    ptr = _aligned_malloc(size, alignment);
+    int rc = ptr ? 0 : -1;
+#else
+    int rc = ::posix_memalign(&ptr, alignment, size);
+#endif
+
+    return (rc == 0) ? ptr : 0;
+}
+
+void free(void *p) {
+#ifdef _WIN32
+    _aligned_free(p);
+#else
+    ::free(p);
+#endif
+}
+
+// Atomic operations
+int32_t fetch_and_add(int32_t *dst, int32_t val) {
+#ifdef _WIN32
+    return InterlockedExchangeAdd(reinterpret_cast<long*>(dst), val);
+#else
+    return __sync_fetch_and_add(dst, val);
+#endif
+}
+
+static int jit_dump_flag = 0;
+static bool jit_dump_flag_initialized = false;
+bool jit_dump_enabled() {
+    if (!jit_dump_flag_initialized) {
+        jit_dump_flag = getenv_int("MKLDNN_JIT_DUMP");
+        jit_dump_flag_initialized = true;
+    }
+    return jit_dump_flag != 0;
+}
+
+}
+}
+
+mkldnn_status_t mkldnn_set_jit_dump(int enabled) {
+    using namespace mkldnn::impl::status;
+    mkldnn::impl::jit_dump_flag = enabled;
+    mkldnn::impl::jit_dump_flag_initialized = true;
+    return success;
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/utils.hpp b/thirdparty/oidn/mkl-dnn/src/common/utils.hpp
new file mode 100644
index 0000000000..d5a8ec5139
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/utils.hpp
@@ -0,0 +1,370 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef UTILS_HPP
+#define UTILS_HPP
+
+#include <stddef.h>
+#include <stdio.h>
+#include <stdlib.h>
+#include <assert.h>
+#include <stdint.h>
+
+#if defined(__x86_64__) || defined(_M_X64)
+#define MKLDNN_X86_64
+#endif
+
+#define MSAN_ENABLED 0
+#if defined(__has_feature)
+#if __has_feature(memory_sanitizer)
+#undef MSAN_ENABLED
+#define MSAN_ENABLED 1
+#include <sanitizer/msan_interface.h>
+#endif
+#endif
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "z_magic.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+// Sanity check for 64 bits
+static_assert(sizeof(void*) == 8, "Intel(R) MKL-DNN supports 64 bit only");
+
+#define CHECK(f) do { \
+    status_t status = f; \
+    if (status != status::success) \
+    return status; \
+} while (0)
+
+#define IMPLICATION(cause, effect) (!(cause) || !!(effect))
+
+namespace utils {
+
+/* a bunch of std:: analogues to be compliant with any msvs version
+ *
+ * Rationale: msvs c++ (and even some c) headers contain special pragma that
+ * injects msvs-version check into object files in order to abi-mismatches
+ * during the static linking. This makes sense if e.g. std:: objects are passed
+ * through between application and library, which is not the case for mkl-dnn
+ * (since there is no any c++-rt dependent stuff, ideally...). */
+
+/* SFINAE helper -- analogue to std::enable_if */
+template<bool expr, class T = void> struct enable_if {};
+template<class T> struct enable_if<true, T> { typedef T type; };
+
+/* analogue std::conditional */
+template <bool, typename, typename> struct conditional {};
+template <typename T, typename F> struct conditional<true, T, F>
+{ typedef T type; };
+template <typename T, typename F> struct conditional<false, T, F>
+{ typedef F type; };
+
+template <bool, typename, bool, typename, typename> struct conditional3 {};
+template <typename T, typename FT, typename FF>
+struct conditional3<true, T, false, FT, FF> { typedef T type; };
+template <typename T, typename FT, typename FF>
+struct conditional3<false, T, true, FT, FF> { typedef FT type; };
+template <typename T, typename FT, typename FF>
+struct conditional3<false, T, false, FT, FF> { typedef FF type; };
+
+template <bool, typename U, U, U> struct conditional_v {};
+template <typename U, U t, U f> struct conditional_v<true, U, t, f>
+{ static constexpr U value = t; };
+template <typename U, U t, U f> struct conditional_v<false, U, t, f>
+{ static constexpr U value = f; };
+
+template <typename T> struct remove_reference { typedef T type; };
+template <typename T> struct remove_reference<T&> { typedef T type; };
+template <typename T> struct remove_reference<T&&> { typedef T type; };
+
+template <typename T>
+inline T&& forward(typename utils::remove_reference<T>::type &t)
+{ return static_cast<T&&>(t); }
+template <typename T>
+inline T&& forward(typename utils::remove_reference<T>::type &&t)
+{ return static_cast<T&&>(t); }
+
+template <typename T>
+inline typename remove_reference<T>::type zero()
+{ auto zero = typename remove_reference<T>::type(); return zero; }
+
+template <typename T, typename P>
+inline bool everyone_is(T val, P item) { return val == item; }
+template <typename T, typename P, typename... Args>
+inline bool everyone_is(T val, P item, Args... item_others) {
+    return val == item && everyone_is(val, item_others...);
+}
+
+template <typename T, typename P>
+constexpr bool one_of(T val, P item) { return val == item; }
+template <typename T, typename P, typename... Args>
+constexpr bool one_of(T val, P item, Args... item_others) {
+    return val == item || one_of(val, item_others...);
+}
+
+template <typename... Args>
+inline bool any_null(Args... ptrs) { return one_of(nullptr, ptrs...); }
+
+template<typename T>
+inline void array_copy(T *dst, const T *src, size_t size) {
+    for (size_t i = 0; i < size; ++i) dst[i] = src[i];
+}
+template<typename T>
+inline bool array_cmp(const T *a1, const T *a2, size_t size) {
+    for (size_t i = 0; i < size; ++i) if (a1[i] != a2[i]) return false;
+    return true;
+}
+template<typename T, typename U>
+inline void array_set(T *arr, const U& val, size_t size) {
+    for (size_t i = 0; i < size; ++i) arr[i] = static_cast<T>(val);
+}
+
+namespace product_impl {
+template<size_t> struct int2type{};
+
+template <typename T>
+constexpr int product_impl(const T *arr, int2type<0>) { return arr[0]; }
+
+template <typename T, size_t num>
+inline T product_impl(const T *arr, int2type<num>) {
+    return arr[0]*product_impl(arr+1, int2type<num-1>()); }
+}
+
+template <size_t num, typename T>
+inline T array_product(const T *arr) {
+    return product_impl::product_impl(arr, product_impl::int2type<num-1>());
+}
+
+template<typename T, typename R = T>
+inline R array_product(const T *arr, size_t size) {
+    R prod = 1;
+    for (size_t i = 0; i < size; ++i) prod *= arr[i];
+    return prod;
+}
+
+/** sorts an array of values using @p comparator. While sorting the array
+ * of value, the function permutes an array of @p keys accordingly.
+ *
+ * @note The arrays of @p keys can be omitted. In this case the function
+ *       sorts the array of @vals only.
+ */
+template <typename T, typename U, typename F>
+inline void simultaneous_sort(T *vals, U *keys, size_t size, F comparator) {
+    if (size == 0) return;
+
+    for (size_t i = 0; i < size - 1; ++i) {
+        bool swapped = false;
+
+        for (size_t j = 0; j < size - i - 1; j++) {
+            if (comparator(vals[j], vals[j + 1]) > 0) {
+                nstl::swap(vals[j], vals[j + 1]);
+                if (keys) nstl::swap(keys[j], keys[j + 1]);
+                swapped = true;
+            }
+        }
+
+        if (swapped == false) break;
+    }
+}
+
+template <typename T, typename U>
+inline typename remove_reference<T>::type div_up(const T a, const U b) {
+    assert(b);
+    return (a + b - 1) / b;
+}
+
+template <typename T, typename U>
+inline typename remove_reference<T>::type rnd_up(const T a, const U b) {
+    return div_up(a, b) * b;
+}
+
+template <typename T, typename U>
+inline typename remove_reference<T>::type rnd_dn(const T a, const U b) {
+    return (a / b) * b;
+}
+
+template <typename T> T *align_ptr(T *ptr, uintptr_t alignment)
+{ return (T *)(((uintptr_t)ptr + alignment - 1) & ~(alignment - 1)); }
+
+template <typename T, typename U, typename V>
+inline U this_block_size(const T offset, const U max, const V block_size) {
+    assert(offset < max);
+    // TODO (Roma): can't use nstl::max() due to circular dependency... we
+    // need to fix this
+    const T block_boundary = offset + block_size;
+    if (block_boundary > max)
+        return max - offset;
+    else
+        return block_size;
+}
+
+template<typename T>
+inline T nd_iterator_init(T start) { return start; }
+template<typename T, typename U, typename W, typename... Args>
+inline T nd_iterator_init(T start, U &x, const W &X, Args &&... tuple) {
+    start = nd_iterator_init(start, utils::forward<Args>(tuple)...);
+    x = start % X;
+    return start / X;
+}
+
+inline bool nd_iterator_step() { return true; }
+template<typename U, typename W, typename... Args>
+inline bool nd_iterator_step(U &x, const W &X, Args &&... tuple) {
+    if (nd_iterator_step(utils::forward<Args>(tuple)...) ) {
+        x = (x + 1) % X;
+        return x == 0;
+    }
+    return false;
+}
+
+template<typename U, typename W, typename Y>
+inline bool nd_iterator_jump(U &cur, const U end, W &x, const Y &X)
+{
+    U max_jump = end - cur;
+    U dim_jump = X - x;
+    if (dim_jump <= max_jump) {
+        x = 0;
+        cur += dim_jump;
+        return true;
+    } else {
+        cur += max_jump;
+        x += max_jump;
+        return false;
+    }
+}
+template<typename U, typename W, typename Y, typename... Args>
+inline bool nd_iterator_jump(U &cur, const U end, W &x, const Y &X,
+        Args &&... tuple)
+{
+    if (nd_iterator_jump(cur, end, utils::forward<Args>(tuple)...)) {
+        x = (x + 1) % X;
+        return x == 0;
+    }
+    return false;
+}
+
+template <typename T>
+inline T pick(size_t i, const T &x0) { return x0; }
+template <typename T, typename ...Args>
+inline T pick(size_t i, const T &x0, Args &&... args) {
+    return i == 0 ? x0 : pick(i - 1, utils::forward<Args>(args)...);
+}
+
+template <typename T>
+T pick_by_prop_kind(prop_kind_t prop_kind, const T &val_fwd_inference,
+        const T &val_fwd_training, const T &val_bwd_d, const T &val_bwd_w) {
+    switch (prop_kind) {
+    case prop_kind::forward_inference: return val_fwd_inference;
+    case prop_kind::forward_training: return val_fwd_training;
+    case prop_kind::backward_data: return val_bwd_d;
+    case prop_kind::backward_weights: return val_bwd_w;
+    default: assert(!"unsupported prop_kind");
+    }
+    return T();
+}
+
+template <typename T>
+T pick_by_prop_kind(prop_kind_t prop_kind,
+        const T &val_fwd, const T &val_bwd_d, const T &val_bwd_w)
+{ return pick_by_prop_kind(prop_kind, val_fwd, val_fwd, val_bwd_d, val_bwd_w); }
+
+template <typename Telem, size_t Tdims>
+struct array_offset_calculator {
+    template <typename... Targs>
+    array_offset_calculator(Telem *base, Targs... Fargs) : _dims{ Fargs... }
+    {
+        _base_ptr = base;
+    }
+    template <typename... Targs>
+    inline Telem &operator()(Targs... Fargs)
+    {
+        return *(_base_ptr + _offset(1, Fargs...));
+    }
+
+private:
+    template <typename... Targs>
+    inline size_t _offset(size_t const dimension, size_t element)
+    {
+        return element;
+    }
+
+    template <typename... Targs>
+    inline size_t _offset(size_t const dimension, size_t theta, size_t element)
+    {
+        return element + (_dims[dimension] * theta);
+    }
+
+    template <typename... Targs>
+    inline size_t _offset(size_t const dimension, size_t theta, size_t element,
+            Targs... Fargs)
+    {
+        size_t t_prime = element + (_dims[dimension] * theta);
+        return _offset(dimension + 1, t_prime, Fargs...);
+    }
+
+    Telem *_base_ptr;
+    const int _dims[Tdims];
+};
+
+}
+
+int32_t fetch_and_add(int32_t *dst, int32_t val);
+inline void yield_thread() {}
+
+// Reads an environment variable 'name' and stores its string value in the
+// 'buffer' of 'buffer_size' bytes on success.
+//
+// - Returns the length of the environment variable string value (excluding
+// the terminating 0) if it is set and its contents (including the terminating
+// 0) can be stored in the 'buffer' without truncation.
+//
+// - Returns negated length of environment variable string value and writes
+// "\0" to the buffer (if it is not NULL) if the 'buffer_size' is to small to
+// store the value (including the terminating 0) without truncation.
+//
+// - Returns 0 and writes "\0" to the buffer (if not NULL) if the environment
+// variable is not set.
+//
+// - Returns INT_MIN if the 'name' is NULL.
+//
+// - Returns INT_MIN if the 'buffer_size' is negative.
+//
+// - Returns INT_MIN if the 'buffer' is NULL and 'buffer_size' is greater than
+// zero. Passing NULL 'buffer' with 'buffer_size' set to 0 can be used to
+// retrieve the length of the environment variable value string.
+//
+int getenv(const char *name, char *buffer, int buffer_size);
+// Reads an integer from the environment
+int getenv_int(const char *name, int default_value = 0);
+bool jit_dump_enabled();
+FILE *fopen(const char *filename, const char *mode);
+
+constexpr int msan_enabled = MSAN_ENABLED;
+inline void msan_unpoison(void *ptr, size_t size) {
+#if MSAN_ENABLED
+    __msan_unpoison(ptr, size);
+#endif
+}
+
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/common/verbose.cpp b/thirdparty/oidn/mkl-dnn/src/common/verbose.cpp
new file mode 100644
index 0000000000..89a57772cf
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/verbose.cpp
@@ -0,0 +1,665 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <stdlib.h>
+#ifndef _WIN32
+#include <sys/time.h>
+#endif
+
+#include "mkldnn.h"
+#include "mkldnn_version.h"
+#include "c_types_map.hpp"
+#include "verbose.hpp"
+#include "cpu/cpu_isa_traits.hpp"
+
+#include "batch_normalization_pd.hpp"
+#include "pooling_pd.hpp"
+#include "concat_pd.hpp"
+#include "reorder_pd.hpp"
+#include "convolution_pd.hpp"
+#include "rnn_pd.hpp"
+#include "deconvolution_pd.hpp"
+#include "shuffle_pd.hpp"
+#include "eltwise_pd.hpp"
+#include "softmax_pd.hpp"
+#include "inner_product_pd.hpp"
+#include "sum_pd.hpp"
+#include "lrn_pd.hpp"
+
+/* MKL-DNN CPU ISA info */
+#define ISA_ANY "No instruction set specific optimizations"
+#define SSE42 "Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2)"
+#define AVX "Intel(R) Advanced Vector Extensions (Intel(R) AVX)"
+#define AVX2 "Intel(R) Advanced Vector Extensions 2 (Intel(R) AVX2)"
+#define AVX512_COMMON "Intel(R) Advanced Vector Extensions 512 (Intel(R) " \
+                      "AVX-512)"
+#define AVX512_CORE "Intel(R) Advanced Vector Extensions 512 (Intel(R) " \
+                    "AVX-512) with AVX512BW, AVX512VL, and AVX512DQ extensions"
+#define AVX512_CORE_VNNI "Intel(R) AVX512-Deep Learning Boost (Intel(R) " \
+                         "AVX512-DL Boost)"
+#define AVX512_MIC "Intel(R) Advanced Vector Extensions 512 (Intel(R) " \
+                   "AVX-512) with AVX512CD, AVX512ER, and AVX512PF extensions"
+#define AVX512_MIC_4OPS "Intel(R) Advanced Vector Extensions 512 (Intel(R) " \
+                   "AVX-512) with AVX512_4FMAPS and AVX512_4VNNIW extensions"
+
+namespace mkldnn {
+namespace impl {
+
+static verbose_t verbose;
+static bool initialized;
+static bool version_printed = false;
+
+const verbose_t *mkldnn_verbose() {
+#if !defined(DISABLE_VERBOSE)
+    if (!initialized) {
+        const int len = 2;
+        char val[len] = {0};
+        if (getenv("MKLDNN_VERBOSE", val, len) == 1)
+            verbose.level = atoi(val);
+        initialized = true;
+    }
+    if (!version_printed && verbose.level > 0) {
+         printf("mkldnn_verbose,info,"
+                 "Intel(R) MKL-DNN v%d.%d.%d (Git Hash %s),%s\n",
+                mkldnn_version()->major, mkldnn_version()->minor,
+                mkldnn_version()->patch, mkldnn_version()->hash,
+                get_isa_info());
+         version_printed = true;
+    }
+#else
+    verbose.level = 0;
+#endif
+    return &verbose;
+}
+
+double get_msec() {
+#ifdef _WIN32
+    static LARGE_INTEGER frequency;
+    if (frequency.QuadPart == 0)
+        QueryPerformanceFrequency(&frequency);
+    LARGE_INTEGER now;
+    QueryPerformanceCounter(&now);
+    return 1e+3 * now.QuadPart / frequency.QuadPart;
+#else
+    struct timeval time;
+    gettimeofday(&time, NULL);
+    return 1e+3 * time.tv_sec + 1e-3 * time.tv_usec;
+#endif
+}
+
+const char *get_isa_info() {
+    using namespace mkldnn::impl::cpu;
+    if (mayiuse(avx512_mic_4ops))  return AVX512_MIC_4OPS;
+    if (mayiuse(avx512_mic))       return AVX512_MIC;
+    if (mayiuse(avx512_core_vnni)) return AVX512_CORE_VNNI;
+    if (mayiuse(avx512_core))      return AVX512_CORE;
+    if (mayiuse(avx512_common))    return AVX512_COMMON;
+    if (mayiuse(avx2))             return AVX2;
+    if (mayiuse(avx))              return AVX;
+    if (mayiuse(sse42))            return SSE42;
+    return ISA_ANY;
+}
+
+/* init_info section */
+namespace {
+#if !defined(DISABLE_VERBOSE)
+#define MKLDNN_VERBOSE_DAT_LEN 256
+#define MKLDNN_VERBOSE_AUX_LEN 384
+#define MKLDNN_VERBOSE_PRB_LEN 384
+
+#define DECL_DAT_AUX_PRB_STRS() \
+    int dat_written = 0, aux_written = 0, prb_written = 0; \
+    MAYBE_UNUSED((dat_written * aux_written * prb_written)); \
+    char dat_str[MKLDNN_VERBOSE_DAT_LEN] = {'\0'}; MAYBE_UNUSED(dat_str); \
+    char aux_str[MKLDNN_VERBOSE_AUX_LEN] = {'\0'}; MAYBE_UNUSED(aux_str); \
+    char prb_str[MKLDNN_VERBOSE_PRB_LEN] = {'\0'}; MAYBE_UNUSED(prb_str)
+
+#define DFMT "%" PRId64
+
+void clear_buf(char *buf, int &written) {
+    /* TODO: do it better */
+    buf[0] = '#';
+    buf[1] = '\0';
+    written = 1;
+}
+
+#define DPRINT(buf, buf_len, written, ...) do { \
+    int l = snprintf(buf + written, buf_len - written, __VA_ARGS__); \
+    if (l < 0 || written + l > buf_len) { \
+        clear_buf(buf, written); \
+    } else { \
+        written += l; \
+    } \
+} while(0)
+
+// XXX: Outputs strings corresponding to memory formats used for data tensors.
+void format_prb_desc_str(char *str, int len, const memory_desc_t *md) {
+    const auto dims = md->dims;
+	int written = 0;
+    if (md->ndims == 1)
+        DPRINT(str, len, written,
+                "x" DFMT, dims[0]);
+    else if (md->ndims == 2)
+        DPRINT(str, len, written,
+                "mb" DFMT "ic" DFMT, dims[0], dims[1]);
+    else if (md->ndims == 3)
+        DPRINT(str, len, written,
+                "mb" DFMT "ic" DFMT "iw" DFMT,
+                dims[0], dims[1], dims[2]);
+    else if (md->ndims == 4)
+        DPRINT(str, len, written,
+                "mb" DFMT "ic" DFMT "ih" DFMT "iw" DFMT,
+                dims[0], dims[1], dims[2], dims[3]);
+    else if (md->ndims == 5)
+        DPRINT(str, len, written,
+                "mb" DFMT "ic" DFMT "id" DFMT "ih" DFMT "iw" DFMT,
+                dims[0], dims[1], dims[2], dims[3], dims[4]);
+    else
+        mkldnn_md2dim_str(str, len, md);
+}
+
+void verbose_templ(char *buffer, mkldnn_primitive_kind_t prim_kind,
+        const char *impl_str, mkldnn_prop_kind_t prop_kind,
+        const char *data_str, const char *aux_str, const char *prb_str) {
+    MAYBE_UNUSED(verbose_templ);
+    int written = 0;
+    DPRINT(buffer, MKLDNN_VERBOSE_BUF_LEN, written, "%s,%s,%s,%s,%s,%s",
+            mkldnn_prim_kind2str(prim_kind), impl_str,
+            mkldnn_prop_kind2str(prop_kind), data_str, aux_str, prb_str);
+}
+
+template <typename pd_t> static void init_info_bnorm(pd_t *s, char *buffer) {
+    DECL_DAT_AUX_PRB_STRS();
+
+    if (1) { // data
+        auto md = s->src_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "data_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // diff data
+        auto md = s->diff_src_md();
+        if (md) {
+            DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " diff_");
+            int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                    MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+            if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+        }
+    }
+
+    DPRINT(aux_str, MKLDNN_VERBOSE_AUX_LEN, aux_written,
+            "flags:%u", s->desc()->flags);
+
+    format_prb_desc_str(prb_str, MKLDNN_VERBOSE_PRB_LEN, s->src_md());
+
+    verbose_templ(buffer, s->kind(), s->name(), s->desc()->prop_kind, dat_str,
+            aux_str, prb_str);
+}
+
+template <typename pd_t> static void init_info_conv(pd_t *s, char *buffer) {
+    DECL_DAT_AUX_PRB_STRS();
+
+    if (1) { // src
+        auto md = s->desc()->prop_kind == prop_kind::backward_data
+            ? s->diff_src_md() : s->src_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "src_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // wei
+        auto md = s->desc()->prop_kind == prop_kind::backward_weights
+            ? s->diff_weights_md() : s->weights_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " wei_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // bia
+        auto md = s->desc()->prop_kind == prop_kind::backward_weights
+            ? s->diff_weights_md(1) : s->weights_md(1);
+        if (md) {
+            DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " bia_");
+            int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                    MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+            if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+        }
+    }
+    if (1) { // dst
+        auto md = !s->is_fwd() ? s->diff_dst_md() : s->dst_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " dst_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+
+    DPRINT(aux_str, MKLDNN_VERBOSE_AUX_LEN, aux_written,
+            "alg:%s", mkldnn_alg_kind2str(s->desc()->alg_kind));
+
+    if (s->ndims() == 5) {
+        if (s->with_groups())
+            DPRINT(prb_str, MKLDNN_VERBOSE_PRB_LEN, prb_written,
+                "mb" DFMT "_g" DFMT "ic" DFMT "oc" DFMT
+                "_id" DFMT "od" DFMT "kd" DFMT "sd" DFMT "dd" DFMT "pd" DFMT
+                "_ih" DFMT "oh" DFMT "kh" DFMT "sh" DFMT "dh" DFMT "ph" DFMT
+                "_iw" DFMT "ow" DFMT "kw" DFMT "sw" DFMT "dw" DFMT "pw" DFMT,
+                s->MB(), s->G(), s->IC(), s->OC(),
+                s->ID(), s->OD(), s->KD(), s->KSD(), s->KDD(), s->padFront(),
+                s->IH(), s->OH(), s->KH(), s->KSH(), s->KDH(), s->padT(),
+                s->IW(), s->OW(), s->KW(), s->KSW(), s->KDW(), s->padL());
+        else
+            DPRINT(prb_str, MKLDNN_VERBOSE_PRB_LEN, prb_written,
+                "mb" DFMT "_ic" DFMT "oc" DFMT
+                "_id" DFMT "od" DFMT "kd" DFMT "sd" DFMT "dd" DFMT "pd" DFMT
+                "_ih" DFMT "oh" DFMT "kh" DFMT "sh" DFMT "dh" DFMT "ph" DFMT
+                "_iw" DFMT "ow" DFMT "kw" DFMT "sw" DFMT "dw" DFMT "pw" DFMT,
+                s->MB(), s->IC(), s->OC(),
+                s->ID(), s->OD(), s->KD(), s->KSD(), s->KDD(), s->padFront(),
+                s->IH(), s->OH(), s->KH(), s->KSH(), s->KDH(), s->padT(),
+                s->IW(), s->OW(), s->KW(), s->KSW(), s->KDW(), s->padL());
+    } else {
+        if (s->with_groups())
+            DPRINT(prb_str, MKLDNN_VERBOSE_PRB_LEN, prb_written,
+                "mb" DFMT "_g" DFMT "ic" DFMT "oc" DFMT
+                "_ih" DFMT "oh" DFMT "kh" DFMT "sh" DFMT "dh" DFMT "ph" DFMT
+                "_iw" DFMT "ow" DFMT "kw" DFMT "sw" DFMT "dw" DFMT "pw" DFMT,
+                s->MB(), s->G(), s->IC(), s->OC(),
+                s->IH(), s->OH(), s->KH(), s->KSH(), s->KDH(), s->padT(),
+                s->IW(), s->OW(), s->KW(), s->KSW(), s->KDW(), s->padL());
+        else
+            DPRINT(prb_str, MKLDNN_VERBOSE_PRB_LEN, prb_written,
+                "mb" DFMT "_ic" DFMT "oc" DFMT
+                "_ih" DFMT "oh" DFMT "kh" DFMT "sh" DFMT "dh" DFMT "ph" DFMT
+                "_iw" DFMT "ow" DFMT "kw" DFMT "sw" DFMT "dw" DFMT "pw" DFMT,
+                s->MB(), s->IC(), s->OC(),
+                s->IH(), s->OH(), s->KH(), s->KSH(), s->KDH(), s->padT(),
+                s->IW(), s->OW(), s->KW(), s->KSW(), s->KDW(), s->padL());
+    }
+
+    verbose_templ(buffer, s->kind(), s->name(), s->desc()->prop_kind, dat_str,
+            aux_str, prb_str);
+}
+
+template <typename pd_t> static void init_info_shuffle(pd_t *s, char *buffer) {
+    DECL_DAT_AUX_PRB_STRS();
+
+    auto md = s->is_fwd() ? s->src_md() : s->diff_dst_md();
+
+    if (1) { // data
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "data_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+
+    DPRINT(aux_str, MKLDNN_VERBOSE_AUX_LEN, aux_written,
+            "axis:%d group_size:" DFMT, s->axis(), s->group_size());
+
+    mkldnn_md2dim_str(prb_str, MKLDNN_VERBOSE_PRB_LEN, md);
+
+    verbose_templ(buffer, s->kind(), s->name(), s->desc()->prop_kind, dat_str,
+            aux_str, prb_str);
+}
+
+template <typename pd_t> static void init_info_eltwise(pd_t *s, char *buffer) {
+    DECL_DAT_AUX_PRB_STRS();
+
+    if (1) { // data
+        auto md = s->src_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "data_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // diff data
+        auto md = s->diff_src_md();
+        if (md) {
+            DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " diff_");
+            int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                    MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+            if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+        }
+    }
+
+    DPRINT(aux_str, MKLDNN_VERBOSE_AUX_LEN, aux_written,
+            "alg:%s", mkldnn_alg_kind2str(s->desc()->alg_kind));
+
+    mkldnn_md2dim_str(prb_str, MKLDNN_VERBOSE_PRB_LEN, s->src_md());
+
+    verbose_templ(buffer, s->kind(), s->name(), s->desc()->prop_kind, dat_str,
+            aux_str, prb_str);
+}
+
+template <typename pd_t> static void init_info_iprod(pd_t *s, char *buffer) {
+    DECL_DAT_AUX_PRB_STRS();
+
+    if (1) { // src
+        auto md = s->desc()->prop_kind == prop_kind::backward_data
+            ? s->diff_src_md() : s->src_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "src_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // wei
+        auto md = s->desc()->prop_kind == prop_kind::backward_weights
+            ? s->diff_weights_md() : s->weights_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " wei_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // bia
+        auto md = s->desc()->prop_kind == prop_kind::backward_weights
+            ? s->diff_weights_md(1) : s->weights_md(1);
+        if (md) {
+            DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " bia_");
+            int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                    MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+            if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+        }
+    }
+    if (1) { // dst
+        auto md = !s->is_fwd() ? s->diff_dst_md() : s->dst_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " dst_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+
+    DPRINT(prb_str, MKLDNN_VERBOSE_PRB_LEN, prb_written,
+            "mb" DFMT "ic" DFMT "oc" DFMT, s->MB(), s->IC_total(), s->OC());
+
+    verbose_templ(buffer, s->kind(), s->name(), s->desc()->prop_kind, dat_str,
+            aux_str, prb_str);
+}
+
+template <typename pd_t> static void init_info_lrn(pd_t *s, char *buffer) {
+    DECL_DAT_AUX_PRB_STRS();
+
+    if (1) { // data
+        auto md = s->src_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "data_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // diff data
+        auto md = s->diff_src_md();
+        if (md) {
+            DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " diff_");
+            int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                    MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+            if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+        }
+    }
+
+    DPRINT(aux_str, MKLDNN_VERBOSE_AUX_LEN, aux_written,
+            "alg:%s", mkldnn_alg_kind2str(s->desc()->alg_kind));
+
+    format_prb_desc_str(prb_str, MKLDNN_VERBOSE_PRB_LEN, s->src_md());
+
+    verbose_templ(buffer, s->kind(), s->name(), s->desc()->prop_kind, dat_str,
+            aux_str, prb_str);
+}
+
+template <typename pd_t> static void init_info_mem(pd_t *s, char *buffer) {
+    DECL_DAT_AUX_PRB_STRS();
+
+    if (1) { // src
+        auto md = s->src_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "src_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // dst
+        auto md = s->dst_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " dst_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+
+    DPRINT(aux_str, MKLDNN_VERBOSE_AUX_LEN, aux_written,
+            "num:%d", s->n_inputs());
+
+    mkldnn_md2dim_str(prb_str, MKLDNN_VERBOSE_PRB_LEN, s->dst_md());
+
+    verbose_templ(buffer, s->kind(), s->name(), prop_kind::undef, dat_str,
+            aux_str, prb_str);
+}
+
+template <typename pd_t> static void init_info_pool(pd_t *s, char *buffer) {
+    DECL_DAT_AUX_PRB_STRS();
+
+    if (1) { // src
+        auto md = s->is_fwd() ? s->src_md() : s->diff_src_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "src_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // dst
+        auto md = s->is_fwd() ? s->dst_md() : s->diff_dst_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " dst_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // ws
+        auto md = s->workspace_md();
+        if (md) {
+            DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " ws_");
+            int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                    MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+            if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+        }
+    }
+
+    DPRINT(aux_str, MKLDNN_VERBOSE_AUX_LEN, aux_written,
+            "alg:%s", mkldnn_alg_kind2str(s->desc()->alg_kind));
+
+    if (s->is_3d()) {
+        DPRINT(prb_str, MKLDNN_VERBOSE_PRB_LEN, prb_written,
+            "mb" DFMT "ic" DFMT "_"
+            "id" DFMT "od" DFMT "kd" DFMT "sd" DFMT "pd" DFMT "_"
+            "ih" DFMT "oh" DFMT "kh" DFMT "sh" DFMT "ph" DFMT "_"
+            "iw" DFMT "ow" DFMT "kw" DFMT "sw" DFMT "pw" DFMT "",
+            s->MB(), s->C(),
+            s->ID(), s->OD(), s->KD(), s->KSD(), s->padFront(),
+            s->IH(), s->OH(), s->KH(), s->KSH(), s->padT(),
+            s->IW(), s->OW(), s->KW(), s->KSW(), s->padL());
+    } else {
+        DPRINT(prb_str, MKLDNN_VERBOSE_PRB_LEN, prb_written,
+            "mb" DFMT "ic" DFMT "_"
+            "ih" DFMT "oh" DFMT "kh" DFMT "sh" DFMT "ph" DFMT "_"
+            "iw" DFMT "ow" DFMT "kw" DFMT "sw" DFMT "pw" DFMT,
+            s->MB(), s->C(),
+            s->IH(), s->OH(), s->KH(), s->KSH(), s->padT(),
+            s->IW(), s->OW(), s->KW(), s->KSW(), s->padL());
+    }
+
+    verbose_templ(buffer, s->kind(), s->name(), s->desc()->prop_kind, dat_str,
+            aux_str, prb_str);
+}
+
+template <typename pd_t> static void init_info_softmax(pd_t *s, char *buffer) {
+    DECL_DAT_AUX_PRB_STRS();
+
+    if (1) { // data
+        auto md = s->dst_md();
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "data_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // diff data
+        auto md = s->diff_src_md();
+        if (md) {
+            DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " diff_");
+            int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                    MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+            if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+        }
+    }
+
+    mkldnn_md2dim_str(prb_str, MKLDNN_VERBOSE_PRB_LEN, s->dst_md());
+
+    verbose_templ(buffer, s->kind(), s->name(), s->desc()->prop_kind, dat_str,
+            aux_str, prb_str);
+}
+
+template <typename pd_t> static void init_info_rnn(pd_t *s, char *buffer) {
+    DECL_DAT_AUX_PRB_STRS();
+
+    if (1) { // src layer
+        auto md = s->is_fwd() ? s->src_md(0) : s->diff_src_md(0);
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "src_layer_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // src iter
+        auto md = s->is_fwd() ? s->src_md(1) : s->diff_src_md(1);
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "src_iter_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // wei_layer
+        auto md = s->is_fwd() ? s->weights_md(0) : s->diff_weights_md(0);
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " wei_layer_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // wei_iter
+        auto md = s->is_fwd() ? s->weights_md(1) : s->diff_weights_md(1);
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " wei_layer_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // bias
+        auto md = s->is_fwd() ? s->weights_md(2) : s->diff_weights_md(2);
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, " bias_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // dst layer
+        auto md = s->is_fwd() ? s->dst_md(0) : s->diff_dst_md(0);
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "dst_layer_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+    if (1) { // dst iter
+        auto md = s->is_fwd() ? s->dst_md(1) : s->diff_dst_md(1);
+        DPRINT(dat_str, MKLDNN_VERBOSE_DAT_LEN, dat_written, "dst_iter_");
+        int l = mkldnn_md2fmt_str(dat_str + dat_written,
+                MKLDNN_VERBOSE_DAT_LEN - dat_written, md);
+        if (l >= 0) dat_written += l; else clear_buf(dat_str, dat_written);
+    }
+
+    alg_kind_t alg_kind = s->cell_kind();
+    rnn_direction_t rnn_dir = s->direction();
+    DPRINT(aux_str, MKLDNN_VERBOSE_AUX_LEN, aux_written,
+            "alg:%s_%s", mkldnn_alg_kind2str(alg_kind),
+            mkldnn_rnn_direction2str(rnn_dir));
+
+    DPRINT(prb_str, MKLDNN_VERBOSE_PRB_LEN, prb_written,
+            "l" DFMT "t" DFMT "mb" DFMT
+            "sic" DFMT "slc" DFMT "dic" DFMT "dlc" DFMT,
+             s->L(), s->T(), s->MB(),
+             s->SIC(), s->SLC(), s->DIC(), s->DLC());
+
+    verbose_templ(buffer, s->kind(), s->name(), s->desc()->prop_kind, dat_str,
+            aux_str, prb_str);
+}
+
+#undef DPRINT
+
+#else // !defined(DISABLE_VERBOSE)
+
+#define DEFINE_STUB(name) \
+    template <typename pd_t> \
+    static void CONCAT2(init_info_, name)(pd_t *s, char *buffer) \
+    { UNUSED(s); UNUSED(buffer); }
+
+DEFINE_STUB(bnorm);
+DEFINE_STUB(conv);
+DEFINE_STUB(eltwise);
+DEFINE_STUB(iprod);
+DEFINE_STUB(lrn);
+DEFINE_STUB(mem);
+DEFINE_STUB(pool);
+DEFINE_STUB(softmax);
+DEFINE_STUB(rnn);
+DEFINE_STUB(shuffle);
+#undef DEFINE_STUB
+
+#endif // !defined(DISABLE_VERBOSE)
+}
+
+void init_info(batch_normalization_pd_t *s, char *b)
+{ init_info_bnorm(s, b); }
+void init_info(concat_pd_t *s, char *b)
+{ init_info_mem(s, b); }
+void init_info(convolution_pd_t *s, char *b)
+{ init_info_conv(s, b); }
+void init_info(deconvolution_pd_t *s, char *b)
+{ init_info_conv(s, b); }
+void init_info(eltwise_pd_t *s, char *b)
+{ init_info_eltwise(s, b); }
+void init_info(inner_product_pd_t *s, char *b)
+{ init_info_iprod(s, b); }
+void init_info(lrn_pd_t *s, char *b)
+{ init_info_lrn(s, b); }
+void init_info(pooling_pd_t *s, char *b)
+{ init_info_pool(s, b); }
+void init_info(reorder_pd_t *s, char *b)
+{ init_info_mem(s, b); }
+void init_info(rnn_pd_t *s, char *b)
+{ init_info_rnn(s, b); }
+void init_info(shuffle_pd_t *s, char *b)
+{ init_info_shuffle(s, b); }
+void init_info(softmax_pd_t *s, char *b)
+{ init_info_softmax(s, b); }
+void init_info(sum_pd_t *s, char *b)
+{ init_info_mem(s, b); }
+
+}
+}
+
+mkldnn_status_t mkldnn_set_verbose(int level) {
+    using namespace mkldnn::impl::status;
+    if (level < 0 || level > 2) return invalid_arguments;
+    mkldnn::impl::verbose.level = level;
+    mkldnn::impl::initialized = true;
+    return success;
+}
+
+const mkldnn_version_t *mkldnn_version() {
+    static mkldnn_version_t ver = {
+        MKLDNN_VERSION_MAJOR,
+        MKLDNN_VERSION_MINOR,
+        MKLDNN_VERSION_PATCH,
+        MKLDNN_VERSION_HASH};
+    return &ver;
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/common/verbose.hpp b/thirdparty/oidn/mkl-dnn/src/common/verbose.hpp
new file mode 100644
index 0000000000..e3049750cb
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/verbose.hpp
@@ -0,0 +1,62 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef VERBOSE_HPP
+#define VERBOSE_HPP
+
+#include <stdio.h>
+#include <cinttypes>
+
+#include "mkldnn_debug.h"
+#include "c_types_map.hpp"
+#include "utils.hpp"
+#include "z_magic.hpp"
+
+namespace mkldnn {
+namespace impl {
+
+struct verbose_t {
+    int level;
+};
+
+const verbose_t *mkldnn_verbose();
+double get_msec();
+const char *get_isa_info();
+
+#if !defined(DISABLE_VERBOSE)
+#define MKLDNN_VERBOSE_BUF_LEN 1024
+#else
+#define MKLDNN_VERBOSE_BUF_LEN 1
+#endif
+
+void init_info(batch_normalization_pd_t *s, char *buffer);
+void init_info(concat_pd_t *s, char *buffer);
+void init_info(convolution_pd_t *s, char *buffer);
+void init_info(deconvolution_pd_t *s, char *buffer);
+void init_info(eltwise_pd_t *s, char *buffer);
+void init_info(inner_product_pd_t *s, char *buffer);
+void init_info(lrn_pd_t *s, char *buffer);
+void init_info(pooling_pd_t *s, char *buffer);
+void init_info(reorder_pd_t *s, char *buffer);
+void init_info(rnn_pd_t *s, char *buffer);
+void init_info(shuffle_pd_t *s, char *buffer);
+void init_info(softmax_pd_t *s, char *buffer);
+void init_info(sum_pd_t *s, char *buffer);
+
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/common/z_magic.hpp b/thirdparty/oidn/mkl-dnn/src/common/z_magic.hpp
new file mode 100644
index 0000000000..520bd4710b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/common/z_magic.hpp
@@ -0,0 +1,46 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef Z_MAGIC_HPP
+#define Z_MAGIC_HPP
+
+#define CHAIn2(a,b) a b
+#define CHAIN2(a,b) CHAIn2(a,b)
+
+#define CONCAt2(a,b) a ## b
+#define CONCAT2(a,b) CONCAt2(a,b)
+
+#define STRINGIFy(s) #s
+#define STRINGIFY(s) STRINGIFy(s)
+
+#ifdef _MSC_VER
+#   define PRAGMA_MACRo(x) __pragma(x)
+#   define PRAGMA_MACRO(x) PRAGMA_MACRo(x)
+#else
+#   define PRAGMA_MACRo(x) _Pragma(#x)
+#   define PRAGMA_MACRO(x) PRAGMA_MACRo(x)
+#endif
+
+#define UNUSED(x) ((void)x)
+#define MAYBE_UNUSED(x) UNUSED(x)
+
+#if defined(_WIN32) && !defined(__GNUC__)
+#define __PRETTY_FUNCTION__ __FUNCSIG__
+#endif
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_barrier.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_barrier.cpp
new file mode 100644
index 0000000000..7cf7822d90
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_barrier.cpp
@@ -0,0 +1,112 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "cpu_barrier.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace simple_barrier {
+
+void generate(jit_generator &code, Xbyak::Reg64 reg_ctx,
+            Xbyak::Reg64 reg_nthr) {
+#   define BAR_CTR_OFF offsetof(ctx_t, ctr)
+#   define BAR_SENSE_OFF offsetof(ctx_t, sense)
+    using namespace Xbyak;
+
+    Xbyak::Reg64 reg_tmp = [&]() {
+        /* returns register which is neither reg_ctx nor reg_nthr */
+        Xbyak::Reg64 regs[] = { util::rax, util::rbx, util::rcx };
+        for (size_t i = 0; i < sizeof(regs) / sizeof(regs[0]); ++i)
+            if (!utils::one_of(regs[i], reg_ctx, reg_nthr))
+                return regs[i];
+        return regs[0]; /* should not happen */
+    }();
+
+    Label barrier_exit_label, barrier_exit_restore_label, spin_label;
+
+    code.cmp(reg_nthr, 1);
+    code.jbe(barrier_exit_label);
+
+    code.push(reg_tmp);
+
+    /* take and save current sense */
+    code.mov(reg_tmp, code.ptr[reg_ctx + BAR_SENSE_OFF]);
+    code.push(reg_tmp);
+    code.mov(reg_tmp, 1);
+
+    if (mayiuse(avx512_mic)) {
+        code.prefetchwt1(code.ptr[reg_ctx + BAR_CTR_OFF]);
+        code.prefetchwt1(code.ptr[reg_ctx + BAR_CTR_OFF]);
+    }
+
+    code.lock(); code.xadd(code.ptr[reg_ctx + BAR_CTR_OFF], reg_tmp);
+    code.add(reg_tmp, 1);
+    code.cmp(reg_tmp, reg_nthr);
+    code.pop(reg_tmp); /* restore previous sense */
+    code.jne(spin_label);
+
+    /* the last thread {{{ */
+    code.mov(code.qword[reg_ctx + BAR_CTR_OFF], 0); // reset ctx
+
+    // notify waiting threads
+    code.not_(reg_tmp);
+    code.mov(code.ptr[reg_ctx + BAR_SENSE_OFF], reg_tmp);
+    code.jmp(barrier_exit_restore_label);
+    /* }}} the last thread */
+
+    code.CodeGenerator::L(spin_label);
+    code.pause();
+    code.cmp(reg_tmp, code.ptr[reg_ctx + BAR_SENSE_OFF]);
+    code.je(spin_label);
+
+    code.CodeGenerator::L(barrier_exit_restore_label);
+    code.pop(reg_tmp);
+
+    code.CodeGenerator::L(barrier_exit_label);
+#    undef BAR_CTR_OFF
+#    undef BAR_SENSE_OFF
+}
+
+/** jit barrier generator */
+struct jit_t: public jit_generator {
+    void (*barrier)(ctx_t *ctx, size_t nthr);
+
+    jit_t() {
+        generate(*this, abi_param1, abi_param2);
+        ret();
+        barrier = reinterpret_cast<decltype(barrier)>(const_cast<uint8_t*>(
+                    this->getCode()));
+    }
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_t)
+};
+
+void barrier(ctx_t *ctx, int nthr) {
+    static jit_t j; /* XXX: constructed on load ... */
+    j.barrier(ctx, nthr);
+}
+
+}
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_barrier.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_barrier.hpp
new file mode 100644
index 0000000000..0f55e33aa8
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_barrier.hpp
@@ -0,0 +1,60 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_BARRIER_HPP
+#define CPU_BARRIER_HPP
+
+#include <assert.h>
+
+#include "jit_generator.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace simple_barrier {
+
+STRUCT_ALIGN(64,
+struct ctx_t {
+    enum { CACHE_LINE_SIZE = 64 };
+    volatile size_t ctr;
+    char pad1[CACHE_LINE_SIZE - 1 * sizeof(size_t)];
+    volatile size_t sense;
+    char pad2[CACHE_LINE_SIZE - 1 * sizeof(size_t)];
+});
+
+inline void ctx_init(ctx_t *ctx) { *ctx = utils::zero<ctx_t>(); }
+void barrier(ctx_t *ctx, int nthr);
+
+/** injects actual barrier implementation into another jitted code
+ * @params:
+ *   code      -- jit_generator object where the barrier is to be injected
+ *   reg_ctx   -- read-only register with pointer to the barrier context
+ *   reg_nnthr -- read-only register with the # of synchronizing threads
+ */
+void generate(jit_generator &code, Xbyak::Reg64 reg_ctx,
+        Xbyak::Reg64 reg_nthr);
+
+}
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_batch_normalization_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_batch_normalization_pd.hpp
new file mode 100644
index 0000000000..1ed5ad57b9
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_batch_normalization_pd.hpp
@@ -0,0 +1,40 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_BATCH_NORMALIZATION_PD_HPP
+#define CPU_BATCH_NORMALIZATION_PD_HPP
+
+#include "batch_normalization_pd.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_batch_normalization_fwd_pd_t: public batch_normalization_fwd_pd_t {
+    using batch_normalization_fwd_pd_t::batch_normalization_fwd_pd_t;
+};
+
+struct cpu_batch_normalization_bwd_pd_t: public batch_normalization_bwd_pd_t {
+    using batch_normalization_bwd_pd_t::batch_normalization_bwd_pd_t;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_batch_normalization_utils.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_batch_normalization_utils.cpp
new file mode 100644
index 0000000000..b8d5c4fcaf
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_batch_normalization_utils.cpp
@@ -0,0 +1,140 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "utils.hpp"
+
+#include "jit_generator.hpp"
+
+#include "cpu_batch_normalization_utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+namespace bnorm_utils {
+
+void cache_balance(size_t working_set_size, dim_t C_blks,
+        dim_t &C_blks_per_iter, int64_t &iters) {
+    int nthrs = mkldnn_get_max_threads();
+    int l3_size = get_cache_size(3, true) * nthrs / 2;
+
+    C_blks_per_iter = l3_size / working_set_size;
+
+    if (C_blks_per_iter == 0)
+        C_blks_per_iter = 1;
+    if (C_blks_per_iter > C_blks)
+        C_blks_per_iter = C_blks;
+
+    iters = (C_blks + C_blks_per_iter - 1) / C_blks_per_iter;
+}
+
+bool thread_balance(bool do_blocking, bool spatial_thr_allowed, int ithr,
+        int nthr, dim_t N, dim_t C_blks, dim_t SP, int &C_ithr, int &C_nthr,
+        dim_t &C_blk_s, dim_t &C_blk_e, int &N_ithr, int &N_nthr, dim_t &N_s,
+        dim_t &N_e, int &S_ithr, int &S_nthr, dim_t &S_s, dim_t &S_e) {
+    if (nthr <= C_blks || !mkldnn_thr_syncable()) {
+        C_ithr = ithr; C_nthr = nthr;
+        N_ithr = 0; N_nthr = 1;
+        S_ithr = 0; S_nthr = 1;
+        N_s = 0; N_e = N; S_s = 0; S_e = SP;
+        balance211(C_blks, C_nthr, C_ithr, C_blk_s, C_blk_e);
+    } else {
+        if (do_blocking) {
+            N_nthr = (int)nstl::min<dim_t>(N, nthr);
+            C_nthr = (int)nstl::min<dim_t>(C_blks, nthr / N_nthr);
+            S_nthr = (int)nstl::min<dim_t>(SP, nthr / (C_nthr * N_nthr));
+        } else {
+            C_nthr = (int)math::gcd((dim_t)nthr, C_blks);
+            N_nthr = (int)nstl::min<dim_t>(N, nthr / C_nthr);
+            S_nthr = (int)nstl::min<dim_t>(SP, nthr / (C_nthr * N_nthr));
+        }
+
+        if (!spatial_thr_allowed)
+            S_nthr = 1;
+
+        if (S_nthr < 1) S_nthr = 1;
+        if (ithr < C_nthr * N_nthr * S_nthr) {
+            N_ithr = (ithr / S_nthr) % N_nthr ;
+            C_ithr = ithr / (N_nthr * S_nthr);
+            S_ithr = ithr % S_nthr;
+            balance211(C_blks, C_nthr, C_ithr, C_blk_s, C_blk_e);
+            balance211(N, N_nthr, N_ithr, N_s, N_e);
+            balance211(SP, S_nthr, S_ithr, S_s, S_e);
+        } else {
+            S_ithr = N_ithr = C_ithr = -ithr;
+            S_s = S_e = N_s = N_e = C_blk_s = C_blk_e = -1;
+        }
+    }
+
+    // spatial_thr_allowed is meant to help maintain
+    // consistent decisions about spatial threading
+    // between mutiple invocations of this routine.
+    // It is caller's responsibility to check the
+    // return value and pass it as a flag to the
+    // next call if needed.
+    if (S_nthr == 1)
+        spatial_thr_allowed = false;
+
+    return spatial_thr_allowed;
+}
+
+bool is_spatial_thr(const batch_normalization_pd_t *bdesc, int simd_w,
+        int data_size) {
+    if (!mkldnn_thr_syncable()) return false;
+
+    dim_t nthr = mkldnn_get_max_threads();
+    dim_t SP = bdesc->W() * bdesc->D() * bdesc->H();
+    dim_t C_PADDED = memory_desc_wrapper(bdesc->src_md())
+        .padded_dims()[1];
+    assert(C_PADDED % simd_w == 0);
+
+    size_t data = bdesc->MB() * C_PADDED * SP * data_size;
+    size_t l3_size_ = get_cache_size(3, true) * nthr / 2;
+    bool do_blocking = (data >= l3_size_ / 2 && l3_size_ > 0);
+    dim_t C_blks_per_iter{ 1 }, iters{ 1 };
+    dim_t C_blks = C_PADDED / simd_w;
+
+    if (do_blocking) {
+        int num_tensors = bdesc->is_fwd() ? 1 : 2;
+        size_t working_set_size
+            = (bdesc->MB() * SP * simd_w * data_size) * num_tensors;
+        cache_balance(working_set_size, C_blks, C_blks_per_iter, iters);
+    }
+
+    // Spatial threading decision made in this function shall be consistent
+    // with thread_balance() behavior.
+    C_blks = do_blocking ? C_blks_per_iter : C_blks;
+
+    if (nthr <= C_blks) return false;
+
+    dim_t S_nthr = 1;
+    if (do_blocking) {
+        dim_t N_nthr = nstl::min(bdesc->MB(), nthr);
+        dim_t C_nthr = nstl::min(C_blks, nthr / N_nthr);
+        S_nthr = nstl::min(SP, nthr / (C_nthr * N_nthr));
+    } else {
+        dim_t C_nthr = math::gcd(nthr, C_blks);
+        dim_t N_nthr = nstl::min(bdesc->MB(), nthr / C_nthr);
+        S_nthr = nstl::min(SP, nthr / (C_nthr * N_nthr));
+    }
+
+    return S_nthr > 1;
+}
+
+}
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_batch_normalization_utils.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_batch_normalization_utils.hpp
new file mode 100644
index 0000000000..0daef0716c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_batch_normalization_utils.hpp
@@ -0,0 +1,43 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_BATCH_NORMALIZATION_UTILS_HPP
+#define CPU_BATCH_NORMALIZATION_UTILS_HPP
+
+#include "batch_normalization_pd.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+namespace bnorm_utils {
+
+void cache_balance(size_t working_set_size, dim_t C_blks,
+        dim_t &C_blks_per_iter, int64_t &iters);
+
+bool thread_balance(bool do_blocking, bool spatial_thr_allowed, int ithr,
+        int nthr, dim_t N, dim_t C_blks, dim_t SP, int &C_ithr, int &C_nthr,
+        dim_t &C_blk_s, dim_t &C_blk_e, int &N_ithr, int &N_nthr, dim_t &N_s,
+        dim_t &N_e, int &S_ithr, int &S_nthr, dim_t &S_s, dim_t &S_e);
+
+bool is_spatial_thr(const batch_normalization_pd_t *bdesc, int simd_w,
+        int data_size);
+
+}
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_concat.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_concat.cpp
new file mode 100644
index 0000000000..b926491202
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_concat.cpp
@@ -0,0 +1,51 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "cpu_engine.hpp"
+
+/*
+#include "cpu/ref_concat.hpp"
+#include "cpu/simple_concat.hpp"
+*/
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using cpd_create_f = mkldnn::impl::engine_t::concat_primitive_desc_create_f;
+
+namespace {
+#define INSTANCE(...) __VA_ARGS__::pd_t::create
+static const cpd_create_f cpu_concat_impl_list[] = {
+    /*
+    INSTANCE(simple_concat_t<data_type::f32>),
+    INSTANCE(simple_concat_t<data_type::u8>),
+    INSTANCE(simple_concat_t<data_type::s8>),
+    INSTANCE(simple_concat_t<data_type::s32>),
+    INSTANCE(ref_concat_t),
+    */
+    nullptr,
+};
+#undef INSTANCE
+}
+
+const cpd_create_f *cpu_engine_t::get_concat_implementation_list() const {
+    return cpu_concat_impl_list;
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_concat_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_concat_pd.hpp
new file mode 100644
index 0000000000..0b01bcf163
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_concat_pd.hpp
@@ -0,0 +1,41 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_CONCAT_PD_HPP
+#define CPU_CONCAT_PD_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "concat_pd.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_concat_pd_t: public concat_pd_t {
+    using concat_pd_t::concat_pd_t;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_convolution_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_convolution_pd.hpp
new file mode 100644
index 0000000000..52a38a2294
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_convolution_pd.hpp
@@ -0,0 +1,74 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_CONVOLUTION_PD_HPP
+#define CPU_CONVOLUTION_PD_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "convolution_pd.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_convolution_fwd_pd_t: public convolution_fwd_pd_t {
+    using convolution_fwd_pd_t::convolution_fwd_pd_t;
+
+    bool has_padded_dst() const {
+        memory_desc_wrapper dst_d(&dst_md_);
+        return OC() != dst_d.padded_dims()[1];
+    }
+
+    bool wants_padded_bias() const {
+        if (!with_bias()) return false;
+        return has_padded_dst();
+    }
+
+    bool wants_zero_pad_dst(bool jit_impl = true) const {
+        if (!has_padded_dst()) return false;
+        const auto &po = attr()->post_ops_;
+        int idx;
+        if ((idx = po.find(primitive_kind::eltwise)) == -1) return false;
+        return !math::eltwise_fwd_preserves_zero(po.entry_[idx].eltwise.alg,
+                jit_impl);
+    }
+};
+
+struct cpu_convolution_bwd_data_pd_t: public convolution_bwd_data_pd_t {
+    using convolution_bwd_data_pd_t::convolution_bwd_data_pd_t;
+};
+
+struct cpu_convolution_bwd_weights_pd_t: public convolution_bwd_weights_pd_t {
+    using convolution_bwd_weights_pd_t::convolution_bwd_weights_pd_t;
+
+    bool wants_padded_bias() const {
+        if (!with_bias()) return false;
+        memory_desc_wrapper diff_dst_d(&diff_dst_md_);
+        return OC() != diff_dst_d.padded_dims()[1];
+    }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_deconvolution_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_deconvolution_pd.hpp
new file mode 100644
index 0000000000..164c8601d7
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_deconvolution_pd.hpp
@@ -0,0 +1,46 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_DECONVOLUTION_PD_HPP
+#define CPU_DECONVOLUTION_PD_HPP
+
+#include <assert.h>
+
+#include "deconvolution_pd.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_deconvolution_fwd_pd_t: public deconvolution_fwd_pd_t {
+    using deconvolution_fwd_pd_t::deconvolution_fwd_pd_t;
+};
+
+struct cpu_deconvolution_bwd_data_pd_t: public deconvolution_bwd_data_pd_t {
+    using deconvolution_bwd_data_pd_t::deconvolution_bwd_data_pd_t;
+};
+
+struct cpu_deconvolution_bwd_weights_pd_t: public deconvolution_bwd_weights_pd_t {
+    using deconvolution_bwd_weights_pd_t::deconvolution_bwd_weights_pd_t;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_eltwise_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_eltwise_pd.hpp
new file mode 100644
index 0000000000..c52f00026e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_eltwise_pd.hpp
@@ -0,0 +1,45 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_ELTWISE_PD_HPP
+#define CPU_ELTWISE_PD_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "eltwise_pd.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_eltwise_fwd_pd_t: public eltwise_fwd_pd_t {
+    using eltwise_fwd_pd_t::eltwise_fwd_pd_t;
+};
+
+struct cpu_eltwise_bwd_pd_t: public eltwise_bwd_pd_t {
+    using eltwise_bwd_pd_t::eltwise_bwd_pd_t;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_engine.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_engine.cpp
new file mode 100644
index 0000000000..ce0a3667ad
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_engine.cpp
@@ -0,0 +1,324 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "type_helpers.hpp"
+#include "verbose.hpp"
+
+#include "cpu_engine.hpp"
+#include "cpu_memory.hpp"
+
+//#include "cpu/rnn/ref_rnn.hpp"
+
+//#include "cpu/jit_avx512_core_x8s8s32x_1x1_convolution.hpp"
+//#include "cpu/jit_avx512_common_1x1_convolution.hpp"
+#include "cpu/jit_avx512_core_fp32_wino_conv_4x3.hpp"
+#include "cpu/jit_avx512_common_convolution_winograd.hpp"
+//#include "cpu/jit_avx512_core_x8s8s32x_convolution.hpp"
+#include "cpu/jit_avx512_common_convolution.hpp"
+//#include "cpu/jit_avx2_1x1_convolution.hpp"
+//#include "cpu/jit_sse42_1x1_convolution.hpp"
+#include "cpu/jit_avx2_convolution.hpp"
+#include "cpu/jit_sse42_convolution.hpp"
+//#include "cpu/gemm_convolution.hpp"
+//#include "cpu/gemm_x8s8s32x_convolution.hpp"
+//#include "cpu/ref_convolution.hpp"
+//#include "cpu/jit_avx512_core_x8s8s32x_deconvolution.hpp"
+//#include "cpu/jit_avx512_core_x8s8s32x_1x1_deconvolution.hpp"
+//#include "cpu/ref_deconvolution.hpp"
+//#include "cpu/ref_shuffle.hpp"
+//#include "cpu/jit_uni_eltwise.hpp"
+//#include "cpu/ref_eltwise.hpp"
+//#include "cpu/ref_softmax.hpp"
+#include "cpu/jit_uni_pooling.hpp"
+//#include "cpu/jit_uni_i8i8_pooling.hpp"
+//#include "cpu/ref_pooling.hpp"
+//#include "cpu/nchw_pooling.hpp"
+//#include "cpu/nhwc_pooling.hpp"
+//#include "cpu/jit_avx512_common_lrn.hpp"
+//#include "cpu/jit_uni_lrn.hpp"
+//#include "cpu/ref_lrn.hpp"
+//#include "cpu/jit_uni_batch_normalization.hpp"
+//#include "cpu/ref_batch_normalization.hpp"
+//#include "cpu/ncsp_batch_normalization.hpp"
+//#include "cpu/nspc_batch_normalization.hpp"
+//#include "cpu/ref_inner_product.hpp"
+//#include "cpu/gemm_inner_product.hpp"
+//#include "cpu/gemm_x8s8s32x_inner_product.hpp"
+//#include "cpu/jit_uni_dw_convolution.hpp"
+//#include "cpu/jit_avx512_core_u8s8s32x_wino_convolution.hpp"
+#include "cpu/jit_avx512_core_fp32_wino_conv_2x3.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+status_t cpu_engine_t::memory_create(memory_t **memory,
+        const memory_desc_t *md, void *handle) {
+    auto _memory = new cpu_memory_t(this, md, handle);
+    if (_memory == nullptr)
+        return status::out_of_memory;
+
+    status_t status = _memory->init();
+    if (status != status::success) {
+        delete _memory;
+        return status;
+    }
+
+    return safe_ptr_assign<memory_t>(*memory, _memory);
+}
+
+using pd_create_f = mkldnn::impl::engine_t::primitive_desc_create_f;
+
+namespace {
+using namespace mkldnn::impl::data_type;
+
+#define INSTANCE(...) &primitive_desc_t::create<__VA_ARGS__::pd_t>
+static const pd_create_f cpu_impl_list[] = {
+    /* RNN */
+    /*
+    INSTANCE(ref_rnn_fwd_f32_t),
+    INSTANCE(ref_rnn_fwd_u8s8_t),
+    INSTANCE(ref_rnn_bwd_f32_t),
+    */
+    /* conv */
+    /*
+    INSTANCE(jit_avx512_common_dw_convolution_fwd_t),
+    INSTANCE(jit_avx512_common_dw_convolution_bwd_data_t),
+    INSTANCE(jit_avx512_common_dw_convolution_bwd_weights_t),
+    INSTANCE(jit_avx512_common_1x1_convolution_fwd_f32_t),
+    INSTANCE(jit_avx512_common_1x1_convolution_bwd_data_f32_t),
+    INSTANCE(jit_avx512_common_1x1_convolution_bwd_weights_t),
+    */
+    INSTANCE(jit_avx512_core_fp32_wino_conv_2x3_fwd_t),
+    INSTANCE(jit_avx512_core_fp32_wino_conv_4x3_fwd_t),
+    //INSTANCE(jit_avx512_core_fp32_wino_conv_4x3_bwd_data_t),
+    //INSTANCE(jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_t),
+    INSTANCE(jit_avx512_common_convolution_winograd_fwd_t),
+    //INSTANCE(jit_avx512_common_convolution_winograd_bwd_data_t),
+    //INSTANCE(jit_avx512_common_convolution_winograd_bwd_weights_t),
+    INSTANCE(jit_avx512_common_convolution_fwd_t<f32>),
+    //INSTANCE(jit_avx512_common_convolution_bwd_data_t<f32>),
+    //INSTANCE(jit_avx512_common_convolution_bwd_weights_t<f32>),
+    /*
+    INSTANCE(jit_avx2_dw_convolution_fwd_t),
+    INSTANCE(jit_avx2_dw_convolution_bwd_data_t),
+    INSTANCE(jit_avx2_dw_convolution_bwd_weights_t),
+    INSTANCE(jit_avx2_1x1_convolution_fwd_t),
+    INSTANCE(jit_avx2_1x1_convolution_bwd_data_t),
+    INSTANCE(jit_avx2_1x1_convolution_bwd_weights_t),
+    INSTANCE(jit_sse42_dw_convolution_fwd_t),
+    INSTANCE(jit_sse42_dw_convolution_bwd_data_t),
+    INSTANCE(jit_sse42_dw_convolution_bwd_weights_t),
+    INSTANCE(jit_sse42_1x1_convolution_fwd_t),
+    */
+    INSTANCE(jit_avx2_convolution_fwd_t),
+    //INSTANCE(jit_avx2_convolution_bwd_data_t),
+    //INSTANCE(jit_avx2_convolution_bwd_weights_t),
+    INSTANCE(jit_sse42_convolution_fwd_t),
+    /*
+    INSTANCE(gemm_convolution_fwd_t),
+    INSTANCE(gemm_convolution_bwd_data_t),
+    INSTANCE(gemm_convolution_bwd_weights_t),
+    INSTANCE(ref_convolution_fwd_t<f32>),
+    INSTANCE(ref_convolution_bwd_data_t<f32, f32, f32, f32>),
+    INSTANCE(ref_convolution_bwd_weights_t<f32, f32, f32, f32>),
+    */
+    /* conv (int) */
+    /*
+    INSTANCE(jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<f32>),
+    INSTANCE(jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<s32>),
+    INSTANCE(jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<s8>),
+    INSTANCE(jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<u8>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<u8,f32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<u8,s32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<u8,u8>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<u8,s8>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<s8,f32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<s8,s32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<s8,u8>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<s8,s8>),
+    INSTANCE(jit_avx512_core_x8s8s32x_convolution_fwd_t<u8,f32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_convolution_fwd_t<u8,s32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_convolution_fwd_t<u8,u8>),
+    INSTANCE(jit_avx512_core_x8s8s32x_convolution_fwd_t<u8,s8>),
+    INSTANCE(jit_avx512_core_x8s8s32x_convolution_fwd_t<s8,f32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_convolution_fwd_t<s8,s32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_convolution_fwd_t<s8,u8>),
+    INSTANCE(jit_avx512_core_x8s8s32x_convolution_fwd_t<s8,s8>),
+    INSTANCE(_gemm_x8s8s32x_convolution_fwd_t<u8, s32>),
+    INSTANCE(_gemm_x8s8s32x_convolution_fwd_t<u8, u8>),
+    INSTANCE(_gemm_x8s8s32x_convolution_fwd_t<u8, s8>),
+    INSTANCE(_gemm_x8s8s32x_convolution_fwd_t<u8, f32>),
+    INSTANCE(_gemm_x8s8s32x_convolution_fwd_t<s8, s32>),
+    INSTANCE(_gemm_x8s8s32x_convolution_fwd_t<s8, u8>),
+    INSTANCE(_gemm_x8s8s32x_convolution_fwd_t<s8, s8>),
+    INSTANCE(_gemm_x8s8s32x_convolution_fwd_t<s8, f32>),
+    INSTANCE(_gemm_u8s8s32x_convolution_bwd_data_t<s32>),
+    INSTANCE(_gemm_u8s8s32x_convolution_bwd_data_t<u8>),
+    INSTANCE(_gemm_u8s8s32x_convolution_bwd_data_t<s8>),
+    INSTANCE(_gemm_u8s8s32x_convolution_bwd_data_t<f32>),
+    INSTANCE(ref_convolution_fwd_t<u8, s8, f32, s32>),
+    INSTANCE(ref_convolution_fwd_t<u8, s8, s32, s32>),
+    INSTANCE(ref_convolution_fwd_t<u8, s8, s8, s32>),
+    INSTANCE(ref_convolution_fwd_t<u8, s8, u8, s32>),
+    INSTANCE(ref_convolution_bwd_data_t<f32, s8, u8, s32>),
+    INSTANCE(ref_convolution_bwd_data_t<s32, s8, u8, s32>),
+    INSTANCE(ref_convolution_bwd_data_t<s8, s8, u8, s32>),
+    INSTANCE(ref_convolution_bwd_data_t<u8, s8, u8, s32>),
+    */
+    /* deconv */
+    /*
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t<u8,f32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t<u8,s32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t<u8,u8>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t<u8,s8>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t<s8,f32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t<s8,s32>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t<s8,u8>),
+    INSTANCE(jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t<s8,s8>),
+    INSTANCE(_jit_avx512_core_x8s8s32x_deconvolution_fwd_t<u8,s32>),
+    INSTANCE(_jit_avx512_core_x8s8s32x_deconvolution_fwd_t<u8,u8>),
+    INSTANCE(_jit_avx512_core_x8s8s32x_deconvolution_fwd_t<u8,s8>),
+    INSTANCE(_jit_avx512_core_x8s8s32x_deconvolution_fwd_t<u8,f32>),
+    INSTANCE(_jit_avx512_core_x8s8s32x_deconvolution_fwd_t<s8,s32>),
+    INSTANCE(_jit_avx512_core_x8s8s32x_deconvolution_fwd_t<s8,u8>),
+    INSTANCE(_jit_avx512_core_x8s8s32x_deconvolution_fwd_t<s8,s8>),
+    INSTANCE(_jit_avx512_core_x8s8s32x_deconvolution_fwd_t<s8,f32>),
+    INSTANCE(ref_deconvolution_bwd_weights_t),
+    INSTANCE(ref_deconvolution_bwd_data_t),
+    INSTANCE(ref_deconvolution_fwd_t),
+    */
+    /* shuffle */
+    /*
+    INSTANCE(ref_shuffle_t<4>), // f32 or s32
+    INSTANCE(ref_shuffle_t<1>), // s8 or u8
+    */
+    /* eltwise */
+    /*
+    INSTANCE(jit_uni_eltwise_fwd_t<avx512_common>),
+    INSTANCE(jit_uni_eltwise_bwd_t<avx512_common>),
+    INSTANCE(jit_uni_eltwise_fwd_t<avx2>),
+    INSTANCE(jit_uni_eltwise_bwd_t<avx2>),
+    INSTANCE(jit_uni_eltwise_fwd_t<sse42>),
+    INSTANCE(jit_uni_eltwise_bwd_t<sse42>),
+    INSTANCE(ref_eltwise_fwd_t<f32>),
+    INSTANCE(ref_eltwise_bwd_t<f32>),
+    */
+    /* eltwise (int) */
+    /*
+    INSTANCE(ref_eltwise_fwd_t<s32>),
+    INSTANCE(ref_eltwise_fwd_t<s8>),
+    INSTANCE(ref_eltwise_fwd_t<u8>),
+    INSTANCE(ref_eltwise_bwd_t<s32>),
+    */
+    /* softmax */
+    /*
+    INSTANCE(ref_softmax_fwd_t<f32>),
+    INSTANCE(ref_softmax_bwd_t<f32>),
+    */
+    /* pool */
+    INSTANCE(jit_uni_pooling_fwd_t<avx512_common>),
+    //INSTANCE(jit_uni_pooling_bwd_t<avx512_common>),
+    INSTANCE(jit_uni_pooling_fwd_t<avx>),
+    //INSTANCE(jit_uni_pooling_bwd_t<avx>),
+    INSTANCE(jit_uni_pooling_fwd_t<sse42>),
+    //INSTANCE(jit_uni_pooling_bwd_t<sse42>),
+    /*
+    INSTANCE(nchw_pooling_fwd_t<f32>),
+    INSTANCE(nchw_pooling_bwd_t<f32>),
+    INSTANCE(nhwc_pooling_fwd_t<f32>),
+    INSTANCE(nhwc_pooling_bwd_t<f32>),
+    INSTANCE(ref_pooling_fwd_t<f32>),
+    INSTANCE(ref_pooling_bwd_t<f32>),
+    */
+    /* pool (int) */
+    /*
+    INSTANCE(jit_uni_i8i8_pooling_fwd_t<avx512_core>),
+    INSTANCE(jit_uni_i8i8_pooling_fwd_t<avx2>),
+    INSTANCE(ref_pooling_fwd_t<s32>),
+    INSTANCE(ref_pooling_fwd_t<s8, s32>),
+    INSTANCE(ref_pooling_fwd_t<u8, s32>),
+    INSTANCE(ref_pooling_bwd_t<s32>),
+    */
+    /* lrn */
+    /*
+    INSTANCE(jit_avx512_common_lrn_fwd_t),
+    INSTANCE(jit_avx512_common_lrn_bwd_t),
+    INSTANCE(jit_uni_lrn_fwd_t<avx2>),
+    INSTANCE(jit_uni_lrn_bwd_t<avx2>),
+    INSTANCE(jit_uni_lrn_fwd_t<sse42>),
+    INSTANCE(ref_lrn_fwd_t<f32>),
+    INSTANCE(ref_lrn_bwd_t<f32>),
+    */
+    /* batch normalization */
+    /*
+    INSTANCE(jit_uni_batch_normalization_fwd_t<avx512_common>),
+    INSTANCE(jit_uni_batch_normalization_bwd_t<avx512_common>),
+    INSTANCE(jit_uni_batch_normalization_fwd_t<avx2>),
+    INSTANCE(jit_uni_batch_normalization_bwd_t<avx2>),
+    INSTANCE(jit_uni_batch_normalization_fwd_t<sse42>),
+    INSTANCE(jit_uni_batch_normalization_bwd_t<sse42>),
+    INSTANCE(ncsp_batch_normalization_fwd_t),
+    INSTANCE(ncsp_batch_normalization_bwd_t),
+    INSTANCE(nspc_batch_normalization_fwd_t),
+    INSTANCE(nspc_batch_normalization_bwd_t),
+    INSTANCE(ref_batch_normalization_fwd_t<f32>),
+    INSTANCE(ref_batch_normalization_bwd_t<f32>),
+    INSTANCE(ref_batch_normalization_fwd_t<s8>),
+    */
+    /* inner product */
+    /*
+    INSTANCE(gemm_inner_product_fwd_t<f32>),
+    INSTANCE(gemm_inner_product_bwd_data_t<f32>),
+    INSTANCE(gemm_inner_product_bwd_weights_t<f32>),
+    INSTANCE(ref_inner_product_fwd_t<f32>),
+    INSTANCE(ref_inner_product_bwd_data_t<f32, f32, f32, f32>),
+    INSTANCE(ref_inner_product_bwd_weights_t<f32>),
+    */
+    /* inner product (int) */
+    /*
+    INSTANCE(gemm_x8s8s32x_inner_product_fwd_t<u8, u8>),
+    INSTANCE(gemm_x8s8s32x_inner_product_fwd_t<u8, s8>),
+    INSTANCE(gemm_x8s8s32x_inner_product_fwd_t<u8, s32>),
+    INSTANCE(gemm_x8s8s32x_inner_product_fwd_t<u8, f32>),
+    INSTANCE(gemm_x8s8s32x_inner_product_fwd_t<s8, u8>),
+    INSTANCE(gemm_x8s8s32x_inner_product_fwd_t<s8, s8>),
+    INSTANCE(gemm_x8s8s32x_inner_product_fwd_t<s8, s32>),
+    INSTANCE(gemm_x8s8s32x_inner_product_fwd_t<s8, f32>),
+    INSTANCE(ref_inner_product_fwd_t<u8, s8, u8, s32>),
+    INSTANCE(ref_inner_product_fwd_t<u8, s8, s8, s32>),
+    INSTANCE(ref_inner_product_fwd_t<u8, s8, s32, s32>),
+    INSTANCE(ref_inner_product_fwd_t<u8, s8, f32, s32>),
+    */
+    /* eol */
+    nullptr,
+};
+#undef INSTANCE
+}
+
+const pd_create_f* cpu_engine_t::get_implementation_list() const {
+    return cpu_impl_list;
+}
+
+cpu_engine_factory_t engine_factory;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_engine.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_engine.hpp
new file mode 100644
index 0000000000..e4c877ee05
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_engine.hpp
@@ -0,0 +1,70 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_ENGINE_HPP
+#define CPU_ENGINE_HPP
+
+#include <assert.h>
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "../common/engine.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+class cpu_engine_t: public engine_t {
+public:
+    cpu_engine_t(): engine_t(engine_kind::cpu) {}
+
+    /* implementation part */
+
+    virtual status_t memory_create(memory_t **memory,
+            const memory_desc_t *md, void *handle) override;
+
+    virtual const concat_primitive_desc_create_f*
+        get_concat_implementation_list() const override;
+    virtual const reorder_primitive_desc_create_f*
+        get_reorder_implementation_list() const override;
+    virtual const sum_primitive_desc_create_f*
+        get_sum_implementation_list() const override;
+    virtual const primitive_desc_create_f*
+        get_implementation_list() const override;
+};
+
+class cpu_engine_factory_t: public engine_factory_t {
+public:
+    virtual size_t count() const override { return 1; }
+    virtual engine_kind_t kind() const override { return engine_kind::cpu; }
+    virtual status_t engine_create(engine_t **engine,
+            size_t index) const override {
+        assert(index == 0);
+        *engine = new cpu_engine_t();
+        return status::success;
+    };
+};
+
+extern cpu_engine_factory_t engine_factory;
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_inner_product_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_inner_product_pd.hpp
new file mode 100644
index 0000000000..5880d3450c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_inner_product_pd.hpp
@@ -0,0 +1,84 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_INNER_PRODUCT_PD_HPP
+#define CPU_INNER_PRODUCT_PD_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "inner_product_pd.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace {
+inline bool dense_gemm_consitency_check(const memory_desc_wrapper &src_d,
+        const memory_desc_wrapper &wei_d, const memory_desc_wrapper &dst_d) {
+    using namespace utils;
+
+    auto strides_compatible = [&]() {
+        bool ok = true;
+        auto w_str = wei_d.blocking_desc().strides;
+        auto d_str = src_d.blocking_desc().strides;
+        for (int i = 1; i < src_d.ndims() - 1; i++) {
+            ok = ok && w_str[i] / d_str[i] == w_str[i + 1] / d_str[i + 1];
+        }
+        return ok && one_of(w_str[1] / d_str[1], 1, wei_d.padded_dims()[0]);
+    };
+    return true && src_d.is_blocking_desc() && wei_d.is_blocking_desc()
+            && src_d.ndims() == wei_d.ndims()
+            && src_d.blocking_desc().inner_nblks
+            == wei_d.blocking_desc().inner_nblks
+            && utils::one_of(src_d.blocking_desc().inner_nblks, 0, 1)
+            && array_cmp(src_d.blocking_desc().inner_blks,
+                       wei_d.blocking_desc().inner_blks,
+                       wei_d.blocking_desc().inner_nblks)
+            && array_cmp(src_d.blocking_desc().inner_idxs,
+                       wei_d.blocking_desc().inner_idxs,
+                       wei_d.blocking_desc().inner_nblks)
+            && strides_compatible()
+            && dst_d.matches_tag(format_tag::nc)
+            && src_d.only_padded_dim(1)
+            && wei_d.only_padded_dim(1)
+            && src_d.padded_dims()[1] == wei_d.padded_dims()[1]
+            && src_d.is_dense(true)
+            && dst_d.is_dense()
+            && wei_d.is_dense(true);
+}
+}
+
+struct cpu_inner_product_fwd_pd_t: public inner_product_fwd_pd_t {
+    using inner_product_fwd_pd_t::inner_product_fwd_pd_t;
+};
+
+struct cpu_inner_product_bwd_data_pd_t: public inner_product_bwd_data_pd_t {
+    using inner_product_bwd_data_pd_t::inner_product_bwd_data_pd_t;
+};
+
+struct cpu_inner_product_bwd_weights_pd_t: public inner_product_bwd_weights_pd_t {
+    using inner_product_bwd_weights_pd_t::inner_product_bwd_weights_pd_t;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_isa_traits.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_isa_traits.hpp
new file mode 100644
index 0000000000..da6e9dac8e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_isa_traits.hpp
@@ -0,0 +1,151 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_ISA_TRAITS_HPP
+#define CPU_ISA_TRAITS_HPP
+
+#include <type_traits>
+
+#define XBYAK64
+#define XBYAK_NO_OP_NAMES
+/* in order to make selinux happy memory that would be marked with X-bit should
+ * be obtained with mmap */
+#define XBYAK_USE_MMAP_ALLOCATOR
+#if defined(_MSC_VER) && !defined(__INTEL_COMPILER)
+/* turn off `size_t to other-type implicit casting` warning
+ * currently we have a lot of jit-generated instructions that
+ * take uint32_t, but we pass size_t (e.g. due to using sizeof).
+ * FIXME: replace size_t parameters with the appropriate ones */
+#pragma warning (disable: 4267)
+#endif
+#include "xbyak/xbyak.h"
+#include "xbyak/xbyak_util.h"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+typedef enum {
+    isa_any,
+    sse41,
+    sse42,
+    avx,
+    avx2,
+    avx512_common,
+    avx512_core,
+    avx512_core_vnni,
+    avx512_mic,
+    avx512_mic_4ops,
+} cpu_isa_t;
+
+template <cpu_isa_t> struct cpu_isa_traits {}; /* ::vlen -> 32 (for avx2) */
+
+template <> struct cpu_isa_traits<sse42> {
+    typedef Xbyak::Xmm Vmm;
+    static constexpr int vlen_shift = 4;
+    static constexpr int vlen = 16;
+    static constexpr int n_vregs = 16;
+};
+template <> struct cpu_isa_traits<avx> {
+    typedef Xbyak::Ymm Vmm;
+    static constexpr int vlen_shift = 5;
+    static constexpr int vlen = 32;
+    static constexpr int n_vregs = 16;
+};
+template <> struct cpu_isa_traits<avx2>:
+    public cpu_isa_traits<avx> {};
+
+template <> struct cpu_isa_traits<avx512_common> {
+    typedef Xbyak::Zmm Vmm;
+    static constexpr int vlen_shift = 6;
+    static constexpr int vlen = 64;
+    static constexpr int n_vregs = 32;
+};
+template <> struct cpu_isa_traits<avx512_core>:
+    public cpu_isa_traits<avx512_common> {};
+
+template <> struct cpu_isa_traits<avx512_mic>:
+    public cpu_isa_traits<avx512_common> {};
+
+template <> struct cpu_isa_traits<avx512_mic_4ops>:
+    public cpu_isa_traits<avx512_common> {};
+
+namespace {
+
+static Xbyak::util::Cpu cpu;
+static inline bool mayiuse(const cpu_isa_t cpu_isa) {
+    using namespace Xbyak::util;
+
+    switch (cpu_isa) {
+    case sse41:
+    case sse42:
+        // FIXME: SSE4.2 is actually NOT required
+        //return cpu.has(Cpu::tSSE42);
+        return cpu.has(Cpu::tSSE41);
+    case avx:
+        return cpu.has(Cpu::tAVX);
+    case avx2:
+        return cpu.has(Cpu::tAVX2);
+    case avx512_common:
+        return cpu.has(Cpu::tAVX512F);
+    case avx512_core:
+        return true
+            && cpu.has(Cpu::tAVX512F)
+            && cpu.has(Cpu::tAVX512BW)
+            && cpu.has(Cpu::tAVX512VL)
+            && cpu.has(Cpu::tAVX512DQ);
+    case avx512_core_vnni:
+        return true
+            && cpu.has(Cpu::tAVX512F)
+            && cpu.has(Cpu::tAVX512BW)
+            && cpu.has(Cpu::tAVX512VL)
+            && cpu.has(Cpu::tAVX512DQ)
+            && cpu.has(Cpu::tAVX512_VNNI);
+    case avx512_mic:
+        return true
+            && cpu.has(Cpu::tAVX512F)
+            && cpu.has(Cpu::tAVX512CD)
+            && cpu.has(Cpu::tAVX512ER)
+            && cpu.has(Cpu::tAVX512PF);
+    case avx512_mic_4ops:
+        return true
+            && mayiuse(avx512_mic)
+            && cpu.has(Cpu::tAVX512_4FMAPS)
+            && cpu.has(Cpu::tAVX512_4VNNIW);
+    case isa_any:
+        return true;
+    }
+    return false;
+}
+}
+
+/* whatever is required to generate string literals... */
+#include "z_magic.hpp"
+#define JIT_IMPL_NAME_HELPER(prefix, isa, suffix_if_any) \
+    (isa == sse42 ? prefix STRINGIFY(sse42) : \
+    (isa == avx ? prefix STRINGIFY(avx) : \
+    (isa == avx2 ? prefix STRINGIFY(avx2) : \
+    (isa == avx512_common ? prefix STRINGIFY(avx512_common) : \
+    (isa == avx512_core ? prefix STRINGIFY(avx512_core) : \
+    (isa == avx512_mic ? prefix STRINGIFY(avx512_mic) : \
+    (isa == avx512_mic_4ops ? prefix STRINGIFY(avx512_mic_4ops) : \
+    prefix suffix_if_any)))))))
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_lrn_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_lrn_pd.hpp
new file mode 100644
index 0000000000..49988f4c2d
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_lrn_pd.hpp
@@ -0,0 +1,42 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_LRN_PD_HPP
+#define CPU_LRN_PD_HPP
+
+#include <assert.h>
+
+#include "lrn_pd.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_lrn_fwd_pd_t: public lrn_fwd_pd_t {
+    using lrn_fwd_pd_t::lrn_fwd_pd_t;
+};
+
+struct cpu_lrn_bwd_pd_t: public lrn_bwd_pd_t {
+    using lrn_bwd_pd_t::lrn_bwd_pd_t;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_memory.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_memory.cpp
new file mode 100644
index 0000000000..3c0624cf46
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_memory.cpp
@@ -0,0 +1,277 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "mkldnn_traits.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_memory.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::data_type;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::format_tag;
+
+enum blk_kind_t { a, b, c, ab, ba, bc, cb };
+
+template <data_type_t dt, blk_kind_t blk_kind, int blksize>
+void typed_zero_pad_blk(
+        const memory_desc_wrapper &m_d, typename prec_traits<dt>::type *data) {
+    using data_t = typename prec_traits<dt>::type;
+    const auto &dims = m_d.dims();
+    const auto &pdims = m_d.padded_dims();
+    const auto &blk = m_d.blocking_desc();
+    auto dim_is_blocked = [&](int dim) {
+        for (int i = 0; i < blk.inner_nblks; i++)
+            if (blk.inner_idxs[i] == dim)
+                return true;
+        return false;
+    };
+    bool A_blocked = dim_is_blocked(0), B_blocked = dim_is_blocked(1),
+         C_blocked = dim_is_blocked(2);
+
+    assert(blk.inner_nblks < 4);
+    assert((A_blocked || B_blocked || C_blocked) || (A_blocked && B_blocked)
+            || (C_blocked && B_blocked));
+
+    const int a_tail_s = A_blocked ? dims[0] % blksize : 0;
+    const int b_tail_s = B_blocked ? dims[1] % blksize : 0;
+    const int c_tail_s = C_blocked ? dims[2] % blksize : 0;
+    assert(a_tail_s || b_tail_s || c_tail_s);
+
+    const int A = A_blocked ? pdims[0] / blksize : dims[0];
+    const int B = B_blocked ? pdims[1] / blksize : dims[1];
+    const int C = C_blocked ? pdims[2] / blksize : dims[2];
+    const int D = m_d.ndims() > 3 ? dims[3] : 1;
+    const int E = m_d.ndims() > 4 ? dims[4] : 1;
+    const int F = m_d.ndims() > 5 ? dims[5] : 1;
+    const int inner_blk = blk.inner_nblks == 3 ? blk.inner_blks[2] : 1;
+
+    auto zeroize_tail = [&](data_t *d, const int tail_s) {
+        for (int b = tail_s; b < blksize; ++b)
+            d[b] = 0;
+    };
+    auto zeroize_tail_inner = [&](data_t *d, const int tail_s) {
+        for (int b1 = 0; b1 < blksize; ++b1)
+            for (int b2 = tail_s; b2 < blksize; ++b2)
+                d[(b1 / inner_blk) * blksize * inner_blk + inner_blk * b2
+                        + b1 % inner_blk]
+                        = 0;
+    };
+    auto zeroize_tail_outer = [&](data_t *d, const int tail_s) {
+        for (int b1 = tail_s; b1 < blksize; ++b1)
+            for (int b2 = 0; b2 < blksize; ++b2)
+                d[(b1 / inner_blk) * blksize * inner_blk + inner_blk * b2
+                        + b1 % inner_blk]
+                        = 0;
+    };
+
+    if (c_tail_s) {
+        parallel_nd(A, B, D, E, F, [&](int a, int b, int d, int e, int f) {
+            auto x = &data[m_d.blk_off(a, b, C - 1, d, e, f)];
+            if (blk_kind == c)
+                zeroize_tail(x, c_tail_s);
+            else if (blk_kind == bc)
+                zeroize_tail_inner(x, c_tail_s);
+            else if (blk_kind == cb)
+                zeroize_tail_outer(x, c_tail_s);
+        });
+    }
+
+    if (b_tail_s) {
+        parallel_nd(A, C, D, E, F, [&](int a, int c, int d, int e, int f) {
+            auto x = &data[m_d.blk_off(a, B - 1, c, d, e, f)];
+            if (blk_kind == b)
+                zeroize_tail(x, b_tail_s);
+            else if (blk_kind == ab || blk_kind == cb)
+                zeroize_tail_inner(x, b_tail_s);
+            else if (blk_kind == ba || blk_kind == bc)
+                zeroize_tail_outer(x, b_tail_s);
+        });
+    }
+
+    if (a_tail_s) {
+        parallel_nd(B, C, D, E, F, [&](int b, int c, int d, int e, int f) {
+            auto x = &data[m_d.blk_off(A - 1, b, c, d, e, f)];
+            if (blk_kind == a)
+                zeroize_tail(x, a_tail_s);
+            else if (blk_kind == ba)
+                zeroize_tail_inner(x, a_tail_s);
+            else if (blk_kind == ab)
+                zeroize_tail_outer(x, a_tail_s);
+        });
+    }
+}
+
+/*
+ * all
+ */
+template <data_type_t dt>
+void typed_zero_pad_generic_blocked(
+        const memory_desc_wrapper &m_d, typename prec_traits<dt>::type *data) {
+    const int ndims = m_d.ndims();
+    const auto &dims = m_d.dims();
+    const auto &pdims = m_d.padded_dims();
+
+    const ptrdiff_t nelems = (ptrdiff_t)m_d.nelems(true);
+
+    /* [D_0] .. [D_k][D_k+1] .. [D_ndim - 1]
+     *            |  \                     /
+     *            |   ---------------------
+     *           has        contiguous
+     *         padding
+     *
+     * step     <-- D_k+1 * ... * D_ndims-1
+     * step_dim <-- k
+     */
+
+    ptrdiff_t step = 1;
+    int step_dim = ndims - 1;
+    for (; step_dim >= 0; --step_dim) {
+        if (dims[step_dim] != pdims[step_dim])
+            break;
+        step *= dims[step_dim];
+    }
+
+    assert(step_dim >= 0 && "no zero padding is required");
+    if (step_dim < 0)
+        return;
+
+    parallel_nd(nelems / step, [&](ptrdiff_t e1) {
+        bool need_zero = false;
+
+        ptrdiff_t idx = e1;
+        for (int d = step_dim; d >= 0; --d) {
+            if (idx % pdims[d] >= dims[d]) {
+                need_zero = true;
+                break;
+            }
+            idx /= pdims[d];
+        }
+
+        if (need_zero) {
+            for (ptrdiff_t e0 = 0; e0 < step; ++e0)
+                data[m_d.off_l(e1 * step + e0, true)] = 0;
+        }
+    });
+}
+
+template <data_type_t dt>
+status_t cpu_memory_t::typed_zero_pad() const {
+    const memory_desc_wrapper mdw(md());
+
+    if (mdw.format_kind() != format_kind::blocked)
+        return unimplemented;
+
+    if (mdw.nelems(false) == mdw.nelems(true))
+        return success;
+
+    auto *data = (typename prec_traits<dt>::type *)data_;
+    auto blk = mdw.blocking_desc();
+
+    auto get_blksize = [&](int ind) {
+        int blksize = 1;
+        for (int i = 0; i < blk.inner_nblks; i++) {
+            if (blk.inner_idxs[i] == ind)
+                blksize *= blk.inner_blks[i];
+        }
+        return blksize;
+    };
+    const int blksize = get_blksize(blk.inner_idxs[0]);
+
+#   define CASE(blksize_, blk_kind) \
+    do { \
+        if (blksize == blksize_) { \
+            typed_zero_pad_blk<dt, blk_kind, blksize_>(mdw, data); \
+            return success; \
+        } \
+    } while(0)
+
+    switch (blk.inner_nblks) {
+    case 1:
+        if (blk.inner_idxs[0] == 0) {
+            CASE(4, a);
+            CASE(8, a);
+            CASE(16, a);
+        } else if (blk.inner_idxs[0] == 1) {
+            CASE(4, b);
+            CASE(8, b);
+            CASE(16, b);
+        }
+        break;
+    case 2:
+    case 3:
+        if (!IMPLICATION(blk.inner_nblks == 3,
+                    blk.inner_idxs[0] == blk.inner_idxs[2]))
+            break;
+
+        if (blk.inner_idxs[0] == 0 && blk.inner_idxs[1] == 1) {
+            CASE(4, ab);
+            CASE(8, ab);
+            CASE(16, ab);
+        } else if (blk.inner_idxs[0] == 1 && blk.inner_idxs[1] == 0) {
+            CASE(4, ba);
+            CASE(8, ba);
+            CASE(16, ba);
+        }
+        if (blk.inner_idxs[0] == 1 && blk.inner_idxs[1] == 2) {
+            CASE(4, bc);
+            CASE(8, bc);
+            CASE(16, bc);
+        } else if (blk.inner_idxs[0] == 2 && blk.inner_idxs[1] == 1) {
+            CASE(4, cb);
+            CASE(8, cb);
+            CASE(16, cb);
+        }
+        break;
+    default: break;
+    }
+
+#   undef CASE
+
+    // the last line of defence
+    typed_zero_pad_generic_blocked<dt>(mdw, data);
+    return success;
+}
+
+status_t cpu_memory_t::zero_pad() const {
+    memory_desc_wrapper mdw(md());
+    const bool skip_zeroing = false
+        || data_ == nullptr
+        || mdw.is_zero()
+        || !mdw.is_blocking_desc();
+    if (skip_zeroing) return success;
+
+    switch (mdw.data_type()) {
+        case f32: return typed_zero_pad<f32>();
+        case s32: return typed_zero_pad<s32>();
+        case s8: return typed_zero_pad<s8>();
+        case u8: return typed_zero_pad<u8>();
+        default: assert(!"memory is undefined"); return unimplemented;
+    }
+    return unimplemented;
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_memory.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_memory.hpp
new file mode 100644
index 0000000000..2c01bcc6af
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_memory.hpp
@@ -0,0 +1,89 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_MEMORY_HPP
+#define CPU_MEMORY_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory.hpp"
+#include "memory_desc_wrapper.hpp"
+
+#include "cpu_engine.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_memory_t: public memory_t {
+    cpu_memory_t(cpu_engine_t *engine, const memory_desc_t *md, void *handle)
+        : memory_t(engine, md)
+        , own_data_(handle == MKLDNN_NATIVE_HANDLE_ALLOCATE)
+        , data_((char *)handle) {}
+
+    cpu_memory_t(cpu_engine_t *engine, const memory_desc_t *md)
+        : cpu_memory_t(engine, md, nullptr) {}
+
+    ~cpu_memory_t() { if (own_data_) free(data_); }
+
+    virtual status_t init() override {
+        if (own_data_) {
+            data_ = nullptr;
+            const size_t size = memory_desc_wrapper(this->md()).size();
+            if (size) {
+                data_ = (char *)malloc(size, 64);
+                if (data_ == nullptr)
+                    return status::out_of_memory;
+            }
+        }
+        return zero_pad();
+    }
+
+    cpu_engine_t *engine() const { return (cpu_engine_t *)memory_t::engine(); }
+
+    virtual status_t get_data_handle(void **handle) const override {
+        *handle = static_cast<void *>(data_);
+        return status::success;
+    }
+
+    virtual mkldnn::impl::status_t set_data_handle(void *handle) override {
+        if (own_data_) { free(data_); own_data_ = false; }
+        data_ = static_cast<char *>(handle);
+        return zero_pad();
+    }
+
+    virtual mkldnn::impl::status_t zero_pad() const override;
+
+private:
+    bool own_data_;
+    char *data_;
+
+    template <mkldnn::impl::data_type_t>
+    mkldnn::impl::status_t typed_zero_pad() const;
+
+    cpu_memory_t(const cpu_memory_t &) = delete;
+    cpu_memory_t &operator=(const cpu_memory_t &) = delete;
+    cpu_memory_t &operator=(cpu_memory_t &&) = delete;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_pooling_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_pooling_pd.hpp
new file mode 100644
index 0000000000..ac2daa415e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_pooling_pd.hpp
@@ -0,0 +1,40 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_POOLING_PD_HPP
+#define CPU_POOLING_PD_HPP
+
+#include "pooling_pd.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_pooling_fwd_pd_t: public pooling_fwd_pd_t {
+    using pooling_fwd_pd_t::pooling_fwd_pd_t;
+};
+
+struct cpu_pooling_bwd_pd_t: public pooling_bwd_pd_t {
+    using pooling_bwd_pd_t::pooling_bwd_pd_t;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_primitive.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_primitive.hpp
new file mode 100644
index 0000000000..56127f36c2
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_primitive.hpp
@@ -0,0 +1,83 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_PRIMITIVE_HPP
+#define CPU_PRIMITIVE_HPP
+
+#include "mkldnn.h"
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "primitive.hpp"
+#include "scratchpad.hpp"
+
+#define CTX_IN_MEM(type, arg) static_cast<type>(ctx.input(arg))
+#define CTX_OUT_MEM(type, arg) static_cast<type>(ctx.output(arg))
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_memory_t;
+
+struct cpu_primitive_t: public primitive_t {
+    cpu_primitive_t(const primitive_desc_t *pd,
+            bool use_global_scratchpad = false)
+        : primitive_t(pd)
+        , scratchpad_buffer_(nullptr)
+        , global_scratchpad_(nullptr)
+    {
+        const size_t scratchpad_size =
+            this->pd()->scratchpad_size(scratchpad_mode::library);
+
+        if (scratchpad_size) {
+            if (use_global_scratchpad)
+                global_scratchpad_ = create_scratchpad(scratchpad_size);
+            else
+                scratchpad_buffer_ = malloc(scratchpad_size, 64);
+        }
+    }
+
+    virtual ~cpu_primitive_t() {
+        delete global_scratchpad_;
+        free(scratchpad_buffer_);
+    }
+
+protected:
+    memory_tracking::grantor_t scratchpad(const exec_ctx_t &ctx) const {
+        void *ptr = nullptr;
+        if (pd()->attr()->scratchpad_mode_ == scratchpad_mode::user) {
+            ptr = CTX_OUT_MEM(void *, MKLDNN_ARG_SCRATCHPAD);
+        } else {
+            ptr = global_scratchpad_
+                ? global_scratchpad_->get() : scratchpad_buffer_;
+        }
+
+        return pd()->scratchpad_registry().grantor(ptr);
+    }
+
+private:
+    void *scratchpad_buffer_;
+    scratchpad_t *global_scratchpad_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reducer.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reducer.cpp
new file mode 100644
index 0000000000..1d41ac5cea
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reducer.cpp
@@ -0,0 +1,544 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "mkldnn_thread.hpp"
+#include "mkldnn_types.h"
+#include "nstl.hpp"
+#include "utils.hpp"
+
+#include "cpu_reducer.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace memory_tracking::names;
+
+void reduce_balancer_t::balance() {
+    using namespace nstl;
+    using namespace utils;
+
+    assert(nthr_ > 0 && job_size_ > 0 && njobs_ > 0 && reduction_size_ > 0);
+
+    const int job_complexity = 1;
+
+    const int min_njobs_per_group = max(1, njobs_ / nthr_);
+    const int max_njobs_per_group = max(1,
+            static_cast<int>(max_buffer_size_ / (nthr_ * job_size_)));
+
+    /* initial guess */
+    int ngroups = min(njobs_ / min_njobs_per_group, nthr_);
+    int nthr_per_group = syncable_ ? min(nthr_ / ngroups, reduction_size_) : 1;
+    int njobs_per_group_ub = div_up(njobs_, ngroups);
+
+    /* rough upper-bound estimation, will be fixed during brute force */
+    size_t thread_complexity_ub = njobs_ * job_size_ * reduction_size_;
+
+    /* brute force parameters for the best balance... */
+    for (int c_njobs_per_group = min_njobs_per_group;
+            c_njobs_per_group < njobs_; ++c_njobs_per_group) {
+        /* current assumption */
+        int c_ngroups = min(njobs_ / c_njobs_per_group, nthr_);
+        int c_nthr_per_group = syncable_
+            ? min(nthr_ / c_ngroups, reduction_size_) : 1;
+        int c_njobs_per_group_ub = div_up(njobs_, c_ngroups);
+
+        if (c_nthr_per_group > 1 && c_njobs_per_group_ub > max_njobs_per_group)
+            continue;
+
+        int c_thread_reduction_ub = div_up(reduction_size_, c_nthr_per_group);
+        size_t c_group_size_ub = job_size_ * c_njobs_per_group_ub;
+        size_t c_thread_complexity_ub = c_group_size_ub * (
+                job_complexity * c_thread_reduction_ub
+                + (c_nthr_per_group != 1));
+
+        if (c_thread_complexity_ub < thread_complexity_ub) {
+            ngroups = c_ngroups;
+            nthr_per_group = c_nthr_per_group;
+            njobs_per_group_ub = c_njobs_per_group_ub;
+            thread_complexity_ub = c_thread_complexity_ub;
+        }
+    }
+
+    assert(njobs_per_group_ub <= max_njobs_per_group || nthr_per_group == 1);
+    assert(ngroups * nthr_per_group <= nthr_);
+    assert((size_t)njobs_per_group_ub * job_size_ * nthr_ <= max_buffer_size_
+            || nthr_per_group == 1); /* no reduction buffer overflow */
+    assert(IMPLICATION(!syncable_, nthr_per_group == 1));
+
+    ngroups_ = ngroups;
+    nthr_per_group_ = nthr_per_group;
+    njobs_per_group_ub_ = njobs_per_group_ub;
+}
+
+/* reducer jit-ted driver */
+
+using namespace Xbyak;
+
+template <impl::data_type_t data_type>
+struct reducer_2d_driver_t: public c_compatible {
+    typedef typename prec_traits<data_type>::type data_t;
+
+    reducer_2d_driver_t(int n_src, size_t src_ld,
+            size_t src_step, size_t dst_step, bool nullify_dst)
+        : n_src_(n_src), src_ld_(src_ld), src_step_(src_step)
+        , dst_step_(dst_step), nullify_dst_(nullify_dst), ker_(nullptr) {}
+    virtual ~reducer_2d_driver_t() {}
+    void operator()(data_t *dst, const data_t *srcs, size_t ny, size_t nx)
+    { assert(ker_); ker_(dst, srcs, ny, nx); }
+
+protected:
+    int n_src_;
+    size_t src_ld_, src_step_, dst_step_;
+    bool nullify_dst_;
+    void (*ker_)(data_t *dst, const data_t *srcs, size_t ny, size_t nx);
+};
+
+template <impl::data_type_t data_type, cpu_isa_t isa>
+struct reducer_2d_driver_f_s_32_t: public reducer_2d_driver_t<data_type>,
+    public jit_generator
+{
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(reducer_2d_driver_f_s_32_t)
+
+    /* cpu specific part */
+    using Vmm = typename utils::conditional<isa == avx2, Ymm, Zmm>::type;
+    const AddressFrame &vmmword = (isa == avx2) ? yword : zword;
+    void uni_vadd(const Xmm& x1, const Xmm& x2, const Operand& op)
+    { if (data_type == data_type::f32) vaddps(x1, x2, op);
+      else vpaddd(x1, x2, op); }
+    void uni_add(const Xmm& x1, const Operand& op)
+    { if (data_type == data_type::f32) addss(x1, op); else paddd(x1, op); }
+
+    const int vlen = cpu_isa_traits<isa>::vlen;
+    const int typesize
+        = sizeof(typename mkldnn::impl::prec_traits<data_type>::type);
+    Xbyak::Reg64 reg_dst = abi_param1;
+    Xbyak::Reg64 reg_src = abi_param2;
+    Xbyak::Reg64 reg_ny = abi_param3;
+    Xbyak::Reg64 reg_nx = abi_param4;
+
+    Xbyak::Reg64 reg_x = rax;
+    Xbyak::Reg64 reg_src_id = r10;
+
+    reducer_2d_driver_f_s_32_t(int n_src, size_t src_ld, size_t src_step,
+            size_t dst_step, bool nullify_dst)
+        : reducer_2d_driver_t<data_type>(n_src, src_ld, src_step,
+                dst_step, nullify_dst)
+    { generate(); }
+
+    void nullify_dst(int nloads, int load_len) {
+        UNUSED(load_len);
+        for (int i = 0; i < nloads; ++i)
+            uni_vpxor(Vmm(i), Vmm(i), Vmm(i));
+        /* prefetches[dst] ? */
+    }
+
+    void load_dst(int nloads, int load_len) {
+        for (int i = 0; i < nloads; ++i) {
+            if (load_len == typesize)
+                movd(Xmm(i), ptr[reg_dst + i * load_len]);
+            else if (load_len == vlen)
+                vmovups(Vmm(i), ptr[reg_dst + i * load_len]);
+            else
+                assert(!"unsupported");
+        }
+    }
+
+    void store_dst(int nloads, int load_len) {
+        for (int i = 0; i < nloads; ++i) {
+            if (load_len == typesize)
+                movd(ptr[reg_dst + i * load_len], Xmm(i));
+            else if (load_len == vlen)
+                vmovups(ptr[reg_dst + i * load_len], Vmm(i));
+            else
+                assert(!"unsupported");
+        }
+    }
+
+    void accumulate(int nloads, int load_len, size_t base_off) {
+        for (int i = 0; i < nloads; ++i) {
+            size_t off = base_off + i * load_len;
+
+            if (load_len == typesize)
+                uni_add(Xmm(i), ptr[reg_src + off]);
+            else if (load_len == vlen)
+                uni_vadd(Vmm(i), Vmm(i), vmmword[reg_src + off]);
+            else
+                assert(!"unsupported");
+        }
+    }
+
+    void loop_x() {
+        const int nloads[] = {cpu_isa_traits<isa>::n_vregs, 1, 1};
+        const int nbranches = sizeof(nloads) / sizeof(nloads[0]);
+
+        const int load_len[nbranches] = {vlen, vlen, typesize};
+        Label loop_x_label[nbranches + 1];
+
+        mov(reg_x, reg_nx);
+
+        for (int id = 0; id < nbranches; ++id) {
+            L(loop_x_label[id]);
+
+            cmp(reg_x, nloads[id] * load_len[id]);
+            jl(loop_x_label[id + 1], T_NEAR);
+
+            if (this->nullify_dst_)
+                nullify_dst(nloads[id], load_len[id]);
+            else
+                load_dst(nloads[id], load_len[id]);
+
+            if (nloads[id] > 1) {
+                Label loop_srcs;
+                mov(reg_src_id, this->n_src_);
+                L(loop_srcs);
+
+                accumulate(nloads[id], load_len[id], 0);
+                add(reg_src, this->src_ld_ * typesize);
+
+                dec(reg_src_id);
+                jnz(loop_srcs, T_NEAR);
+
+                sub(reg_src, this->n_src_ * this->src_ld_ * typesize);
+            } else {
+                for (int src_id = 0; src_id < this->n_src_; ++src_id) {
+                    const size_t base_off = src_id * this->src_ld_ * typesize;
+                    accumulate(nloads[id], load_len[id], base_off);
+                }
+            }
+
+            store_dst(nloads[id], load_len[id]);
+
+            add(reg_src, nloads[id] * load_len[id]);
+            add(reg_dst, nloads[id] * load_len[id]);
+
+            sub(reg_x, nloads[id] * load_len[id]);
+
+            jmp(loop_x_label[id], T_NEAR);
+        }
+
+        L(loop_x_label[nbranches]);
+
+        /* restore address registers */
+        sub(reg_src, reg_nx);
+        sub(reg_dst, reg_nx);
+    }
+
+    void generate() {
+        assert(isa == avx2 || isa == avx512_common || isa == avx512_mic);
+
+        preamble();
+
+        shl(reg_nx, 2);
+
+        Label ny_loop;
+        L(ny_loop);
+
+        loop_x();
+
+        add(reg_dst, this->dst_step_ * typesize);
+        add(reg_src, this->src_step_ * typesize);
+
+        dec(reg_ny);
+        jnz(ny_loop, T_NEAR);
+
+        postamble();
+        this->ker_ = reinterpret_cast<decltype(this->ker_)>(
+            const_cast<uint8_t*>(this->getCode()));
+    }
+};
+
+template <impl::data_type_t data_type>
+inline reducer_2d_driver_t<data_type> *create_reduce_2d_drv(int n_src,
+        size_t src_ld, size_t src_step, size_t dst_step, bool nullify_dst) {
+    if (mayiuse(avx512_common))
+        return new reducer_2d_driver_f_s_32_t<data_type, avx512_common>(n_src,
+            src_ld, src_step, dst_step, nullify_dst);
+    else if (mayiuse(avx2))
+        return new reducer_2d_driver_f_s_32_t<data_type, avx2>(n_src, src_ld,
+            src_step, dst_step, nullify_dst);
+    assert(!"unimplemented");
+    return nullptr;
+}
+
+/* cpu_reducer_t */
+
+template <impl::data_type_t data_type>
+void cpu_reducer_t<data_type>::conf_t::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad) const {
+    if (balancer_.nthr_per_group_ == 1) return;
+
+    const size_t space_size = balancer_.ngroups_
+        * (balancer_.nthr_per_group_ - 1)
+        * cpu_reducer_t<data_type>::space_per_thread(balancer_);
+    scratchpad.book(key_reducer_space, sizeof(data_t) * space_size, PAGE_4K);
+    scratchpad.book(key_reducer_space_bctx,
+            sizeof(simple_barrier::ctx_t) * balancer_.ngroups_);
+}
+
+template <impl::data_type_t data_type>
+cpu_reducer_t<data_type>::cpu_reducer_t(const conf_t &conf)
+    : conf_(conf), drv_(nullptr)
+{
+    if (balancer().nthr_per_group_ == 1) return;
+
+    drv_ = create_reduce_2d_drv<data_type>(balancer().nthr_per_group_ - 1,
+            space_per_thread(balancer()), 0, 0, false);
+}
+
+template <impl::data_type_t data_type>
+cpu_reducer_t<data_type>::~cpu_reducer_t() { delete drv_; }
+
+template <impl::data_type_t data_type>
+typename cpu_reducer_t<data_type>::data_t *
+cpu_reducer_t<data_type>::get_local_ptr(int ithr, data_t *dst,
+        const memory_tracking::grantor_t &scratchpad) const {
+    const int id_in_grp = balancer().id_in_group(ithr);
+
+    /* threads 0 from each group writes directly to the destination */
+    if (id_in_grp == 0)
+        return dst + balancer().ithr_job_off(ithr) * balancer().job_size_;
+
+    const int grp_id = balancer().group_id(ithr);
+    const int offset_factor = grp_id * (balancer().nthr_per_group_ - 1)
+        + (id_in_grp - 1);
+
+    auto space = scratchpad.template get<data_t>(key_reducer_space);
+    return space + offset_factor * space_per_thread(balancer());
+}
+
+template <impl::data_type_t data_type>
+void cpu_reducer_t<data_type>::reduce_nolock(int ithr, data_t *dst,
+        const memory_tracking::grantor_t &scratchpad) const {
+    bool redundant_reduction = balancer().nthr_per_group_ == 1
+        || balancer().idle(ithr);
+    if (redundant_reduction) return;
+
+#ifdef SIMPLE_IMPL
+    if (balancer().id_in_group(ithr) != 0)
+        return; /* only threads 0 do the reduction */
+
+    const int njobs_in_grp = balancer().ithr_njobs(ithr);
+    data_t *d = get_local_ptr(ithr, dst, scratchpad);
+    for (int id_in_grp = 1; id_in_grp < balancer_.nthr_per_group_; ++id_in_grp)
+    {
+        const data_t *space = get_local_ptr(ithr + id_in_grp, dst, scratchpad);
+        for (size_t i = 0; i < (size_t)njobs_in_grp * balancer().job_size_; ++i)
+            d[i] += space[i];
+    }
+#else
+    using namespace utils;
+
+    const int id_in_grp = balancer().id_in_group(ithr);
+    const int njobs_in_grp = balancer().ithr_njobs(ithr);
+    const size_t cl = 64 / sizeof(data_t);
+
+    const size_t reduction_size = njobs_in_grp * balancer().job_size_;
+    size_t start{0}, end{0};
+    balance211(div_up(reduction_size, cl), balancer().nthr_per_group_,
+            id_in_grp, start, end);
+
+    if (start == end) return;
+
+    data_t *d = get_local_ptr(ithr - id_in_grp, dst, scratchpad) + start * cl;
+    const data_t *space = get_local_ptr(ithr - id_in_grp + 1, dst, scratchpad)
+        + start * cl;
+    const size_t len = nstl::min(end * cl, reduction_size) - start * cl;
+
+    (*drv_)(d, space, 1, len);
+#endif
+}
+
+template struct cpu_reducer_t<data_type::f32>;
+template struct cpu_reducer_t<data_type::s32>;
+
+/* cpu_reducer_2d_t */
+
+template <impl::data_type_t data_type>
+void cpu_reducer_2d_t<data_type>::conf_t::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad) const {
+    if (balancer_.nthr_per_group_ == 1) return;
+
+    const size_t space_size = balancer_.ngroups_ * balancer_.nthr_per_group_
+        * cpu_reducer_2d_t<data_type>::space_per_thread(balancer_);
+    scratchpad.book(key_reducer_space, sizeof(data_t) * space_size);
+    scratchpad.book(key_reducer_space_bctx,
+            sizeof(simple_barrier::ctx_t) * balancer_.ngroups_);
+}
+
+template <impl::data_type_t data_type>
+cpu_reducer_2d_t<data_type>::cpu_reducer_2d_t(const conf_t &conf)
+    : conf_(conf), drv_(nullptr)
+{
+    if (balancer().nthr_per_group_ == 1) return;
+
+    drv_ = create_reduce_2d_drv<data_type>(balancer().nthr_per_group_,
+            space_per_thread(balancer()), conf_.job_size_x_, conf_.dst_x_,
+            true);
+}
+
+template <impl::data_type_t data_type>
+cpu_reducer_2d_t<data_type>::~cpu_reducer_2d_t() { delete drv_; }
+
+template <impl::data_type_t data_type>
+typename cpu_reducer_2d_t<data_type>::data_t *cpu_reducer_2d_t<data_type>::
+get_local_ptr(int ithr, const memory_tracking::grantor_t &scratchpad) const {
+    const int id_in_grp = balancer().id_in_group(ithr);
+    const int grp_id = balancer().group_id(ithr);
+    const int offset_factor = grp_id * balancer().nthr_per_group_ + id_in_grp;
+    auto space = scratchpad.template get<data_t>(key_reducer_space);
+    return space + offset_factor * space_per_thread(balancer());
+}
+
+template <impl::data_type_t data_type>
+int cpu_reducer_2d_t<data_type>::choose_x_blocking(int nx, int ny,
+        int nthr_per_grp) const {
+    // find x_blocking for better balance reducing work between threads
+    assert(conf_.x_block_ > 0 && nx > conf_.x_block_
+            && nx % conf_.x_block_ == 0);
+    int x_blocking = nx / conf_.x_block_;
+    int min_x_blocking =
+            utils::div_up(x_blocking, nstl::max(1, nthr_per_grp / ny));
+    while (true) {
+        if (x_blocking % 2 == 0 && x_blocking >= min_x_blocking * 2)
+            x_blocking /= 2;
+        else if (x_blocking % 3 == 0 && x_blocking >= min_x_blocking * 3)
+            x_blocking /= 3;
+        else
+            break;
+    }
+    if (x_blocking >= min_x_blocking * 4) x_blocking = 1;
+    x_blocking *= conf_.x_block_;
+    return x_blocking;
+}
+
+template <impl::data_type_t data_type>
+void cpu_reducer_2d_t<data_type>::reduce_block(const data_t* space_base,
+        data_t *dst, int job, int start_y, int start_x,
+        int ny_start, int nx_start, int ny_step, int nx_step) const {
+    data_t *d = dst + (start_y + ny_start) * conf_.dst_x_
+                    + start_x + nx_start;
+    const data_t *space = space_base + job * balancer().job_size_
+                            + ny_start * conf_.job_size_x_ + nx_start;
+#ifdef SIMPLE_IMPL
+    for (int idg = 0; idg < balancer().nthr_per_group_; ++idg) {
+        const data_t *w = &space[idg * space_per_thread(balancer())];
+        for (int y = 0; y < ny_step; ++y)
+            for (int x = 0; x < nx_step; ++x) {
+                d[y * conf_.dst_x_ + x]
+                    = (idg == 0 ? 0 : d[y * conf_.dst_x_ + x])
+                    + w[y * conf_.job_size_x_ + x];
+            }
+    }
+#else
+    (*drv_)(d, space, ny_step, nx_step);
+#endif
+}
+
+template <impl::data_type_t data_type>
+void cpu_reducer_2d_t<data_type>::reduce_nolock(int ithr, data_t *dst,
+        const memory_tracking::grantor_t &scratchpad) const {
+    bool redundant_reduction = balancer().nthr_per_group_ == 1
+        || balancer().idle(ithr);
+    if (redundant_reduction) return;
+
+    const int id_in_grp = balancer().id_in_group(ithr);
+    const int njobs_in_grp = balancer().ithr_njobs(ithr);
+    const int njobs_x = utils::div_up(conf_.dst_x_, conf_.job_size_x_);
+    const int global_job_start = balancer().ithr_job_off(ithr);
+
+    const data_t *space_base = get_local_ptr(ithr - id_in_grp, scratchpad);
+
+    const int pr_grps = nstl::min(njobs_in_grp, balancer().nthr_per_group_);
+    const int pr_nthr_per_grp = balancer().nthr_per_group_ / pr_grps;
+
+    if (id_in_grp >= pr_grps * pr_nthr_per_grp)
+        return; /* idle */
+
+    const int pr_my_grp = id_in_grp / pr_nthr_per_grp;
+    const int pr_my_id = id_in_grp % pr_nthr_per_grp;
+
+    int pr_job_start{0}, pr_job_end{0};
+    balance211(njobs_in_grp, pr_grps, pr_my_grp, pr_job_start, pr_job_end);
+
+    for (int j = pr_job_start; j < pr_job_end; ++j) {
+        const int global_job = global_job_start + j;
+        const int j_y = global_job / njobs_x;
+        const int j_x = global_job % njobs_x;
+        const int start_y = j_y * conf_.job_size_y_;
+        const int start_x = j_x * conf_.job_size_x_;
+        const int ny = nstl::min(conf_.dst_y_ - start_y, conf_.job_size_y_);
+        const int nx = nstl::min(conf_.dst_x_ - start_x, conf_.job_size_x_);
+        int x_blocking = choose_x_blocking(nx, ny, pr_nthr_per_grp);
+
+        int nxy_start{0}, nxy_end{0};
+        balance211(ny * nx / x_blocking, pr_nthr_per_grp, pr_my_id,
+                    nxy_start, nxy_end);
+        if (nxy_start == nxy_end) continue;
+        nxy_start *= x_blocking;
+        nxy_end *= x_blocking;
+
+        int nxy = nxy_start;
+        if (nxy % nx != 0) {
+            int nx_step = nstl::min(nx - nxy % nx, nxy_end - nxy);
+            reduce_block(space_base, dst, j, start_y, start_x,
+                        nxy / nx, nxy % nx, 1, nx_step);
+            nxy += nx_step;
+        }
+        if ((nxy_end - nxy) > nx) {
+            int ny_step = (nxy_end - nxy) / nx;
+            reduce_block(space_base, dst, j, start_y, start_x,
+                        nxy / nx, nxy % nx, ny_step, nx);
+            nxy += nx * ny_step;
+        }
+        if ((nxy_end - nxy) > 0) {
+            reduce_block(space_base, dst, j, start_y, start_x,
+                        nxy / nx, nxy % nx, 1, nxy_end - nxy);
+        }
+    }
+}
+
+template struct cpu_reducer_2d_t<data_type::f32>;
+template struct cpu_reducer_2d_t<data_type::s32>;
+
+/* accumulator section */
+
+template <impl::data_type_t data_type>
+cpu_accumulator_1d_t<data_type>::cpu_accumulator_1d_t(): drv_(nullptr) {
+    drv_ = create_reduce_2d_drv<data_type>(1, 0, 0, 0, false);
+}
+
+template <impl::data_type_t data_type>
+cpu_accumulator_1d_t<data_type>::~cpu_accumulator_1d_t() {
+    delete drv_;
+}
+
+template <impl::data_type_t data_type>
+void cpu_accumulator_1d_t<data_type>::accumulate(data_t *dst,
+        const data_t *src, size_t size) {
+    (*drv_)(dst, src, 1, size);
+}
+
+template struct cpu_accumulator_1d_t<data_type::f32>;
+template struct cpu_accumulator_1d_t<data_type::s32>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reducer.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reducer.hpp
new file mode 100644
index 0000000000..27f5939cd2
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reducer.hpp
@@ -0,0 +1,334 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REDUCER_HPP
+#define CPU_REDUCER_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+#include "mkldnn_types.h"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+
+#include "cpu_barrier.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+/** class to perform balancing over 3D array
+ *
+ * Conceptually the reduction happens according to the picture below:
+ *
+ *  <--job_size->
+ *  +-----------+   +-----------+           +-----------+  ^
+ *  |           |   |           |           |           |  |
+ *  |           |   |           |           |           |  |
+ *  |     1     |   |     2     |   . . .   |   njobs   |  | reduction_size
+ *  |           |   |           |           |           |  |
+ *  |           |   |           |           |           |  |
+ *  +-----------+   +-----------+           +-----------+  v
+ *
+ *    |   |   |       |   |   |               |   |   |
+ *    v   v   v       v   v   v               v   v   v
+ *  ===================================================== vertical reduction
+ *
+ *  +-----------+   +-----------+   . . .   +-----------+ result
+ *
+ * In a simple case the result must be contiguous in memory.
+ * @class cpu_reducer_t is an implementation.
+ *
+ * Threads are divided into groups. The groups are independent of each other.
+ * Each group may work on several jobs (the distribution is not uniform, since
+ * njobs might be not a multiple of groups). Threads within a group work on
+ * different parts of the reduction dimension. Thread 0 in each group is called
+ * master (@sa reduce_balancer_t::master()).
+ *
+ * If threading driver does not allow sync between sub-group of threads (e.g.
+ * Intel(R) TBB) the # of thread per group is enforced to be 1.
+ */
+struct reduce_balancer_t {
+    reduce_balancer_t() { init(1, 1, 1, 1, 0); } /* trivial balance */
+    reduce_balancer_t(int nthr, int job_size, int njobs, int reduction_size,
+            size_t max_buffer_size)
+    { init(nthr, job_size, njobs, reduction_size, max_buffer_size); }
+
+    reduce_balancer_t &init(int nthr, int job_size, int njobs,
+            int reduction_size, size_t max_buffer_size)
+    {
+        syncable_ = mkldnn_thr_syncable();
+        nthr_ = nthr;
+        job_size_ = job_size;
+        njobs_ = njobs;
+        reduction_size_ = reduction_size;
+        max_buffer_size_ = max_buffer_size;
+        balance();
+        return *this;
+    }
+
+    bool syncable_;
+    int nthr_;
+    int job_size_, njobs_, reduction_size_;
+
+    int ngroups_; /** number of independent work (thread) groups */
+    int nthr_per_group_; /** number of threads within a single work group */
+    int njobs_per_group_ub_; /** the max # of jobs within a work group */
+
+    bool master(int ithr) const { return id_in_group(ithr) == 0; }
+    bool idle(int ithr) const { return ithr >= nthr_per_group_ * ngroups_; }
+
+    int group_id(int ithr) const { return ithr / nthr_per_group_; }
+    int id_in_group(int ithr) const { return ithr % nthr_per_group_; }
+
+    int grp_njobs(int grp) const {
+        if (grp >= ngroups_) return 0;
+        return njobs_ / ngroups_ + (grp < njobs_ % ngroups_);
+    }
+    int grp_job_off(int grp) const {
+        if (grp >= ngroups_) return njobs_;
+        return njobs_ / ngroups_ * grp + nstl::min(grp, njobs_ % ngroups_);
+    }
+
+    int ithr_njobs(int ithr) const { return grp_njobs(group_id(ithr)); }
+    int ithr_job_off(int ithr) const { return grp_job_off(group_id(ithr)); }
+
+private:
+    size_t max_buffer_size_;
+    void balance();
+};
+
+/** forward declaration of reduce driver */
+template <impl::data_type_t data_type> struct reducer_2d_driver_t;
+
+/** class to perform a reduction over 3D array
+ *
+ * Balancing is based on @class reduce_balancer_t.
+ * Restrictions: the result of the reduction must be contiguous in memory. *
+ * The reduction happens according to the picture below (once more):
+ *
+ *  <--job_size->
+ *  +-----------+   +-----------+           +-----------+  ^
+ *  |           |   |           |           |           |  |
+ *  |           |   |           |           |           |  |
+ *  |     1     |   |     2     |   . . .   |   njobs   |  | reduction_size
+ *  |           |   |           |           |           |  |
+ *  |           |   |           |           |           |  |
+ *  +-----------+   +-----------+           +-----------+  v
+ *
+ *    |   |   |       |   |   |               |   |   |
+ *    v   v   v       v   v   v               v   v   v
+ *  ===================================================== vertical reduction
+ *
+ *  +-----------+   +-----------+   . . .   +-----------+ (contiguous) result
+ *
+ * An example how work might be shared is shown below.
+ *
+ * In this example group 0 owns 2 (independent) jobs -- 2 big squares.
+ * The number of threads per group is also 2 (thread 0 of group 0 and thread 1
+ * of group 0). Master threads (i.e. threads with id 0 in corresponding group)
+ * from each group put the partial result directly into destination memory,
+ * while all the other threads with-in the group use workspace (on the picture
+ * the only thread 1). Once intermediate results obtained each group reduces
+ * corresponding part (own jobs) to the destination memory.
+ *
+ *  <-------   group 0   ------->
+ *
+ *  +-----------+   +-----------+  ^
+ *  |           |   |           |  | thread 0 of  reduces to the dest-memory
+ *  |           |   |           |  | group 0      +-----------+   +-----------+
+ *  |- - - - - -|   |- - - - - -|  X
+ *  |           |   |           |  | thread 1 of  reduces to workspace[tid=1]:
+ *  |           |   |           |  | group 0      +-----------+   +-----------+
+ *  +-----------+   +-----------+  v
+ *                                                  |   |   |       |   |   |
+ *                                                  v   v   v       v   v   v
+ *                                   ((barrier))  =============================
+ *
+ *                                  dest-memory:  +-----------+   +-----------+
+ */
+template <impl::data_type_t data_type>
+struct cpu_reducer_t {
+    typedef typename prec_traits<data_type>::type data_t;
+
+    struct conf_t {
+        conf_t() = default;
+        conf_t &init(const reduce_balancer_t &balancer)
+        { balancer_ = balancer; return *this; }
+
+        void init_scratchpad(memory_tracking::registrar_t &scratchpad) const;
+
+        reduce_balancer_t balancer_;
+    };
+
+    cpu_reducer_t(const conf_t &conf);
+    ~cpu_reducer_t();
+
+    /** initializes reducer.
+     * Must be called from a single thread prior to actual usage */
+    void init(const memory_tracking::grantor_t &scratchpad) const {
+        if (balancer().nthr_per_group_ == 1) return;
+
+        auto bctx = scratchpad.template get<simple_barrier::ctx_t>(
+                memory_tracking::names::key_reducer_space_bctx);
+        for (int i = 0; i < balancer().ngroups_; ++i)
+            simple_barrier::ctx_init(&bctx[i]);
+    }
+
+    /** for given thread returns the pointer where to put partial results.
+     * Reduction destination @p dst must be provided as well (master threads
+     * from each group will use it for partial result to reduce memory
+     * pressure).
+     *
+     * @note: job offset is already applied by get_local_ptr(), which means all
+     *        threads should start writing from the very beginning of returned
+     *        address.
+     */
+    data_t *get_local_ptr(int ithr, data_t *dst,
+            const memory_tracking::grantor_t &scratchpad) const;
+
+    /** performs the reduction with built-in synchronization. */
+    void reduce(int ithr, data_t *dst,
+            const memory_tracking::grantor_t &scratchpad) const {
+        bool redundant_reduction = balancer().nthr_per_group_ == 1
+            || balancer().idle(ithr);
+        if (redundant_reduction) return;
+
+        auto bctx = scratchpad.template get<simple_barrier::ctx_t>(
+                memory_tracking::names::key_reducer_space_bctx);
+        simple_barrier::barrier(&bctx[balancer().group_id(ithr)],
+                balancer().nthr_per_group_);
+
+        reduce_nolock(ithr, dst, scratchpad);
+    }
+
+    const reduce_balancer_t &balancer() const { return conf_.balancer_; }
+
+private:
+    static size_t space_per_thread(const reduce_balancer_t &balancer)
+    { return balancer.njobs_per_group_ub_ * balancer.job_size_; }
+
+    /* The scratchpad is organized as follows:
+     *
+     * data_t space[nthr_][njobs_per_group_ub_][jobs_size_];
+     * simple_barrier::ctx_t barriers[groups_]; */
+
+    const conf_t conf_;
+    reducer_2d_driver_t<data_type> *drv_;
+
+    void reduce_nolock(int ithr, data_t *dst,
+            const memory_tracking::grantor_t &scratchpad) const;
+};
+
+template <impl::data_type_t data_type>
+struct cpu_reducer_2d_t {
+    typedef typename prec_traits<data_type>::type data_t;
+
+    struct conf_t {
+        conf_t() = default;
+        conf_t &init(const reduce_balancer_t &balancer, int job_size_x,
+                int job_size_y, int x_block, int dst_x, int dst_y) {
+            balancer_ = balancer;
+            job_size_x_ = job_size_x;
+            job_size_y_ = job_size_y;
+            x_block_ = x_block;
+            dst_x_ = dst_x;
+            dst_y_ = dst_y;
+            return *this;
+        }
+
+        void init_scratchpad(memory_tracking::registrar_t &scratchpad) const;
+
+        reduce_balancer_t balancer_;
+        int job_size_x_, job_size_y_, x_block_, dst_x_, dst_y_;
+    };
+
+    cpu_reducer_2d_t(const conf_t &conf);
+    ~cpu_reducer_2d_t();
+
+    /** initializes reducer.
+     * Must be called from a single thread prior to actual usage */
+    void init(const memory_tracking::grantor_t &scratchpad) const {
+        if (balancer().nthr_per_group_ == 1) return;
+
+        auto bctx = scratchpad.template get<simple_barrier::ctx_t>(
+                memory_tracking::names::key_reducer_space_bctx);
+        for (int i = 0; i < balancer().ngroups_; ++i)
+            simple_barrier::ctx_init(&bctx[i]);
+    }
+
+    /** for given thread returns the pointer where to put partial results */
+    data_t *get_local_ptr(int ithr,
+            const memory_tracking::grantor_t &scratchpad) const;
+
+    /** performs the reduction with built-in synchronization. */
+    void reduce(int ithr, data_t *dst,
+            const memory_tracking::grantor_t &scratchpad) const {
+        bool redundant_reduction = balancer().nthr_per_group_ == 1
+            || balancer().idle(ithr);
+        if (redundant_reduction) return;
+
+        auto bctx = scratchpad.template get<simple_barrier::ctx_t>(
+                memory_tracking::names::key_reducer_space_bctx);
+        simple_barrier::barrier(&bctx[balancer().group_id(ithr)],
+                balancer().nthr_per_group_);
+
+        reduce_nolock(ithr, dst, scratchpad);
+    }
+
+    const reduce_balancer_t &balancer() const { return conf_.balancer_; }
+
+private:
+    static size_t space_per_thread(const reduce_balancer_t &balancer)
+    { return balancer.njobs_per_group_ub_ * balancer.job_size_; }
+
+    /* The scratchpad is organized as follows:
+     *
+     * data_t space[nthr_][njobs_per_group_ub_][jobs_size_];
+     * simple_barrier::ctx_t barriers[groups_]; */
+
+    const conf_t conf_;
+    reducer_2d_driver_t<data_type> *drv_;
+
+    int choose_x_blocking(int nx, int ny, int nthr_per_grp) const;
+    void reduce_block(const data_t* space_base, data_t *dst,
+            int job, int start_y, int start_x,
+            int ny_start, int nx_start, int ny_step, int nx_step) const;
+    void reduce_nolock(int ithr, data_t *dst,
+            const memory_tracking::grantor_t &scratchpad) const;
+};
+
+/** simple 1d accumulator: y[:] += x[:] */
+template <impl::data_type_t data_type>
+struct cpu_accumulator_1d_t {
+    typedef typename prec_traits<data_type>::type data_t;
+
+    cpu_accumulator_1d_t();
+    ~cpu_accumulator_1d_t();
+    void accumulate(data_t *dst, const data_t *src, size_t size);
+
+    reducer_2d_driver_t<data_type> *drv_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reorder.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reorder.cpp
new file mode 100644
index 0000000000..82be70353d
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reorder.cpp
@@ -0,0 +1,262 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "cpu_engine.hpp"
+#include "cpu_primitive.hpp"
+#include "cpu_reorder_pd.hpp"
+#include "cpu_memory.hpp"
+#include "type_helpers.hpp"
+
+#include "cpu/jit_uni_reorder.hpp"
+#include "cpu/simple_reorder.hpp"
+#include "cpu/wino_reorder.hpp"
+#include "cpu/rnn/rnn_reorders.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using rpd_create_f = mkldnn::impl::engine_t::reorder_primitive_desc_create_f;
+
+namespace {
+using namespace mkldnn::impl::data_type;
+using namespace mkldnn::impl::format_tag;
+
+#define REG_SR(idt, ifmt, odt, ofmt, ...) \
+    simple_reorder_t<idt, ifmt, odt, ofmt, __VA_ARGS__>::pd_t::create
+
+#define REG_SR_BIDIR(idt, ifmt, odt, ofmt) \
+    REG_SR(idt, ifmt, odt, ofmt, fmt_order::keep), \
+    REG_SR(idt, ifmt, odt, ofmt, fmt_order::reverse)
+
+#define REG_SR_DIRECT_COPY(idt, odt) \
+    REG_SR(idt, any, odt, any, fmt_order::any, spec::direct_copy), \
+    REG_SR(idt, any, odt, any, fmt_order::any, spec::direct_copy_except_dim_0)
+
+static const rpd_create_f cpu_reorder_impl_list[] = {
+    /* winograd */
+    wino_reorder_t<f32, f32>::pd_t::create,
+    //wino_reorder_t<f32, s8>::pd_t::create,
+
+    /* rnn reorders */
+    rnn_data_reorder_t<f32, u8>::pd_t::create,
+    rnn_weights_reorder_t<f32, f32>::pd_t::create,
+    rnn_weights_reorder_t<f32, s8>::pd_t::create,
+
+    /* conv reorders w/ compensation */
+    REG_SR(f32, any, s8, hwio, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(f32, any, s8, hwigo, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(s8, any, s8, hwio, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(s8, any, s8, hwigo, fmt_order::keep, spec::conv_s8s8),
+
+    REG_SR(f32, oiw, s8, OIw4i16o4i, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(f32, goiw, s8, gOIw4i16o4i, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(s8, oiw, s8, OIw4i16o4i, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(s8, goiw, s8, gOIw4i16o4i, fmt_order::keep, spec::conv_s8s8),
+
+    REG_SR(f32, oihw, s8, OIhw4i16o4i, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(f32, goihw, s8, gOIhw4i16o4i, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(s8, oihw, s8, OIhw4i16o4i, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(s8, goihw, s8, gOIhw4i16o4i, fmt_order::keep, spec::conv_s8s8),
+
+    REG_SR(f32, goihw, s8, gOIhw2i8o4i, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(s8, goihw, s8, gOIhw2i8o4i, fmt_order::keep, spec::conv_s8s8),
+
+    REG_SR(f32, goihw, s8, gOIhw4o4i, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(s8, goihw, s8, gOIhw4o4i, fmt_order::keep, spec::conv_s8s8),
+
+    REG_SR(f32, goiw, s8, Goiw16g, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(s8, goiw, s8, Goiw16g, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(f32, goihw, s8, Goihw16g, fmt_order::keep, spec::conv_s8s8),
+    REG_SR(s8, goihw, s8, Goihw16g, fmt_order::keep, spec::conv_s8s8),
+
+    /* regular reorders */
+
+#if defined(__INTEL_COMPILER) || (defined(__GNUC__) && !defined(__clang__))
+    /* Direct copy for icc which is faster than jitted code;
+     * Direct copy for gcc which might or might not be faster than jitted
+     * code, but still worth it because doesn't require jitting, i.e. much
+     * faster creation time. This is tentative solution and should be removed
+     * later (when we will cache jitted code?...). */
+    REG_SR_DIRECT_COPY(f32, f32),
+#endif
+
+#ifdef __INTEL_COMPILER
+    /* direct copy for icc, which is faster than jitted code */
+    /*
+    REG_SR_DIRECT_COPY(f32, s32),
+    REG_SR_DIRECT_COPY(f32, s8),
+    REG_SR_DIRECT_COPY(f32, u8),
+    REG_SR_DIRECT_COPY(s32, f32),
+    REG_SR_DIRECT_COPY(s32, s32),
+    REG_SR_DIRECT_COPY(s32, s8),
+    REG_SR_DIRECT_COPY(s32, u8),
+    REG_SR_DIRECT_COPY(s8, f32),
+    REG_SR_DIRECT_COPY(s8, s32),
+    REG_SR_DIRECT_COPY(s8, s8),
+    REG_SR_DIRECT_COPY(s8, u8),
+    REG_SR_DIRECT_COPY(u8, f32),
+    REG_SR_DIRECT_COPY(u8, s32),
+    REG_SR_DIRECT_COPY(u8, s8),
+    REG_SR_DIRECT_COPY(u8, u8),
+    */
+#endif
+
+    /* jit */
+    jit_uni_reorder_create,
+
+    /* fp32: flat <-> blocked with tail */
+    /*
+    REG_SR_BIDIR(f32, any, f32, nCw4c),
+    REG_SR_BIDIR(f32, any, f32, nCw8c),
+    REG_SR_BIDIR(f32, any, f32, OIw4i4o),
+    REG_SR_BIDIR(f32, any, f32, OIw8i8o),
+    REG_SR_BIDIR(f32, any, f32, OIw8o8i),
+    REG_SR_BIDIR(f32, any, f32, gOIw4i4o),
+    REG_SR_BIDIR(f32, any, f32, gOIw8i8o),
+    REG_SR_BIDIR(f32, any, f32, gOIw8o8i),
+
+    REG_SR_BIDIR(f32, any, f32, nCw16c),
+    REG_SR_BIDIR(f32, any, f32, OIw16o16i),
+    REG_SR_BIDIR(f32, any, f32, OIw16i16o),
+    REG_SR_BIDIR(f32, any, f32, IOw16o16i),
+    REG_SR_BIDIR(f32, any, f32, gOIw16o16i),
+    REG_SR_BIDIR(f32, any, f32, gOIw16i16o),
+    REG_SR_BIDIR(f32, any, f32, gIOw16o16i),
+
+    REG_SR_BIDIR(f32, any, f32, nChw4c),
+    REG_SR_BIDIR(f32, any, f32, nChw8c),
+    REG_SR_BIDIR(f32, any, f32, OIhw4i4o),
+    REG_SR_BIDIR(f32, any, f32, Ohwi8o),
+
+    REG_SR_BIDIR(f32, any, f32, OIhw8i8o),
+    REG_SR_BIDIR(f32, any, f32, OIhw8o8i),
+    REG_SR_BIDIR(f32, any, f32, gOIhw4i4o),
+    REG_SR_BIDIR(f32, any, f32, gOIhw4o4i),
+    REG_SR_BIDIR(f32, any, f32, gOhwi8o),
+    REG_SR_BIDIR(f32, any, f32, gOIhw8i8o),
+    REG_SR_BIDIR(f32, any, f32, gOIhw8o8i),
+
+    REG_SR_BIDIR(f32, any, f32, nChw16c),
+    REG_SR_BIDIR(f32, any, f32, Oihw4o),
+    REG_SR_BIDIR(f32, any, f32, Oihw16o),
+    REG_SR_BIDIR(f32, any, f32, Ohwi4o),
+    REG_SR_BIDIR(f32, any, f32, Ohwi16o),
+    REG_SR_BIDIR(f32, any, f32, OIhw16o16i),
+    REG_SR_BIDIR(f32, any, f32, OIhw16i16o),
+    REG_SR_BIDIR(f32, any, f32, IOhw16o16i),
+    REG_SR_BIDIR(f32, any, f32, gOihw4o),
+    REG_SR_BIDIR(f32, any, f32, gOihw16o),
+    REG_SR_BIDIR(f32, any, f32, gOhwi4o),
+    REG_SR_BIDIR(f32, any, f32, gOhwi16o),
+    REG_SR_BIDIR(f32, any, f32, gOIhw16o16i),
+    REG_SR_BIDIR(f32, any, f32, gOIhw16i16o),
+    REG_SR_BIDIR(f32, any, f32, gIOhw16o16i),
+
+    REG_SR_BIDIR(f32, any, f32, nCdhw4c),
+    REG_SR_BIDIR(f32, any, f32, nCdhw8c),
+    REG_SR_BIDIR(f32, any, f32, OIdhw4i4o),
+    REG_SR_BIDIR(f32, any, f32, Odhwi8o),
+    REG_SR_BIDIR(f32, any, f32, OIdhw8i8o),
+    REG_SR_BIDIR(f32, any, f32, OIdhw8o8i),
+    REG_SR_BIDIR(f32, any, f32, gOIdhw4i4o),
+    REG_SR_BIDIR(f32, any, f32, gOdhwi8o),
+    REG_SR_BIDIR(f32, any, f32, gOIdhw8i8o),
+    REG_SR_BIDIR(f32, any, f32, gOIdhw8o8i),
+
+    REG_SR_BIDIR(f32, any, f32, nCdhw16c),
+    REG_SR_BIDIR(f32, any, f32, Oidhw4o),
+    REG_SR_BIDIR(f32, any, f32, Oidhw16o),
+    REG_SR_BIDIR(f32, any, f32, Odhwi16o),
+    REG_SR_BIDIR(f32, any, f32, OIdhw16o16i),
+    REG_SR_BIDIR(f32, any, f32, OIdhw16i16o),
+    REG_SR_BIDIR(f32, any, f32, gOidhw4o),
+    REG_SR_BIDIR(f32, any, f32, gOidhw16o),
+    REG_SR_BIDIR(f32, any, f32, gOdhwi16o),
+    REG_SR_BIDIR(f32, any, f32, gOIdhw16o16i),
+    REG_SR_BIDIR(f32, any, f32, gOIdhw16i16o),
+    */
+
+    /* fp32: blocked <-> blocked with tail */
+    REG_SR_BIDIR(f32, nCw8c, f32, nCw16c),
+    REG_SR_BIDIR(f32, nChw8c, f32, nChw16c),
+    REG_SR_BIDIR(f32, nCdhw8c, f32, nCdhw16c),
+
+    /* int: flat <-> blocked with tail */
+    /*
+    REG_SR_BIDIR(f32, any, s32, nChw16c),
+    REG_SR_BIDIR(f32, any, s8, nChw16c),
+    REG_SR_BIDIR(f32, any, u8, nChw16c),
+    REG_SR_BIDIR(s32, any, f32, nChw16c),
+    REG_SR_BIDIR(s32, any, s32, nChw16c),
+    REG_SR_BIDIR(s32, any, s8, nChw16c),
+    REG_SR_BIDIR(s32, any, u8, nChw16c),
+    REG_SR_BIDIR(s8, any, f32, nChw16c),
+    REG_SR_BIDIR(s8, any, s32, nChw16c),
+    REG_SR_BIDIR(s8, any, s8, nChw16c),
+    REG_SR_BIDIR(s8, any, u8, nChw16c),
+    REG_SR_BIDIR(u8, any, f32, nChw16c),
+    REG_SR_BIDIR(u8, any, s32, nChw16c),
+    REG_SR_BIDIR(u8, any, s8, nChw16c),
+    REG_SR_BIDIR(u8, any, u8, nChw16c),
+
+    REG_SR_BIDIR(f32, any, f32, OIhw4i16o4i),
+    REG_SR_BIDIR(f32, any, s8, OIhw4i16o4i),
+    REG_SR_BIDIR(s8, any, f32, OIhw4i16o4i),
+    REG_SR_BIDIR(s8, any, s8, OIhw4i16o4i),
+    REG_SR_BIDIR(f32, any, s8, gOIhw4i16o4i),
+    REG_SR_BIDIR(s8, any, f32, gOIhw4i16o4i),
+    REG_SR_BIDIR(f32, any, f32, gOIhw4i16o4i),
+    REG_SR_BIDIR(s8, any, s8, gOIhw4i16o4i),
+    */
+
+    /* reference: the last line of defence */
+    /*
+    REG_SR(f32, any, f32, any, fmt_order::any, spec::reference),
+    REG_SR(f32, any, s32, any, fmt_order::any, spec::reference),
+    REG_SR(f32, any, s8, any, fmt_order::any, spec::reference),
+    REG_SR(f32, any, u8, any, fmt_order::any, spec::reference),
+
+    REG_SR(s32, any, f32, any, fmt_order::any, spec::reference),
+    REG_SR(s32, any, s32, any, fmt_order::any, spec::reference),
+    REG_SR(s32, any, s8, any, fmt_order::any, spec::reference),
+    REG_SR(s32, any, u8, any, fmt_order::any, spec::reference),
+
+    REG_SR(s8, any, f32, any, fmt_order::any, spec::reference),
+    REG_SR(s8, any, s32, any, fmt_order::any, spec::reference),
+    REG_SR(s8, any, s8, any, fmt_order::any, spec::reference),
+    REG_SR(s8, any, u8, any, fmt_order::any, spec::reference),
+
+    REG_SR(u8, any, f32, any, fmt_order::any, spec::reference),
+    REG_SR(u8, any, s32, any, fmt_order::any, spec::reference),
+    REG_SR(u8, any, u8, any, fmt_order::any, spec::reference),
+    REG_SR(u8, any, s8, any, fmt_order::any, spec::reference),
+    */
+
+    /* eol */
+    nullptr,
+};
+}
+
+const rpd_create_f *cpu_engine_t::get_reorder_implementation_list() const {
+    return cpu_reorder_impl_list;
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reorder_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reorder_pd.hpp
new file mode 100644
index 0000000000..1622eb6849
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_reorder_pd.hpp
@@ -0,0 +1,48 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REORDER_PD_HPP
+#define CPU_REORDER_PD_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "reorder_pd.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_reorder_pd_t: public reorder_pd_t {
+    using reorder_pd_t::reorder_pd_t;
+
+    status_t init() {
+        const auto &post_ops = attr()->post_ops_;
+        bool args_ok = IMPLICATION(post_ops.len_ != 0, post_ops.len_ == 1
+                && post_ops.entry_[0].kind == primitive_kind::sum);
+        scratchpad_engine_ = src_engine_;
+        return args_ok ? status::success : status::unimplemented;
+    }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_shuffle_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_shuffle_pd.hpp
new file mode 100644
index 0000000000..f16587b99f
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_shuffle_pd.hpp
@@ -0,0 +1,41 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_SHUFFLE_PD_HPP
+#define CPU_SHUFFLE_PD_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "shuffle_pd.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_shuffle_pd_t: public shuffle_pd_t {
+    using shuffle_pd_t::shuffle_pd_t;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_softmax_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_softmax_pd.hpp
new file mode 100644
index 0000000000..3a39eab974
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_softmax_pd.hpp
@@ -0,0 +1,45 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_SOFTMAX_PD_HPP
+#define CPU_SOFTMAX_PD_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "softmax_pd.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_softmax_fwd_pd_t: public softmax_fwd_pd_t {
+    using softmax_fwd_pd_t::softmax_fwd_pd_t;
+};
+
+struct cpu_softmax_bwd_pd_t: public softmax_bwd_pd_t {
+    using softmax_bwd_pd_t::softmax_bwd_pd_t;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_sum.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_sum.cpp
new file mode 100644
index 0000000000..1ab5d9f174
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_sum.cpp
@@ -0,0 +1,48 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "cpu_engine.hpp"
+
+/*
+#include "cpu/ref_sum.hpp"
+#include "cpu/simple_sum.hpp"
+*/
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using spd_create_f = mkldnn::impl::engine_t::sum_primitive_desc_create_f;
+
+namespace {
+#define INSTANCE(...) __VA_ARGS__::pd_t::create
+static const spd_create_f cpu_sum_impl_list[] = {
+    /*
+    INSTANCE(simple_sum_t<data_type::f32>),
+    INSTANCE(ref_sum_t),
+    */
+    nullptr,
+};
+#undef INSTANCE
+}
+
+const spd_create_f *cpu_engine_t::get_sum_implementation_list() const {
+    return cpu_sum_impl_list;
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/cpu_sum_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_sum_pd.hpp
new file mode 100644
index 0000000000..0965129f9b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/cpu_sum_pd.hpp
@@ -0,0 +1,39 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_SUM_PD_HPP
+#define CPU_SUM_PD_HPP
+
+#include "c_types_map.hpp"
+#include "sum_pd.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_sum_pd_t: public sum_pd_t {
+    using sum_pd_t::sum_pd_t;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/gemm_utils_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/gemm_utils_f32.cpp
new file mode 100644
index 0000000000..a9810dec28
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/gemm_utils_f32.cpp
@@ -0,0 +1,372 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+#include <cmath>
+
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+#include "gemm_utils_f32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+namespace gemm_utils {
+#define BM_NOCOPY_AVX 64
+#define BN_NOCOPY_AVX 48
+#define BK_NOCOPY_AVX 384
+#define BN_LARGE_NOCOPY_AVX 192
+#define BM_SMALL_NOCOPY_AVX 16
+#define BN_SMALL_NOCOPY_AVX 1
+#define BK_SMALL_NOCOPY_AVX 4
+// Determine number of threads for each dimension of a 3-D partitioning
+// algorithm based on input parameters
+// m/n/k - First/second/third parameter for GEMM
+// nthrs - total available number of threads
+// nthrs_m/nthrs_n/nthrs_k - number of threads to use in each dimension
+// BM/BN/BK - blocking values
+void calc_nthr_nocopy_avx(int m, int n, int k,
+        int nthrs, int *nthrs_m, int *nthrs_n, int *nthrs_k, int *BM, int *BN,
+        int *BK)
+{
+    int nthr, nthr_m, nthr_n, nthr_k;
+    int MB, NB, KB;
+
+    nthr = nthrs;
+    nthr_m = (m + BM_NOCOPY_AVX - 1) / BM_NOCOPY_AVX;
+    nthr_n = (n + BN_NOCOPY_AVX - 1) / BN_NOCOPY_AVX;
+    nthr_k = 1;
+
+    // Partition along K dimension
+    //  - if threading allows having barriers (e.g. OMP)
+    //  - if there is not enough parallelism along M or N
+    if (mkldnn_thr_syncable()) {
+        int nthr_other = nthr_k = 1;
+        while ((nthr_m * nthr_n * nthr_other < nthr)
+                && (k / (nthr_other + 1) > BK_NOCOPY_AVX)) {
+            nthr_other++;
+            if ((nthr / nthr_other) * nthr_other > 0.9 * nthr)
+                nthr_k = nthr_other;
+        }
+    }
+    nthr /= nthr_k;
+
+    if (nthr_m == 1)
+        nthr_n = nthr;
+    if (nthr_n == 1)
+        nthr_m = nthr;
+
+    // Simple partition reduction
+    while (nthr_m * nthr_n > nthr)
+        if (nthr_m > nthr_n)
+            nthr_m--;
+        else
+            nthr_n--;
+    while (nthr_m * nthr_n < nthr)
+        if (nthr_m < nthr_n)
+            nthr_m++;
+        else
+            nthr_n++;
+
+    if ((nthr_m * nthr_n > nthr) && (nthr_m > 1) && (nthr_n > 1)) {
+
+        if (nthr_m <= nthr_n) {
+            nthr_m = (int)sqrt((double)nthr);
+            if (nthr_m > (m + BM_SMALL_NOCOPY_AVX - 1) / BM_SMALL_NOCOPY_AVX)
+                nthr_m = (m + BM_SMALL_NOCOPY_AVX - 1) / BM_SMALL_NOCOPY_AVX;
+            nthr_n = nthr / nthr_m;
+
+            while ((nthr_m > 1) && (nthr_m * nthr_n != nthr)) {
+                nthr_m--;
+                nthr_n = nthr / nthr_m;
+            }
+        } else {
+            nthr_n = (int)sqrt((double)nthr);
+            if (nthr_n > (n + BN_SMALL_NOCOPY_AVX - 1) / BN_SMALL_NOCOPY_AVX)
+                nthr_n = (n + BN_SMALL_NOCOPY_AVX - 1) / BN_SMALL_NOCOPY_AVX;
+            nthr_m = nthr / nthr_n;
+
+            while ((nthr_n > 1) && (nthr_m * nthr_n != nthr)) {
+                nthr_n--;
+                nthr_m = nthr / nthr_n;
+            }
+        }
+    }
+
+    MB = (m + nthr_m - 1) / nthr_m + BM_SMALL_NOCOPY_AVX - 1;
+    MB -= MB % BM_SMALL_NOCOPY_AVX;
+    NB = (n + nthr_n - 1) / nthr_n + BN_SMALL_NOCOPY_AVX - 1;
+    NB -= NB % BN_SMALL_NOCOPY_AVX;
+    KB = (k + nthr_k - 1) / nthr_k + BK_SMALL_NOCOPY_AVX - 1;
+    KB -= KB % BK_SMALL_NOCOPY_AVX;
+
+    if (MB * nthr_m > m)
+        nthr_m = (m + MB - 1) / MB;
+    if (NB * nthr_n > n)
+        nthr_n = (n + NB - 1) / NB;
+    if (KB * nthr_k > k)
+        nthr_k = (k + KB - 1) / KB;
+
+    *nthrs_m = nthr_m;
+    *nthrs_n = nthr_n;
+    *nthrs_k = nthr_k;
+
+    *BM = MB;
+    *BN = NB;
+    *BK = KB;
+}
+#undef BM_NOCOPY_AVX
+#undef BN_NOCOPY_AVX
+#undef BK_NOCOPY_AVX
+#undef BN_LARGE_NOCOPY_AVX
+#undef BM_SMALL_NOCOPY_AVX
+#undef BN_SMALL_NOCOPY_AVX
+#undef BK_SMALL_NOCOPY_AVX
+
+#define BM_NOCOPY_AVX512_COMMON 32
+#define BN_NOCOPY_AVX512_COMMON 64
+#define BK_NOCOPY_AVX512_COMMON 192
+#define BN_LARGE_NOCOPY_AVX512_COMMON 192
+#define BM_SMALL_NOCOPY_AVX512_COMMON 16
+#define BN_SMALL_NOCOPY_AVX512_COMMON 1
+#define BK_SMALL_NOCOPY_AVX512_COMMON 4
+// Determine number of threads for each dimension of a 3-D partitioning
+// algorithm based on input parameters
+// m/n/k - First/second/third parameter for GEMM
+// nthrs - total available number of threads
+// nthrs_m/nthrs_n/nthrs_k - number of threads to use in each dimension
+// BM/BN/BK - blocking values
+void calc_nthr_nocopy_avx512_common(int m,
+        int n, int k, int nthrs, int *nthrs_m, int *nthrs_n, int *nthrs_k,
+        int *BM, int *BN, int *BK)
+{
+    int nthr, nthr_m, nthr_n, nthr_k = 1;
+    int MB, NB, KB;
+    nthr = nthrs;
+
+    int counter = 0;
+    float ratio_float = 1.;
+    int ratio = 1;
+    nthr = nthrs;
+    int nthr_m_gt_n;
+
+    // Partition along K dimension
+    //  - if threading allows having barriers (e.g. OMP)
+    //  - if there is not enough parallelism along M or N
+    if (mkldnn_thr_syncable()) {
+        if (n <= 2 * BN_NOCOPY_AVX512_COMMON &&
+                m <= 2 * BM_NOCOPY_AVX512_COMMON * nthr) {
+            nthr_k = k / BK_NOCOPY_AVX512_COMMON;
+            if (nthr_k > nthr / 4)
+                nthr_k = nthr / 4;
+            if (nthr_k < 1)
+                nthr_k = 1;
+
+            while ((nthr_k > 1) && (nthr % nthr_k)) {
+                nthr_k--;
+            }
+            nthr /= nthr_k;
+        } else {
+            nthr_k = 1;
+        }
+    }
+    nthr_m = (m + BM_NOCOPY_AVX512_COMMON - 1) / BM_NOCOPY_AVX512_COMMON;
+    nthr_n = (n + BN_NOCOPY_AVX512_COMMON - 1) / BN_NOCOPY_AVX512_COMMON;
+
+    if (nthr_m < 1)
+        nthr_m = 1;
+    if (nthr_n < 1)
+        nthr_n = 1;
+
+    nthr_m_gt_n = nthr_m > nthr_n ? 1 : 0;
+    ratio_float = (float)nthr_m / nthr_n;
+
+    if (nthr_m_gt_n)
+        ratio = (int)ratio_float;
+    else
+        ratio = (int)(1. / ratio_float);
+
+    // scale down nthr_m and nthr_n if they are too large
+    while (nthr_m * nthr_n > 4 * nthr) {
+        nthr_m /= 2;
+        nthr_n /= 2;
+    }
+
+    if (nthr_m < 1)
+        nthr_m = 1;
+    if (nthr_n < 1)
+        nthr_n = 1;
+
+    // Simple partition reduction
+    counter = 0;
+    while (nthr_m * nthr_n > nthr) {
+        if (nthr_m > nthr_n) {
+            if (counter < ratio)
+                nthr_m--;
+            else {
+                nthr_n--;
+                counter = -1;
+            }
+        } else {
+            if (counter < ratio)
+                nthr_n--;
+            else {
+                nthr_m--;
+                counter = -1;
+            }
+        }
+        counter++;
+    }
+
+    // Simple partition increment
+    counter = 0;
+    while (nthr_m * nthr_n < 0.95 * nthr) {
+        if (nthr_m > nthr_n) {
+            if (counter < ratio)
+                nthr_m++;
+            else {
+                nthr_n++;
+                counter = -1;
+            }
+        } else {
+            if (counter < ratio)
+                nthr_n++;
+            else {
+                nthr_m++;
+                counter = -1;
+            }
+        }
+        counter++;
+    }
+
+    // if nothing works out, then this should work
+    if ((nthr_m * nthr_n > nthr)) {
+
+        if (nthr_m <= nthr_n) {
+            nthr_m = (int)sqrt((double)nthr);
+            if (nthr_m > (m + BM_SMALL_NOCOPY_AVX512_COMMON - 1)
+                            / BM_SMALL_NOCOPY_AVX512_COMMON)
+                nthr_m = (m + BM_SMALL_NOCOPY_AVX512_COMMON - 1)
+                        / BM_SMALL_NOCOPY_AVX512_COMMON;
+            nthr_n = nthr / nthr_m;
+
+            while ((nthr_m > 1) && (nthr_m * nthr_n != nthr)) {
+                nthr_m--;
+                nthr_n = nthr / nthr_m;
+            }
+        } else {
+            nthr_n = (int)sqrt((double)nthr);
+            if (nthr_n > (n + BN_SMALL_NOCOPY_AVX512_COMMON - 1)
+                            / BN_SMALL_NOCOPY_AVX512_COMMON)
+                nthr_n = (n + BN_SMALL_NOCOPY_AVX512_COMMON - 1)
+                        / BN_SMALL_NOCOPY_AVX512_COMMON;
+            nthr_m = nthr / nthr_n;
+
+            while ((nthr_n > 1) && (nthr_m * nthr_n != nthr)) {
+                nthr_n--;
+                nthr_m = nthr / nthr_n;
+            }
+        }
+    }
+
+    MB = (m + nthr_m - 1) / nthr_m + BM_SMALL_NOCOPY_AVX512_COMMON - 1;
+    MB -= MB % BM_SMALL_NOCOPY_AVX512_COMMON;
+    NB = (n + nthr_n - 1) / nthr_n + BN_SMALL_NOCOPY_AVX512_COMMON - 1;
+    NB -= NB % BN_SMALL_NOCOPY_AVX512_COMMON;
+    KB = (k + nthr_k - 1) / nthr_k + BK_SMALL_NOCOPY_AVX512_COMMON - 1;
+    KB -= KB % BK_SMALL_NOCOPY_AVX512_COMMON;
+
+    if (MB * nthr_m > m)
+        nthr_m = (m + MB - 1) / MB;
+    if (NB * nthr_n > n)
+        nthr_n = (n + NB - 1) / NB;
+    if (KB * nthr_k > k)
+        nthr_k = (k + KB - 1) / KB;
+
+    *nthrs_m = nthr_m;
+    *nthrs_n = nthr_n;
+    *nthrs_k = nthr_k;
+
+    *BM = MB;
+    *BN = NB;
+    *BK = KB;
+}
+#undef BM_NOCOPY_AVX512_COMMON
+#undef BN_NOCOPY_AVX512_COMMON
+#undef BK_NOCOPY_AVX512_COMMON
+#undef BN_LARGE_NOCOPY_AVX512_COMMON
+#undef BM_SMALL_NOCOPY_AVX512_COMMON
+#undef BN_SMALL_NOCOPY_AVX512_COMMON
+#undef BK_SMALL_NOCOPY_AVX512_COMMON
+
+// Partition n values as equally as possible among nthr threads
+// and set the offset (t_offset) and number of values (t_block) for ithr
+// Assumption: 0 <= ithr < nthr
+void partition_unit_diff(
+        int ithr, int nthr, int n, int *t_offset, int *t_block)
+{
+    int band = n / nthr;
+    if (band == 0)
+        band = 1;
+    int tail = n - band * nthr;
+    if (tail < 0)
+        tail = 0;
+
+    if (ithr < tail) {
+        band++;
+        *t_offset = band * ithr;
+        *t_block = band;
+    } else {
+        *t_offset = band * ithr + tail;
+        *t_block = band;
+    }
+
+    if (*t_offset >= n) {
+        *t_offset = 0;
+        *t_block = 0;
+    }
+
+    if (*t_offset + *t_block > n) {
+        *t_block = n - *t_offset;
+    }
+}
+
+// Sum the m*n values from p_src into p_dst, assuming the two-dimensional
+// arrays have leading dimensions ld_src and ld_dst, respectively
+template<typename data_t>
+void sum_two_matrices(int m, int n,
+        data_t * __restrict p_src, dim_t ld_src,
+        data_t * __restrict p_dst, dim_t ld_dst)
+{
+    int i, j;
+    for (j = 0; j < n; j++) {
+        for (i = 0; i < m; i++) {
+            p_dst[i + j * ld_dst] += p_src[i + j * ld_src];
+        }
+    }
+}
+
+template
+void sum_two_matrices<float>(int m, int n,
+        float * __restrict p_src, dim_t ld_src,
+        float * __restrict p_dst, dim_t ld_dst);
+
+template
+void sum_two_matrices<double>(int m, int n,
+        double * __restrict p_src, dim_t ld_src,
+        double * __restrict p_dst, dim_t ld_dst);
+}
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/gemm_utils_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/gemm_utils_f32.hpp
new file mode 100644
index 0000000000..3352298b4a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/gemm_utils_f32.hpp
@@ -0,0 +1,72 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef GEMM_UTILS_HPP
+#define GEMM_UTILS_HPP
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace gemm_utils {
+// Alias for any dimension related variable.
+typedef ptrdiff_t dim_t;
+
+template <typename T, bool isTransA, bool isTransB>
+struct gemm_traits {};
+
+template <bool isTransA, bool isTransB>
+struct gemm_traits<double, isTransA, isTransB> {
+    static constexpr int m = 8;
+    static constexpr int n = 6;
+    static constexpr int BM = 4032;
+    static constexpr int BN = isTransA ? 96 : 192;
+    static constexpr int BK = isTransB ? 96 : 512;
+};
+
+template <bool isTransA, bool isTransB>
+struct gemm_traits<float, isTransA, isTransB> {
+    static constexpr int m = 16;
+    static constexpr int n = 6;
+    static constexpr int BM = 4032;
+    static constexpr int BN = isTransA ? 96 : 48;
+    static constexpr int BK = isTransB ? 96 : 256;
+};
+
+template <typename T>
+using unroll_factor = gemm_traits<T, false, false>;
+
+template <typename data_t>
+void sum_two_matrices(int m, int n,
+        data_t * __restrict p_src, dim_t ld_src,
+        data_t * __restrict p_dst, dim_t ld_dst);
+
+void calc_nthr_nocopy_avx512_common(int m,
+        int n, int k, int nthrs, int *nthrs_m, int *nthrs_n, int *nthrs_k,
+        int *BM, int *BN, int *BK);
+
+void calc_nthr_nocopy_avx(int m, int n, int k,
+        int nthrs, int *nthrs_m, int *nthrs_n, int *nthrs_k, int *BM, int *BN,
+        int *BK);
+
+void partition_unit_diff(
+        int ithr, int nthr, int n, int *t_offset, int *t_block);
+};
+
+}
+}
+}
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx512_common_gemm_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx512_common_gemm_f32.cpp
new file mode 100644
index 0000000000..d7be43e392
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx512_common_gemm_f32.cpp
@@ -0,0 +1,2131 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <cmath>
+#include <mutex>
+
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "ref_gemm_f32.hpp"
+#include "gemm_utils_f32.hpp"
+#include "jit_avx512_common_gemm_f32.hpp"
+
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+#define CACHE_LINE_SIZE 64
+
+#define STACKSIZE get_size_of_abi_save_regs()
+#ifdef _WIN32
+#define STACK_K_CAPACITY 32
+#else
+#define STACK_K_CAPACITY 2048
+#endif
+#define SIZE 4
+#define OFFSET 128
+#define BASE_SHIFT 2
+#define SECOND_FETCH unroll_n
+#define UNROLL_M 48
+#define UNROLL_N 8
+
+namespace avx512_common_gemm_f32 {
+using namespace gemm_utils;
+
+struct xbyak_gemm : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_common_gemm_f32_xbyak_gemm)
+
+    xbyak_gemm(char isTransA, char isTransB, float beta, bool hasBias = false,
+            void *code_ptr = nullptr,
+            size_t code_size = 80 * Xbyak::DEFAULT_MAX_CODE_SIZE)
+        : jit_generator(code_ptr, code_size)
+    {
+        using namespace Xbyak;
+
+        enum { ver_avx512_core, ver_avx512_mic } ver =
+            mayiuse(avx512_core) ? ver_avx512_core : ver_avx512_mic;
+
+        bool isBeta0 = (beta == 0.0);
+        bool isBetaN = (!isBeta0 && beta != 1.0);
+
+        // various definitions for convenience
+        auto ARG_M = abi_param1;
+        auto ARG_N = abi_param2;
+        auto K = abi_param3;
+        auto ARG_ALPHA = abi_param4;
+#ifdef _WIN32
+        auto ARG_A = ptr[rsp + OFFSET_SHADOWSPACE + STACKSIZE];
+        auto ARG_LDA = qword[rsp + OFFSET_SHADOWSPACE +
+            sizeof(float *) + STACKSIZE];
+        const auto stackOffset = OFFSET_SHADOWSPACE +
+            sizeof(float *) + STACKSIZE;
+        auto A = rsi;
+        auto LDA = rdi;
+#else
+        auto ARG_A = r8;
+        auto ARG_LDA = r9;
+        const auto stackOffset = STACKSIZE;
+        auto A = ARG_A;
+        auto LDA = ARG_LDA;
+#endif
+        auto ARG_B = ptr[rsp + 8 + stackOffset];
+        auto ARG_LDB = ptr[rsp + 16 + stackOffset];
+        auto ARG_BETA = ptr[rsp + 24 + stackOffset];
+        auto ARG_C = ptr[rsp + 32 + stackOffset];
+        auto ARG_LDC = ptr[rsp + 40 + stackOffset];
+        auto ARG_BIAS = ptr[rsp + 48 + stackOffset];
+        auto ARG_WS = ptr[rsp + 56 + stackOffset];
+
+        auto B = r11;
+        auto LDB = rbx;
+        auto LDC = r13;
+        auto LL = rax;
+        auto AO1 = abi_param2;
+        auto BO1 = abi_param4;
+        auto BO2 = rbp;
+        auto CO1 = r14;
+        auto CO2 = r15;
+        auto LDB3 = r10;
+        auto LDA4 = abi_param1;
+        auto AA = r12;
+        auto BIAS1 = abi_param1;
+
+        auto M = qword[rsp + 0];
+        auto N = qword[rsp + 8];
+        auto FLAG = qword[rsp + 16];
+        auto I = qword[rsp + 24];
+        auto C = qword[rsp + 32];
+        auto BIAS = qword[rsp + 40];
+        auto ALPHA = qword[rsp + 48];
+        auto BETA = qword[rsp + 64];
+        auto ORIG_A = qword[rsp + 80];
+        auto ORIG_SP = qword[rsp + 120];
+
+        auto ZSTRIDE = zmm4;
+        auto VALPHA = zmm6;
+        auto VBETA = zmm7;
+        auto VBIAS1 = zmm1;
+        auto VBIAS2 = zmm2;
+        auto VBIAS3 = zmm3;
+
+        auto PREFETCHSIZEA = ver == ver_avx512_core ? 48 : 80;
+        auto PREFETCHSIZEB = 16;
+
+        Zmm regs[] = { zmm8, zmm9, zmm10, zmm11, zmm12, zmm13, zmm14, zmm15,
+            zmm16, zmm17, zmm18, zmm19, zmm20, zmm21, zmm22, zmm23, zmm24,
+            zmm25, zmm26, zmm27, zmm28, zmm29, zmm30, zmm31 };
+
+        // Function for packing if needed
+        auto do_pack = [&](int unroll_m) {
+            Label pack2, pack3, pack4, pack10;
+
+            mov(BO1, A);
+            lea(AO1, ptr[rsp + 128 + OFFSET * SIZE]);
+            mov(LL, K);
+            sar(LL, 2);
+            jle(pack3, T_NEAR);
+            align(16);
+
+            L(pack2);
+            if (!isTransA) {
+                for (int i = 0; i < 4; i++) {
+                    vmovups(zmm0 | k1, ptr[BO1 + (0 * 16 - OFFSET) * SIZE]);
+                    if (unroll_m > 16)
+                        vmovups(zmm1 | k2, ptr[BO1 + (1 * 16 - OFFSET) * SIZE]);
+                    if (unroll_m > 32)
+                        vmovups(zmm2 | k3, ptr[BO1 + (2 * 16 - OFFSET) * SIZE]);
+                    add(BO1, LDA);
+
+                    vmovups(ptr[AO1 + (unroll_m * i + 0 * 16 - OFFSET) * SIZE]
+                                    | k1,
+                            zmm0);
+                    if (unroll_m > 16)
+                        vmovups(ptr[AO1
+                                        + (unroll_m * i + 1 * 16 - OFFSET)
+                                                * SIZE]
+                                        | k2,
+                                zmm1);
+                    if (unroll_m > 32)
+                        vmovups(ptr[AO1
+                                        + (unroll_m * i + 2 * 16 - OFFSET)
+                                                * SIZE]
+                                        | k3,
+                                zmm2);
+                }
+            } else {
+                for (int i = 0; i < 4; i++) {
+                    kmovw(k4, k1);
+                    vgatherqps(ymm5 | k4,
+                            ptr[BO1 + ZSTRIDE + (i - OFFSET) * SIZE]);
+                    lea(BO2, ptr[BO1 + LDA * 8]);
+                    kshiftrw(k4, k1, 8);
+                    vgatherqps(ymm6 | k4,
+                            ptr[BO2 + ZSTRIDE + (i - OFFSET) * SIZE]);
+                    vshuff64x2(zmm0, zmm5, zmm6, 0x44);
+
+                    if (unroll_m > 16) {
+                        lea(BO2, ptr[BO2 + LDA * 8]);
+                        kmovw(k4, k2);
+                        vgatherqps(ymm5 | k4,
+                                ptr[BO2 + ZSTRIDE + (i - OFFSET) * SIZE]);
+                        lea(BO2, ptr[BO2 + LDA * 8]);
+                        kshiftrw(k4, k2, 8);
+                        vgatherqps(ymm6 | k4,
+                                ptr[BO2 + ZSTRIDE + (i - OFFSET) * SIZE]);
+                        vshuff64x2(zmm1, zmm5, zmm6, 0x44);
+                    }
+
+                    if (unroll_m > 32) {
+                        lea(BO2, ptr[BO2 + LDA * 8]);
+                        kmovw(k4, k3);
+                        vgatherqps(ymm5 | k4,
+                                ptr[BO2 + ZSTRIDE + (i - OFFSET) * SIZE]);
+                        lea(BO2, ptr[BO2 + LDA * 8]);
+                        kshiftrw(k4, k3, 8);
+                        vgatherqps(ymm6 | k4,
+                                ptr[BO2 + ZSTRIDE + (i - OFFSET) * SIZE]);
+                        lea(BO2, ptr[BO2 + LDA * 8]);
+                        vshuff64x2(zmm2, zmm5, zmm6, 0x44);
+                    }
+
+                    vmovups(ptr[AO1 + (unroll_m * i + 0 * 16 - OFFSET) * SIZE],
+                            zmm0 | k1);
+                    if (unroll_m > 16)
+                        vmovups(ptr[AO1
+                                        + (unroll_m * i + 1 * 16 - OFFSET)
+                                                * SIZE],
+                                zmm1 | k2);
+                    if (unroll_m > 32)
+                        vmovups(ptr[AO1
+                                        + (unroll_m * i + 2 * 16 - OFFSET)
+                                                * SIZE],
+                                zmm2 | k3);
+                }
+                add(BO1, 4 * SIZE);
+            }
+            add(AO1, unroll_m * 4 * SIZE);
+
+            sub(LL, 1);
+            jg(pack2, T_NEAR);
+            align(16);
+
+            L(pack3);
+            mov(LL, K);
+            and_(LL, 3);
+            jle(pack10, T_NEAR);
+            align(16);
+
+            L(pack4);
+            if (!isTransA) {
+                vmovups(zmm0 | k1, ptr[BO1 + (0 * 16 - OFFSET) * SIZE]);
+                if (unroll_m > 16)
+                    vmovups(zmm1 | k2, ptr[BO1 + (1 * 16 - OFFSET) * SIZE]);
+                if (unroll_m > 32)
+                    vmovups(zmm2 | k3, ptr[BO1 + (2 * 16 - OFFSET) * SIZE]);
+                add(BO1, LDA);
+            } else {
+                kmovw(k4, k1);
+                vgatherqps(ymm5 | k4, ptr[BO1 + ZSTRIDE + (0 - OFFSET) * SIZE]);
+                lea(BO2, ptr[BO1 + LDA * 8]);
+                kshiftrw(k4, k1, 8);
+                vgatherqps(ymm6 | k4, ptr[BO2 + ZSTRIDE + (0 - OFFSET) * SIZE]);
+                vshuff64x2(zmm0, zmm5, zmm6, 0x44);
+
+                if (unroll_m > 16) {
+                    lea(BO2, ptr[BO2 + LDA * 8]);
+                    kmovw(k4, k2);
+                    vgatherqps(ymm5 | k4,
+                            ptr[BO2 + ZSTRIDE + (0 - OFFSET) * SIZE]);
+                    lea(BO2, ptr[BO2 + LDA * 8]);
+                    kshiftrw(k4, k2, 8);
+                    vgatherqps(ymm6 | k4,
+                            ptr[BO2 + ZSTRIDE + (0 - OFFSET) * SIZE]);
+                    vshuff64x2(zmm1, zmm5, zmm6, 0x44);
+                }
+
+                if (unroll_m > 32) {
+                    lea(BO2, ptr[BO2 + LDA * 8]);
+                    kmovw(k4, k3);
+                    vgatherqps(ymm5 | k4,
+                            ptr[BO2 + ZSTRIDE + (0 - OFFSET) * SIZE]);
+                    lea(BO2, ptr[BO2 + LDA * 8]);
+                    kshiftrw(k4, k3, 8);
+                    vgatherqps(ymm6 | k4,
+                            ptr[BO2 + ZSTRIDE + (0 - OFFSET) * SIZE]);
+                    lea(BO2, ptr[BO2 + LDA * 8]);
+                    vshuff64x2(zmm2, zmm5, zmm6, 0x44);
+                }
+                add(BO1, SIZE);
+            }
+
+            vmovups(ptr[AO1 + (unroll_m * 0 + 0 * 16 - OFFSET) * SIZE],
+                    zmm0 | k1);
+            if (unroll_m > 16)
+                vmovups(ptr[AO1 + (unroll_m * 0 + 1 * 16 - OFFSET) * SIZE],
+                        zmm1 | k2);
+            if (unroll_m > 32)
+                vmovups(ptr[AO1 + (unroll_m * 0 + 2 * 16 - OFFSET) * SIZE],
+                        zmm2 | k3);
+
+            add(AO1, unroll_m * SIZE);
+            sub(LL, 1);
+            jg(pack4, T_NEAR);
+            align(16);
+
+            L(pack10);
+        };
+
+        // Function to update C, covering masking and other considerations
+        auto update = [&](Zmm reg, bool useCO1, int offset, int mask,
+                bool useScale = false) {
+            vmulps(reg, reg, VALPHA);
+            if (!isBeta0) {
+                if (!useScale) {
+                    switch (mask) {
+                    case 0:
+                        if (useCO1)
+                            vmovups(zmm0, ptr[CO1 + offset * SIZE]);
+                        else
+                            vmovups(zmm0, ptr[CO2 + offset * SIZE]);
+                        break;
+                    case 1:
+                        if (useCO1)
+                            vmovups(zmm0 | k1 | T_z, ptr[CO1 + offset * SIZE]);
+                        else
+                            vmovups(zmm0 | k1 | T_z, ptr[CO2 + offset * SIZE]);
+                        break;
+                    case 2:
+                        if (useCO1)
+                            vmovups(zmm0 | k2 | T_z, ptr[CO1 + offset * SIZE]);
+                        else
+                            vmovups(zmm0 | k2 | T_z, ptr[CO2 + offset * SIZE]);
+                        break;
+                    case 3:
+                        if (useCO1)
+                            vmovups(zmm0 | k3 | T_z, ptr[CO1 + offset * SIZE]);
+                        else
+                            vmovups(zmm0 | k3 | T_z, ptr[CO2 + offset * SIZE]);
+                        break;
+                    }
+                } else {
+                    switch (mask) {
+                    case 0:
+                        if (useCO1)
+                            vmovups(zmm0, ptr[CO1 + LDC + offset * SIZE]);
+                        else
+                            vmovups(zmm0, ptr[CO2 + LDC + offset * SIZE]);
+                        break;
+                    case 1:
+                        if (useCO1)
+                            vmovups(zmm0 | k1 | T_z,
+                                    ptr[CO1 + LDC + offset * SIZE]);
+                        else
+                            vmovups(zmm0 | k1 | T_z,
+                                    ptr[CO2 + LDC + offset * SIZE]);
+                        break;
+                    case 2:
+                        if (useCO1)
+                            vmovups(zmm0 | k2 | T_z,
+                                    ptr[CO1 + LDC + offset * SIZE]);
+                        else
+                            vmovups(zmm0 | k2 | T_z,
+                                    ptr[CO2 + LDC + offset * SIZE]);
+                        break;
+                    case 3:
+                        if (useCO1)
+                            vmovups(zmm0 | k3 | T_z,
+                                    ptr[CO1 + LDC + offset * SIZE]);
+                        else
+                            vmovups(zmm0 | k3 | T_z,
+                                    ptr[CO2 + LDC + offset * SIZE]);
+                        break;
+                    }
+                }
+                if (!isBetaN) {
+                    vaddps(zmm0, reg, zmm0);
+                } else {
+                    vfmadd132ps(zmm0, reg, VBETA);
+                }
+                if (!useScale) {
+                    switch (mask) {
+                    case 0:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + offset * SIZE], zmm0);
+                        else
+                            vmovups(ptr[CO2 + offset * SIZE], zmm0);
+                        break;
+                    case 1:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + offset * SIZE], zmm0 | k1);
+                        else
+                            vmovups(ptr[CO2 + offset * SIZE], zmm0 | k1);
+                        break;
+                    case 2:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + offset * SIZE], zmm0 | k2);
+                        else
+                            vmovups(ptr[CO2 + offset * SIZE], zmm0 | k2);
+                        break;
+                    case 3:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + offset * SIZE], zmm0 | k3);
+                        else
+                            vmovups(ptr[CO2 + offset * SIZE], zmm0 | k3);
+                        break;
+                    }
+                } else {
+                    switch (mask) {
+                    case 0:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + LDC + offset * SIZE], zmm0);
+                        else
+                            vmovups(ptr[CO2 + LDC + offset * SIZE], zmm0);
+                        break;
+                    case 1:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + LDC + offset * SIZE], zmm0 | k1);
+                        else
+                            vmovups(ptr[CO2 + LDC + offset * SIZE], zmm0 | k1);
+                        break;
+                    case 2:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + LDC + offset * SIZE], zmm0 | k2);
+                        else
+                            vmovups(ptr[CO2 + LDC + offset * SIZE], zmm0 | k2);
+                        break;
+                    case 3:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + LDC + offset * SIZE], zmm0 | k3);
+                        else
+                            vmovups(ptr[CO2 + LDC + offset * SIZE], zmm0 | k3);
+                        break;
+                    }
+                }
+            } else {
+                if (!useScale) {
+                    switch (mask) {
+                    case 0:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + offset * SIZE], reg);
+                        else
+                            vmovups(ptr[CO2 + offset * SIZE], reg);
+                        break;
+                    case 1:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + offset * SIZE], reg | k1);
+                        else
+                            vmovups(ptr[CO2 + offset * SIZE], reg | k1);
+                        break;
+                    case 2:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + offset * SIZE], reg | k2);
+                        else
+                            vmovups(ptr[CO2 + offset * SIZE], reg | k2);
+                        break;
+                    case 3:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + offset * SIZE], reg | k3);
+                        else
+                            vmovups(ptr[CO2 + offset * SIZE], reg | k3);
+                        break;
+                    }
+                } else {
+                    switch (mask) {
+                    case 0:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + LDC + offset * SIZE], reg);
+                        else
+                            vmovups(ptr[CO2 + LDC + offset * SIZE], reg);
+                        break;
+                    case 1:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + LDC + offset * SIZE], reg | k1);
+                        else
+                            vmovups(ptr[CO2 + LDC + offset * SIZE], reg | k1);
+                        break;
+                    case 2:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + LDC + offset * SIZE], reg | k2);
+                        else
+                            vmovups(ptr[CO2 + LDC + offset * SIZE], reg | k2);
+                        break;
+                    case 3:
+                        if (useCO1)
+                            vmovups(ptr[CO1 + LDC + offset * SIZE], reg | k3);
+                        else
+                            vmovups(ptr[CO2 + LDC + offset * SIZE], reg | k3);
+                        break;
+                    }
+                }
+            }
+            vpxorq(reg, reg, reg);
+        };
+
+        // Loop with unroll_n - 2 FMAs; called by innerkernel
+        auto fmaloop = [&](int unroll_m, int unroll_n, int iteration) {
+            for (int i = 2; i < unroll_n; i++) {
+                if (ver == ver_avx512_core) {
+                    if (!isTransB) {
+                        switch (i) {
+                        case 2:
+                            vbroadcastss(
+                                    zmm3,
+                                    ptr[BO1 + LDB * 2
+                                            + (iteration - OFFSET) * SIZE]);
+                            break;
+                        case 3:
+                            vbroadcastss(
+                                    zmm3,
+                                    ptr[BO1 + LDB3
+                                            + (iteration - OFFSET) * SIZE]);
+                            break;
+                        case 4:
+                            vbroadcastss(zmm3,
+                                    ptr[BO2 + (iteration - OFFSET) * SIZE]);
+                            break;
+                        case 5:
+                            vbroadcastss(
+                                    zmm3,
+                                    ptr[BO2 + LDB * 1
+                                            + (iteration - OFFSET) * SIZE]);
+                            break;
+                        case 6:
+                            vbroadcastss(
+                                    zmm3,
+                                    ptr[BO2 + LDB * 2
+                                            + (iteration - OFFSET) * SIZE]);
+                            break;
+                        case 7:
+                            vbroadcastss(
+                                    zmm3,
+                                    ptr[BO2 + LDB3
+                                            + (iteration - OFFSET) * SIZE]);
+                            break;
+                        }
+                    } else {
+                        vbroadcastss(zmm3, ptr[BO1 + (i - OFFSET) * SIZE]);
+                    }
+                    vfmadd231ps(regs[i], zmm3, zmm0);
+                    if (unroll_m >= 32)
+                        vfmadd231ps(regs[i + 8], zmm3, zmm1);
+                    if (unroll_m >= 48)
+                        vfmadd231ps(regs[i + 16], zmm3, zmm2);
+                } else {
+                    if (!isTransB) {
+                        switch (i) {
+                        case 2:
+                            vfmadd231ps(regs[i], zmm0,
+                                    zword_b[BO1 + LDB * 2
+                                    + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 32)
+                                vfmadd231ps(regs[i + 8], zmm1,
+                                        zword_b[BO1 + LDB * 2
+                                        + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 48)
+                                vfmadd231ps(regs[i + 16], zmm2,
+                                        zword_b[BO1 + LDB * 2
+                                        + (iteration - OFFSET) * SIZE]);
+                            break;
+                        case 3:
+                            vfmadd231ps(regs[i], zmm0,
+                                    zword_b[BO1 + LDB3
+                                    + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 32)
+                                vfmadd231ps(regs[i + 8], zmm1,
+                                        zword_b[BO1 + LDB3
+                                        + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 48)
+                                vfmadd231ps(regs[i + 16], zmm2,
+                                        zword_b[BO1 + LDB3
+                                        + (iteration - OFFSET) * SIZE]);
+                            break;
+                        case 4:
+                            vfmadd231ps(regs[i], zmm0,
+                                    zword_b[BO2 + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 32)
+                                vfmadd231ps(regs[i + 8], zmm1,
+                                        zword_b[BO2 + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 48)
+                                vfmadd231ps(regs[i + 16], zmm2,
+                                        zword_b[BO2 + (iteration - OFFSET) * SIZE]);
+                            break;
+                        case 5:
+                            vfmadd231ps(regs[i], zmm0,
+                                    zword_b[BO2 + LDB * 1
+                                    + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 32)
+                                vfmadd231ps(regs[i + 8], zmm1,
+                                        zword_b[BO2 + LDB * 1
+                                        + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 48)
+                                vfmadd231ps(regs[i + 16], zmm2,
+                                        zword_b[BO2 + LDB * 1
+                                        + (iteration - OFFSET) * SIZE]);
+                            break;
+                        case 6:
+                            vfmadd231ps(regs[i], zmm0,
+                                    zword_b[BO2 + LDB * 2
+                                    + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 32)
+                                vfmadd231ps(regs[i + 8], zmm1,
+                                        zword_b[BO2 + LDB * 2
+                                        + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 48)
+                                vfmadd231ps(regs[i + 16], zmm2,
+                                        zword_b[BO2 + LDB * 2
+                                        + (iteration - OFFSET) * SIZE]);
+                            break;
+                        case 7:
+                            vfmadd231ps(regs[i], zmm0,
+                                    zword_b[BO2 + LDB3
+                                    + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 32)
+                                vfmadd231ps(regs[i + 8], zmm1,
+                                        zword_b[BO2 + LDB3
+                                        + (iteration - OFFSET) * SIZE]);
+                            if (unroll_m >= 48)
+                                vfmadd231ps(regs[i + 16], zmm2,
+                                        zword_b[BO2 + LDB3
+                                        + (iteration - OFFSET) * SIZE]);
+                            break;
+                        }
+                    } else {
+                        vfmadd231ps(
+                                regs[i], zmm0, zword_b[BO1 + (i - OFFSET) * SIZE]);
+                        if (unroll_m >= 32)
+                            vfmadd231ps(regs[i + 8], zmm1,
+                                    zword_b[BO1 + (i - OFFSET) * SIZE]);
+                        if (unroll_m >= 48)
+                            vfmadd231ps(regs[i + 16], zmm2,
+                                    zword_b[BO1 + (i - OFFSET) * SIZE]);
+                    }
+                }
+            }
+        };
+
+        // Innerkernel; called by kernel
+        auto innerkernel = [&](int unroll_m, int unroll_n, bool isDirect,
+                bool isCopy, bool doCPrefetch, bool isUnmasked = true) {
+            for (int i = 0; i < 8; i++) {
+                if (!isDirect) {
+                    prefetcht0(ptr[AO1
+                            + (PREFETCHSIZEA + i * unroll_m + 0 * 16 - OFFSET)
+                                    * SIZE]);
+                    if (unroll_m >= 32)
+                        prefetcht0(ptr[AO1
+                            + (PREFETCHSIZEA + i * unroll_m + 1 * 16 - OFFSET)
+                                    * SIZE]);
+                    if (unroll_m >= 48)
+                        prefetcht0(ptr[AO1
+                            + (PREFETCHSIZEA + i * unroll_m + 2 * 16 - OFFSET)
+                                    * SIZE]);
+                } else {
+                    prefetcht0(ptr[AO1 + LDA4 + (16 * 0 * SIZE)]);
+                    if (unroll_m >= 32)
+                        prefetcht0(ptr[AO1 + LDA4 + (16 * 1 * SIZE)]);
+                    if (unroll_m >= 48)
+                        prefetcht0(ptr[AO1 + LDA4 + (16 * 2 * SIZE)]);
+                }
+
+                if (!isDirect) {
+                    if (i != 0) {
+                        if (isUnmasked || unroll_m > 16) {
+                            vmovups(zmm0,
+                                    ptr[AO1
+                                            + (unroll_m * i + 0 * 16 - OFFSET)
+                                                    * SIZE]);
+                        } else {
+                            vmovups(zmm0 | k1 | T_z,
+                                    ptr[AO1
+                                            + (unroll_m * i + 0 * 16 - OFFSET)
+                                                    * SIZE]);
+                        }
+                        if (unroll_m >= 32) {
+                            if (isUnmasked || unroll_m > 32) {
+                                vmovups(zmm1, ptr[AO1
+                                                      + (unroll_m * i + 1 * 16
+                                                                - OFFSET)
+                                                              * SIZE]);
+                            } else {
+                                vmovups(zmm1 | k2 | T_z,
+                                        ptr[AO1
+                                                + (unroll_m * i + 1 * 16
+                                                          - OFFSET)
+                                                        * SIZE]);
+                            }
+                        }
+                        if (unroll_m >= 48) {
+                            if (isUnmasked) {
+                                vmovups(zmm2, ptr[AO1
+                                                      + (unroll_m * i + 2 * 16
+                                                                - OFFSET)
+                                                              * SIZE]);
+                            } else {
+                                vmovups(zmm2 | k3 | T_z,
+                                        ptr[AO1
+                                                + (unroll_m * i + 2 * 16
+                                                          - OFFSET)
+                                                        * SIZE]);
+                            }
+                        }
+                    }
+                } else {
+                    if (isUnmasked || unroll_m > 16) {
+                        vmovups(zmm0, ptr[AO1 + (0 * 16 - OFFSET) * SIZE]);
+                    } else {
+                        vmovups(zmm0 | k1 | T_z,
+                                ptr[AO1 + (0 * 16 - OFFSET) * SIZE]);
+                    }
+                    if (unroll_m >= 32) {
+                        if (isUnmasked || unroll_m > 32) {
+                            vmovups(zmm1, ptr[AO1 + (1 * 16 - OFFSET) * SIZE]);
+                        } else {
+                            vmovups(zmm1 | k2 | T_z,
+                                    ptr[AO1 + (1 * 16 - OFFSET) * SIZE]);
+                        }
+                    }
+                    if (unroll_m >= 48) {
+                        if (isUnmasked) {
+                            vmovups(zmm2, ptr[AO1 + (2 * 16 - OFFSET) * SIZE]);
+                        } else {
+                            vmovups(zmm2 | k3 | T_z,
+                                    ptr[AO1 + (2 * 16 - OFFSET) * SIZE]);
+                        }
+                    }
+                    add(AO1, LDA);
+                }
+
+                if (ver == ver_avx512_core) {
+                    if (!isTransB) {
+                        vbroadcastss(zmm3, ptr[BO1 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(zmm3, ptr[BO1 + (0 - OFFSET) * SIZE]);
+                    }
+                    vfmadd231ps(regs[0], zmm3, zmm0);
+                    if (unroll_m >= 32)
+                        vfmadd231ps(regs[0 + 8], zmm3, zmm1);
+                    if (unroll_m >= 48)
+                        vfmadd231ps(regs[0 + 16], zmm3, zmm2);
+                } else {
+                    if (!isTransB) {
+                        vfmadd231ps(regs[0], zmm0,
+                                zword_b[BO1 + (i - OFFSET) * SIZE]);
+                        if (unroll_m >= 32)
+                            vfmadd231ps(regs[0 + 8], zmm1,
+                                    zword_b[BO1 + (i - OFFSET) * SIZE]);
+                        if (unroll_m >= 48)
+                            vfmadd231ps(regs[0 + 16], zmm2,
+                                    zword_b[BO1 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vfmadd231ps(regs[0], zmm0,
+                                zword_b[BO1 + (0 - OFFSET) * SIZE]);
+                        if (unroll_m >= 32)
+                            vfmadd231ps(regs[0 + 8], zmm1,
+                                    zword_b[BO1 + (0 - OFFSET) * SIZE]);
+                        if (unroll_m >= 48)
+                            vfmadd231ps(regs[0 + 16], zmm2,
+                                    zword_b[BO1 + (0 - OFFSET) * SIZE]);
+                    }
+                }
+
+                if (unroll_n >= i + 1) {
+                    if (!isTransB) {
+                        switch (i) {
+                        case 0:
+                            prefetcht0(
+                                    ptr[BO1 + (PREFETCHSIZEB - OFFSET) * SIZE]);
+                            break;
+                        case 1:
+                            prefetcht0(ptr[BO1 + LDB
+                                    + (PREFETCHSIZEB - OFFSET) * SIZE]);
+                            break;
+                        case 2:
+                            prefetcht0(ptr[BO1 + LDB * 2
+                                    + (PREFETCHSIZEB - OFFSET) * SIZE]);
+                            break;
+                        case 3:
+                            prefetcht0(ptr[BO1 + LDB3
+                                    + (PREFETCHSIZEB - OFFSET) * SIZE]);
+                            break;
+                        case 4:
+                            prefetcht0(
+                                    ptr[BO2 + (PREFETCHSIZEB - OFFSET) * SIZE]);
+                            break;
+                        case 5:
+                            prefetcht0(ptr[BO2 + LDB
+                                    + (PREFETCHSIZEB - OFFSET) * SIZE]);
+                            break;
+                        case 6:
+                            prefetcht0(ptr[BO2 + LDB * 2
+                                    + (PREFETCHSIZEB - OFFSET) * SIZE]);
+                            break;
+                        case 7:
+                            prefetcht0(ptr[BO2 + LDB3
+                                    + (PREFETCHSIZEB - OFFSET) * SIZE]);
+                            break;
+                        }
+                    }
+                }
+
+                if (unroll_n >= 2) {
+                    if (ver == ver_avx512_core) {
+                        if (!isTransB) {
+                            vbroadcastss(zmm3,
+                                    ptr[BO1 + LDB * 1 + (i - OFFSET) * SIZE]);
+                        } else {
+                            vbroadcastss(zmm3, ptr[BO1 + (1 - OFFSET) * SIZE]);
+                        }
+                        vfmadd231ps(regs[1], zmm3, zmm0);
+                        if (unroll_m >= 32)
+                            vfmadd231ps(regs[1 + 8], zmm3, zmm1);
+                        if (unroll_m >= 48)
+                            vfmadd231ps(regs[1 + 16], zmm3, zmm2);
+                    } else {
+                        if (!isTransB) {
+                            vfmadd231ps(regs[1], zmm0,
+                                    zword_b[BO1 + LDB * 1 + (i - OFFSET) * SIZE]);
+                            if (unroll_m >= 32)
+                                vfmadd231ps(regs[1 + 8], zmm1,
+                                        zword_b[BO1 + LDB * 1
+                                        + (i - OFFSET) * SIZE]);
+                            if (unroll_m >= 48)
+                                vfmadd231ps(regs[1 + 16], zmm2,
+                                        zword_b[BO1 + LDB * 1
+                                        + (i - OFFSET) * SIZE]);
+                        } else {
+                            vfmadd231ps(regs[1], zmm0,
+                                    zword_b[BO1 + (1 - OFFSET) * SIZE]);
+                            if (unroll_m >= 32)
+                                vfmadd231ps(regs[1 + 8], zmm1,
+                                        zword_b[BO1 + (1 - OFFSET) * SIZE]);
+                            if (unroll_m >= 48)
+                                vfmadd231ps(regs[1 + 16], zmm2,
+                                        zword_b[BO1 + (1 - OFFSET) * SIZE]);
+                        }
+                    }
+                }
+
+                if (isCopy) {
+                    if (isUnmasked || unroll_m > 16) {
+                        vmovups(ptr[LDA4
+                                        + (unroll_m * i + 0 * 16 - OFFSET)
+                                                * SIZE],
+                                zmm0);
+                    } else {
+                        vmovups(ptr[LDA4
+                                        + (unroll_m * i + 0 * 16 - OFFSET)
+                                                * SIZE],
+                                zmm0 | k1);
+                    }
+                    if (unroll_m >= 32) {
+                        if (isUnmasked || unroll_m > 32) {
+                            vmovups(ptr[LDA4
+                                            + (unroll_m * i + 1 * 16 - OFFSET)
+                                                    * SIZE],
+                                    zmm1);
+                        } else {
+                            vmovups(ptr[LDA4
+                                            + (unroll_m * i + 1 * 16 - OFFSET)
+                                                    * SIZE],
+                                    zmm1 | k2);
+                        }
+                    }
+                    if (unroll_m >= 48) {
+                        if (isUnmasked) {
+                            vmovups(ptr[LDA4
+                                            + (unroll_m * i + 2 * 16 - OFFSET)
+                                                    * SIZE],
+                                    zmm2);
+                        } else {
+                            vmovups(ptr[LDA4
+                                            + (unroll_m * i + 2 * 16 - OFFSET)
+                                                    * SIZE],
+                                    zmm2 | k3);
+                        }
+                    }
+                    if (i == 7)
+                        sub(LDA4, -unroll_m * 8 * SIZE);
+                }
+                fmaloop(unroll_m, unroll_n, i);
+
+                if (i == 1) {
+                    if (doCPrefetch) {
+                        if (ver == ver_avx512_core)
+                            prefetchw(ptr[CO2 + 0 * 16 * SIZE]);
+                        else
+                            prefetcht0(ptr[CO2 + 0 * 16 * SIZE]);
+                    }
+                }
+                if (i == 3) {
+                    if (doCPrefetch && unroll_m >= 32) {
+                        if (ver == ver_avx512_core)
+                            prefetchw(ptr[CO2 + 1 * 16 * SIZE]);
+                        else
+                            prefetcht0(ptr[CO2 + 1 * 16 * SIZE]);
+                    }
+                    if (!isTransA) {
+                        if (ver == ver_avx512_core)
+                            prefetcht0(ptr[AA + 16 * 0 * SIZE]);
+                        else
+                            prefetcht2(ptr[AA + 16 * 0 * SIZE]);
+                    }
+                }
+                if (i == 5) {
+                    if (doCPrefetch) {
+                        if (unroll_m >= 48) {
+                            if (ver == ver_avx512_core)
+                                prefetchw(ptr[CO2 + 2 * 16 * SIZE]);
+                            else
+                                prefetcht0(ptr[CO2 + 2 * 16 * SIZE]);
+                        }
+                        add(CO2, LDC);
+                    }
+                    if (!isTransA) {
+                        if (unroll_m >= 32) {
+                            if (ver == ver_avx512_core)
+                                prefetcht0(ptr[AA + 16 * 1 * SIZE]);
+                            else
+                                prefetcht2(ptr[AA + 16 * 1 * SIZE]);
+                        }
+                    }
+                }
+
+                if (isTransB) {
+                    prefetcht0(ptr[BO1 + BO2]);
+                    add(BO1, LDB);
+                }
+            } // end of for loop
+
+            if (!isTransB) {
+                sub(BO1, -8 * SIZE);
+                if (unroll_n >= 4)
+                    sub(BO2, -8 * SIZE);
+            }
+            if (!isTransA) {
+                if (unroll_m >= 48) {
+                    if (ver == ver_avx512_core)
+                        prefetcht0(ptr[AA + 16 * 2 * SIZE]);
+                    else
+                        prefetcht2(ptr[AA + 16 * 2 * SIZE]);
+                }
+                lea(AA, ptr[AA + LDA]);
+            }
+
+            if (!isDirect) {
+                if (isUnmasked || unroll_m > 16) {
+                    vmovups(zmm0,
+                            ptr[AO1 + (unroll_m * 8 + 0 * 16 - OFFSET) * SIZE]);
+                } else {
+                    vmovups(zmm0 | k1 | T_z,
+                            ptr[AO1 + (unroll_m * 8 + 0 * 16 - OFFSET) * SIZE]);
+                }
+                if (unroll_m >= 32) {
+                    if (isUnmasked || unroll_m > 32) {
+                        vmovups(zmm1, ptr[AO1
+                                              + (unroll_m * 8 + 1 * 16 - OFFSET)
+                                                      * SIZE]);
+                    } else {
+                        vmovups(zmm1 | k2 | T_z,
+                                ptr[AO1
+                                        + (unroll_m * 8 + 1 * 16 - OFFSET)
+                                                * SIZE]);
+                    }
+                }
+                if (unroll_m >= 48) {
+                    if (isUnmasked) {
+                        vmovups(zmm2, ptr[AO1
+                                              + (unroll_m * 8 + 2 * 16 - OFFSET)
+                                                      * SIZE]);
+                    } else {
+                        vmovups(zmm2 | k3 | T_z,
+                                ptr[AO1
+                                        + (unroll_m * 8 + 2 * 16 - OFFSET)
+                                                * SIZE]);
+                    }
+                }
+                sub(AO1, -unroll_m * 8 * SIZE);
+            }
+
+            sub(LL, 1);
+        };
+
+        // Main kernel; does prefetching and calls innerkernel
+        // After calculating results in registers, writes back to C matrix by
+        // calling update
+        auto kernel = [&](int unroll_m, int unroll_n, bool isDirect,
+                bool isCopy, bool isUnmasked = true) {
+            if (!isDirect) {
+                lea(AO1, ptr[rsp + 128 + OFFSET * SIZE]);
+            } else {
+                mov(AO1, A);
+            }
+
+            if (isCopy) {
+                lea(LDA4, ptr[rsp + 128 + OFFSET * SIZE]);
+            } else {
+                auto step = ver == ver_avx512_core ? 2 : 4;
+                lea(LDA4, ptr[LDA * step + (16 - 1 - OFFSET) * SIZE]);
+            }
+
+            if (isTransB) {
+                lea(BO2, ptr[LDB * 4 + (16 / 2 - 1 - OFFSET) * SIZE]);
+            }
+
+            if (!isDirect) {
+                if (isUnmasked || unroll_m > 16) {
+                    vmovups(zmm0,
+                            ptr[AO1 + (unroll_m * 0 + 0 * 16 - OFFSET) * SIZE]);
+                } else {
+                    vmovups(zmm0 | k1 | T_z,
+                            ptr[AO1 + (unroll_m * 0 + 0 * 16 - OFFSET) * SIZE]);
+                }
+                if (unroll_m >= 32) {
+                    if (isUnmasked || unroll_m > 32) {
+                        vmovups(zmm1, ptr[AO1
+                                              + (unroll_m * 0 + 1 * 16 - OFFSET)
+                                                      * SIZE]);
+                    } else {
+                        vmovups(zmm1 | k2 | T_z,
+                                ptr[AO1
+                                        + (unroll_m * 0 + 1 * 16 - OFFSET)
+                                                * SIZE]);
+                    }
+                }
+                if (unroll_m >= 48) {
+                    if (isUnmasked) {
+                        vmovups(zmm2, ptr[AO1
+                                              + (unroll_m * 0 + 2 * 16 - OFFSET)
+                                                      * SIZE]);
+                    } else {
+                        vmovups(zmm2 | k3 | T_z,
+                                ptr[AO1
+                                        + (unroll_m * 0 + 2 * 16 - OFFSET)
+                                                * SIZE]);
+                    }
+                }
+            }
+
+            Label kernel12, kernel13, kernel14, kernel15, kernel16, kernel18;
+
+            mov(LL, K);
+            sar(LL, 3);
+            sub(LL, SECOND_FETCH);
+            jle(kernel13, T_NEAR);
+            align(16);
+
+            L(kernel12);
+            innerkernel(
+                    unroll_m, unroll_n, isDirect, isCopy, false, isUnmasked);
+            jg(kernel12, T_NEAR);
+            align(16);
+
+            L(kernel13);
+            lea(CO2, ptr[CO1 + (16 - 1) * SIZE]);
+            add(LL, unroll_n);
+            jle(kernel15, T_NEAR);
+            align(16);
+
+            L(kernel14);
+            innerkernel(unroll_m, unroll_n, isDirect, isCopy, true, isUnmasked);
+            jg(kernel14, T_NEAR);
+            align(16);
+
+            L(kernel15);
+            mov(LL, K);
+            and_(LL, 7);
+            jle(kernel18, T_NEAR);
+            align(16);
+
+            L(kernel16);
+            if (isDirect) {
+                if (isUnmasked || unroll_m > 16) {
+                    vmovups(zmm0, ptr[AO1 + (0 * 16 - OFFSET) * SIZE]);
+                } else {
+                    vmovups(zmm0 | k1 | T_z,
+                            ptr[AO1 + (0 * 16 - OFFSET) * SIZE]);
+                }
+                if (unroll_m >= 32) {
+                    if (isUnmasked || unroll_m > 32) {
+                        vmovups(zmm1, ptr[AO1 + (1 * 16 - OFFSET) * SIZE]);
+                    } else {
+                        vmovups(zmm1 | k2 | T_z,
+                                ptr[AO1 + (1 * 16 - OFFSET) * SIZE]);
+                    }
+                }
+                if (unroll_m >= 48) {
+                    if (isUnmasked) {
+                        vmovups(zmm2, ptr[AO1 + (2 * 16 - OFFSET) * SIZE]);
+                    } else {
+                        vmovups(zmm2 | k3 | T_z,
+                                ptr[AO1 + (2 * 16 - OFFSET) * SIZE]);
+                    }
+                }
+                add(AO1, LDA);
+            }
+
+            for (int i = 0; i < unroll_n; i++) {
+                if (!isTransB) {
+                    switch (i) {
+                    case 0:
+                        vbroadcastss(zmm3, ptr[BO1 + (0 - OFFSET) * SIZE]);
+                        break;
+                    case 1:
+                        vbroadcastss(
+                                zmm3, ptr[BO1 + LDB * 1 + (0 - OFFSET) * SIZE]);
+                        break;
+                    case 2:
+                        vbroadcastss(
+                                zmm3, ptr[BO1 + LDB * 2 + (0 - OFFSET) * SIZE]);
+                        break;
+                    case 3:
+                        vbroadcastss(
+                                zmm3, ptr[BO1 + LDB3 + (0 - OFFSET) * SIZE]);
+                        break;
+                    case 4:
+                        vbroadcastss(zmm3, ptr[BO2 + (0 - OFFSET) * SIZE]);
+                        break;
+                    case 5:
+                        vbroadcastss(
+                                zmm3, ptr[BO2 + LDB * 1 + (0 - OFFSET) * SIZE]);
+                        break;
+                    case 6:
+                        vbroadcastss(
+                                zmm3, ptr[BO2 + LDB * 2 + (0 - OFFSET) * SIZE]);
+                        break;
+                    case 7:
+                        vbroadcastss(
+                                zmm3, ptr[BO2 + LDB3 + (0 - OFFSET) * SIZE]);
+                        break;
+                    }
+                } else {
+                    vbroadcastss(zmm3, ptr[BO1 + (i - OFFSET) * SIZE]);
+                }
+                vfmadd231ps(regs[i], zmm3, zmm0);
+                if (unroll_m >= 32) {
+                    vfmadd231ps(regs[i + 8], zmm3, zmm1);
+                }
+                if (unroll_m >= 48) {
+                    vfmadd231ps(regs[i + 16], zmm3, zmm2);
+                }
+            }
+
+            if (isCopy) {
+                if (isUnmasked || unroll_m > 16) {
+                    vmovups(ptr[LDA4 + (unroll_m * 0 + 0 * 16 - OFFSET) * SIZE],
+                            zmm0);
+                } else {
+                    vmovups(ptr[LDA4 + (unroll_m * 0 + 0 * 16 - OFFSET) * SIZE],
+                            zmm0 | k1);
+                }
+                if (unroll_m >= 32) {
+                    if (isUnmasked || unroll_m > 32) {
+                        vmovups(ptr[LDA4
+                                        + (unroll_m * 0 + 1 * 16 - OFFSET)
+                                                * SIZE],
+                                zmm1);
+                    } else {
+                        vmovups(ptr[LDA4
+                                        + (unroll_m * 0 + 1 * 16 - OFFSET)
+                                                * SIZE],
+                                zmm1 | k2);
+                    }
+                }
+                if (unroll_m >= 48) {
+                    if (isUnmasked) {
+                        vmovups(ptr[LDA4
+                                        + (unroll_m * 0 + 2 * 16 - OFFSET)
+                                                * SIZE],
+                                zmm2);
+                    } else {
+                        vmovups(ptr[LDA4
+                                        + (unroll_m * 0 + 2 * 16 - OFFSET)
+                                                * SIZE],
+                                zmm2 | k3);
+                    }
+                }
+                sub(LDA4, -unroll_m * SIZE);
+            }
+
+            if (!isDirect) {
+                if (isUnmasked || unroll_m > 16) {
+                    vmovups(zmm0,
+                            ptr[AO1 + (unroll_m * 1 + 0 * 16 - OFFSET) * SIZE]);
+                } else {
+                    vmovups(zmm0 | k1 | T_z,
+                            ptr[AO1 + (unroll_m * 1 + 0 * 16 - OFFSET) * SIZE]);
+                }
+                if (unroll_m >= 32) {
+                    if (isUnmasked || unroll_m > 32) {
+                        vmovups(zmm1, ptr[AO1
+                                              + (unroll_m * 1 + 1 * 16 - OFFSET)
+                                                      * SIZE]);
+                    } else {
+                        vmovups(zmm1 | k2 | T_z,
+                                ptr[AO1
+                                        + (unroll_m * 1 + 1 * 16 - OFFSET)
+                                                * SIZE]);
+                    }
+                }
+                if (unroll_m >= 48) {
+                    if (isUnmasked) {
+                        vmovups(zmm2, ptr[AO1
+                                              + (unroll_m * 1 + 2 * 16 - OFFSET)
+                                                      * SIZE]);
+                    } else {
+                        vmovups(zmm2 | k3 | T_z,
+                                ptr[AO1
+                                        + (unroll_m * 1 + 2 * 16 - OFFSET)
+                                                * SIZE]);
+                    }
+                }
+                sub(AO1, -unroll_m * SIZE);
+            }
+
+            if (!isTransB) {
+                sub(BO1, -SIZE);
+                if (unroll_n >= 4) {
+                    sub(BO2, -SIZE);
+                }
+            } else {
+                add(BO1, LDB);
+            }
+
+            sub(LL, 1);
+            jg(kernel16, T_NEAR);
+            align(16);
+
+            L(kernel18);
+            vbroadcastss(VALPHA, ALPHA);
+
+            if (isBetaN) {
+                vbroadcastss(VBETA, BETA);
+            }
+
+            // Write back the results; all beta cases need to be handled
+            if (hasBias) {
+                mov(BIAS1, BIAS);
+                if (isUnmasked || unroll_m > 16)
+                    vmovups(VBIAS1, ptr[BIAS1 + 0 * SIZE]);
+                else
+                    vmovups(VBIAS1 | k1 | T_z, ptr[BIAS1 + 0 * SIZE]);
+                if (unroll_m >= 32) {
+                    if (isUnmasked || unroll_m > 32)
+                        vmovups(VBIAS2, ptr[BIAS1 + 16 * SIZE]);
+                    else
+                        vmovups(VBIAS2 | k2 | T_z, ptr[BIAS1 + 16 * SIZE]);
+                }
+                if (unroll_m >= 48) {
+                    if (isUnmasked)
+                        vmovups(VBIAS3, ptr[BIAS1 + 32 * SIZE]);
+                    else
+                        vmovups(VBIAS3 | k3 | T_z, ptr[BIAS1 + 32 * SIZE]);
+                }
+            }
+
+            for (int i = 0; i < unroll_n; i++) {
+                bool useScale = i % 2 != 0;
+                bool useCO1 = i < 2;
+                if (i == 2)
+                    lea(CO2, ptr[CO1 + LDC * 2]);
+                if (i == 4 || i == 6)
+                    lea(CO2, ptr[CO2 + LDC * 2]);
+                if (hasBias)
+                    vaddps(regs[i], VBIAS1, regs[i]);
+                if (isUnmasked || unroll_m > 16) {
+                    update(regs[i], useCO1, 0, 0, useScale);
+                } else {
+                    update(regs[i], useCO1, 0, 1, useScale);
+                }
+                if (unroll_m >= 32) {
+                    if (hasBias)
+                        vaddps(regs[i + 8], VBIAS2, regs[i + 8]);
+                    if (isUnmasked || unroll_m > 32) {
+                        update(regs[i + 8], useCO1, 16, 0, useScale);
+                    } else {
+                        update(regs[i + 8], useCO1, 16, 2, useScale);
+                    }
+                }
+                if (unroll_m >= 48) {
+                    if (hasBias)
+                        vaddps(regs[i + 16], VBIAS3, regs[i + 16]);
+                    if (isUnmasked) {
+                        update(regs[i + 16], useCO1, 32, 0, useScale);
+                    } else {
+                        update(regs[i + 16], useCO1, 32, 3, useScale);
+                    }
+                }
+            }
+
+            switch (unroll_n) {
+            case 1: add(CO1, LDC); break;
+            case 2: lea(CO1, ptr[CO1 + LDC * 2]); break;
+            case 3: lea(CO1, ptr[CO2 + LDC * 1]); break;
+            case 4: lea(CO1, ptr[CO2 + LDC * 2]); break;
+            case 5: lea(CO1, ptr[CO2 + LDC * 1]); break;
+            case 6: lea(CO1, ptr[CO2 + LDC * 2]); break;
+            case 7: lea(CO1, ptr[CO2 + LDC * 1]); break;
+            case 8: lea(CO1, ptr[CO2 + LDC * 2]); break;
+            }
+
+            // Compute next address of B
+            if (!isTransB) {
+                lea(rax, ptr[K * SIZE]);
+                switch (unroll_n) {
+                case 1:
+                    add(BO1, LDB);
+                    add(BO2, LDB);
+                    break;
+                case 2:
+                    lea(BO1, ptr[BO1 + LDB * 2]);
+                    lea(BO2, ptr[BO2 + LDB * 2]);
+                    break;
+                case 3:
+                    lea(BO1, ptr[BO1 + LDB3]);
+                    lea(BO2, ptr[BO2 + LDB3]);
+                    break;
+                case 4:
+                    lea(BO1, ptr[BO1 + LDB * 4]);
+                    lea(BO2, ptr[BO2 + LDB * 4]);
+                    break;
+                case 5:
+                    lea(BO1, ptr[BO1 + LDB * 4]);
+                    add(BO1, LDB);
+                    lea(BO2, ptr[BO2 + LDB * 4]);
+                    add(BO2, LDB);
+                    break;
+                case 6:
+                    lea(BO1, ptr[BO1 + LDB3 * 2]);
+                    lea(BO2, ptr[BO2 + LDB3 * 2]);
+                    break;
+                case 7:
+                    lea(BO1, ptr[BO1 + LDB * 8]);
+                    sub(BO1, LDB);
+                    lea(BO2, ptr[BO2 + LDB * 8]);
+                    sub(BO2, LDB);
+                    break;
+                case 8:
+                    lea(BO1, ptr[BO1 + LDB * 8]);
+                    lea(BO2, ptr[BO2 + LDB * 8]);
+                    break;
+                }
+                sub(BO1, rax);
+                sub(BO2, rax);
+            } else {
+                mov(rax, LDB);
+                imul(rax, K);
+                sub(BO1, rax);
+                add(BO1, unroll_n * SIZE);
+            }
+        };
+
+        // High-level subroutine; does packing if needed, then splits C matrix.
+        // Operates on chunks of 48 rows, 8 columns at a time (handling tail
+        // cases appropriately by doing 32 or 16 rows, and/or with masking,
+        // and/or fewer columns).
+        auto subloop = [&](int unroll_m) {
+            Label l_subloop_20x[8], l_subloop_mask_20x[8];
+            Label l_subloop_30x[8], l_subloop_mask_30x[8];
+
+            Label subloop11, subloop11mask;
+            Label subloop30, subloop30mask;
+            Label subloop31, subloop31mask;
+            Label subloop96;
+            Label subloop98, subloop98mask;
+            Label subloop99;
+
+            // Create mask
+            mov(BO1, rcx);
+            mov(rcx, M);
+            sub(rcx, unroll_m - 16);
+            mov(CO1, 16);
+            cmp(rcx, 16);
+
+            cmovg(rcx, CO1);
+            mov(rax, 1);
+            sal(rax, cl);
+            sub(rax, 1);
+            mov(rcx, 0xffff);
+
+            if (unroll_m == 16) {
+                kmovw(k1, eax);
+            } else if (unroll_m == 32) {
+                kmovw(k1, ecx);
+                kmovw(k2, eax);
+            } else {
+                kmovw(k1, ecx);
+                kmovw(k2, ecx);
+                kmovw(k3, eax);
+            }
+            mov(rcx, BO1);
+
+            and_(rax, 0xffff);
+            cmp(rax, 0xffff);
+            jne(subloop96, T_NEAR);
+
+            if (isTransA) {
+                do_pack(unroll_m);
+            }
+
+            mov(CO1, C);
+            add(C, unroll_m * SIZE);
+
+            mov(BO1, B);
+            if (!isTransB) {
+                lea(BO2, ptr[B + LDB * 4]);
+            }
+
+            if (!isTransA) {
+                lea(AA, ptr[A + (unroll_m + 16 - 1 - OFFSET) * SIZE]);
+                cmp(M, UNROLL_M);
+                jg(subloop98, T_NEAR);
+
+                mov(AA, ORIG_A);
+                lea(AA, ptr[AA + (16 - 1 - OFFSET) * SIZE]);
+                L(subloop98);
+            }
+
+            mov(LL, N);
+            mov(I, LL);
+            if (!isTransA) {
+                // If N is too small, skip copy operation
+                cmp(LL, UNROLL_N * 3);
+                jle(subloop30, T_NEAR);
+
+                // If A is not aligned to cache line
+                cmp(FLAG, 0);
+                je(subloop30, T_NEAR);
+            } else {
+                cmp(LL, UNROLL_N);
+                jl(l_subloop_20x[1], T_NEAR);
+            }
+            align(16);
+
+            if (!isTransA) {
+                kernel(unroll_m, UNROLL_N, true, true);
+            } else {
+                kernel(unroll_m, UNROLL_N, false, false);
+            }
+
+            sub(I, UNROLL_N);
+            cmp(I, UNROLL_N);
+            jl(l_subloop_20x[1], T_NEAR);
+            align(16);
+
+            L(subloop11);
+            kernel(unroll_m, UNROLL_N, false, false);
+            sub(I, UNROLL_N);
+            cmp(I, UNROLL_N);
+            jge(subloop11, T_NEAR);
+            align(16);
+
+            for (int i = 1; i <= 7; i++) {
+                L(l_subloop_20x[i]);
+                cmp(I, i);
+                if (i < 7) {
+                    jne(l_subloop_20x[i + 1], T_NEAR);
+                } else {
+                    jne(subloop99, T_NEAR);
+                }
+                kernel(unroll_m, i, false, false);
+                jmp(subloop99, T_NEAR);
+                align(16);
+            }
+
+            if (!isTransA) {
+                L(subloop30);
+                cmp(I, UNROLL_N);
+                jl(l_subloop_30x[1], T_NEAR);
+                align(16);
+
+                L(subloop31);
+                kernel(unroll_m, UNROLL_N, true, false);
+                sub(I, UNROLL_N);
+                cmp(I, UNROLL_N);
+                jge(subloop31, T_NEAR);
+                align(16);
+
+                for (int i = 1; i <= 7; i++) {
+                    L(l_subloop_30x[i]);
+                    cmp(I, i);
+                    if (i < 7) {
+                        jne(l_subloop_30x[i + 1], T_NEAR);
+                    } else {
+                        jne(subloop99, T_NEAR);
+                    }
+                    kernel(unroll_m, i, true, false);
+                    if (i < 7)
+                        jmp(subloop99, T_NEAR);
+                    align(16);
+                }
+            }
+            jmp(subloop99, T_NEAR);
+            align(16);
+
+            L(subloop96);
+            if (isTransA) {
+                do_pack(unroll_m);
+            }
+
+            mov(CO1, C);
+            add(C, unroll_m * SIZE);
+            mov(BO1, B);
+            if (!isTransB) {
+                lea(BO2, ptr[B + LDB * 4]);
+            }
+
+            if (!isTransA) {
+                lea(AA, ptr[A + (unroll_m + 16 - 1 - OFFSET) * SIZE]);
+                cmp(M, UNROLL_M);
+                jg(subloop98mask, T_NEAR);
+                mov(AA, ORIG_A);
+                lea(AA, ptr[AA + (16 - 1 - OFFSET) * SIZE]);
+                L(subloop98mask);
+            }
+
+            mov(LL, N);
+            mov(I, LL);
+            if (!isTransA) {
+                // If N is too small, skip copy operation
+                cmp(LL, UNROLL_N * 3);
+                jle(subloop30mask, T_NEAR);
+
+                // If A is not aligned to cache line
+                cmp(FLAG, 0);
+                je(subloop30mask, T_NEAR);
+            } else {
+                cmp(LL, UNROLL_N);
+                jl(l_subloop_mask_20x[1], T_NEAR);
+            }
+            align(16);
+
+            if (!isTransA) {
+                kernel(unroll_m, UNROLL_N, true, true, false);
+            } else {
+                kernel(unroll_m, UNROLL_N, false, false, false);
+            }
+
+            sub(I, UNROLL_N);
+            cmp(I, UNROLL_N);
+            jl(l_subloop_mask_20x[1], T_NEAR);
+            align(16);
+
+            L(subloop11mask);
+            kernel(unroll_m, UNROLL_N, false, false, false);
+            sub(I, UNROLL_N);
+            cmp(I, UNROLL_N);
+            jge(subloop11mask, T_NEAR);
+            align(16);
+
+            for (int i = 1; i <= 7; i++) {
+                L(l_subloop_mask_20x[i]);
+                cmp(I, i);
+                if (i < 7) {
+                    jne(l_subloop_mask_20x[i + 1], T_NEAR);
+                } else {
+                    jne(subloop99, T_NEAR);
+                }
+                kernel(unroll_m, i, false, false, false);
+                jmp(subloop99, T_NEAR);
+                align(16);
+            }
+
+            if (!isTransA) {
+                L(subloop30mask);
+                cmp(I, UNROLL_N);
+                jl(l_subloop_mask_30x[1], T_NEAR);
+                align(16);
+
+                L(subloop31mask);
+                kernel(unroll_m, UNROLL_N, true, false, false);
+                sub(I, UNROLL_N);
+                cmp(I, UNROLL_N);
+                jge(subloop31mask, T_NEAR);
+                align(16);
+
+                for (int i = 1; i <= 7; i++) {
+                    L(l_subloop_mask_30x[i]);
+                    cmp(I, i);
+                    if (i < 7) {
+                        jne(l_subloop_mask_30x[i + 1], T_NEAR);
+                    } else {
+                        jne(subloop99, T_NEAR);
+                    }
+                    kernel(unroll_m, i, true, false, false);
+                    if (i < 7)
+                        jmp(subloop99, T_NEAR);
+                    align(16);
+                }
+            }
+
+            L(subloop99);
+            // Compute address for A
+            if (!isTransA) {
+                add(A, unroll_m * SIZE);
+            } else {
+                mov(rax, LDA);
+                imul(rax, rax, unroll_m);
+                add(A, rax);
+            }
+
+            // Compute next address of BIAS
+            if (hasBias) {
+                add(BIAS, unroll_m * SIZE);
+            }
+        };
+
+        preamble();
+
+        Label buffer_in_ws, buffer_allocated;
+
+        // Get the registers
+        mov(B, ARG_B);
+        mov(LDB, ARG_LDB);
+        mov(r15, ARG_BETA);
+        mov(r12, ARG_C);
+        if (hasBias)
+            mov(r10, ARG_BIAS);
+        mov(LDC, ARG_LDC);
+        mov(rbp, rsp);
+
+        vmovss(xmm0, ptr[ARG_ALPHA]);
+        vmovss(xmm1, ptr[r15]);
+
+#if _WIN32
+        mov(A, ARG_A);
+        mov(LDA, ARG_LDA);
+#endif
+
+        cmp(K, STACK_K_CAPACITY);
+        jg(buffer_in_ws, T_NEAR);
+
+        // Create buffer and align to 4kB page
+        lea(rax, ptr[K * SIZE]);
+        imul(rax, rax, 0x30);
+        add(rax, 256);
+        sub(rsp, rax);
+        and_(rsp, -PAGE_4K);
+        jmp(buffer_allocated, T_NEAR);
+
+        L(buffer_in_ws);
+        mov(rsp, ARG_WS);
+
+        L(buffer_allocated);
+
+        mov(ORIG_SP, rbp);
+        mov(M, ARG_M);
+        mov(N, ARG_N);
+        mov(C, r12);
+        if (hasBias)
+            mov(BIAS, r10);
+        vmovss(ALPHA, xmm0);
+        vmovss(BETA, xmm1);
+        sub(A, -OFFSET * SIZE);
+        sub(B, -OFFSET * SIZE);
+        mov(ORIG_A, A);
+        sal(LDA, BASE_SHIFT);
+        sal(LDB, BASE_SHIFT);
+        sal(LDC, BASE_SHIFT);
+        lea(LDB3, ptr[LDB + LDB * 2]);
+
+        if (isTransA) {
+            vpbroadcastq(zmm2, LDA);
+            vpxorq(ZSTRIDE, ZSTRIDE, ZSTRIDE);
+            mov(rax, -2);
+            kmovw(k4, eax);
+
+            for (int i = 0; i < 6; i++) {
+                vpaddq(ZSTRIDE | k4, ZSTRIDE, zmm2);
+                kshiftlw(k4, k4, 1);
+            }
+            vpaddq(ZSTRIDE | k4, ZSTRIDE, zmm2);
+        }
+
+        // Check A alignment and leading dimension; take copy-based path as
+        // needed
+        mov(rax, LDA);
+        or_(rax, A);
+        and_(rax, ver == ver_avx512_core ? 0x07 : 0x3f);
+        mov(FLAG, rax);
+
+        for (int i = 8; i < 16; i++) {
+            for (int j = 0; j < 3; j++) {
+                vpxorq(Zmm(i + 8 * j), Zmm(i + 8 * j), Zmm(i + 8 * j));
+            }
+        }
+
+        Label main0, main1, main2, main999;
+
+        cmp(M, 32);
+        jle(main0, T_NEAR);
+        align(16);
+
+        L(main1);
+        subloop(48);
+        sub(M, UNROLL_M);
+        cmp(M, 32);
+        jg(main1, T_NEAR);
+        align(16);
+
+        L(main0);
+        cmp(M, 16);
+        jle(main2, T_NEAR);
+
+        subloop(32);
+        jmp(main999, T_NEAR);
+        align(16);
+
+        L(main2);
+        cmp(M, 0);
+        jle(main999, T_NEAR);
+        subloop(16);
+        align(16);
+
+        L(main999);
+        // Restore original stack
+        mov(rsp, ORIG_SP);
+
+        vzeroupper();
+        postamble();
+
+        ker_ = this->getCode<ker_t>();
+    }
+
+    typedef void (*ker_t)(dim_t m, dim_t n, dim_t k,
+            const float *alpha, const float *a, dim_t lda,
+            const float *b, dim_t ldb, const float *beta, float *c,
+            dim_t ldc, const float *bias, float *ws);
+
+    void operator()(dim_t m, dim_t n, dim_t k,
+            const float *alpha, const float *a, dim_t lda,
+            const float *b, dim_t ldb, const float *beta, float *c,
+            dim_t ldc, const float *bias, float *ws) const
+    {
+        ker_(m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, bias, ws);
+    }
+
+private:
+    ker_t ker_;
+};
+
+const xbyak_gemm *get_xbyak_gemm(
+        bool isTransA, bool isTransB, float beta, bool hasBias) {
+    auto beta_idx = [](float beta) {
+        return (beta == 0.0) ? 0 : (beta == 1.0 ? 1 : 2);
+    };
+
+    // Kernel table [isTransA][isTransB][hasBias][beta (0, 1, other)]
+    static xbyak_gemm *kernel_table[2][2][2][3];
+    static std::once_flag initialized;
+    std::call_once(initialized, [=]{
+            for (bool isTransA: {false, true})
+            for (bool isTransB: {false, true})
+            for (bool hasBias: {false, true})
+            for (float beta: {0.0f, 1.0f, 2.0f}) {
+                // nocopy sgemm with bias for beta != 0.0 is not supported
+                if (hasBias && beta != 0.0)
+                    continue;
+                kernel_table[isTransA][isTransB][hasBias][beta_idx(beta)] =
+                    new xbyak_gemm(isTransA, isTransB, beta, hasBias);
+            }
+    });
+
+    return kernel_table[isTransA][isTransB][hasBias][beta_idx(beta)];
+}
+
+void sgemm_nocopy_driver(const char *transa,
+        const char *transb, int m, int n, int k, const float *alpha,
+        const float *a, dim_t lda, const float *b, dim_t ldb, const float *beta,
+        float *c, dim_t ldc, const float *bias, float *ws)
+{
+    bool isTransA = (*transa == 'T' || *transa == 't');
+    bool isTransB = (*transb == 'T' || *transb == 't');
+
+    int Bm, sizeM, Bn, sizeN, Bk, sizeK;
+
+    int i, j;
+
+    if ((m <= 0) || (n <= 0))
+        return;
+
+    if ((k <= 0) || (alpha[0] == 0.)) {
+
+        if (beta[0] == 0.) {
+            for (j = 0; j < n; j++)
+                for (i = 0; i < m; i++)
+                    c[i + j * ldc] = 0.0;
+        } else if (beta[0] != 1.) {
+            for (j = 0; j < n; j++)
+                for (i = 0; i < m; i++)
+                    c[i + j * ldc] *= beta[0];
+        }
+
+        return;
+    }
+
+    assert(IMPLICATION(bias != nullptr, *beta == 0.0));
+
+    // XXX: this happens on every thread...
+    bool hasBias = (bias != nullptr);
+    auto ker_bn = get_xbyak_gemm(isTransA, isTransB, *beta, hasBias);
+    auto ker_b1 = get_xbyak_gemm(isTransA, isTransB, 1.0, false);
+    auto ker_b0 = get_xbyak_gemm(isTransA, isTransB, 0.0, false);
+    assert(ker_bn && ker_b1 && ker_b0);
+
+    int BM = 4032, BN, BK;
+    if (mayiuse(avx512_core)) {
+        BN = isTransA ? 384 : 64;
+        BK = 384;
+    } else {
+        BN = isTransA ? 96 : 64;
+        BK = isTransB ? 96 : 192;
+        if (!isTransA && !isTransB)
+            BK = 128;
+    }
+    const float *curA, *curB, *curBias = nullptr;
+    float *curC;
+
+    for (Bk = 0; Bk < k; Bk += sizeK) {
+        sizeK = k - Bk;
+        if (sizeK >= BK * 2)
+            sizeK = BK;
+        else {
+            if (sizeK > BK)
+                sizeK = (sizeK + 1) / 2;
+        }
+
+        for (Bm = 0; Bm < m; Bm += sizeM) {
+            sizeM = m - Bm;
+            if (sizeM >= BM * 2)
+                sizeM = BM;
+            else {
+                if (sizeM > BM + BM / 2)
+                    sizeM = (sizeM + 1) / 2;
+            }
+
+            for (Bn = 0; Bn < n; Bn += sizeN) {
+                sizeN = n - Bn;
+                if (sizeN >= BN * 2)
+                    sizeN = BN;
+                else {
+                    if (sizeN > BN + BN / 2)
+                        sizeN = (sizeN + 1) / 2;
+                }
+
+                if (!isTransA) {
+                    curA = a + Bm + Bk * lda;
+                } else {
+                    curA = a + Bk + Bm * lda;
+                }
+                if (!isTransB) {
+                    curB = b + Bk + Bn * ldb;
+                } else {
+                    curB = b + Bn + Bk * ldb;
+                }
+                curC = c + Bm + (size_t)Bn * ldc;
+                if (bias != nullptr) {
+                    if (Bk == 0) {
+                        curBias = bias + Bm;
+                    } else {
+                        curBias = nullptr;
+                    }
+                }
+                if (Bk == 0) {
+                    if (*beta == 0.0 && bias == nullptr)
+                        (*ker_b0)((dim_t)sizeM, (dim_t)sizeN, (dim_t)sizeK,
+                                alpha, curA, lda, curB, ldb, beta, curC, ldc,
+                                curBias, ws);
+                    else
+                        (*ker_bn)((dim_t)sizeM, (dim_t)sizeN, (dim_t)sizeK,
+                                alpha, curA, lda, curB, ldb, beta, curC, ldc,
+                                curBias, ws);
+                } else {
+                    (*ker_b1)((dim_t)sizeM, (dim_t)sizeN, (dim_t)sizeK,
+                            alpha, curA, lda, curB, ldb, beta, curC, ldc,
+                            curBias, ws);
+                }
+            }
+        }
+    }
+}
+
+}
+
+mkldnn_status_t jit_avx512_common_gemm_f32(
+        const char *transa, const char *transb,
+        const int *p_m, const int *p_n, const int *p_k, const float *p_alpha,
+        const float *A, const int *p_lda, const float *B, const int *p_ldb,
+        const float *p_beta, float *C, const int *p_ldc, const float *bias)
+{
+    using namespace mkldnn::impl::utils;
+    using namespace avx512_common_gemm_f32;
+    using namespace gemm_utils;
+
+    if (*p_beta != 0 && bias)
+        return ref_gemm(transa, transb, p_m, p_n, p_k,
+                p_alpha, A, p_lda, B, p_lda, p_beta, C, p_ldc, bias);
+
+    int nthr = (mkldnn_in_parallel()) ? 1 : mkldnn_get_max_threads();
+
+    int m = *p_m;
+    int n = *p_n;
+    int k = *p_k;
+    dim_t lda = *p_lda;
+    dim_t ldb = *p_ldb;
+    dim_t ldc = *p_ldc;
+    float beta = *p_beta;
+    int MB, NB, KB;
+
+    int nthr_m, nthr_n, nthr_k, nthr_mn;
+
+    // Determine threading partitioning
+    calc_nthr_nocopy_avx512_common(
+            m, n, k, nthr, &nthr_m, &nthr_n, &nthr_k, &MB, &NB, &KB);
+    assert(IMPLICATION(!mkldnn_thr_syncable(), nthr_k == 1));
+
+    // May not happen, but just in case
+    if (nthr < nthr_m * nthr_n * nthr_k)
+        nthr = nthr_m * nthr_n * nthr_k;
+
+    nthr_mn = nthr_m * nthr_n;
+
+    unsigned char * ompstatus_ = nullptr;
+    unsigned char volatile *ompstatus = nullptr;
+
+    float *c_buffers = nullptr;
+    float *ws_buffers = nullptr;
+
+    if (nthr_k > 1) {
+        ompstatus_ = (unsigned char *) malloc(
+                nthr * CACHE_LINE_SIZE,
+                CACHE_LINE_SIZE);
+        ompstatus = (unsigned char volatile *) ompstatus_;
+        assert(ompstatus);
+
+        for (int i = 0; i < nthr; i++)
+            ompstatus[i * CACHE_LINE_SIZE] = 0;
+
+        c_buffers = (float *)malloc(nthr_m * nthr_n * (nthr_k - 1) * MB * NB
+                * sizeof(float), PAGE_4K);
+    }
+
+    const size_t ws_elems_per_thr = (size_t)k * 48 + 64;
+    const size_t ws_size_per_thr
+            = rnd_up(ws_elems_per_thr * sizeof(float), PAGE_4K);
+    if (k > STACK_K_CAPACITY) {
+        ws_buffers = (float *)malloc(nthr * ws_size_per_thr, PAGE_4K);
+    }
+
+    parallel_nd(nthr, [&](const int ithr) {
+        int ithr_m, ithr_n, ithr_k, ithr_mn;
+        int m_from, m_to, myM;
+        int n_from, n_to, myN;
+        int k_from, k_to, myK;
+        int cbase, ibase;
+        const float *myA, *myB, *myBias = nullptr;
+        float *myC = C, myBeta;
+        float *ws = ws_buffers ?
+                ws_buffers + ithr * ws_size_per_thr / sizeof(float) : 0;
+        dim_t ld = ldc;
+
+        int sum_later = (mkldnn_get_num_threads() < nthr_m * nthr_n * nthr_k);
+
+        if (ithr < nthr_m * nthr_n * nthr_k) {
+
+            ithr_mn = ithr % nthr_mn;
+            ithr_m = ithr_mn % nthr_m;
+            ithr_n = ithr_mn / nthr_m;
+            ithr_k = ithr / nthr_mn;
+
+            /* swap ithr_k for performance improvement */
+            if (ithr_k == 0)
+                ithr_k = nthr_k - 1;
+            else if (ithr_k == nthr_k - 1)
+                ithr_k = 0;
+
+            m_from = MB * (ithr_m);
+            m_to = MB * (ithr_m + 1);
+            if (m_to > m)
+                m_to = m;
+            myM = m_to - m_from;
+
+            n_from = NB * (ithr_n);
+            n_to = NB * (ithr_n + 1);
+            if (n_to > n)
+                n_to = n;
+            myN = n_to - n_from;
+
+            k_from = KB * (ithr_k);
+            k_to = KB * (ithr_k + 1);
+            if (k_to > k)
+                k_to = k;
+            myK = k_to - k_from;
+
+            cbase = (ithr_m + nthr_m * ithr_n) * (nthr_k - 1);
+            ibase = (ithr_m + nthr_m * ithr_n) * nthr_k;
+
+            if ((myM > 0) && (myN > 0)) {
+
+                if (*transa == 'N' || *transa == 'n') {
+                    myA = &(A[m_from + k_from * lda]);
+                } else {
+                    myA = &(A[k_from + m_from * lda]);
+                }
+                if (*transb == 'N' || *transb == 'n') {
+                    myB = &(B[k_from + n_from * ldb]);
+                } else {
+                    myB = &(B[n_from + k_from * ldb]);
+                }
+                if (ithr_k == 0) {
+                    myC = &(C[m_from + n_from * ldc]);
+                    myBeta = beta;
+                    ld = ldc;
+                    if (bias)
+                        myBias = &(bias[m_from]);
+                } else {
+                    myC = c_buffers + (dim_t)MB * NB * (cbase + ithr_k - 1);
+                    myBeta = 0.0;
+                    ld = MB;
+                    myBias = nullptr;
+                }
+
+                sgemm_nocopy_driver(transa, transb, myM, myN, myK, p_alpha, myA,
+                        lda, myB, ldb, &myBeta, myC, ld, myBias, ws);
+
+                if (nthr_k > 1 && !sum_later)
+                    ompstatus[(ibase + ithr_k) * CACHE_LINE_SIZE] = 1;
+            }
+
+            if (nthr_k > 1 && !sum_later) {
+
+                // sum matrices partitioned along K dimension
+                int n1, n2;
+
+                partition_unit_diff(ithr_k, nthr_k, myN, &n1, &n2);
+
+                if (ithr_k > 0) {
+
+                    myC = c_buffers + (dim_t)MB * NB * (cbase + ithr_k - 1)
+                        + (dim_t)n1 * MB;
+                    /* need to wait until main thread finishes */
+                    while (ompstatus[ibase * CACHE_LINE_SIZE] != 1) {
+                    };
+
+                    /* my cache is hot */
+                    sum_two_matrices(myM, n2, myC, MB,
+                            &C[m_from + (n_from + n1) * ldc], ldc);
+                }
+
+                for (int ik = 1; ik < nthr_k; ++ik) {
+                    if (ik != ithr_k) {
+
+                        myC = c_buffers + (dim_t)MB * NB * (cbase + ik - 1)
+                            + (dim_t)n1 * MB;
+
+                        while (ompstatus[(ibase + ik) * CACHE_LINE_SIZE] != 1) {
+                        };
+
+                        sum_two_matrices(myM, n2, myC, MB,
+                                &C[m_from + (n_from + n1) * ldc], ldc);
+                    }
+                }
+            }
+        }
+    });
+
+
+    // handle C summation later
+    if (nthr_k > 1 && ompstatus[0] == 0) {
+
+        parallel_nd(nthr, [&](const int ithr) {
+            int ithr_m, ithr_n, ithr_k, ithr_mn;
+            int m_from, m_to, myM;
+            int n_from, n_to, myN;
+            int cbase;
+            float *myC = C;
+
+            if (ithr < nthr_m * nthr_n * nthr_k) {
+
+                ithr_mn = ithr % nthr_mn;
+                ithr_m = ithr_mn % nthr_m;
+                ithr_n = ithr_mn / nthr_m;
+                ithr_k = ithr / nthr_mn;
+
+                /* swap ithr_k for performance improvement */
+                if (ithr_k == 0)
+                    ithr_k = nthr_k - 1;
+                else if (ithr_k == nthr_k - 1)
+                    ithr_k = 0;
+
+                m_from = MB * (ithr_m);
+                m_to = MB * (ithr_m + 1);
+                if (m_to > m)
+                    m_to = m;
+                myM = m_to - m_from;
+
+                n_from = NB * (ithr_n);
+                n_to = NB * (ithr_n + 1);
+                if (n_to > n)
+                    n_to = n;
+                myN = n_to - n_from;
+
+                cbase = (ithr_m + nthr_m * ithr_n) * (nthr_k - 1);
+
+                if (nthr_k > 1) {
+                    // sum matrices partitioned along K dimension
+                    int n1, n2;
+
+                    partition_unit_diff(ithr_k, nthr_k, myN, &n1, &n2);
+
+                    if (ithr_k > 0) {
+
+                        myC = c_buffers + (dim_t)MB * NB * (cbase + ithr_k - 1)
+                            + (dim_t)n1 * MB;
+
+                        /* my cache is hot */
+                        sum_two_matrices(myM, n2, myC, MB,
+                                         &C[m_from + (n_from + n1) * ldc], ldc);
+                    }
+
+                    for (int ik = 1; ik < nthr_k; ++ik) {
+                        if (ik != ithr_k) {
+
+                            myC = c_buffers + (dim_t)MB * NB * (cbase + ik - 1)
+                                + (dim_t)n1 * MB;
+
+                            sum_two_matrices(myM, n2, myC, MB,
+                                             &C[m_from + (n_from + n1) * ldc], ldc);
+                        }
+                    }
+                }
+            }
+        });
+    }
+
+    free(c_buffers);
+    free(ompstatus_);
+    free(ws_buffers);
+
+    return mkldnn_success;
+}
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx512_common_gemm_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx512_common_gemm_f32.hpp
new file mode 100644
index 0000000000..d581b7fd71
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx512_common_gemm_f32.hpp
@@ -0,0 +1,36 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_AVX512_COMMON_GEMM_F32_HPP
+#define JIT_AVX512_COMMON_GEMM_F32_HPP
+
+#include "mkldnn_types.h"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+mkldnn_status_t jit_avx512_common_gemm_f32(
+        const char *transa, const char *transb, const int *M,
+        const int *N, const int *K, const float *alpha, const float *A,
+        const int *lda, const float *B, const int *ldb, const float *beta,
+        float *C, const int *ldc, const float *bias = nullptr);
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx_gemm_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx_gemm_f32.cpp
new file mode 100644
index 0000000000..60d4220837
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx_gemm_f32.cpp
@@ -0,0 +1,2705 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <cmath>
+#include <mutex>
+
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "ref_gemm_f32.hpp"
+#include "gemm_utils_f32.hpp"
+#include "jit_avx_gemm_f32.hpp"
+
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+#define CACHE_LINE_SIZE 64
+
+#define STACKSIZE get_size_of_abi_save_regs()
+#if _WIN32
+#define STACK_K_CAPACITY 128
+#else
+#define STACK_K_CAPACITY 8192
+#endif
+#define SIZE 4
+#define OFFSET 32
+#define BASE_SHIFT 2
+#define SECOND_FETCH 14
+
+namespace avx_gemm_f32 {
+using namespace gemm_utils;
+
+struct xbyak_gemm : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx_gemm_f32_xbyak_gemm)
+
+    xbyak_gemm(char isTransA, char isTransB, float beta, bool hasBias = false,
+            void *code_ptr = nullptr,
+            size_t code_size = 80 * Xbyak::DEFAULT_MAX_CODE_SIZE)
+        : jit_generator(code_ptr, code_size)
+    {
+        using namespace Xbyak;
+
+        const bool is_avx2 = mayiuse(avx2);
+        assert(IMPLICATION(!is_avx2, mayiuse(avx)));
+
+        const int UNROLL_M = is_avx2 ? 16 : 8;
+        const int UNROLL_N = 6;
+
+        bool isBeta0 = (beta == 0.0);
+        bool isBetaN = (!isBeta0 && beta != 1.0);
+
+        // various definitions for convenience
+        auto ARG_M = abi_param1;
+        auto ARG_N = abi_param2;
+        auto K = abi_param3;
+        auto ARG_ALPHA = abi_param4;
+#ifdef _WIN32
+        auto ARG_A = ptr[rsp + OFFSET_SHADOWSPACE + STACKSIZE];
+        auto ARG_LDA = qword[rsp + OFFSET_SHADOWSPACE +
+            sizeof(float *) + STACKSIZE];
+        const auto stackOffset = OFFSET_SHADOWSPACE +
+            sizeof(float *) + STACKSIZE;
+        auto A = rsi;
+        auto LDA = rdi;
+#else
+        auto ARG_A = r8;
+        auto ARG_LDA = r9;
+        const auto stackOffset = STACKSIZE;
+        auto A = ARG_A;
+        auto LDA = ARG_LDA;
+#endif
+        auto ARG_B = ptr[rsp + 8 + stackOffset];
+        auto ARG_LDB = ptr[rsp + 16 + stackOffset];
+        auto ARG_BETA = ptr[rsp + 24 + stackOffset];
+        auto ARG_C = ptr[rsp + 32 + stackOffset];
+        auto ARG_LDC = ptr[rsp + 40 + stackOffset];
+        auto ARG_BIAS = ptr[rsp + 48 + stackOffset];
+        auto ARG_WS = ptr[rsp + 56 + stackOffset];
+
+        auto B = r11;
+        auto LDB = rbx;
+        auto LDC = r13;
+        auto LL = rax;
+        auto AO1 = abi_param2;
+        auto BO1 = abi_param4;
+        auto BO2 = rbp;
+        auto CO1 = r14;
+        auto CO2 = r15;
+        auto LDB3 = r10;
+        auto LDA4 = abi_param1;
+        auto AA = r12;
+        auto BIAS1 = abi_param1;
+
+        auto M = qword[rsp + 0];
+        auto N = qword[rsp + 8];
+        auto FLAG = qword[rsp + 16];
+        auto I = qword[rsp + 24];
+        auto C = qword[rsp + 32];
+        auto BIAS = qword[rsp + 40];
+        auto ALPHA = qword[rsp + 48];
+        auto BETA = qword[rsp + 64];
+        auto ORIG_A = qword[rsp + 80];
+        auto MASK = dword[rsp + 88];
+        auto STRIDE = qword[rsp + 120];
+        auto ORIG_SP = qword[rsp + 152];
+
+        auto VALPHA = ymm1;
+        auto VBETA = ymm2;
+        auto VMASK = ymm3;
+        auto VBIAS1 = ymm2;
+        auto VBIAS2 = ymm4;
+
+        auto PREFETCHSIZEA = 128;
+        auto PREFETCHSIZEB = (!isTransB) ? -16 : 0;
+
+        // Function for packing if needed
+        auto do_pack = [&](
+                int unroll_m, bool isLoad1Unmasked, bool isLoad2Unmasked) {
+            Label pack2, pack3, pack4, pack10;
+
+            int regIdx;
+            Reg64 reg;
+
+            mov(BO1, A);
+            lea(AO1, ptr[rsp + 256 + OFFSET * SIZE]);
+
+            if (isTransA) {
+                lea(BO2, ptr[BO1 + LDA * 4]);
+                lea(CO1, ptr[LDA + LDA * 2]);
+                vmovupd(ymm7, STRIDE);
+            }
+
+            mov(LL, K);
+            sar(LL, 2);
+            jle(pack3, T_NEAR);
+            align(16);
+
+            L(pack2);
+            if (!isTransA) {
+                for (int i = 0; i < 4; i++) {
+                    regIdx = (i % 2 == 0) ? 4 : 6;
+                    if (isLoad1Unmasked) {
+                        vmovups(Ymm(regIdx),
+                                ptr[BO1 + (0 * 8 - OFFSET) * SIZE]);
+                    } else {
+                        vmaskmovps(Ymm(regIdx), VMASK,
+                                ptr[BO1 + (0 * 8 - OFFSET) * SIZE]);
+                    }
+                    if (unroll_m > 8) {
+                        if (isLoad2Unmasked) {
+                            vmovups(Ymm(regIdx + 1),
+                                    ptr[BO1 + (1 * 8 - OFFSET) * SIZE]);
+                        } else {
+                            vmaskmovps(Ymm(regIdx + 1), VMASK,
+                                    ptr[BO1 + (1 * 8 - OFFSET) * SIZE]);
+                        }
+                    }
+                    add(BO1, LDA);
+
+                    vmovups(ptr[AO1 + (unroll_m * i + 0 * 8 - OFFSET) * SIZE],
+                            Ymm(regIdx));
+                    if (unroll_m > 8) {
+                        vmovups(ptr[AO1
+                                        + (unroll_m * i + 1 * 8 - OFFSET)
+                                                * SIZE],
+                                Ymm(regIdx + 1));
+                    }
+                }
+
+            } else {
+                if (isLoad1Unmasked) {
+                    for (int i = 0; i < 2; i++) {
+                        reg = (i % 2 == 0) ? BO1 : BO2;
+                        vmovups(xmm0, ptr[reg + (0 * 8 - OFFSET) * SIZE]);
+                        vmovups(xmm1,
+                                ptr[reg + LDA * 1 + (0 * 8 - OFFSET) * SIZE]);
+                        lea(BO2, ptr[reg + LDA * 2]);
+                        vunpcklps(xmm4, xmm0, xmm1);
+                        vunpckhps(xmm5, xmm0, xmm1);
+                        vmovups(xmm0, ptr[BO2 + (0 * 8 - OFFSET) * SIZE]);
+                        vmovups(xmm1,
+                                ptr[BO2 + LDA * 1 + (0 * 8 - OFFSET) * SIZE]);
+                        lea(BO2, ptr[BO2 + LDA * 2]);
+                        vunpcklps(xmm6, xmm0, xmm1);
+                        vunpckhps(xmm2, xmm0, xmm1);
+
+                        vunpcklpd(xmm0, xmm4, xmm6);
+                        vunpckhpd(xmm1, xmm4, xmm6);
+                        vmovups(ptr[AO1
+                                        + (unroll_m * 0 + i * 4 - OFFSET)
+                                                * SIZE],
+                                xmm0);
+                        vmovups(ptr[AO1
+                                        + (unroll_m * 1 + i * 4 - OFFSET)
+                                                * SIZE],
+                                xmm1);
+                        vunpcklpd(xmm0, xmm5, xmm2);
+                        vunpckhpd(xmm1, xmm5, xmm2);
+                        vmovups(ptr[AO1
+                                        + (unroll_m * 2 + i * 4 - OFFSET)
+                                                * SIZE],
+                                xmm0);
+                        vmovups(ptr[AO1
+                                        + (unroll_m * 3 + i * 4 - OFFSET)
+                                                * SIZE],
+                                xmm1);
+                    }
+                } else if (is_avx2) {
+                    for (int i = 0; i < 2; i++) {
+                        vmovaps(xmm4, xmm3);
+                        vgatherqps(xmm0,
+                                ptr[BO1 + ymm7 + ((2 * i) - OFFSET) * SIZE],
+                                xmm4);
+                        vmovaps(xmm4, xmm3);
+                        vgatherqps(xmm1,
+                                ptr[BO1 + ymm7 + ((2 * i + 1) - OFFSET) * SIZE],
+                                xmm4);
+
+                        vmovups(ptr[AO1
+                                        + (unroll_m * (2 * i) + 0 * 4 - OFFSET)
+                                                * SIZE],
+                                xmm0);
+                        vmovups(ptr[AO1
+                                        + (unroll_m * (2 * i + 1) + 0 * 4
+                                                  - OFFSET)
+                                                * SIZE],
+                                xmm1);
+                    }
+
+                    lea(BO2, ptr[BO1 + LDA * 4]);
+
+                    for (int i = 0; i < 2; i++) {
+                        vextractf128(xmm4, ymm3, 1);
+                        vgatherqps(xmm0,
+                                ptr[BO2 + ymm7 + ((2 * i) - OFFSET) * SIZE],
+                                xmm4);
+                        vextractf128(xmm4, ymm3, 1);
+                        vgatherqps(xmm1,
+                                ptr[BO2 + ymm7 + ((2 * i + 1) - OFFSET) * SIZE],
+                                xmm4);
+
+                        vmovups(ptr[AO1
+                                        + (unroll_m * (2 * i) + 1 * 4 - OFFSET)
+                                                * SIZE],
+                                xmm0);
+                        vmovups(ptr[AO1
+                                        + (unroll_m * (2 * i + 1) + 1 * 4
+                                                  - OFFSET)
+                                                * SIZE],
+                                xmm1);
+                    }
+
+                    lea(BO2, ptr[BO2 + LDA * 4]);
+                } else {
+                    vxorps(xmm4, xmm4, xmm4);
+                    lea(BO2, ptr[BO1 + LDA * 4]);
+
+                    auto el_cp = [&](int section, int ld_step) {
+                        RegExp src_addr = section == 0 ? BO1 : BO2;
+                        if (ld_step == 1 || ld_step == 2)
+                            src_addr = src_addr + LDA * ld_step;
+                        else if (ld_step == 3)
+                            src_addr = src_addr + CO1;
+                        src_addr = src_addr - OFFSET * SIZE;
+
+                        vmovups(Xmm(ld_step % 2), ptr[src_addr]);
+                        RegExp dst_addr = AO1
+                            + (ld_step + section * 4 - OFFSET) * SIZE;
+                        for (int off = 0; off < 4; ++off)
+                            pextrd(ptr[dst_addr + unroll_m * off * SIZE],
+                                    Xmm(ld_step % 2), off);
+                    };
+
+                    Label l_end;
+                    el_cp(0, 0); cmp(M, 4 * 0 + 0 + 1); je(l_end, T_NEAR);
+                    el_cp(0, 1); cmp(M, 4 * 0 + 1 + 1); je(l_end, T_NEAR);
+                    el_cp(0, 2); cmp(M, 4 * 0 + 2 + 1); je(l_end, T_NEAR);
+                    el_cp(0, 3); cmp(M, 4 * 0 + 3 + 1); je(l_end, T_NEAR);
+                    el_cp(1, 0); cmp(M, 4 * 1 + 0 + 1); je(l_end, T_NEAR);
+                    el_cp(1, 1); cmp(M, 4 * 1 + 1 + 1); je(l_end, T_NEAR);
+                    el_cp(1, 2);
+                    L(l_end);
+
+                    lea(BO2, ptr[BO2 + LDA * 4]);
+                }
+
+                if (unroll_m >= 16) {
+                    assert(is_avx2);
+                    if (isLoad2Unmasked) {
+                        for (int i = 0; i < 2; i++) {
+                            vmovups(xmm0, ptr[BO2 + (0 * 8 - OFFSET) * SIZE]);
+                            vmovups(xmm1, ptr[BO2 + LDA * 1
+                                                  + (0 * 8 - OFFSET) * SIZE]);
+                            lea(BO2, ptr[BO2 + LDA * 2]);
+                            vunpcklps(xmm4, xmm0, xmm1);
+                            vunpckhps(xmm5, xmm0, xmm1);
+                            vmovups(xmm0, ptr[BO2 + (0 * 8 - OFFSET) * SIZE]);
+                            vmovups(xmm1, ptr[BO2 + LDA * 1
+                                                  + (0 * 8 - OFFSET) * SIZE]);
+                            if (i == 0)
+                                lea(BO2, ptr[BO2 + LDA * 2]);
+                            vunpcklps(xmm6, xmm0, xmm1);
+                            vunpckhps(xmm2, xmm0, xmm1);
+
+                            vunpcklpd(xmm0, xmm4, xmm6);
+                            vunpckhpd(xmm1, xmm4, xmm6);
+                            vmovups(ptr[AO1
+                                            + (unroll_m * 0 + (i + 2) * 4
+                                                      - OFFSET)
+                                                    * SIZE],
+                                    xmm0);
+                            vmovups(ptr[AO1
+                                            + (unroll_m * 1 + (i + 2) * 4
+                                                      - OFFSET)
+                                                    * SIZE],
+                                    xmm1);
+                            vunpcklpd(xmm0, xmm5, xmm2);
+                            vunpckhpd(xmm1, xmm5, xmm2);
+                            vmovups(ptr[AO1
+                                            + (unroll_m * 2 + (i + 2) * 4
+                                                      - OFFSET)
+                                                    * SIZE],
+                                    xmm0);
+                            vmovups(ptr[AO1
+                                            + (unroll_m * 3 + (i + 2) * 4
+                                                      - OFFSET)
+                                                    * SIZE],
+                                    xmm1);
+                        }
+                    } else {
+                        for (int i = 0; i < 2; i++) {
+                            vmovaps(xmm4, xmm3);
+                            vgatherqps(xmm0,
+                                    ptr[BO2 + ymm7 + ((2 * i) - OFFSET) * SIZE],
+                                    xmm4);
+                            vmovaps(xmm4, xmm3);
+                            vgatherqps(xmm1,
+                                    ptr[BO2 + ymm7
+                                               + ((2 * i + 1) - OFFSET) * SIZE],
+                                    xmm4);
+
+                            vmovups(ptr[AO1
+                                            + (unroll_m * (2 * i) + 2 * 4
+                                                      - OFFSET)
+                                                    * SIZE],
+                                    xmm0);
+                            vmovups(ptr[AO1
+                                            + (unroll_m * (2 * i + 1) + 2 * 4
+                                                      - OFFSET)
+                                                    * SIZE],
+                                    xmm1);
+                        }
+
+                        lea(BO2, ptr[BO2 + LDA * 4]);
+
+                        for (int i = 0; i < 2; i++) {
+                            vextractf128(xmm4, ymm3, 1);
+                            vgatherqps(xmm0,
+                                    ptr[BO2 + ymm7 + ((2 * i) - OFFSET) * SIZE],
+                                    xmm4);
+                            vextractf128(xmm4, ymm3, 1);
+                            vgatherqps(xmm1,
+                                    ptr[BO2 + ymm7
+                                               + ((2 * i + 1) - OFFSET) * SIZE],
+                                    xmm4);
+
+                            vmovups(ptr[AO1
+                                            + (unroll_m * (2 * i) + 3 * 4
+                                                      - OFFSET)
+                                                    * SIZE],
+                                    xmm0);
+                            vmovups(ptr[AO1
+                                            + (unroll_m * (2 * i + 1) + 3 * 4
+                                                      - OFFSET)
+                                                    * SIZE],
+                                    xmm1);
+                        }
+
+                        lea(BO2, ptr[BO2 + LDA * 4]);
+                    }
+                }
+                add(BO1, (4 * SIZE));
+            }
+
+            add(AO1, unroll_m * 4 * SIZE);
+            sub(LL, 1);
+            jg(pack2, T_NEAR);
+            align(16);
+
+            L(pack3);
+            mov(LL, K);
+            and_(LL, 3);
+            jle(pack10, T_NEAR);
+            align(16);
+
+            L(pack4);
+            if (!isTransA) {
+                if (isLoad1Unmasked) {
+                    vmovups(ymm4, ptr[BO1 + (0 * 8 - OFFSET) * SIZE]);
+                } else {
+                    vmaskmovps(ymm4, VMASK, ptr[BO1 + (0 * 8 - OFFSET) * SIZE]);
+                }
+                if (unroll_m > 8) {
+                    if (isLoad2Unmasked) {
+                        vmovups(ymm5, ptr[BO1 + (1 * 8 - OFFSET) * SIZE]);
+                    } else {
+                        vmaskmovps(ymm5, VMASK,
+                                ptr[BO1 + (1 + 8 - OFFSET) * SIZE]);
+                    }
+                }
+                add(BO1, LDA);
+                vmovups(ptr[AO1 + (unroll_m * 0 + 0 * 8 - OFFSET) * SIZE],
+                        ymm4);
+                if (unroll_m > 8) {
+                    vmovups(ptr[AO1 + (unroll_m * 0 + 1 * 8 - OFFSET) * SIZE],
+                            ymm5);
+                }
+            } else {
+                if (isLoad1Unmasked) {
+                    for (int i = 0; i < 2; i++) {
+                        reg = (i % 2 == 0) ? BO1 : BO2;
+                        vmovss(Xmm(i + 1), ptr[reg + (0 * 8 - OFFSET) * SIZE]);
+                        vmovss(xmm0,
+                                ptr[reg + LDA * 1 + (0 * 8 - OFFSET) * SIZE]);
+                        lea(BO2, ptr[reg + LDA * 2]);
+                        vunpcklps(Xmm(i + 1), Xmm(i + 1), Xmm(0));
+                    }
+                    vunpcklpd(xmm1, xmm1, xmm2);
+                    vmovups(ptr[AO1 + (unroll_m * 0 + 0 * 4 - OFFSET) * SIZE],
+                            xmm1);
+
+                    for (int i = 0; i < 2; i++) {
+                        vmovss(Xmm(i + 1), ptr[BO2 + (0 * 8 - OFFSET) * SIZE]);
+                        vmovss(xmm0,
+                                ptr[BO2 + LDA * 1 + (0 * 8 - OFFSET) * SIZE]);
+                        lea(BO2, ptr[BO2 + LDA * 2]);
+                        vunpcklps(Xmm(i + 1), Xmm(i + 1), Xmm(0));
+                    }
+                    vunpcklpd(xmm1, xmm1, xmm2);
+                    vmovups(ptr[AO1 + (unroll_m * 0 + 1 * 4 - OFFSET) * SIZE],
+                            xmm1);
+                } else if (is_avx2) {
+                    vmovaps(xmm4, xmm3);
+                    vgatherqps(xmm1, ptr[BO1 + ymm7 + (0 * 8 - OFFSET) * SIZE],
+                            xmm4);
+                    lea(BO2, ptr[BO1 + LDA * 4]);
+                    vmovups(ptr[AO1 + (unroll_m * 0 + 0 * 4 - OFFSET) * SIZE],
+                            xmm1);
+
+                    vextractf128(xmm4, ymm3, 1);
+                    vgatherqps(xmm1, ptr[BO2 + ymm7 + (0 * 8 - OFFSET) * SIZE],
+                            xmm4);
+                    lea(BO2, ptr[BO2 + LDA * 4]);
+                    vmovups(ptr[AO1 + (unroll_m * 0 + 1 * 4 - OFFSET) * SIZE],
+                            xmm1);
+                } else {
+                    vxorps(xmm4, xmm4, xmm4);
+                    lea(BO2, ptr[BO1 + LDA * 4]);
+
+                    auto el_cp = [&](int section, int ld_step) {
+                        RegExp src_addr = section == 0 ? BO1 : BO2;
+                        if (ld_step == 1 || ld_step == 2)
+                            src_addr = src_addr + LDA * ld_step;
+                        else if (ld_step == 3)
+                            src_addr = src_addr + CO1;
+                        src_addr = src_addr - OFFSET * SIZE;
+
+                        vmovss(xmm1, ptr[src_addr]);
+                        RegExp dst_addr = AO1
+                            + (ld_step + section * 4 - OFFSET) * SIZE;
+                        movss(ptr[dst_addr], xmm1);
+                    };
+
+                    Label l_end;
+                    el_cp(0, 0); cmp(M, 4 * 0 + 0 + 1); je(l_end, T_NEAR);
+                    el_cp(0, 1); cmp(M, 4 * 0 + 1 + 1); je(l_end, T_NEAR);
+                    el_cp(0, 2); cmp(M, 4 * 0 + 2 + 1); je(l_end, T_NEAR);
+                    el_cp(0, 3); cmp(M, 4 * 0 + 3 + 1); je(l_end, T_NEAR);
+                    el_cp(1, 0); cmp(M, 4 * 1 + 0 + 1); je(l_end, T_NEAR);
+                    el_cp(1, 1); cmp(M, 4 * 1 + 1 + 1); je(l_end, T_NEAR);
+                    el_cp(1, 2);
+                    L(l_end);
+
+                    lea(BO2, ptr[BO2 + LDA * 4]);
+                }
+
+                if (unroll_m >= 16) {
+                    assert(is_avx2);
+                    if (isLoad2Unmasked) {
+                        for (int i = 0; i < 2; i++) {
+                            vmovss(Xmm(i + 1),
+                                    ptr[BO2 + (0 * 8 - OFFSET) * SIZE]);
+                            vmovss(xmm0, ptr[BO2 + LDA * 1
+                                                 + (0 * 8 - OFFSET) * SIZE]);
+                            lea(BO2, ptr[BO2 + LDA * 2]);
+                            vunpcklps(Xmm(i + 1), Xmm(i + 1), Xmm(0));
+                        }
+                        vunpcklpd(xmm1, xmm1, xmm2);
+                    } else {
+                        vmovaps(xmm4, xmm3);
+                        vgatherqps(xmm1,
+                                ptr[BO2 + ymm7 + (0 * 8 - OFFSET) * SIZE],
+                                xmm4);
+                        lea(BO2, ptr[BO2 + LDA * 4]);
+                    }
+                    vmovups(ptr[AO1 + (unroll_m * 0 + 2 * 4 - OFFSET) * SIZE],
+                            xmm1);
+
+                    if (isLoad2Unmasked) {
+                        for (int i = 0; i < 2; i++) {
+                            vmovss(Xmm(i + 1),
+                                    ptr[BO2 + (0 * 8 - OFFSET) * SIZE]);
+                            vmovss(xmm0, ptr[BO2 + LDA * 1
+                                                 + (0 * 8 - OFFSET) * SIZE]);
+                            lea(BO2, ptr[BO2 + LDA * 2]);
+                            vunpcklps(Xmm(i + 1), Xmm(i + 1), Xmm(0));
+                        }
+                        vunpcklpd(xmm1, xmm1, xmm2);
+                    } else {
+                        vextractf128(xmm4, ymm3, 1);
+                        vgatherqps(xmm1,
+                                ptr[BO2 + ymm7 + (0 * 8 - OFFSET) * SIZE],
+                                xmm4);
+                    }
+                    vmovups(ptr[AO1 + (unroll_m * 0 + 3 * 4 - OFFSET) * SIZE],
+                            xmm1);
+                }
+                add(BO1, SIZE);
+            }
+
+            add(AO1, unroll_m * SIZE);
+            sub(LL, 1);
+            jg(pack4, T_NEAR);
+            align(16);
+
+            L(pack10);
+        };
+
+        // Fused multiply add; may become one or two instructions
+        auto fma = [&](bool useFma, Ymm reg0, Ymm reg1, Ymm reg2,
+                bool overWrite = false) {
+            if (useFma) {
+                if (is_avx2) {
+                    vfmadd231ps(reg2, reg1, reg0);
+                } else {
+                    assert(UNROLL_M == 8);
+                    auto tent_vreg = overWrite ? reg1 : ymm1;
+                    vmulps(tent_vreg, reg1, reg0);
+                    vaddps(reg2, reg2, tent_vreg);
+                }
+            } else {
+                if (!overWrite) {
+                    vmulps(ymm15, reg1, reg0);
+                    vaddps(reg2, reg2, ymm15);
+                } else {
+                    vmulps(reg1, reg1, reg0);
+                    vaddps(reg2, reg2, reg1);
+                }
+            }
+        };
+
+        // Inner kernel with k=8
+        auto innerkernel8 = [&](int unroll_m, int unroll_n,
+                bool isLoad1Unmasked, bool isLoad2Unmasked, bool isDirect,
+                bool isCopy, bool useFma, Ymm reg00, Ymm reg01, Ymm reg02,
+                Ymm reg03, Ymm reg04, Ymm reg05, Ymm reg06, Ymm reg07,
+                Ymm reg08, Ymm reg09, Ymm reg10, Ymm reg11, Ymm reg12,
+                Ymm reg13, Ymm reg14, Ymm reg15, Ymm reg16, Ymm reg17,
+                Ymm reg18, Ymm reg19, Ymm reg20, Ymm reg21, Ymm reg22,
+                Ymm reg23) {
+
+            Ymm fmareg;
+
+            if (!isDirect) {
+                prefetcht0(ptr[AO1 + (PREFETCHSIZEA + 0) * SIZE]);
+            } else {
+                prefetcht0(ptr[AO1 + LDA4]);
+            }
+
+            for (int i = 0; i < 8; i++) {
+                if (isDirect) {
+                    if (isLoad1Unmasked) {
+                        vmovups(ymm0, ptr[AO1 + (0 * 8 - OFFSET) * SIZE]);
+                    } else {
+                        vmaskmovps(ymm0, VMASK,
+                                ptr[AO1 + (0 * 8 - OFFSET) * SIZE]);
+                    }
+                    if (unroll_m >= 16) {
+                        if (isLoad2Unmasked) {
+                            vmovups(ymm1, ptr[AO1 + (1 * 8 - OFFSET) * SIZE]);
+                        } else {
+                            vmaskmovps(ymm1, VMASK,
+                                    ptr[AO1 + (1 * 8 - OFFSET) * SIZE]);
+                        }
+                    }
+                    add(AO1, LDA);
+                }
+
+                if (!isTransB) {
+                    vbroadcastss(ymm2, ptr[BO1 + (i - OFFSET) * SIZE]);
+                } else {
+                    vbroadcastss(ymm2, ptr[BO1 + (0 - OFFSET) * SIZE]);
+                }
+                fmareg = (i % 2 == 0) ? reg00 : reg12;
+                fma(useFma, ymm0, ymm2, fmareg);
+                if (unroll_m >= 16) {
+                    fmareg = (i % 2 == 0) ? reg06 : reg18;
+                    fma(useFma, ymm1, ymm2, fmareg);
+                }
+                if (i == 0) {
+                    if (!isTransB) {
+                        prefetcht0(ptr[BO1 + PREFETCHSIZEB * SIZE]);
+                    }
+                }
+                if (unroll_n >= 2) {
+                    if (!isTransB) {
+                        if (i == 1) {
+                            prefetcht0(ptr[BO1 + LDB + PREFETCHSIZEB * SIZE]);
+                        }
+                        vbroadcastss(
+                                ymm2, ptr[BO1 + LDB * 1 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (1 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg01 : reg13;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg07 : reg19;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (isCopy) {
+                    vmovups(ptr[LDA4 + (unroll_m * i + 0 * 8 - OFFSET) * SIZE],
+                            ymm0);
+                    if (unroll_m >= 16) {
+                        vmovups(ptr[LDA4
+                                        + (unroll_m * i + 1 * 8 - OFFSET)
+                                                * SIZE],
+                                ymm1);
+                    }
+                    if (i == 7) {
+                        sub(LDA4, -unroll_m * 8 * SIZE);
+                    }
+                }
+
+                if (unroll_n >= 3) {
+                    if (!isTransB) {
+                        if (i == 2) {
+                            prefetcht0(
+                                    ptr[BO1 + LDB * 2 + PREFETCHSIZEB * SIZE]);
+                        }
+                        vbroadcastss(
+                                ymm2, ptr[BO1 + LDB * 2 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (2 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg02 : reg14;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg08 : reg20;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (i == 7) {
+                    if (!isTransB) {
+                        sub(BO1, -8 * SIZE);
+                    }
+                }
+
+                if (unroll_n >= 4) {
+                    if (!isTransB) {
+                        if (i == 3) {
+                            prefetcht0(ptr[BO2 + PREFETCHSIZEB * SIZE]);
+                        }
+                        vbroadcastss(ymm2, ptr[BO2 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (3 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg03 : reg15;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg09 : reg21;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (unroll_n >= 5) {
+                    if (!isTransB) {
+                        if (i == 4) {
+                            prefetcht0(ptr[BO2 + LDB + PREFETCHSIZEB * SIZE]);
+                        }
+                        vbroadcastss(
+                                ymm2, ptr[BO2 + LDB * 1 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (4 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg04 : reg16;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg10 : reg22;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (unroll_n >= 6) {
+                    if (!isTransB) {
+                        if (i == 5) {
+                            prefetcht0(
+                                    ptr[BO2 + LDB * 2 + PREFETCHSIZEB * SIZE]);
+                        }
+                        vbroadcastss(
+                                ymm2, ptr[BO2 + LDB * 2 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (5 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg05 : reg17;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg11 : reg23;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+                if (isTransB) {
+                    prefetcht0(ptr[BO1 + BO2]);
+                    add(BO1, LDB);
+                }
+
+                if (i == 0) {
+                    if (unroll_m >= 4) {
+                        if (!isDirect) {
+                            prefetcht0(
+                                    ptr[AO1 + (PREFETCHSIZEA + 2 * 8) * SIZE]);
+                        } else {
+                            prefetcht0(ptr[AO1 + LDA4]);
+                        }
+                    }
+                }
+                if (i == 1 || i == 2) {
+                    if (unroll_m >= 8) {
+                        if (!isDirect) {
+                            prefetcht0(ptr[AO1
+                                    + (PREFETCHSIZEA + (2 + 2 * i) * 8)
+                                            * SIZE]);
+                        } else {
+                            prefetcht0(ptr[AO1 + LDA4]);
+                        }
+                    }
+                }
+                if (i == 3 || i == 4 || i == 5 || i == 6) {
+                    if (unroll_m >= 16) {
+                        if (!isDirect) {
+                            prefetcht0(ptr[AO1
+                                    + (PREFETCHSIZEA + (2 + 2 * i) * 8)
+                                            * SIZE]);
+                        } else {
+                            prefetcht0(ptr[AO1 + LDA4]);
+                        }
+                    }
+                }
+                if (i == 7) {
+                    if (!isTransB) {
+                        if (unroll_n >= 4) {
+                            sub(BO2, -8 * SIZE);
+                        }
+                    }
+                    if (!isTransA) {
+                        prefetcht2(ptr[AA]);
+                        lea(AA, ptr[AA + LDA]);
+                    }
+                }
+
+                if (!isDirect) {
+                    if (isLoad1Unmasked) {
+                        vmovups(ymm0,
+                                ptr[AO1
+                                        + (unroll_m * (i + 1) + 0 * 8 - OFFSET)
+                                                * SIZE]);
+                    } else {
+                        vmaskmovps(
+                                ymm0, VMASK,
+                                ptr[AO1
+                                        + (unroll_m * (i + 1) + 0 * 8 - OFFSET)
+                                                * SIZE]);
+                    }
+                    if (unroll_m >= 16) {
+                        if (isLoad2Unmasked) {
+                            vmovups(ymm1, ptr[AO1
+                                                  + (unroll_m * (i + 1) + 1 * 8
+                                                            - OFFSET)
+                                                          * SIZE]);
+                        } else {
+                            vmaskmovps(ymm1, VMASK,
+                                    ptr[AO1
+                                               + (unroll_m * (i + 1) + 1 * 8
+                                                         - OFFSET)
+                                                       * SIZE]);
+                        }
+                    }
+                }
+            }
+
+            if (!isDirect) {
+                sub(AO1, -unroll_m * 8 * SIZE);
+            }
+            sub(LL, 1);
+
+        };
+
+        // Inner kernel with k=4
+        auto innerkernel4 = [&](int unroll_m, int unroll_n,
+                bool isLoad1Unmasked, bool isLoad2Unmasked, bool isDirect,
+                bool isCopy, bool useFma, Ymm reg00, Ymm reg01, Ymm reg02,
+                Ymm reg03, Ymm reg04, Ymm reg05, Ymm reg06, Ymm reg07,
+                Ymm reg08, Ymm reg09, Ymm reg10, Ymm reg11, Ymm reg12,
+                Ymm reg13, Ymm reg14, Ymm reg15, Ymm reg16, Ymm reg17,
+                Ymm reg18, Ymm reg19, Ymm reg20, Ymm reg21, Ymm reg22,
+                Ymm reg23) {
+
+            Ymm fmareg;
+
+            if (!isDirect) {
+                prefetcht0(ptr[AO1 + (PREFETCHSIZEA + 0) * SIZE]);
+            } else {
+                prefetcht0(ptr[AO1 + LDA4]);
+            }
+
+            for (int i = 0; i < 4; i++) {
+                if (isDirect) {
+                    if (isLoad1Unmasked) {
+                        vmovups(ymm0, ptr[AO1 + (0 * 8 - OFFSET) * SIZE]);
+                    } else {
+                        vmaskmovps(ymm0, VMASK,
+                                ptr[AO1 + (0 * 8 - OFFSET) * SIZE]);
+                    }
+                    if (unroll_m >= 16) {
+                        if (isLoad2Unmasked) {
+                            vmovups(ymm1, ptr[AO1 + (1 * 8 - OFFSET) * SIZE]);
+                        } else {
+                            vmaskmovps(ymm1, VMASK,
+                                    ptr[AO1 + (1 * 8 - OFFSET) * SIZE]);
+                        }
+                    }
+                    add(AO1, LDA);
+                }
+
+                if (!isTransB) {
+                    vbroadcastss(ymm2, ptr[BO1 + (i - OFFSET) * SIZE]);
+                } else {
+                    vbroadcastss(ymm2, ptr[BO1 + (0 - OFFSET) * SIZE]);
+                }
+                fmareg = (i % 2 == 0) ? reg00 : reg12;
+                fma(useFma, ymm0, ymm2, fmareg);
+                if (unroll_m >= 16) {
+                    fmareg = (i % 2 == 0) ? reg06 : reg18;
+                    fma(useFma, ymm1, ymm2, fmareg);
+                }
+                if (i == 0) {
+                    if (!isTransB) {
+                        prefetcht0(ptr[BO1 + PREFETCHSIZEB * SIZE]);
+                    }
+                }
+                if (unroll_n >= 2) {
+                    if (!isTransB) {
+                        if (i == 1) {
+                            prefetcht0(ptr[BO1 + LDB + PREFETCHSIZEB * SIZE]);
+                        }
+                        vbroadcastss(
+                                ymm2, ptr[BO1 + LDB * 1 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (1 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg01 : reg13;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg07 : reg19;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (isCopy) {
+                    vmovups(ptr[LDA4 + (unroll_m * i + 0 * 8 - OFFSET) * SIZE],
+                            ymm0);
+                    if (unroll_m >= 16) {
+                        vmovups(ptr[LDA4
+                                        + (unroll_m * i + 1 * 8 - OFFSET)
+                                                * SIZE],
+                                ymm1);
+                    }
+                    if (i == 3) {
+                        sub(LDA4, -unroll_m * 4 * SIZE);
+                    }
+                }
+
+                if (unroll_n >= 3) {
+                    if (!isTransB) {
+                        if (i == 2) {
+                            prefetcht0(
+                                    ptr[BO1 + LDB * 2 + PREFETCHSIZEB * SIZE]);
+                        }
+                        vbroadcastss(
+                                ymm2, ptr[BO1 + LDB * 2 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (2 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg02 : reg14;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg08 : reg20;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (i == 7) {
+                    if (!isTransB) {
+                        sub(BO1, -8 * SIZE);
+                    }
+                }
+
+                if (unroll_n >= 4) {
+                    if (!isTransB) {
+                        if (i == 3) {
+                            prefetcht0(ptr[BO2 + PREFETCHSIZEB * SIZE]);
+                        }
+                        vbroadcastss(ymm2, ptr[BO2 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (3 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg03 : reg15;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg09 : reg21;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (unroll_n >= 5) {
+                    if (!isTransB) {
+                        if (i == 4) {
+                            prefetcht0(ptr[BO2 + LDB + PREFETCHSIZEB * SIZE]);
+                        }
+                        vbroadcastss(
+                                ymm2, ptr[BO2 + LDB * 1 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (4 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg04 : reg16;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg10 : reg22;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (unroll_n >= 6) {
+                    if (!isTransB) {
+                        if (i == 5) {
+                            prefetcht0(
+                                    ptr[BO2 + LDB * 2 + PREFETCHSIZEB * SIZE]);
+                        }
+                        vbroadcastss(
+                                ymm2, ptr[BO2 + LDB * 2 + (i - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (5 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg05 : reg17;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg11 : reg23;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+                if (isTransB) {
+                    prefetcht0(ptr[BO1 + BO2]);
+                    add(BO1, LDB);
+                }
+
+                if (i == 0) {
+                    if (unroll_m >= 4) {
+                        if (!isDirect) {
+                            prefetcht0(
+                                    ptr[AO1 + (PREFETCHSIZEA + 2 * 8) * SIZE]);
+                        } else {
+                            prefetcht0(ptr[AO1 + LDA4]);
+                        }
+                    }
+                }
+                if (i == 1 || i == 2) {
+                    if (unroll_m >= 8) {
+                        if (!isDirect) {
+                            prefetcht0(ptr[AO1
+                                    + (PREFETCHSIZEA + (2 + 2 * i) * 8)
+                                            * SIZE]);
+                        } else {
+                            prefetcht0(ptr[AO1 + LDA4]);
+                        }
+                    }
+                }
+                if (i == 3) {
+                    if (!isTransB) {
+                        sub(BO1, -4 * SIZE);
+                        if (unroll_n >= 4) {
+                            sub(BO2, -4 * SIZE);
+                        }
+                    }
+                }
+
+                if (!isDirect) {
+                    if (isLoad1Unmasked) {
+                        vmovups(ymm0,
+                                ptr[AO1
+                                        + (unroll_m * (i + 1) + 0 * 8 - OFFSET)
+                                                * SIZE]);
+                    } else {
+                        vmaskmovps(
+                                ymm0, VMASK,
+                                ptr[AO1
+                                        + (unroll_m * (i + 1) + 0 * 8 - OFFSET)
+                                                * SIZE]);
+                    }
+                    if (unroll_m >= 16) {
+                        if (isLoad2Unmasked) {
+                            vmovups(ymm1, ptr[AO1
+                                                  + (unroll_m * (i + 1) + 1 * 8
+                                                            - OFFSET)
+                                                          * SIZE]);
+                        } else {
+                            vmaskmovps(ymm1, VMASK,
+                                    ptr[AO1
+                                               + (unroll_m * (i + 1) + 1 * 8
+                                                         - OFFSET)
+                                                       * SIZE]);
+                        }
+                    }
+                }
+            }
+
+            if (!isDirect) {
+                sub(AO1, -unroll_m * 4 * SIZE);
+            }
+
+        };
+
+        // Inner kernel with k=2
+        auto innerkernel2 = [&](int unroll_m, int unroll_n,
+                bool isLoad1Unmasked, bool isLoad2Unmasked, bool isDirect,
+                bool isCopy, bool useFma, Ymm reg00, Ymm reg01, Ymm reg02,
+                Ymm reg03, Ymm reg04, Ymm reg05, Ymm reg06, Ymm reg07,
+                Ymm reg08, Ymm reg09, Ymm reg10, Ymm reg11, Ymm reg12,
+                Ymm reg13, Ymm reg14, Ymm reg15, Ymm reg16, Ymm reg17,
+                Ymm reg18, Ymm reg19, Ymm reg20, Ymm reg21, Ymm reg22,
+                Ymm reg23) {
+
+            Ymm fmareg;
+
+            for (int i = 0; i < 2; i++) {
+                if (isDirect) {
+                    if (isLoad1Unmasked) {
+                        vmovups(ymm0, ptr[AO1 + (0 * 8 - OFFSET) * SIZE]);
+                    } else {
+                        vmaskmovps(ymm0, VMASK,
+                                ptr[AO1 + (0 * 8 - OFFSET) * SIZE]);
+                    }
+                    if (unroll_m >= 16) {
+                        if (isLoad2Unmasked) {
+                            vmovups(ymm1, ptr[AO1 + (1 * 8 - OFFSET) * SIZE]);
+                        } else {
+                            vmaskmovps(ymm1, VMASK,
+                                    ptr[AO1 + (1 * 8 - OFFSET) * SIZE]);
+                        }
+                    }
+                    add(AO1, LDA);
+                }
+
+                if (!isTransB) {
+                    vbroadcastss(ymm2, ptr[BO1 + (0 - OFFSET) * SIZE]);
+                } else {
+                    vbroadcastss(ymm2, ptr[BO1 + (0 - OFFSET) * SIZE]);
+                }
+                fmareg = (i % 2 == 0) ? reg00 : reg12;
+                fma(useFma, ymm0, ymm2, fmareg);
+                if (unroll_m >= 16) {
+                    fmareg = (i % 2 == 0) ? reg06 : reg18;
+                    fma(useFma, ymm1, ymm2, fmareg);
+                }
+                if (unroll_n >= 2) {
+                    if (!isTransB) {
+                        vbroadcastss(
+                                ymm2, ptr[BO1 + LDB * 1 + (0 - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (1 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg01 : reg13;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg07 : reg19;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (unroll_n >= 3) {
+                    if (!isTransB) {
+                        if (i == 2) {
+                            prefetcht0(
+                                    ptr[BO1 + LDB * 2 + PREFETCHSIZEB * SIZE]);
+                        }
+                        vbroadcastss(
+                                ymm2, ptr[BO1 + LDB * 2 + (0 - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (2 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg02 : reg14;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg08 : reg20;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (unroll_n >= 4) {
+                    if (!isTransB) {
+                        vbroadcastss(ymm2, ptr[BO2 + (0 - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (3 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg03 : reg15;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg09 : reg21;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (unroll_n >= 5) {
+                    if (!isTransB) {
+                        vbroadcastss(
+                                ymm2, ptr[BO2 + LDB * 1 + (0 - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (4 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg04 : reg16;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg10 : reg22;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (unroll_n >= 6) {
+                    if (!isTransB) {
+                        vbroadcastss(
+                                ymm2, ptr[BO2 + LDB * 2 + (0 - OFFSET) * SIZE]);
+                    } else {
+                        vbroadcastss(ymm2, ptr[BO1 + (5 - OFFSET) * SIZE]);
+                    }
+                    fmareg = (i % 2 == 0) ? reg05 : reg17;
+                    fma(useFma, ymm0, ymm2, fmareg);
+                    if (unroll_m >= 16) {
+                        fmareg = (i % 2 == 0) ? reg11 : reg23;
+                        fma(useFma, ymm1, ymm2, fmareg);
+                    }
+                }
+
+                if (isCopy) {
+                    vmovups(ptr[LDA4 + (unroll_m * 0 + 0 * 8 - OFFSET) * SIZE],
+                            ymm0);
+                    if (unroll_m >= 16) {
+                        vmovups(ptr[LDA4
+                                        + (unroll_m * 0 + 1 * 8 - OFFSET)
+                                                * SIZE],
+                                ymm1);
+                    }
+                    sub(LDA4, -unroll_m * SIZE);
+                }
+
+                if (!isDirect) {
+                    if (isLoad1Unmasked) {
+                        vmovups(ymm0, ptr[AO1
+                                              + (unroll_m * 1 + 0 * 8 - OFFSET)
+                                                      * SIZE]);
+                    } else {
+                        vmaskmovps(ymm0, VMASK,
+                                ptr[AO1
+                                           + (unroll_m * 1 + 0 * 8 - OFFSET)
+                                                   * SIZE]);
+                    }
+                    if (unroll_m >= 16) {
+                        if (isLoad2Unmasked) {
+                            vmovups(ymm1,
+                                    ptr[AO1
+                                            + (unroll_m * 1 + 1 * 8 - OFFSET)
+                                                    * SIZE]);
+                        } else {
+                            vmaskmovps(ymm1, VMASK,
+                                    ptr[AO1
+                                               + (unroll_m * 1 + 1 * 8 - OFFSET)
+                                                       * SIZE]);
+                        }
+                    }
+                    sub(AO1, -unroll_m * SIZE);
+                }
+
+                if (!isTransB) {
+                    sub(BO1, -SIZE);
+                    if (unroll_n >= 4) {
+                        sub(BO2, -SIZE);
+                    }
+                } else {
+                    add(BO1, LDB);
+                }
+            }
+
+        };
+
+        // Inner kernel with k=1
+        auto innerkernel1 = [&](int unroll_m, int unroll_n,
+                bool isLoad1Unmasked, bool isLoad2Unmasked, bool isDirect,
+                bool isCopy, bool useFma, Ymm reg00, Ymm reg01, Ymm reg02,
+                Ymm reg03, Ymm reg04, Ymm reg05, Ymm reg06, Ymm reg07,
+                Ymm reg08, Ymm reg09, Ymm reg10, Ymm reg11) {
+
+            if (isDirect) {
+                if (isLoad1Unmasked) {
+                    vmovups(ymm0, ptr[AO1 + (0 * 8 - OFFSET) * SIZE]);
+                } else {
+                    vmaskmovps(ymm0, VMASK, ptr[AO1 + (0 * 8 - OFFSET) * SIZE]);
+                }
+                if (unroll_m >= 16) {
+                    if (isLoad2Unmasked) {
+                        vmovups(ymm1, ptr[AO1 + (1 * 8 - OFFSET) * SIZE]);
+                    } else {
+                        vmaskmovps(ymm1, VMASK,
+                                ptr[AO1 + (1 * 8 - OFFSET) * SIZE]);
+                    }
+                }
+                add(AO1, LDA);
+            }
+
+            if (!isTransB) {
+                vbroadcastss(ymm2, ptr[BO1 + (0 - OFFSET) * SIZE]);
+            } else {
+                vbroadcastss(ymm2, ptr[BO1 + (0 - OFFSET) * SIZE]);
+            }
+            fma(useFma, ymm0, ymm2, reg00);
+            if (unroll_m >= 16) {
+                fma(useFma, ymm1, ymm2, reg06);
+            }
+
+            if (unroll_n >= 2) {
+                if (!isTransB) {
+                    vbroadcastss(
+                            ymm2, ptr[BO1 + LDB * 1 + (0 - OFFSET) * SIZE]);
+                } else {
+                    vbroadcastss(ymm2, ptr[BO1 + (1 - OFFSET) * SIZE]);
+                }
+                fma(useFma, ymm0, ymm2, reg01);
+                if (unroll_m >= 16) {
+                    fma(useFma, ymm1, ymm2, reg07);
+                }
+            }
+
+            if (unroll_n >= 3) {
+                if (!isTransB) {
+                    vbroadcastss(
+                            ymm2, ptr[BO1 + LDB * 2 + (0 - OFFSET) * SIZE]);
+                } else {
+                    vbroadcastss(ymm2, ptr[BO1 + (2 - OFFSET) * SIZE]);
+                }
+                fma(useFma, ymm0, ymm2, reg02);
+                if (unroll_m >= 16) {
+                    fma(useFma, ymm1, ymm2, reg08);
+                }
+            }
+
+            if (unroll_n >= 4) {
+                if (!isTransB) {
+                    vbroadcastss(ymm2, ptr[BO2 + (0 - OFFSET) * SIZE]);
+                } else {
+                    vbroadcastss(ymm2, ptr[BO1 + (3 - OFFSET) * SIZE]);
+                }
+                fma(useFma, ymm0, ymm2, reg03);
+                if (unroll_m >= 16) {
+                    fma(useFma, ymm1, ymm2, reg09);
+                }
+            }
+
+            if (unroll_n >= 5) {
+                if (!isTransB) {
+                    vbroadcastss(
+                            ymm2, ptr[BO2 + LDB * 1 + (0 - OFFSET) * SIZE]);
+                } else {
+                    vbroadcastss(ymm2, ptr[BO1 + (4 - OFFSET) * SIZE]);
+                }
+                fma(useFma, ymm0, ymm2, reg04);
+                if (unroll_m >= 16) {
+                    fma(useFma, ymm1, ymm2, reg10);
+                }
+            }
+
+            if (unroll_n >= 6) {
+                if (!isTransB) {
+                    vbroadcastss(
+                            ymm2, ptr[BO2 + LDB * 2 + (0 - OFFSET) * SIZE]);
+                } else {
+                    vbroadcastss(ymm2, ptr[BO1 + (5 - OFFSET) * SIZE]);
+                }
+                fma(useFma, ymm0, ymm2, reg05);
+                if (unroll_m >= 16) {
+                    fma(useFma, ymm1, ymm2, reg11);
+                }
+            }
+
+            if (isCopy) {
+                vmovups(ptr[LDA4 + (unroll_m * 0 + 0 * 8 - OFFSET) * SIZE],
+                        ymm0);
+                if (unroll_m >= 16) {
+                    vmovups(ptr[LDA4 + (unroll_m * 0 + 1 * 8 - OFFSET) * SIZE],
+                            ymm1);
+                }
+                sub(LDA4, -unroll_m * SIZE);
+            }
+
+            if (!isDirect) {
+                if (isLoad1Unmasked) {
+                    vmovups(ymm0,
+                            ptr[AO1 + (unroll_m * 1 + 0 * 8 - OFFSET) * SIZE]);
+                } else {
+                    vmaskmovps(ymm0, VMASK,
+                            ptr[AO1 + (unroll_m * 1 + 0 * 8 - OFFSET) * SIZE]);
+                }
+                if (unroll_m >= 16) {
+                    if (isLoad2Unmasked) {
+                        vmovups(ymm1, ptr[AO1
+                                              + (unroll_m * 1 + 1 * 8 - OFFSET)
+                                                      * SIZE]);
+                    } else {
+                        vmaskmovps(ymm1, VMASK,
+                                ptr[AO1
+                                           + (unroll_m * 1 + 1 * 8 - OFFSET)
+                                                   * SIZE]);
+                    }
+                }
+                sub(AO1, -unroll_m * SIZE);
+            }
+
+            if (!isTransB) {
+                sub(BO1, -SIZE);
+                if (unroll_n >= 4) {
+                    sub(BO2, -SIZE);
+                }
+            } else {
+                add(BO1, LDB);
+            }
+
+        };
+
+        // Main kernel; does prefetching and calls innerkernel{1,2,4,8} as
+        // appropriate
+        // After calculating results in registers, writes back to C matrix
+        auto kernel = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy, bool useFma,
+                Ymm reg00 = Ymm(4), Ymm reg01 = Ymm(5), Ymm reg02 = Ymm(6),
+                Ymm reg03 = Ymm(7), Ymm reg04 = Ymm(8), Ymm reg05 = Ymm(9),
+                Ymm reg06 = Ymm(10), Ymm reg07 = Ymm(11), Ymm reg08 = Ymm(12),
+                Ymm reg09 = Ymm(13), Ymm reg10 = Ymm(14), Ymm reg11 = Ymm(15),
+                Ymm reg12 = Ymm(4), Ymm reg13 = Ymm(5), Ymm reg14 = Ymm(6),
+                Ymm reg15 = Ymm(7), Ymm reg16 = Ymm(8), Ymm reg17 = Ymm(9),
+                Ymm reg18 = Ymm(10), Ymm reg19 = Ymm(11), Ymm reg20 = Ymm(12),
+                Ymm reg21 = Ymm(13), Ymm reg22 = Ymm(14), Ymm reg23 = Ymm(15)) {
+            if (!isDirect) {
+                lea(AO1, ptr[rsp + 256 + OFFSET * SIZE]);
+            } else {
+                mov(AO1, A);
+            }
+
+            if (isCopy) {
+                lea(LDA4, ptr[rsp + 256 + OFFSET * SIZE]);
+            } else {
+                lea(LDA4, ptr[LDA * 8 + (8 - 1 - OFFSET) * SIZE]);
+            }
+
+            if (isTransB) {
+                lea(BO2, ptr[LDB * 4 + (8 - 1 - OFFSET) * SIZE]);
+                lea(BO2, ptr[BO2 + LDB * 2]);
+            }
+
+            if (!isDirect) {
+                if (isLoad1Unmasked) {
+                    vmovups(ymm0,
+                            ptr[AO1 + (unroll_m * 0 + 0 * 8 - OFFSET) * SIZE]);
+                } else {
+                    vmaskmovps(ymm0, VMASK,
+                            ptr[AO1 + (unroll_m * 0 + 0 * 8 - OFFSET) * SIZE]);
+                }
+                if (unroll_m >= 16) {
+                    if (isLoad2Unmasked) {
+                        vmovups(ymm1, ptr[AO1
+                                              + (unroll_m * 0 + 1 * 8 - OFFSET)
+                                                      * SIZE]);
+                    } else {
+                        vmaskmovps(ymm1, VMASK,
+                                ptr[AO1
+                                           + (unroll_m * 0 + 1 * 8 - OFFSET)
+                                                   * SIZE]);
+                    }
+                }
+            }
+
+            for (int i = 4; i < 10; i++) {
+                vxorps(Ymm(i), Ymm(i), Ymm(i));
+                vxorps(Ymm(i + 6), Ymm(i + 6), Ymm(i + 6));
+            }
+
+            mov(LL, K);
+            sar(LL, 3);
+
+            Label kernel12, kernel13, kernel14, kernel15;
+            Label kernel16, kernel17, kernel18;
+
+            sub(LL, SECOND_FETCH);
+            jle(kernel13, T_NEAR);
+            align(16);
+
+            L(kernel12);
+            innerkernel8(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, useFma, reg00, reg01, reg02, reg03, reg04,
+                    reg05, reg06, reg07, reg08, reg09, reg10, reg11, reg12,
+                    reg13, reg14, reg15, reg16, reg17, reg18, reg19, reg20,
+                    reg21, reg22, reg23);
+            jg(kernel12, T_NEAR);
+            align(16);
+
+            L(kernel13);
+            prefetcht0(ptr[CO1 + (unroll_m - 1) * SIZE]);
+            if (unroll_n >= 2)
+                prefetcht0(ptr[CO1 + LDC + (unroll_m - 1) * SIZE]);
+            if (unroll_n >= 3)
+                prefetcht0(ptr[CO1 + LDC * 2 + (unroll_m - 1) * SIZE]);
+            if (unroll_n >= 4)
+                prefetcht0(ptr[CO2 + (unroll_m - 1) * SIZE]);
+            if (unroll_n >= 5)
+                prefetcht0(ptr[CO2 + LDC + (unroll_m - 1) * SIZE]);
+            if (unroll_n >= 6)
+                prefetcht0(ptr[CO2 + LDC * 2 + (unroll_m - 1) * SIZE]);
+
+            add(LL, SECOND_FETCH);
+            jle(kernel15, T_NEAR);
+            align(16);
+
+            L(kernel14);
+            innerkernel8(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, useFma, reg00, reg01, reg02, reg03, reg04,
+                    reg05, reg06, reg07, reg08, reg09, reg10, reg11, reg12,
+                    reg13, reg14, reg15, reg16, reg17, reg18, reg19, reg20,
+                    reg21, reg22, reg23);
+            jg(kernel14, T_NEAR);
+            align(16);
+
+            L(kernel15);
+            test(K, 4);
+            jle(kernel16, T_NEAR);
+            innerkernel4(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, useFma, reg00, reg01, reg02, reg03, reg04,
+                    reg05, reg06, reg07, reg08, reg09, reg10, reg11, reg12,
+                    reg13, reg14, reg15, reg16, reg17, reg18, reg19, reg20,
+                    reg21, reg22, reg23);
+
+            L(kernel16);
+            test(K, 2);
+            jle(kernel17, T_NEAR);
+            innerkernel2(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, useFma, reg00, reg01, reg02, reg03, reg04,
+                    reg05, reg06, reg07, reg08, reg09, reg10, reg11, reg12,
+                    reg13, reg14, reg15, reg16, reg17, reg18, reg19, reg20,
+                    reg21, reg22, reg23);
+            align(16);
+
+            L(kernel17);
+            if (unroll_m == 16) {
+                if (unroll_n <= 3) {
+                    vaddps(reg00, reg00, reg12);
+                    vaddps(reg01, reg01, reg13);
+                    vaddps(reg02, reg02, reg14);
+                    vaddps(reg06, reg06, reg18);
+                    vaddps(reg07, reg07, reg19);
+                    vaddps(reg08, reg08, reg20);
+                }
+            }
+
+            if (unroll_m <= 8) {
+                vaddps(reg00, reg00, reg12);
+                vaddps(reg01, reg01, reg13);
+                vaddps(reg02, reg02, reg14);
+                vaddps(reg03, reg03, reg15);
+                vaddps(reg04, reg04, reg16);
+                vaddps(reg05, reg05, reg17);
+            }
+
+            test(K, 1);
+            jle(kernel18, T_NEAR);
+            innerkernel1(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, useFma, reg00, reg01, reg02, reg03, reg04,
+                    reg05, reg06, reg07, reg08, reg09, reg10, reg11);
+            align(16);
+
+            L(kernel18);
+            vbroadcastss(VALPHA, ALPHA);
+
+            if (isBetaN) {
+                vbroadcastss(VBETA, BETA);
+            }
+
+            // Write back the results; all beta and bias cases need to be
+            // handled
+            switch (unroll_n) {
+            case 1: mov(rax, LDC); break;
+            case 2: lea(rax, ptr[LDC * 2]); break;
+            case 3: lea(rax, ptr[LDC + LDC * 2]); break;
+            case 4: lea(rax, ptr[LDC + LDC * 4]); break;
+            case 5:
+                lea(rax, ptr[LDC * 4]);
+                add(rax, LDC);
+                break;
+            case 6:
+                lea(rax, ptr[LDC + LDC * 2]);
+                add(rax, rax);
+                break;
+            }
+
+            if (hasBias) {
+                mov(BIAS1, BIAS);
+                if (isLoad1Unmasked) {
+                    vmovups(VBIAS1, ptr[BIAS1 + 0 * SIZE]);
+                } else {
+                    vmaskmovps(VBIAS1, VMASK, ptr[BIAS1 + 0 * SIZE]);
+                }
+            }
+
+            for (int i = 0; i < unroll_n; i++) {
+                vmulps(Ymm(i + 4), Ymm(i + 4), VALPHA);
+                if (!isBeta0) {
+                    if (isLoad1Unmasked) {
+                        switch (i) {
+                        case 0: vmovups(ymm0, ptr[CO1 + 0 * SIZE]); break;
+                        case 1: vmovups(ymm0, ptr[CO1 + LDC + 0 * SIZE]); break;
+                        case 2:
+                            vmovups(ymm0, ptr[CO1 + LDC * 2 + 0 * SIZE]);
+                            break;
+                        case 3: vmovups(ymm0, ptr[CO2 + 0 * SIZE]); break;
+                        case 4: vmovups(ymm0, ptr[CO2 + LDC + 0 * SIZE]); break;
+                        case 5:
+                            vmovups(ymm0, ptr[CO2 + LDC * 2 + 0 * SIZE]);
+                            break;
+                        }
+                    } else {
+                        switch (i) {
+                        case 0:
+                            vmaskmovps(ymm0, VMASK, ptr[CO1 + 0 * SIZE]);
+                            break;
+                        case 1:
+                            vmaskmovps(ymm0, VMASK, ptr[CO1 + LDC + 0 * SIZE]);
+                            break;
+                        case 2:
+                            vmaskmovps(
+                                    ymm0, VMASK, ptr[CO1 + LDC * 2 + 0 * SIZE]);
+                            break;
+                        case 3:
+                            vmaskmovps(ymm0, VMASK, ptr[CO2 + 0 * SIZE]);
+                            break;
+                        case 4:
+                            vmaskmovps(ymm0, VMASK, ptr[CO2 + LDC + 0 * SIZE]);
+                            break;
+                        case 5:
+                            vmaskmovps(
+                                    ymm0, VMASK, ptr[CO2 + LDC * 2 + 0 * SIZE]);
+                            break;
+                        }
+                    }
+
+                    if (!isBetaN) {
+                        vaddps(Ymm(i + 4), ymm0, Ymm(i + 4));
+                    } else {
+                        fma(useFma, VBETA, ymm0, Ymm(i + 4), true);
+                    }
+                }
+                if (hasBias) {
+                    vaddps(Ymm(i + 4), VBIAS1, Ymm(i + 4));
+                }
+                if (isLoad1Unmasked) {
+                    switch (i) {
+                    case 0: vmovups(ptr[CO1 + 0 * SIZE], Ymm(i + 4)); break;
+                    case 1:
+                        vmovups(ptr[CO1 + LDC + 0 * SIZE], Ymm(i + 4));
+                        break;
+                    case 2:
+                        vmovups(ptr[CO1 + LDC * 2 + 0 * SIZE], Ymm(i + 4));
+                        break;
+                    case 3: vmovups(ptr[CO2 + 0 * SIZE], Ymm(i + 4)); break;
+                    case 4:
+                        vmovups(ptr[CO2 + LDC + 0 * SIZE], Ymm(i + 4));
+                        break;
+                    case 5:
+                        vmovups(ptr[CO2 + LDC * 2 + 0 * SIZE], Ymm(i + 4));
+                        break;
+                    }
+                } else {
+                    switch (i) {
+                    case 0:
+                        vmaskmovps(ptr[CO1 + 0 * SIZE], VMASK, Ymm(i + 4));
+                        break;
+                    case 1:
+                        vmaskmovps(
+                                ptr[CO1 + LDC + 0 * SIZE], VMASK, Ymm(i + 4));
+                        break;
+                    case 2:
+                        vmaskmovps(ptr[CO1 + LDC * 2 + 0 * SIZE], VMASK,
+                                Ymm(i + 4));
+                        break;
+                    case 3:
+                        vmaskmovps(ptr[CO2 + 0 * SIZE], VMASK, Ymm(i + 4));
+                        break;
+                    case 4:
+                        vmaskmovps(
+                                ptr[CO2 + LDC + 0 * SIZE], VMASK, Ymm(i + 4));
+                        break;
+                    case 5:
+                        vmaskmovps(ptr[CO2 + LDC * 2 + 0 * SIZE], VMASK,
+                                Ymm(i + 4));
+                        break;
+                    }
+                }
+
+                if (unroll_m >= 16) {
+                    // Re-use ymm4 (VBIAS2)
+                    if (i == 0) {
+                        if (hasBias) {
+                            if (isLoad1Unmasked) {
+                                vmovups(VBIAS2, ptr[BIAS1 + 8 * SIZE]);
+                            } else {
+                                vmaskmovps(
+                                        VBIAS2, VMASK, ptr[BIAS1 + 8 * SIZE]);
+                            }
+                        }
+                    }
+                    vmulps(Ymm(i + 10), Ymm(i + 10), VALPHA);
+                    if (!isBeta0) {
+                        if (isLoad2Unmasked) {
+                            switch (i) {
+                            case 0: vmovups(ymm0, ptr[CO1 + 8 * SIZE]); break;
+                            case 1:
+                                vmovups(ymm0, ptr[CO1 + LDC + 8 * SIZE]);
+                                break;
+                            case 2:
+                                vmovups(ymm0, ptr[CO1 + LDC * 2 + 8 * SIZE]);
+                                break;
+                            case 3: vmovups(ymm0, ptr[CO2 + 8 * SIZE]); break;
+                            case 4:
+                                vmovups(ymm0, ptr[CO2 + LDC + 8 * SIZE]);
+                                break;
+                            case 5:
+                                vmovups(ymm0, ptr[CO2 + LDC * 2 + 8 * SIZE]);
+                                break;
+                            }
+                        } else {
+                            switch (i) {
+                            case 0:
+                                vmaskmovps(ymm0, VMASK, ptr[CO1 + 8 * SIZE]);
+                                break;
+                            case 1:
+                                vmaskmovps(
+                                        ymm0, VMASK, ptr[CO1 + LDC + 8 * SIZE]);
+                                break;
+                            case 2:
+                                vmaskmovps(ymm0, VMASK,
+                                        ptr[CO1 + LDC * 2 + 8 * SIZE]);
+                                break;
+                            case 3:
+                                vmaskmovps(ymm0, VMASK, ptr[CO2 + 8 * SIZE]);
+                                break;
+                            case 4:
+                                vmaskmovps(
+                                        ymm0, VMASK, ptr[CO2 + LDC + 8 * SIZE]);
+                                break;
+                            case 5:
+                                vmaskmovps(ymm0, VMASK,
+                                        ptr[CO2 + LDC * 2 + 8 * SIZE]);
+                                break;
+                            }
+                        }
+                        if (!isBetaN) {
+                            vaddps(Ymm(i + 10), ymm0, Ymm(i + 10));
+                        } else {
+                            fma(useFma, VBETA, ymm0, Ymm(i + 10), true);
+                        }
+                    }
+                    if (hasBias) {
+                        vaddps(Ymm(i + 10), VBIAS2, Ymm(i + 10));
+                    }
+                    if (isLoad2Unmasked) {
+                        switch (i) {
+                        case 0:
+                            vmovups(ptr[CO1 + 8 * SIZE], Ymm(i + 10));
+                            break;
+                        case 1:
+                            vmovups(ptr[CO1 + LDC + 8 * SIZE], Ymm(i + 10));
+                            break;
+                        case 2:
+                            vmovups(ptr[CO1 + LDC * 2 + 8 * SIZE], Ymm(i + 10));
+                            break;
+                        case 3:
+                            vmovups(ptr[CO2 + 8 * SIZE], Ymm(i + 10));
+                            break;
+                        case 4:
+                            vmovups(ptr[CO2 + LDC + 8 * SIZE], Ymm(i + 10));
+                            break;
+                        case 5:
+                            vmovups(ptr[CO2 + LDC * 2 + 8 * SIZE], Ymm(i + 10));
+                            break;
+                        }
+                    } else {
+                        switch (i) {
+                        case 0:
+                            vmaskmovps(ptr[CO1 + 8 * SIZE], VMASK, Ymm(i + 10));
+                            break;
+                        case 1:
+                            vmaskmovps(ptr[CO1 + LDC + 8 * SIZE], VMASK,
+                                    Ymm(i + 10));
+                            break;
+                        case 2:
+                            vmaskmovps(ptr[CO1 + LDC * 2 + 8 * SIZE], VMASK,
+                                    Ymm(i + 10));
+                            break;
+                        case 3:
+                            vmaskmovps(ptr[CO2 + 8 * SIZE], VMASK, Ymm(i + 10));
+                            break;
+                        case 4:
+                            vmaskmovps(ptr[CO2 + LDC + 8 * SIZE], VMASK,
+                                    Ymm(i + 10));
+                            break;
+                        case 5:
+                            vmaskmovps(ptr[CO2 + LDC * 2 + 8 * SIZE], VMASK,
+                                    Ymm(i + 10));
+                            break;
+                        }
+                    }
+                }
+                if (i == 2)
+                    add(CO1, rax);
+            }
+            if (unroll_n >= 4) {
+                add(CO2, rax);
+            }
+
+            // Compute next address of B
+            if (!isTransB) {
+                lea(rax, ptr[K * SIZE]);
+                switch (unroll_n) {
+                case 1:
+                    add(BO1, LDB);
+                    add(BO2, LDB);
+                    break;
+                case 2:
+                    lea(BO1, ptr[BO1 + LDB * 2]);
+                    lea(BO2, ptr[BO2 + LDB * 2]);
+                    break;
+                case 3:
+                    lea(BO1, ptr[BO1 + LDB3]);
+                    lea(BO2, ptr[BO2 + LDB3]);
+                    break;
+                case 4:
+                    lea(BO1, ptr[BO1 + LDB * 4]);
+                    lea(BO2, ptr[BO2 + LDB * 4]);
+                    break;
+                case 5:
+                    lea(BO1, ptr[BO1 + LDB * 4]);
+                    add(BO1, LDB);
+                    lea(BO2, ptr[BO2 + LDB * 4]);
+                    add(BO2, LDB);
+                    break;
+                case 6:
+                    lea(BO1, ptr[BO1 + LDB3 * 2]);
+                    lea(BO2, ptr[BO2 + LDB3 * 2]);
+                    break;
+                }
+                sub(BO1, rax);
+                sub(BO2, rax);
+            } else {
+                mov(rax, LDB);
+                imul(rax, K);
+                sub(BO1, rax);
+                add(BO1, unroll_n * SIZE);
+            }
+        };
+
+        auto kernel_16x6 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy) {
+            kernel(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, true);
+        };
+
+        auto kernel_16x5 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy) {
+            kernel(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, true);
+        };
+
+        auto kernel_16x4 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy) {
+            kernel(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, true);
+        };
+
+        auto kernel_16x3 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy,
+                bool useFma = true) {
+            kernel(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, useFma, Ymm(4), Ymm(5), Ymm(6), Ymm(7),
+                    Ymm(8), Ymm(9), Ymm(10), Ymm(11), Ymm(12), Ymm(13), Ymm(14),
+                    Ymm(15), Ymm(7), Ymm(8), Ymm(9), Ymm(7), Ymm(8), Ymm(9),
+                    Ymm(13), Ymm(14), Ymm(15));
+        };
+
+        auto kernel_16x2 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy) {
+            kernel_16x3(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, false);
+        };
+
+        auto kernel_16x1 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy) {
+            kernel_16x3(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, false);
+        };
+
+        auto kernel_8x6 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy,
+                bool useFma = true) {
+            kernel(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, useFma, Ymm(4), Ymm(5), Ymm(6), Ymm(7),
+                    Ymm(8), Ymm(9), Ymm(10), Ymm(11), Ymm(12), Ymm(13), Ymm(14),
+                    Ymm(15), Ymm(10), Ymm(11), Ymm(12), Ymm(13), Ymm(14),
+                    Ymm(15));
+        };
+
+        auto kernel_8x5 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy) {
+            kernel_8x6(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy);
+        };
+
+        auto kernel_8x4 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy) {
+            kernel_8x6(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy);
+        };
+
+        auto kernel_8x3 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy,
+                bool useFma = true) {
+            kernel(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, useFma, Ymm(4), Ymm(5), Ymm(6), Ymm(7),
+                    Ymm(8), Ymm(9), Ymm(10), Ymm(11), Ymm(12), Ymm(13), Ymm(14),
+                    Ymm(15), Ymm(7), Ymm(8), Ymm(9), Ymm(7), Ymm(8), Ymm(9),
+                    Ymm(13), Ymm(14), Ymm(15));
+        };
+
+        auto kernel_8x2 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy) {
+            kernel_8x3(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, false);
+        };
+
+        auto kernel_8x1 = [&](int unroll_m, int unroll_n, bool isLoad1Unmasked,
+                bool isLoad2Unmasked, bool isDirect, bool isCopy) {
+            kernel_8x3(unroll_m, unroll_n, isLoad1Unmasked, isLoad2Unmasked,
+                    isDirect, isCopy, false);
+        };
+
+        // High-level subroutine; does packing if needed, then splits C matrix.
+        // Operates on chunks of 16 rows, 6 columns at a time (handling tail
+        // cases appropriately).
+        // Masking is used for tail cases where M is not divisible by 8.
+        auto subloop = [&](
+                int unroll_m, bool isLoad1Unmasked, bool isLoad2Unmasked) {
+            if (isTransA) {
+                do_pack(unroll_m, isLoad1Unmasked, isLoad2Unmasked);
+            }
+
+            Label subloop11, subloop11mask;
+            Label subloop20, subloop21, subloop22, subloop23;
+            Label subloop24, subloop25;
+            Label subloop30, subloop31, subloop32, subloop33;
+            Label subloop34, subloop35;
+            Label subloop98, subloop98mask;
+            Label subloop99, subloop99mask;
+
+            mov(CO1, C);
+            lea(CO2, ptr[CO1 + LDC * 2]);
+            add(CO2, LDC);
+            add(C, unroll_m * SIZE);
+            mov(BO1, B);
+            if (!isTransB) {
+                lea(BO2, qword[B + LDB3]);
+            }
+
+            if (!isTransA) {
+                lea(AA, ptr[A + (unroll_m * 2 - 1 - OFFSET) * SIZE]);
+                cmp(M, UNROLL_M);
+                jg(subloop98, T_NEAR);
+
+                mov(AA, ORIG_A);
+                lea(AA, ptr[AA + (unroll_m - 1 - OFFSET) * SIZE]);
+                L(subloop98);
+            }
+
+            mov(LL, N);
+            mov(I, LL);
+            if (!isTransA) {
+                // If N is too small, skip copy operation
+                cmp(LL, UNROLL_N * 3);
+                jle(subloop30, T_NEAR);
+
+                // If A is not aligned to cache line
+                cmp(FLAG, 0);
+                je(subloop30, T_NEAR);
+            } else {
+                cmp(LL, UNROLL_N);
+                jl(subloop20, T_NEAR);
+            }
+            align(16);
+
+            if (!isTransA) {
+                if (unroll_m == 16) {
+                    kernel_16x6(unroll_m, UNROLL_N, isLoad1Unmasked,
+                            isLoad2Unmasked, true, true);
+                } else {
+                    kernel_8x6(unroll_m, UNROLL_N, isLoad1Unmasked,
+                            isLoad2Unmasked, true, true);
+                }
+            } else {
+                if (unroll_m == 16) {
+                    kernel_16x6(unroll_m, UNROLL_N, isLoad1Unmasked,
+                            isLoad2Unmasked, false, false);
+                } else {
+                    kernel_8x6(unroll_m, UNROLL_N, isLoad1Unmasked,
+                            isLoad2Unmasked, false, false);
+                }
+            }
+
+            sub(I, UNROLL_N);
+            cmp(I, UNROLL_N);
+            jl(subloop20, T_NEAR);
+            align(16);
+
+            L(subloop11);
+            if (unroll_m == 16) {
+                kernel_16x6(unroll_m, UNROLL_N, isLoad1Unmasked,
+                        isLoad2Unmasked, false, false);
+            } else {
+                kernel_8x6(unroll_m, UNROLL_N, isLoad1Unmasked, isLoad2Unmasked,
+                        false, false);
+            }
+            sub(I, UNROLL_N);
+            cmp(I, UNROLL_N);
+            jge(subloop11, T_NEAR);
+            align(16);
+
+            L(subloop20);
+            cmp(I, 1);
+            jne(subloop21, T_NEAR);
+            if (unroll_m == 16) {
+                kernel_16x1(unroll_m, 1, isLoad1Unmasked, isLoad2Unmasked,
+                        false, false);
+            } else {
+                kernel_8x1(unroll_m, 1, isLoad1Unmasked, isLoad2Unmasked, false,
+                        false);
+            }
+            jmp(subloop99, T_NEAR);
+            align(16);
+
+            L(subloop21);
+            cmp(I, 2);
+            jne(subloop22, T_NEAR);
+            if (unroll_m == 16) {
+                kernel_16x2(unroll_m, 2, isLoad1Unmasked, isLoad2Unmasked,
+                        false, false);
+            } else {
+                kernel_8x2(unroll_m, 2, isLoad1Unmasked, isLoad2Unmasked, false,
+                        false);
+            }
+            jmp(subloop99, T_NEAR);
+            align(16);
+
+            L(subloop22);
+            cmp(I, 3);
+            jne(subloop23, T_NEAR);
+            if (unroll_m == 16) {
+                kernel_16x3(unroll_m, 3, isLoad1Unmasked, isLoad2Unmasked,
+                        false, false);
+            } else {
+                kernel_8x3(unroll_m, 3, isLoad1Unmasked, isLoad2Unmasked, false,
+                        false);
+            }
+            jmp(subloop99, T_NEAR);
+            align(16);
+
+            L(subloop23);
+            cmp(I, 4);
+            jne(subloop24, T_NEAR);
+            if (unroll_m == 16) {
+                kernel_16x4(unroll_m, 4, isLoad1Unmasked, isLoad2Unmasked,
+                        false, false);
+            } else {
+                kernel_8x4(unroll_m, 4, isLoad1Unmasked, isLoad2Unmasked, false,
+                        false);
+            }
+            jmp(subloop99, T_NEAR);
+            align(16);
+
+            L(subloop24);
+            cmp(I, 5);
+            jne(subloop99, T_NEAR);
+            if (unroll_m == 16) {
+                kernel_16x5(unroll_m, 5, isLoad1Unmasked, isLoad2Unmasked,
+                        false, false);
+            } else {
+                kernel_8x5(unroll_m, 5, isLoad1Unmasked, isLoad2Unmasked, false,
+                        false);
+            }
+            jmp(subloop99, T_NEAR);
+            align(16);
+
+            if (!isTransA) {
+                L(subloop30);
+                cmp(I, UNROLL_N);
+                jl(subloop25, T_NEAR);
+                align(16);
+
+                L(subloop31);
+                if (unroll_m == 16) {
+                    kernel_16x6(unroll_m, UNROLL_N, isLoad1Unmasked,
+                            isLoad2Unmasked, true, false);
+                } else {
+                    kernel_8x6(unroll_m, UNROLL_N, isLoad1Unmasked,
+                            isLoad2Unmasked, true, false);
+                }
+                sub(I, UNROLL_N);
+                cmp(I, UNROLL_N);
+                jge(subloop31, T_NEAR);
+                align(16);
+
+                L(subloop25);
+                cmp(I, 1);
+                jne(subloop32, T_NEAR);
+                if (unroll_m == 16) {
+                    kernel_16x1(unroll_m, 1, isLoad1Unmasked, isLoad2Unmasked,
+                            true, false);
+                } else {
+                    kernel_8x1(unroll_m, 1, isLoad1Unmasked, isLoad2Unmasked,
+                            true, false);
+                }
+                jmp(subloop99, T_NEAR);
+                align(16);
+
+                L(subloop32);
+                cmp(I, 2);
+                jne(subloop33, T_NEAR);
+                if (unroll_m == 16) {
+                    kernel_16x2(unroll_m, 2, isLoad1Unmasked, isLoad2Unmasked,
+                            true, false);
+                } else {
+                    kernel_8x2(unroll_m, 2, isLoad1Unmasked, isLoad2Unmasked,
+                            true, false);
+                }
+                jmp(subloop99, T_NEAR);
+                align(16);
+
+                L(subloop33);
+                cmp(I, 3);
+                jne(subloop34, T_NEAR);
+                if (unroll_m == 16) {
+                    kernel_16x3(unroll_m, 3, isLoad1Unmasked, isLoad2Unmasked,
+                            true, false);
+                } else {
+                    kernel_8x3(unroll_m, 3, isLoad1Unmasked, isLoad2Unmasked,
+                            true, false);
+                }
+                jmp(subloop99, T_NEAR);
+                align(16);
+
+                L(subloop34);
+                cmp(I, 4);
+                jne(subloop35, T_NEAR);
+                if (unroll_m == 16) {
+                    kernel_16x4(unroll_m, 4, isLoad1Unmasked, isLoad2Unmasked,
+                            true, false);
+                } else {
+                    kernel_8x4(unroll_m, 4, isLoad1Unmasked, isLoad2Unmasked,
+                            true, false);
+                }
+                jmp(subloop99, T_NEAR);
+                align(16);
+
+                L(subloop35);
+                cmp(I, 5);
+                jne(subloop99, T_NEAR);
+                if (unroll_m == 16) {
+                    kernel_16x5(unroll_m, 5, isLoad1Unmasked, isLoad2Unmasked,
+                            true, false);
+                } else {
+                    kernel_8x5(unroll_m, 5, isLoad1Unmasked, isLoad2Unmasked,
+                            true, false);
+                }
+                align(16);
+            }
+
+            L(subloop99);
+            // Compute address for A
+            if (!isTransA) {
+                add(A, unroll_m * SIZE);
+            } else {
+                mov(rax, LDA);
+                imul(rax, rax, unroll_m);
+                add(A, rax);
+            }
+
+            // Compute next address of BIAS
+            if (hasBias) {
+                add(BIAS, unroll_m * SIZE);
+            }
+        };
+
+        preamble();
+
+        Label buffer_in_ws, buffer_allocated;
+
+        // Get the registers
+        mov(B, ARG_B);
+        mov(LDB, ARG_LDB);
+        mov(r15, ARG_BETA);
+        mov(r12, ARG_C);
+        if (hasBias)
+            mov(r10, ARG_BIAS);
+        mov(LDC, ARG_LDC);
+        mov(rbp, rsp);
+
+        vmovss(xmm0, ptr[ARG_ALPHA]);
+        vmovss(xmm1, ptr[r15]);
+
+#if _WIN32
+        mov(A, ARG_A);
+        mov(LDA, ARG_LDA);
+#endif
+
+        cmp(K, STACK_K_CAPACITY);
+        jg(buffer_in_ws, T_NEAR);
+
+        // Create buffer and align to 4kB page
+        lea(rax, ptr[K * SIZE]);
+        sal(rax, 4);
+        add(rax, 256);
+        sub(rsp, rax);
+        and_(rsp, -PAGE_4K);
+        jmp(buffer_allocated, T_NEAR);
+
+        L(buffer_in_ws);
+        mov(rsp, ARG_WS);
+
+        L(buffer_allocated);
+
+        mov(ORIG_SP, rbp);
+        mov(M, ARG_M);
+        mov(N, ARG_N);
+        mov(C, r12);
+        if (hasBias)
+            mov(BIAS, r10);
+        vmovss(ALPHA, xmm0);
+        vmovss(BETA, xmm1);
+        sub(A, -OFFSET * SIZE);
+        sub(B, -OFFSET * SIZE);
+        mov(ORIG_A, A);
+        sal(LDA, BASE_SHIFT);
+        sal(LDB, BASE_SHIFT);
+        sal(LDC, BASE_SHIFT);
+        lea(LDB3, ptr[LDB + LDB * 2]);
+
+        for (int i = 0; i < 8; i++) {
+            mov(dword[rsp + 88 + i * 4], i);
+        }
+
+        if (isTransA && is_avx2) {
+            movq(xmm0, LDA);
+            vpbroadcastq(ymm1, xmm0);
+            vinsertf128(ymm0, ymm0, xmm0, 1);
+            vpermilpd(ymm0, ymm0, 5);
+            vpaddq(ymm1, ymm1, ymm1);
+            vperm2f128(ymm1, ymm1, ymm1, 8);
+            vpaddq(ymm0, ymm0, ymm1);
+            vmovups(STRIDE, ymm0);
+        }
+
+        // Check A alignment and leading dimension; take copy-based path as
+        // needed
+        mov(rax, LDA);
+        or_(rax, A);
+        and_(rax, 0x1f);
+        mov(FLAG, rax);
+
+        Label main0, main1, main2, main3, main999;
+
+        cmp(M, UNROLL_M);
+        jl(main0, T_NEAR);
+        align(16);
+
+        L(main1);
+        subloop(UNROLL_M, true, true);
+        sub(M, UNROLL_M);
+        cmp(M, UNROLL_M);
+        jge(main1, T_NEAR);
+        align(16);
+
+        L(main0);
+        cmp(M, 0);
+        jle(main999, T_NEAR);
+
+        if (UNROLL_M > 8) {
+            cmp(M, 8);
+            jle(main2, T_NEAR);
+
+            sub(M, 8);
+            vbroadcastss(VMASK, M);
+            vpcmpgtd(VMASK, VMASK, MASK);
+
+            subloop(16, true, false);
+            jmp(main999, T_NEAR);
+            align(16);
+
+            L(main2);
+            cmp(M, 8);
+            jne(main3, T_NEAR);
+            subloop(8, true, true);
+            jmp(main999, T_NEAR);
+        }
+
+        align(16);
+
+        L(main3);
+        vbroadcastss(VMASK, M);
+        if (is_avx2) {
+            vpcmpgtd(VMASK, VMASK, MASK);
+        } else {
+            auto xmask = Xmm(VMASK.getIdx());
+            auto xmm_tmp = xmm4;
+
+            vextractf128(xmm_tmp, VMASK, 1);
+            vpcmpgtd(xmask, xmask, MASK);
+            vpcmpgtd(xmm_tmp, xmm_tmp, dword[rsp + 88 + 4 * 4]); // MASK + 4
+            vinsertf128(VMASK, VMASK, xmm_tmp, 1);
+        }
+        subloop(8, false, false);
+        align(16);
+
+        L(main999);
+        // Restore original stack
+        mov(rsp, ORIG_SP);
+
+        vzeroupper();
+        postamble();
+
+        ker_ = this->getCode<ker_t>();
+    }
+
+    typedef void (*ker_t)(dim_t m, dim_t n, dim_t k,
+            const float *alpha, const float *a, dim_t lda,
+            const float *b, dim_t ldb, const float *beta, float *c,
+            dim_t ldc, const float *bias, float *ws);
+
+    void operator()(dim_t  m, dim_t n, dim_t k,
+            const float *alpha, const float *a, dim_t lda,
+            const float *b, dim_t ldb, const float *beta, float *c,
+            dim_t ldc, const float *bias, float *ws) const
+    {
+        ker_(m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, bias, ws);
+    }
+
+private:
+    ker_t ker_;
+};
+
+const xbyak_gemm *get_xbyak_gemm(
+        bool isTransA, bool isTransB, float beta, bool hasBias) {
+    auto beta_idx = [](float beta) {
+        return (beta == 0.0) ? 0 : (beta == 1.0 ? 1 : 2);
+    };
+
+    // Kernel table [isTransA][isTransB][hasBias][beta (0, 1, other)]
+    static xbyak_gemm *kernel_table[2][2][2][3];
+    static std::once_flag initialized;
+    std::call_once(initialized, [=]{
+            for (bool isTransA: {false, true})
+            for (bool isTransB: {false, true})
+            for (bool hasBias: {false, true})
+            for (float beta: {0.0f, 1.0f, 2.0f}) {
+                // nocopy sgemm with bias for beta != 0.0 is not supported
+                if (hasBias && beta != 0.0)
+                    continue;
+                kernel_table[isTransA][isTransB][hasBias][beta_idx(beta)] =
+                    new xbyak_gemm(isTransA, isTransB, beta, hasBias);
+            }
+    });
+
+    return kernel_table[isTransA][isTransB][hasBias][beta_idx(beta)];
+}
+
+void sgemm_nocopy_driver(const char *transa,
+        const char *transb, int m, int n, int k, const float *alpha,
+        const float *a, dim_t lda, const float *b, dim_t ldb, const float *beta,
+        float *c, dim_t ldc, const float *bias, float *ws)
+{
+    bool isTransA = (*transa == 'T' || *transa == 't');
+    bool isTransB = (*transb == 'T' || *transb == 't');
+
+    int Bm, sizeM, Bn, sizeN, Bk, sizeK;
+
+    int i, j;
+
+    if ((m <= 0) || (n <= 0))
+        return;
+
+    if ((k <= 0) || (alpha[0] == 0.)) {
+
+        if (beta[0] == 0.) {
+            for (j = 0; j < n; j++)
+                for (i = 0; i < m; i++)
+                    c[i + j * ldc] = 0.0;
+        } else if (beta[0] != 1.) {
+            for (j = 0; j < n; j++)
+                for (i = 0; i < m; i++)
+                    c[i + j * ldc] *= beta[0];
+        }
+
+        return;
+    }
+
+    assert(IMPLICATION(bias != nullptr, *beta == 0.0));
+
+    // XXX: this happens on every thread...
+    bool hasBias = (bias != nullptr);
+    auto ker_bn = get_xbyak_gemm(isTransA, isTransB, *beta, hasBias);
+    auto ker_b1 = get_xbyak_gemm(isTransA, isTransB, 1.0, false);
+    auto ker_b0 = get_xbyak_gemm(isTransA, isTransB, 0.0, false);
+    assert(ker_bn && ker_b1 && ker_b0);
+
+    int BM = 4032;
+    int BN = isTransA ? 96 : 48;
+    int BK = isTransB ? 96 : 256;
+    const float *curA, *curB, *curBias = nullptr;
+    float *curC;
+
+    for (Bk = 0; Bk < k; Bk += sizeK) {
+        sizeK = k - Bk;
+        if (sizeK >= BK * 2)
+            sizeK = BK;
+        else {
+            if (sizeK > BK)
+                sizeK = (sizeK + 1) / 2;
+        }
+
+        for (Bm = 0; Bm < m; Bm += sizeM) {
+            sizeM = m - Bm;
+            if (sizeM >= BM * 2)
+                sizeM = BM;
+            else {
+                if (sizeM > BM + BM / 2)
+                    sizeM = (sizeM + 1) / 2;
+            }
+
+            for (Bn = 0; Bn < n; Bn += sizeN) {
+                sizeN = n - Bn;
+                if (sizeN >= BN * 2)
+                    sizeN = BN;
+                else {
+                    if (sizeN > BN + BN / 2)
+                        sizeN = (sizeN + 1) / 2;
+                }
+
+                if (!isTransA) {
+                    curA = a + Bm + Bk * lda;
+                } else {
+                    curA = a + Bk + Bm * lda;
+                }
+                if (!isTransB) {
+                    curB = b + Bk + Bn * ldb;
+                } else {
+                    curB = b + Bn + Bk * ldb;
+                }
+                curC = c + Bm + (size_t)Bn * ldc;
+                if (bias != nullptr) {
+                    if (Bk == 0) {
+                        curBias = bias + Bm;
+                    } else {
+                        curBias = nullptr;
+                    }
+                }
+                if (Bk == 0) {
+                    if (*beta == 0.0 && bias == nullptr)
+                        (*ker_b0)((dim_t)sizeM, (dim_t)sizeN, (dim_t)sizeK,
+                                alpha, curA, lda, curB, ldb, beta, curC, ldc,
+                                curBias, ws);
+                    else
+                        (*ker_bn)((dim_t)sizeM, (dim_t)sizeN, (dim_t)sizeK,
+                                alpha, curA, lda, curB, ldb, beta, curC, ldc,
+                                curBias, ws);
+                } else {
+                    (*ker_b1)((dim_t)sizeM, (dim_t)sizeN, (dim_t)sizeK,
+                            alpha, curA, lda, curB, ldb, beta, curC, ldc,
+                            curBias, ws);
+                }
+            }
+        }
+    }
+}
+
+}
+
+mkldnn_status_t jit_avx_gemm_f32(
+        const char *transa, const char *transb,
+        const int *p_m, const int *p_n, const int *p_k, const float *p_alpha,
+        const float *A, const int *p_lda, const float *B, const int *p_ldb,
+        const float *p_beta, float *C, const int *p_ldc, const float *bias)
+{
+    using namespace mkldnn::impl::utils;
+    using namespace avx_gemm_f32;
+    using namespace gemm_utils;
+
+    if (*p_beta != 0 && bias)
+        return ref_gemm(transa, transb, p_m, p_n, p_k,
+                p_alpha, A, p_lda, B, p_lda, p_beta, C, p_ldc, bias);
+
+    int nthr = (mkldnn_in_parallel()) ? 1 : mkldnn_get_max_threads();
+
+    int m = *p_m;
+    int n = *p_n;
+    int k = *p_k;
+    dim_t lda = *p_lda;
+    dim_t ldb = *p_ldb;
+    dim_t ldc = *p_ldc;
+    float beta = *p_beta;
+    int MB, NB, KB;
+
+    int nthr_m, nthr_n, nthr_k, nthr_mn;
+
+    // Determine threading partitioning
+    calc_nthr_nocopy_avx(
+            m, n, k, nthr, &nthr_m, &nthr_n, &nthr_k, &MB, &NB, &KB);
+    assert(IMPLICATION(!mkldnn_thr_syncable(), nthr_k == 1));
+
+    // May not happen, but just in case
+    if (nthr < nthr_m * nthr_n * nthr_k)
+        nthr = nthr_m * nthr_n * nthr_k;
+
+    nthr_mn = nthr_m * nthr_n;
+
+    unsigned char * ompstatus_ = nullptr;
+    unsigned char volatile *ompstatus = nullptr;
+
+    float *c_buffers = nullptr;
+    float *ws_buffers = nullptr;
+
+    if (nthr_k > 1) {
+        ompstatus_ = (unsigned char *) malloc(
+                nthr * CACHE_LINE_SIZE,
+                CACHE_LINE_SIZE);
+        ompstatus = (unsigned char volatile *) ompstatus_;
+        assert(ompstatus);
+
+        for (int i = 0; i < nthr; i++)
+            ompstatus[i * CACHE_LINE_SIZE] = 0;
+
+        c_buffers = (float *)malloc(nthr_m * nthr_n * (nthr_k - 1) * MB * NB
+                * sizeof(float), PAGE_4K);
+    }
+
+    const size_t ws_elems_per_thr = (size_t)k * 16 + 64;
+    const size_t ws_size_per_thr
+            = rnd_up(ws_elems_per_thr * sizeof(float), PAGE_4K);
+    if (k > STACK_K_CAPACITY) {
+        ws_buffers = (float *)malloc(nthr * ws_size_per_thr, PAGE_4K);
+    }
+
+    parallel_nd(nthr, [&](const int ithr) {
+        int ithr_m, ithr_n, ithr_k, ithr_mn;
+        int m_from, m_to, myM;
+        int n_from, n_to, myN;
+        int k_from, k_to, myK;
+        int cbase, ibase;
+        const float *myA, *myB, *myBias = nullptr;
+        float *myC = C, myBeta;
+        float *ws = ws_buffers ?
+                ws_buffers + ithr * ws_size_per_thr / sizeof(float) : 0;
+        dim_t ld = ldc;
+
+        int sum_later = (mkldnn_get_num_threads() < nthr_m * nthr_n * nthr_k);
+
+        if (ithr < nthr_m * nthr_n * nthr_k) {
+
+            ithr_mn = ithr % nthr_mn;
+            ithr_m = ithr_mn % nthr_m;
+            ithr_n = ithr_mn / nthr_m;
+            ithr_k = ithr / nthr_mn;
+
+            /* swap ithr_k for performance improvement */
+            if (ithr_k == 0)
+                ithr_k = nthr_k - 1;
+            else if (ithr_k == nthr_k - 1)
+                ithr_k = 0;
+
+            m_from = MB * (ithr_m);
+            m_to = MB * (ithr_m + 1);
+            if (m_to > m)
+                m_to = m;
+            myM = m_to - m_from;
+
+            n_from = NB * (ithr_n);
+            n_to = NB * (ithr_n + 1);
+            if (n_to > n)
+                n_to = n;
+            myN = n_to - n_from;
+
+            k_from = KB * (ithr_k);
+            k_to = KB * (ithr_k + 1);
+            if (k_to > k)
+                k_to = k;
+            myK = k_to - k_from;
+
+            cbase = (ithr_m + nthr_m * ithr_n) * (nthr_k - 1);
+            ibase = (ithr_m + nthr_m * ithr_n) * nthr_k;
+
+            if ((myM > 0) && (myN > 0)) {
+
+                if (*transa == 'N' || *transa == 'n') {
+                    myA = &(A[m_from + k_from * lda]);
+                } else {
+                    myA = &(A[k_from + m_from * lda]);
+                }
+                if (*transb == 'N' || *transb == 'n') {
+                    myB = &(B[k_from + n_from * ldb]);
+                } else {
+                    myB = &(B[n_from + k_from * ldb]);
+                }
+                if (ithr_k == 0) {
+                    myC = &(C[m_from + n_from * ldc]);
+                    myBeta = beta;
+                    ld = ldc;
+                    if (bias)
+                        myBias = &(bias[m_from]);
+                } else {
+                    myC = c_buffers + (dim_t)MB * NB * (cbase + ithr_k - 1);
+                    myBeta = 0.0;
+                    ld = MB;
+                    myBias = nullptr;
+                }
+
+                sgemm_nocopy_driver(transa, transb, myM, myN, myK, p_alpha, myA,
+                        lda, myB, ldb, &myBeta, myC, ld, myBias, ws);
+
+                if (nthr_k > 1 && !sum_later)
+                    ompstatus[(ibase + ithr_k) * CACHE_LINE_SIZE] = 1;
+            }
+
+            if (nthr_k > 1 && !sum_later) {
+
+                // sum matrices partitioned along K dimension
+                int n1, n2;
+
+                partition_unit_diff(ithr_k, nthr_k, myN, &n1, &n2);
+
+                if (ithr_k > 0) {
+
+                    myC = c_buffers + (dim_t)MB * NB * (cbase + ithr_k - 1)
+                        + (dim_t)n1 * MB;
+                    /* need to wait until main thread finishes */
+                    while (ompstatus[ibase * CACHE_LINE_SIZE] != 1) {
+                    };
+
+                    /* my cache is hot */
+                    sum_two_matrices(myM, n2, myC, MB,
+                            &C[m_from + (n_from + n1) * ldc], ldc);
+                }
+
+                for (int ik = 1; ik < nthr_k; ++ik) {
+                    if (ik != ithr_k) {
+
+                        myC = c_buffers + (dim_t)MB * NB * (cbase + ik - 1)
+                            + (dim_t)n1 * MB;
+
+                        while (ompstatus[(ibase + ik) * CACHE_LINE_SIZE] != 1) {
+                        };
+
+                        sum_two_matrices(myM, n2, myC, MB,
+                                &C[m_from + (n_from + n1) * ldc], ldc);
+                    }
+                }
+            }
+        }
+    });
+
+    // handle C summation later
+    if (nthr_k > 1 && ompstatus[0] == 0) {
+
+        parallel_nd(nthr, [&](const int ithr) {
+            int ithr_m, ithr_n, ithr_k, ithr_mn;
+            int m_from, m_to, myM;
+            int n_from, n_to, myN;
+            int cbase;
+            float *myC = C;
+
+            if (ithr < nthr_m * nthr_n * nthr_k) {
+
+                ithr_mn = ithr % nthr_mn;
+                ithr_m = ithr_mn % nthr_m;
+                ithr_n = ithr_mn / nthr_m;
+                ithr_k = ithr / nthr_mn;
+
+                /* swap ithr_k for performance improvement */
+                if (ithr_k == 0)
+                    ithr_k = nthr_k - 1;
+                else if (ithr_k == nthr_k - 1)
+                    ithr_k = 0;
+
+                m_from = MB * (ithr_m);
+                m_to = MB * (ithr_m + 1);
+                if (m_to > m)
+                    m_to = m;
+                myM = m_to - m_from;
+
+                n_from = NB * (ithr_n);
+                n_to = NB * (ithr_n + 1);
+                if (n_to > n)
+                    n_to = n;
+                myN = n_to - n_from;
+
+                cbase = (ithr_m + nthr_m * ithr_n) * (nthr_k - 1);
+
+                if (nthr_k > 1) {
+                    // sum matrices partitioned along K dimension
+                    int n1, n2;
+
+                    partition_unit_diff(ithr_k, nthr_k, myN, &n1, &n2);
+
+                    if (ithr_k > 0) {
+
+                        myC = c_buffers + (dim_t)MB * NB * (cbase + ithr_k - 1)
+                            + (dim_t)n1 * MB;
+
+                        /* my cache is hot */
+                        sum_two_matrices(myM, n2, myC, MB,
+                                         &C[m_from + (n_from + n1) * ldc], ldc);
+                    }
+
+                    for (int ik = 1; ik < nthr_k; ++ik) {
+                        if (ik != ithr_k) {
+
+                            myC = c_buffers + (dim_t)MB * NB * (cbase + ik - 1)
+                                + (dim_t)n1 * MB;
+
+                            sum_two_matrices(myM, n2, myC, MB,
+                                             &C[m_from + (n_from + n1) * ldc], ldc);
+                        }
+                    }
+                }
+            }
+        });
+    }
+
+
+    free(c_buffers);
+    free(ompstatus_);
+    free(ws_buffers);
+
+    return mkldnn_success;
+}
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx_gemm_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx_gemm_f32.hpp
new file mode 100644
index 0000000000..aabf520a3c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/jit_avx_gemm_f32.hpp
@@ -0,0 +1,37 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_AVX_GEMM_F32_HPP
+#define JIT_AVX_GEMM_F32_HPP
+
+#include "mkldnn_types.h"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+mkldnn_status_t jit_avx_gemm_f32(
+        const char *transa, const char *transb, const int *M,
+        const int *N, const int *K, const float *alpha, const float *A,
+        const int *lda, const float *B, const int *ldb, const float *beta,
+        float *C, const int *ldc, const float *bias = nullptr);
+
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/ref_gemm_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/ref_gemm_f32.cpp
new file mode 100644
index 0000000000..5147885a89
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/ref_gemm_f32.cpp
@@ -0,0 +1,346 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn_types.h"
+
+#include "mkldnn_thread.hpp"
+#include "nstl.hpp"
+#include "utils.hpp"
+
+#include "jit_generator.hpp"
+
+#include "gemm_utils_f32.hpp"
+#include "ref_gemm_f32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::utils;
+using namespace gemm_utils;
+
+namespace {
+
+template <typename data_t>
+void copy_A(
+        bool isTransA, int K, const data_t *A, const dim_t lda, data_t *ws) {
+    for (int k = 0; k < K; k++) {
+        PRAGMA_OMP_SIMD()
+        for (int i = 0; i < unroll_factor<data_t>::m; i++) {
+            ws[i] = isTransA ? A[i * lda + k] : A[i + k * lda];
+        }
+        ws += unroll_factor<data_t>::m;
+    }
+}
+
+template <typename data_t, bool isTransA, bool isTransB>
+void kernel_mxn(int K, const data_t *A, const dim_t lda,
+        const data_t *B, const dim_t ldb, data_t *C, const dim_t ldc,
+        const data_t alpha, const data_t beta) {
+    data_t c[unroll_factor<data_t>::m * unroll_factor<data_t>::n] =
+        { static_cast<data_t>(0.) };
+    for (int k = 0; k < K; k++) {
+        for (int j = 0; j < unroll_factor<data_t>::n; j++) {
+            data_t b = isTransB ? B[j + k * ldb] : B[k + j * ldb];
+            PRAGMA_OMP_SIMD()
+            for (int i = 0; i < unroll_factor<data_t>::m; i++) {
+                data_t a = isTransA ? A[i * lda + k] : A[i + lda * k];
+                c[i + unroll_factor<data_t>::m * j] += a * b;
+            }
+        }
+    }
+    for (int j = 0; j < unroll_factor<data_t>::n; j++) {
+        PRAGMA_OMP_SIMD()
+        for (int i = 0; i < unroll_factor<data_t>::m; i++) {
+            C[i + j * ldc] = (beta == static_cast<data_t>(0.))
+            ? alpha * c[i + unroll_factor<data_t>::m * j]
+            : alpha * c[i + unroll_factor<data_t>::m * j]
+                + beta * C[i + j * ldc];
+        }
+    }
+}
+
+template <typename data_t, bool isTransA, bool isTransB>
+void block_ker(const int M, const int N, const int K,
+        const data_t *A, const dim_t lda, const data_t *B, const dim_t ldb,
+        data_t *C, const dim_t ldc, const data_t alpha, const data_t beta,
+        data_t *ws, bool do_copy) {
+    int Nu = rnd_dn(N, unroll_factor<data_t>::n);
+    int Mu = rnd_dn(M, unroll_factor<data_t>::m);
+    for (int i = 0; i < Mu; i += unroll_factor<data_t>::m) {
+        for (int j = 0; j < Nu; j += unroll_factor<data_t>::n) {
+            const data_t *b = isTransB ? &B[j] : &B[j * ldb];
+            const data_t *a = isTransA ? &A[i * lda] : &A[i];
+            if (do_copy) {
+                if (j == 0) {
+                    copy_A<data_t>(isTransA, K, a, lda, ws);
+                }
+                kernel_mxn<data_t, false, isTransB>(
+                        K, ws, unroll_factor<data_t>::m, b, ldb,
+                        &C[i + j * ldc], ldc, alpha, beta);
+            } else {
+                kernel_mxn<data_t, isTransA, isTransB>(
+                        K, a, lda, b, ldb, &C[i + j * ldc], ldc, alpha, beta);
+            }
+        }
+    }
+    // tail processing
+    for (int i = 0; i < M; i++) {
+        for (int j = Nu; j < N; j++) {
+            data_t c = beta == static_cast<data_t>(0.)
+                ? static_cast<data_t>(0.)
+                : beta * C[i + j * ldc];
+            for (int p = 0; p < K; p++) {
+                data_t b = isTransB ? B[j + p * ldb] : B[p + j * ldb];
+                data_t a = isTransA ? A[p + i * lda] : A[i + p * lda];
+                c += alpha * a * b;
+            }
+            C[i + j * ldc] = c;
+        }
+    }
+    for (int i = Mu; i < M; i++) {
+        for (int j = 0; j < Nu; j++) {
+            data_t c = beta == static_cast<data_t>(0.)
+                ? static_cast<data_t>(0.)
+                : beta * C[i + j * ldc];
+            for (int p = 0; p < K; p++) {
+                data_t b = isTransB ? B[j + p * ldb] : B[p + j * ldb];
+                data_t a = isTransA ? A[p + i * lda] : A[i + p * lda];
+                c += alpha * a * b;
+            }
+            C[i + j * ldc] = c;
+        }
+    }
+}
+
+template <typename data_t, bool isTransA, bool isTransB>
+void gemm_ithr(const int M, const int N, const int K, const data_t alpha,
+        const data_t *A, const dim_t lda, const data_t *B, const dim_t ldb,
+        const data_t beta, data_t *C, const dim_t ldc, bool do_copy,
+        data_t *ws) {
+    constexpr int BM = gemm_traits<data_t, isTransA, isTransB>::BM;
+    constexpr int BN = gemm_traits<data_t, isTransA, isTransB>::BN;
+    constexpr int BK = gemm_traits<data_t, isTransA, isTransB>::BK;
+
+    const data_t *curA;
+    const data_t *curB;
+    data_t *curC;
+
+    if ((M <= 0) || (N <= 0))
+        return;
+
+    if ((K <= 0) || (alpha == static_cast<data_t>(0))) {
+        dim_t MN = N * M;
+        if (beta == static_cast<data_t>(0.)) {
+            for (dim_t j = 0; j < MN; j++)
+                C[j] = static_cast<data_t>(0.);
+        } else if (beta != static_cast<data_t>(1.)) {
+            for (dim_t j = 0; j < MN; j++)
+                C[j] *= beta;
+        }
+        return;
+    }
+
+    for (int Bk = 0; Bk < K; Bk += BK) {
+        int kb = nstl::min(K - Bk, BK);
+        for (int Bm = 0; Bm < M; Bm += BM) {
+            int mb = nstl::min(M - Bm, BM);
+            for (int Bn = 0; Bn < N; Bn += BN) {
+                int nb = nstl::min(N - Bn, BN);
+                curA = isTransA ? A + Bk + Bm * lda : A + Bm + Bk * lda;
+                curB = isTransB ? B + Bn + Bk * ldb : B + Bk + Bn * ldb;
+                curC = C + Bm + Bn * ldc;
+                if (Bk == 0) {
+                    block_ker<data_t, isTransA, isTransB>(mb, nb, kb, curA, lda,
+                        curB, ldb, curC, ldc, alpha, beta, ws, do_copy);
+                } else {
+                    block_ker<data_t, isTransA, isTransB>(mb, nb, kb, curA, lda,
+                        curB, ldb, curC, ldc, alpha, static_cast<data_t>(1.0),
+                        ws, do_copy);
+                }
+            }
+        }
+    }
+}
+
+}
+
+template <typename data_t>
+mkldnn_status_t ref_gemm(
+        const char *transa_, const char *transb_, const int *M_,
+        const int *N_, const int *K_, const data_t *alpha_, const data_t *A,
+        const int *lda_, const data_t *B, const int *ldb_, const data_t *beta_,
+        data_t *C, const int *ldc_, const data_t *bias) {
+
+    bool isTransA = (*transa_ == 'T' || *transa_ == 't');
+    bool isTransB = (*transb_ == 'T' || *transb_ == 't');
+    const int M = *M_, N = *N_, K = *K_;
+    const dim_t lda = *lda_, ldb = *ldb_, ldc = *ldc_;
+    const data_t alpha = *alpha_, beta = *beta_;
+
+    int max_nthr = mkldnn_in_parallel() ? 1 : mkldnn_get_max_threads();
+    int nthr_m, nthr_n, nthr_k;
+    int MB, NB, KB;
+    // thread balancing over M, N, K & size of blocking dimensions
+    calc_nthr_nocopy_avx(
+            M, N, K, max_nthr, &nthr_m, &nthr_n, &nthr_k, &MB, &NB, &KB);
+    assert(IMPLICATION(!mkldnn_thr_syncable(), nthr_k == 1));
+
+    data_t *c_buffers = nullptr;
+    data_t *ws_buffers = nullptr;
+    if (nthr_k > 1) {
+        c_buffers = (data_t *)malloc(nthr_m * nthr_n * (nthr_k - 1) * MB * NB
+                * sizeof(data_t), PAGE_4K);
+        if (!c_buffers) {
+            nthr_k = 1;
+            KB = K;
+        }
+    }
+
+    bool do_copy = (NB / unroll_factor<data_t>::n > 3);
+    const int nthr_mn = nthr_m * nthr_n;
+    const int nthr = nthr_mn * nthr_k;
+    const size_t ws_elems_per_thr = K * unroll_factor<data_t>::m;
+    const size_t ws_size_per_thr
+            = rnd_up(ws_elems_per_thr * sizeof(data_t), PAGE_4K);
+    if (do_copy) {
+        ws_buffers = (data_t*)malloc(nthr * ws_size_per_thr, PAGE_4K);
+        if (!ws_buffers)
+            do_copy = false;
+    }
+
+    auto get_thr_block = [&](int &from, int &to, int &myN, int NB, int N,
+                             int ithr) {
+        from = NB * (ithr);
+        to = NB * (ithr + 1);
+        if (to > N)
+            to = N;
+        myN = to - from;
+    };
+
+    parallel_nd(nthr, [&](const int ithr) {
+        int ithr_mn = ithr % nthr_mn;
+        int ithr_m = ithr_mn % nthr_m;
+        int ithr_n = ithr_mn / nthr_m;
+        int ithr_k = ithr / nthr_mn;
+
+        int cbase = (ithr_m + nthr_m * ithr_n) * (nthr_k - 1);
+
+        data_t *ws = do_copy
+                ? ws_buffers + ithr * ws_size_per_thr / sizeof(data_t)
+                : nullptr;
+
+        int m_from = 0, m_to = 0, myM = 0, n_from = 0, n_to = 0, myN = 0,
+                k_from = 0, k_to = 0, myK = 0;
+
+        get_thr_block(m_from, m_to, myM, MB, M, ithr_m);
+        get_thr_block(n_from, n_to, myN, NB, N, ithr_n);
+        get_thr_block(k_from, k_to, myK, KB, K, ithr_k);
+
+        if (myM > 0 && myN > 0) {
+            data_t myBeta, *myC;
+            dim_t ld;
+            if (ithr_k == 0) {
+                myC = &(C[m_from + n_from * ldc]);
+                myBeta = beta;
+                ld = ldc;
+            } else {
+                myC = c_buffers + (dim_t)MB * NB * (cbase + ithr_k - 1);
+                myBeta = 0.0f;
+                ld = MB;
+            }
+            const data_t *myA = isTransA
+                    ? &(A[k_from + m_from * lda])
+                    : &(A[m_from + k_from * lda]);
+            const data_t *myB = isTransB
+                    ? &(B[n_from + k_from * ldb])
+                    : &(B[k_from + n_from * ldb]);
+
+            if (!isTransA) {
+                if (!isTransB) {
+                    gemm_ithr<data_t, false, false>(myM, myN, myK, alpha, myA,
+                        lda, myB, ldb, myBeta, myC, ld, do_copy, ws);
+                } else {
+                    gemm_ithr<data_t, false, true>(myM, myN, myK, alpha, myA,
+                        lda, myB, ldb, myBeta, myC, ld, do_copy, ws);
+                }
+            } else {
+                if (!isTransB) {
+                    gemm_ithr<data_t, true, false>(myM, myN, myK, alpha, myA,
+                        lda, myB, ldb, myBeta, myC, ld, do_copy, ws);
+                } else {
+                    gemm_ithr<data_t, true, true>(myM, myN, myK, alpha, myA,
+                        lda, myB, ldb, myBeta, myC, ld, do_copy, ws);
+                }
+            }
+        }
+    });
+
+    if (nthr_k > 1) {
+        parallel_nd(nthr, [&](const int ithr) {
+            int ithr_mn = ithr % nthr_mn;
+            int ithr_m = ithr_mn % nthr_m;
+            int ithr_k = ithr / nthr_mn;
+            int ithr_n = ithr_mn / nthr_m;
+
+            int n_from = 0, n_to = 0, myN = 0;
+            int m_from = 0, m_to = 0, myM = 0;
+
+            int cbase = (ithr_m + nthr_m * ithr_n) * (nthr_k - 1);
+
+            get_thr_block(n_from, n_to, myN, NB, N, ithr_n);
+            get_thr_block(m_from, m_to, myM, MB, M, ithr_m);
+
+            // sum matrices partitioned along K dimension
+            int offset = 0, block = 0;
+            gemm_utils::partition_unit_diff(ithr_k, nthr_k, myN, &offset,
+                    &block);
+            for (int ik = 1; ik < nthr_k; ++ik) {
+                data_t *myC = c_buffers
+                            + MB * ((dim_t)NB * (cbase + ik - 1) + offset);
+
+                gemm_utils::sum_two_matrices(myM, block, myC, MB,
+                        &C[m_from + (n_from + offset) * ldc], ldc);
+            }
+        });
+    }
+
+    if (bias) {
+        parallel_nd(N, M, [&](int i, int j) {
+            C[i*ldc + j] += bias[j];
+        });
+    }
+
+    free(ws_buffers);
+    free(c_buffers);
+
+    return mkldnn_success;
+}
+
+template mkldnn_status_t ref_gemm<float>(
+        const char *transa_, const char *transb_,
+        const int *M_, const int *N_, const int *K_, const float *alpha_,
+        const float *A, const int *lda_, const float *B, const int *ldb_,
+        const float *beta_, float *C, const int *ldc_, const float *bias);
+
+template mkldnn_status_t ref_gemm<double>(
+        const char *transa_, const char *transb_,
+        const int *M_, const int *N_, const int *K_, const double *alpha_,
+        const double *A, const int *lda_, const double *B, const int *ldb_,
+        const double *beta_, double *C, const int *ldc_, const double *bias);
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/ref_gemm_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/ref_gemm_f32.hpp
new file mode 100644
index 0000000000..7c90ba6277
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/f32/ref_gemm_f32.hpp
@@ -0,0 +1,36 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef REF_GEMM_F32_HPP
+#define REF_GEMM_F32_HPP
+
+#include "mkldnn_types.h"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <typename data_t>
+mkldnn_status_t ref_gemm(const char *transa, const char *transb, const int *M,
+        const int *N, const int *K, const data_t *alpha, const data_t *A,
+        const int *lda, const data_t *B, const int *ldb, const data_t *beta,
+        data_t *C, const int *ldc, const data_t *bias);
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/gemm.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/gemm.cpp
new file mode 100644
index 0000000000..3dbe07d743
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/gemm.cpp
@@ -0,0 +1,280 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn.h"
+
+#include "mkldnn_traits.hpp"
+#include "nstl.hpp"
+
+#include "jit_generator.hpp"
+
+#include "gemm.hpp"
+
+#include "f32/jit_avx512_common_gemm_f32.hpp"
+#include "f32/jit_avx_gemm_f32.hpp"
+#include "f32/ref_gemm_f32.hpp"
+
+#include "s8x8s32/jit_avx512_core_gemm_s8u8s32.hpp"
+#include "s8x8s32/simple_gemm_s8s8s32.hpp"
+#include "s8x8s32/ref_gemm_s8x8s32.hpp"
+
+#include "os_blas.hpp"
+
+/* USE_MKL      USE_CBLAS       effect
+ * -------      ---------       ------
+ * yes          yes             use Intel(R) MKL CBLAS
+ * yes          no              use jit
+ * no           yes             system-dependent CBLAS
+ * no           no              use jit
+ */
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+mkldnn_status_t check_gemm_input(const char *transa, const char *transb,
+        const int *M, const int *N, const int *K, const int *lda,
+        const int *ldb, const int *ldc, const float *alpha, const float *beta,
+        const bool with_bias) {
+    if (utils::any_null(transa, transb, M, N, K, lda, ldb, ldc, alpha, beta))
+        return mkldnn_invalid_arguments;
+    if (with_bias && *beta != 0)
+        return mkldnn_unimplemented;
+    bool consistency = true
+        && utils::one_of(*transa, 'T', 't', 'N', 'n')
+        && utils::one_of(*transb, 'T', 't', 'N', 'n')
+        && *M >= 0
+        && *N >= 0
+        && *K >= 0;
+
+    if (!consistency)
+        return mkldnn_invalid_arguments;
+    bool isTransA = utils::one_of(*transa, 'T', 't');
+    bool isTransB = utils::one_of(*transb, 'T', 't');
+    int nrowA = isTransA ? *K : *M;
+    int nrowB = isTransB ? *N : *K;
+    consistency = true
+        && *lda >= nstl::max(1, nrowA)
+        && *ldb >= nstl::max(1, nrowB)
+        && *ldc >= nstl::max(1, *M);
+    if (!consistency)
+        return mkldnn_invalid_arguments;
+
+    return mkldnn_success;
+}
+
+mkldnn_status_t check_gemm_x8x8x32_input(const char *offsetc,
+        const char *transa, const char *transb, const int *M, const int *N,
+        const int *K, const int *lda, const int *ldb, const int *ldc,
+        const float *alpha, const float *beta, const bool with_bias) {
+    if (offsetc == nullptr)
+        return mkldnn_invalid_arguments;
+    if (!utils::one_of(*offsetc, 'F', 'f', 'C', 'c', 'R', 'r'))
+        return mkldnn_invalid_arguments;
+
+    return check_gemm_input(transa, transb, M, N, K, lda, ldb, ldc, alpha,
+        beta, with_bias);
+}
+
+mkldnn_status_t extended_sgemm(const char *transa, const char *transb,
+        const int *M, const int *N, const int *K, const float *alpha,
+        const float *A, const int *lda, const float *B, const int *ldb,
+        const float *beta, float *C, const int *ldc,
+        const float *bias, const bool force_jit_gemm) {
+    mkldnn_status_t status = check_gemm_input(transa, transb, M, N, K,
+            lda, ldb, ldc, alpha, beta, bias != nullptr);
+    if (status != mkldnn_success)
+        return status;
+
+#ifdef USE_CBLAS
+    if (!force_jit_gemm) {
+        bool trA = *transa == 't' || *transa == 'T';
+        bool trB = *transb == 't' || *transb == 'T';
+        CBLAS_TRANSPOSE Cblas_trA = trA ? CblasTrans : CblasNoTrans;
+        CBLAS_TRANSPOSE Cblas_trB = trB ? CblasTrans : CblasNoTrans;
+        cblas_sgemm(CblasColMajor, Cblas_trA, Cblas_trB,
+                *M, *N, *K, *alpha, A, *lda, B, *ldb, *beta, C, *ldc);
+
+        if (bias) {
+            // Add bias if necessary (bias is applied to columns of C)
+            cblas_int incx = 1, incy = 1;
+            parallel_nd(*N, [&](int n) {
+                ptrdiff_t offset = (ptrdiff_t)n * (*ldc);
+                cblas_saxpy(*M, 1.0, bias, incx, C + offset, incy);
+            });
+        }
+        return mkldnn_success;
+    }
+#endif
+
+    if (mayiuse(avx512_common))
+        return jit_avx512_common_gemm_f32(transa, transb,
+                M, N, K, alpha, A, lda, B, ldb, beta, C, ldc, bias);
+    else if (mayiuse(avx))
+        return jit_avx_gemm_f32(transa, transb,
+                M, N, K, alpha, A, lda, B, ldb, beta, C, ldc, bias);
+    else
+        return ref_gemm<float>(transa, transb,
+                M, N, K, alpha, A, lda, B, ldb, beta, C, ldc, bias);
+}
+
+template <typename b_dt>
+mkldnn_status_t gemm_s8x8s32(const char *transa, const char *transb,
+        const char *offsetc, const int *M, const int *N, const int *K,
+        const float *alpha, const int8_t *A, const int *LDA, const int8_t *ao,
+        const b_dt *B, const int *LDB, const int8_t *bo, const float *beta,
+        int32_t *C, const int *LDC, const int32_t *co) {
+    mkldnn_status_t status = check_gemm_x8x8x32_input(offsetc, transa, transb,
+        M, N, K, LDA, LDB, LDC, alpha, beta, false);
+    if (status != mkldnn_success)
+        return status;
+
+    if (*M == 0 || *N == 0 || *K == 0)
+        return mkldnn_success;
+
+#if USE_MKL_IGEMM
+        bool OCisR = (*offsetc == 'R' || *offsetc == 'r');
+        bool OCisC = (*offsetc == 'C' || *offsetc == 'c');
+        bool AisN = (*transa == 'N' || *transa == 'n');
+        bool BisN = (*transb == 'N' || *transb == 'n');
+
+    if (data_traits<b_dt>::data_type == data_type::u8) {
+        CBLAS_TRANSPOSE Cblas_trA = AisN ? CblasNoTrans : CblasTrans;
+        CBLAS_TRANSPOSE Cblas_trB = BisN ? CblasNoTrans : CblasTrans;
+        CBLAS_OFFSET Cblas_offsetc =
+            OCisR
+            ? CblasRowOffset
+            : OCisC
+            ? CblasColOffset
+            : CblasFixOffset;
+        cblas_gemm_s8u8s32(CblasColMajor, Cblas_trA, Cblas_trB, Cblas_offsetc,
+                *M, *N, *K, *alpha, A, *LDA, *ao, (uint8_t *)B, *LDB, *bo,
+                *beta, C, *LDC, co);
+        return mkldnn_success;
+    } else {
+        assert(data_traits<b_dt>::data_type == data_type::s8);
+        // TODO CBLAS implementation of gemm_s8s8s32 goes here.
+        // mkldnn_gemm_s8s8s32 doesn't support non-zero ao and bo
+        if (utils::everyone_is(0, *ao, *bo)) {
+            return simple_gemm_s8s8s32(transa, transb, offsetc, M,
+                    N, K, alpha, A, LDA, ao, (int8_t *)B, LDB, bo, beta,
+                    C, LDC, co);
+        } else {
+            return ref_gemm_s8x8s32(transa, transb, offsetc, M, N, K,
+                    alpha, A, LDA, ao, B, LDB, bo, beta, C, LDC, co);
+        }
+    }
+#else
+    cpu_isa_t isa = isa_any;
+    if (mayiuse(avx512_core_vnni)) {
+        isa = avx512_core_vnni;
+    } else if (mayiuse(avx512_core)) {
+        isa = avx512_core;
+    }
+
+    if (data_traits<b_dt>::data_type == data_type::u8) {
+        switch (isa) {
+        case avx512_core:
+        case avx512_core_vnni:
+            return jit_avx512_core_gemm_s8u8s32(transa, transb, offsetc, M,
+                    N, K, alpha, A, LDA, ao, (uint8_t *)B, LDB, bo, beta,
+                    C, LDC, co);
+        default:
+            return ref_gemm_s8x8s32(transa, transb, offsetc, M, N, K,
+                    alpha, A, LDA, ao, B, LDB, bo, beta, C, LDC, co);
+        }
+    } else {
+        assert(data_traits<b_dt>::data_type == data_type::s8);
+        // mkldnn_gemm_s8s8s32 doesn't support non-zero ao and bo
+        if ((mayiuse(avx512_core) || mayiuse(avx512_core_vnni))
+                && *ao == 0 && *bo == 0) {
+            return simple_gemm_s8s8s32(transa, transb, offsetc, M,
+                    N, K, alpha, A, LDA, ao, (int8_t *)B, LDB, bo, beta,
+                    C, LDC, co);
+        } else {
+            return ref_gemm_s8x8s32(transa, transb, offsetc, M, N, K,
+                    alpha, A, LDA, ao, B, LDB, bo, beta, C, LDC, co);
+        }
+    }
+#endif
+}
+
+template
+mkldnn_status_t gemm_s8x8s32(const char *transa, const char *transb,
+        const char *offsetc, const int *M, const int *N, const int *K,
+        const float *alpha, const int8_t *A, const int *LDA, const int8_t *ao,
+        const int8_t *B, const int *LDB, const int8_t *bo, const float *beta,
+        int32_t *C, const int *LDC, const int32_t *co);
+
+template
+mkldnn_status_t gemm_s8x8s32(const char *transa, const char *transb,
+        const char *offsetc, const int *M, const int *N, const int *K,
+        const float *alpha, const int8_t *A, const int *LDA, const int8_t *ao,
+        const uint8_t *B, const int *LDB, const int8_t *bo, const float *beta,
+        int32_t *C, const int *LDC, const int32_t *co);
+
+}
+}
+}
+
+using namespace mkldnn::impl;
+using namespace mkldnn::impl::cpu;
+
+mkldnn_status_t mkldnn_sgemm(const char *transa, const char *transb,
+        const int64_t *M, const int64_t *N, const int64_t *K, const float *alpha,
+        const float *A, const int64_t *lda, const float *B, const int64_t *ldb,
+        const float *beta, float *C, const int64_t *ldc) {
+    int M_s32 = (int)*M;
+    int N_s32 = (int)*N;
+    int K_s32 = (int)*K;
+    int lda_s32 = (int)*lda;
+    int ldb_s32 = (int)*ldb;
+    int ldc_s32 = (int)*ldc;
+
+    return extended_sgemm(transa, transb, &M_s32, &N_s32, &K_s32,
+            alpha, A, &lda_s32, B, &ldb_s32, beta, C, &ldc_s32);
+}
+
+mkldnn_status_t mkldnn_gemm_s8u8s32(const char *transa, const char *transb,
+        const char *offsetc, const int64_t *M, const int64_t *N, const int64_t *K,
+        const float *alpha, const int8_t *A, const int64_t *lda, const int8_t *ao,
+        const uint8_t *B, const int64_t *ldb, const int8_t *bo, const float *beta,
+        int32_t *C, const int64_t *ldc, const int32_t *co) {
+    int M_s32 = (int)*M;
+    int N_s32 = (int)*N;
+    int K_s32 = (int)*K;
+    int lda_s32 = (int)*lda;
+    int ldb_s32 = (int)*ldb;
+    int ldc_s32 = (int)*ldc;
+    return gemm_s8x8s32(transa, transb, offsetc, &M_s32, &N_s32, &K_s32,
+            alpha, A, &lda_s32, ao, B, &ldb_s32, bo, beta, C, &ldc_s32, co);
+}
+
+mkldnn_status_t mkldnn_gemm_s8s8s32(const char *transa, const char *transb,
+        const char *offsetc, const int64_t *M, const int64_t *N, const int64_t *K,
+        const float *alpha, const int8_t *A, const int64_t *lda, const int8_t *ao,
+        const int8_t *B, const int64_t *ldb, const int8_t *bo, const float *beta,
+        int32_t *C, const int64_t *ldc, const int32_t *co) {
+    int M_s32 = (int)*M;
+    int N_s32 = (int)*N;
+    int K_s32 = (int)*K;
+    int lda_s32 = (int)*lda;
+    int ldb_s32 = (int)*ldb;
+    int ldc_s32 = (int)*ldc;
+
+    return gemm_s8x8s32<int8_t>(transa, transb, offsetc, &M_s32, &N_s32, &K_s32,
+            alpha, A, &lda_s32, ao, B, &ldb_s32, bo, beta, C, &ldc_s32, co);
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/gemm.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/gemm.hpp
new file mode 100644
index 0000000000..dc15ff7130
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/gemm.hpp
@@ -0,0 +1,58 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef GEMM_HPP
+#define GEMM_HPP
+
+#include "mkldnn_types.h"
+#include "os_blas.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+mkldnn_status_t extended_sgemm(const char *transa, const char *transb,
+        const int *M, const int *N, const int *K, const float *alpha,
+        const float *A, const int *lda, const float *B, const int *ldb,
+        const float *beta, float *C, const int *ldc,
+        const float *bias = nullptr, bool force_jit_gemm = false);
+
+template <typename b_dt>
+mkldnn_status_t gemm_s8x8s32(const char *transa, const char *transb,
+        const char *offsetc, const int *M, const int *N, const int *K,
+        const float *alpha, const int8_t *A, const int *lda, const int8_t *ao,
+        const b_dt *B, const int *ldb, const int8_t *bo, const float *beta,
+        int32_t *c, const int *ldc, const int32_t *co);
+
+#ifdef USE_CBLAS
+#define GEMM_IMPL_STR "gemm:blas"
+#else
+#define GEMM_IMPL_STR "gemm:jit"
+#endif
+
+#if USE_MKL_IGEMM
+#define IGEMM_S8U8S32_IMPL_STR "igemm_s8u8s32:blas"
+#define IGEMM_S8S8S32_IMPL_STR "igemm_s8s8s32:blas"
+#else
+#define IGEMM_S8U8S32_IMPL_STR "igemm_s8u8s32:jit"
+#define IGEMM_S8S8S32_IMPL_STR "igemm_s8s8s32:jit"
+#endif
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/os_blas.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/os_blas.hpp
new file mode 100644
index 0000000000..4d34ede0bd
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/os_blas.hpp
@@ -0,0 +1,86 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef OS_BLAS_HPP
+#define OS_BLAS_HPP
+
+/** \file
+ * Common stuff respecting USE_MKL and USE_CBLAS compile flags
+ *
+ *  USE_MKL  USE_CBLAS effect
+ *  -------  --------- ------
+ *  yes      yes       normal compile: jit *may* be preferred over Intel(R) MKL CBLAS
+ *  yes      no        jit calls OK; assert if cblas is ever called
+ *  no       yes       system-dependent CBLAS
+ *  no       no        gemm convolution (or other blas) N/A; create stubs
+ */
+
+#if defined(USE_MKL)
+
+#include "mkl_version.h"
+
+#define USE_MKL_PACKED_GEMM (INTEL_MKL_VERSION >= 20190001)
+#define USE_MKL_IGEMM \
+    (INTEL_MKL_VERSION >= 20180000 && __INTEL_MKL_BUILD_DATE >= 20170628)
+
+#include "mkl_cblas.h"
+#if !defined(USE_CBLAS)
+#define cblas_sgemm(...) assert(!"CBLAS is unavailable")
+#endif
+
+#else /* defined(USE_MKL) */
+
+#define USE_MKL_PACKED_GEMM 0
+#define USE_MKL_IGEMM 0
+
+#if defined(_SX)
+/* TODO: _SX should also define USE_CBLAS in case the later is available */
+extern "C" {
+#include "cblas.h" // CHECK: does SX also have a fortran API sgemm?
+}
+
+#elif defined(USE_CBLAS)
+#include "cblas.h" // Maybe a system/cmake cblas works for you?
+#else
+/* put the stubs to make a code compilable but not workable */
+#define cblas_sgemm(...) assert(!"CBLAS is unavailable")
+#endif /* defined(_SX) */
+
+#endif /* defined(USE_MKL) */
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+#if defined(USE_MKL) && defined(USE_CBLAS)
+typedef MKL_INT cblas_int;
+
+#elif defined(USE_CBLAS)
+typedef int cblas_int;
+
+#if defined(_SX)
+/* this cblas.h is peculiar... */
+typedef CBLAS_ORDER CBLAS_LAYOUT;
+#endif
+#endif
+
+}
+}
+}
+
+#endif /* OS_BLAS_HPP */
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/common.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/common.hpp
new file mode 100644
index 0000000000..dde72f4a17
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/common.hpp
@@ -0,0 +1,206 @@
+/*******************************************************************************
+* Copyright 2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef COMMON_H
+#define COMMON_H
+
+#define GEMM_CODE_SIZE          (4096L * 32)
+
+#define AVX512_UNROLL_M                   48
+#define AVX512_UNROLL_N                    8
+#define AVX512_UNROLL_K                    1
+#define AVX512_BM                       9984
+#define AVX512_BN                        384
+#define AVX512_BK                        768
+#define AVX512_BK_VNNI                  1536
+#define AVX512_BK_TRADITIONAL            384
+#define AVX512_BLOCKING_SMALL_K           48
+#define AVX512_BN_SMALL_K                 24
+
+
+#define PAGESIZE 4096
+
+#define PADD_BYTESIZE_ONPAGE(x, size) (((x) * (size) + PAGESIZE - 1) / PAGESIZE) * PAGESIZE
+#define NEXT_THR_STRIDE(x, size) (PADD_BYTESIZE_ONPAGE(x, size)) / size
+
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+enum {
+    PARTITION_1D_ROW,
+    PARTITION_1D_COL,
+    PARTITION_2D_COL_MAJOR,
+    PARTITION_2D = PARTITION_2D_COL_MAJOR,
+};
+
+enum {
+    COPY_NONE,
+    COPY_A,
+};
+
+enum {
+    NO_OFFSET,
+    FIX_OFFSET,
+    COL_OFFSET,
+    ROW_OFFSET,
+};
+
+// Alias for any dimension related variable.
+typedef long long int dim_t;
+
+typedef struct {
+    // Interface arguments.
+    int transa, transb, offsetc;
+    dim_t m, n, k;
+    dim_t lda, ldb, ldc;
+    const int8_t *a;
+    const uint8_t *b;
+    int32_t *c;
+    const float *alpha, *beta;
+
+    int8_t ao, bo;
+    const int32_t *co;
+
+    // Kernel parameters.
+    dim_t um, un, uk, bm, bn, bk;
+    dim_t bn_small_k, bk_traditional, blocking_small_k;
+
+    int (*copyA)(const dim_t *m, const dim_t *n, const int8_t *a,
+            const dim_t *lda, const int8_t *alpha, int8_t *b,
+            const dim_t *dummy1, const dim_t *dummy2, int32_t *row_col_sum);
+
+    int (*copyB)(const dim_t *m, const dim_t *n, const uint8_t *a,
+            const dim_t *lda, const uint8_t *alpha, uint8_t *b,
+            const dim_t *dummy1, const dim_t *dummy2, int32_t *row_col_sum);
+
+    int (*kernel)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    int (*kernel_b)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    int (*kernel_r)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    int (*kernel_c)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    int (*kernel_b0)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    int (*kernel_b0_b)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    int (*kernel_b0_r)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    int (*kernel_b0_c)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    // Gemv kernels
+    void (*gemv_s8u8s32_kernel)(const dim_t, const dim_t, const float,
+                                const int8_t*, const dim_t, const uint8_t*,
+                                const float, int32_t*);
+
+    void (*gemv_u8s8s32_kernel)(const dim_t, const dim_t, const float,
+                                const uint8_t*, const dim_t, const int8_t*,
+                                const float, int32_t*);
+
+    // Gemv parameters
+    int swap;
+
+} blas_t;
+
+
+class jit_avx512_core_u8_copy_an_kern : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_u8_copy_an_kern);
+
+    public:
+        jit_avx512_core_u8_copy_an_kern();
+};
+
+class jit_avx512_core_u8_copy_at_kern : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_u8_copy_at_kern);
+
+    public:
+        jit_avx512_core_u8_copy_at_kern();
+};
+
+class jit_avx512_core_u8_copy_bn_kern : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_u8_copy_bn_kern);
+
+    public:
+        jit_avx512_core_u8_copy_bn_kern();
+};
+
+class jit_avx512_core_u8_copy_bt_kern : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_u8_copy_bt_kern);
+
+    public:
+        jit_avx512_core_u8_copy_bt_kern();
+};
+
+class jit_avx512_core_u8_copy_sum_an_kern : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_u8_copy_sum_an_kern);
+
+    public:
+        jit_avx512_core_u8_copy_sum_an_kern();
+};
+
+class jit_avx512_core_u8_copy_sum_at_kern : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_u8_copy_sum_at_kern);
+
+    public:
+        jit_avx512_core_u8_copy_sum_at_kern();
+};
+
+class jit_avx512_core_u8_copy_sum_bn_kern : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_u8_copy_sum_bn_kern);
+
+    public:
+        jit_avx512_core_u8_copy_sum_bn_kern();
+};
+
+class jit_avx512_core_u8_copy_sum_bt_kern : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_u8_copy_sum_bt_kern);
+
+    public:
+        jit_avx512_core_u8_copy_sum_bt_kern();
+};
+
+}
+}
+}
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/gemv.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/gemv.hpp
new file mode 100644
index 0000000000..db9dd9ef97
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/gemv.hpp
@@ -0,0 +1,28 @@
+/*******************************************************************************
+* Copyright 2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "common.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+int gemm_s8u8s32_jump_to_gemv_s8u8s32(blas_t *arg);
+int gemv_threading_driver(blas_t *arg);
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32.cpp
new file mode 100644
index 0000000000..e4b8e1cde2
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32.cpp
@@ -0,0 +1,1409 @@
+/*******************************************************************************
+* Copyright 2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <cstdint>
+#include <mutex>
+
+#include "common.hpp"
+#include "mkldnn_types.h"
+#include "nstl.hpp"
+#include "utils.hpp"
+
+#include "jit_avx512_core_gemm_s8u8s32.hpp"
+#include "jit_avx512_core_gemm_s8u8s32_kern.hpp"
+#include "jit_avx512_core_kernel_gemv_s8u8s32_kern.hpp"
+#include "gemv.hpp"
+
+#if defined(_MSC_VER)
+#include <malloc.h>
+#endif
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+typedef struct {
+    int nthrs_m, nthrs_n;
+    int partition;
+    int copy_type;
+} blas_thread_t;
+
+static inline void round_to_nearest(int32_t *rounded_val, double fp_val) {
+    if (fp_val >= 0.) {
+        fp_val += 0.5;
+        if (fp_val > INT32_MAX) {
+            fp_val = INT32_MAX;
+        }
+    } else {
+        fp_val -= 0.5;
+        if (fp_val < INT32_MIN) {
+            fp_val = INT32_MIN;
+        }
+    }
+    *rounded_val = (int32_t) fp_val;
+}
+
+static inline void add_results(const dim_t m, const dim_t n, const dim_t k,
+        const float alpha, const float beta, const int32_t *c_partial_sum,
+        const dim_t ldcp, int32_t *c_data, const dim_t ldc,
+        const int32_t *a_row_sum, const int32_t *b_col_sum, const int8_t ao,
+        const int8_t bo, const int32_t *co, const int offsetc)
+{
+    for (dim_t j = 0; j < n; ++j) {
+        for (dim_t i = 0; i < m; ++i) {
+            int32_t ctemp = c_partial_sum[i + j * ldcp];
+
+            if (alpha == 1.0f) {
+                if (beta == 0.0f) {
+                    c_data[i + j * ldc] = ctemp;
+                } else {
+                    double c_float = (double) beta
+                        * (double) c_data[i + j * ldc];
+                    c_float += (double) ctemp;
+                    round_to_nearest(&c_data[i + j * ldc], c_float);
+                }
+            } else if (alpha == -1.0f) {
+                if (beta == 0.0f) {
+                    c_data[i + j * ldc] = -ctemp;
+                } else {
+                    double c_float = (double) beta
+                        * (double) c_data[i + j * ldc];
+                    c_float -= (double) ctemp;
+                    round_to_nearest(&c_data[i + j * ldc], c_float);
+                }
+            } else {
+                if (beta == 0.0f) {
+                    double c_float = alpha * (double) ctemp;
+                    round_to_nearest(&c_data[i + j * ldc], c_float);
+                } else {
+                    double c_float = alpha * (double) ctemp +
+                        beta * (double) c_data[i + j * ldc];
+                    round_to_nearest(&c_data[i + j * ldc], c_float);
+                }
+            }
+
+            if (offsetc == FIX_OFFSET) {
+                c_data[i + j * ldc] += co[0];
+            } else if (offsetc == ROW_OFFSET) {
+                c_data[i + j * ldc] += co[j];
+            } else if (offsetc == COL_OFFSET) {
+                c_data[i + j * ldc] += co[i];
+            }
+        }
+    }
+}
+
+// TODO Find a better place for those functions.
+static inline dim_t ld_padd(const dim_t x)
+{
+    return ((x + ((2048 / sizeof(int32_t)) - 1)) / (2048 / sizeof(int32_t)))
+        * (2048 / sizeof(int32_t)) +  (64 / sizeof(int32_t));
+}
+
+void igemm_inner_kernel(const dim_t m, const dim_t n, const dim_t k,
+        const int8_t *a, const uint8_t *b, float beta, int32_t *c,
+        const dim_t ldc, const int32_t *a_row_sum, const int32_t *b_col_sum,
+        const int32_t *co, const int offsetc, const blas_t *arg)
+{
+    int8_t ao = arg->ao;
+    int8_t bo = arg->bo;
+    int32_t co_0 = (offsetc == NO_OFFSET)? 0 : co[0];
+
+    // Since m and n are limited by blocking, stack overflow may not happen;
+    // it's up to 32kB
+#if !defined(_MSC_VER)
+    int32_t col_offset[m];
+    int32_t row_offset[n];
+#else
+    int32_t *col_offset = (int32_t *) _alloca(sizeof(*col_offset) * m);
+    int32_t *row_offset = (int32_t *) _alloca(sizeof(*row_offset) * n);
+#endif
+
+    int col_req = 0;
+    int row_req = 0;
+
+    if ((bo != 0) || (offsetc == COL_OFFSET))
+        col_req = 1;
+    if ((ao != 0) || (offsetc == ROW_OFFSET))
+        row_req = 1;
+
+    // It needs one of colum or row offsets, but it doesn't need both
+    if (((ao != 0) && (bo != 0)) || ((offsetc == FIX_OFFSET) && (co_0 != 0))) {
+        if ((col_req == 0) && (row_req == 0)) {
+            if (m <= n) {
+                col_req = 1;
+            } else {
+                row_req = 1;
+            }
+        }
+    }
+
+    if (col_req) {
+        for (dim_t i = 0; i < m; i++)
+            col_offset[i] = 0;
+
+        if (offsetc == COL_OFFSET) {
+            for (dim_t i = 0; i < m; i++)
+                col_offset[i] += co[i];
+        }
+
+        if (bo != 0) {
+            for (dim_t i = 0; i < m; i++)
+                col_offset[i] += bo * a_row_sum[i];
+        }
+    }
+
+    if (row_req) {
+        for (dim_t i = 0; i < n; i++)
+            row_offset[i] = 0;
+
+        if (offsetc == ROW_OFFSET) {
+            for (dim_t i = 0; i < n; i++)
+                row_offset[i] += co[i];
+        }
+
+        if (ao != 0) {
+            for (dim_t i = 0; i < n; i++)
+                row_offset[i] += ao * b_col_sum[i];
+        }
+    }
+
+    if ((offsetc == FIX_OFFSET) && (co_0 != 0)) {
+        if (col_req) {
+            for (dim_t i = 0; i < m; i++)
+                col_offset[i] += co_0;
+        } else {
+            for (dim_t i = 0; i < n; i++)
+                row_offset[i] += co_0;
+        }
+    }
+
+    if ((ao != 0) && (bo != 0)) {
+        if (col_req) {
+            for (dim_t i = 0; i < m; i++)
+                col_offset[i] += (int32_t) k * ao * bo;
+        } else {
+            for (dim_t i = 0; i < n; i++)
+                row_offset[i] += (int32_t) k * ao * bo;
+        }
+    }
+
+    if (col_req == 0) {
+        if (row_req == 0) {
+            if (beta == 0.0) {
+                arg->kernel_b0(&m, &n, &k, NULL, a, b, c, ldc, col_offset,
+                        row_offset);
+            } else {
+                arg->kernel(&m, &n, &k, NULL, a, b, c, ldc, col_offset,
+                        row_offset);
+            }
+        } else {
+            if (beta == 0.0) {
+                arg->kernel_b0_r(&m, &n, &k, NULL, a, b, c, ldc, col_offset,
+                        row_offset);
+            } else {
+                arg->kernel_r(&m, &n, &k, NULL, a, b, c, ldc, col_offset,
+                        row_offset);
+            }
+        }
+    } else {
+        if (row_req == 0) {
+            if (beta == 0.0) {
+                arg->kernel_b0_c(&m, &n, &k, NULL, a, b, c, ldc, col_offset,
+                        row_offset);
+            } else {
+                arg->kernel_c(&m, &n, &k, NULL, a, b, c, ldc, col_offset,
+                        row_offset);
+            }
+        } else {
+            if (beta == 0.0) {
+                arg->kernel_b0_b(&m, &n, &k, NULL, a, b, c, ldc, col_offset,
+                        row_offset);
+            } else {
+                arg->kernel_b(&m, &n, &k, NULL, a, b, c, ldc, col_offset,
+                        row_offset);
+            }
+        }
+    }
+}
+
+static inline void *align(void *ptr, size_t alignment)
+{
+    return (void *) utils::rnd_up((uintptr_t) ptr, alignment);
+}
+
+static int gemm_kernel_driver(const dim_t m, const dim_t n, const dim_t k,
+        const int8_t *a, const uint8_t *b, int32_t *c, const int32_t *co,
+        const blas_t *arg)
+{
+    dim_t   lda   = arg->lda;
+    dim_t   ldb   = arg->ldb;
+    dim_t   ldc   = arg->ldc;
+    int8_t  ao    = arg->ao;
+    int8_t  bo    = arg->bo;
+    float   alpha = *arg->alpha;
+    float   beta  = *arg->beta;
+
+    if (m <= 0 || n <= 0) {
+        return 0;
+    }
+
+    // Padding along K dimension.
+    dim_t k_padd = 0;
+    if (k <= arg->bk_traditional) {
+        k_padd = utils::rnd_up(k, arg->uk);
+        k_padd = nstl::max(128LL, k_padd);
+    } else if (k < 2 * arg->bk) {
+        k_padd = utils::rnd_up(k / 2, arg->uk);
+    } else {
+        k_padd = arg->bk;
+    }
+
+    // Padding along M dimension.
+    dim_t m_padd = utils::rnd_up(nstl::min(nstl::max(m, arg->um), arg->bm),
+            arg->um);
+
+    // Padding along N dimension.
+    dim_t n_padd = 0;
+    if (k < arg->blocking_small_k) {
+        n_padd = utils::rnd_up(nstl::min(nstl::max(n, arg->un),
+                    arg->bn_small_k), arg->un);
+    } else {
+        n_padd = utils::rnd_up(nstl::min(nstl::max(n, arg->un), arg->bn),
+                arg->un);
+    }
+
+    // Padding for temporary buffer for C
+    dim_t ldc_buf = ld_padd(m_padd);
+
+    dim_t strideAm = (arg->transa == 0)? 1 : lda;
+    dim_t strideAn = (arg->transa != 0)? 1 : lda;
+    dim_t strideBm = (arg->transb == 0)? 1 : ldb;
+    dim_t strideBn = (arg->transb != 0)? 1 : ldb;
+
+    size_t a_buf_nelems = m_padd * k_padd;
+    size_t b_buf_nelems = k_padd * n_padd;
+    size_t a_row_sum_nelems = m_padd;
+    size_t b_col_sum_nelems = n_padd;
+
+    size_t mem_size = a_buf_nelems * sizeof(*a) + PAGE_4K
+        + b_buf_nelems * sizeof(*b) + PAGE_4K
+        + a_row_sum_nelems * sizeof(*c) + PAGE_4K
+        + b_col_sum_nelems * sizeof(*c) + PAGE_4K;
+
+    bool need_c_buffer = alpha != 1.0f || (beta != 1 && beta != 0);
+    if (need_c_buffer) {
+        size_t c_buf_nelems = ldc_buf * n_padd;
+        mem_size += c_buf_nelems * sizeof(*c) + PAGE_4K;
+    }
+
+    char *mem = (char *) malloc(mem_size, 128);
+
+    if (!mem) {
+        return -1;
+    }
+
+    int8_t *bufferA = (int8_t *) align(mem, PAGE_4K);
+    uint8_t *bufferB = (uint8_t *) align(bufferA + a_buf_nelems, PAGE_4K);
+    int32_t *a_row_sum = (int32_t *) align(bufferB + b_buf_nelems, PAGE_4K);
+    int32_t *b_col_sum = (int32_t *) align(a_row_sum + a_row_sum_nelems,
+            PAGE_4K);
+
+    int32_t *bufferC = NULL;
+    if (need_c_buffer) {
+        bufferC = (int32_t *) align(b_col_sum + b_col_sum_nelems, PAGE_4K);
+    }
+
+    float beta_saved = beta;
+
+    int a_block_copied = 0;
+    dim_t sizeM = 0;
+    for (dim_t Bm = 0; Bm < m; Bm += sizeM) {
+        sizeM = m - Bm;
+        if (sizeM > m_padd)
+            sizeM = m_padd;
+
+        dim_t sizeK = 0;
+        for (dim_t Bk = 0; Bk < k; Bk += sizeK) {
+            sizeK = k - Bk;
+            if (sizeK > k_padd)
+                sizeK = k_padd;
+
+            // Scale C blocks by beta only for the first time
+            if (Bk == 0)
+                beta = beta_saved;
+            else
+                beta = 1.0f;
+
+            // Apply C offset when to the last k-block of the partial sum.
+            int offsetc = NO_OFFSET;
+            if (Bk + sizeK == k)
+                offsetc = arg->offsetc;
+
+            dim_t sizeN = 0;
+            for (dim_t Bn = 0; Bn < n; Bn += sizeN) {
+                sizeN = n - Bn;
+                if (sizeN > n_padd)
+                    sizeN = n_padd;
+
+                const uint8_t *b_block = b + Bk * strideBm + Bn * strideBn;
+                arg->copyB(&sizeK, &sizeN, b_block, &ldb, NULL, bufferB, NULL,
+                        NULL, b_col_sum);
+
+                dim_t sizeUM = 0;
+                for (dim_t Um = 0; Um < sizeM; Um += sizeUM) {
+                    sizeUM = sizeM - Um;
+                    if (sizeUM > arg->um)
+                        sizeUM = arg->um;
+
+                    /*
+                     * Use the whole A buffer only if we have multiple B blocks
+                     * for k-dimension, otherwise we are wasting cache to store
+                     * B and C blocks.
+                     */
+                    dim_t Um_forA = 0;
+                    if (sizeN < n)
+                        Um_forA = Um;
+
+                    const int8_t *a_block = a + (Bm + Um) * strideAm
+                        + Bk * strideAn;
+                    if (!a_block_copied) {
+                        arg->copyA(&sizeK, &sizeUM, a_block, &lda, NULL,
+                                bufferA + Um_forA * sizeK, NULL, NULL,
+                                a_row_sum + Um_forA);
+                    }
+
+                    int32_t *c_block = c + (Bm + Um) + Bn * ldc;
+                    dim_t co_stride = 0;
+                    if (offsetc == FIX_OFFSET) {
+                        co_stride = 0;
+                    } else if (offsetc == ROW_OFFSET) {
+                        co_stride = Bn;
+                    } else if (offsetc == COL_OFFSET) {
+                        co_stride = Bm + Um;
+                    }
+                    if (need_c_buffer) {
+                        igemm_inner_kernel(sizeUM, sizeN, sizeK,
+                                bufferA + Um_forA * sizeK, bufferB, 0.0f,
+                                bufferC + Um, ldc_buf, a_row_sum + Um_forA,
+                                b_col_sum, NULL, NO_OFFSET, arg);
+
+                        // Finish the block adding the necessary alpha, beta
+                        // and offsets.
+                        add_results(sizeUM, sizeN, sizeK, alpha, beta,
+                                bufferC + Um, ldc_buf, c_block, ldc,
+                                a_row_sum + Um_forA, b_col_sum, ao, bo,
+                                co + co_stride, offsetc);
+                    } else {
+                        igemm_inner_kernel(sizeUM, sizeN, sizeK,
+                                bufferA + Um_forA * sizeK, bufferB, beta,
+                                c_block, ldc, a_row_sum + Um_forA, b_col_sum,
+                                co + co_stride, offsetc, arg);
+                    }
+                }
+                a_block_copied = 1;
+            }
+            a_block_copied = 0;
+        }
+    }
+
+    free(mem);
+
+    return 0;
+}
+
+static int kernel_driver_parallel_acopiedbcopy(const dim_t m, const dim_t n,
+        const dim_t k, const int8_t *bufferA, const uint8_t *b,
+        const float beta, int32_t *c, const int offsetc, const int32_t *co,
+        const int32_t *a_row_sum, const blas_t *arg)
+{
+    dim_t   ldb   = arg->ldb;
+    dim_t   ldc   = arg->ldc;
+    int8_t  ao    = arg->ao;
+    int8_t  bo    = arg->bo;
+    float   alpha = *arg->alpha;
+
+    if (m <= 0 || n <= 0) {
+        return 0;
+    }
+
+    // Padding along N dimension.
+    dim_t n_padd = 0;
+    if (k < arg->blocking_small_k) {
+        n_padd = utils::rnd_up(nstl::min(nstl::max(n, arg->un),
+                    arg->bn_small_k), arg->un);
+    } else {
+        n_padd = utils::rnd_up(nstl::min(nstl::max(n, arg->un), arg->bn),
+                arg->un);
+    }
+
+    // Padding for temporary buffer for C
+    dim_t ldc_buf = ld_padd(m);
+
+    dim_t strideBn = (arg->transb != 0)? 1 : ldb;
+
+    size_t b_buf_nelems = k * n_padd;
+    size_t b_col_sum_nelems = n_padd;
+
+    size_t mem_size = b_buf_nelems * sizeof(*b) + PAGE_4K
+        + b_col_sum_nelems * sizeof(*c) + PAGE_4K;
+
+    bool need_c_buffer = alpha != 1.0f || (beta != 1 && beta != 0);
+    if (need_c_buffer) {
+        size_t c_buf_nelems = ldc_buf * n_padd;
+        mem_size += c_buf_nelems * sizeof(*c) + PAGE_4K;
+    }
+
+    char *mem = (char *) malloc(mem_size, 128);
+
+    if (!mem) {
+        return -1;
+    }
+
+    uint8_t *bufferB = (uint8_t *) align(mem, PAGE_4K);
+    int32_t *b_col_sum = (int32_t *) align(bufferB + b_buf_nelems, PAGE_4K);
+
+    int32_t *bufferC = NULL;
+    if (need_c_buffer) {
+        bufferC = (int32_t *) align(b_col_sum + b_col_sum_nelems, PAGE_4K);
+    }
+
+    dim_t sizeN = 0;
+    for (dim_t Bn = 0; Bn < n; Bn += sizeN) {
+        sizeN = n - Bn;
+        if (sizeN > n_padd)
+            sizeN = n_padd;
+
+        // Implement the kernel here.
+        const uint8_t *b_block = b + Bn * strideBn;
+        arg->copyB(&k, &sizeN, b_block, &ldb, NULL, bufferB, NULL, NULL,
+                b_col_sum);
+
+            dim_t co_stride = 0;
+            if (offsetc == FIX_OFFSET) {
+                co_stride = 0;
+            } else if (offsetc == ROW_OFFSET) {
+                co_stride = Bn;
+            } else if (offsetc == COL_OFFSET) {
+                co_stride = 0;
+            }
+        int32_t *c_block = c + Bn * ldc;
+        if (need_c_buffer) {
+            igemm_inner_kernel(m, sizeN, k, bufferA, bufferB, 0.0f, bufferC,
+                    ldc_buf, a_row_sum, b_col_sum, NULL, NO_OFFSET, arg);
+
+            // Finish the block adding the necessary alpha, beta and offsets.
+            add_results(m, sizeN, k, alpha, beta, bufferC, ldc_buf, c_block,
+                    ldc, a_row_sum, b_col_sum, ao, bo, co + co_stride,
+                    offsetc);
+        } else {
+            igemm_inner_kernel(m, sizeN, k, bufferA, bufferB, beta, c_block,
+                    ldc, a_row_sum, b_col_sum, co + co_stride, offsetc, arg);
+        }
+    }
+
+    free(mem);
+
+    return 0;
+
+}
+
+#define N2D_MAX_AVX512 384
+#define M2D_MIN_AVX512 384
+#define VECLEN         16
+#define NCONS          1
+static inline void set_thread_opts_avx512(int *p_nthrs,
+        blas_thread_t *thread_info, const blas_t *arg)
+{
+    int nthrs = *p_nthrs;
+    dim_t m = arg->m;
+    dim_t n = arg->n;
+
+    thread_info->nthrs_m = 0;
+    thread_info->nthrs_n = 0;
+    thread_info->copy_type = COPY_NONE; // By default don't do parallel copy.
+
+    int condition_2D_bsrc = -1;
+    if ((256 * m > nthrs * n) && (nthrs * m < 256 * n)) {
+        condition_2D_bsrc = 1;
+    } else {
+        condition_2D_bsrc = 0;
+    }
+
+    int condition_1D_copya = 0;
+    if ((m >= 1000) && (n >= nthrs * N2D_MAX_AVX512 / 4)) {
+        condition_2D_bsrc  = 0;
+        condition_1D_copya = 1;
+    }
+
+    // If offset is non-zero, we need to keep 1D_copya to reduce update overhead
+    if (arg->ao != 0 || arg->bo != 0 || arg->co[0] != 0
+            || arg->offsetc != FIX_OFFSET) {
+        condition_2D_bsrc  = 0;
+        condition_1D_copya = 1;
+    }
+
+    if (condition_2D_bsrc == 1) {
+        int nthrs_m = 1;
+        int nthrs_n = nthrs;
+
+        while ((nthrs_n % 2 == 0) &&
+                (n / nthrs > N2D_MAX_AVX512 ||
+                 n / nthrs_n <= N2D_MAX_AVX512 / 2) &&
+                (m / nthrs_m >= 2 * M2D_MIN_AVX512) &&
+                (nthrs_m < 4)) {
+            nthrs_m *= 2;
+            nthrs_n /= 2;
+        }
+
+        thread_info->nthrs_m = nthrs_m;
+        thread_info->nthrs_n = nthrs_n;
+        thread_info->partition = PARTITION_2D;
+
+        // Reset the total number of threads that will be used.
+        *p_nthrs = nthrs_m * nthrs_n;
+
+    } else if (condition_1D_copya && mkldnn_thr_syncable()) {
+        // Use parallel copy A algorithm
+        thread_info->copy_type = COPY_A;
+        thread_info->partition = PARTITION_1D_COL;
+    } else {
+        if ((m > n) && (m / nthrs >= VECLEN || n < NCONS * nthrs)) {
+            thread_info->partition = PARTITION_1D_ROW;
+        } else {
+            thread_info->partition = PARTITION_1D_COL;
+        }
+    }
+}
+#undef N2D_MAX_AVX512
+#undef M2D_MIN_AVX512
+#undef VECLEN
+#undef NCONS
+
+static inline void partition_1d(const int ithr, const int nthrs, const dim_t n,
+        dim_t *t_offset, dim_t *t_block)
+{
+    dim_t band = n / nthrs;
+
+    dim_t tail = n - (nthrs - 1) * band;
+    if (tail > (band + 1))
+        band++;
+    tail = n - (nthrs - 1) * band;
+
+    if (ithr < (nthrs - 1))
+        *t_block = band;
+    else
+        *t_block = tail;
+
+    *t_offset = ithr * band;
+
+    if (*t_offset >= n) {
+        *t_block = 0;
+        *t_offset = 0;
+    } else if ((*t_offset + *t_block) > n) {
+        *t_block = n - *t_offset;
+    }
+}
+
+static inline void partition_2d(const int ithr, int *nthrs, const int ithr_i,
+        const int ithr_j, const int nthrs_m, const int nthrs_n, const dim_t m,
+        const dim_t n, dim_t *p_m_disp, dim_t *p_m_band, dim_t *p_n_disp,
+        dim_t *p_n_band)
+{
+    dim_t m_disp = 0, n_disp = 0;
+    dim_t m_band = 0, n_band = 0;
+
+    int mdiv = nthrs_m;
+    int ndiv = nthrs_n;
+
+    dim_t m_bandt = m / mdiv; /* size per thread */
+    dim_t n_bandt = n / ndiv; /* size per thread */
+    int firstmgroup = mdiv - 1;
+    int firstngroup = ndiv - 1;
+    dim_t firstmval = m_bandt;
+    dim_t firstnval = n_bandt;
+
+    int mthr_used = mdiv;
+    if (m - (mdiv - 1) * m_bandt > m_bandt + 1) {
+        if (m - (mdiv - 1) * m_bandt > mdiv)
+            ++m_bandt;
+
+        firstmval = m_bandt + 1;
+        mthr_used = (int) (m / firstmval);
+
+        if (mthr_used * firstmval < m)
+            ++mthr_used;
+
+        firstmgroup = mthr_used - 1;
+    }
+
+    int nthr_used = ndiv;
+    if (n - (ndiv - 1) * n_bandt > n_bandt + 1) {
+        firstnval = n_bandt + 1;
+        nthr_used = (int) (n / firstnval);
+
+        if (nthr_used * firstnval < n)
+            ++nthr_used;
+
+        firstngroup = nthr_used - 1;
+    }
+
+    *nthrs = mthr_used * nthr_used;
+
+    if (ithr < *nthrs) {
+        if (ithr_i < firstmgroup) {
+            m_band = firstmval;
+            m_disp = ithr_i * firstmval;
+        } else if (ithr_i <= mthr_used - 2) {
+            m_band = m_bandt;
+            m_disp = firstmgroup * firstmval + (ithr_i - firstmgroup) * m_bandt;
+        } else {
+            m_disp = firstmgroup * firstmval
+                + (mthr_used - 1 - firstmgroup) * m_bandt;
+            m_band = nstl::max(0LL, m - m_disp);
+        }
+
+        if (ithr_j < firstngroup) {
+            n_band = firstnval;
+            n_disp = ithr_j * firstnval;
+        } else if (ithr_j <= nthr_used - 2) {
+            n_band = n_bandt;
+            n_disp = firstngroup * firstnval + (ithr_j - firstngroup) * n_bandt;
+        } else {
+            n_disp = firstngroup * firstnval
+                + (nthr_used - 1 - firstngroup) * n_bandt;
+            n_band = nstl::max(0LL, n - n_disp);
+        }
+        m_disp = nstl::max(nstl::min(m_disp, m - 1), 0LL);
+        n_disp = nstl::max(nstl::min(n_disp, n - 1), 0LL);
+    }
+
+    if (ithr < *nthrs) {
+        *p_m_disp = m_disp;
+        *p_n_disp = n_disp;
+        *p_m_band = m_band;
+        *p_n_band = n_band;
+    } else {
+        *p_m_disp = 0;
+        *p_n_disp = 0;
+        *p_m_band = 0;
+        *p_n_band = 0;
+    }
+
+    return;
+}
+
+static inline void decompose_matrices(const int ithr, int *nthrs, dim_t *m,
+        dim_t *n, dim_t *k, const int8_t **a, const uint8_t **b, int32_t **c,
+        const int32_t **co, const blas_thread_t *thread_info, const blas_t *arg)
+{
+    dim_t strideAm = (arg->transa == 0)? 1 : arg->lda;
+    dim_t strideBn = (arg->transb != 0)? 1 : arg->ldb;
+    int offsetc = arg->offsetc;
+
+    switch (thread_info->partition) {
+    case PARTITION_1D_ROW:
+        {
+            dim_t offset = 0;
+            dim_t block = 0;
+            partition_1d(ithr, *nthrs, arg->m, &offset, &block);
+
+            *m = block;
+            *n = arg->n;
+            *k = arg->k;
+
+            // Set matrix A.
+            *a = arg->a + offset * strideAm;
+
+            // Set matrix B.
+            *b = arg->b;
+
+            // Set matrix C.
+            *c = arg->c + offset;
+
+            // Set offset vector for C matrix
+            dim_t co_stride = 0;
+            if (offsetc == FIX_OFFSET) {
+                co_stride = 0;
+            } else if (offsetc == ROW_OFFSET) {
+                co_stride = 0;
+            } else if (offsetc == COL_OFFSET) {
+                co_stride = offset;
+            }
+            *co = arg->co + co_stride;
+            break;
+        }
+
+    case PARTITION_1D_COL:
+        {
+            dim_t offset = 0;
+            dim_t block = 0;
+            partition_1d(ithr, *nthrs, arg->n, &offset, &block);
+
+            *m = arg->m;
+            *n = block;
+            *k = arg->k;
+
+            // Set matrix A.
+            *a = arg->a;
+
+            // Set matrix B.
+            *b = arg->b + offset * strideBn;
+
+            // Set matrix C.
+            *c = arg->c + offset * arg->ldc;
+
+            // Set offset vector for C matrix
+            dim_t co_stride = 0;
+            if (offsetc == FIX_OFFSET) {
+                co_stride = 0;
+            } else if (offsetc == ROW_OFFSET) {
+                co_stride = offset;
+            } else if (offsetc == COL_OFFSET) {
+                co_stride = 0;
+            }
+            *co = arg->co + co_stride;
+            break;
+        }
+
+    case PARTITION_2D_COL_MAJOR:
+        {
+            int nthrs_m = thread_info->nthrs_m;
+            int nthrs_n = thread_info->nthrs_n;
+            int ithr_i = ithr % nthrs_m;
+            int ithr_j = ithr / nthrs_m;
+
+            dim_t m_disp = 0;
+            dim_t m_band = 0;
+            dim_t n_disp = 0;
+            dim_t n_band = 0;
+
+            partition_2d(ithr, nthrs, ithr_i, ithr_j, nthrs_m, nthrs_n,
+                    arg->m, arg->n, &m_disp, &m_band, &n_disp, &n_band);
+
+            *m = m_band;
+            *n = n_band;
+            *k = arg->k;
+
+            // Set matrix A.
+            *a = arg->a + m_disp * strideAm;
+
+            // Set matrix B.
+            *b = arg->b + n_disp * strideBn;
+
+            // Set matrix C.
+            *c = arg->c + m_disp + n_disp * arg->ldc;
+
+            // Set offset vector for C matrix
+            dim_t co_stride = 0;
+            if (offsetc == FIX_OFFSET) {
+                co_stride = 0;
+            } else if (offsetc == ROW_OFFSET) {
+                co_stride = n_disp;
+            } else if (offsetc == COL_OFFSET) {
+                co_stride = m_disp;
+            }
+            *co = arg->co + co_stride;
+            break;
+        }
+    }
+}
+
+#define MULTIPLIER 10
+static int parallel_a_copy(const int ithr, const int nthrs, const dim_t m,
+        const dim_t n, const dim_t k, const int8_t *a, const uint8_t *b,
+        int32_t *c, const int32_t *co, const blas_t *arg,
+        char **p_shared_mem)
+{
+    const dim_t lda = arg->lda;
+    const dim_t ldb = arg->ldb;
+    const dim_t strideAm = (arg->transa == 0)? 1 : lda;
+    const dim_t strideAn = (arg->transa != 0)? 1 : lda;
+    const dim_t strideBm = (arg->transb == 0)? 1 : ldb;
+
+    // Padding along M dimension.
+    dim_t m_padd = utils::rnd_up(nstl::min(nstl::max(m, arg->um), arg->bm),
+            arg->um);
+
+    // Padding along K dimension.
+    dim_t k_padd = 0;
+    if (k <= arg->bk_traditional) {
+        k_padd = utils::rnd_up(k, arg->uk);
+        k_padd = nstl::max(128LL, k_padd);
+    } else if (k < 2 * arg->bk) {
+        k_padd = utils::rnd_up(k / 2, arg->uk);
+    } else {
+        k_padd = arg->bk;
+    }
+
+    m_padd *= nthrs > MULTIPLIER ? MULTIPLIER : nthrs;
+    if (m_padd > m) {
+        m_padd = utils::rnd_up(m, arg->um);
+    }
+
+    size_t a_buf_nelems = m_padd * k_padd;
+
+    // Allocate shared memory for A and its row sum buffers in master thread.
+    if (ithr == 0) { // If thread master
+        size_t a_row_sum_nelems = m_padd;
+
+        size_t mem_size = (a_buf_nelems * sizeof(*a) + PAGE_4K)
+            + a_row_sum_nelems * sizeof(*c) + PAGE_4K;
+
+        *p_shared_mem = (char *) malloc(mem_size, 128);
+
+    }
+    mkldnn_thr_barrier();
+
+    char *mem = *p_shared_mem;
+    int8_t *bufferA = (int8_t *) align(mem, PAGE_4K);
+    int32_t *a_row_sum = (int32_t *) align(bufferA + a_buf_nelems, PAGE_4K);
+
+    if (!mem) {
+        return -1;
+    }
+
+    int result = 0; // Return status
+
+    dim_t sizeK = 0;
+    for (dim_t Bk = 0; Bk < k; Bk += sizeK) {
+        sizeK = k - Bk;
+        if (sizeK > k_padd)
+            sizeK = k_padd;
+
+        // Scale C blocks by beta only for the first term of partial sum.
+        float beta = 1.0f;
+        if (Bk == 0)
+            beta = *(arg->beta);
+
+        // Apply C offset for the last k-block of the partial sum.
+        int offsetc = NO_OFFSET;
+        if (Bk + sizeK == k)
+            offsetc = arg->offsetc;
+
+        dim_t sizeM = 0;
+        for (dim_t Bm = 0; Bm < m; Bm += sizeM) {
+            sizeM = m - Bm;
+            if (sizeM > m_padd)
+                sizeM = m_padd;
+
+            if (ithr < nthrs) {
+                dim_t band = (sizeM + nthrs - 1) / nthrs;
+                band = utils::rnd_up(band, arg->um);
+
+                dim_t offset = band * ithr;
+
+                // If offset is too large don't use that thread for copying.
+                if (offset >= sizeM) {
+                    offset = 0;
+                    band = 0;
+                }
+
+                // Handle the tail of the copy.
+                if (offset + band > sizeM) {
+                    band = sizeM - offset;
+                }
+
+                if (band > 0) {
+                    const int8_t *a_block = a + (Bm + offset) * strideAm
+                        + Bk * strideAn;
+                    arg->copyA(&sizeK, &band, a_block, &lda, NULL,
+                            bufferA + offset * sizeK, NULL, NULL,
+                            a_row_sum + offset);
+                }
+            }
+            mkldnn_thr_barrier(); // Wait for finishing parallel copy.
+
+            const uint8_t *b_block = b + Bk * strideBm;
+            int32_t *c_block = c + Bm;
+            dim_t co_stride = 0;
+            if (offsetc == FIX_OFFSET) {
+                co_stride = 0;
+            } else if (offsetc == ROW_OFFSET) {
+                co_stride = 0;
+            } else if (offsetc == COL_OFFSET) {
+                co_stride = Bm;
+            }
+
+            result = kernel_driver_parallel_acopiedbcopy(sizeM, n, sizeK,
+                    bufferA, b_block, beta, c_block, offsetc, co + co_stride,
+                    a_row_sum, arg);
+
+            mkldnn_thr_barrier(); // Wait for kernel computations to finish.
+        }
+    }
+
+    // Free memory allocated in master thread
+    if (ithr == 0) {
+        free(mem);
+    }
+
+    return result;
+}
+#undef MULTIPLIER
+
+static inline void get_omp_thread_count(dim_t m, dim_t n, dim_t k,
+        double fp_per_cycle, int *nthrs)
+{
+    double omp_overhead_small_core = 3.0e+3;
+    double omp_intercept_big_core = 4.0e+3;
+    double omp_slope_big_core = 5.0e+2;
+
+    double gemm_cycles = 8.0 * m * n * k / fp_per_cycle;
+
+    int i = *nthrs;
+
+    // Use a different model for omp overheads if nthrs is <= 4
+    if (*nthrs <= 4 && omp_overhead_small_core > 0) {
+        double omp_cycles = omp_overhead_small_core;
+        if (gemm_cycles < omp_cycles) {
+            *nthrs = 1;
+            return;
+        } else {
+            while (i > 1) {
+                if (omp_cycles * i < gemm_cycles * (i - 1)) break;
+                --i;
+            }
+        }
+    } else {
+        if (gemm_cycles < (omp_intercept_big_core + 2 * omp_slope_big_core)) {
+            *nthrs = 1;
+            return;
+        }
+
+        // adaptive decrement to march faster·
+        while (i > 1) {
+            double omp_cycles = omp_intercept_big_core + i * omp_slope_big_core;
+            if (omp_cycles * i < gemm_cycles * (i - 1))
+                break;
+
+            if (i < 10)
+                i -= 2;
+            else if (i < 30)
+                i -= 4;
+            else
+                i -= 8;
+        }
+    }
+
+    if (i < 1)
+        i = 1;
+
+    *nthrs = i;
+}
+
+#define CACHE_LINE_SIZE 64
+static int gemm_threading_driver(blas_t *arg)
+{
+    if ((arg->m <= 0) || (arg->n <= 0))
+        return mkldnn_success;
+
+    if (gemm_s8u8s32_jump_to_gemv_s8u8s32(arg)) {
+        return mkldnn_success;
+    }
+
+    int nthr = (mkldnn_in_parallel()) ? 1 : mkldnn_get_max_threads();
+    get_omp_thread_count(arg->m, arg->n, arg->k, 64.0, &nthr);
+
+    if (nthr == 1) {
+        return gemm_kernel_driver(arg->m, arg->n, arg->k, arg->a, arg->b,
+                arg->c, arg->co, arg);
+    }
+
+    int *results = (int *) malloc(sizeof(*results) * nthr * CACHE_LINE_SIZE,
+            PAGE_4K);
+
+    if (!results) {
+        return -1;
+    }
+
+    for (int i = 0; i < nthr; i++) {
+        results[i * CACHE_LINE_SIZE] = 0; // Initialize to success
+    }
+
+    char *shared_mem = NULL;
+
+    parallel(nthr, [&](const int ithr, const int nthr) {
+        int nthrs = nthr;
+        if (nthrs == 1) {
+            results[0] = gemm_kernel_driver(arg->m, arg->n, arg->k, arg->a,
+                arg->b, arg->c, arg->co, arg);
+        } else {
+            blas_thread_t thread_info;
+            set_thread_opts_avx512(&nthrs, &thread_info, arg);
+
+            const int8_t *a = NULL;
+            const uint8_t *b = NULL;
+            int32_t *c = NULL;
+            const int32_t *co = NULL;
+            dim_t m = -1;
+            dim_t n = -1;
+            dim_t k = -1;
+            decompose_matrices(ithr, &nthrs, &m, &n, &k, &a, &b, &c, &co,
+                &thread_info, arg);
+
+            if (ithr < nthrs) {
+                switch (thread_info.copy_type) {
+                case COPY_A:
+                    results[ithr * CACHE_LINE_SIZE] =
+                        parallel_a_copy(ithr, nthrs, m, n, k, a, b, c, co, arg,
+                                &shared_mem);
+                    break;
+
+                default:
+                case COPY_NONE:
+                    results[ithr * CACHE_LINE_SIZE] =
+                        gemm_kernel_driver(m, n, k, a, b, c, co, arg);
+                    break;
+                }
+            }
+        }
+    });
+
+    int result = 0;  // Initialize to success
+    for (int i = 0; i < nthr; i++) {
+        if (results[i] != 0) {
+            result = results[i * CACHE_LINE_SIZE];
+            break;
+        }
+    }
+
+    free(results);
+
+    return result;
+}
+#undef CACHE_LINE_SIZE
+
+static jit_avx512_core_u8_copy_an_kern *copy_an;
+static jit_avx512_core_u8_copy_at_kern *copy_at;
+static jit_avx512_core_u8_copy_bn_kern *copy_bn;
+static jit_avx512_core_u8_copy_bt_kern *copy_bt;
+static jit_avx512_core_u8_copy_sum_an_kern *copy_sum_an;
+static jit_avx512_core_u8_copy_sum_at_kern *copy_sum_at;
+static jit_avx512_core_u8_copy_sum_bn_kern *copy_sum_bn;
+static jit_avx512_core_u8_copy_sum_bt_kern *copy_sum_bt;
+static jit_avx512_core_gemm_s8u8s32_kern *kernel;
+static jit_avx512_core_gemm_s8u8s32_kern *kernel_b;
+static jit_avx512_core_gemm_s8u8s32_kern *kernel_r;
+static jit_avx512_core_gemm_s8u8s32_kern *kernel_c;
+static jit_avx512_core_gemm_s8u8s32_kern *kernel_b0;
+static jit_avx512_core_gemm_s8u8s32_kern *kernel_b0_b;
+static jit_avx512_core_gemm_s8u8s32_kern *kernel_b0_r;
+static jit_avx512_core_gemm_s8u8s32_kern *kernel_b0_c;
+static jit_avx512_core_gemv_s8u8s32_kern *gemv_s8u8s32_kernel;
+static jit_avx512_core_gemv_s8u8s32_kern *gemv_u8s8s32_kernel;
+
+static void jit_init(blas_t *arg)
+{
+    static int (*copyAn)(const dim_t *m, const dim_t *n, const int8_t *a,
+            const dim_t *lda, const int8_t *alpha, int8_t *b,
+            const dim_t *dummy1, const dim_t *dummy2, int32_t *row_col_sum);
+
+    static int (*copyAt)(const dim_t *m, const dim_t *n, const int8_t  *a,
+            const dim_t *lda, const int8_t  *alpha, int8_t  *b,
+            const dim_t *dummy1, const dim_t *dummy2, int32_t *row_col_sum);
+
+    static int (*copyBn)(const dim_t *m, const dim_t *n, const uint8_t *a,
+            const dim_t *lda, const uint8_t *alpha, uint8_t *b,
+            const dim_t *dummy1, const dim_t *dummy2, int32_t *row_col_sum);
+
+    static int (*copyBt)(const dim_t *m, const dim_t *n, const uint8_t *a,
+            const dim_t *lda, const uint8_t *alpha, uint8_t *b,
+            const dim_t *dummy1, const dim_t *dummy2, int32_t *row_col_sum);
+
+    static int (*copySumAn)(const dim_t *m, const dim_t *n, const int8_t  *a,
+            const dim_t *lda, const int8_t  *alpha, int8_t  *b,
+            const dim_t *dummy1, const dim_t *dummy2, int32_t *row_col_sum);
+
+    static int (*copySumAt)(const dim_t *m, const dim_t *n, const int8_t  *a,
+            const dim_t *lda, const int8_t  *alpha, int8_t  *b,
+            const dim_t *dummy1, const dim_t *dummy2, int32_t *row_col_sum);
+
+    static int (*copySumBn)(const dim_t *m, const dim_t *n, const uint8_t *a,
+            const dim_t *lda, const uint8_t *alpha, uint8_t *b,
+            const dim_t *dummy1, const dim_t *dummy2, int32_t *row_col_sum);
+
+    static int (*copySumBt)(const dim_t *m, const dim_t *n, const uint8_t *a,
+            const dim_t *lda, const uint8_t *alpha, uint8_t *b,
+            const dim_t *dummy1, const dim_t *dummy2, int32_t *row_col_sum);
+
+    static int (*kern)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    static int (*kern_b)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    static int (*kern_r)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    static int (*kern_c)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    static int (*kern_b0)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    static int (*kern_b0_b)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    static int (*kern_b0_r)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    static int (*kern_b0_c)(const dim_t *m, const dim_t *n, const dim_t *k,
+            const float *alpha, const int8_t *a, const uint8_t *b, int32_t *c,
+            const dim_t ldc, const int32_t *col_offset,
+            const int32_t *row_offset);
+
+    static void (*gemv_s8u8s32_kern)(const dim_t, const dim_t, const float,
+                                     const int8_t*, const dim_t, const uint8_t*,
+                                     const float, int32_t*);
+
+    static void (*gemv_u8s8s32_kern)(const dim_t, const dim_t, const float,
+                                     const uint8_t*, const dim_t, const int8_t*,
+                                     const float, int32_t*);
+
+    if (mayiuse(avx512_core_vnni)) {
+            arg->um = AVX512_UNROLL_M;
+            arg->un = AVX512_UNROLL_N;
+            arg->uk = AVX512_UNROLL_K;
+            arg->bm = AVX512_BM;
+            arg->bn = AVX512_BN;
+            arg->bk = AVX512_BK_VNNI;
+
+            arg->bk_traditional   = AVX512_BK_TRADITIONAL;
+            arg->bn_small_k       = AVX512_BN_SMALL_K;
+            arg->blocking_small_k = AVX512_BLOCKING_SMALL_K;
+    } else {
+            arg->um = AVX512_UNROLL_M;
+            arg->un = AVX512_UNROLL_N;
+            arg->uk = AVX512_UNROLL_K;
+            arg->bm = AVX512_BM;
+            arg->bn = AVX512_BN;
+            arg->bk = AVX512_BK;
+
+            arg->bk_traditional   = AVX512_BK_TRADITIONAL;
+            arg->bn_small_k       = AVX512_BN_SMALL_K;
+            arg->blocking_small_k = AVX512_BLOCKING_SMALL_K;
+    }
+
+    static std::once_flag initialized;
+    std::call_once(initialized, []{
+
+        copy_an = new jit_avx512_core_u8_copy_an_kern();
+        copy_at = new jit_avx512_core_u8_copy_at_kern();
+        copy_bn = new jit_avx512_core_u8_copy_bn_kern();
+        copy_bt = new jit_avx512_core_u8_copy_bt_kern();
+
+        copy_sum_an = new jit_avx512_core_u8_copy_sum_an_kern();
+        copy_sum_at = new jit_avx512_core_u8_copy_sum_at_kern();
+        copy_sum_bn = new jit_avx512_core_u8_copy_sum_bn_kern();
+        copy_sum_bt = new jit_avx512_core_u8_copy_sum_bt_kern();
+
+        kernel      = new jit_avx512_core_gemm_s8u8s32_kern(false, false, false);
+        kernel_b    = new jit_avx512_core_gemm_s8u8s32_kern(false, true,  true);
+        kernel_r    = new jit_avx512_core_gemm_s8u8s32_kern(false, false, true);
+        kernel_c    = new jit_avx512_core_gemm_s8u8s32_kern(false, true,  false);
+        kernel_b0   = new jit_avx512_core_gemm_s8u8s32_kern(true,  false, false);
+        kernel_b0_b = new jit_avx512_core_gemm_s8u8s32_kern(true,  true,  true);
+        kernel_b0_r = new jit_avx512_core_gemm_s8u8s32_kern(true,  false, true);
+        kernel_b0_c = new jit_avx512_core_gemm_s8u8s32_kern(true,  true,  false);
+
+        gemv_s8u8s32_kernel = new jit_avx512_core_gemv_s8u8s32_kern();
+        gemv_u8s8s32_kernel = new jit_avx512_core_gemv_s8u8s32_kern();
+
+
+        copyAn = copy_an->getCode<int (*)(const dim_t *, const dim_t *,
+                const int8_t *, const dim_t *, const int8_t *, int8_t *,
+                const dim_t *, const dim_t *, int32_t *)>();
+
+        copyAt = copy_at->getCode<int (*)(const dim_t *, const dim_t *,
+                const int8_t *, const dim_t *, const int8_t *, int8_t *,
+                const dim_t *, const dim_t *, int32_t *)>();
+
+        copyBn = copy_bn->getCode<int (*)(const dim_t *, const dim_t *,
+                const uint8_t *, const dim_t *, const uint8_t *, uint8_t *,
+                const dim_t *, const dim_t *, int32_t *)>();
+
+        copyBt = copy_bt->getCode<int (*)(const dim_t *, const dim_t *,
+                const uint8_t *, const dim_t *, const uint8_t *, uint8_t *,
+                const dim_t *, const dim_t *, int32_t *)>();
+
+        copySumAn = copy_sum_an->getCode<int (*)(const dim_t *, const dim_t *,
+                const int8_t *, const dim_t *, const int8_t *, int8_t *,
+                const dim_t *, const dim_t *, int32_t *)>();
+
+        copySumAt = copy_sum_at->getCode<int (*)(const dim_t *, const dim_t *,
+                const int8_t *, const dim_t *, const int8_t *, int8_t *,
+                const dim_t *, const dim_t *, int32_t *)>();
+
+        copySumBn = copy_sum_bn->getCode<int (*)(const dim_t *, const dim_t *,
+                const uint8_t *, const dim_t *, const uint8_t *, uint8_t *,
+                const dim_t *, const dim_t *, int32_t *)>();
+
+        copySumBt = copy_sum_bt->getCode<int (*)(const dim_t *, const dim_t *,
+                const uint8_t *, const dim_t *, const uint8_t *, uint8_t *,
+                const dim_t *, const dim_t *, int32_t *)>();
+
+        kern = kernel->getCode<int (*)(const dim_t *, const dim_t *,
+                const dim_t *, const float *, const int8_t *, const uint8_t *,
+                int32_t *, const dim_t, const int32_t *, const int32_t *)>();
+
+        kern_b = kernel_b->getCode<int (*)(const dim_t *, const dim_t *,
+                const dim_t *, const float *, const int8_t *, const uint8_t *,
+                int32_t *, const dim_t, const int32_t *, const int32_t *)>();
+
+        kern_r = kernel_r->getCode<int (*)(const dim_t *, const dim_t *,
+                const dim_t *, const float *, const int8_t *, const uint8_t *,
+                int32_t *, const dim_t, const int32_t *, const int32_t *)>();
+
+        kern_c = kernel_c->getCode<int (*)(const dim_t *, const dim_t *,
+                const dim_t *, const float *, const int8_t *, const uint8_t *,
+                int32_t *, const dim_t, const int32_t *, const int32_t *)>();
+
+        kern_b0 = kernel_b0->getCode<int (*)(const dim_t *, const dim_t *,
+                const dim_t *, const float *, const int8_t *, const uint8_t *,
+                int32_t *, const dim_t, const int32_t *, const int32_t *)>();
+
+        kern_b0_b = kernel_b0_b->getCode<int (*)(const dim_t *, const dim_t *,
+                const dim_t *, const float *, const int8_t *, const uint8_t *,
+                int32_t *, const dim_t, const int32_t *, const int32_t *)>();
+
+        kern_b0_r = kernel_b0_r->getCode<int (*)(const dim_t *, const dim_t *,
+                const dim_t *, const float *, const int8_t *, const uint8_t *,
+                int32_t *, const dim_t, const int32_t *, const int32_t *)>();
+
+        kern_b0_c = kernel_b0_c->getCode<int (*)(const dim_t *, const dim_t *,
+                const dim_t *, const float *, const int8_t *, const uint8_t *,
+                int32_t *, const dim_t, const int32_t *, const int32_t *)>();
+
+        gemv_s8u8s32_kern =
+            gemv_s8u8s32_kernel -> generate<jit_avx512_core_gemv_s8u8s32_kern::gemv_s8u8s32_kernel_t>
+            (mayiuse(avx512_core_vnni));
+        gemv_u8s8s32_kern =
+            gemv_u8s8s32_kernel -> generate<jit_avx512_core_gemv_s8u8s32_kern::gemv_u8s8s32_kernel_t>
+            (mayiuse(avx512_core_vnni));
+    });
+
+    if (arg->bo == 0) { // No need to compute A row sum if bo is zero
+        if (arg->transa == 0) {
+            arg->copyA = copyAn;
+        } else {
+            arg->copyA = copyAt;
+        }
+    } else {
+        if (arg->transa == 0) {
+            arg->copyA = copySumAn;
+        } else {
+            arg->copyA = copySumAt;
+        }
+    }
+
+    if (arg->ao == 0) { // No need to compute B column sum if ao is zero
+        if (arg->transb == 0) {
+            arg->copyB = copyBn;
+        } else {
+            arg->copyB = copyBt;
+        }
+    } else {
+        if (arg->transb == 0) {
+            arg->copyB = copySumBn;
+        } else {
+            arg->copyB = copySumBt;
+        }
+    }
+
+    arg->kernel      = kern;
+    arg->kernel_b    = kern_b;
+    arg->kernel_r    = kern_r;
+    arg->kernel_c    = kern_c;
+    arg->kernel_b0   = kern_b0;
+    arg->kernel_b0_b = kern_b0_b;
+    arg->kernel_b0_r = kern_b0_r;
+    arg->kernel_b0_c = kern_b0_c;
+    arg -> gemv_s8u8s32_kernel = gemv_s8u8s32_kern;
+    arg -> gemv_u8s8s32_kernel = gemv_u8s8s32_kern;
+}
+
+mkldnn_status_t jit_avx512_core_gemm_s8u8s32(
+        const char *transA, const char *transB, const char *offsetC,
+        const int *m, const int *n, const int *k,
+        const float *alpha, const int8_t *a, const int *lda, const int8_t *oa,
+        const uint8_t *b, const int *ldb, const int8_t *ob,
+        const float *beta, int32_t *c, const int *ldc, const int32_t *oc)
+{
+    char transa  = *transA;
+    char transb  = *transB;
+    char offsetc = *offsetC;
+
+    blas_t args;
+
+    // Initialize blas structure
+    args.m         = *m;
+    args.n         = *n;
+    args.k         = *k;
+    args.alpha     = alpha;
+    args.a         = a;
+    args.lda       = *lda;
+    args.b         = b;
+    args.ldb       = *ldb;
+    args.beta      = beta;
+    args.c         = c;
+    args.ldc       = *ldc;
+    args.transa    = (transa == 'N' || transa == 'n') ? 0 : 1;
+    args.transb    = (transb == 'N' || transb == 'n') ? 0 : 1;
+    args.um        = 0;
+    args.un        = 0;
+    args.bm        = 0;
+    args.bn        = 0;
+    args.bk        = 0;
+    args.copyA     = NULL;
+    args.copyB     = NULL;
+    args.kernel    = NULL;
+    args.kernel_b0 = NULL;
+    args.ao        = *oa;
+    args.bo        = *ob;
+    args.co        = oc;
+
+    if (offsetc == 'F' || offsetc == 'f') {
+        args.offsetc = FIX_OFFSET;
+    } else if (offsetc == 'R' || offsetc == 'r') {
+        args.offsetc = ROW_OFFSET;
+    } else { // offsetc == 'C' || offsetc == 'c'
+        args.offsetc = COL_OFFSET;
+    }
+
+    jit_init(&args);
+    int result = gemm_threading_driver(&args);
+
+    return (result < 0) ? mkldnn_out_of_memory : mkldnn_success;
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32.hpp
new file mode 100644
index 0000000000..b2e2902a12
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32.hpp
@@ -0,0 +1,38 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_AVX512_CORE_GEMM_S8U8S32_HPP
+#define JIT_AVX512_CORE_GEMM_S8U8S32_HPP
+
+#include <cstdint>
+#include "mkldnn_types.h"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+mkldnn_status_t jit_avx512_core_gemm_s8u8s32(
+        const char *transA, const char *transB, const char *offsetC,
+        const int *m, const int *n, const int *k,
+        const float *alpha, const int8_t *a, const int *lda, const int8_t *oa,
+        const uint8_t *b, const int *ldb, const int8_t *ob,
+        const float *beta, int32_t *c, const int *ldc, const int32_t *oc);
+
+}
+}
+}
+
+#endif // JIT_AVX512_CORE_GEMM_S8U8S32_HPP
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32_kern.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32_kern.cpp
new file mode 100644
index 0000000000..57554a1852
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32_kern.cpp
@@ -0,0 +1,539 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "jit_avx512_core_gemm_s8u8s32_kern.hpp"
+
+
+#ifdef _WIN32
+static const bool is_windows = 1;
+#else
+static const bool is_windows = 0;
+#endif
+
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace Xbyak;
+
+
+
+
+// Convert between vector register lengths.
+static inline Xmm make_xmm(const Xmm &v) { return Xmm(v.getIdx()); }
+static inline Ymm make_ymm(const Xmm &v) { return Ymm(v.getIdx()); }
+
+// Load from or store to C.
+void jit_avx512_core_gemm_s8u8s32_kern::c_load(const Xbyak::Xmm &dst,
+    const Xbyak::Address &src, int nelems)
+{
+    switch (nelems) {
+    default: vmovups(dst, src); break;
+    case 8:  vmovups(make_ymm(dst), src); break;
+    case 4:  vmovups(make_xmm(dst), src); break;
+    case 2:  vmovlps(make_xmm(dst), src); break;
+    case 1:  vmovss(make_xmm(dst), src); break;
+    }
+}
+void jit_avx512_core_gemm_s8u8s32_kern::c_store(const Xbyak::Address &dst,
+    const Xbyak::Xmm &src, int nelems)
+{
+    switch (nelems) {
+    default: vmovups(dst, src); break;
+    case 8:  vmovups(dst, make_ymm(src)); break;
+    case 4:  vmovups(dst, make_xmm(src)); break;
+    case 2:  vmovsd(dst, make_xmm(src)); break;
+    case 1:  vmovss(dst, make_xmm(src)); break;
+    }
+}
+
+// Perform length-4 dot product accumulations of unsigned and signed bytes
+//  in parallel.
+// Use vpdpbusd if VNNI available, otherwise emulate.
+void jit_avx512_core_gemm_s8u8s32_kern::dot_product(const Xmm &dst,
+    const Xmm &src1, const Xmm &src2)
+{
+    if (vnni)
+        vpdpbusd(dst, src1, src2);
+    else {
+        vpmaddubsw(dp_scratch, src1, src2);
+        vpmaddwd(dp_scratch, ones, dp_scratch);
+        vpaddd(dst, dst, dp_scratch);
+    }
+}
+
+// Inner kernel.
+void jit_avx512_core_gemm_s8u8s32_kern::kernel_loop(int unroll_m, int unroll_n,
+        bool cfetch)
+{
+    int um_vecs = (unroll_m + 15) >> 4;
+    Label label_kernel_loop;
+
+    L_aligned(label_kernel_loop); {
+        for (int h = 0; h < 4; h++) {
+            for (int j = 0; j < unroll_n; j++) {
+                const Zmm b = b_regs[j & 1];
+
+                vpbroadcastd(b, ptr[BO + isize *
+                    (2 * j + 2 * h * unroll_n - offset_b)]);
+                dot_product(c_regs[0][j], b, a_regs[0]);
+
+                if (j == 1 && !(h & 1))
+                    prefetch_b(ptr[BO + isize * (prefetch_size_b
+                        + 2 * h * unroll_n - offset_b)]);
+                else if (j % 3 == 0)
+                    prefetch_a(ptr[AO + isize * (prefetch_size_a
+                        + 32 * (j / 3) + 2 * h * unroll_m - offset_a)]);
+
+                for (int i = 1; i < um_vecs; i++)
+                    dot_product(c_regs[i][j], b, a_regs[i]);
+
+                if (cfetch && (j == std::min(1, unroll_n - 1))) {
+                    if (h == 3)
+                        lea(CO2, ptr[CO2 + LDC]);
+                    else if (h < um_vecs)
+                        prefetch_c(ptr[CO2 + (16 * h * size)]);
+                }
+
+                if (h == 3 && j == std::min(3, unroll_n - 1))
+                    lea(AA, ptr[AA + (32 * isize)]);
+            }
+
+            for (int i = 0; i < um_vecs; i++)
+                vmovups(a_regs[i], ptr[AO + isize *
+                (32 * i + 2 * (h + 1) * unroll_m - offset_a)]);
+
+            if (h == 2)
+                prefetch_x(ptr[AA - (offset_a * isize)]);
+        }
+
+        add(AO, 8 * isize * unroll_m);
+        add(BO, 8 * isize * unroll_n);
+        sub(LoopCount, 1);
+        jg(label_kernel_loop, T_NEAR);
+    }
+}
+
+// k remainder loop for kernel.
+void jit_avx512_core_gemm_s8u8s32_kern::remainder_kernel(int unroll_m,
+        int unroll_n, int unroll_k, int bwidth)
+{
+    if ((unroll_m > IGEMM_UNROLL_M) || (unroll_n > IGEMM_UNROLL_N)
+            || (unroll_m < 0)  || (unroll_n < 0))
+        return;
+
+    int um_vecs = (unroll_m + 15) >> 4;
+
+    for (int h = 0; h < unroll_k; h++) {
+        for (int j = 0; j < unroll_n; j++) {
+            Zmm b = b_regs[j & 1];
+            auto b_src = ptr[BO + (-isize * offset_b
+                + bwidth * (j + h * unroll_n))];
+
+            switch (bwidth) {
+            case 4:
+                vpbroadcastd(b, b_src);
+                break;
+            case 2:
+                vpbroadcastw(b, b_src);
+                break;
+            case 1:
+                vpbroadcastb(b, b_src);
+                break;
+            }
+            for (int i = 0; i < um_vecs; i++)
+                dot_product(c_regs[i][j], b, a_regs[i]);
+        }
+
+        if (unroll_k > 1) {
+            for (int i = 0; i < um_vecs; i++)
+                vmovups(a_regs[i], ptr[AO + isize * (32 * i
+                    + (h + 1) * 2 * unroll_m - offset_a)]);
+        }
+    }
+
+    add(AO, unroll_k * unroll_m * bwidth);
+    add(BO, unroll_k * unroll_n * bwidth);
+}
+
+// Inner loop.
+void jit_avx512_core_gemm_s8u8s32_kern::innerloop(int unroll_m, int unroll_n)
+{
+    if ((unroll_m > IGEMM_UNROLL_M) || (unroll_n > IGEMM_UNROLL_N)
+            || (unroll_m < 0)  || (unroll_n < 0))
+        return;
+
+    int um_vecs = (unroll_m + 15) >> 4;
+    int stage1 = unroll_n, stage2 = unroll_n;
+
+    Label label_kernel_loop_1, label_k_main_loop_2, label_kernel_loop_2;
+    Label label_k_main_loop_3, label_kernel_loop_3;
+    Label label_k_remainder_loop_begin, label_k_rem_4, label_k_rem_2;
+    Label label_k_rem_1, label_update_begin;
+
+    mov(AO, A);
+    for (int i = 0; i < um_vecs; i++)
+        vmovups(a_regs[i], ptr[AO + isize * (32 * i - offset_a)]);
+
+    mov(LoopCount, K);
+    sar(LoopCount, 4);
+    jle(label_k_remainder_loop_begin, T_NEAR);
+
+    // Main k loops, broken into three parts to time C prefetching.
+    sub(LoopCount, stage1 + stage2);
+    jle(label_k_main_loop_2, T_NEAR);
+
+    kernel_loop(unroll_m, unroll_n, false);
+
+    L_aligned(label_k_main_loop_2);
+    lea(CO2, ptr[CO1 + size * (std::min(unroll_m, 16) - 1)]);
+    add(LoopCount, stage1);
+    jle(label_k_main_loop_3, T_NEAR);
+
+    kernel_loop(unroll_m, unroll_n, true);
+
+    L_aligned(label_k_main_loop_3);
+    lea(CO2, ptr[CO1 + size * (std::min(unroll_m, 16) - 1)]);
+    add(LoopCount, stage2);
+    jle(label_k_remainder_loop_begin, T_NEAR);
+
+    kernel_loop(unroll_m, unroll_n, true);
+
+    // k remainder handling
+    L_aligned(label_k_remainder_loop_begin);
+    mov(LoopCount, K);
+    test(LoopCount, 8);
+    je(label_k_rem_4, T_NEAR);
+
+    remainder_kernel(unroll_m, unroll_n, 2, 4);
+
+    L_aligned(label_k_rem_4);
+    mov(LoopCount, K);
+    test(LoopCount, 4);
+    je(label_k_rem_2, T_NEAR);
+
+    remainder_kernel(unroll_m, unroll_n, 1, 4);
+
+    L_aligned(label_k_rem_2);
+    mov(LoopCount, K);
+    test(LoopCount, 2);
+    je(label_k_rem_1, T_NEAR);
+
+    Zmm zero = zmm6;
+    Zmm tmp = zmm5;
+
+    vpxorq(zero, zero, zero);
+    for (int i = 0; i < um_vecs; i++) {
+        Zmm a = a_regs[i];
+        vbroadcasti64x4(a, ptr[AO + isize * (16 * i - offset_a)]);
+        vpunpcklwd(tmp, a, zero);
+        vpunpckhwd(a, a, zero);
+        vshufi32x4(a, tmp, a, 0x44);
+        vshufi32x4(a, a, a, 0xD8);
+    }
+
+    remainder_kernel(unroll_m, unroll_n, 1, 2);
+
+    L_aligned(label_k_rem_1);
+    mov(LoopCount, K);
+    test(LoopCount, 1);
+    je(label_update_begin, T_NEAR);
+
+    vpxorq(zero, zero, zero);
+    for (int i = 0; i < um_vecs; i++) {
+        Zmm a = a_regs[i];
+        vbroadcasti32x4(a, ptr[AO + isize * (8 * i - offset_a)]);
+        vpunpcklbw(tmp, a, zero);
+        vpunpckhbw(a, a, zero);
+        vinsertf128(make_ymm(a), make_ymm(tmp), make_xmm(a), 1);
+        vpunpcklwd(tmp, a, zero);
+        vpunpckhwd(a, a, zero);
+        vshufi32x4(a, tmp, a, 0x44);
+        vshufi32x4(a, a, a, 0xD8);
+    }
+
+    remainder_kernel(unroll_m, unroll_n, 1, 1);
+
+    // Add offsets and update C.
+    L_aligned(label_update_begin);
+
+    if (enable_offset_r) {
+        // Add row offsets.
+        mov(rax, coffset_ry);
+        for (int j = 0; j < unroll_n; j++) {
+            Zmm row_offset = zmm0;
+
+            vbroadcastss(row_offset, ptr[rax + size * j]);
+
+            for (int i = 0; i < um_vecs; i++)
+                vpaddd(c_regs[i][j], c_regs[i][j], row_offset);
+        }
+        add(coffset_ry, size * unroll_n);
+    }
+
+    if (enable_offset_c) {
+        // Add column offsets.
+        mov(rax, coffset_cy);
+        for (int i = 0; i < um_vecs; i++) {
+            Zmm col_offset = zmm0;
+
+            c_load(col_offset, ptr[rax + size * 16 * i], unroll_m);
+
+            for (int j = 0; j < unroll_n; j++)
+                vpaddd(c_regs[i][j], c_regs[i][j], col_offset);
+        }
+    }
+
+    Reg64 LDC3 = rax;
+    lea(LDC3, ptr[LDC + LDC * 2]);
+
+    // C updates.
+    int c_off_j = 0;
+    for (int j = 0; j < unroll_n; j++) {
+        if (j > 0 && (j & 3) == 0) {
+            lea(CO1, ptr[CO1 + LDC * 4]);
+            c_off_j += 4;
+        }
+
+        int jj = j - c_off_j;
+
+        for (int i = 0; i < um_vecs; i++) {
+            Zmm c = c_regs[i][j];
+            Zmm c_old = zmm0;
+            decltype(LDC * jj) ldc_mult = (jj == 3) ? LDC3 : LDC * jj;
+
+            auto c_mem = ptr[CO1 + ldc_mult + size * 16 * i];
+
+            if (beta_zero)
+                c_store(c_mem, c, unroll_m);
+            else {
+                c_load(c_old, c_mem, unroll_m);
+                vpaddd(c_old, c, c_old);
+                c_store(c_mem, c_old, unroll_m);
+            }
+
+            vpxorq(c, c, c);
+        }
+    }
+
+    lea(CO1, ptr[CO1 + LDC * (unroll_n - c_off_j)]);
+}
+
+// Outer loop.
+void jit_avx512_core_gemm_s8u8s32_kern::outerloop(int unroll_x, int unroll_y,
+    Label *&cur_outerloop_label)
+{
+    Label label_m_loop, label_n_loop, label_n_remainder_loops[6];
+
+    L(*cur_outerloop_label);
+    cur_outerloop_label++;
+    if (unroll_x >= IGEMM_UNROLL_M) {
+        mov(J, M);
+        cmp(J, unroll_x);
+        jl(*cur_outerloop_label, T_NEAR);    // Jump to next outerloop label.
+    } else {
+        test(J, unroll_x);
+        jle(*cur_outerloop_label, T_NEAR);
+    }
+
+    L_aligned(label_m_loop); {
+        mov(CO1, C);
+        add(C, unroll_x * size);
+
+        mov(BO, B);
+
+        mov(AA, K);
+        imul(AA, AA, unroll_x * isize);
+        lea(AA, ptr[A + AA + isize * prefetch_size_a]);
+
+        if (enable_offset_c) {
+            mov(rax, coffset_cx);
+            mov(coffset_cy, rax);
+            add(rax, unroll_x * size);
+            mov(coffset_cx, rax);
+        }
+
+        if (enable_offset_r) {
+            mov(rax, coffset_rx);
+            mov(coffset_ry, rax);
+        }
+
+        mov(I, N);
+        cmp(I, unroll_y);
+        jl(label_n_remainder_loops[0], T_NEAR);
+
+        L_aligned(label_n_loop); {
+            innerloop(unroll_x, unroll_y);
+            sub(I, unroll_y);
+            cmp(I, unroll_y);
+            jge(label_n_loop, T_NEAR);
+        }
+
+        align(16);
+
+        int label_idx = 0;
+        for (int uy = 16; uy > 0; uy >>= 1) {
+            L(label_n_remainder_loops[label_idx++]);
+            if (unroll_y > uy) {
+                test(I, uy);
+                jle(label_n_remainder_loops[label_idx], T_NEAR);
+
+                innerloop(unroll_x, uy);
+                align(16);
+            }
+        }
+        L(label_n_remainder_loops[label_idx]);
+
+        mov(A, AO);
+        if (unroll_x >= IGEMM_UNROLL_M) {
+            sub(J, unroll_x);
+            cmp(J, unroll_x);
+            jge(label_m_loop);
+        }
+    }
+
+    align(16);
+}
+
+void jit_avx512_core_gemm_s8u8s32_kern::generate()
+{
+    // Prologue
+    preamble();
+    sub(rsp, stack_alloc_size);
+
+    if (is_windows) {
+        mov(A, arg_a);
+        mov(B, arg_b);
+    }
+
+    mov(C, arg_c);
+    mov(LDC, arg_ldc);
+
+    sub(A, -offset_a * isize);
+    sub(B, -offset_b * isize);
+
+    mov(M, qword[M]);
+    mov(N, qword[N]);
+    mov(K, qword[K]);
+
+    lea(LDC, ptr[LDC * size]);
+
+    if (enable_offset_c) {
+        mov(rax, arg_coffset_c);
+        mov(coffset_cx, rax);
+    }
+    if (enable_offset_r) {
+        mov(rax, arg_coffset_r);
+        mov(coffset_rx, rax);
+    }
+
+    for (int i = 0; i < (max_unroll_m >> 4); i++) {
+        for (int j = 0; j < max_unroll_n; j++) {
+            auto &c = c_regs[i][j];
+            vpxorq(c, c, c);
+        }
+    }
+
+    if (!vnni) {
+        mov(rax, 1);
+        movq(make_xmm(ones), rax);
+        vpbroadcastw(ones, make_xmm(ones));
+    }
+
+    Label outerloop_labels[8];
+    Label *cur_outerloop_label = &outerloop_labels[0];
+
+    // Main m loop.
+    outerloop(IGEMM_UNROLL_M, IGEMM_UNROLL_N, cur_outerloop_label);
+
+    // m remainder loops.
+    for (int um = 32; um > 0; um >>= 1)
+        if (IGEMM_UNROLL_M > um)
+            outerloop(um, IGEMM_UNROLL_N, cur_outerloop_label);
+
+    L(*cur_outerloop_label);
+
+    // Epilogue.
+    add(rsp, stack_alloc_size);
+    postamble();
+}
+
+
+jit_avx512_core_gemm_s8u8s32_kern::jit_avx512_core_gemm_s8u8s32_kern(bool
+        beta_zero_, bool enable_offset_c_, bool enable_offset_r_) :
+    jit_generator(nullptr, 100000), arg_a(0), arg_b(0), arg_c(0), arg_ldc(0),
+    arg_coffset_c(0), arg_coffset_r(0), coffset_cx(0), coffset_cy(0),
+    coffset_rx(0), coffset_ry(0)
+{
+    beta_zero = beta_zero_;
+    enable_offset_c = enable_offset_c_;
+    enable_offset_r = enable_offset_r_;
+    vnni = mayiuse(avx512_core_vnni);
+
+    // Assign integer registers
+    M = is_windows ? rcx : rdi;
+    N = is_windows ? rdx : rsi;
+    K = is_windows ? r8 : rdx;
+    A = is_windows ? rsi : r8;
+    B = r9;
+    C = r10;
+    LDC = r11;
+    I = r12;
+    J = r13;
+    LoopCount = rax;
+    AO = r14;
+    BO = r15;
+    CO1 = rbx;
+    CO2 = rbp;
+    AA = is_windows ? rdi : rcx;
+
+    // Assign vector registers
+    dp_scratch = zmm6;
+    ones = zmm7;
+    for (int i = 0; i < (max_unroll_m >> 4); i++)
+        a_regs[i] = Zmm(i);
+    b_regs[0] = zmm4;
+    b_regs[1] = zmm5;
+
+    int rn = 0;
+    for (int i = 0; i < (max_unroll_m >> 4); i++)
+        for (int j = 0; j < max_unroll_n; j++)
+            c_regs[i][j] = Zmm(8 + rn++);
+
+    // Assign stack variables.
+    stack_alloc_size = 32;
+    auto args_offset = stack_alloc_size + get_size_of_abi_save_regs()
+        + 8 + (is_windows ? 48 : 0);
+
+    arg_a         = ptr[rsp + (args_offset - 16)];
+    arg_b         = ptr[rsp + (args_offset - 8)];
+    arg_c         = ptr[rsp + (args_offset + 0)];
+    arg_ldc       = ptr[rsp + (args_offset + 8)];
+    arg_coffset_c = ptr[rsp + (args_offset + 16)];
+    arg_coffset_r = ptr[rsp + (args_offset + 24)];
+
+    coffset_cx = qword[rsp + 0];
+    coffset_cy = qword[rsp + 8];
+    coffset_rx = qword[rsp + 16];
+    coffset_ry = qword[rsp + 24];
+
+    generate();
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32_kern.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32_kern.hpp
new file mode 100644
index 0000000000..e8efcc1cc8
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemm_s8u8s32_kern.hpp
@@ -0,0 +1,101 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef IGEMM_KERNEL_GENERATOR_HPP
+#define IGEMM_KERNEL_GENERATOR_HPP
+
+#include "jit_generator.hpp"
+
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+class jit_avx512_core_gemm_s8u8s32_kern : public jit_generator {
+public:
+    jit_avx512_core_gemm_s8u8s32_kern(bool beta_zero_, bool enable_offset_c_,
+        bool enable_offset_r_);
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_gemm_s8u8s32_kern);
+
+protected:
+    bool beta_zero;
+    bool enable_offset_c, enable_offset_r;
+    bool vnni;
+
+    void prefetch_a(const Xbyak::Address &src) {
+        prefetcht0(src);
+    }
+    void prefetch_b(const Xbyak::Address &src) {
+        prefetcht0(src);
+    }
+    void prefetch_c(const Xbyak::Address &src) {
+        prefetchw(src);
+    }
+    void prefetch_x(const Xbyak::Address &src) {
+        prefetcht0(src);
+    }
+
+    void c_load(const Xbyak::Xmm &dst, const Xbyak::Address &src, int nelems);
+    void c_store(const Xbyak::Address &dst, const Xbyak::Xmm &src, int nelems);
+
+    void dot_product(const Xbyak::Xmm &dst, const Xbyak::Xmm &src1,
+        const Xbyak::Xmm &src2);
+    void kernel_loop(int unroll_m, int unroll_n, bool cfetch);
+    void remainder_kernel(int unroll_m, int unroll_n, int unroll_k, int bwidth);
+    void innerloop(int unroll_m, int unroll_n);
+    void outerloop(int unroll_x, int unroll_y, Xbyak::Label *&outerloop_label);
+
+    void generate();
+
+
+private:
+    static const int IGEMM_UNROLL_M = 48;
+    static const int IGEMM_UNROLL_N = 8;
+
+    static const int isize = 2;
+    static const int size = 4;
+
+    // Prefetch configuration
+    static const int prefetch_size_a = 32 * 5;
+    static const int prefetch_size_b = 32 * 4;
+
+    static const int offset_a = 256, offset_b = 256;
+    static const int max_unroll_m = 48, max_unroll_n = 8;
+
+    // Integer register assignments
+    Xbyak::Reg64 M, N, K, A, B, C, LDC, I, J, LoopCount;
+    Xbyak::Reg64 AO, BO, CO1, CO2, AA;
+
+    // Vector register assignments
+    Xbyak::Zmm dp_scratch, ones, a_regs[max_unroll_m >> 4], b_regs[2];
+    Xbyak::Zmm c_regs[max_unroll_m >> 4][max_unroll_n];
+
+    // Stack variable assignments
+    int stack_alloc_size;
+    Xbyak::Address arg_a, arg_b, arg_c, arg_ldc, arg_coffset_c, arg_coffset_r;
+    Xbyak::Address coffset_cx, coffset_cy, coffset_rx, coffset_ry;
+
+    void L_aligned(Xbyak::Label &label, int alignment = 16) {
+        align(alignment);
+        L(label);
+    }
+};
+
+}
+}
+}
+
+#endif /* header guard */
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemv_s8u8s32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemv_s8u8s32.cpp
new file mode 100644
index 0000000000..4f0b10dadd
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_gemv_s8u8s32.cpp
@@ -0,0 +1,290 @@
+/*******************************************************************************
+ * Copyright 2019 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *******************************************************************************/
+
+#include "gemv.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+int gemm_s8u8s32_jump_to_gemv_s8u8s32(blas_t *arg) {
+
+    blas_t arg_gemv = *arg;
+
+    if ((arg -> offsetc == FIX_OFFSET) && // Fix offset
+        (arg -> ao == 0) &&
+        (arg -> bo == 0) &&
+        (arg -> co[0] == 0) &&
+        (*(arg -> alpha) == 1.0f) &&
+        ((*(arg -> beta) == 1.0f) || *(arg -> beta) == 0.0f)) {
+
+        if (arg -> n == 1) {
+
+            if (arg -> transa == 1) { // A transpose
+                arg_gemv.n = arg -> k;
+                arg_gemv.ldc = 1;
+                arg_gemv.swap = 0;
+                if (arg -> transb == 0) { // B non transpose
+                    arg_gemv.ldb = 1;
+                }
+                // B transpose arg_gemv.ldb = arg -> ldb
+                gemv_threading_driver(&arg_gemv);
+                return 1;
+            }
+        }
+
+        if (arg -> m == 1) {
+
+            if (arg -> transb == 0) { // B non transpose
+                arg_gemv.transa = 1;
+                arg_gemv.m = arg -> n;
+                arg_gemv.n = arg -> k;
+                arg_gemv.a = (int8_t *) arg -> b;
+                arg_gemv.lda = arg -> ldb;
+                arg_gemv.b = (uint8_t *) arg -> a;
+                arg_gemv.swap = 1;
+                if (arg -> transa == 0) { // A non transpose
+                    arg_gemv.ldb = arg -> lda;
+                }
+                else { // A transpose
+                    arg_gemv.ldb = 1;
+                }
+                gemv_threading_driver(&arg_gemv);
+                return 1;
+            }
+        }
+    }
+
+    return 0;
+}
+
+
+int gemv_kernel_driver(blas_t *arg) {
+
+    dim_t m = arg -> m;
+    dim_t n = arg -> n;
+    uint8_t *a = (uint8_t *) arg -> a;
+    dim_t lda = arg -> lda;
+    int8_t *b = (int8_t *) arg -> b;
+    float beta = *(arg -> beta);
+
+    if (arg -> swap) {
+        arg -> gemv_u8s8s32_kernel(m, n, 1.0f, a, lda, b, beta, arg -> c);
+    }
+    else {
+        arg -> gemv_s8u8s32_kernel(arg -> m, arg -> n, 1.0f, arg -> a,
+                                   arg -> lda, arg -> b, *(arg -> beta), arg -> c);
+    }
+
+    return 0;
+}
+
+int gemv_threading_driver(blas_t *arg) {
+
+    dim_t nthr_m, nthr_n = 1;
+    dim_t MB, NB, UM = 16, UN = 64;
+    dim_t BLOCKM = 192, BLOCKN = 3072;
+    int status;
+    dim_t i;
+
+    dim_t nthr = (mkldnn_in_parallel()) ? 1 : mkldnn_get_max_threads();
+
+    uint8_t *new_x = NULL;
+    int32_t *tmp_y = NULL, *new_y = NULL;
+
+    dim_t m = arg -> m, n = arg -> n;
+
+    blas_t arg_seq = *arg;
+    float zero = 0.0f;
+
+    nthr_m = std::min(std::max(m / BLOCKM, (dim_t) 1), nthr);
+    MB = m / nthr_m;
+    MB = (((MB / UM) * UM) == MB) ? MB : (MB / UM) * UM + UM;
+    nthr_m = (((m / MB) * MB) == m) ? m / MB : m / MB + 1;
+    nthr_m = std::min(std::max(nthr_m, (dim_t) 1), nthr);
+
+    while ((nthr_m * (nthr_n + 1) <= nthr) && ((n / (nthr_n + 1)) >= BLOCKN)) {
+        nthr_n++;
+    }
+
+    NB = n / nthr_n;
+    NB = (((NB / UN) * UN) == NB) ? NB : (NB / UN) * UN + UN;
+    nthr_n = (((n / NB) * NB) == n) ? n / NB : n / NB + 1;
+    nthr_n = std::min(std::max(nthr_n, (dim_t) 1), nthr / nthr_m);
+
+    nthr = nthr_m * nthr_n;
+
+    if (arg -> ldb != 1) {
+        new_x = (uint8_t *)malloc(n, 64);
+        if (new_x == NULL)
+            return 1;
+        for (i = 0; i < n; i++) {
+            new_x[i] = (arg -> b)[i * arg -> ldb];
+        }
+        arg_seq.b = new_x;
+        arg_seq.ldb = 1;
+    }
+    else new_x = (uint8_t *) arg -> b;
+
+    if (arg -> ldc != 1) {
+        new_y = (int32_t *) malloc(nthr_m * PADD_BYTESIZE_ONPAGE(MB, sizeof(int32_t)), 64);
+        if (new_y == NULL) {
+            if (arg -> ldb != 1) {
+                free(new_x);
+            }
+            return 1;
+        }
+    }
+
+    // GEMV computation
+    if (nthr == 1) {
+
+        if (arg -> ldc != 1) {
+            if (*(arg -> beta) != 0.0f) {
+                for (i = 0; i < m; i++) {
+                    new_y[i] = arg -> c[i * arg -> ldc];
+                }
+            }
+        }
+
+        status = gemv_kernel_driver(&arg_seq);
+
+        if (arg -> ldc != 1) {
+            for (i = 0; i < m; i++) {
+                arg -> c[i * arg -> ldc] = new_y[i];
+            }
+        }
+
+        if (arg -> ldb != 1) {
+            free(new_x);
+        }
+        if (arg -> ldc != 1) {
+            free(new_y);
+        }
+        return status;
+    }
+
+    if (nthr_n > 1) {
+        tmp_y = (int32_t *) malloc((nthr_n - 1) * PADD_BYTESIZE_ONPAGE(m, sizeof(int32_t)), PAGESIZE);
+        if (tmp_y == NULL) {
+            if (arg -> ldb != 1) {
+                free(new_x);
+            }
+            return 1;
+        }
+    }
+
+    parallel_nd((int) nthr, [&](const dim_t ithr) {
+
+            dim_t m_from, m_to, myM;
+            dim_t n_from, n_to, myN;
+
+            dim_t n_id, m_id;
+            dim_t loc_incy = 1;
+            int32_t *loc_y;
+
+            blas_t arg_loc = arg_seq;
+            int j;
+
+            m_id = ithr / nthr_n;
+            n_id = ithr % nthr_n;
+
+            m_from = MB * m_id;
+            m_to = MB * (m_id + 1);
+            if ((m_to > m) || (m_id == nthr_m - 1))
+                m_to = m;
+
+            myM = m_to - m_from;
+
+            n_from = NB * n_id;
+            n_to = NB * (n_id + 1);
+            if ((n_to > n) || (n_id == nthr_n - 1))
+                n_to = n;
+
+            myN = n_to - n_from;
+
+            if (n_id != 0) {
+                arg_loc.beta = &zero;
+                loc_y = tmp_y + (NEXT_THR_STRIDE(m, sizeof(int32_t))) * (n_id - 1) + m_from;
+            }
+            else {
+                if (arg -> ldc == 1) {
+                    loc_y = arg_seq.c + m_from;
+                }
+                else {
+                    // need to copy the block of c in new_y
+                    loc_y = new_y + m_id * NEXT_THR_STRIDE(MB, sizeof(int32_t));
+                    if (*(arg -> beta) != 0.0f) {
+                        for (j = 0; j < myM; j++) {
+                            loc_y[j] = arg -> c[(m_from + j) * arg -> ldc];
+                        }
+                    }
+                }
+            }
+
+            arg_loc.m = myM;
+            arg_loc.n = myN;
+            arg_loc.a = arg_seq.a + m_from * arg_seq.lda + n_from;
+            arg_loc.b = arg_seq.b + n_from;
+            arg_loc.c = loc_y;
+            arg_loc.ldc = loc_incy;
+
+            gemv_kernel_driver(&arg_loc);
+
+            if ((n_id == 0) && (arg -> ldc != 1)) {
+                for (j = 0; j < myM; j++) {
+                    arg -> c[(m_from + j) * arg -> ldc] = loc_y[j];
+                }
+            }
+
+        });
+
+    if (nthr_n > 1) {
+        parallel_nd((int) nthr_m, [&](const dim_t ithr) {
+
+                dim_t j, j_from, j_to, ii;
+                int32_t acc;
+
+                j_from = MB * ithr;
+                j_to = MB * (ithr + 1);
+                if ((j_to > m) || (ithr == nthr - 1))
+                    j_to = m;
+
+                for (j = j_from; j < j_to; j++) {
+                    acc = 0;
+                    for (ii = 0; ii < nthr_n - 1; ii++) {
+                        acc += tmp_y[ii * NEXT_THR_STRIDE(m, sizeof(int32_t)) + j];
+                    }
+                    (arg -> c)[j * arg -> ldc] += acc;
+                }
+            });
+        free(tmp_y);
+    }
+
+    if (arg -> ldb != 1) {
+        free(new_x);
+    }
+
+    if (arg -> ldc != 1) {
+        free(new_y);
+    }
+
+    return 0;
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_kernel_gemv_s8u8s32_kern.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_kernel_gemv_s8u8s32_kern.cpp
new file mode 100644
index 0000000000..c57a8c1d12
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_kernel_gemv_s8u8s32_kern.cpp
@@ -0,0 +1,411 @@
+/*******************************************************************************
+ * Copyright 2019 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *******************************************************************************/
+
+#include "jit_avx512_core_kernel_gemv_s8u8s32_kern.hpp"
+
+#ifdef _WIN32
+#define is_windows 1
+#else
+#define is_windows 0
+#endif
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+void jit_avx512_core_gemv_s8u8s32_kern::vnni(Xbyak::Zmm acc, Xbyak::Zmm b,
+                                             Xbyak::Zmm a, Xbyak::Zmm tmp,
+                                             Xbyak::Zmm one, bool swap,
+                                             int use_vnni) {
+
+    if (use_vnni) {
+        if (swap)
+            vpdpbusd(acc, a, b);
+        else
+            vpdpbusd(acc, b, a);
+    }
+
+    else {
+        if (swap)
+            vpmaddubsw(tmp, a, b);
+        else
+            vpmaddubsw(tmp, b, a);
+        vpmaddwd(tmp, tmp, one);
+        vpaddd(acc, tmp, acc);
+    }
+
+}
+
+void jit_avx512_core_gemv_s8u8s32_kern::n_loop_body(int start_a_idx, int start_acc_idx,
+                                                    int b_idx, int nreg_acc,
+                                                    Xbyak::Reg64 A, Xbyak::Reg64 lda,
+                                                    Xbyak::Reg64 X, Xbyak::Zmm tmp,
+                                                    Xbyak::Zmm one, bool swap, int use_vnni,
+                                                    int use_mask, Xbyak::Opmask mask_n) {
+
+    int i;
+    int nreg_A = nreg_acc / 2 + (nreg_acc % 2);
+
+    // load X + j
+    if (use_mask)
+        vmovdqu8(Xbyak::Zmm(b_idx) | mask_n | T_z, ptr[X]);
+    else
+        vmovdqu8(Xbyak::Zmm(b_idx), ptr[X]);
+
+    xor_(r14, r14);
+    // load values of A
+    for (i = 0; i < nreg_A; i++) {
+        if (use_mask)
+            vmovdqu8(Xbyak::Zmm(start_a_idx + i) | mask_n | T_z, ptr[A + r14]);
+        else
+            vmovdqu8(Xbyak::Zmm(start_a_idx + i), ptr[A + r14]);
+        add(r14, lda);
+    }
+
+    for (i = 0; i < nreg_A; i++) {
+        // vnni (acc, b, a, tmp, one, swap, use_vnni)
+        vnni(Xbyak::Zmm(start_acc_idx + i), Xbyak::Zmm(b_idx),
+             Xbyak::Zmm(start_a_idx + i), tmp, one, swap, use_vnni);
+    }
+
+    for (i = 0; i < nreg_A - (nreg_acc % 2); i++) {
+        if (use_mask)
+            vmovdqu8(Xbyak::Zmm(start_a_idx + i) | mask_n | T_z, ptr[A + r14]);
+        else
+            vmovdqu8(Xbyak::Zmm(start_a_idx + i), ptr[A + r14]);
+        add(r14, lda);
+    }
+
+    for (i = 0; i < nreg_A - (nreg_acc % 2); i++) {
+        vnni(Xbyak::Zmm(start_acc_idx + i + nreg_A), Xbyak::Zmm(b_idx),
+             Xbyak::Zmm(start_a_idx + i), tmp, one, swap, use_vnni);
+    }
+
+}
+
+void jit_avx512_core_gemv_s8u8s32_kern::shuffle_and_add(Xbyak::Zmm dest, Xbyak::Zmm A,
+                                                        Xbyak::Zmm B, Xbyak::Zmm C,
+                                                        Xbyak::Zmm D) {
+
+    vshufi32x4(dest, A, C, 0x44);
+    vshufi32x4(A, A, C, 0xEE);
+    vpaddd(C, dest, A); // C = A0 + A2|A1 + A3|C0 + C2|C1 + C3
+
+    vshufi32x4(dest, B, D, 0x44);
+    vshufi32x4(B, B, D, 0xEE);
+    vpaddd(D, dest, B); // D = B0 + B2|B1 + B3|D0 + D2|D1 + D3
+
+    vshufi32x4(A, C, D, 0x88);
+    vshufi32x4(B, C, D, 0xDD);
+    vpaddd(dest, A, B); // dest = SAi|SBi|SCi|SDi
+
+}
+
+void jit_avx512_core_gemv_s8u8s32_kern::update_c(int nreg_acc, Xbyak::Reg64 Y,
+                                                 int start_a_idx, int start_acc_idx,
+                                                 Xbyak::Xmm beta, int use_mask,
+                                                 Xbyak::Opmask mask_m) {
+
+    int l, i, k, j, last_it;
+    Xbyak::Label store_label;
+
+    l = 0;
+    for (k = 0; k < nreg_acc; k += 8) {
+        for (i = 0, j = k; i < 8; i += 4, j += 2) {
+            if (j < nreg_acc) {
+                // shuffle per block of 4 registers
+                shuffle_and_add(Xbyak::Zmm(start_a_idx + l), // dest
+                                Xbyak::Zmm(start_acc_idx + j), // A = acc0
+                                Xbyak::Zmm(start_acc_idx + 1 + j), // B = acc1
+                                Xbyak::Zmm(start_acc_idx + 4 + j), // C = acc4
+                                Xbyak::Zmm(start_acc_idx + 5 + j)); // D = acc5
+
+                // extract low and high from dest and hadd
+                vextracti32x8(Xbyak::Ymm(start_a_idx + l + 1), Xbyak::Zmm(start_a_idx + l), 0);
+                vextracti32x8(Xbyak::Ymm(start_a_idx + l + 2), Xbyak::Zmm(start_a_idx + l), 1);
+                vphaddd(Xbyak::Ymm(start_a_idx + l),
+                        Xbyak::Ymm(start_a_idx + l + 1),
+                        Xbyak::Ymm(start_a_idx + l + 2));
+            }
+            l++;
+        }
+
+        vphaddd(Xbyak::Ymm(start_a_idx + l),
+                Xbyak::Ymm(start_a_idx + l - 2),
+                Xbyak::Ymm(start_a_idx + l - 1));
+
+        l++;
+    }
+
+    // eventually add with C and store new value
+    vxorps(Xbyak::Ymm(start_a_idx),
+           Xbyak::Ymm(start_a_idx),
+           Xbyak::Ymm(start_a_idx));
+    vucomiss(beta, Xbyak::Ymm(start_a_idx));
+    je(store_label, T_NEAR);
+
+    // beta = 1
+    for (k = 0, l = 2; k < nreg_acc; k += 8, l += 3) {
+        // load Y and add
+        last_it = (k + 8) > nreg_acc;
+        if (use_mask && last_it)
+            vmovdqu32(Xbyak::Ymm(start_a_idx + k / 8) | mask_m | T_z, ptr[Y + (k / 8) * 32]);
+        else
+            vmovdqu32(Xbyak::Ymm(start_a_idx + k / 8), ptr[Y + (k / 8) * 32]);
+
+        vpaddd(Xbyak::Ymm(start_a_idx + l),
+               Xbyak::Ymm(start_a_idx + l),
+               Xbyak::Ymm(start_a_idx + k / 8));
+    }
+
+    // store
+    aligned_label(store_label);
+    for (k = 0, l = 2; k < nreg_acc; k += 8, l += 3) {
+        last_it = (k + 8) > nreg_acc;
+        if (use_mask && last_it)
+            vmovdqu32(ptr[Y + (k / 8) * 32], Xbyak::Ymm(start_a_idx + l) | mask_m);
+        else
+            vmovdqu32(ptr[Y + (k / 8) * 32], Xbyak::Ymm(start_a_idx + l));
+    }
+
+}
+
+template <typename T>
+T jit_avx512_core_gemv_s8u8s32_kern::generate(int use_vnni) {
+
+    Xbyak::Opmask mask_n = k1, mask_m = k2;
+    Xbyak::Label one_label, m_tail_label, m_loop_label, n_loop_label;
+    Xbyak::Label n_tail_label, update_c_label, end_label;
+    constexpr unsigned int n_labels = (1 << unroll_m) - 1;
+    Xbyak::Label m_tail_label_case[n_labels];
+    Xbyak::Label n_loop_label_case[n_labels];
+    Xbyak::Label n_tail_label_case[n_labels];
+    Xbyak::Label update_c_label_case[n_labels];
+
+    int i, ii;
+
+    Xbyak::Zmm one, tmp;
+    Xbyak::Reg64 n = abi_param2, m = abi_param1;
+    Xbyak::Reg64 A = is_windows ? abi_param4 : abi_param3;
+    Xbyak::Reg64 lda = is_windows ? abi_param3 : abi_param4;
+    Xbyak::Reg64 X = is_windows ? rdi : r8;
+    Xbyak::Xmm beta = xmm1;
+    Xbyak::Reg64 Y = is_windows ? rsi : r9;
+
+    bool swap = !std::is_same<T, gemv_s8u8s32_kernel_t>::value;
+
+    // Windows: read on the stack lda, X, beta, Y
+
+    int zmm_idx = 1;
+    int nreg_acc = 1 << unroll_m;
+    int nreg_A = 1 << (unroll_m - 1);
+    int nreg_A_acc = nreg_acc + nreg_A;
+
+    if (!use_vnni) {
+        // set a zmm register to one
+        tmp = Xbyak::Zmm(0);
+        one = Xbyak::Zmm(zmm_idx + 1);
+        zmm_idx += 2; // one + tmp
+    }
+    else {
+        beta = xmm0;
+    }
+
+    preamble();
+
+    if (is_windows) {
+        mov(lda, ptr[rsp + get_size_of_abi_save_regs() + 40]);
+        mov(X, ptr[rsp + get_size_of_abi_save_regs() + 48]);
+        movss(beta, ptr[rsp + get_size_of_abi_save_regs() + 56]);
+        mov(Y, ptr[rsp + get_size_of_abi_save_regs() + 64]);
+    }
+
+    if (use_vnni && !is_windows) {
+        movaps(beta, xmm1);
+    }
+
+    mov(rax, (1 << unroll_n) - 1);
+    kmovq(k3, rax);
+
+    and_(rax, n); // rax contains n & ((1 << unroll_n) - 1)
+    mov(rbx, 1);
+    shlx(rbx, rbx, rax);
+    sub(rbx, 1);
+    kmovq(mask_n, rbx);
+    // mask_n set (AVX512 only), can use rax and rbx again
+
+    // set mask_m for update of the C matrix
+    // load/store on the C matrix use Ymm so tail according to Ymm size
+    mov(rax, 7); // 8 * 32 = 256 Ymm size
+    and_(rax, m); // rax contains m & 7
+    mov(rbx, 1);
+    shlx(rbx, rbx, rax);
+    sub(rbx, 1);
+    kmovq(mask_m, rbx);
+    // mask_m set (AVX512 only), can use rax and rbx again
+
+    // setup register of ones when VNNI instructions not available
+    if (!use_vnni) {
+        vmovdqu16(one, ptr[rip + one_label]);
+    }
+
+    // M loop
+    // base pointer for A rax contains a + i * lda
+    // Loop stop when rax >= a + (m & mask_um) * lda = rbx
+    // loop increment r10 = um * lda
+    // rbp = Y + i
+    mov(rax, A); // i = 0
+    mov(rbx, m);
+    and_(rbx, mask_um);
+    imul(rbx, lda);
+    add(rbx, A);
+    mov(r10, lda);
+    sal(r10, unroll_m);
+    mov(rbp, Y);
+
+    // N loop
+    // base pointer for X r11 contains x + j
+    // Loop stop when r11 >= x + n & mask_un = r12
+    // loop increment un
+    // r13 = rax + j = A + i * lda + j
+    mov(r12, n);
+    and_(r12, mask_un);
+    add(r12, X);
+
+    // M loop
+    aligned_label(m_loop_label);
+    cmp(rax, rbx);
+    jge(m_tail_label, T_NEAR);
+
+    // enter M loop
+    for(i = 0; i < nreg_acc; i++) {
+        vpxorq(Xbyak::Zmm(i + zmm_idx + nreg_A),
+               Xbyak::Zmm(i + zmm_idx + nreg_A),
+               Xbyak::Zmm(i + zmm_idx + nreg_A));
+    }
+
+    // N loop
+    mov(r11, X); // j = 0
+    mov(r13, rax);
+    aligned_label(n_loop_label);
+    cmp(r11, r12);
+    jge(n_tail_label, T_NEAR);
+
+    // enter N loop
+
+    n_loop_body(zmm_idx, zmm_idx + nreg_A, zmm_idx + nreg_A_acc, nreg_acc,
+                r13, lda, r11, tmp, one, swap, use_vnni, 0, mask_n);
+
+    // increment rax with un
+    add(r11, 1 << unroll_n);
+    add(r13, 1 << unroll_n);
+    jmp(n_loop_label, T_NEAR);
+    // end N loop
+
+    // N tail
+    aligned_label(n_tail_label);
+
+    ktestq(mask_n, k3);
+    je(update_c_label, T_NEAR);
+    n_loop_body(zmm_idx, zmm_idx + nreg_A, zmm_idx + nreg_A_acc, nreg_acc,
+                r13, lda, r11, tmp, one, swap, use_vnni, 1, mask_n);
+
+    // update C matrix
+    aligned_label(update_c_label);
+
+    update_c(nreg_acc, rbp, zmm_idx, zmm_idx + nreg_A, beta, 0, mask_m);
+
+    // increment rax with um * lda
+    add(rax, r10);
+    add(rbp, 1 << (unroll_m + 2));
+    jmp(m_loop_label, T_NEAR);
+    // end M loop
+
+    // M tail
+    aligned_label(m_tail_label);
+
+    // r10 will contain m_tail = m % unroll_m = m & (1 << unroll_m) - 1
+    mov(r10, m);
+    and_(r10, (1 << unroll_m) - 1);
+    for (ii = 1; ii < 1 << unroll_m; ii++) {
+        aligned_label(m_tail_label_case[ii-1]);
+        cmp(r10, ii);
+        if (ii == (1 << unroll_m) - 1)
+            jne(end_label, T_NEAR);
+        else
+            jne(m_tail_label_case[ii], T_NEAR);
+
+        // m_tail = i, use i accumulators
+
+        for(i = 0; i < ii; i++) {
+            vpxorq(Xbyak::Zmm(i + zmm_idx + nreg_A),
+                   Xbyak::Zmm(i + zmm_idx + nreg_A),
+                   Xbyak::Zmm(i + zmm_idx + nreg_A));
+        }
+
+        // N loop
+        mov(r11, X); // j = 0
+        mov(r13, rax);
+        aligned_label(n_loop_label_case[ii - 1]);
+        cmp(r11, r12);
+        jge(n_tail_label_case[ii - 1], T_NEAR);
+
+        n_loop_body(zmm_idx, zmm_idx + nreg_A, zmm_idx + nreg_A_acc, ii, r13,
+                    lda, r11, tmp, one, swap, use_vnni, 0, mask_n);
+
+        // increment rax with un
+        add(r11, 1 << unroll_n);
+        add(r13, 1 << unroll_n);
+        jmp(n_loop_label_case[ii - 1], T_NEAR);
+        // end N loop
+
+        // N tail
+        aligned_label(n_tail_label_case[ii - 1]);
+        ktestq(mask_n, k3);
+        je(update_c_label_case[ii - 1], T_NEAR);
+        n_loop_body(zmm_idx, zmm_idx + nreg_A, zmm_idx + nreg_A_acc, ii, r13,
+                    lda, r11, tmp, one, swap, use_vnni, 1, mask_n);
+
+        // update C matrix
+        aligned_label(update_c_label_case[ii - 1]);
+        update_c(ii, rbp, zmm_idx, zmm_idx + nreg_A, beta, 1, mask_m);
+
+        if (ii < ((1 << unroll_m) - 1))
+            jmp(end_label, T_NEAR);
+    }
+
+    aligned_label(end_label);
+
+    postamble();
+
+    if (!use_vnni) {
+        aligned_label(one_label);
+        for (i = 0; i < size_vec_reg/8; i++)
+            dq(0x0001000100010001);
+    }
+
+    return (T) getCode();
+}
+
+template jit_avx512_core_gemv_s8u8s32_kern::gemv_s8u8s32_kernel_t
+jit_avx512_core_gemv_s8u8s32_kern::generate<jit_avx512_core_gemv_s8u8s32_kern::gemv_s8u8s32_kernel_t>(int);
+
+template jit_avx512_core_gemv_s8u8s32_kern::gemv_u8s8s32_kernel_t
+jit_avx512_core_gemv_s8u8s32_kern::generate<jit_avx512_core_gemv_s8u8s32_kern::gemv_u8s8s32_kernel_t>(int);
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_kernel_gemv_s8u8s32_kern.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_kernel_gemv_s8u8s32_kern.hpp
new file mode 100644
index 0000000000..9ea23a5f56
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_kernel_gemv_s8u8s32_kern.hpp
@@ -0,0 +1,64 @@
+/*******************************************************************************
+ * Copyright 2019 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *******************************************************************************/
+
+#include "jit_generator.hpp"
+#include "common.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+class jit_avx512_core_gemv_s8u8s32_kern : jit_generator {
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_gemv_s8u8s32_kern);
+
+    // assumes untoll_{m,n} are a power of 2
+    static constexpr unsigned int unroll_m = 4; // real unrolling factor is 2^unroll_m
+    const int mask_um = 0xFFFFFFF0;
+    static constexpr unsigned int unroll_n = 6; // real unrolling factor is 2^unroll_n
+    const int mask_un = 0xFFFFFFC0;
+    const int size_vec_reg = 64; // bytes
+
+    void aligned_label(Xbyak::Label &label, int alignment = 16) {
+        align(alignment);
+        L(label);
+    }
+
+    void vnni(Xbyak::Zmm, Xbyak::Zmm, Xbyak::Zmm, Xbyak::Zmm, Xbyak::Zmm, bool, int);
+    void n_loop_body(int, int, int, int, Xbyak::Reg64, Xbyak::Reg64,
+                     Xbyak::Reg64, Xbyak::Zmm, Xbyak::Zmm, bool, int, int, Xbyak::Opmask);
+    void shuffle_and_add(Xbyak::Zmm, Xbyak::Zmm, Xbyak::Zmm, Xbyak::Zmm, Xbyak::Zmm);
+    void update_c(int, Xbyak::Reg64, int, int, Xbyak::Xmm, int, Xbyak::Opmask);
+
+public:
+    jit_avx512_core_gemv_s8u8s32_kern() : jit_generator(nullptr, GEMM_CODE_SIZE) {};
+
+    // m, n, alpha, a, lda, x, beta, y
+    typedef void (*gemv_s8u8s32_kernel_t)(const dim_t, const dim_t, const float,
+                                          const int8_t*, const dim_t, const uint8_t*,
+                                          const float, int32_t*);
+    typedef void (*gemv_u8s8s32_kernel_t)(const dim_t, const dim_t, const float,
+                                          const uint8_t*, const dim_t, const int8_t*,
+                                          const float, int32_t*);
+
+    template <typename T>
+    T generate(int use_vnni);
+
+};
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_an_kern.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_an_kern.cpp
new file mode 100644
index 0000000000..544cd2ff25
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_an_kern.cpp
@@ -0,0 +1,819 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "jit_generator.hpp"
+#include "common.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+jit_avx512_core_u8_copy_an_kern::jit_avx512_core_u8_copy_an_kern(): jit_generator(nullptr, GEMM_CODE_SIZE)
+{
+
+#ifndef _WIN32
+#define M	rdi
+#define N	rsi
+#define A	rdx
+#define LDA	rcx
+#define ALPHA	r8
+#define B	r9
+
+#define I	rax
+#define A1	r10
+#define A2	r8
+#define LDA3	r11
+
+#else
+
+#define M	rcx
+#define N	rdx
+#define A	r8
+#define LDA	r9
+#define ALPHA	rax
+#define B	rdi
+
+#define I	rax
+#define A1	rsi
+#define A2	r10
+#define LDA3	r11
+
+#define ARG_ALPHA	40+stacksize+rsp
+#define ARG_B		48+stacksize+rsp
+
+#endif
+
+inLocalLabel();
+{
+
+Xbyak::Label l170;
+Xbyak::Label l1f0;
+Xbyak::Label l20;
+Xbyak::Label l224;
+Xbyak::Label l234;
+Xbyak::Label l240;
+Xbyak::Label l254;
+Xbyak::Label l32c;
+Xbyak::Label l34;
+Xbyak::Label l388;
+Xbyak::Label l3b0;
+Xbyak::Label l3c0;
+Xbyak::Label l3cc;
+Xbyak::Label l3dc;
+Xbyak::Label l454;
+Xbyak::Label l48c;
+Xbyak::Label l4a8;
+Xbyak::Label l4b8;
+Xbyak::Label l4c4;
+Xbyak::Label l4d8;
+Xbyak::Label l570;
+Xbyak::Label l5c4;
+Xbyak::Label l5f0;
+Xbyak::Label l60c;
+Xbyak::Label l61c;
+Xbyak::Label l628;
+Xbyak::Label l638;
+Xbyak::Label l6b0;
+Xbyak::Label l6f4;
+Xbyak::Label l720;
+Xbyak::Label l73c;
+Xbyak::Label l74c;
+Xbyak::Label l758;
+Xbyak::Label l76c;
+Xbyak::Label l804;
+Xbyak::Label l858;
+Xbyak::Label l88c;
+Xbyak::Label l8a4;
+Xbyak::Label l8b2;
+Xbyak::Label l8bc;
+Xbyak::Label l8cc;
+Xbyak::Label l944;
+Xbyak::Label l98c;
+Xbyak::Label l9b0;
+Xbyak::Label l9c8;
+Xbyak::Label l9d8;
+
+	preamble();
+#ifdef _WIN32
+	auto stacksize = get_size_of_abi_save_regs();
+	mov(ALPHA, ptr[ARG_ALPHA]);
+	mov(B, ptr[ARG_B]);
+#endif
+
+	mov(M, qword[M]);
+	mov(N, qword[N]);
+	mov(LDA, qword[LDA]);
+	lea(LDA3, ptr[LDA+LDA*2]);
+	sub(A, -128);
+	sub(B, -128);
+	cmp(N, 0x30);
+	jl(l234, T_NEAR);
+	align(4);
+
+L(l20);
+	mov(A1, A);
+	add(A, 0x30);
+	mov(I, M);
+	sar(I, 0x2);
+	jle(l170, T_NEAR);
+	align(4);
+
+L(l34);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	movdqa(xmm4, xmm0);
+	punpcklbw(xmm0, xmm1);
+	punpckhbw(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpcklbw(xmm2, xmm3);
+	punpckhbw(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqa(xmm2, xmm4);
+	punpcklwd(xmm4, xmm5);
+	punpckhwd(xmm2, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	movdqu(xword[B-0x60], xmm4);
+	movdqu(xword[B-0x50], xmm2);
+	movdqu(xmm0, xword[A1-0x70]);
+	movdqu(xmm1, xword[A1+LDA*1-0x70]);
+	movdqu(xmm2, xword[A1+LDA*2-0x70]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x70]);
+	movdqa(xmm4, xmm0);
+	punpcklbw(xmm0, xmm1);
+	punpckhbw(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpcklbw(xmm2, xmm3);
+	punpckhbw(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqa(xmm2, xmm4);
+	punpcklwd(xmm4, xmm5);
+	punpckhwd(xmm2, xmm5);
+	movdqu(xword[B-0x40], xmm0);
+	movdqu(xword[B-0x30], xmm1);
+	movdqu(xword[B-0x20], xmm4);
+	movdqu(xword[B-0x10], xmm2);
+	movdqu(xmm0, xword[A1-0x60]);
+	movdqu(xmm1, xword[A1+LDA*1-0x60]);
+	movdqu(xmm2, xword[A1+LDA*2-0x60]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x60]);
+	lea(A1, ptr[A1+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpcklbw(xmm0, xmm1);
+	punpckhbw(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpcklbw(xmm2, xmm3);
+	punpckhbw(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqa(xmm2, xmm4);
+	punpcklwd(xmm4, xmm5);
+	punpckhwd(xmm2, xmm5);
+	movdqu(xword[B], xmm0);
+	movdqu(xword[B+0x10], xmm1);
+	movdqu(xword[B+0x20], xmm4);
+	movdqu(xword[B+0x30], xmm2);
+	sub(B, -192);
+	dec(I);
+	jg(l34, T_NEAR);
+	align(4);
+
+L(l170);
+	test(M, 0x2);
+	jle(l1f0, T_NEAR);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1-0x70]);
+	movdqu(xmm2, xword[A1-0x60]);
+	add(A1, LDA);
+	movdqu(xmm3, xword[A1-0x80]);
+	movdqu(xmm4, xword[A1-0x70]);
+	movdqu(xmm5, xword[A1-0x60]);
+	add(A1, LDA);
+	movdqa(xmm6, xmm0);
+	punpcklbw(xmm0, xmm3);
+	punpckhbw(xmm6, xmm3);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm6);
+	movdqa(xmm6, xmm1);
+	punpcklbw(xmm1, xmm4);
+	punpckhbw(xmm6, xmm4);
+	movdqu(xword[B-0x60], xmm1);
+	movdqu(xword[B-0x50], xmm6);
+	movdqa(xmm6, xmm2);
+	punpcklbw(xmm2, xmm5);
+	punpckhbw(xmm6, xmm5);
+	movdqu(xword[B-0x40], xmm2);
+	movdqu(xword[B-0x30], xmm6);
+	sub(B, -96);
+	align(4);
+
+L(l1f0);
+	test(M, 0x1);
+	jle(l224, T_NEAR);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1-0x70]);
+	movdqu(xmm2, xword[A1-0x60]);
+	add(A1, LDA);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	movdqu(xword[B-0x60], xmm2);
+	sub(B, -48);
+	align(4);
+
+L(l224);
+	sub(N, 0x30);
+	cmp(N, 0x30);
+	jge(l20, T_NEAR);
+	align(4);
+
+L(l234);
+	cmp(N, 0x20);
+	jl(l3c0, T_NEAR);
+	align(4);
+
+L(l240);
+	mov(A1, A);
+	add(A, 0x20);
+	mov(I, M);
+	sar(I, 0x2);
+	jle(l32c, T_NEAR);
+	align(4);
+
+L(l254);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	movdqa(xmm4, xmm0);
+	punpcklbw(xmm0, xmm1);
+	punpckhbw(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpcklbw(xmm2, xmm3);
+	punpckhbw(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqa(xmm2, xmm4);
+	punpcklwd(xmm4, xmm5);
+	punpckhwd(xmm2, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	movdqu(xword[B-0x60], xmm4);
+	movdqu(xword[B-0x50], xmm2);
+	movdqu(xmm0, xword[A1-0x70]);
+	movdqu(xmm1, xword[A1+LDA*1-0x70]);
+	movdqu(xmm2, xword[A1+LDA*2-0x70]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x70]);
+	lea(A1, ptr[A1+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpcklbw(xmm0, xmm1);
+	punpckhbw(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpcklbw(xmm2, xmm3);
+	punpckhbw(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqa(xmm2, xmm4);
+	punpcklwd(xmm4, xmm5);
+	punpckhwd(xmm2, xmm5);
+	movdqu(xword[B-0x40], xmm0);
+	movdqu(xword[B-0x30], xmm1);
+	movdqu(xword[B-0x20], xmm4);
+	movdqu(xword[B-0x10], xmm2);
+	sub(B, -128);
+	dec(I);
+	jg(l254, T_NEAR);
+	align(4);
+
+L(l32c);
+	test(M, 0x2);
+	jle(l388, T_NEAR);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1-0x70]);
+	add(A1, LDA);
+	movdqu(xmm2, xword[A1-0x80]);
+	movdqu(xmm3, xword[A1-0x70]);
+	add(A1, LDA);
+	movdqa(xmm4, xmm0);
+	punpcklbw(xmm0, xmm2);
+	punpckhbw(xmm4, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm4);
+	movdqa(xmm4, xmm1);
+	punpcklbw(xmm1, xmm3);
+	punpckhbw(xmm4, xmm3);
+	movdqu(xword[B-0x60], xmm1);
+	movdqu(xword[B-0x50], xmm4);
+	sub(B, -64);
+	align(4);
+
+L(l388);
+	test(M, 0x1);
+	jle(l3b0, T_NEAR);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1-0x70]);
+	add(A1, LDA);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	sub(B, -32);
+	align(4);
+
+L(l3b0);
+	sub(N, 0x20);
+	cmp(N, 0x20);
+	jge(l240, T_NEAR);
+	align(4);
+
+L(l3c0);
+	cmp(N, 0x10);
+	jl(l4b8, T_NEAR);
+	align(4);
+
+L(l3cc);
+	mov(A1, A);
+	add(A, 0x10);
+	mov(I, M);
+	sar(I, 0x2);
+	jle(l454, T_NEAR);
+	align(4);
+
+L(l3dc);
+	movdqu(xmm0, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqu(xmm1, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqu(xmm2, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqu(xmm3, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqa(xmm4, xmm0);
+	punpcklbw(xmm0, xmm1);
+	punpckhbw(xmm4, xmm1);
+	movdqa(xmm1, xmm2);
+	punpcklbw(xmm2, xmm3);
+	punpckhbw(xmm1, xmm3);
+	movdqa(xmm3, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm3, xmm2);
+	movdqa(xmm2, xmm4);
+	punpcklwd(xmm4, xmm1);
+	punpckhwd(xmm2, xmm1);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm3);
+	movdqu(xword[B-0x60], xmm4);
+	movdqu(xword[B-0x50], xmm2);
+	sub(B, -64);
+	dec(I);
+	jg(l3dc, T_NEAR);
+	align(4);
+
+L(l454);
+	test(M, 0x2);
+	jle(l48c, T_NEAR);
+	movdqu(xmm0, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqu(xmm1, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqa(xmm2, xmm0);
+	punpcklbw(xmm0, xmm1);
+	punpckhbw(xmm2, xmm1);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm2);
+	sub(B, -32);
+	align(4);
+
+L(l48c);
+	test(M, 0x1);
+	jle(l4a8, T_NEAR);
+	movdqu(xmm0, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l4a8);
+	sub(N, 0x10);
+	cmp(N, 0x10);
+	jge(l3cc, T_NEAR);
+	align(4);
+
+L(l4b8);
+	cmp(N, 0x8);
+	jl(l61c, T_NEAR);
+	align(4);
+
+L(l4c4);
+	mov(A1, A);
+	add(A, 0x8);
+	mov(I, M);
+	sar(I, 0x3);
+	jle(l570, T_NEAR);
+	align(4);
+
+L(l4d8);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqu(xword[B-0x60], xmm0);
+	movdqu(xword[B-0x50], xmm1);
+	sub(B, -64);
+	dec(I);
+	jg(l4d8, T_NEAR);
+	align(4);
+
+L(l570);
+	test(M, 0x4);
+	jle(l5c4, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	sub(B, -32);
+	align(4);
+
+L(l5c4);
+	test(M, 0x2);
+	jle(l5f0, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l5f0);
+	test(M, 0x1);
+	jle(l60c, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l60c);
+	sub(N, 0x8);
+	cmp(N, 0x8);
+	jge(l4c4, T_NEAR);
+	align(4);
+
+L(l61c);
+	cmp(N, 0x4);
+	jl(l74c, T_NEAR);
+	align(4);
+
+L(l628);
+	mov(A1, A);
+	add(A, 0x4);
+	mov(I, M);
+	sar(I, 0x3);
+	jle(l6b0, T_NEAR);
+	align(4);
+
+L(l638);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	sub(B, -32);
+	dec(I);
+	jg(l638, T_NEAR);
+	align(4);
+
+L(l6b0);
+	test(M, 0x4);
+	jle(l6f4, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l6f4);
+	test(M, 0x2);
+	jle(l720, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l720);
+	test(M, 0x1);
+	jle(l73c, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l73c);
+	sub(N, 0x4);
+	cmp(N, 0x4);
+	jge(l628, T_NEAR);
+	align(4);
+
+L(l74c);
+	cmp(N, 0x2);
+	jl(l8b2, T_NEAR);
+	align(4);
+
+L(l758);
+	mov(A1, A);
+	add(A, 0x2);
+	mov(LDA3, M);
+	sar(LDA3, 0x3);
+	jle(l804, T_NEAR);
+	align(4);
+
+L(l76c);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm4, eax, 0x0);
+	punpcklbw(xmm1, xmm2);
+	punpcklbw(xmm3, xmm4);
+	punpcklwd(xmm1, xmm3);
+	punpcklqdq(xmm0, xmm1);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	dec(LDA3);
+	jg(l76c, T_NEAR);
+	align(4);
+
+L(l804);
+	test(M, 0x4);
+	jle(l858, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l858);
+	test(M, 0x2);
+	jle(l88c, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l88c);
+	test(M, 0x1);
+	jle(l8a4, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	mov(word[B-0x80], ax);
+	sub(B, -2);
+	align(4);
+
+L(l8a4);
+	sub(N, 0x2);
+	cmp(N, 0x2);
+	jge(l758, T_NEAR);
+	align(4);
+
+L(l8b2);
+	cmp(N, 0x1);
+	jl(l9d8, T_NEAR);
+	align(4);
+
+L(l8bc);
+	mov(A1, A);
+	add(A, 0x1);
+	mov(LDA3, M);
+	sar(LDA3, 0x3);
+	jle(l944, T_NEAR);
+	align(4);
+
+L(l8cc);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x7);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	dec(LDA3);
+	jg(l8cc, T_NEAR);
+	align(4);
+
+L(l944);
+	test(M, 0x4);
+	jle(l98c, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x3);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l98c);
+	test(M, 0x2);
+	jle(l9b0, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	mov(byte[B-0x80], al);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	mov(byte[B-0x7f], al);
+	sub(B, -2);
+	align(4);
+
+L(l9b0);
+	test(M, 0x1);
+	jle(l9c8, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	mov(byte[B-0x80], al);
+	sub(B, -1);
+	align(4);
+
+L(l9c8);
+	sub(N, 0x1);
+	cmp(N, 0x1);
+	jge(l8bc, T_NEAR);
+	align(4);
+
+L(l9d8);
+
+	postamble();
+}
+outLocalLabel();
+
+#undef M
+#undef N
+#undef A
+#undef LDA
+#undef ALPHA
+#undef B
+#undef I
+#undef A1
+#undef A2
+#undef LDA3
+#ifdef _WIN32
+#undef ARG_ALPHA
+#undef ARG_B
+#endif
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_at_kern.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_at_kern.cpp
new file mode 100644
index 0000000000..1c11fc6cef
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_at_kern.cpp
@@ -0,0 +1,2209 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "jit_generator.hpp"
+#include "common.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+jit_avx512_core_u8_copy_at_kern::jit_avx512_core_u8_copy_at_kern(): jit_generator(nullptr, GEMM_CODE_SIZE)
+{
+
+#ifndef _WIN32
+#define M	rdi
+#define N	rsi
+#define A	rdx
+#define LDA	rcx
+#define ALPHA	r8
+#define B	r9
+
+#define I	rax
+#define A1	r10
+#define A2	r8
+#define LDA3	r11
+
+#else
+
+#define M	rcx
+#define N	rdx
+#define A	r8
+#define LDA	r9
+#define ALPHA	rax
+#define B	rdi
+
+#define I	rax
+#define A1	rsi
+#define A2	r10
+#define LDA3	r11
+
+#define ARG_ALPHA	40+stacksize+rsp
+#define ARG_B		48+stacksize+rsp
+
+#endif
+
+inLocalLabel();
+{
+
+Xbyak::Label l1014;
+Xbyak::Label l1390;
+Xbyak::Label l159c;
+Xbyak::Label l173c;
+Xbyak::Label l18e4;
+Xbyak::Label l1a7c;
+Xbyak::Label l1a8c;
+Xbyak::Label l1a98;
+Xbyak::Label l1ab4;
+Xbyak::Label l1c64;
+Xbyak::Label l1d74;
+Xbyak::Label l1e50;
+Xbyak::Label l1f2c;
+Xbyak::Label l1ffc;
+Xbyak::Label l20;
+Xbyak::Label l200c;
+Xbyak::Label l2018;
+Xbyak::Label l2034;
+Xbyak::Label l2110;
+Xbyak::Label l21a0;
+Xbyak::Label l2210;
+Xbyak::Label l2284;
+Xbyak::Label l22f0;
+Xbyak::Label l2300;
+Xbyak::Label l230c;
+Xbyak::Label l2324;
+Xbyak::Label l2398;
+Xbyak::Label l23e8;
+Xbyak::Label l242c;
+Xbyak::Label l2474;
+Xbyak::Label l24b4;
+Xbyak::Label l24c4;
+Xbyak::Label l24d0;
+Xbyak::Label l24e8;
+Xbyak::Label l2520;
+Xbyak::Label l254c;
+Xbyak::Label l2578;
+Xbyak::Label l25a8;
+Xbyak::Label l25c8;
+Xbyak::Label l25d6;
+Xbyak::Label l25e0;
+Xbyak::Label l25f0;
+Xbyak::Label l260c;
+Xbyak::Label l262c;
+Xbyak::Label l264c;
+Xbyak::Label l2668;
+Xbyak::Label l2680;
+Xbyak::Label l2690;
+Xbyak::Label l44;
+Xbyak::Label l58c;
+Xbyak::Label l8b0;
+Xbyak::Label lb14;
+Xbyak::Label ld84;
+Xbyak::Label lfdc;
+Xbyak::Label lfec;
+Xbyak::Label lff8;
+
+	preamble();
+#ifdef _WIN32
+	auto stacksize = get_size_of_abi_save_regs();
+	mov(ALPHA, ptr[ARG_ALPHA]);
+	mov(B, ptr[ARG_B]);
+#endif
+
+	mov(N, qword[N]);
+	mov(M, qword[M]);
+	mov(LDA, qword[LDA]);
+	sub(A, -128);
+	sub(B, -128);
+	lea(LDA3, ptr[LDA+LDA*2]);
+	cmp(N, 0x30);
+	jl(lfec, T_NEAR);
+	align(4);
+
+L(l20);
+	mov(A1, A);
+	mov(I, LDA);
+	shl(I, 0x5);
+	lea(I, ptr[I+LDA*8]);
+	lea(I, ptr[I+LDA*8]);
+	add(A, I);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l58c, T_NEAR);
+	align(4);
+
+L(l44);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B+0x40], xmm1);
+	movdqu(xword[B+0x100], xmm4);
+	movdqu(xword[B+0x1c0], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	movdqu(xword[B+0x50], xmm1);
+	movdqu(xword[B+0x110], xmm4);
+	movdqu(xword[B+0x1d0], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x60], xmm0);
+	movdqu(xword[B+0x60], xmm1);
+	movdqu(xword[B+0x120], xmm4);
+	movdqu(xword[B+0x1e0], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x50], xmm0);
+	movdqu(xword[B+0x70], xmm1);
+	movdqu(xword[B+0x130], xmm4);
+	movdqu(xword[B+0x1f0], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x40], xmm0);
+	movdqu(xword[B+0x80], xmm1);
+	movdqu(xword[B+0x140], xmm4);
+	movdqu(xword[B+0x200], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x30], xmm0);
+	movdqu(xword[B+0x90], xmm1);
+	movdqu(xword[B+0x150], xmm4);
+	movdqu(xword[B+0x210], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x20], xmm0);
+	movdqu(xword[B+0xa0], xmm1);
+	movdqu(xword[B+0x160], xmm4);
+	movdqu(xword[B+0x220], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x10], xmm0);
+	movdqu(xword[B+0xb0], xmm1);
+	movdqu(xword[B+0x170], xmm4);
+	movdqu(xword[B+0x230], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B], xmm0);
+	movdqu(xword[B+0xc0], xmm1);
+	movdqu(xword[B+0x180], xmm4);
+	movdqu(xword[B+0x240], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B+0x10], xmm0);
+	movdqu(xword[B+0xd0], xmm1);
+	movdqu(xword[B+0x190], xmm4);
+	movdqu(xword[B+0x250], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B+0x20], xmm0);
+	movdqu(xword[B+0xe0], xmm1);
+	movdqu(xword[B+0x1a0], xmm4);
+	movdqu(xword[B+0x260], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B+0x30], xmm0);
+	movdqu(xword[B+0xf0], xmm1);
+	movdqu(xword[B+0x1b0], xmm4);
+	movdqu(xword[B+0x270], xmm3);
+	sub(A1, -16);
+	sub(B, -768);
+	dec(I);
+	jg(l44, T_NEAR);
+	align(4);
+
+L(l58c);
+	test(M, 0x8);
+	jle(l8b0, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	movq(xmm2, qword[A1+LDA*2-0x80]);
+	movq(xmm3, qword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B+0x40], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	movdqu(xword[B+0x50], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x60], xmm0);
+	movdqu(xword[B+0x60], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x50], xmm0);
+	movdqu(xword[B+0x70], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x40], xmm0);
+	movdqu(xword[B+0x80], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x30], xmm0);
+	movdqu(xword[B+0x90], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x20], xmm0);
+	movdqu(xword[B+0xa0], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x10], xmm0);
+	movdqu(xword[B+0xb0], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B], xmm0);
+	movdqu(xword[B+0xc0], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B+0x10], xmm0);
+	movdqu(xword[B+0xd0], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B+0x20], xmm0);
+	movdqu(xword[B+0xe0], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B+0x30], xmm0);
+	movdqu(xword[B+0xf0], xmm1);
+	sub(A1, -8);
+	sub(B, -384);
+	align(4);
+
+L(l8b0);
+	test(M, 0x4);
+	jle(lb14, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	movd(xmm2, dword[A1+LDA*2-0x80]);
+	movd(xmm3, dword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x60], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x50], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x40], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x30], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x20], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x10], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B+0x10], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B+0x20], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B+0x30], xmm0);
+	sub(A1, -4);
+	sub(B, -192);
+	align(4);
+
+L(lb14);
+	test(M, 0x2);
+	jle(ld84, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A1+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x7);
+	movdqu(xword[B-0x80], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqu(xword[B-0x70], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqu(xword[B-0x60], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqu(xword[B-0x50], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqu(xword[B-0x40], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqu(xword[B-0x30], xmm0);
+	sub(A1, -2);
+	sub(B, -96);
+	align(4);
+
+L(ld84);
+	test(M, 0x1);
+	jle(lfdc, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xf);
+	movdqu(xword[B-0x80], xmm0);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xf);
+	movdqu(xword[B-0x70], xmm0);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xf);
+	movdqu(xword[B-0x60], xmm0);
+	sub(B, -48);
+	align(4);
+
+L(lfdc);
+	sub(N, 0x30);
+	cmp(N, 0x30);
+	jge(l20, T_NEAR);
+	align(4);
+
+L(lfec);
+	cmp(N, 0x20);
+	jl(l1a8c, T_NEAR);
+	align(4);
+
+L(lff8);
+	mov(A1, A);
+	mov(I, LDA);
+	shl(I, 0x5);
+	add(A, I);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l1390, T_NEAR);
+	align(4);
+
+L(l1014);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B], xmm1);
+	movdqu(xword[B+0x80], xmm4);
+	movdqu(xword[B+0x100], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	movdqu(xword[B+0x10], xmm1);
+	movdqu(xword[B+0x90], xmm4);
+	movdqu(xword[B+0x110], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x60], xmm0);
+	movdqu(xword[B+0x20], xmm1);
+	movdqu(xword[B+0xa0], xmm4);
+	movdqu(xword[B+0x120], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x50], xmm0);
+	movdqu(xword[B+0x30], xmm1);
+	movdqu(xword[B+0xb0], xmm4);
+	movdqu(xword[B+0x130], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x40], xmm0);
+	movdqu(xword[B+0x40], xmm1);
+	movdqu(xword[B+0xc0], xmm4);
+	movdqu(xword[B+0x140], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x30], xmm0);
+	movdqu(xword[B+0x50], xmm1);
+	movdqu(xword[B+0xd0], xmm4);
+	movdqu(xword[B+0x150], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x20], xmm0);
+	movdqu(xword[B+0x60], xmm1);
+	movdqu(xword[B+0xe0], xmm4);
+	movdqu(xword[B+0x160], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x10], xmm0);
+	movdqu(xword[B+0x70], xmm1);
+	movdqu(xword[B+0xf0], xmm4);
+	movdqu(xword[B+0x170], xmm3);
+	sub(A1, -16);
+	sub(B, -512);
+	dec(I);
+	jg(l1014, T_NEAR);
+	align(4);
+
+L(l1390);
+	test(M, 0x8);
+	jle(l159c, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	movq(xmm2, qword[A1+LDA*2-0x80]);
+	movq(xmm3, qword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	movdqu(xword[B+0x10], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x60], xmm0);
+	movdqu(xword[B+0x20], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x50], xmm0);
+	movdqu(xword[B+0x30], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x40], xmm0);
+	movdqu(xword[B+0x40], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x30], xmm0);
+	movdqu(xword[B+0x50], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x20], xmm0);
+	movdqu(xword[B+0x60], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x10], xmm0);
+	movdqu(xword[B+0x70], xmm1);
+	sub(A1, -8);
+	sub(B, -256);
+	align(4);
+
+L(l159c);
+	test(M, 0x4);
+	jle(l173c, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	movd(xmm2, dword[A1+LDA*2-0x80]);
+	movd(xmm3, dword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x60], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x50], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x40], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x30], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x20], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x10], xmm0);
+	sub(A1, -4);
+	sub(B, -128);
+	align(4);
+
+L(l173c);
+	test(M, 0x2);
+	jle(l18e4, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A1+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x7);
+	movdqu(xword[B-0x80], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqu(xword[B-0x70], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqu(xword[B-0x60], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqu(xword[B-0x50], xmm0);
+	sub(A1, -2);
+	sub(B, -64);
+	align(4);
+
+L(l18e4);
+	test(M, 0x1);
+	jle(l1a7c, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xf);
+	movdqu(xword[B-0x80], xmm0);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xf);
+	movdqu(xword[B-0x70], xmm0);
+	sub(B, -32);
+	align(4);
+
+L(l1a7c);
+	sub(N, 0x20);
+	cmp(N, 0x20);
+	jge(lff8, T_NEAR);
+	align(4);
+
+L(l1a8c);
+	cmp(N, 0x10);
+	jl(l200c, T_NEAR);
+	align(4);
+
+L(l1a98);
+	mov(A1, A);
+	mov(I, LDA);
+	shl(I, 0x4);
+	add(A, I);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l1c64, T_NEAR);
+	align(4);
+
+L(l1ab4);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x40], xmm1);
+	movdqu(xword[B], xmm4);
+	movdqu(xword[B+0x40], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	movdqu(xword[B-0x30], xmm1);
+	movdqu(xword[B+0x10], xmm4);
+	movdqu(xword[B+0x50], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x60], xmm0);
+	movdqu(xword[B-0x20], xmm1);
+	movdqu(xword[B+0x20], xmm4);
+	movdqu(xword[B+0x60], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x50], xmm0);
+	movdqu(xword[B-0x10], xmm1);
+	movdqu(xword[B+0x30], xmm4);
+	movdqu(xword[B+0x70], xmm3);
+	sub(A1, -16);
+	sub(B, -256);
+	dec(I);
+	jg(l1ab4, T_NEAR);
+	align(4);
+
+L(l1c64);
+	test(M, 0x8);
+	jle(l1d74, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	movq(xmm2, qword[A1+LDA*2-0x80]);
+	movq(xmm3, qword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x40], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	movdqu(xword[B-0x30], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x60], xmm0);
+	movdqu(xword[B-0x20], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x50], xmm0);
+	movdqu(xword[B-0x10], xmm1);
+	sub(A1, -8);
+	sub(B, -128);
+	align(4);
+
+L(l1d74);
+	test(M, 0x4);
+	jle(l1e50, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	movd(xmm2, dword[A1+LDA*2-0x80]);
+	movd(xmm3, dword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x60], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x50], xmm0);
+	sub(A1, -4);
+	sub(B, -64);
+	align(4);
+
+L(l1e50);
+	test(M, 0x2);
+	jle(l1f2c, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A1+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x7);
+	movdqu(xword[B-0x80], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	movdqu(xword[B-0x70], xmm0);
+	sub(A1, -2);
+	sub(B, -32);
+	align(4);
+
+L(l1f2c);
+	test(M, 0x1);
+	jle(l1ffc, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	pinsrb(xmm0, eax, 0xf);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l1ffc);
+	sub(N, 0x10);
+	cmp(N, 0x10);
+	jge(l1a98, T_NEAR);
+	align(4);
+
+L(l200c);
+	cmp(N, 0x8);
+	jl(l2300, T_NEAR);
+	align(4);
+
+L(l2018);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*4]);
+	lea(I, ptr[A1+LDA*8]);
+	mov(A, I);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l2110, T_NEAR);
+	align(4);
+
+L(l2034);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	sub(A1, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x60], xmm1);
+	movdqu(xword[B-0x40], xmm4);
+	movdqu(xword[B-0x20], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	sub(A2, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	movdqu(xword[B-0x50], xmm1);
+	movdqu(xword[B-0x30], xmm4);
+	movdqu(xword[B-0x10], xmm3);
+	sub(B, -128);
+	dec(I);
+	jg(l2034, T_NEAR);
+	align(4);
+
+L(l2110);
+	test(M, 0x8);
+	jle(l21a0, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	movq(xmm2, qword[A1+LDA*2-0x80]);
+	movq(xmm3, qword[A1+LDA3*1-0x80]);
+	sub(A1, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x60], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	movdqu(xword[B-0x50], xmm1);
+	sub(B, -64);
+	align(4);
+
+L(l21a0);
+	test(M, 0x4);
+	jle(l2210, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	movd(xmm2, dword[A1+LDA*2-0x80]);
+	movd(xmm3, dword[A1+LDA3*1-0x80]);
+	sub(A1, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	sub(B, -32);
+	align(4);
+
+L(l2210);
+	test(M, 0x2);
+	jle(l2284, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A1+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A1+LDA3*1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x7);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l2284);
+	test(M, 0x1);
+	jle(l22f0, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1+LDA3*1-0x80]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	pinsrb(xmm0, eax, 0x7);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l22f0);
+	sub(N, 0x8);
+	cmp(N, 0x8);
+	jge(l2018, T_NEAR);
+	align(4);
+
+L(l2300);
+	cmp(N, 0x4);
+	jl(l24c4, T_NEAR);
+	align(4);
+
+L(l230c);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*2]);
+	lea(I, ptr[A1+LDA*4]);
+	mov(A, I);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l2398, T_NEAR);
+	align(4);
+
+L(l2324);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	sub(A1, -16);
+	movdqu(xmm2, xword[A2-0x80]);
+	movdqu(xmm3, xword[A2+LDA*1-0x80]);
+	sub(A2, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	movdqu(xword[B-0x60], xmm4);
+	movdqu(xword[B-0x50], xmm3);
+	sub(B, -64);
+	dec(I);
+	jg(l2324, T_NEAR);
+	align(4);
+
+L(l2398);
+	test(M, 0x8);
+	jle(l23e8, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	sub(A1, -8);
+	movq(xmm2, qword[A2-0x80]);
+	movq(xmm3, qword[A2+LDA*1-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	sub(B, -32);
+	align(4);
+
+L(l23e8);
+	test(M, 0x4);
+	jle(l242c, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	sub(A1, -4);
+	movd(xmm2, dword[A2-0x80]);
+	movd(xmm3, dword[A2+LDA*1-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l242c);
+	test(M, 0x2);
+	jle(l2474, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA*1-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x3);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l2474);
+	test(M, 0x1);
+	jle(l24b4, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x3);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l24b4);
+	sub(N, 0x4);
+	cmp(N, 0x4);
+	jge(l230c, T_NEAR);
+	align(4);
+
+L(l24c4);
+	cmp(N, 0x2);
+	jl(l25d6, T_NEAR);
+	align(4);
+
+L(l24d0);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*1]);
+	lea(I, ptr[A1+LDA*2]);
+	mov(A, I);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l2520, T_NEAR);
+	align(4);
+
+L(l24e8);
+	movdqu(xmm0, xword[A1-0x80]);
+	sub(A1, -16);
+	movdqu(xmm1, xword[A2-0x80]);
+	sub(A2, -16);
+	movdqa(xmm2, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm2, xmm1);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm2);
+	sub(B, -32);
+	dec(I);
+	jg(l24e8, T_NEAR);
+	align(4);
+
+L(l2520);
+	test(M, 0x8);
+	jle(l254c, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	sub(A1, -8);
+	movq(xmm1, qword[A2-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l254c);
+	test(M, 0x4);
+	jle(l2578, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	sub(A1, -4);
+	movd(xmm1, dword[A2-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l2578);
+	test(M, 0x2);
+	jle(l25a8, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x1);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l25a8);
+	test(M, 0x1);
+	jle(l25c8, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	mov(byte[B-0x80], al);
+	mov(al, byte[A2-0x80]);
+	mov(byte[B-0x7f], al);
+	sub(B, -2);
+	align(4);
+
+L(l25c8);
+	sub(N, 0x2);
+	cmp(N, 0x2);
+	jge(l24d0, T_NEAR);
+	align(4);
+
+L(l25d6);
+	cmp(N, 0x1);
+	jl(l2690, T_NEAR);
+	align(4);
+
+L(l25e0);
+	mov(A1, A);
+	add(A, LDA);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l260c, T_NEAR);
+	align(4);
+
+L(l25f0);
+	movdqu(xmm0, xword[A1-0x80]);
+	sub(A1, -16);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	dec(I);
+	jg(l25f0, T_NEAR);
+	align(4);
+
+L(l260c);
+	test(M, 0x8);
+	jle(l262c, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	sub(A1, -8);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l262c);
+	test(M, 0x4);
+	jle(l264c, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	sub(A1, -4);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l264c);
+	test(M, 0x2);
+	jle(l2668, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	mov(word[B-0x80], ax);
+	sub(A1, -2);
+	sub(B, -2);
+	align(4);
+
+L(l2668);
+	test(M, 0x1);
+	jle(l2680, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	mov(byte[B-0x80], al);
+	sub(B, -1);
+	align(4);
+
+L(l2680);
+	sub(N, 0x1);
+	cmp(N, 0x1);
+	jge(l25e0, T_NEAR);
+	align(4);
+
+L(l2690);
+
+	postamble();
+}
+outLocalLabel();
+
+#undef M
+#undef N
+#undef A
+#undef LDA
+#undef ALPHA
+#undef B
+#undef I
+#undef A1
+#undef A2
+#undef LDA3
+#ifdef _WIN32
+#undef ARG_ALPHA
+#undef ARG_B
+#endif
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_bn_kern.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_bn_kern.cpp
new file mode 100644
index 0000000000..56c36ee14a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_bn_kern.cpp
@@ -0,0 +1,564 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "jit_generator.hpp"
+#include "common.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+jit_avx512_core_u8_copy_bn_kern::jit_avx512_core_u8_copy_bn_kern(): jit_generator(nullptr, GEMM_CODE_SIZE)
+{
+
+#ifndef _WIN32
+#define M	rdi
+#define N	rsi
+#define A	rdx
+#define LDA	rcx
+#define ALPHA	r8
+#define B	r9
+
+#define I	rax
+#define A1	r10
+#define A2	r8
+#define LDA3	r11
+
+#else
+
+#define M	rcx
+#define N	rdx
+#define A	r8
+#define LDA	r9
+#define ALPHA	rax
+#define B	rdi
+
+#define I	rax
+#define A1	rsi
+#define A2	r10
+#define LDA3	r11
+
+#define ARG_ALPHA	40+stacksize+rsp
+#define ARG_B		48+stacksize+rsp
+
+#endif
+
+inLocalLabel();
+{
+
+Xbyak::Label l118;
+Xbyak::Label l1a8;
+Xbyak::Label l20;
+Xbyak::Label l218;
+Xbyak::Label l28c;
+Xbyak::Label l2f8;
+Xbyak::Label l308;
+Xbyak::Label l314;
+Xbyak::Label l32c;
+Xbyak::Label l3a0;
+Xbyak::Label l3c;
+Xbyak::Label l3f0;
+Xbyak::Label l434;
+Xbyak::Label l47c;
+Xbyak::Label l4bc;
+Xbyak::Label l4cc;
+Xbyak::Label l4d8;
+Xbyak::Label l4f0;
+Xbyak::Label l528;
+Xbyak::Label l554;
+Xbyak::Label l580;
+Xbyak::Label l5b0;
+Xbyak::Label l5d0;
+Xbyak::Label l5de;
+Xbyak::Label l5e8;
+Xbyak::Label l5f8;
+Xbyak::Label l614;
+Xbyak::Label l634;
+Xbyak::Label l654;
+Xbyak::Label l670;
+Xbyak::Label l688;
+Xbyak::Label l698;
+
+	preamble();
+#ifdef _WIN32
+	auto stacksize = get_size_of_abi_save_regs();
+	mov(ALPHA, ptr[ARG_ALPHA]);
+	mov(B, ptr[ARG_B]);
+#endif
+
+	mov(N, qword[N]);
+	mov(M, qword[M]);
+	mov(LDA, qword[LDA]);
+	sub(A, -128);
+	sub(B, -128);
+	lea(LDA3, ptr[LDA+LDA*2]);
+	cmp(N, 0x8);
+	jl(l308, T_NEAR);
+	align(4);
+
+L(l20);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*4]);
+	lea(I, ptr[A1+LDA*8]);
+	mov(A, I);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l118, T_NEAR);
+	align(4);
+
+L(l3c);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	sub(A1, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x60], xmm1);
+	movdqu(xword[B-0x40], xmm4);
+	movdqu(xword[B-0x20], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	sub(A2, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	movdqu(xword[B-0x50], xmm1);
+	movdqu(xword[B-0x30], xmm4);
+	movdqu(xword[B-0x10], xmm3);
+	sub(B, -128);
+	dec(I);
+	jg(l3c, T_NEAR);
+	align(4);
+
+L(l118);
+	test(M, 0x8);
+	jle(l1a8, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	movq(xmm2, qword[A1+LDA*2-0x80]);
+	movq(xmm3, qword[A1+LDA3*1-0x80]);
+	sub(A1, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x60], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	movdqu(xword[B-0x50], xmm1);
+	sub(B, -64);
+	align(4);
+
+L(l1a8);
+	test(M, 0x4);
+	jle(l218, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	movd(xmm2, dword[A1+LDA*2-0x80]);
+	movd(xmm3, dword[A1+LDA3*1-0x80]);
+	sub(A1, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	sub(B, -32);
+	align(4);
+
+L(l218);
+	test(M, 0x2);
+	jle(l28c, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A1+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A1+LDA3*1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x7);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l28c);
+	test(M, 0x1);
+	jle(l2f8, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1+LDA3*1-0x80]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	pinsrb(xmm0, eax, 0x7);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l2f8);
+	sub(N, 0x8);
+	cmp(N, 0x8);
+	jge(l20, T_NEAR);
+	align(4);
+
+L(l308);
+	cmp(N, 0x4);
+	jl(l4cc, T_NEAR);
+	align(4);
+
+L(l314);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*2]);
+	lea(I, ptr[A1+LDA*4]);
+	mov(A, I);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l3a0, T_NEAR);
+	align(4);
+
+L(l32c);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	sub(A1, -16);
+	movdqu(xmm2, xword[A2-0x80]);
+	movdqu(xmm3, xword[A2+LDA*1-0x80]);
+	sub(A2, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	movdqu(xword[B-0x60], xmm4);
+	movdqu(xword[B-0x50], xmm3);
+	sub(B, -64);
+	dec(I);
+	jg(l32c, T_NEAR);
+	align(4);
+
+L(l3a0);
+	test(M, 0x8);
+	jle(l3f0, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	sub(A1, -8);
+	movq(xmm2, qword[A2-0x80]);
+	movq(xmm3, qword[A2+LDA*1-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	sub(B, -32);
+	align(4);
+
+L(l3f0);
+	test(M, 0x4);
+	jle(l434, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	sub(A1, -4);
+	movd(xmm2, dword[A2-0x80]);
+	movd(xmm3, dword[A2+LDA*1-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l434);
+	test(M, 0x2);
+	jle(l47c, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA*1-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x3);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l47c);
+	test(M, 0x1);
+	jle(l4bc, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x3);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l4bc);
+	sub(N, 0x4);
+	cmp(N, 0x4);
+	jge(l314, T_NEAR);
+	align(4);
+
+L(l4cc);
+	cmp(N, 0x2);
+	jl(l5de, T_NEAR);
+	align(4);
+
+L(l4d8);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*1]);
+	lea(I, ptr[A1+LDA*2]);
+	mov(A, I);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l528, T_NEAR);
+	align(4);
+
+L(l4f0);
+	movdqu(xmm0, xword[A1-0x80]);
+	sub(A1, -16);
+	movdqu(xmm1, xword[A2-0x80]);
+	sub(A2, -16);
+	movdqa(xmm2, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm2, xmm1);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm2);
+	sub(B, -32);
+	dec(I);
+	jg(l4f0, T_NEAR);
+	align(4);
+
+L(l528);
+	test(M, 0x8);
+	jle(l554, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	sub(A1, -8);
+	movq(xmm1, qword[A2-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l554);
+	test(M, 0x4);
+	jle(l580, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	sub(A1, -4);
+	movd(xmm1, dword[A2-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l580);
+	test(M, 0x2);
+	jle(l5b0, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x1);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l5b0);
+	test(M, 0x1);
+	jle(l5d0, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	mov(byte[B-0x80], al);
+	mov(al, byte[A2-0x80]);
+	mov(byte[B-0x7f], al);
+	sub(B, -2);
+	align(4);
+
+L(l5d0);
+	sub(N, 0x2);
+	cmp(N, 0x2);
+	jge(l4d8, T_NEAR);
+	align(4);
+
+L(l5de);
+	cmp(N, 0x1);
+	jl(l698, T_NEAR);
+	align(4);
+
+L(l5e8);
+	mov(A1, A);
+	add(A, LDA);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l614, T_NEAR);
+	align(4);
+
+L(l5f8);
+	movdqu(xmm0, xword[A1-0x80]);
+	sub(A1, -16);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	dec(I);
+	jg(l5f8, T_NEAR);
+	align(4);
+
+L(l614);
+	test(M, 0x8);
+	jle(l634, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	sub(A1, -8);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l634);
+	test(M, 0x4);
+	jle(l654, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	sub(A1, -4);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l654);
+	test(M, 0x2);
+	jle(l670, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	mov(word[B-0x80], ax);
+	sub(A1, -2);
+	sub(B, -2);
+	align(4);
+
+L(l670);
+	test(M, 0x1);
+	jle(l688, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	mov(byte[B-0x80], al);
+	sub(B, -1);
+	align(4);
+
+L(l688);
+	sub(N, 0x1);
+	cmp(N, 0x1);
+	jge(l5e8, T_NEAR);
+	align(4);
+
+L(l698);
+
+	postamble();
+}
+outLocalLabel();
+
+#undef M
+#undef N
+#undef A
+#undef LDA
+#undef ALPHA
+#undef B
+#undef I
+#undef A1
+#undef A2
+#undef LDA3
+#ifdef _WIN32
+#undef ARG_ALPHA
+#undef ARG_B
+#endif
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_bt_kern.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_bt_kern.cpp
new file mode 100644
index 0000000000..53e99d94de
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_bt_kern.cpp
@@ -0,0 +1,501 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "jit_generator.hpp"
+#include "common.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+jit_avx512_core_u8_copy_bt_kern::jit_avx512_core_u8_copy_bt_kern(): jit_generator(nullptr, GEMM_CODE_SIZE)
+{
+
+#ifndef _WIN32
+#define M	rdi
+#define N	rsi
+#define A	rdx
+#define LDA	rcx
+#define ALPHA	r8
+#define B	r9
+
+#define I	rax
+#define A1	r10
+#define A2	r8
+#define LDA3	r11
+
+#else
+
+#define M	rcx
+#define N	rdx
+#define A	r8
+#define LDA	r9
+#define ALPHA	rax
+#define B	rdi
+
+#define I	rax
+#define A1	rsi
+#define A2	r10
+#define LDA3	r11
+
+#define ARG_ALPHA	40+stacksize+rsp
+#define ARG_B		48+stacksize+rsp
+
+#endif
+
+inLocalLabel();
+{
+
+Xbyak::Label l120;
+Xbyak::Label l14c;
+Xbyak::Label l168;
+Xbyak::Label l178;
+Xbyak::Label l184;
+Xbyak::Label l194;
+Xbyak::Label l20;
+Xbyak::Label l20c;
+Xbyak::Label l250;
+Xbyak::Label l27c;
+Xbyak::Label l298;
+Xbyak::Label l2a8;
+Xbyak::Label l2b4;
+Xbyak::Label l2c8;
+Xbyak::Label l34;
+Xbyak::Label l360;
+Xbyak::Label l3b4;
+Xbyak::Label l3e8;
+Xbyak::Label l400;
+Xbyak::Label l40e;
+Xbyak::Label l418;
+Xbyak::Label l428;
+Xbyak::Label l4a0;
+Xbyak::Label l4e8;
+Xbyak::Label l50c;
+Xbyak::Label l524;
+Xbyak::Label l534;
+Xbyak::Label lcc;
+
+	preamble();
+#ifdef _WIN32
+	auto stacksize = get_size_of_abi_save_regs();
+	mov(ALPHA, ptr[ARG_ALPHA]);
+	mov(B, ptr[ARG_B]);
+#endif
+
+	mov(M, qword[M]);
+	mov(N, qword[N]);
+	mov(LDA, qword[LDA]);
+	lea(LDA3, ptr[LDA+LDA*2]);
+	sub(A, -128);
+	sub(B, -128);
+	cmp(N, 0x8);
+	jl(l178, T_NEAR);
+	align(4);
+
+L(l20);
+	mov(A1, A);
+	add(A, 0x8);
+	mov(I, M);
+	sar(I, 0x3);
+	jle(lcc, T_NEAR);
+	align(4);
+
+L(l34);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqu(xword[B-0x60], xmm0);
+	movdqu(xword[B-0x50], xmm1);
+	sub(B, -64);
+	dec(I);
+	jg(l34, T_NEAR);
+	align(4);
+
+L(lcc);
+	test(M, 0x4);
+	jle(l120, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	sub(B, -32);
+	align(4);
+
+L(l120);
+	test(M, 0x2);
+	jle(l14c, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l14c);
+	test(M, 0x1);
+	jle(l168, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l168);
+	sub(N, 0x8);
+	cmp(N, 0x8);
+	jge(l20, T_NEAR);
+	align(4);
+
+L(l178);
+	cmp(N, 0x4);
+	jl(l2a8, T_NEAR);
+	align(4);
+
+L(l184);
+	mov(A1, A);
+	add(A, 0x4);
+	mov(I, M);
+	sar(I, 0x3);
+	jle(l20c, T_NEAR);
+	align(4);
+
+L(l194);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	movdqu(xword[B-0x70], xmm0);
+	sub(B, -32);
+	dec(I);
+	jg(l194, T_NEAR);
+	align(4);
+
+L(l20c);
+	test(M, 0x4);
+	jle(l250, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l250);
+	test(M, 0x2);
+	jle(l27c, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l27c);
+	test(M, 0x1);
+	jle(l298, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l298);
+	sub(N, 0x4);
+	cmp(N, 0x4);
+	jge(l184, T_NEAR);
+	align(4);
+
+L(l2a8);
+	cmp(N, 0x2);
+	jl(l40e, T_NEAR);
+	align(4);
+
+L(l2b4);
+	mov(A1, A);
+	add(A, 0x2);
+	mov(LDA3, M);
+	sar(LDA3, 0x3);
+	jle(l360, T_NEAR);
+	align(4);
+
+L(l2c8);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm4, eax, 0x0);
+	punpcklbw(xmm1, xmm2);
+	punpcklbw(xmm3, xmm4);
+	punpcklwd(xmm1, xmm3);
+	punpcklqdq(xmm0, xmm1);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	dec(LDA3);
+	jg(l2c8, T_NEAR);
+	align(4);
+
+L(l360);
+	test(M, 0x4);
+	jle(l3b4, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l3b4);
+	test(M, 0x2);
+	jle(l3e8, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l3e8);
+	test(M, 0x1);
+	jle(l400, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	mov(word[B-0x80], ax);
+	sub(B, -2);
+	align(4);
+
+L(l400);
+	sub(N, 0x2);
+	cmp(N, 0x2);
+	jge(l2b4, T_NEAR);
+	align(4);
+
+L(l40e);
+	cmp(N, 0x1);
+	jl(l534, T_NEAR);
+	align(4);
+
+L(l418);
+	mov(A1, A);
+	add(A, 0x1);
+	mov(LDA3, M);
+	sar(LDA3, 0x3);
+	jle(l4a0, T_NEAR);
+	align(4);
+
+L(l428);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x7);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	dec(LDA3);
+	jg(l428, T_NEAR);
+	align(4);
+
+L(l4a0);
+	test(M, 0x4);
+	jle(l4e8, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x3);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l4e8);
+	test(M, 0x2);
+	jle(l50c, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	mov(byte[B-0x80], al);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	mov(byte[B-0x7f], al);
+	sub(B, -2);
+	align(4);
+
+L(l50c);
+	test(M, 0x1);
+	jle(l524, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	mov(byte[B-0x80], al);
+	sub(B, -1);
+	align(4);
+
+L(l524);
+	sub(N, 0x1);
+	cmp(N, 0x1);
+	jge(l418, T_NEAR);
+	align(4);
+
+L(l534);
+
+	postamble();
+}
+outLocalLabel();
+
+#undef M
+#undef N
+#undef A
+#undef LDA
+#undef ALPHA
+#undef B
+#undef I
+#undef A1
+#undef A2
+#undef LDA3
+#ifdef _WIN32
+#undef ARG_ALPHA
+#undef ARG_B
+#endif
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_an_kern.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_an_kern.cpp
new file mode 100644
index 0000000000..49a312fc88
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_an_kern.cpp
@@ -0,0 +1,1283 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "jit_generator.hpp"
+#include "common.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+jit_avx512_core_u8_copy_sum_an_kern::jit_avx512_core_u8_copy_sum_an_kern(): jit_generator(nullptr, GEMM_CODE_SIZE)
+{
+
+#ifndef _WIN32
+#define M	rdi
+#define N	rsi
+#define A	rdx
+#define LDA	rcx
+#define ALPHA	r8
+#define B	r9
+
+#define I	rax
+#define A1	r10
+#define A2	r8
+#define LDA3	r11
+
+#define ARG_BIAS	24+stacksize+rsp
+
+#else
+
+#define M	rcx
+#define N	rdx
+#define A	r8
+#define LDA	r9
+#define ALPHA	rax
+#define B	rdi
+
+#define I	rax
+#define A1	rsi
+#define A2	r10
+#define LDA3	r11
+
+#define ARG_ALPHA	40+stacksize+rsp
+#define ARG_B		48+stacksize+rsp
+#define ARG_BIAS	72+stacksize+rsp
+
+#endif
+
+inLocalLabel();
+{
+
+Xbyak::Label l1024;
+Xbyak::Label l1090;
+Xbyak::Label l10d4;
+Xbyak::Label l10fc;
+Xbyak::Label l111a;
+Xbyak::Label l1124;
+Xbyak::Label l113c;
+Xbyak::Label l11d4;
+Xbyak::Label l1234;
+Xbyak::Label l1278;
+Xbyak::Label l129c;
+Xbyak::Label l12bc;
+Xbyak::Label l20;
+Xbyak::Label l2a0;
+Xbyak::Label l3c0;
+Xbyak::Label l438;
+Xbyak::Label l480;
+Xbyak::Label l48c;
+Xbyak::Label l4c8;
+Xbyak::Label l5c;
+Xbyak::Label l6a8;
+Xbyak::Label l7b4;
+Xbyak::Label l850;
+Xbyak::Label l89c;
+Xbyak::Label l8a8;
+Xbyak::Label l8d0;
+Xbyak::Label l9d0;
+Xbyak::Label la64;
+Xbyak::Label lab8;
+Xbyak::Label lae8;
+Xbyak::Label laf4;
+Xbyak::Label lb14;
+Xbyak::Label lc30;
+Xbyak::Label lcc8;
+Xbyak::Label ld1c;
+Xbyak::Label ld54;
+Xbyak::Label ld78;
+Xbyak::Label ld84;
+Xbyak::Label ld9c;
+Xbyak::Label le58;
+Xbyak::Label lebc;
+Xbyak::Label lef8;
+Xbyak::Label lf1c;
+Xbyak::Label lf3c;
+Xbyak::Label lf48;
+Xbyak::Label lf60;
+
+	preamble();
+	auto stacksize = get_size_of_abi_save_regs();
+#ifdef _WIN32
+	mov(ALPHA, ptr[ARG_ALPHA]);
+	mov(B, ptr[ARG_B]);
+#endif
+
+	mov(M, qword[M]);
+	mov(N, qword[N]);
+	mov(LDA, qword[LDA]);
+	lea(LDA3, ptr[LDA+LDA*2]);
+	sub(A, -128);
+	sub(B, -128);
+	cmp(N, 0x30);
+	jl(l480, T_NEAR);
+	align(4);
+
+L(l20);
+	mov(A1, A);
+	add(A, 0x30);
+	vxorps(ymm8, ymm8, ymm8);
+	vxorps(ymm9, ymm9, ymm9);
+	vxorps(ymm10, ymm10, ymm10);
+	vxorps(ymm11, ymm11, ymm11);
+	vxorps(ymm12, ymm12, ymm12);
+	vxorps(ymm13, ymm13, ymm13);
+	vxorps(ymm14, ymm14, ymm14);
+	vxorps(ymm15, ymm15, ymm15);
+	mov(I, M);
+	sar(I, 0x2);
+	jle(l2a0, T_NEAR);
+	align(4);
+
+L(l5c);
+	vmovdqu(xmm0, xword[A1-0x80]);
+	vmovdqu(xmm1, xword[A1+LDA*1-0x80]);
+	vmovdqu(xmm2, xword[A1+LDA*2-0x80]);
+	vmovdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	vpunpcklbw(xmm4, xmm0, xmm1);
+	vpunpckhbw(xmm5, xmm0, xmm1);
+	vpunpcklbw(xmm6, xmm2, xmm3);
+	vpunpckhbw(xmm7, xmm2, xmm3);
+	vpunpcklwd(xmm0, xmm4, xmm6);
+	vpunpckhwd(xmm1, xmm4, xmm6);
+	vpunpcklwd(xmm2, xmm5, xmm7);
+	vpunpckhwd(xmm3, xmm5, xmm7);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm1);
+	vmovhlps(xmm7, xmm1, xmm1);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm8, ymm8, ymm5);
+	vmovdqu(xword[B-0x80], xmm0);
+	vmovdqu(xword[B-0x70], xmm1);
+	vpmovsxbw(ymm5, xmm2);
+	vmovhlps(xmm6, xmm2, xmm2);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm3);
+	vmovhlps(xmm7, xmm3, xmm3);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm9, ymm9, ymm5);
+	vmovdqu(xword[B-0x60], xmm2);
+	vmovdqu(xword[B-0x50], xmm3);
+	vmovdqu(xmm0, xword[A1-0x70]);
+	vmovdqu(xmm1, xword[A1+LDA*1-0x70]);
+	vmovdqu(xmm2, xword[A1+LDA*2-0x70]);
+	vmovdqu(xmm3, xword[A1+LDA3*1-0x70]);
+	vpunpcklbw(xmm4, xmm0, xmm1);
+	vpunpckhbw(xmm5, xmm0, xmm1);
+	vpunpcklbw(xmm6, xmm2, xmm3);
+	vpunpckhbw(xmm7, xmm2, xmm3);
+	vpunpcklwd(xmm0, xmm4, xmm6);
+	vpunpckhwd(xmm1, xmm4, xmm6);
+	vpunpcklwd(xmm2, xmm5, xmm7);
+	vpunpckhwd(xmm3, xmm5, xmm7);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm1);
+	vmovhlps(xmm7, xmm1, xmm1);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm10, ymm10, ymm5);
+	vmovdqu(xword[B-0x40], xmm0);
+	vmovdqu(xword[B-0x30], xmm1);
+	vpmovsxbw(ymm5, xmm2);
+	vmovhlps(xmm6, xmm2, xmm2);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm3);
+	vmovhlps(xmm7, xmm3, xmm3);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm11, ymm11, ymm5);
+	vmovdqu(xword[B-0x20], xmm2);
+	vmovdqu(xword[B-0x10], xmm3);
+	vmovdqu(xmm0, xword[A1-0x60]);
+	vmovdqu(xmm1, xword[A1+LDA*1-0x60]);
+	vmovdqu(xmm2, xword[A1+LDA*2-0x60]);
+	vmovdqu(xmm3, xword[A1+LDA3*1-0x60]);
+	lea(A1, ptr[A1+LDA*4]);
+	vpunpcklbw(xmm4, xmm0, xmm1);
+	vpunpckhbw(xmm5, xmm0, xmm1);
+	vpunpcklbw(xmm6, xmm2, xmm3);
+	vpunpckhbw(xmm7, xmm2, xmm3);
+	vpunpcklwd(xmm0, xmm4, xmm6);
+	vpunpckhwd(xmm1, xmm4, xmm6);
+	vpunpcklwd(xmm2, xmm5, xmm7);
+	vpunpckhwd(xmm3, xmm5, xmm7);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm1);
+	vmovhlps(xmm7, xmm1, xmm1);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm12, ymm12, ymm5);
+	vmovdqu(xword[B], xmm0);
+	vmovdqu(xword[B+0x10], xmm1);
+	vpmovsxbw(ymm5, xmm2);
+	vmovhlps(xmm6, xmm2, xmm2);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm3);
+	vmovhlps(xmm7, xmm3, xmm3);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm13, ymm13, ymm5);
+	vmovdqu(xword[B+0x20], xmm2);
+	vmovdqu(xword[B+0x30], xmm3);
+	sub(B, -192);
+	dec(I);
+	jg(l5c, T_NEAR);
+	align(4);
+
+L(l2a0);
+	test(M, 0x2);
+	jle(l3c0, T_NEAR);
+	vmovdqu(xmm0, xword[A1-0x80]);
+	vmovdqu(xmm1, xword[A1-0x70]);
+	vmovdqu(xmm2, xword[A1-0x60]);
+	add(A1, LDA);
+	vmovdqu(xmm6, xword[A1-0x80]);
+	vmovdqu(xmm4, xword[A1-0x70]);
+	vmovdqu(xmm5, xword[A1-0x60]);
+	add(A1, LDA);
+	vpunpcklbw(xmm3, xmm0, xmm6);
+	vpunpckhbw(xmm0, xmm0, xmm6);
+	vpmovsxbw(ymm7, xmm3);
+	vmovhlps(xmm6, xmm3, xmm3);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm7, ymm7, ymm6);
+	vpmovsxwd(ymm7, xmm7);
+	vpaddd(ymm8, ymm8, ymm7);
+	vmovdqu(xword[B-0x80], xmm3);
+	vpmovsxbw(ymm7, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm7, ymm7, ymm6);
+	vpmovsxwd(ymm7, xmm7);
+	vpaddd(ymm9, ymm9, ymm7);
+	vmovdqu(xword[B-0x70], xmm0);
+	vpunpcklbw(xmm3, xmm1, xmm4);
+	vpunpckhbw(xmm0, xmm1, xmm4);
+	vpmovsxbw(ymm7, xmm3);
+	vmovhlps(xmm6, xmm3, xmm3);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm7, ymm7, ymm6);
+	vpmovsxwd(ymm7, xmm7);
+	vpaddd(ymm10, ymm10, ymm7);
+	vmovdqu(xword[B-0x60], xmm3);
+	vpmovsxbw(ymm7, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm7, ymm7, ymm6);
+	vpmovsxwd(ymm7, xmm7);
+	vpaddd(ymm11, ymm11, ymm7);
+	vmovdqu(xword[B-0x50], xmm0);
+	vpunpcklbw(xmm3, xmm2, xmm5);
+	vpunpckhbw(xmm0, xmm2, xmm5);
+	vpmovsxbw(ymm7, xmm3);
+	vmovhlps(xmm6, xmm3, xmm3);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm7, ymm7, ymm6);
+	vpmovsxwd(ymm7, xmm7);
+	vpaddd(ymm12, ymm12, ymm7);
+	vmovdqu(xword[B-0x40], xmm3);
+	vpmovsxbw(ymm7, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm7, ymm7, ymm6);
+	vpmovsxwd(ymm7, xmm7);
+	vpaddd(ymm13, ymm13, ymm7);
+	vmovdqu(xword[B-0x30], xmm0);
+	sub(B, -96);
+	align(4);
+
+L(l3c0);
+	test(M, 0x1);
+	jle(l438, T_NEAR);
+	vmovdqu(xmm0, xword[A1-0x80]);
+	vmovdqu(xmm1, xword[A1-0x70]);
+	vmovdqu(xmm2, xword[A1-0x60]);
+	add(A1, LDA);
+	vpmovsxbd(ymm7, xmm0);
+	vpaddd(ymm8, ymm8, ymm7);
+	vmovhlps(xmm7, xmm0, xmm0);
+	vpmovsxbd(ymm7, xmm7);
+	vpaddd(ymm9, ymm9, ymm7);
+	vmovdqu(xword[B-0x80], xmm0);
+	vpmovsxbd(ymm7, xmm1);
+	vpaddd(ymm10, ymm10, ymm7);
+	vmovhlps(xmm7, xmm1, xmm1);
+	vpmovsxbd(ymm7, xmm7);
+	vpaddd(ymm11, ymm11, ymm7);
+	vmovdqu(xword[B-0x70], xmm1);
+	vpmovsxbd(ymm7, xmm2);
+	vpaddd(ymm12, ymm12, ymm7);
+	vmovhlps(xmm7, xmm2, xmm2);
+	vpmovsxbd(ymm7, xmm7);
+	vpaddd(ymm13, ymm13, ymm7);
+	vmovdqu(xword[B-0x60], xmm2);
+	sub(B, -48);
+	align(4);
+
+L(l438);
+	mov(A1, qword[ARG_BIAS]);
+	vmovdqu(yword[A1], ymm8);
+	vmovdqu(yword[A1+0x20], ymm9);
+	vmovdqu(yword[A1+0x40], ymm10);
+	vmovdqu(yword[A1+0x60], ymm11);
+	vmovdqu(yword[A1+0x80], ymm12);
+	vmovdqu(yword[A1+0xa0], ymm13);
+	add(qword[ARG_BIAS], 0xc0);
+	sub(N, 0x30);
+	cmp(N, 0x30);
+	jge(l20, T_NEAR);
+	vzeroupper();
+	align(4);
+
+L(l480);
+	cmp(N, 0x20);
+	jl(l89c, T_NEAR);
+	align(4);
+
+L(l48c);
+	mov(A1, A);
+	add(A, 0x20);
+	pxor(xmm8, xmm8);
+	pxor(xmm9, xmm9);
+	pxor(xmm10, xmm10);
+	pxor(xmm11, xmm11);
+	pxor(xmm12, xmm12);
+	pxor(xmm13, xmm13);
+	pxor(xmm14, xmm14);
+	pxor(xmm15, xmm15);
+	mov(I, M);
+	sar(I, 0x2);
+	jle(l6a8, T_NEAR);
+	align(4);
+
+L(l4c8);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	movdqa(xmm4, xmm0);
+	punpcklbw(xmm0, xmm1);
+	punpckhbw(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpcklbw(xmm2, xmm3);
+	punpckhbw(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqa(xmm2, xmm4);
+	punpcklwd(xmm4, xmm5);
+	punpckhwd(xmm2, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B-0x60], xmm4);
+	pmovsxbw(xmm5, xmm2);
+	movhlps(xmm6, xmm2);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B-0x50], xmm2);
+	movdqu(xmm0, xword[A1-0x70]);
+	movdqu(xmm1, xword[A1+LDA*1-0x70]);
+	movdqu(xmm2, xword[A1+LDA*2-0x70]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x70]);
+	lea(A1, ptr[A1+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpcklbw(xmm0, xmm1);
+	punpckhbw(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpcklbw(xmm2, xmm3);
+	punpckhbw(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	movdqa(xmm2, xmm4);
+	punpcklwd(xmm4, xmm5);
+	punpckhwd(xmm2, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm12, xmm5);
+	movdqu(xword[B-0x40], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm13, xmm5);
+	movdqu(xword[B-0x30], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	movdqu(xword[B-0x20], xmm4);
+	pmovsxbw(xmm5, xmm2);
+	movhlps(xmm6, xmm2);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm15, xmm5);
+	movdqu(xword[B-0x10], xmm2);
+	sub(B, -128);
+	dec(I);
+	jg(l4c8, T_NEAR);
+	align(4);
+
+L(l6a8);
+	test(M, 0x2);
+	jle(l7b4, T_NEAR);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1-0x70]);
+	add(A1, LDA);
+	movdqu(xmm2, xword[A1-0x80]);
+	movdqu(xmm3, xword[A1-0x70]);
+	add(A1, LDA);
+	movdqa(xmm4, xmm0);
+	punpcklbw(xmm0, xmm2);
+	punpckhbw(xmm4, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm4);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm11, xmm6);
+	movdqu(xword[B-0x70], xmm4);
+	movdqa(xmm4, xmm1);
+	punpcklbw(xmm1, xmm3);
+	punpckhbw(xmm4, xmm3);
+	pmovsxbw(xmm5, xmm1);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm12, xmm5);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm13, xmm6);
+	movdqu(xword[B-0x60], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm15, xmm6);
+	movdqu(xword[B-0x50], xmm4);
+	sub(B, -64);
+	align(4);
+
+L(l7b4);
+	test(M, 0x1);
+	jle(l850, T_NEAR);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1-0x70]);
+	add(A1, LDA);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm8, xmm5);
+	pshufd(xmm6, xmm0, 0x55);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	pshufd(xmm5, xmm0, 0xaa);
+	pmovsxbd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	pshufd(xmm6, xmm0, 0xff);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm11, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbd(xmm5, xmm1);
+	paddd(xmm12, xmm5);
+	pshufd(xmm6, xmm1, 0x55);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm13, xmm6);
+	pshufd(xmm5, xmm1, 0xaa);
+	pmovsxbd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	pshufd(xmm6, xmm1, 0xff);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm15, xmm6);
+	movdqu(xword[B-0x70], xmm1);
+	sub(B, -32);
+	align(4);
+
+L(l850);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm8);
+	movdqu(xword[A1+0x10], xmm9);
+	movdqu(xword[A1+0x20], xmm10);
+	movdqu(xword[A1+0x30], xmm11);
+	movdqu(xword[A1+0x40], xmm12);
+	movdqu(xword[A1+0x50], xmm13);
+	movdqu(xword[A1+0x60], xmm14);
+	movdqu(xword[A1+0x70], xmm15);
+	add(qword[ARG_BIAS], 0x80);
+	sub(N, 0x20);
+	cmp(N, 0x20);
+	jge(l48c, T_NEAR);
+	align(4);
+
+L(l89c);
+	cmp(N, 0x10);
+	jl(lae8, T_NEAR);
+	align(4);
+
+L(l8a8);
+	mov(A1, A);
+	add(A, 0x10);
+	pxor(xmm8, xmm8);
+	pxor(xmm9, xmm9);
+	pxor(xmm10, xmm10);
+	pxor(xmm11, xmm11);
+	mov(I, M);
+	sar(I, 0x2);
+	jle(l9d0, T_NEAR);
+	align(4);
+
+L(l8d0);
+	movdqu(xmm0, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqu(xmm1, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqu(xmm2, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqu(xmm3, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqa(xmm4, xmm0);
+	punpcklbw(xmm0, xmm1);
+	punpckhbw(xmm4, xmm1);
+	movdqa(xmm1, xmm2);
+	punpcklbw(xmm2, xmm3);
+	punpckhbw(xmm1, xmm3);
+	movdqa(xmm3, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm3, xmm2);
+	movdqa(xmm2, xmm4);
+	punpcklwd(xmm4, xmm1);
+	punpckhwd(xmm2, xmm1);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm3);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	pmovsxbw(xmm5, xmm2);
+	movhlps(xmm6, xmm2);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B-0x60], xmm4);
+	movdqu(xword[B-0x50], xmm2);
+	sub(B, -64);
+	dec(I);
+	jg(l8d0, T_NEAR);
+	align(4);
+
+L(l9d0);
+	test(M, 0x2);
+	jle(la64, T_NEAR);
+	movdqu(xmm0, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqu(xmm1, xword[A1-0x80]);
+	add(A1, LDA);
+	movdqa(xmm2, xmm0);
+	punpcklbw(xmm0, xmm1);
+	punpckhbw(xmm2, xmm1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	pmovsxbw(xmm5, xmm2);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movhlps(xmm6, xmm2);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm11, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm2);
+	sub(B, -32);
+	align(4);
+
+L(la64);
+	test(M, 0x1);
+	jle(lab8, T_NEAR);
+	movdqu(xmm0, xword[A1-0x80]);
+	add(A1, LDA);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm8, xmm5);
+	pshufd(xmm6, xmm0, 0x55);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	pshufd(xmm5, xmm0, 0xaa);
+	pmovsxbd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	pshufd(xmm6, xmm0, 0xff);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm11, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(lab8);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm8);
+	movdqu(xword[A1+0x10], xmm9);
+	movdqu(xword[A1+0x20], xmm10);
+	movdqu(xword[A1+0x30], xmm11);
+	add(qword[ARG_BIAS], 0x40);
+	sub(N, 0x10);
+	cmp(N, 0x10);
+	jge(l8a8, T_NEAR);
+	align(4);
+
+L(lae8);
+	cmp(N, 0x8);
+	jl(ld78, T_NEAR);
+	align(4);
+
+L(laf4);
+	mov(A1, A);
+	add(A, 0x8);
+	pxor(xmm8, xmm8);
+	pxor(xmm9, xmm9);
+	mov(I, M);
+	sar(I, 0x3);
+	jle(lc30, T_NEAR);
+	align(4);
+
+L(lb14);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x60], xmm0);
+	movdqu(xword[B-0x50], xmm1);
+	sub(B, -64);
+	dec(I);
+	jg(lb14, T_NEAR);
+	align(4);
+
+L(lc30);
+	test(M, 0x4);
+	jle(lcc8, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	sub(B, -32);
+	align(4);
+
+L(lcc8);
+	test(M, 0x2);
+	jle(ld1c, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(ld1c);
+	test(M, 0x1);
+	jle(ld54, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	pmovsxbd(xmm5, xmm0);
+	pshufd(xmm6, xmm0, 0x55);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm8, xmm5);
+	paddd(xmm9, xmm6);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(ld54);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm8);
+	movdqu(xword[A1+0x10], xmm9);
+	add(qword[ARG_BIAS], 0x20);
+	sub(N, 0x8);
+	cmp(N, 0x8);
+	jge(laf4, T_NEAR);
+	align(4);
+
+L(ld78);
+	cmp(N, 0x4);
+	jl(lf3c, T_NEAR);
+	align(4);
+
+L(ld84);
+	mov(A1, A);
+	add(A, 0x4);
+	pxor(xmm7, xmm7);
+	mov(I, M);
+	sar(I, 0x3);
+	jle(le58, T_NEAR);
+	align(4);
+
+L(ld9c);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	sub(B, -32);
+	dec(I);
+	jg(ld9c, T_NEAR);
+	align(4);
+
+L(le58);
+	test(M, 0x4);
+	jle(lebc, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(lebc);
+	test(M, 0x2);
+	jle(lef8, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(lef8);
+	test(M, 0x1);
+	jle(lf1c, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(lf1c);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x10);
+	sub(N, 0x4);
+	cmp(N, 0x4);
+	jge(ld84, T_NEAR);
+	align(4);
+
+L(lf3c);
+	cmp(N, 0x2);
+	jl(l111a, T_NEAR);
+	align(4);
+
+L(lf48);
+	mov(A1, A);
+	add(A, 0x2);
+	pxor(xmm7, xmm7);
+	mov(LDA3, M);
+	sar(LDA3, 0x3);
+	jle(l1024, T_NEAR);
+	align(4);
+
+L(lf60);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm4, eax, 0x0);
+	punpcklbw(xmm1, xmm2);
+	punpcklbw(xmm3, xmm4);
+	punpcklwd(xmm1, xmm3);
+	punpcklqdq(xmm0, xmm1);
+	pshufd(xmm6, xmm0, 0xd8);
+	pmovsxbw(xmm5, xmm6);
+	movhlps(xmm6, xmm6);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	dec(LDA3);
+	jg(lf60, T_NEAR);
+	align(4);
+
+L(l1024);
+	test(M, 0x4);
+	jle(l1090, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l1090);
+	test(M, 0x2);
+	jle(l10d4, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l10d4);
+	test(M, 0x1);
+	jle(l10fc, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	mov(word[B-0x80], ax);
+	sub(B, -2);
+	align(4);
+
+L(l10fc);
+	mov(A1, qword[ARG_BIAS]);
+	movq(qword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x8);
+	sub(N, 0x2);
+	cmp(N, 0x2);
+	jge(lf48, T_NEAR);
+	align(4);
+
+L(l111a);
+	cmp(N, 0x1);
+	jl(l12bc, T_NEAR);
+	align(4);
+
+L(l1124);
+	mov(A1, A);
+	add(A, 0x1);
+	pxor(xmm7, xmm7);
+	mov(LDA3, M);
+	sar(LDA3, 0x3);
+	jle(l11d4, T_NEAR);
+	align(4);
+
+L(l113c);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x7);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	dec(LDA3);
+	jg(l113c, T_NEAR);
+	align(4);
+
+L(l11d4);
+	test(M, 0x4);
+	jle(l1234, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x3);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l1234);
+	test(M, 0x2);
+	jle(l1278, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x0);
+	mov(byte[B-0x80], al);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	mov(byte[B-0x7f], al);
+	sub(B, -2);
+	align(4);
+
+L(l1278);
+	test(M, 0x1);
+	jle(l129c, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	mov(byte[B-0x80], al);
+	sub(B, -1);
+	align(4);
+
+L(l129c);
+	mov(A1, qword[ARG_BIAS]);
+	movd(dword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x4);
+	sub(N, 0x1);
+	cmp(N, 0x1);
+	jge(l1124, T_NEAR);
+	align(4);
+
+L(l12bc);
+
+	postamble();
+}
+outLocalLabel();
+
+#undef M
+#undef N
+#undef A
+#undef LDA
+#undef ALPHA
+#undef B
+#undef I
+#undef A1
+#undef A2
+#undef LDA3
+#ifdef _WIN32
+#undef ARG_ALPHA
+#undef ARG_B
+#endif
+#undef ARG_BIAS
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_at_kern.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_at_kern.cpp
new file mode 100644
index 0000000000..a4f4ff09c6
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_at_kern.cpp
@@ -0,0 +1,3163 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "jit_generator.hpp"
+#include "common.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+jit_avx512_core_u8_copy_sum_at_kern::jit_avx512_core_u8_copy_sum_at_kern(): jit_generator(nullptr, GEMM_CODE_SIZE)
+{
+
+#ifndef _WIN32
+#define M	rdi
+#define N	rsi
+#define A	rdx
+#define LDA	rcx
+#define ALPHA	r8
+#define B	r9
+
+#define I	rax
+#define A1	r10
+#define A2	r8
+#define LDA3	r11
+
+#define ARG_BIAS	24+stacksize+rsp
+
+#else
+
+#define M	rcx
+#define N	rdx
+#define A	r8
+#define LDA	r9
+#define ALPHA	rax
+#define B	rdi
+
+#define I	rax
+#define A1	rsi
+#define A2	r10
+#define LDA3	r11
+
+#define ARG_ALPHA	40+stacksize+rsp
+#define ARG_B		48+stacksize+rsp
+#define ARG_BIAS	72+stacksize+rsp
+
+#endif
+
+inLocalLabel();
+{
+
+Xbyak::Label l1750;
+Xbyak::Label l1b6c;
+Xbyak::Label l1e14;
+Xbyak::Label l20;
+Xbyak::Label l2068;
+Xbyak::Label l226c;
+Xbyak::Label l22b8;
+Xbyak::Label l22c4;
+Xbyak::Label l22f4;
+Xbyak::Label l26b4;
+Xbyak::Label l28cc;
+Xbyak::Label l2a2c;
+Xbyak::Label l2b5c;
+Xbyak::Label l2c64;
+Xbyak::Label l2c94;
+Xbyak::Label l2ca0;
+Xbyak::Label l2cc8;
+Xbyak::Label l2eac;
+Xbyak::Label l2fc0;
+Xbyak::Label l3078;
+Xbyak::Label l3118;
+Xbyak::Label l319c;
+Xbyak::Label l31c0;
+Xbyak::Label l31cc;
+Xbyak::Label l31ec;
+Xbyak::Label l32e4;
+Xbyak::Label l3378;
+Xbyak::Label l33dc;
+Xbyak::Label l3434;
+Xbyak::Label l347c;
+Xbyak::Label l349c;
+Xbyak::Label l34a8;
+Xbyak::Label l34c8;
+Xbyak::Label l3558;
+Xbyak::Label l35b0;
+Xbyak::Label l35f4;
+Xbyak::Label l3638;
+Xbyak::Label l366c;
+Xbyak::Label l368a;
+Xbyak::Label l3694;
+Xbyak::Label l36a8;
+Xbyak::Label l36ec;
+Xbyak::Label l3728;
+Xbyak::Label l3760;
+Xbyak::Label l3794;
+Xbyak::Label l37b8;
+Xbyak::Label l37d8;
+Xbyak::Label l5cc;
+Xbyak::Label l6c;
+Xbyak::Label l968;
+Xbyak::Label lc80;
+Xbyak::Label lf1c;
+Xbyak::Label lf64;
+Xbyak::Label lf70;
+Xbyak::Label lfb4;
+
+	preamble();
+	auto stacksize = get_size_of_abi_save_regs();
+#ifdef _WIN32
+	mov(ALPHA, ptr[ARG_ALPHA]);
+	mov(B, ptr[ARG_B]);
+#endif
+
+	mov(N, qword[N]);
+	mov(M, qword[M]);
+	mov(LDA, qword[LDA]);
+	sub(A, -128);
+	sub(B, -128);
+	lea(LDA3, ptr[LDA+LDA*2]);
+	cmp(N, 0x30);
+	jl(lf64, T_NEAR);
+	align(4);
+
+L(l20);
+	mov(A1, A);
+	mov(I, LDA);
+	shl(I, 0x5);
+	lea(I, ptr[I+LDA*8]);
+	lea(I, ptr[I+LDA*8]);
+	add(A, I);
+	vxorps(ymm8, ymm8, ymm8);
+	vxorps(ymm9, ymm9, ymm9);
+	vxorps(ymm10, ymm10, ymm10);
+	vxorps(ymm11, ymm11, ymm11);
+	vxorps(ymm12, ymm12, ymm12);
+	vxorps(ymm13, ymm13, ymm13);
+	vxorps(ymm14, ymm14, ymm14);
+	vxorps(ymm15, ymm15, ymm15);
+	mov(I, M);
+	sar(I, 0x3);
+	jle(l5cc, T_NEAR);
+	align(4);
+
+L(l6c);
+	vmovq(xmm0, qword[A1-0x80]);
+	vmovq(xmm1, qword[A1+LDA*1-0x80]);
+	vmovq(xmm2, qword[A1+LDA*2-0x80]);
+	vmovq(xmm3, qword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	vpunpckldq(xmm1, xmm0, xmm1);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm1, xmm3);
+	vpunpckhqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B-0x80], xmm0);
+	vmovdqu(xword[B+0x40], xmm1);
+	vmovq(xmm2, qword[A2-0x80]);
+	vmovq(xmm3, qword[A2+LDA*1-0x80]);
+	vmovq(xmm4, qword[A2+LDA*2-0x80]);
+	vmovq(xmm5, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpckldq(xmm5, xmm4, xmm5);
+	vpunpcklqdq(xmm2, xmm3, xmm5);
+	vpunpckhqdq(xmm3, xmm3, xmm5);
+	vmovdqu(xword[B-0x70], xmm2);
+	vmovdqu(xword[B+0x50], xmm3);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm2);
+	vmovhlps(xmm7, xmm2, xmm2);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm8, ymm8, ymm5);
+	vpmovsxbw(ymm5, xmm1);
+	vmovhlps(xmm6, xmm1, xmm1);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm3);
+	vmovhlps(xmm7, xmm3, xmm3);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm8, ymm8, ymm5);
+	vmovq(xmm0, qword[A2-0x80]);
+	vmovq(xmm1, qword[A2+LDA*1-0x80]);
+	vmovq(xmm2, qword[A2+LDA*2-0x80]);
+	vmovq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm1, xmm0, xmm1);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm1, xmm3);
+	vpunpckhqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B-0x60], xmm0);
+	vmovdqu(xword[B+0x60], xmm1);
+	vmovq(xmm2, qword[A2-0x80]);
+	vmovq(xmm3, qword[A2+LDA*1-0x80]);
+	vmovq(xmm4, qword[A2+LDA*2-0x80]);
+	vmovq(xmm5, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpckldq(xmm5, xmm4, xmm5);
+	vpunpcklqdq(xmm2, xmm3, xmm5);
+	vpunpckhqdq(xmm3, xmm3, xmm5);
+	vmovdqu(xword[B-0x50], xmm2);
+	vmovdqu(xword[B+0x70], xmm3);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm2);
+	vmovhlps(xmm7, xmm2, xmm2);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm9, ymm9, ymm5);
+	vpmovsxbw(ymm5, xmm1);
+	vmovhlps(xmm6, xmm1, xmm1);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm3);
+	vmovhlps(xmm7, xmm3, xmm3);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm9, ymm9, ymm5);
+	vmovq(xmm0, qword[A2-0x80]);
+	vmovq(xmm1, qword[A2+LDA*1-0x80]);
+	vmovq(xmm2, qword[A2+LDA*2-0x80]);
+	vmovq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm1, xmm0, xmm1);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm1, xmm3);
+	vpunpckhqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B-0x40], xmm0);
+	vmovdqu(xword[B+0x80], xmm1);
+	vmovq(xmm2, qword[A2-0x80]);
+	vmovq(xmm3, qword[A2+LDA*1-0x80]);
+	vmovq(xmm4, qword[A2+LDA*2-0x80]);
+	vmovq(xmm5, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpckldq(xmm5, xmm4, xmm5);
+	vpunpcklqdq(xmm2, xmm3, xmm5);
+	vpunpckhqdq(xmm3, xmm3, xmm5);
+	vmovdqu(xword[B-0x30], xmm2);
+	vmovdqu(xword[B+0x90], xmm3);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm2);
+	vmovhlps(xmm7, xmm2, xmm2);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm10, ymm10, ymm5);
+	vpmovsxbw(ymm5, xmm1);
+	vmovhlps(xmm6, xmm1, xmm1);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm3);
+	vmovhlps(xmm7, xmm3, xmm3);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm10, ymm10, ymm5);
+	vmovq(xmm0, qword[A2-0x80]);
+	vmovq(xmm1, qword[A2+LDA*1-0x80]);
+	vmovq(xmm2, qword[A2+LDA*2-0x80]);
+	vmovq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm1, xmm0, xmm1);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm1, xmm3);
+	vpunpckhqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B-0x20], xmm0);
+	vmovdqu(xword[B+0xa0], xmm1);
+	vmovq(xmm2, qword[A2-0x80]);
+	vmovq(xmm3, qword[A2+LDA*1-0x80]);
+	vmovq(xmm4, qword[A2+LDA*2-0x80]);
+	vmovq(xmm5, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpckldq(xmm5, xmm4, xmm5);
+	vpunpcklqdq(xmm2, xmm3, xmm5);
+	vpunpckhqdq(xmm3, xmm3, xmm5);
+	vmovdqu(xword[B-0x10], xmm2);
+	vmovdqu(xword[B+0xb0], xmm3);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm2);
+	vmovhlps(xmm7, xmm2, xmm2);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm11, ymm11, ymm5);
+	vpmovsxbw(ymm5, xmm1);
+	vmovhlps(xmm6, xmm1, xmm1);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm3);
+	vmovhlps(xmm7, xmm3, xmm3);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm11, ymm11, ymm5);
+	vmovq(xmm0, qword[A2-0x80]);
+	vmovq(xmm1, qword[A2+LDA*1-0x80]);
+	vmovq(xmm2, qword[A2+LDA*2-0x80]);
+	vmovq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm1, xmm0, xmm1);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm1, xmm3);
+	vpunpckhqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B], xmm0);
+	vmovdqu(xword[B+0xc0], xmm1);
+	vmovq(xmm2, qword[A2-0x80]);
+	vmovq(xmm3, qword[A2+LDA*1-0x80]);
+	vmovq(xmm4, qword[A2+LDA*2-0x80]);
+	vmovq(xmm5, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpckldq(xmm5, xmm4, xmm5);
+	vpunpcklqdq(xmm2, xmm3, xmm5);
+	vpunpckhqdq(xmm3, xmm3, xmm5);
+	vmovdqu(xword[B+0x10], xmm2);
+	vmovdqu(xword[B+0xd0], xmm3);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm2);
+	vmovhlps(xmm7, xmm2, xmm2);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm12, ymm12, ymm5);
+	vpmovsxbw(ymm5, xmm1);
+	vmovhlps(xmm6, xmm1, xmm1);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm3);
+	vmovhlps(xmm7, xmm3, xmm3);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm12, ymm12, ymm5);
+	vmovq(xmm0, qword[A2-0x80]);
+	vmovq(xmm1, qword[A2+LDA*1-0x80]);
+	vmovq(xmm2, qword[A2+LDA*2-0x80]);
+	vmovq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm1, xmm0, xmm1);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm1, xmm3);
+	vpunpckhqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B+0x20], xmm0);
+	vmovdqu(xword[B+0xe0], xmm1);
+	vmovq(xmm2, qword[A2-0x80]);
+	vmovq(xmm3, qword[A2+LDA*1-0x80]);
+	vmovq(xmm4, qword[A2+LDA*2-0x80]);
+	vmovq(xmm5, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm3, xmm2, xmm3);
+	vpunpckldq(xmm5, xmm4, xmm5);
+	vpunpcklqdq(xmm2, xmm3, xmm5);
+	vpunpckhqdq(xmm3, xmm3, xmm5);
+	vmovdqu(xword[B+0x30], xmm2);
+	vmovdqu(xword[B+0xf0], xmm3);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm2);
+	vmovhlps(xmm7, xmm2, xmm2);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm13, ymm13, ymm5);
+	vpmovsxbw(ymm5, xmm1);
+	vmovhlps(xmm6, xmm1, xmm1);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm3);
+	vmovhlps(xmm7, xmm3, xmm3);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm13, ymm13, ymm5);
+	sub(A1, -8);
+	sub(B, -384);
+	dec(I);
+	jg(l6c, T_NEAR);
+	align(4);
+
+L(l5cc);
+	test(M, 0x4);
+	jle(l968, T_NEAR);
+	vmovd(xmm0, dword[A1-0x80]);
+	vmovd(xmm1, dword[A1+LDA*1-0x80]);
+	vmovd(xmm2, dword[A1+LDA*2-0x80]);
+	vmovd(xmm3, dword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	vpunpckldq(xmm0, xmm0, xmm1);
+	vpunpckldq(xmm2, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm0, xmm2);
+	vmovdqu(xword[B-0x80], xmm0);
+	vmovd(xmm1, dword[A2-0x80]);
+	vmovd(xmm2, dword[A2+LDA*1-0x80]);
+	vmovd(xmm3, dword[A2+LDA*2-0x80]);
+	vmovd(xmm4, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm1, xmm1, xmm2);
+	vpunpckldq(xmm3, xmm3, xmm4);
+	vpunpcklqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B-0x70], xmm1);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm1);
+	vmovhlps(xmm7, xmm1, xmm1);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm8, ymm8, ymm5);
+	vmovd(xmm0, dword[A2-0x80]);
+	vmovd(xmm1, dword[A2+LDA*1-0x80]);
+	vmovd(xmm2, dword[A2+LDA*2-0x80]);
+	vmovd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm0, xmm0, xmm1);
+	vpunpckldq(xmm2, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm0, xmm2);
+	vmovdqu(xword[B-0x60], xmm0);
+	vmovd(xmm1, dword[A2-0x80]);
+	vmovd(xmm2, dword[A2+LDA*1-0x80]);
+	vmovd(xmm3, dword[A2+LDA*2-0x80]);
+	vmovd(xmm4, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm1, xmm1, xmm2);
+	vpunpckldq(xmm3, xmm3, xmm4);
+	vpunpcklqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B-0x50], xmm1);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm1);
+	vmovhlps(xmm7, xmm1, xmm1);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm9, ymm9, ymm5);
+	vmovd(xmm0, dword[A2-0x80]);
+	vmovd(xmm1, dword[A2+LDA*1-0x80]);
+	vmovd(xmm2, dword[A2+LDA*2-0x80]);
+	vmovd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm0, xmm0, xmm1);
+	vpunpckldq(xmm2, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm0, xmm2);
+	vmovdqu(xword[B-0x40], xmm0);
+	vmovd(xmm1, dword[A2-0x80]);
+	vmovd(xmm2, dword[A2+LDA*1-0x80]);
+	vmovd(xmm3, dword[A2+LDA*2-0x80]);
+	vmovd(xmm4, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm1, xmm1, xmm2);
+	vpunpckldq(xmm3, xmm3, xmm4);
+	vpunpcklqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B-0x30], xmm1);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm1);
+	vmovhlps(xmm7, xmm1, xmm1);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm10, ymm10, ymm5);
+	vmovd(xmm0, dword[A2-0x80]);
+	vmovd(xmm1, dword[A2+LDA*1-0x80]);
+	vmovd(xmm2, dword[A2+LDA*2-0x80]);
+	vmovd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm0, xmm0, xmm1);
+	vpunpckldq(xmm2, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm0, xmm2);
+	vmovdqu(xword[B-0x20], xmm0);
+	vmovd(xmm1, dword[A2-0x80]);
+	vmovd(xmm2, dword[A2+LDA*1-0x80]);
+	vmovd(xmm3, dword[A2+LDA*2-0x80]);
+	vmovd(xmm4, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm1, xmm1, xmm2);
+	vpunpckldq(xmm3, xmm3, xmm4);
+	vpunpcklqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B-0x10], xmm1);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm1);
+	vmovhlps(xmm7, xmm1, xmm1);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm11, ymm11, ymm5);
+	vmovd(xmm0, dword[A2-0x80]);
+	vmovd(xmm1, dword[A2+LDA*1-0x80]);
+	vmovd(xmm2, dword[A2+LDA*2-0x80]);
+	vmovd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm0, xmm0, xmm1);
+	vpunpckldq(xmm2, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm0, xmm2);
+	vmovdqu(xword[B], xmm0);
+	vmovd(xmm1, dword[A2-0x80]);
+	vmovd(xmm2, dword[A2+LDA*1-0x80]);
+	vmovd(xmm3, dword[A2+LDA*2-0x80]);
+	vmovd(xmm4, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm1, xmm1, xmm2);
+	vpunpckldq(xmm3, xmm3, xmm4);
+	vpunpcklqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B+0x10], xmm1);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm1);
+	vmovhlps(xmm7, xmm1, xmm1);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm12, ymm12, ymm5);
+	vmovd(xmm0, dword[A2-0x80]);
+	vmovd(xmm1, dword[A2+LDA*1-0x80]);
+	vmovd(xmm2, dword[A2+LDA*2-0x80]);
+	vmovd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm0, xmm0, xmm1);
+	vpunpckldq(xmm2, xmm2, xmm3);
+	vpunpcklqdq(xmm0, xmm0, xmm2);
+	vmovdqu(xword[B+0x20], xmm0);
+	vmovd(xmm1, dword[A2-0x80]);
+	vmovd(xmm2, dword[A2+LDA*1-0x80]);
+	vmovd(xmm3, dword[A2+LDA*2-0x80]);
+	vmovd(xmm4, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpunpckldq(xmm1, xmm1, xmm2);
+	vpunpckldq(xmm3, xmm3, xmm4);
+	vpunpcklqdq(xmm1, xmm1, xmm3);
+	vmovdqu(xword[B+0x30], xmm1);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxbw(ymm6, xmm1);
+	vmovhlps(xmm7, xmm1, xmm1);
+	vpmovsxbw(ymm7, xmm7);
+	vphaddw(ymm6, ymm6, ymm7);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm13, ymm13, ymm5);
+	sub(A1, -4);
+	sub(B, -192);
+	align(4);
+
+L(l968);
+	test(M, 0x2);
+	jle(lc80, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x1);
+	mov(ax, word[A1+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x2);
+	mov(ax, word[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	vpinsrw(xmm0, xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrw(xmm0, xmm0, eax, 0x7);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm8, ymm8, ymm5);
+	vmovdqu(xword[B-0x80], xmm0);
+	mov(ax, word[A2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrw(xmm0, xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm9, ymm9, ymm5);
+	vmovdqu(xword[B-0x70], xmm0);
+	mov(ax, word[A2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrw(xmm0, xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm10, ymm10, ymm5);
+	vmovdqu(xword[B-0x60], xmm0);
+	mov(ax, word[A2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrw(xmm0, xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm11, ymm11, ymm5);
+	vmovdqu(xword[B-0x50], xmm0);
+	mov(ax, word[A2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrw(xmm0, xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm12, ymm12, ymm5);
+	vmovdqu(xword[B-0x40], xmm0);
+	mov(ax, word[A2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrw(xmm0, xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	vpinsrw(xmm0, xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	vpmovsxbw(ymm5, xmm0);
+	vmovhlps(xmm6, xmm0, xmm0);
+	vpmovsxbw(ymm6, xmm6);
+	vphaddw(ymm5, ymm5, ymm6);
+	vpmovsxwd(ymm5, xmm5);
+	vpaddd(ymm13, ymm13, ymm5);
+	vmovdqu(xword[B-0x30], xmm0);
+	sub(A1, -2);
+	sub(B, -96);
+	align(4);
+
+L(lc80);
+	test(M, 0x1);
+	jle(lf1c, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x1);
+	mov(al, byte[A1+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x2);
+	mov(al, byte[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0xf);
+	vpmovsxbd(ymm7, xmm0);
+	vpaddd(ymm8, ymm8, ymm7);
+	vmovhlps(xmm7, xmm0, xmm0);
+	vpmovsxbd(ymm7, xmm7);
+	vpaddd(ymm9, ymm9, ymm7);
+	vmovdqu(xword[B-0x80], xmm0);
+	mov(al, byte[A2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x0);
+	mov(al, byte[A2+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x1);
+	mov(al, byte[A2+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x2);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0xf);
+	vpmovsxbd(ymm7, xmm0);
+	vpaddd(ymm10, ymm10, ymm7);
+	vmovhlps(xmm7, xmm0, xmm0);
+	vpmovsxbd(ymm7, xmm7);
+	vpaddd(ymm11, ymm11, ymm7);
+	vmovdqu(xword[B-0x70], xmm0);
+	mov(al, byte[A2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x0);
+	mov(al, byte[A2+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x1);
+	mov(al, byte[A2+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x2);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	vpinsrb(xmm0, xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	vpinsrb(xmm0, xmm0, eax, 0xf);
+	vpmovsxbd(ymm7, xmm0);
+	vpaddd(ymm12, ymm12, ymm7);
+	vmovhlps(xmm7, xmm0, xmm0);
+	vpmovsxbd(ymm7, xmm7);
+	vpaddd(ymm13, ymm13, ymm7);
+	vmovdqu(xword[B-0x60], xmm0);
+	sub(B, -48);
+	align(4);
+
+L(lf1c);
+	mov(A1, qword[ARG_BIAS]);
+	vmovdqu(yword[A1], ymm8);
+	vmovdqu(yword[A1+0x20], ymm9);
+	vmovdqu(yword[A1+0x40], ymm10);
+	vmovdqu(yword[A1+0x60], ymm11);
+	vmovdqu(yword[A1+0x80], ymm12);
+	vmovdqu(yword[A1+0xa0], ymm13);
+	add(qword[ARG_BIAS], 0xc0);
+	sub(N, 0x30);
+	cmp(N, 0x30);
+	jge(l20, T_NEAR);
+	vzeroupper();
+	align(4);
+
+L(lf64);
+	cmp(N, 0x20);
+	jl(l22b8, T_NEAR);
+	align(4);
+
+L(lf70);
+	mov(A1, A);
+	mov(I, LDA);
+	shl(I, 0x5);
+	add(A, I);
+	pxor(xmm8, xmm8);
+	pxor(xmm9, xmm9);
+	pxor(xmm10, xmm10);
+	pxor(xmm11, xmm11);
+	pxor(xmm12, xmm12);
+	pxor(xmm13, xmm13);
+	pxor(xmm14, xmm14);
+	pxor(xmm15, xmm15);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l1750, T_NEAR);
+	align(4);
+
+L(lfb4);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B+0x80], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B+0x100], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B+0x10], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B+0x90], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B+0x110], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B-0x60], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B+0x20], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B+0xa0], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B+0x120], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B-0x50], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B+0x30], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B+0xb0], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B+0x130], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm12, xmm5);
+	movdqu(xword[B-0x40], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm12, xmm5);
+	movdqu(xword[B+0x40], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm12, xmm5);
+	movdqu(xword[B+0xc0], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm12, xmm5);
+	movdqu(xword[B+0x140], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm13, xmm5);
+	movdqu(xword[B-0x30], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm13, xmm5);
+	movdqu(xword[B+0x50], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm13, xmm5);
+	movdqu(xword[B+0xd0], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm13, xmm5);
+	movdqu(xword[B+0x150], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	movdqu(xword[B-0x20], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	movdqu(xword[B+0x60], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	movdqu(xword[B+0xe0], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	movdqu(xword[B+0x160], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm15, xmm5);
+	movdqu(xword[B-0x10], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm15, xmm5);
+	movdqu(xword[B+0x70], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm15, xmm5);
+	movdqu(xword[B+0xf0], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm15, xmm5);
+	movdqu(xword[B+0x170], xmm3);
+	sub(A1, -16);
+	sub(B, -512);
+	dec(I);
+	jg(lfb4, T_NEAR);
+	align(4);
+
+L(l1750);
+	test(M, 0x8);
+	jle(l1b6c, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	movq(xmm2, qword[A1+LDA*2-0x80]);
+	movq(xmm3, qword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B+0x10], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B-0x60], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B+0x20], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B-0x50], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B+0x30], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm12, xmm5);
+	movdqu(xword[B-0x40], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm12, xmm5);
+	movdqu(xword[B+0x40], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm13, xmm5);
+	movdqu(xword[B-0x30], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm13, xmm5);
+	movdqu(xword[B+0x50], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	movdqu(xword[B-0x20], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	movdqu(xword[B+0x60], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm15, xmm5);
+	movdqu(xword[B-0x10], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm15, xmm5);
+	movdqu(xword[B+0x70], xmm1);
+	sub(A1, -8);
+	sub(B, -256);
+	align(4);
+
+L(l1b6c);
+	test(M, 0x4);
+	jle(l1e14, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	movd(xmm2, dword[A1+LDA*2-0x80]);
+	movd(xmm3, dword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B-0x60], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B-0x50], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm12, xmm5);
+	movdqu(xword[B-0x40], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm13, xmm5);
+	movdqu(xword[B-0x30], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	movdqu(xword[B-0x20], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm15, xmm5);
+	movdqu(xword[B-0x10], xmm0);
+	sub(A1, -4);
+	sub(B, -128);
+	align(4);
+
+L(l1e14);
+	test(M, 0x2);
+	jle(l2068, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A1+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x7);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm11, xmm6);
+	movdqu(xword[B-0x70], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm12, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm13, xmm6);
+	movdqu(xword[B-0x60], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	lea(A2, ptr[A2+LDA*4]);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm15, xmm6);
+	movdqu(xword[B-0x50], xmm0);
+	sub(A1, -2);
+	sub(B, -64);
+	align(4);
+
+L(l2068);
+	test(M, 0x1);
+	jle(l226c, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xf);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm8, xmm5);
+	pshufd(xmm6, xmm0, 0x55);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	pshufd(xmm5, xmm0, 0xaa);
+	pmovsxbd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	pshufd(xmm6, xmm0, 0xff);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm11, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xf);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm12, xmm5);
+	pshufd(xmm6, xmm0, 0x55);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm13, xmm6);
+	pshufd(xmm5, xmm0, 0xaa);
+	pmovsxbd(xmm5, xmm5);
+	paddd(xmm14, xmm5);
+	pshufd(xmm6, xmm0, 0xff);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm15, xmm6);
+	movdqu(xword[B-0x70], xmm0);
+	sub(B, -32);
+	align(4);
+
+L(l226c);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm8);
+	movdqu(xword[A1+0x10], xmm9);
+	movdqu(xword[A1+0x20], xmm10);
+	movdqu(xword[A1+0x30], xmm11);
+	movdqu(xword[A1+0x40], xmm12);
+	movdqu(xword[A1+0x50], xmm13);
+	movdqu(xword[A1+0x60], xmm14);
+	movdqu(xword[A1+0x70], xmm15);
+	add(qword[ARG_BIAS], 0x80);
+	sub(N, 0x20);
+	cmp(N, 0x20);
+	jge(lf70, T_NEAR);
+	align(4);
+
+L(l22b8);
+	cmp(N, 0x10);
+	jl(l2c94, T_NEAR);
+	align(4);
+
+L(l22c4);
+	mov(A1, A);
+	mov(I, LDA);
+	shl(I, 0x4);
+	add(A, I);
+	pxor(xmm8, xmm8);
+	pxor(xmm9, xmm9);
+	pxor(xmm10, xmm10);
+	pxor(xmm11, xmm11);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l26b4, T_NEAR);
+	align(4);
+
+L(l22f4);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x40], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B+0x40], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x30], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B+0x10], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B+0x50], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B-0x60], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B-0x20], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B+0x20], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B+0x60], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B-0x50], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B-0x10], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B+0x30], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B+0x70], xmm3);
+	sub(A1, -16);
+	sub(B, -256);
+	dec(I);
+	jg(l22f4, T_NEAR);
+	align(4);
+
+L(l26b4);
+	test(M, 0x8);
+	jle(l28cc, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	movq(xmm2, qword[A1+LDA*2-0x80]);
+	movq(xmm3, qword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x40], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x30], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B-0x60], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B-0x20], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B-0x50], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B-0x10], xmm1);
+	sub(A1, -8);
+	sub(B, -128);
+	align(4);
+
+L(l28cc);
+	test(M, 0x4);
+	jle(l2a2c, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	movd(xmm2, dword[A1+LDA*2-0x80]);
+	movd(xmm3, dword[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movdqu(xword[B-0x60], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm11, xmm5);
+	movdqu(xword[B-0x50], xmm0);
+	sub(A1, -4);
+	sub(B, -64);
+	align(4);
+
+L(l2a2c);
+	test(M, 0x2);
+	jle(l2b5c, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A1+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x7);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	pinsrw(xmm0, eax, 0x7);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm11, xmm6);
+	movdqu(xword[B-0x70], xmm0);
+	sub(A1, -2);
+	sub(B, -32);
+	align(4);
+
+L(l2b5c);
+	test(M, 0x1);
+	jle(l2c64, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1+LDA3*1-0x80]);
+	lea(A2, ptr[A1+LDA*4]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0x7);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x8);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x9);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xa);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	lea(A2, ptr[A2+LDA*4]);
+	pinsrb(xmm0, eax, 0xb);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0xc);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0xd);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0xe);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	pinsrb(xmm0, eax, 0xf);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm8, xmm5);
+	pshufd(xmm6, xmm0, 0x55);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	pshufd(xmm5, xmm0, 0xaa);
+	pmovsxbd(xmm5, xmm5);
+	paddd(xmm10, xmm5);
+	pshufd(xmm6, xmm0, 0xff);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm11, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l2c64);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm8);
+	movdqu(xword[A1+0x10], xmm9);
+	movdqu(xword[A1+0x20], xmm10);
+	movdqu(xword[A1+0x30], xmm11);
+	add(qword[ARG_BIAS], 0x40);
+	sub(N, 0x10);
+	cmp(N, 0x10);
+	jge(l22c4, T_NEAR);
+	align(4);
+
+L(l2c94);
+	cmp(N, 0x8);
+	jl(l31c0, T_NEAR);
+	align(4);
+
+L(l2ca0);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*4]);
+	lea(I, ptr[A1+LDA*8]);
+	mov(A, I);
+	pxor(xmm8, xmm8);
+	pxor(xmm9, xmm9);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l2eac, T_NEAR);
+	align(4);
+
+L(l2cc8);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	sub(A1, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x60], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x40], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x20], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	sub(A2, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x50], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x30], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x10], xmm3);
+	sub(B, -128);
+	dec(I);
+	jg(l2cc8, T_NEAR);
+	align(4);
+
+L(l2eac);
+	test(M, 0x8);
+	jle(l2fc0, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	movq(xmm2, qword[A1+LDA*2-0x80]);
+	movq(xmm3, qword[A1+LDA3*1-0x80]);
+	sub(A1, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x60], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x50], xmm1);
+	sub(B, -64);
+	align(4);
+
+L(l2fc0);
+	test(M, 0x4);
+	jle(l3078, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	movd(xmm2, dword[A1+LDA*2-0x80]);
+	movd(xmm3, dword[A1+LDA3*1-0x80]);
+	sub(A1, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	sub(B, -32);
+	align(4);
+
+L(l3078);
+	test(M, 0x2);
+	jle(l3118, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A1+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A1+LDA3*1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x7);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l3118);
+	test(M, 0x1);
+	jle(l319c, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1+LDA3*1-0x80]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	pinsrb(xmm0, eax, 0x7);
+	pmovsxbd(xmm5, xmm0);
+	pshufd(xmm6, xmm0, 0x55);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm8, xmm5);
+	paddd(xmm9, xmm6);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l319c);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm8);
+	movdqu(xword[A1+0x10], xmm9);
+	add(qword[ARG_BIAS], 0x20);
+	sub(N, 0x8);
+	cmp(N, 0x8);
+	jge(l2ca0, T_NEAR);
+	align(4);
+
+L(l31c0);
+	cmp(N, 0x4);
+	jl(l349c, T_NEAR);
+	align(4);
+
+L(l31cc);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*2]);
+	lea(I, ptr[A1+LDA*4]);
+	mov(A, I);
+	pxor(xmm7, xmm7);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l32e4, T_NEAR);
+	align(4);
+
+L(l31ec);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	sub(A1, -16);
+	movdqu(xmm2, xword[A2-0x80]);
+	movdqu(xmm3, xword[A2+LDA*1-0x80]);
+	sub(A2, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x70], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x60], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x50], xmm3);
+	sub(B, -64);
+	dec(I);
+	jg(l31ec, T_NEAR);
+	align(4);
+
+L(l32e4);
+	test(M, 0x8);
+	jle(l3378, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	sub(A1, -8);
+	movq(xmm2, qword[A2-0x80]);
+	movq(xmm3, qword[A2+LDA*1-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x70], xmm1);
+	sub(B, -32);
+	align(4);
+
+L(l3378);
+	test(M, 0x4);
+	jle(l33dc, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	sub(A1, -4);
+	movd(xmm2, dword[A2-0x80]);
+	movd(xmm3, dword[A2+LDA*1-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l33dc);
+	test(M, 0x2);
+	jle(l3434, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA*1-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x3);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l3434);
+	test(M, 0x1);
+	jle(l347c, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x3);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l347c);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x10);
+	sub(N, 0x4);
+	cmp(N, 0x4);
+	jge(l31cc, T_NEAR);
+	align(4);
+
+L(l349c);
+	cmp(N, 0x2);
+	jl(l368a, T_NEAR);
+	align(4);
+
+L(l34a8);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*1]);
+	lea(I, ptr[A1+LDA*2]);
+	mov(A, I);
+	pxor(xmm7, xmm7);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l3558, T_NEAR);
+	align(4);
+
+L(l34c8);
+	movdqu(xmm0, xword[A1-0x80]);
+	sub(A1, -16);
+	movdqu(xmm1, xword[A2-0x80]);
+	sub(A2, -16);
+	movdqa(xmm2, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm2, xmm1);
+	pshufd(xmm6, xmm0, 0xd8);
+	pmovsxbw(xmm5, xmm6);
+	movhlps(xmm6, xmm6);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pshufd(xmm6, xmm2, 0xd8);
+	pmovsxbw(xmm5, xmm6);
+	movhlps(xmm6, xmm6);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x70], xmm2);
+	sub(B, -32);
+	dec(I);
+	jg(l34c8, T_NEAR);
+	align(4);
+
+L(l3558);
+	test(M, 0x8);
+	jle(l35b0, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	sub(A1, -8);
+	movq(xmm1, qword[A2-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	pshufd(xmm6, xmm0, 0xd8);
+	pmovsxbw(xmm5, xmm6);
+	movhlps(xmm6, xmm6);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l35b0);
+	test(M, 0x4);
+	jle(l35f4, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	sub(A1, -4);
+	movd(xmm1, dword[A2-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l35f4);
+	test(M, 0x2);
+	jle(l3638, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l3638);
+	test(M, 0x1);
+	jle(l366c, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(byte[B-0x80], al);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(byte[B-0x7f], al);
+	sub(B, -2);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	align(4);
+
+L(l366c);
+	mov(A1, qword[ARG_BIAS]);
+	movq(qword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x8);
+	sub(N, 0x2);
+	cmp(N, 0x2);
+	jge(l34a8, T_NEAR);
+	align(4);
+
+L(l368a);
+	cmp(N, 0x1);
+	jl(l37d8, T_NEAR);
+	align(4);
+
+L(l3694);
+	mov(A1, A);
+	add(A, LDA);
+	pxor(xmm7, xmm7);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l36ec, T_NEAR);
+	align(4);
+
+L(l36a8);
+	movdqu(xmm0, xword[A1-0x80]);
+	sub(A1, -16);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	dec(I);
+	jg(l36a8, T_NEAR);
+	align(4);
+
+L(l36ec);
+	test(M, 0x8);
+	jle(l3728, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	sub(A1, -8);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l3728);
+	test(M, 0x4);
+	jle(l3760, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	sub(A1, -4);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l3760);
+	test(M, 0x2);
+	jle(l3794, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	mov(word[B-0x80], ax);
+	sub(A1, -2);
+	sub(B, -2);
+	align(4);
+
+L(l3794);
+	test(M, 0x1);
+	jle(l37b8, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	mov(byte[B-0x80], al);
+	sub(B, -1);
+	align(4);
+
+L(l37b8);
+	mov(A1, qword[ARG_BIAS]);
+	movd(dword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x4);
+	sub(N, 0x1);
+	cmp(N, 0x1);
+	jge(l3694, T_NEAR);
+	align(4);
+
+L(l37d8);
+
+	postamble();
+}
+outLocalLabel();
+
+#undef M
+#undef N
+#undef A
+#undef LDA
+#undef ALPHA
+#undef B
+#undef I
+#undef A1
+#undef A2
+#undef LDA3
+#ifdef _WIN32
+#undef ARG_ALPHA
+#undef ARG_B
+#endif
+#undef ARG_BIAS
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_bn_kern.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_bn_kern.cpp
new file mode 100644
index 0000000000..c7f1393c9d
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_bn_kern.cpp
@@ -0,0 +1,821 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "jit_generator.hpp"
+#include "common.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+jit_avx512_core_u8_copy_sum_bn_kern::jit_avx512_core_u8_copy_sum_bn_kern(): jit_generator(nullptr, GEMM_CODE_SIZE)
+{
+
+#ifndef _WIN32
+#define M	rdi
+#define N	rsi
+#define A	rdx
+#define LDA	rcx
+#define ALPHA	r8
+#define B	r9
+
+#define I	rax
+#define A1	r10
+#define A2	r8
+#define LDA3	r11
+
+#define ARG_BIAS	24+stacksize+rsp
+
+#else
+
+#define M	rcx
+#define N	rdx
+#define A	r8
+#define LDA	r9
+#define ALPHA	rax
+#define B	rdi
+
+#define I	rax
+#define A1	rsi
+#define A2	r10
+#define LDA3	r11
+
+#define ARG_ALPHA	40+stacksize+rsp
+#define ARG_B		48+stacksize+rsp
+#define ARG_BIAS	72+stacksize+rsp
+
+#endif
+
+inLocalLabel();
+{
+
+Xbyak::Label l20;
+Xbyak::Label l22c;
+Xbyak::Label l340;
+Xbyak::Label l3f8;
+Xbyak::Label l48;
+Xbyak::Label l498;
+Xbyak::Label l51c;
+Xbyak::Label l540;
+Xbyak::Label l54c;
+Xbyak::Label l56c;
+Xbyak::Label l664;
+Xbyak::Label l6f8;
+Xbyak::Label l75c;
+Xbyak::Label l7b4;
+Xbyak::Label l7fc;
+Xbyak::Label l81c;
+Xbyak::Label l828;
+Xbyak::Label l848;
+Xbyak::Label l8d8;
+Xbyak::Label l930;
+Xbyak::Label l974;
+Xbyak::Label l9b8;
+Xbyak::Label l9ec;
+Xbyak::Label la0a;
+Xbyak::Label la14;
+Xbyak::Label la28;
+Xbyak::Label la6c;
+Xbyak::Label laa8;
+Xbyak::Label lae0;
+Xbyak::Label lb14;
+Xbyak::Label lb38;
+Xbyak::Label lb58;
+
+	preamble();
+	auto stacksize = get_size_of_abi_save_regs();
+#ifdef _WIN32
+	mov(ALPHA, ptr[ARG_ALPHA]);
+	mov(B, ptr[ARG_B]);
+#endif
+
+	mov(N, qword[N]);
+	mov(M, qword[M]);
+	mov(LDA, qword[LDA]);
+	sub(A, -128);
+	sub(B, -128);
+	lea(LDA3, ptr[LDA+LDA*2]);
+	cmp(N, 0x8);
+	jl(l540, T_NEAR);
+	align(4);
+
+L(l20);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*4]);
+	lea(I, ptr[A1+LDA*8]);
+	mov(A, I);
+	pxor(xmm8, xmm8);
+	pxor(xmm9, xmm9);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l22c, T_NEAR);
+	align(4);
+
+L(l48);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	movdqu(xmm2, xword[A1+LDA*2-0x80]);
+	movdqu(xmm3, xword[A1+LDA3*1-0x80]);
+	sub(A1, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x60], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x40], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x20], xmm3);
+	movdqu(xmm0, xword[A2-0x80]);
+	movdqu(xmm1, xword[A2+LDA*1-0x80]);
+	movdqu(xmm2, xword[A2+LDA*2-0x80]);
+	movdqu(xmm3, xword[A2+LDA3*1-0x80]);
+	sub(A2, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x50], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x30], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x10], xmm3);
+	sub(B, -128);
+	dec(I);
+	jg(l48, T_NEAR);
+	align(4);
+
+L(l22c);
+	test(M, 0x8);
+	jle(l340, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	movq(xmm2, qword[A1+LDA*2-0x80]);
+	movq(xmm3, qword[A1+LDA3*1-0x80]);
+	sub(A1, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x60], xmm1);
+	movq(xmm0, qword[A2-0x80]);
+	movq(xmm1, qword[A2+LDA*1-0x80]);
+	movq(xmm2, qword[A2+LDA*2-0x80]);
+	movq(xmm3, qword[A2+LDA3*1-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x50], xmm1);
+	sub(B, -64);
+	align(4);
+
+L(l340);
+	test(M, 0x4);
+	jle(l3f8, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	movd(xmm2, dword[A1+LDA*2-0x80]);
+	movd(xmm3, dword[A1+LDA3*1-0x80]);
+	sub(A1, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A2-0x80]);
+	movd(xmm1, dword[A2+LDA*1-0x80]);
+	movd(xmm2, dword[A2+LDA*2-0x80]);
+	movd(xmm3, dword[A2+LDA3*1-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	sub(B, -32);
+	align(4);
+
+L(l3f8);
+	test(M, 0x2);
+	jle(l498, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A1+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A1+LDA3*1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x3);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x4);
+	mov(ax, word[A2+LDA*1-0x80]);
+	pinsrw(xmm0, eax, 0x5);
+	mov(ax, word[A2+LDA*2-0x80]);
+	pinsrw(xmm0, eax, 0x6);
+	mov(ax, word[A2+LDA3*1-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x7);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l498);
+	test(M, 0x1);
+	jle(l51c, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1+LDA3*1-0x80]);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A2+LDA*2-0x80]);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A2+LDA3*1-0x80]);
+	pinsrb(xmm0, eax, 0x7);
+	pmovsxbd(xmm5, xmm0);
+	pshufd(xmm6, xmm0, 0x55);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm8, xmm5);
+	paddd(xmm9, xmm6);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l51c);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm8);
+	movdqu(xword[A1+0x10], xmm9);
+	add(qword[ARG_BIAS], 0x20);
+	sub(N, 0x8);
+	cmp(N, 0x8);
+	jge(l20, T_NEAR);
+	align(4);
+
+L(l540);
+	cmp(N, 0x4);
+	jl(l81c, T_NEAR);
+	align(4);
+
+L(l54c);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*2]);
+	lea(I, ptr[A1+LDA*4]);
+	mov(A, I);
+	pxor(xmm7, xmm7);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l664, T_NEAR);
+	align(4);
+
+L(l56c);
+	movdqu(xmm0, xword[A1-0x80]);
+	movdqu(xmm1, xword[A1+LDA*1-0x80]);
+	sub(A1, -16);
+	movdqu(xmm2, xword[A2-0x80]);
+	movdqu(xmm3, xword[A2+LDA*1-0x80]);
+	sub(A2, -16);
+	movdqa(xmm4, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm4, xmm1);
+	movdqa(xmm5, xmm2);
+	punpckldq(xmm2, xmm3);
+	punpckhdq(xmm5, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	movdqa(xmm3, xmm4);
+	punpcklqdq(xmm4, xmm5);
+	punpckhqdq(xmm3, xmm5);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x70], xmm1);
+	pmovsxbw(xmm5, xmm4);
+	movhlps(xmm6, xmm4);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x60], xmm4);
+	pmovsxbw(xmm5, xmm3);
+	movhlps(xmm6, xmm3);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x50], xmm3);
+	sub(B, -64);
+	dec(I);
+	jg(l56c, T_NEAR);
+	align(4);
+
+L(l664);
+	test(M, 0x8);
+	jle(l6f8, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	movq(xmm1, qword[A1+LDA*1-0x80]);
+	sub(A1, -8);
+	movq(xmm2, qword[A2-0x80]);
+	movq(xmm3, qword[A2+LDA*1-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklqdq(xmm0, xmm2);
+	punpckhqdq(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x70], xmm1);
+	sub(B, -32);
+	align(4);
+
+L(l6f8);
+	test(M, 0x4);
+	jle(l75c, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	movd(xmm1, dword[A1+LDA*1-0x80]);
+	sub(A1, -4);
+	movd(xmm2, dword[A2-0x80]);
+	movd(xmm3, dword[A2+LDA*1-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	punpckldq(xmm2, xmm3);
+	punpcklqdq(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l75c);
+	test(M, 0x2);
+	jle(l7b4, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1+LDA*1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x1);
+	mov(ax, word[A2-0x80]);
+	pinsrw(xmm0, eax, 0x2);
+	mov(ax, word[A2+LDA*1-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x3);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l7b4);
+	test(M, 0x1);
+	jle(l7fc, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A2+LDA*1-0x80]);
+	pinsrb(xmm0, eax, 0x3);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l7fc);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x10);
+	sub(N, 0x4);
+	cmp(N, 0x4);
+	jge(l54c, T_NEAR);
+	align(4);
+
+L(l81c);
+	cmp(N, 0x2);
+	jl(la0a, T_NEAR);
+	align(4);
+
+L(l828);
+	mov(A1, A);
+	lea(A2, ptr[A1+LDA*1]);
+	lea(I, ptr[A1+LDA*2]);
+	mov(A, I);
+	pxor(xmm7, xmm7);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(l8d8, T_NEAR);
+	align(4);
+
+L(l848);
+	movdqu(xmm0, xword[A1-0x80]);
+	sub(A1, -16);
+	movdqu(xmm1, xword[A2-0x80]);
+	sub(A2, -16);
+	movdqa(xmm2, xmm0);
+	punpckldq(xmm0, xmm1);
+	punpckhdq(xmm2, xmm1);
+	pshufd(xmm6, xmm0, 0xd8);
+	pmovsxbw(xmm5, xmm6);
+	movhlps(xmm6, xmm6);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	pshufd(xmm6, xmm2, 0xd8);
+	pmovsxbw(xmm5, xmm6);
+	movhlps(xmm6, xmm6);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x70], xmm2);
+	sub(B, -32);
+	dec(I);
+	jg(l848, T_NEAR);
+	align(4);
+
+L(l8d8);
+	test(M, 0x8);
+	jle(l930, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	sub(A1, -8);
+	movq(xmm1, qword[A2-0x80]);
+	sub(A2, -8);
+	punpckldq(xmm0, xmm1);
+	pshufd(xmm6, xmm0, 0xd8);
+	pmovsxbw(xmm5, xmm6);
+	movhlps(xmm6, xmm6);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l930);
+	test(M, 0x4);
+	jle(l974, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	sub(A1, -4);
+	movd(xmm1, dword[A2-0x80]);
+	sub(A2, -4);
+	punpckldq(xmm0, xmm1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l974);
+	test(M, 0x2);
+	jle(l9b8, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	sub(A1, -2);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A2-0x80]);
+	sub(A2, -2);
+	pinsrw(xmm0, eax, 0x1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l9b8);
+	test(M, 0x1);
+	jle(l9ec, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	mov(byte[B-0x80], al);
+	mov(al, byte[A2-0x80]);
+	pinsrb(xmm0, eax, 0x1);
+	mov(byte[B-0x7f], al);
+	sub(B, -2);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	align(4);
+
+L(l9ec);
+	mov(A1, qword[ARG_BIAS]);
+	movq(qword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x8);
+	sub(N, 0x2);
+	cmp(N, 0x2);
+	jge(l828, T_NEAR);
+	align(4);
+
+L(la0a);
+	cmp(N, 0x1);
+	jl(lb58, T_NEAR);
+	align(4);
+
+L(la14);
+	mov(A1, A);
+	add(A, LDA);
+	pxor(xmm7, xmm7);
+	mov(I, M);
+	sar(I, 0x4);
+	jle(la6c, T_NEAR);
+	align(4);
+
+L(la28);
+	movdqu(xmm0, xword[A1-0x80]);
+	sub(A1, -16);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	dec(I);
+	jg(la28, T_NEAR);
+	align(4);
+
+L(la6c);
+	test(M, 0x8);
+	jle(laa8, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	sub(A1, -8);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(laa8);
+	test(M, 0x4);
+	jle(lae0, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	sub(A1, -4);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(lae0);
+	test(M, 0x2);
+	jle(lb14, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	mov(word[B-0x80], ax);
+	sub(A1, -2);
+	sub(B, -2);
+	align(4);
+
+L(lb14);
+	test(M, 0x1);
+	jle(lb38, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrb(xmm0, eax, 0x0);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	mov(byte[B-0x80], al);
+	sub(B, -1);
+	align(4);
+
+L(lb38);
+	mov(A1, qword[ARG_BIAS]);
+	movd(dword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x4);
+	sub(N, 0x1);
+	cmp(N, 0x1);
+	jge(la14, T_NEAR);
+	align(4);
+
+L(lb58);
+
+	postamble();
+}
+outLocalLabel();
+
+#undef M
+#undef N
+#undef A
+#undef LDA
+#undef ALPHA
+#undef B
+#undef I
+#undef A1
+#undef A2
+#undef LDA3
+#ifdef _WIN32
+#undef ARG_ALPHA
+#undef ARG_B
+#endif
+#undef ARG_BIAS
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_bt_kern.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_bt_kern.cpp
new file mode 100644
index 0000000000..afe4f1713e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/jit_avx512_core_u8_copy_sum_bt_kern.cpp
@@ -0,0 +1,647 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "jit_generator.hpp"
+#include "common.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+jit_avx512_core_u8_copy_sum_bt_kern::jit_avx512_core_u8_copy_sum_bt_kern(): jit_generator(nullptr, GEMM_CODE_SIZE)
+{
+
+#ifndef _WIN32
+#define M	rdi
+#define N	rsi
+#define A	rdx
+#define LDA	rcx
+#define ALPHA	r8
+#define B	r9
+
+#define I	rax
+#define A1	r10
+#define A2	r8
+#define LDA3	r11
+
+#define ARG_BIAS	24+stacksize+rsp
+
+#else
+
+#define M	rcx
+#define N	rdx
+#define A	r8
+#define LDA	r9
+#define ALPHA	rax
+#define B	rdi
+
+#define I	rax
+#define A1	rsi
+#define A2	r10
+#define LDA3	r11
+
+#define ARG_ALPHA	40+stacksize+rsp
+#define ARG_B		48+stacksize+rsp
+#define ARG_BIAS	72+stacksize+rsp
+
+#endif
+
+inLocalLabel();
+{
+
+Xbyak::Label l15c;
+Xbyak::Label l1f4;
+Xbyak::Label l20;
+Xbyak::Label l248;
+Xbyak::Label l280;
+Xbyak::Label l2a4;
+Xbyak::Label l2b0;
+Xbyak::Label l2c8;
+Xbyak::Label l384;
+Xbyak::Label l3e8;
+Xbyak::Label l40;
+Xbyak::Label l424;
+Xbyak::Label l448;
+Xbyak::Label l468;
+Xbyak::Label l474;
+Xbyak::Label l48c;
+Xbyak::Label l550;
+Xbyak::Label l5bc;
+Xbyak::Label l600;
+Xbyak::Label l628;
+Xbyak::Label l646;
+Xbyak::Label l650;
+Xbyak::Label l668;
+Xbyak::Label l700;
+Xbyak::Label l760;
+Xbyak::Label l7a4;
+Xbyak::Label l7c8;
+Xbyak::Label l7e8;
+
+	preamble();
+	auto stacksize = get_size_of_abi_save_regs();
+#ifdef _WIN32
+	mov(ALPHA, ptr[ARG_ALPHA]);
+	mov(B, ptr[ARG_B]);
+#endif
+
+	mov(M, qword[M]);
+	mov(N, qword[N]);
+	mov(LDA, qword[LDA]);
+	lea(LDA3, ptr[LDA+LDA*2]);
+	sub(A, -128);
+	sub(B, -128);
+	cmp(N, 0x8);
+	jl(l2a4, T_NEAR);
+	align(4);
+
+L(l20);
+	mov(A1, A);
+	add(A, 0x8);
+	pxor(xmm8, xmm8);
+	pxor(xmm9, xmm9);
+	mov(I, M);
+	sar(I, 0x3);
+	jle(l15c, T_NEAR);
+	align(4);
+
+L(l40);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x60], xmm0);
+	movdqu(xword[B-0x50], xmm1);
+	sub(B, -64);
+	dec(I);
+	jg(l40, T_NEAR);
+	align(4);
+
+L(l15c);
+	test(M, 0x4);
+	jle(l1f4, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm2, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm3, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	movdqa(xmm1, xmm0);
+	punpcklwd(xmm0, xmm2);
+	punpckhwd(xmm1, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	pmovsxbw(xmm5, xmm1);
+	movhlps(xmm6, xmm1);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm9, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movdqu(xword[B-0x70], xmm1);
+	sub(B, -32);
+	align(4);
+
+L(l1f4);
+	test(M, 0x2);
+	jle(l248, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	movq(xmm1, qword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm8, xmm5);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm6, xmm6);
+	pmovsxwd(xmm6, xmm6);
+	paddd(xmm9, xmm6);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l248);
+	test(M, 0x1);
+	jle(l280, T_NEAR);
+	movq(xmm0, qword[A1-0x80]);
+	add(A1, LDA);
+	pmovsxbd(xmm5, xmm0);
+	pshufd(xmm6, xmm0, 0x55);
+	pmovsxbd(xmm6, xmm6);
+	paddd(xmm8, xmm5);
+	paddd(xmm9, xmm6);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l280);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm8);
+	movdqu(xword[A1+0x10], xmm9);
+	add(qword[ARG_BIAS], 0x20);
+	sub(N, 0x8);
+	cmp(N, 0x8);
+	jge(l20, T_NEAR);
+	align(4);
+
+L(l2a4);
+	cmp(N, 0x4);
+	jl(l468, T_NEAR);
+	align(4);
+
+L(l2b0);
+	mov(A1, A);
+	add(A, 0x4);
+	pxor(xmm7, xmm7);
+	mov(I, M);
+	sar(I, 0x3);
+	jle(l384, T_NEAR);
+	align(4);
+
+L(l2c8);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x70], xmm0);
+	sub(B, -32);
+	dec(I);
+	jg(l2c8, T_NEAR);
+	align(4);
+
+L(l384);
+	test(M, 0x4);
+	jle(l3e8, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm2, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm3, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	movhlps(xmm6, xmm0);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	align(4);
+
+L(l3e8);
+	test(M, 0x2);
+	jle(l424, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	add(A1, LDA);
+	movd(xmm1, dword[A1-0x80]);
+	add(A1, LDA);
+	punpcklbw(xmm0, xmm1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l424);
+	test(M, 0x1);
+	jle(l448, T_NEAR);
+	movd(xmm0, dword[A1-0x80]);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l448);
+	mov(A1, qword[ARG_BIAS]);
+	movdqu(xword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x10);
+	sub(N, 0x4);
+	cmp(N, 0x4);
+	jge(l2b0, T_NEAR);
+	align(4);
+
+L(l468);
+	cmp(N, 0x2);
+	jl(l646, T_NEAR);
+	align(4);
+
+L(l474);
+	mov(A1, A);
+	add(A, 0x2);
+	pxor(xmm7, xmm7);
+	mov(LDA3, M);
+	sar(LDA3, 0x3);
+	jle(l550, T_NEAR);
+	align(4);
+
+L(l48c);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm4, eax, 0x0);
+	punpcklbw(xmm1, xmm2);
+	punpcklbw(xmm3, xmm4);
+	punpcklwd(xmm1, xmm3);
+	punpcklqdq(xmm0, xmm1);
+	pshufd(xmm6, xmm0, 0xd8);
+	pmovsxbw(xmm5, xmm6);
+	movhlps(xmm6, xmm6);
+	pmovsxbw(xmm6, xmm6);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movdqu(xword[B-0x80], xmm0);
+	sub(B, -16);
+	dec(LDA3);
+	jg(l48c, T_NEAR);
+	align(4);
+
+L(l550);
+	test(M, 0x4);
+	jle(l5bc, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm2, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm3, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	punpcklbw(xmm2, xmm3);
+	punpcklwd(xmm0, xmm2);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	align(4);
+
+L(l5bc);
+	test(M, 0x2);
+	jle(l600, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm0, eax, 0x0);
+	mov(ax, word[A1-0x80]);
+	add(A1, LDA);
+	pinsrw(xmm1, eax, 0x0);
+	punpcklbw(xmm0, xmm1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l600);
+	test(M, 0x1);
+	jle(l628, T_NEAR);
+	mov(ax, word[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	mov(word[B-0x80], ax);
+	sub(B, -2);
+	align(4);
+
+L(l628);
+	mov(A1, qword[ARG_BIAS]);
+	movq(qword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x8);
+	sub(N, 0x2);
+	cmp(N, 0x2);
+	jge(l474, T_NEAR);
+	align(4);
+
+L(l646);
+	cmp(N, 0x1);
+	jl(l7e8, T_NEAR);
+	align(4);
+
+L(l650);
+	mov(A1, A);
+	add(A, 0x1);
+	pxor(xmm7, xmm7);
+	mov(LDA3, M);
+	sar(LDA3, 0x3);
+	jle(l700, T_NEAR);
+	align(4);
+
+L(l668);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x3);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x4);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x5);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x6);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x7);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm6);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movq(qword[B-0x80], xmm0);
+	sub(B, -8);
+	dec(LDA3);
+	jg(l668, T_NEAR);
+	align(4);
+
+L(l700);
+	test(M, 0x4);
+	jle(l760, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x0);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x1);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x2);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x3);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	movd(dword[B-0x80], xmm0);
+	sub(B, -4);
+	align(4);
+
+L(l760);
+	test(M, 0x2);
+	jle(l7a4, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x0);
+	mov(byte[B-0x80], al);
+	mov(al, byte[A1-0x80]);
+	add(A1, LDA);
+	pinsrb(xmm0, eax, 0x1);
+	pmovsxbw(xmm5, xmm0);
+	phaddw(xmm5, xmm5);
+	pmovsxwd(xmm5, xmm5);
+	paddd(xmm7, xmm5);
+	mov(byte[B-0x7f], al);
+	sub(B, -2);
+	align(4);
+
+L(l7a4);
+	test(M, 0x1);
+	jle(l7c8, T_NEAR);
+	mov(al, byte[A1-0x80]);
+	pinsrw(xmm0, eax, 0x0);
+	pmovsxbd(xmm5, xmm0);
+	paddd(xmm7, xmm5);
+	mov(byte[B-0x80], al);
+	sub(B, -1);
+	align(4);
+
+L(l7c8);
+	mov(A1, qword[ARG_BIAS]);
+	movd(dword[A1], xmm7);
+	add(qword[ARG_BIAS], 0x4);
+	sub(N, 0x1);
+	cmp(N, 0x1);
+	jge(l650, T_NEAR);
+	align(4);
+
+L(l7e8);
+
+	postamble();
+}
+outLocalLabel();
+
+#undef M
+#undef N
+#undef A
+#undef LDA
+#undef ALPHA
+#undef B
+#undef I
+#undef A1
+#undef A2
+#undef LDA3
+#ifdef _WIN32
+#undef ARG_ALPHA
+#undef ARG_B
+#endif
+#undef ARG_BIAS
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/ref_gemm_s8x8s32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/ref_gemm_s8x8s32.cpp
new file mode 100644
index 0000000000..4fc11afcbc
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/ref_gemm_s8x8s32.cpp
@@ -0,0 +1,116 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <cstdint>
+
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "../f32/ref_gemm_f32.hpp"
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <typename b_dt>
+mkldnn_status_t ref_gemm_s8x8s32(const char *transa, const char *transb,
+        const char *offsetc, const int *M, const int *N, const int *K,
+        const float *alpha, const int8_t *A, const int *LDA, const int8_t *ao,
+        const b_dt *B, const int *LDB, const int8_t *bo, const float *beta,
+        int32_t *C, const int *LDC, const int32_t *co) {
+
+    if (*M == 0 || *N == 0 || *K == 0)
+        return mkldnn_success;
+
+    bool OCisR = (*offsetc == 'R' || *offsetc == 'r');
+    bool OCisC = (*offsetc == 'C' || *offsetc == 'c');
+    bool AisN = (*transa == 'N' || *transa == 'n');
+    bool BisN = (*transb == 'N' || *transb == 'n');
+
+    int m = *M, n = *N, k = *K, lda = *LDA, ldb = *LDB, ldc = *LDC;
+    size_t sizeA = AisN ? lda * k : lda * m;
+    size_t sizeB = BisN ? ldb * n : ldb * k;
+    size_t sizeC = ldc * n;
+
+    double *dA = (double *)malloc(sizeA * sizeof(double), PAGE_4K);
+    double *dB = (double *)malloc(sizeB * sizeof(double), PAGE_4K);
+    double *dC = (double *)malloc(sizeC * sizeof(double), PAGE_4K);
+
+    if (utils::any_null(dA, dB, dC)) {
+        free(dA);
+        free(dB);
+        free(dC);
+        return mkldnn_out_of_memory;
+    }
+
+    auto da_setter = [=] (int i, int j, double v) { dA[j * lda + i] = v; };
+    auto db_setter = [=] (int i, int j, double v) { dB[j * ldb + i] = v; };
+
+    auto ia_accessor = [=] (int i, int j) { return A[j * lda + i]; };
+    auto ib_accessor = [=] (int i, int j) { return B[j * ldb + i]; };
+
+    const int a_rows = AisN ? m : k;
+    const int a_cols = AisN ? k : m;
+    mkldnn::impl::parallel_nd(a_cols, a_rows, [&](int j, int i) {
+        da_setter(i, j,
+            static_cast<double>(ia_accessor(i, j)) + static_cast<double>(ao[0]));
+    });
+
+    const int b_rows = BisN ? k : n;
+    const int b_cols = BisN ? n : k;
+    mkldnn::impl::parallel_nd(b_cols, b_rows, [&](int j, int i) {
+        db_setter(i, j,
+            static_cast<double>(ib_accessor(i, j)) + static_cast<double>(bo[0]));
+    });
+    double one = 1.0, zero = 0.0;
+    ref_gemm<double>(transa, transb, M, N, K, &one, dA, LDA, dB, LDB, &zero,
+        dC, LDC, nullptr);
+
+    auto i2d = [=] (int32_t v) { return static_cast<double>(v); };
+    auto f2d = [=] (float v)   { return static_cast<double>(v); };
+
+    mkldnn::impl::parallel_nd(n, m, [&] (int j, int i) {
+        double coffset = OCisR ? i2d(co[j]) : OCisC ? i2d(co[i]) : i2d(co[0]);
+        double val = ((*beta == 0.0f) ? 0.0 : f2d(*beta) * i2d(C[i + j * ldc]))
+            + f2d(*alpha) * dC[i + j * ldc] + coffset;
+        C[i + j * ldc] = math::out_round<int32_t>(math::saturate<int32_t>(val));
+    });
+
+    free(dA);
+    free(dB);
+    free(dC);
+    return mkldnn_success;
+}
+
+template mkldnn_status_t ref_gemm_s8x8s32<uint8_t>(
+        const char *transa, const char *transb, const char *offsetc,
+        const int *M, const int *N, const int *K,
+        const float *alpha, const int8_t *A, const int *LDA, const int8_t *ao,
+        const uint8_t *B, const int *LDB, const int8_t *bo,
+        const float *beta, int32_t *C, const int *LDC, const int32_t *co);
+
+template mkldnn_status_t ref_gemm_s8x8s32<int8_t>(
+        const char *transa, const char *transb, const char *offsetc,
+        const int *M, const int *N, const int *K,
+        const float *alpha, const int8_t *A, const int *LDA, const int8_t *ao,
+        const int8_t *B, const int *LDB, const int8_t *bo,
+        const float *beta, int32_t *C, const int *LDC, const int32_t *co);
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/ref_gemm_s8x8s32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/ref_gemm_s8x8s32.hpp
new file mode 100644
index 0000000000..6c0370ae99
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/ref_gemm_s8x8s32.hpp
@@ -0,0 +1,38 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef REF_GEMM_S8X8S32_HPP
+#define REF_GEMM_S8X8S32_HPP
+
+#include <stdint.h>
+
+#include "mkldnn_types.h"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <typename b_dt>
+mkldnn_status_t ref_gemm_s8x8s32(const char *transa, const char *transb,
+        const char *offsetc, const int *M, const int *N, const int *K,
+        const float *alpha, const int8_t *A, const int *LDA, const int8_t *ao,
+        const b_dt *B, const int *LDB, const int8_t *bo, const float *beta,
+        int32_t *C, const int *LDC, const int32_t *co);
+
+}
+}
+}
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/simple_gemm_s8s8s32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/simple_gemm_s8s8s32.cpp
new file mode 100644
index 0000000000..de1035f3b2
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/simple_gemm_s8s8s32.cpp
@@ -0,0 +1,180 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "common.hpp"
+#include "nstl.hpp"
+#include "math_utils.hpp"
+
+#include "../gemm.hpp"
+#include "jit_avx512_core_gemm_s8u8s32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+void compensation_init(const char *offsetC, int32_t *compensation, int len,
+        const int32_t *oc) {
+    bool OCisC = (*offsetC == 'C' || *offsetC == 'c');
+    bool OCisF = (*offsetC == 'F' || *offsetC == 'f');
+
+   if (OCisF && (*oc) != 0) {
+       for (int i = 0; i < len; i++)
+           compensation[i] = *oc;
+   } else if (OCisC) {
+       for (int i = 0; i < len; i++)
+           compensation[i] = oc[i];
+   } else {
+       parallel_nd(len, [=](int i) { compensation[i] = 0; });
+   }
+}
+
+void compensation_compute(bool transa, int m, int k, float alpha,
+        const int8_t *a, int lda, int32_t *compensation) {
+    if (!transa) {
+        const int L2_cache_size = get_cache_size(2, true);
+        const int blocking_factor = nstl::min(k, L2_cache_size / lda + 1);
+        const int npanels = k / blocking_factor;
+        const bool has_tile = k % blocking_factor > 0;
+
+        parallel_nd(npanels, m, [&](int j, int i) {
+            int32_t val = 0;
+            for (int jb = 0; jb < blocking_factor; jb++) {
+                val += a[(i + (ptrdiff_t)j * blocking_factor * lda)
+                    + (ptrdiff_t)jb * lda];
+            }
+            if (alpha != 1.0f) {
+                val = math::out_round<int32_t>(math::saturate<int32_t>(
+                    (double)val * alpha * -128.0));
+            } else {
+                val *= -128;
+            }
+            fetch_and_add(&compensation[i], val);
+        });
+
+        if (has_tile) {
+            parallel_nd(m, [=](int i) {
+                int32_t val = 0;
+                for (int j = npanels * blocking_factor; j < k; j++) {
+                    val += a[i + (ptrdiff_t)j * lda];
+                }
+                if (alpha != 1.0f) {
+                    val = math::out_round<int32_t>(math::saturate<int32_t>(
+                        (double)val * alpha * -128.0));
+                } else {
+                    val *= -128;
+                }
+                fetch_and_add(&compensation[i], val);
+            });
+        }
+    } else {
+        parallel_nd(m, [=](int i) {
+            int32_t val = 0;
+            for (int j = 0; j < k; j++) {
+                val += a[j + (ptrdiff_t)i * lda];
+            }
+            if (alpha != 1.0f) {
+                val = math::out_round<int32_t>(math::saturate<int32_t>(
+                    (double)val * alpha * -128.0));
+            } else {
+                val *= -128;
+            }
+            compensation[i] += val;
+        });
+    }
+}
+
+void copy_and_shift_b(bool transb, int k, int n, uint8_t *b_u8, int ldb_u8,
+        const int8_t *b_s8, int ldb_s8) {
+    const int b_cols = transb ? k : n;
+
+    parallel_nd(b_cols, [=](int j) {
+        const int b_rows = transb ? n : k;
+
+        uint8_t *pb_u8 = b_u8 + j * ldb_u8;
+        const int8_t *pb_s8 = b_s8 + j * ldb_s8;
+
+        for (int i = 0; i < b_rows; i++) {
+            (*pb_u8) = (*pb_s8) + 128;
+            pb_u8++;
+            pb_s8++;
+        }
+    });
+}
+
+/**
+ * gemm_s8s8s32 operation is defined as follows:
+ * C = alpha * op(A) * (op(B) + B_shift) + beta * C + C_offset + compensation
+ *
+ * where
+ *  - compensation is a vector of length m that contains computed compensation
+ *   that may contain C_offset if applicable. The compensation is applied inside
+ *   gemm_s8u8s32 as a C_offset
+ *  - B_shift is a k-by-n matrix, every element of B_shift is equal to 128
+ *
+ *  What is the compensation:
+ *  In order to prepare the matrix B for gemm_s8u8s32 call the B_shift is applied:
+ *  C = alpha * op(A) * (op(B) + B_shift) + beta * C + C_offset =
+ *  alpha * op(A) * op(B) + alpha * op(A) * B_shift + beta * C + C_offset
+ *  compensation = -alpha * op(A) * B_shift
+ *  Since B_shift is a matrix, every element of which is equal to 128 then
+ *  - if op(A) = A: compensation contains sum of the elements in each row
+ *   scaled by -128 * alpha
+ *  - if op(A) = A**T: compensation contains sum of the elements in each column
+ *   scaled by -128 * alpha
+ *
+ * The rest of parameters is described in mkldnn.h
+ */
+mkldnn_status_t simple_gemm_s8s8s32(
+        const char *transA, const char *transB, const char *offsetC,
+        const int *m, const int *n, const int *k,
+        const float *alpha, const int8_t *a, const int *lda, const int8_t *oa,
+        const int8_t *b, const int *ldb, const int8_t *ob,
+        const float *beta, int32_t *c, const int *ldc, const int32_t *oc) {
+    if (*oa != 0 || *ob != 0) return mkldnn_unimplemented;
+
+    int M = *m, N = *n, K = *k;
+    bool transa = (*transA == 'T' || *transA == 't');
+    bool transb = (*transB == 'T' || *transB == 't');
+    int ld = transb ? N : K;
+
+    uint8_t *b_u8 = (uint8_t *)malloc(sizeof(uint8_t) * K * N, 64);
+    int32_t *compensation = (int32_t *)malloc(sizeof(int32_t) * M, 64);
+
+    if (utils::any_null(b_u8, compensation)) {
+        free(b_u8);
+        free(compensation);
+        return mkldnn_out_of_memory;
+    }
+
+    compensation_init(offsetC, compensation, M, oc);
+    compensation_compute(transa, M, K, *alpha, a, *lda, compensation);
+    copy_and_shift_b(transb, K, N, b_u8, ld, b, *ldb);
+
+    gemm_s8x8s32(transA, transB, "C", m, n, k, alpha, a, lda, oa, b_u8,
+        &ld, ob, beta, c, ldc, compensation);
+
+    if ((*offsetC == 'R' || *offsetC == 'r'))
+        parallel_nd(M, N,
+            [=](int i, int j) { c[i + (ptrdiff_t)j * *ldc] += oc[j]; });
+
+    free(b_u8);
+    free(compensation);
+
+    return mkldnn_success;
+}
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/simple_gemm_s8s8s32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/simple_gemm_s8s8s32.hpp
new file mode 100644
index 0000000000..03a3d2f7e0
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm/s8x8s32/simple_gemm_s8s8s32.hpp
@@ -0,0 +1,37 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef SIMPLE_GEMM_S8S8S32_HPP
+#define SIMPLE_GEMM_S8S8S32_HPP
+
+#include <stdint.h>
+#include "mkldnn_types.h"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+mkldnn_status_t simple_gemm_s8s8s32(
+        const char *transA, const char *transB, const char *offsetC,
+        const int *m, const int *n, const int *k,
+        const float *alpha, const int8_t *a, const int *lda, const int8_t *oa,
+        const int8_t *b, const int *ldb, const int8_t *ob,
+        const float *beta, int32_t *c, const int *ldc, const int32_t *oc);
+}
+}
+}
+
+#endif // SIMPLE_GEMM_S8S8S32_HPP
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution.cpp
new file mode 100644
index 0000000000..604a728b47
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution.cpp
@@ -0,0 +1,307 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn_types.h"
+
+#include "c_types_map.hpp"
+#include "gemm_convolution.hpp"
+#include "utils.hpp"
+#include "type_helpers.hpp"
+#include "mkldnn_thread.hpp"
+#include "ref_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+
+void gemm_convolution_fwd_t::execute_forward(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const data_t *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    auto col = scratchpad(ctx).get<data_t>(key_conv_gemm_col);
+
+    const jit_gemm_conv_conf_t &jcp = this->pd()->jcp_;
+
+    const int M = jcp.os * jcp.od;
+    const size_t src_step = jcp.ic * jcp.ih * jcp.iw * jcp.id;
+    const size_t dst_step = jcp.oc * M;
+    const size_t weights_g_size = jcp.ic * jcp.oc * jcp.ks;
+
+    assert(IMPLICATION(
+            jcp.id != 1, jcp.oh_block == jcp.oh && jcp.ow_block == jcp.ow));
+    assert(IMPLICATION(jcp.ow_block != jcp.ow, jcp.oh_block == 1));
+
+    const int K = jcp.ic * jcp.ks;
+    const int N = jcp.oc;
+
+    if (jcp.im2col_sz && jcp.id != 1)
+        parallel_nd(jcp.im2col_sz * jcp.nthr,
+                [&](ptrdiff_t i) { col[i] = (data_t)0; });
+
+    const int nb_oh = div_up(jcp.oh, jcp.oh_block);
+    const int nb_ow = div_up(jcp.ow, jcp.ow_block);
+    const size_t work_amount = jcp.ngroups * jcp.mb * jcp.od * nb_oh * nb_ow;
+    parallel(jcp.nthr, [&](const int ithr, const int nthr) {
+        data_t *_col = col + (ptrdiff_t)ithr * jcp.im2col_sz;
+
+        int g{ 0 }, n{ 0 }, od{ 0 }, ohb{ 0 }, owb{ 0 };
+        size_t start = 0, end = 0;
+
+        balance211(work_amount, nthr, ithr, start, end);
+        nd_iterator_init(start, g, jcp.ngroups, n, jcp.mb, od, jcp.od, ohb,
+                nb_oh, owb, nb_ow);
+        for (size_t iwork = start; iwork < end; ++iwork) {
+            int oh = ohb * jcp.oh_block;
+            int ow = owb * jcp.ow_block;
+            const data_t *_src = src + (n * jcp.ngroups + g) * src_step;
+            const data_t *_weights = weights + g * weights_g_size;
+            data_t *_dst_im = dst + (n * jcp.ngroups + g) * dst_step;
+            const int h_step = nstl::min(jcp.oh_block, jcp.oh - oh);
+            const int w_step = nstl::min(jcp.ow_block, jcp.ow - ow);
+            if (jcp.im2col_sz) {
+                if (jcp.id == 1)
+                    jit_gemm_convolution_utils::im2col(
+                            jcp, _src, _col, oh, h_step, ow, w_step);
+                else
+                    jit_gemm_convolution_utils::im2col_3d(jcp, _src, _col, od);
+            }
+
+            const data_t one = 1.0;
+
+            const int m = h_step * w_step;
+            const int LDA = jcp.im2col_sz ? m : M;
+            data_t *_dst = _dst_im + od * jcp.os + oh * jcp.ow + ow;
+
+            extended_sgemm("N", "N", &m, &N, &K, &one,
+                    jcp.im2col_sz ? _col : _src + od * m, &LDA, _weights, &K,
+                    &this->beta_, _dst, &M);
+
+            data_t *d = _dst;
+            if (eltwise_) {
+                // fast branch for ReLU case
+                if (eltwise_->alg_ == alg_kind::eltwise_relu) {
+                    parallel_nd(jcp.oc, [&](const int oc) {
+                        data_t b = jcp.with_bias ? bias[g * jcp.oc + oc] : 0;
+                        data_t *d_ = d + oc * M;
+                        PRAGMA_OMP_SIMD()
+                        for (int oS = 0; oS < m; ++oS) {
+                            d_[oS] += b;
+                            if (d_[oS] < 0) d_[oS] *= eltwise_->alpha_;
+                        }
+                    });
+                } else {
+                    parallel_nd(jcp.oc, [&](const int oc) {
+                        data_t b = jcp.with_bias ? bias[g * jcp.oc + oc] : 0;
+                        data_t *d_ = d + oc * M;
+                        PRAGMA_OMP_SIMD()
+                        for (int oS = 0; oS < m; ++oS) {
+                            d_[oS] += b;
+                            d_[oS] = eltwise_->compute_scalar(d_[oS]);
+                        }
+                    });
+                }
+            } else if (jcp.with_bias) {
+                parallel_nd(jcp.oc, [&](const int oc) {
+                    data_t b = bias[g * jcp.oc + oc];
+                    data_t *d_ = d + oc * M;
+                    PRAGMA_OMP_SIMD()
+                    for (int oS = 0; oS < m; ++oS) {
+                        d_[oS] += b;
+                    }
+                });
+            }
+            nd_iterator_step(g, jcp.ngroups, n, jcp.mb, od, jcp.od, ohb, nb_oh,
+                    owb, nb_ow);
+        }
+    });
+}
+
+void gemm_convolution_bwd_data_t::execute_backward_data(
+        const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    auto col = scratchpad(ctx).get<data_t>(key_conv_gemm_col);
+
+    const jit_gemm_conv_conf_t &jcp = this->pd()->jcp_;
+
+    const int M = jcp.os * jcp.od;
+    const size_t src_step = jcp.ic * jcp.ih * jcp.iw * jcp.id;
+    const size_t dst_step = jcp.oc * M;
+    const size_t weights_g_size = jcp.ic * jcp.oc * jcp.ks;
+
+    const int m = jcp.os;
+    const int K = jcp.oc;
+    const int N = jcp.ic * jcp.ks;
+    const int LDC = jcp.im2col_sz ? m : M;
+
+    const size_t work_amount = (size_t)jcp.ngroups * jcp.mb;
+
+    if (jcp.id > 1) {
+        const ptrdiff_t diff_src_sz = (ptrdiff_t)(work_amount * src_step);
+        parallel_nd(diff_src_sz, [&](ptrdiff_t i) { diff_src[i] = (data_t)0; });
+    }
+
+    parallel(jcp.nthr, [&](const int ithr, const int nthr) {
+        data_t *_col = col + (ptrdiff_t)ithr * jcp.im2col_sz;
+
+        int g{0}, n{0};
+        size_t start = 0, end = 0;
+        balance211(work_amount, nthr, ithr, start, end);
+        nd_iterator_init(start, g, jcp.ngroups, n, jcp.mb);
+        for (size_t iwork = start; iwork < end; ++iwork) {
+
+            data_t *_diff_src = diff_src + (n * jcp.ngroups + g)*src_step;
+            const data_t *_weights = weights + g * weights_g_size;
+            for (int od = 0; od < jcp.od; ++od) {
+                const data_t *_diff_dst = diff_dst + (n * jcp.ngroups + g)
+                    *dst_step + od * m;
+
+                const data_t zero = 0.0, one = 1.0;
+                extended_sgemm("N", "T", &m, &N, &K, &one, _diff_dst, &M,
+                    _weights, &N, &zero,
+                    jcp.im2col_sz ? _col:_diff_src + od * m, &LDC);
+
+                if (jcp.im2col_sz) {
+                    if (jcp.id == 1)
+                        jit_gemm_convolution_utils::col2im(jcp, _col,
+                            _diff_src);
+                    else
+                        jit_gemm_convolution_utils::col2im_3d(jcp, _col,
+                            _diff_src, od);
+                }
+            }
+            nd_iterator_step(g, jcp.ngroups, n, jcp.mb);
+        }
+    });
+}
+
+void gemm_convolution_bwd_weights_t::execute_backward_weights(
+        const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_weights = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
+    auto diff_bias = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_BIAS);
+
+    auto col = scratchpad(ctx).get<data_t>(key_conv_gemm_col);
+    auto wei_reduction = scratchpad(ctx).get<data_t>(key_conv_wei_reduction);
+
+    const jit_gemm_conv_conf_t &jcp = this->pd()->jcp_;
+
+    const int K = jcp.os * jcp.od;
+    const size_t src_step = jcp.ic * jcp.ih * jcp.iw * jcp.id;
+    const size_t dst_step = jcp.oc * K;
+    const size_t weights_g_size = jcp.ic * jcp.oc * jcp.ks;
+
+    const int k = jcp.os;
+    const int N = jcp.oc;
+    const int M = jcp.ic * jcp.ks;
+    const int LDA = jcp.im2col_sz ? k : K;
+
+    parallel_nd(jcp.im2col_sz * jcp.nthr,
+            [&](ptrdiff_t i) { col[i] = (data_t)0; });
+
+    parallel(jcp.nthr, [&](const int ithr, const int nthr) {
+        int ithr_g, nthr_g, ithr_mb, nthr_mb;
+        size_t g_start{0}, g_end{0}, mb_start{0}, mb_end{0};
+
+        const int mb_for_balance = jcp.need_wei_reduction ? jcp.mb : 1;
+        jit_gemm_convolution_utils::bwd_weights_balance(ithr, nthr, jcp.ngroups,
+                mb_for_balance, ithr_g, nthr_g, ithr_mb, nthr_mb);
+
+        assert(IMPLICATION(!jcp.need_wei_reduction, nthr_mb == 1));
+        const int need_reduction = nthr_mb != 1;
+
+        if (ithr_g != -1 && ithr_mb != -1) {
+            balance211((size_t)jcp.ngroups, nthr_g, ithr_g, g_start, g_end);
+            balance211((size_t)jcp.mb, nthr_mb, ithr_mb, mb_start, mb_end);
+
+            assert(IMPLICATION((g_end - g_start) > 1, need_reduction == 0));
+
+            data_t *_col = col + (ptrdiff_t)ithr * jcp.im2col_sz;
+            data_t *weights_reduce_base = wei_reduction
+                    + ithr_g * nthr_mb * weights_g_size;
+            data_t *weights_reduce = weights_reduce_base
+                    + ithr_mb * weights_g_size;
+
+            for (size_t g = g_start; g < g_end; ++g) {
+                data_t *_diff_weights = need_reduction
+                        ? weights_reduce : (diff_weights + g * weights_g_size);
+                for (size_t mb = mb_start; mb < mb_end; ++mb) {
+                    const data_t *_src = src + (mb*jcp.ngroups+g)*src_step;
+                    for (int od = 0; od < jcp.od; ++od) {
+                    const data_t *_diff_dst = diff_dst
+                            + (mb*jcp.ngroups+g)*dst_step + od * k;
+
+                    if (jcp.im2col_sz) {
+                        if (jcp.id == 1)
+                            jit_gemm_convolution_utils::im2col(
+                                    jcp, _src, _col, 0, jcp.oh, 0, jcp.ow);
+                        else
+                            jit_gemm_convolution_utils::im2col_3d(jcp, _src,
+                                _col, od);
+                    }
+
+                    const data_t zero = 0.0, one = 1.0;
+                    extended_sgemm(
+                        "T", "N", &M, &N, &k, &one,
+                        jcp.im2col_sz ? _col : _src + od * k,
+                        &LDA, _diff_dst, &K,
+                        mb == mb_start && od == 0 ? &zero : &one,
+                        _diff_weights, &M);
+                    }
+                }
+            }
+            if (need_reduction) {
+                mkldnn_thr_barrier();
+                data_t *weights_base = diff_weights + g_start * weights_g_size;
+                jit_gemm_convolution_utils::bwd_weights_reduction_par(
+                    ithr_mb, nthr_mb, jcp, weights_reduce_base, weights_base);
+            }
+        } else
+            if (need_reduction) { mkldnn_thr_barrier(); }
+    });
+
+    if (jcp.with_bias) {
+        parallel_nd(jcp.ngroups, jcp.oc, [&](int g, int oc) {
+            data_t db = 0;
+            size_t offset_ = (size_t)g * dst_step + (size_t)oc * K;
+            for (int mb = 0; mb < jcp.mb; ++mb)
+            {
+                size_t offset = offset_ + (size_t)mb * jcp.ngroups * dst_step;
+                for (int od = 0; od < jcp.od; ++od)
+                for (int oh = 0; oh < jcp.oh; ++oh)
+                PRAGMA_OMP_SIMD(reduction(+:db))
+                for (int ow = 0; ow < jcp.ow; ++ow) {
+                    db += diff_dst[offset];
+                    offset++;
+                }
+            }
+            diff_bias[g*jcp.oc+oc] = db;
+        });
+    }
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution.hpp
new file mode 100644
index 0000000000..302e46369a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution.hpp
@@ -0,0 +1,250 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_GEMM_CONVOLUTION_HPP
+#define CPU_JIT_GEMM_CONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+
+#include "gemm_convolution_utils.hpp"
+#include "gemm/gemm.hpp"
+#include "ref_eltwise.hpp"
+
+#include "cpu_convolution_pd.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct gemm_convolution_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc, const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_convolution_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats_common(dat_tag(), wei_tag(), dat_tag())
+                && post_ops_ok()
+                && memory_desc_matches_tag(*src_md(), dat_tag())
+                && memory_desc_matches_tag(*dst_md(), dat_tag())
+                && memory_desc_matches_tag(*weights_md(), wei_tag());
+            if (!ok) return status::unimplemented;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
+                    *desc(), src_md(), weights_md(0), dst_md(),
+                    mkldnn_get_max_threads());
+        }
+
+        jit_gemm_conv_conf_t jcp_;
+
+    protected:
+        format_tag_t dat_tag() const {
+            using namespace format_tag;
+            return utils::pick(ndims() - 3, ncw, nchw, ncdhw);
+        }
+
+        format_tag_t wei_tag() const {
+            using namespace format_tag;
+            return with_groups()
+                ? utils::pick(ndims() - 3, goiw, goihw, goidhw)
+                : utils::pick(ndims() - 3, oiw, oihw, oidhw);
+        }
+
+        bool post_ops_ok() const {
+            auto const &po = attr()->post_ops_;
+            auto is_eltwise = [&](int idx)
+            { return po.entry_[idx].is_eltwise(); };
+            auto is_sum = [&](int idx) { return po.entry_[idx].is_sum(); };
+
+            switch (po.len_) {
+            case 0: return true; // no post_ops
+            case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise
+            case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise
+            default: return false;
+            }
+            return false;
+        }
+    };
+
+    gemm_convolution_fwd_t(const pd_t *apd)
+        : cpu_primitive_t(apd, true)
+        , eltwise_(nullptr)
+    {
+        const auto &post_ops = pd()->attr()->post_ops_;
+        const data_t one = 1.0, zero = 0.0;
+        beta_ = post_ops.find(primitive_kind::sum) >= 0 ? one : zero;
+
+        const int entry_idx = post_ops.find(primitive_kind::eltwise);
+        if (entry_idx != -1) eltwise_ = new ref_eltwise_scalar_fwd_t(
+                post_ops.entry_[entry_idx].eltwise);
+    }
+
+    ~gemm_convolution_fwd_t() { delete eltwise_; }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    data_t beta_;
+
+    ref_eltwise_scalar_fwd_t* eltwise_;
+};
+
+struct gemm_convolution_bwd_data_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_bwd_data_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc, const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_convolution_bwd_data_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_data
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::undef, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats_common(dat_tag(), wei_tag(), dat_tag())
+                && memory_desc_matches_tag(*diff_src_md(), dat_tag())
+                && memory_desc_matches_tag(*diff_dst_md(), dat_tag())
+                && memory_desc_matches_tag(*weights_md(), wei_tag());
+            if (!ok) return status::unimplemented;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
+                    *desc(), diff_src_md(), weights_md(0), diff_dst_md(),
+                    mkldnn_get_max_threads());
+        }
+
+        jit_gemm_conv_conf_t jcp_;
+
+    protected:
+        format_tag_t dat_tag() const {
+            using namespace format_tag;
+            return utils::pick(ndims() - 3, ncw, nchw, ncdhw);
+        }
+
+        format_tag_t wei_tag() const {
+            using namespace format_tag;
+            return with_groups()
+                ? utils::pick(ndims() - 3, goiw, goihw, goidhw)
+                : utils::pick(ndims() - 3, oiw, oihw, oidhw);
+        }
+    };
+
+    gemm_convolution_bwd_data_t(const pd_t *apd)
+        : cpu_primitive_t(apd, true) {}
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_data(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_data(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+struct gemm_convolution_bwd_weights_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_bwd_weights_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_weights_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_convolution_bwd_weights_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_weights
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats_common(dat_tag(), wei_tag(), dat_tag())
+                && memory_desc_matches_tag(*src_md(), dat_tag())
+                && memory_desc_matches_tag(*diff_dst_md(), dat_tag())
+                && memory_desc_matches_tag(*diff_weights_md(), wei_tag());
+            if (!ok) return status::unimplemented;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
+                    *desc(), src_md(), diff_weights_md(0), diff_dst_md(),
+                    mkldnn_get_max_threads());
+        }
+
+        jit_gemm_conv_conf_t jcp_;
+
+    protected:
+        format_tag_t dat_tag() const {
+            using namespace format_tag;
+            return utils::pick(ndims() - 3, ncw, nchw, ncdhw);
+        }
+
+        format_tag_t wei_tag() const {
+            using namespace format_tag;
+            return with_groups()
+                ? utils::pick(ndims() - 3, goiw, goihw, goidhw)
+                : utils::pick(ndims() - 3, oiw, oihw, oidhw);
+        }
+    };
+
+    gemm_convolution_bwd_weights_t(const pd_t *apd)
+        : cpu_primitive_t(apd, true) {}
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_weights(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_weights(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution_utils.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution_utils.cpp
new file mode 100644
index 0000000000..f133b1e62b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution_utils.cpp
@@ -0,0 +1,771 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn_types.h"
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+#include "cpu_isa_traits.hpp"
+
+#include "gemm_convolution_utils.hpp"
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::utils;
+using namespace prop_kind;
+using namespace data_type;
+
+namespace jit_gemm_convolution_utils {
+
+void im2col_3d(const jit_gemm_conv_conf_t &jcp, const float *im, float *col,
+        int od)
+{
+    const size_t OHW = jcp.oh * jcp.ow;
+    const size_t im_step = jcp.ih * jcp.iw * jcp.id;
+    const size_t col_step = jcp.ks * OHW;
+
+    parallel_nd(jcp.ic, [&](int ic) {
+        const float *__restrict im_loc = im + ic * im_step;
+        float *__restrict col_loc = col + ic * col_step;
+        int id = od * jcp.stride_d - jcp.f_pad;
+        for (int kd = 0; kd < jcp.kd; ++kd) {
+            float *__restrict col_ = col_loc + kd * jcp.kh * jcp.kw * OHW;
+            if (id < 0 || id >= jcp.id) {
+                int ih_ = -jcp.t_pad;
+                for (int kh = 0; kh < jcp.kh; ++kh) {
+                    int ih = ih_;
+                    for (int oh = 0; oh < jcp.oh; ++oh) {
+                        if (ih < 0 || ih >= jcp.ih) {
+                            ih += jcp.stride_h;
+                            continue;
+                        }
+                        int iw_ = -jcp.l_pad;
+                        for (int kw = 0; kw < jcp.kw; ++kw) {
+                            int iw = iw_;
+                            for (int ow = 0; ow < jcp.ow; ++ow) {
+                                if (iw < 0 || iw >= jcp.iw) {
+                                    iw += jcp.stride_w;
+                                    continue;
+                                }
+
+                                const size_t col_idx = kw * OHW + oh * jcp.ow
+                                    + ow;
+
+                                col_[col_idx] = 0;
+                                iw += jcp.stride_w;
+                            }
+                            iw_ += (1 + jcp.dilate_w);
+                        }
+                        ih += jcp.stride_h;
+                    }
+                    ih_ += (1 + jcp.dilate_h);
+                    col_ += jcp.kw * OHW;
+                }
+            } else {
+                const float *__restrict im_ = im_loc + id * jcp.ih * jcp.iw;
+                int ih_ = -jcp.t_pad;
+                for (int kh = 0; kh < jcp.kh; ++kh) {
+                    int ih = ih_;
+                    for (int oh = 0; oh < jcp.oh; ++oh) {
+                        if (ih < 0 || ih >= jcp.ih) {
+                            ih += jcp.stride_h;
+                            continue;
+                        }
+                        int iw_ = -jcp.l_pad;
+                        for (int kw = 0; kw < jcp.kw; ++kw) {
+                            int iw = iw_;
+                            for (int ow = 0; ow < jcp.ow; ++ow) {
+                                if (iw < 0 || iw >= jcp.iw) {
+                                    iw += jcp.stride_w;
+                                    continue;
+                                }
+
+                                const size_t col_idx = kw * OHW + oh * jcp.ow
+                                    + ow;
+                                const size_t im_idx = ih * jcp.iw + iw;
+
+                                col_[col_idx] = im_[im_idx];
+                                iw += jcp.stride_w;
+                            }
+                            iw_ += (1 + jcp.dilate_w);
+                        }
+                        ih += jcp.stride_h;
+                    }
+                    ih_ += (1 + jcp.dilate_h);
+                    col_ += jcp.kw * OHW;
+                }
+            }
+            id += (1 + jcp.dilate_d);
+        }
+    });
+}
+
+/* col[ic][kh][kw][oh][ow] <-- im2col(im[ic][ih][iw]) */
+void im2col(const jit_gemm_conv_conf_t &jcp, const float *__restrict im,
+       float *__restrict col, int hs, int hb, int ws, int wb) {
+    const size_t im_step = jcp.is;
+    const size_t col_step = jcp.ks * hb * wb;
+    if (jcp.stride_w == 1) {
+        // Generated code is more optimized for stride_w == 1
+        // because innermost loop is by width
+        auto ker = [&](int ic, int kh, int kw, int oh) {
+            const float *__restrict im_ = im + ic * im_step;
+            float *__restrict col_
+                = col + ic * col_step + ((kh * jcp.kw + kw) * hb + oh) * wb;
+
+            const int ih = (oh + hs) * jcp.stride_h - jcp.t_pad
+                + kh * (1 + jcp.dilate_h);
+            if (ih < 0 || ih >= jcp.ih) {
+                for (int ow = 0; ow < wb; ++ow)
+                    col_[ow] = 0.f;
+            } else {
+                for (int ow = 0; ow < wb; ++ow) {
+                    const int iw = ow + ws - jcp.l_pad + kw * (1 + jcp.dilate_w);
+                    if (iw < 0 || iw >= jcp.iw)
+                        col_[ow] = 0.f;
+                    else {
+                        const size_t im_idx = ih * jcp.iw + iw;
+                        col_[ow] = im_[im_idx];
+                    }
+                }
+            }
+        };
+
+        if (jcp.outer_threading) {
+            for (int ic = 0; ic < jcp.ic; ic++)
+                for (int kh = 0; kh < jcp.kh; kh++)
+                    for (int kw = 0; kw < jcp.kw; kw++)
+                        for (int oh = 0; oh < hb; oh++)
+                            ker(ic, kh, kw, oh);
+        }
+        else {
+            parallel_nd(jcp.ic, jcp.kh, jcp.kw, hb, ker);
+        }
+    } else if (jcp.ic == 1) {
+        parallel_nd(jcp.kh, hb, [&](int kh, int oh) {
+            const int ih = (oh + hs) * jcp.stride_h - jcp.t_pad
+                    + kh * (1 + jcp.dilate_h);
+            if (ih < 0 || ih >= jcp.ih)
+                for (int kw = 0; kw < jcp.kw; ++kw) {
+                    for (int ow = 0; ow < wb; ++ow) {
+                        const size_t col_idx
+                                = ((kh * jcp.kw + kw) * hb + oh) * wb + ow;
+                        col[col_idx] = 0;
+                    }
+                }
+            else
+                for (int kw = 0; kw < jcp.kw; ++kw) {
+                    for (int ow = 0; ow < wb; ++ow) {
+                        const int iw = (ow + ws) * jcp.stride_w - jcp.l_pad
+                                + kw * (1 + jcp.dilate_w);
+                        const size_t col_idx
+                                = ((kh * jcp.kw + kw) * hb + oh) * wb + ow;
+                        const size_t im_idx = ih * jcp.iw + iw;
+                        if (iw < 0 || iw >= jcp.iw)
+                            col[col_idx] = 0;
+                        else
+                            col[col_idx] = im[im_idx];
+                    }
+                }
+        });
+    } else {
+
+        parallel_nd(jcp.ic, jcp.kh, jcp.kw, hb,
+            [&](int ic, int kh, int kw, int oh) {
+            const float *__restrict im_ = im + ic * im_step;
+            float *__restrict col_ = col + ic * col_step
+                + ((kh * jcp.kw + kw) * hb + oh) * wb;
+
+            const int ih = (oh + hs) * jcp.stride_h - jcp.t_pad
+                + kh * (1 + jcp.dilate_h);
+            if (ih < 0 || ih >= jcp.ih) {
+                for (int ow = 0; ow < wb; ++ow)
+                    col_[ow] = 0.f;
+            } else {
+                for (int ow = 0; ow < wb; ++ow) {
+                    const int iw = (ow + ws) * jcp.stride_w - jcp.l_pad
+                        + kw * (1 + jcp.dilate_w);
+                    const size_t im_idx = ih * jcp.iw + iw;
+                    if (iw < 0 || iw >= jcp.iw)
+                        col_[ow] = 0.f;
+                    else
+                        col_[ow] = im_[im_idx];
+                }
+            }
+        });
+    }
+}
+
+inline int limit(int low, int upper, int value) {
+    return nstl::max(low, nstl::min(upper, value));
+}
+
+/* col[kh][kw][ic][oh][ow] <-- im2col_u8(im[ih][iw][ic]) */
+template <typename T>
+void im2col_u8(const jit_gemm_conv_conf_t &jcp, const T *__restrict im,
+        T *__restrict imtr, uint8_t *__restrict col, int hs, int hb, int ws,
+        int wb) {
+    uint8_t shift = jcp.signed_input ? 128 : 0;
+    const int dh = 1 + jcp.dilate_h;
+    const int dw = 1 + jcp.dilate_w;
+    const int sh = jcp.stride_h;
+    const int sw = jcp.stride_w;
+    const int im_iw_stride = jcp.ic * jcp.ngroups;
+    const int im_ih_stride = jcp.iw * im_iw_stride;
+    const int tp = jcp.t_pad;
+    const int lp = jcp.l_pad;
+
+    if (jcp.outer_threading && sh == 1 && sw == 1 && dh == 1 && dw == 1) {
+        /* im[ih][iw][ic] --> imtr[ic][ih][iw] --> col[kh][kw][ic][oh][ow] */
+        const int hp = hs - tp;
+        const int wp = ws - lp;
+        const int ih_start = limit(0, jcp.ih, hp);
+        const int ih_end = limit(0, jcp.ih, hp + hb + jcp.kh);
+        const int iw_start = limit(0, jcp.iw, wp);
+        const int iw_end = limit(0, jcp.iw, wp + wb + jcp.kw);
+
+        const int ihb = ih_end - ih_start;
+        const int iwb = iw_end - iw_start;
+
+        const int imtr_ic_stride = ihb * iwb;
+        const ptrdiff_t imtr_idx_shift = ih_start * iwb + iw_start;
+        for (int ic = 0; ic < jcp.ic; ic++) {
+            const ptrdiff_t imtr_idx_ic = ic * imtr_ic_stride - imtr_idx_shift;
+            for (int ih = ih_start; ih < ih_end; ih++) {
+                const ptrdiff_t im_idx_ih = ic + ih * im_ih_stride;
+                const ptrdiff_t imtr_idx_ih = imtr_idx_ic + ih * iwb;
+                for (int iw = iw_start; iw < iw_end; iw++)
+                    imtr[imtr_idx_ih + iw] = im[im_idx_ih + iw * im_iw_stride];
+            }
+        }
+
+        const int col_ic_str = hb * wb;
+        const int col_kw_stride = jcp.ic * col_ic_str;
+        const int col_kh_stride = jcp.kw * col_kw_stride;
+
+        const int oh_init = ih_start - hp;
+        const int ow_init = iw_start - wp;
+        for (int kh = 0; kh < jcp.kh; kh++) {
+            const ptrdiff_t col_idx_kh = kh * col_kh_stride;
+            const int oh_kh = oh_init - kh;
+            const int oh_start = limit(0, hb, oh_kh);
+            const int oh_end = limit(0, hb, oh_kh + ihb);
+            for (int kw = 0; kw < jcp.kw; kw++) {
+                const ptrdiff_t col_idx_kw
+                        = col_idx_kh + kw * jcp.ic * col_ic_str;
+                const int ow_kw = ow_init - kw;
+                const int imtr_shift = oh_kh * iwb + ow_kw;
+                const int ow_start = limit(0, wb, ow_kw);
+                const int ow_end = limit(0, wb, ow_kw + iwb);
+                for (int ic = 0; ic < jcp.ic; ic++) {
+                    const ptrdiff_t col_idx_ic = col_idx_kw + ic * col_ic_str;
+                    const int imtr_idx_ic = ic * imtr_ic_stride - imtr_shift;
+                    for (int oh = 0; oh < oh_start; oh++) {
+                        const ptrdiff_t col_idx_oh = col_idx_ic + oh * wb;
+                        for (int ow = 0; ow < wb; ++ow)
+                            col[col_idx_oh + ow] = shift;
+                    }
+                    for (int oh = oh_start; oh < oh_end; oh++) {
+                        const ptrdiff_t col_idx_oh = col_idx_ic + oh * wb;
+                        const ptrdiff_t imtr_idx_oh = imtr_idx_ic + oh * iwb;
+                        for (int ow = 0; ow < ow_start; ++ow)
+                            col[col_idx_oh + ow] = shift;
+                        for (int ow = ow_start; ow < ow_end; ++ow)
+                            col[col_idx_oh + ow]
+                                    = imtr[imtr_idx_oh + ow] + shift;
+                        for (int ow = ow_end; ow < wb; ++ow)
+                            col[col_idx_oh + ow] = shift;
+                    }
+                    for (int oh = oh_end; oh < hb; oh++) {
+                        const ptrdiff_t col_idx_oh = col_idx_ic + oh * wb;
+                        for (int ow = 0; ow < wb; ++ow)
+                            col[col_idx_oh + ow] = shift;
+                    }
+                }
+            }
+        }
+    } else {
+        parallel_nd(jcp.kh, jcp.kw, jcp.ic, hb,
+            [&](int kh, int kw, int ic, int oh) {
+                const int hp = tp - kh * dh;
+                const int ih = (oh + hs) * sh - hp;
+                const ptrdiff_t col_idx_base
+                        = (((kh * jcp.kw + kw) * jcp.ic + ic) * hb + oh) * wb;
+                if (ih < 0 || ih >= jcp.ih)
+                    for (int ow = 0; ow < wb; ow++)
+                        col[col_idx_base + ow] = shift;
+                else {
+                    const int wp = lp - kw * dw;
+                    const int ow_start = limit(0, wb, div_up(wp, sw) - ws);
+                    const int ow_end
+                            = limit(0, wb, div_up(jcp.iw + wp, sw) - ws);
+                    for (int ow = 0; ow < ow_start; ow++)
+                        col[col_idx_base + ow] = shift;
+                    const int iw_base = ws * sw - wp;
+                    const ptrdiff_t im_idx_base = ih * im_ih_stride + ic;
+                    for (int ow = ow_start; ow < ow_end; ow++) {
+                        const int iw = iw_base + ow * sw;
+                        const ptrdiff_t im_idx
+                                = im_idx_base + iw * im_iw_stride;
+                        col[col_idx_base + ow] = im[im_idx] + shift;
+                    }
+                    for (int ow = ow_end; ow < wb; ow++)
+                        col[col_idx_base + ow] = shift;
+                }
+            });
+    }
+}
+
+template void im2col_u8<int8_t>(const jit_gemm_conv_conf_t &jcp,
+        const int8_t *__restrict im, int8_t *__restrict imtr,
+        uint8_t *__restrict col, int hs, int hb, int ws, int wb);
+template void im2col_u8<uint8_t>(const jit_gemm_conv_conf_t &jcp,
+        const uint8_t *__restrict im, uint8_t *__restrict imtr,
+        uint8_t *__restrict col, int hs, int hb, int ws, int wb);
+
+/* im[ih][iw][ic] <-- col2im_s32(col[oh][ow][kh][kw][ic]) */
+void col2im_s32(const jit_gemm_conv_conf_t &jcp, const int32_t *__restrict col,
+        int32_t *__restrict im)
+{
+    parallel(0, [&](const int ithr, const int nthr) {
+        int h_nthr = nstl::min(jcp.ih, nthr);
+        int w_nthr = nstl::min(jcp.iw, nthr / h_nthr);
+        int h_ithr = 1, h_s = 0, h_e = 0, w_ithr = 1, w_s = 0, w_e = 0;
+        if (ithr < h_nthr * w_nthr) {
+            h_ithr = ithr / w_nthr;
+            w_ithr = ithr % w_nthr;
+            balance211(jcp.ih, h_nthr, h_ithr, h_s, h_e);
+            balance211(jcp.iw, w_nthr, w_ithr, w_s, w_e);
+        } else {
+            h_ithr = w_ithr = -ithr;
+            h_s = h_e = w_s = w_e = -1;
+        }
+
+        for (int ih = h_s; ih < h_e; ++ih) {
+            for (int iw = w_s; iw < w_e; ++iw) {
+                PRAGMA_OMP_SIMD()
+                for (int ic = 0; ic < jcp.ic; ++ic) {
+                    im[(ih * jcp.iw + iw) * jcp.ic + ic] = 0;
+                }
+            }
+        }
+
+        // TODO: reduce region: [0.. oh] --> [h_s * sh .. h_e * sh]
+        for (int oh = 0; oh < jcp.oh; ++oh) {
+            for (int ow = 0; ow < jcp.ow; ++ow) {
+                for (int kh = 0; kh < jcp.kh; ++kh) {
+                    const int ih = oh * jcp.stride_h
+                        - jcp.t_pad + kh * (1 + jcp.dilate_h);
+                    if (ih < h_s || ih >= h_e) continue;
+
+                    for (int kw = 0; kw < jcp.kw; ++kw) {
+                        const int iw = ow * jcp.stride_w
+                            - jcp.l_pad + kw * (1 + jcp.dilate_w);
+                        if (iw < w_s || iw >= w_e) continue;
+
+                        const size_t col_idx = (((oh * jcp.ow + ow) * jcp.kh
+                                + kh) * jcp.kw + kw) * jcp.ic;
+                        const size_t im_idx
+                            = (ih * jcp.iw + iw) * jcp.ic;
+                        PRAGMA_OMP_SIMD()
+                        for (int ic = 0; ic < jcp.ic; ++ic) {
+                            im[im_idx + ic] += col[col_idx + ic];
+                        }
+                    }
+                }
+            }
+        }
+    });
+}
+
+void col2im_3d(const jit_gemm_conv_conf_t &jcp, const float *col, float *im,
+        int od)
+{
+    parallel_nd(jcp.ic, [&](int ic) {
+        const float *__restrict col_ = col + (size_t)ic * jcp.ks * jcp.os;
+        float *__restrict im_ic = im + (size_t)ic * jcp.ih * jcp.iw * jcp.id;
+
+        int id = od * jcp.stride_d - jcp.f_pad;
+        for (int kd = 0; kd < jcp.kd; ++kd) {
+            if (id < 0 || id >= jcp.id) {
+                col_ += jcp.kh * jcp.kw * jcp.os;
+                id += (1 + jcp.dilate_d);
+                continue;
+            }
+
+            float *__restrict im_ = im_ic + id * jcp.ih * jcp.iw;
+
+            for (int oh = 0; oh < jcp.oh; ++oh) {
+            for (int kh = 0; kh < jcp.kh; ++kh) {
+                const int ih = oh * jcp.stride_h - jcp.t_pad
+                    + kh * (1 + jcp.dilate_h);
+                if (ih < 0 || ih >= jcp.ih) continue;
+
+                for (int ow = 0; ow < jcp.ow; ++ow) {
+                for (int kw = 0; kw < jcp.kw; ++kw) {
+                    const int iw = ow * jcp.stride_w - jcp.l_pad
+                        + kw * (1 + jcp.dilate_w);
+                    if (iw < 0 || iw >= jcp.iw) continue;
+
+                    const size_t col_idx = ((kh*jcp.kw + kw)*jcp.oh+oh)*jcp.ow+ow;
+                    const size_t im_idx = ih*jcp.iw + iw;
+                    im_[im_idx] += col_[col_idx];
+                }}
+            }}
+
+            col_ += jcp.kh * jcp.kw * jcp.os;
+            id += (1 + jcp.dilate_d);
+        }
+    });
+}
+
+void col2im(const jit_gemm_conv_conf_t &jcp, const float *col, float *im) {
+    const size_t col_step = jcp.ks * jcp.os;
+    const size_t im_step = jcp.ih * jcp.iw;
+    const int iS = jcp.ih * jcp.iw;
+
+    parallel_nd(jcp.ic, [&](int ic) {
+        float *__restrict im_ = im + ic * im_step;
+        const float *__restrict col_ = col + ic * col_step;
+        PRAGMA_OMP_SIMD()
+        for (int is = 0; is < iS; ++is) im_[is] = 0.;
+
+        for (int kh = 0; kh < jcp.kh; ++kh) {
+        for (int oh = 0; oh < jcp.oh; ++oh) {
+            const int ih =
+                    oh * jcp.stride_h - jcp.t_pad + kh * (1 + jcp.dilate_h);
+            if (ih < 0 || ih >= jcp.ih) continue;
+
+            for (int kw = 0; kw < jcp.kw; ++kw) {
+            for (int ow = 0; ow < jcp.ow; ++ow) {
+                const int iw =
+                        ow * jcp.stride_w - jcp.l_pad + kw * (1 + jcp.dilate_w);
+                if (iw < 0 || iw >= jcp.iw) continue;
+
+                const size_t col_idx = ((kh*jcp.kw + kw)*jcp.oh+oh)*jcp.ow+ow;
+                const size_t im_idx = ih*jcp.iw + iw;
+                im_[im_idx] += col_[col_idx];
+            }
+            }
+        }
+        }
+    });
+}
+
+status_t init_conf(jit_gemm_conv_conf_t &jcp,
+        memory_tracking::registrar_t &scratchpad, const convolution_desc_t &cd,
+        const memory_desc_wrapper &src_d, const memory_desc_wrapper &weights_d,
+        const memory_desc_wrapper &dst_d, int max_threads) {
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+    const int ndims = src_d.ndims();
+    const int is_1d = ndims == 3;
+    const int is_3d = ndims == 5;
+
+    jcp.prop_kind = cd.prop_kind;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+    jcp.id = is_3d ? src_d.dims()[2] : 1;
+    jcp.ih = is_1d ? 1 : src_d.dims()[ndims - 2];
+    jcp.iw = src_d.dims()[ndims - 1];
+    jcp.od = is_3d ? dst_d.dims()[2] : 1;
+    jcp.oh = is_1d ? 1 : dst_d.dims()[ndims - 2];
+    jcp.ow = dst_d.dims()[ndims - 1];
+
+    jcp.kd = is_3d ? weights_d.dims()[with_groups + 2] : 1;
+    jcp.kh = is_1d ? 1 : weights_d.dims()[with_groups + ndims - 2];
+    jcp.kw = weights_d.dims()[with_groups + ndims - 1];
+
+    jcp.f_pad = is_3d ? cd.padding[0][0] : 0;
+    jcp.t_pad = is_1d ? 0 : cd.padding[0][ndims - 4];
+    jcp.l_pad = cd.padding[0][ndims - 3];
+
+    jcp.stride_d = is_3d ? cd.strides[0] : 1;
+    jcp.stride_h = is_1d ? 1 : cd.strides[ndims - 4];
+    jcp.stride_w = cd.strides[ndims - 3];
+
+    jcp.dilate_d = is_3d ? cd.dilates[0] : 0;
+    jcp.dilate_h = is_1d ? 0 : cd.dilates[ndims - 4];
+    jcp.dilate_w = cd.dilates[ndims - 3];
+
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef
+        || cd.diff_bias_desc.format_kind != format_kind::undef;
+
+    jcp.is = jcp.ih * jcp.iw;
+    jcp.os = jcp.oh * jcp.ow;
+    jcp.ks = jcp.kh * jcp.kw * jcp.kd;
+
+    jcp.signed_input = src_d.data_type() == data_type::s8;
+
+    jcp.im2col_sz = !everyone_is(true,
+            jcp.ow == jcp.iw, jcp.oh == jcp.ih, jcp.od == jcp.id,
+            jcp.stride_w == 1, jcp.stride_h == 1, jcp.stride_d == 1,
+            jcp.ks == 1, !jcp.signed_input)
+        ? (ptrdiff_t)jcp.ic * jcp.ks * jcp.os : 0;
+
+    jcp.outer_threading = false;
+
+    bool is_int8_conv = utils::one_of(src_d.data_type(), s32, s8, u8)
+        && weights_d.data_type() == s8;
+
+    const int vlen = mayiuse(avx512_common)
+        ? cpu_isa_traits<avx512_common>::vlen
+        : mayiuse(avx)
+            ? cpu_isa_traits<avx>::vlen
+            : mayiuse(sse42) ? cpu_isa_traits<sse42>::vlen : 4;
+    const int simd_w = vlen / (is_int8_conv ? 1 : 4);
+
+    const bool is_bwd_d = jcp.prop_kind == backward_data;
+    const bool is_bwd_w = jcp.prop_kind == backward_weights;
+    const bool is_fwd = !is_bwd_d && !is_bwd_w;
+    jcp.oh_block = is_fwd ? jcp.oh : jcp.ih;
+    jcp.ow_block = is_fwd ? jcp.ow : jcp.iw;
+
+    using namespace memory_tracking::names;
+    bool is_depthwise = jcp.ic == 1 && jcp.oc == 1 && jcp.ngroups != 1;
+
+    // TODO: maybe mitigate blocking restriction
+    const int wei_size = jcp.oc * jcp.ic * jcp.kh * jcp.kw;
+    const int L2 = get_cache_size(2, true)
+          / (is_int8_conv ? sizeof(int8_t) : sizeof(float));
+    bool is_blocking_applicable = true
+            && is_fwd && jcp.im2col_sz
+            && jcp.id == 1 && jcp.od == 1
+            && jcp.dilate_h == 0 && jcp.dilate_w == 0
+            && !is_depthwise
+            && wei_size < L2/2;
+    if (is_blocking_applicable) {
+        // looking for oh and ow blocking
+        int h_block{ jcp.oh_block }, w_block{ jcp.ow_block };
+        const int ic = jcp.ic;
+        const int oc = jcp.oc;
+        const int iw = jcp.iw;
+        const int ow = jcp.ow;
+        const int oh = jcp.oh;
+        const int os = oh * ow;
+
+        // 1. cache requirement
+        int row_size = ic * ow * jcp.ks + 2 * (ic * iw + oc * ow);
+        if (is_int8_conv) {
+            // Heuristic rule: gemm needed a lot of memory for internal usage
+            row_size *= 5;
+            // memory for accumulators
+            row_size += oc * ow * sizeof(uint32_t);
+            // memory for transposition
+            row_size += ic * iw;
+        }
+
+        h_block = nstl::max(1, nstl::min(oh, div_up(L2, row_size)));
+        if (h_block == 1) {
+            int col_size = ic * jcp.ks + 2 * (ic + oc);
+            if (is_int8_conv) {
+                col_size *= 5;
+                col_size += oc * sizeof(uint32_t);
+                col_size += ic;
+            }
+            w_block = nstl::max(1, nstl::min(ow, div_up(L2, col_size)));
+        }
+
+        // 2. threading requirement
+        if (h_block != oh)
+            h_block = nstl::max(1, rnd_dn(h_block, 4));
+        if (w_block != ow)
+            w_block = nstl::max(1, rnd_dn(w_block, simd_w));
+
+        float thr_eff = 0.f;
+        float thr_eff_treshold = 0.9f;
+        if (w_block == ow) {
+            do {
+                int nb_h = div_up(oh, h_block);
+                size_t work = jcp.ngroups * jcp.mb * jcp.od * nb_h;
+                float disb = (float)oh / rnd_up(oh, h_block);
+                thr_eff = (float)work / rnd_up(work, max_threads);
+                thr_eff = (thr_eff + disb) / 2.f;
+                if (thr_eff >= thr_eff_treshold)
+                    break;
+                h_block = rnd_dn(h_block - 4, 4);
+            } while (h_block > 0);
+        }
+        if (thr_eff < thr_eff_treshold) // we didn't find suitable h_block
+        {
+            h_block = 1;
+            int nb_h = oh;
+            do {
+                int nb_w = div_up(ow, w_block);
+                size_t work_amount = jcp.ngroups * jcp.mb * nb_h * nb_w;
+                float disb = (float)ow / rnd_up(ow, w_block);
+                thr_eff = (float)work_amount / rnd_up(work_amount, max_threads);
+                thr_eff = (thr_eff + disb) / 2.f;
+                if (thr_eff > thr_eff_treshold)
+                    break;
+                w_block = rnd_dn(w_block - simd_w, simd_w);
+            } while (w_block > 0);
+        }
+        h_block = nstl::max(1, h_block);
+        w_block = nstl::max(1, w_block);
+        const size_t inner_work = div_up(os, simd_w) * div_up(oc, simd_w);
+        const float inner_thr_eff
+                = (float)inner_work / rnd_up(inner_work, max_threads);
+        if (thr_eff >= inner_thr_eff / 2 && h_block > 0 && w_block > 0) {
+            jcp.oh_block = h_block;
+            jcp.ow_block = w_block;
+            jcp.outer_threading = true;
+        }
+        // updating jcp.im2col_sz
+        if (jcp.oh_block != 1)
+            jcp.ow_block = ow;
+        jcp.im2col_sz = (ptrdiff_t)ic * jcp.ks * jcp.oh_block * jcp.ow_block;
+    }
+    //  For threading selection in bwd_d we do:
+    //  1. Rough estimation of efficiency for inner and outer threading.
+    //  2. Gemm size estimation in assumption that it does not work
+    //  so effectively for small sizes.
+    //  64K - this is heuristic gemm size per thread threshold.
+    const int gemm_thrld = 64 * 1024;
+
+    if (is_int8_conv) {
+        if (is_fwd) {
+            if (!jcp.outer_threading) {
+                bool is_depthwise = jcp.ic == 1 && jcp.oc == 1 && jcp.ngroups != 1;
+                const size_t outer_work = jcp.ngroups * jcp.mb;
+                const float outer_thr_eff
+                    = (float)outer_work / rnd_up(outer_work, max_threads);
+                const size_t inner_work
+                    = div_up(jcp.is, simd_w) * div_up(jcp.ic, simd_w);
+                const float inner_thr_eff
+                    = (float)inner_work / rnd_up(inner_work, max_threads);
+                jcp.outer_threading = (is_depthwise
+                 || (jcp.is / max_threads < 64 && jcp.mb != 1))
+                 && (outer_thr_eff / inner_thr_eff >= 1.f
+                     || (jcp.os * jcp.ic * jcp.oc) / max_threads < gemm_thrld);
+            }
+            jcp.nthr = jcp.outer_threading ? max_threads : 1;
+            scratchpad.book(key_conv_gemm_col,
+                sizeof(int8_t) * jcp.nthr * jcp.im2col_sz);
+            scratchpad.book(key_conv_int_dat_in_acc_dt,
+                sizeof(int32_t) * jcp.nthr * jcp.oh_block * jcp.ow_block * jcp.oc);
+            scratchpad.book(key_conv_gemm_imtr,
+                sizeof(int8_t) * jcp.nthr * jcp.is * jcp.ic);
+        } else if (is_bwd_d) {
+            bool is_depthwise = jcp.ic == 1 && jcp.oc == 1 && jcp.ngroups != 1;
+            const size_t outer_work = jcp.ngroups * jcp.mb;
+            const float outer_thr_eff
+                    = (float)outer_work / rnd_up(outer_work, max_threads);
+            const size_t inner_work
+                    = div_up(jcp.is, simd_w) * div_up(jcp.ic, simd_w);
+            const float inner_thr_eff
+                    = (float)inner_work / rnd_up(inner_work, max_threads);
+            jcp.outer_threading = (is_depthwise
+                 || (jcp.is / max_threads < 64 && jcp.mb != 1))
+                 && (outer_thr_eff / inner_thr_eff >= 1.f
+                     || (jcp.is * jcp.ic * jcp.oc) / max_threads < gemm_thrld);
+
+            jcp.nthr = jcp.outer_threading ? max_threads : 1;
+            scratchpad.book(key_conv_gemm_col,
+                sizeof(int32_t) * jcp.nthr * jcp.im2col_sz);
+            scratchpad.book(key_conv_int_dat_in_acc_dt,
+                sizeof(int32_t) * jcp.nthr * jcp.is * jcp.ic);
+        } else if (is_bwd_w) {
+            assert(!"unimplemented prop_kind");
+            return status::unimplemented;
+        }
+    } else {
+        if (is_fwd) {
+            if (!jcp.outer_threading) {
+                const size_t outer_work_amount = jcp.ngroups * jcp.mb * jcp.od;
+                const float outer_thr_eff = (float)outer_work_amount
+                        / rnd_up(outer_work_amount, max_threads);
+                const size_t inner_work_amount
+                        = div_up(jcp.os, simd_w) * div_up(jcp.oc, simd_w);
+                const float inner_thr_eff = (float)inner_work_amount
+                        / rnd_up(inner_work_amount, max_threads);
+                jcp.outer_threading = jcp.os / max_threads < 512
+                    && IMPLICATION(jcp.od == 1, jcp.mb != 1 || jcp.ngroups > 2)
+                    && (outer_thr_eff / inner_thr_eff >= 1.f
+                      || (jcp.os * jcp.ic * jcp.oc) / max_threads < gemm_thrld);
+            }
+        } else if (is_bwd_d) {
+            const size_t outer_work_amount = jcp.ngroups * jcp.mb;
+            const float outer_thr_eff = (float)outer_work_amount
+                / rnd_up(outer_work_amount, max_threads);
+            const size_t inner_work
+                = div_up(jcp.is, simd_w) * div_up(jcp.ic, simd_w);
+            const float inner_thr_eff = (float)inner_work
+                / rnd_up(inner_work, max_threads);
+            jcp.outer_threading = (jcp.os / max_threads < 512 || jcp.ks < 64)
+                && (jcp.mb != 1 || jcp.ngroups > 2)
+                && (outer_thr_eff / inner_thr_eff >= 1.f
+                    || (jcp.is * jcp.ic * jcp.oc) / max_threads < gemm_thrld);
+        } else if (is_bwd_w)
+            jcp.outer_threading = jcp.os / max_threads < 256
+                && (jcp.mb != 1 || jcp.ngroups > 2);
+
+        jcp.nthr = jcp.outer_threading ? max_threads : 1;
+        scratchpad.book(key_conv_gemm_col,
+                sizeof(float) * jcp.nthr * jcp.im2col_sz);
+
+        if (is_bwd_w) {
+            jcp.need_wei_reduction = mkldnn_thr_syncable()
+                ? jcp.mb != 1 && jcp.nthr != 1 : false;
+            scratchpad.book(key_conv_wei_reduction,
+                    sizeof(float) * jcp.nthr * jcp.ngroups * weights_d.size());
+        }
+    }
+
+    return status::success;
+}
+
+void bwd_weights_balance(int ithr, int nthr, int ngroups, int mb, int &ithr_g,
+        int &nthr_g, int &ithr_mb, int &nthr_mb) {
+    nthr_g = nstl::min(ngroups, nthr);
+    nthr_mb = nstl::min(mb, nthr / nthr_g);
+    if (ithr / nthr_mb >= ngroups) {
+        ithr_g = ithr_mb = -1;
+    } else {
+        ithr_g = ithr / nthr_mb;
+        ithr_mb = ithr % nthr_mb;
+    }
+}
+
+void bwd_weights_reduction_par(int ithr, int nthr,
+        const jit_gemm_conv_conf_t &jcp, const float *weights_reduce_ws,
+        float *weights) {
+    const size_t weights_g_size = jcp.ic * jcp.oc * jcp.ks;
+
+    size_t weights_start{0}, weights_end{0};
+    balance211(weights_g_size, nthr, ithr, weights_start, weights_end);
+
+    for (int i = 0; i < nthr; ++i) {
+        const float *ws_i = weights_reduce_ws + i * weights_g_size;
+        for (size_t s = weights_start; s < weights_end; ++s)
+            weights[s] = (i == 0 ? 0 : weights[s]) + ws_i[s];
+    }
+}
+
+};
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution_utils.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution_utils.hpp
new file mode 100644
index 0000000000..e006789344
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_convolution_utils.hpp
@@ -0,0 +1,66 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_GEMM_CONVOLUTION_UTILS_HPP
+#define CPU_JIT_GEMM_CONVOLUTION_UTILS_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_engine.hpp"
+#include "jit_primitive_conf.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace jit_gemm_convolution_utils {
+
+void im2col_3d(const jit_gemm_conv_conf_t &jcp, const float *im, float *col,
+        int od);
+void im2col(const jit_gemm_conv_conf_t &jcp, const float *__restrict im,
+       float *__restrict col, int hs, int hb, int ws, int wb);
+template <typename T>
+void im2col_u8(const jit_gemm_conv_conf_t &jcp, const T *__restrict im,
+        T* __restrict imtr, uint8_t *__restrict col,
+        int hs, int hb, int ws, int wb);
+
+void col2im_s32(const jit_gemm_conv_conf_t &jcp, const int32_t *__restrict col,
+        int32_t *__restrict im);
+void col2im_3d(const jit_gemm_conv_conf_t &jcp, const float *col, float *im,
+        int od);
+void col2im(const jit_gemm_conv_conf_t &jcp, const float *col, float *im);
+
+status_t init_conf(jit_gemm_conv_conf_t &jcp,
+        memory_tracking::registrar_t &scratchpad, const convolution_desc_t &cd,
+        const memory_desc_wrapper &src_d, const memory_desc_wrapper &weights_d,
+        const memory_desc_wrapper &dst_d, int max_threads);
+
+void bwd_weights_balance(int ithr, int nthr, int ngroups, int mb,
+        int &ithr_g, int &nthr_g, int &ithr_mb, int &nthr_mb);
+void bwd_weights_reduction_par(int ithr, int nthr,
+        const jit_gemm_conv_conf_t &jcp, const float *weights_reduce_ws,
+        float *weights);
+
+}
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_inner_product.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_inner_product.cpp
new file mode 100644
index 0000000000..2872122f0d
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_inner_product.cpp
@@ -0,0 +1,156 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "mkldnn_thread.hpp"
+
+#include "gemm_inner_product.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::data_type;
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::primitive_kind;
+
+template <impl::data_type_t data_type>
+void gemm_inner_product_fwd_t<data_type>::execute_forward(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const data_t *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const int MB = pd()->MB();
+    const int OC = pd()->OC();
+    const int IC = pd()->IC_total_padded();
+
+    bool wei_tr = !memory_desc_matches_one_of_tag(
+            *pd()->weights_md(), hwio, dhwio, io);
+
+    const auto &post_ops = pd()->attr()->post_ops_;
+    const bool do_relu = post_ops.len_ == 1;
+
+    float alpha = 1.0, beta = 0.0;
+    extended_sgemm(wei_tr ? "T" : "N", "N", &OC, &MB, &IC, &alpha, weights,
+            wei_tr ? &IC : &OC, src, &IC, &beta, dst, &OC, bias);
+
+    if (do_relu) {
+        float nslope = post_ops.entry_[0].eltwise.alpha;
+        parallel_nd(MB, OC, [&](int mb, int oc) {
+            size_t dst_off = mb * OC + oc;
+            if (dst[dst_off] < 0)
+                dst[dst_off] *= nslope;
+        });
+    }
+}
+
+template <impl::data_type_t data_type>
+void gemm_inner_product_bwd_data_t<data_type>::execute_backward_data(
+        const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const int MB = pd()->MB();
+    const int OC = pd()->OC();
+    const int IC = pd()->IC_total_padded();
+
+    bool wei_tr = memory_desc_matches_one_of_tag(
+            *pd()->weights_md(), hwio, dhwio, io);
+
+    float alpha = 1.0, beta = 0.0;
+    extended_sgemm(wei_tr ? "T" : "N", "N", &IC, &MB, &OC, &alpha, weights,
+            wei_tr ? &OC : &IC, diff_dst, &OC, &beta, diff_src, &IC);
+}
+
+template <impl::data_type_t data_type>
+void gemm_inner_product_bwd_weights_t<data_type>::execute_backward_weights(
+        const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_weights = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
+    auto diff_bias = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_BIAS);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_bias_d(pd()->diff_weights_md(1));
+
+    diff_dst += diff_dst_d.offset0();
+
+    const int MB = pd()->MB();
+    const int OC = pd()->OC();
+    const int IC = pd()->IC_total_padded();
+
+    bool wei_tr = memory_desc_matches_one_of_tag(
+            *pd()->diff_weights_md(), hwio, dhwio, io);
+
+    float alpha = 1.0, beta = 0.0;
+    if (wei_tr)
+        extended_sgemm("N", "T", &OC, &IC, &MB, &alpha, diff_dst, &OC, src, &IC,
+                &beta, diff_weights, &OC);
+    else
+        extended_sgemm("N", "T", &IC, &OC, &MB, &alpha, src, &IC, diff_dst, &OC,
+                &beta, diff_weights, &IC);
+
+    if (diff_bias) {
+        diff_bias += diff_bias_d.offset0();
+        constexpr int blksize = 8;
+        const int OC_blocks = OC / blksize;
+        const int rem_OC = OC % blksize;
+        parallel(0, [&](const int ithr, const int nthr) {
+            int oc_st{0}, oc_e{0};
+            balance211(OC_blocks, nthr, ithr, oc_st, oc_e);
+            oc_st = oc_st * blksize;
+            oc_e = oc_e * blksize;
+
+            PRAGMA_OMP_SIMD()
+            for (int oc = oc_st; oc < oc_e; ++oc) {
+                diff_bias[oc] = diff_dst[oc];
+            }
+
+            for (int mb = 1; mb < MB; ++mb) {
+                PRAGMA_OMP_SIMD()
+                for (int oc = oc_st; oc < oc_e; ++oc) {
+                    diff_bias[oc] += diff_dst[mb * OC + oc];
+                }
+            }
+
+            if (rem_OC != 0 && ithr == nthr-1) {
+                for (int oc = OC_blocks * blksize; oc < OC; oc++)
+                    diff_bias[oc] = diff_dst[oc];
+                for (int mb = 1; mb < MB; ++mb) {
+                    for (int oc = OC_blocks * blksize; oc < OC; oc++) {
+                        diff_bias[oc] += diff_dst[mb * OC + oc];
+                    }
+                }
+            }
+        });
+    }
+}
+
+template struct gemm_inner_product_fwd_t<data_type::f32>;
+template struct gemm_inner_product_bwd_data_t<data_type::f32>;
+template struct gemm_inner_product_bwd_weights_t<data_type::f32>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_inner_product.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_inner_product.hpp
new file mode 100644
index 0000000000..acf0a49b9a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_inner_product.hpp
@@ -0,0 +1,157 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_GEMM_INNER_PRODUCT_HPP
+#define CPU_GEMM_INNER_PRODUCT_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "gemm/gemm.hpp"
+
+#include "cpu_inner_product_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t data_type>
+struct gemm_inner_product_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_inner_product_fwd_pd_t {
+        using cpu_inner_product_fwd_pd_t::cpu_inner_product_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_inner_product_fwd_t);
+
+        status_t init() {
+            using namespace utils;
+
+            bool ok = true
+                && set_default_params() == status::success
+                && is_fwd()
+                && !has_zero_dim_memory()
+                && everyone_is(data_type,
+                        src_md()->data_type,
+                        weights_md()->data_type,
+                        dst_md()->data_type,
+                        with_bias() ? weights_md(1)->data_type : data_type)
+                && attr()->output_scales_.has_default_values()
+                && attr()->post_ops_.len_ <= 1
+                && IMPLICATION(attr()->post_ops_.len_ == 1,
+                        attr()->post_ops_.entry_[0].is_relu(true, false))
+                && dense_gemm_consitency_check(src_md(), weights_md(),
+                        dst_md());
+            return ok ? status::success : status::unimplemented;
+        }
+    };
+
+    gemm_inner_product_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t data_type>
+struct gemm_inner_product_bwd_data_t: public cpu_primitive_t {
+    struct pd_t: public cpu_inner_product_bwd_data_pd_t {
+        using cpu_inner_product_bwd_data_pd_t::cpu_inner_product_bwd_data_pd_t;
+
+        DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_inner_product_bwd_data_t);
+
+        status_t init() {
+            bool ok = true
+                && set_default_params() == status::success
+                && desc()->prop_kind == prop_kind::backward_data
+                && !has_zero_dim_memory()
+                && utils::everyone_is(data_type,
+                        diff_src_md()->data_type,
+                        weights_md()->data_type,
+                        diff_dst_md()->data_type)
+                && attr()->has_default_values()
+                && dense_gemm_consitency_check(diff_src_md(), weights_md(),
+                        diff_dst_md());
+            return ok ? status::success : status::unimplemented;
+        }
+    };
+
+    gemm_inner_product_bwd_data_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_data(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_data(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t data_type>
+struct gemm_inner_product_bwd_weights_t: public cpu_primitive_t {
+    struct pd_t: public cpu_inner_product_bwd_weights_pd_t {
+        using cpu_inner_product_bwd_weights_pd_t::cpu_inner_product_bwd_weights_pd_t;
+
+        DECLARE_COMMON_PD_T(GEMM_IMPL_STR, gemm_inner_product_bwd_weights_t);
+
+        status_t init() {
+            bool ok = true
+                && set_default_params() == status::success
+                && desc()->prop_kind == prop_kind::backward_weights
+                && !has_zero_dim_memory()
+                && utils::everyone_is(data_type,
+                        src_md()->data_type,
+                        diff_weights_md()->data_type,
+                        diff_dst_md()->data_type,
+                        with_bias() ? diff_weights_md(1)->data_type : data_type)
+                && attr()->has_default_values()
+                && dense_gemm_consitency_check(src_md(), diff_weights_md(),
+                        diff_dst_md());
+
+            return ok ? status::success : status::unimplemented;
+        }
+    };
+
+    gemm_inner_product_bwd_weights_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_weights(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_weights(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_convolution.cpp
new file mode 100644
index 0000000000..fed7e4d693
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_convolution.cpp
@@ -0,0 +1,740 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "utils.hpp"
+#include "type_helpers.hpp"
+#include "mkldnn_thread.hpp"
+#include "math_utils.hpp"
+
+#include "simple_q10n.hpp"
+
+#include "gemm/gemm.hpp"
+#include "gemm_x8s8s32x_convolution.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::math;
+using namespace mkldnn::impl::memory_tracking::names;
+
+template <data_type_t src_type, data_type_t dst_type>
+void _gemm_x8s8s32x_convolution_fwd_t<src_type, dst_type>::
+execute_forward(const exec_ctx_t &ctx) const {
+    auto src_base = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto wei_base = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bia_base = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto dst_base = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    auto scratchpad = this->scratchpad(ctx);
+
+    const jit_gemm_conv_conf_t &jcp = this->pd()->jcp_;
+
+    assert(IMPLICATION(
+            jcp.id != 1, jcp.oh_block == jcp.oh && jcp.ow_block == jcp.ow));
+    assert(IMPLICATION(jcp.ow_block != jcp.ow, jcp.oh_block == 1));
+
+    parallel(jcp.nthr, [&](const int ithr, const int nthr) {
+        execute_forward_thr(ithr, nthr, src_base, wei_base, bia_base, dst_base,
+                scratchpad);
+    });
+}
+
+template <data_type_t src_type, data_type_t dst_type>
+_gemm_x8s8s32x_convolution_fwd_t<src_type, dst_type>::pp_ker_t::pp_ker_t(
+    const pd_t *pd)
+    : ker_(nullptr)
+    , jcp_(pd->jcp_)
+    , OC_(pd->jcp_.oc)
+    , OS_(pd->jcp_.os)
+    , bias_data_type_(data_type::undef)
+    , bias_data_type_size_(0)
+    , scale_idx_mult_(0)
+    , do_bias_(false)
+    , do_relu_(false)
+    , do_sum_(false)
+{
+    using namespace types;
+
+    const auto dst_md = memory_desc_wrapper(pd->dst_md());
+    dst_os_stride_ = dst_md.blk_off(0, 0, 0, 1);
+
+    scale_idx_mult_ = (pd->attr()->output_scales_.mask_ == (1 << 1));
+
+    auto &post_ops = pd->attr()->post_ops_;
+
+    int entry_idx = -1;
+    for (int idx = 0; idx < post_ops.len_; ++idx) {
+        const auto &e = post_ops.entry_[idx];
+        if (e.is_relu(true, false)) {
+            entry_idx = idx;
+            break;
+        }
+    }
+    do_relu_ = entry_idx >= 0;
+
+    do_signed_scaling_ = jcp_.signed_input;
+
+    do_sum_ = post_ops.contain(primitive_kind::sum, 0);
+    do_bias_ = pd->with_bias();
+    bias_data_type_ = pd->desc()->bias_desc.data_type;
+    if (do_bias_) {
+        assert(bias_data_type_ != data_type::undef);
+        bias_data_type_size_ = data_type_size(bias_data_type_);
+    }
+    const size_t vlen_start
+            = cpu_isa_traits<avx512_common>::vlen / sizeof(float);
+
+    for (size_t i = vlen_start; i > 0; i--) {
+        if (OC_ % i == 0) {
+            vlen_ = i;
+            break;
+        }
+    }
+
+    if (!mayiuse(avx512_core))
+        // use fallback code for older CPUs
+        return;
+    else
+        generate();
+}
+
+template <data_type_t src_type, data_type_t dst_type>
+void _gemm_x8s8s32x_convolution_fwd_t<src_type, dst_type>::pp_ker_t::generate()
+{
+    using namespace Xbyak;
+    using namespace utils;
+
+    // TODO: clean-up
+    Reg64 reg_param = abi_param1;
+    Reg64 reg_dst = rdx;
+    Reg64 reg_acc = rax;
+    Reg64 reg_bias = rbx;
+    Reg64 reg_scales = rsi;
+
+    Reg64 reg_len = r8;
+    Reg64 reg_tmp = rcx; // intentional for shifting purposes
+    Reg64 reg_oc_offset = r9;
+    Reg64 reg_rem_mask_short = r10;
+    Reg64 reg_rem_mask_vlen = r11;
+    Opmask kreg_rem_mask_short = k1;
+    Opmask kreg_rem_mask_vlen = k3;
+    Opmask kreg_relu_cmp = k2;
+
+    const size_t vlen = vlen_;
+
+    Zmm vreg_zero = Zmm(0);
+    Zmm vreg_scale = Zmm(1);
+    Zmm vreg_nslope = Zmm(2);
+    Zmm vreg_sum_scale = Zmm(3);
+    Zmm vreg_signed_scale = Zmm(4);
+
+    size_t def_unroll = 4;
+    size_t max_unroll = 12;
+    size_t zmm_step = 2;
+    if (do_sum_) {
+        max_unroll = 8;
+        zmm_step = 3;
+    }
+
+    auto vreg_dst = [&](int idx) {
+        return Zmm(5 + idx * zmm_step + 0);
+    };
+    auto vreg_bias = [&](int idx) {
+        return Zmm(5 + idx * zmm_step + 1);
+    };
+    auto vreg_prev_dst = [&](int idx) {
+        return Zmm(5 + idx * zmm_step + 2);
+    };
+
+    preamble();
+
+#define PARAM_OFF(x) offsetof(ker_args, x)
+    mov(reg_dst, ptr[reg_param + PARAM_OFF(dst)]);
+    mov(reg_acc, ptr[reg_param + PARAM_OFF(acc)]);
+    mov(reg_bias, ptr[reg_param + PARAM_OFF(bias)]);
+    mov(reg_scales, ptr[reg_param + PARAM_OFF(scales)]);
+    mov(reg_len, ptr[reg_param + PARAM_OFF(len)]);
+    mov(reg_oc_offset, ptr[reg_param + PARAM_OFF(oc_offset)]);
+    vbroadcastss(vreg_nslope, ptr[reg_param + PARAM_OFF(nslope)]);
+    vbroadcastss(vreg_sum_scale, ptr[reg_param + PARAM_OFF(sum_scale)]);
+    vbroadcastss(vreg_signed_scale, ptr[reg_param + PARAM_OFF(signed_scale)]);
+    if (scale_idx_mult_ == 0)
+        vbroadcastss(vreg_scale, dword[reg_scales]);
+
+#undef PARAM_OFF
+
+    mov(reg_rem_mask_vlen, 1);
+    shl(reg_rem_mask_vlen, vlen);
+    sub(reg_rem_mask_vlen, 1);
+    kmovq(kreg_rem_mask_vlen, reg_rem_mask_vlen);
+
+    if (do_relu_ || dst_type == data_type::u8)
+        vxorps(vreg_zero, vreg_zero, vreg_zero);
+
+    // Load accumulated value, convert to float, apply sum (if any),
+    // bias (if any), scaling, and relu (if any);
+    // then convert to destination type and store
+    auto compute = [&](size_t offset, int idx, bool apply_mask) {
+        auto acc_addr = ptr[reg_acc + offset * sizeof(acc_data_t)];
+
+        if (scale_idx_mult_ > 0) {
+            assert(scale_idx_mult_ == 1);
+            auto scale_addr = ptr[reg_scales + offset * sizeof(float)];
+            auto vreg_scale_ = vreg_scale;
+            if (apply_mask)
+                vreg_scale_ = vreg_scale_ | kreg_rem_mask_short;
+            else
+                vreg_scale_ = vreg_scale_ | kreg_rem_mask_vlen;
+            vmovups(vreg_scale_, scale_addr);
+        }
+
+        auto vreg_dst_ = vreg_dst(idx);
+        if (apply_mask)
+            vreg_dst_ = vreg_dst_ | kreg_rem_mask_short;
+        else
+            vreg_dst_ = vreg_dst_ | kreg_rem_mask_vlen;
+        vcvtdq2ps(vreg_dst_, acc_addr);
+
+        if (do_signed_scaling_)
+            vmulps(vreg_dst(idx), vreg_dst(idx), vreg_signed_scale);
+
+        if (do_bias_) {
+            auto bias_addr = ptr[reg_bias + offset * bias_data_type_size_];
+            auto vreg_bias_ = vreg_bias(idx);
+            if (apply_mask)
+                vreg_bias_ = vreg_bias_ | kreg_rem_mask_short;
+            else
+                vreg_bias_ = vreg_bias_ | kreg_rem_mask_vlen;
+
+            switch (bias_data_type_) {
+            case data_type::s8:
+                vpmovsxbd(vreg_bias_, bias_addr);
+                break;
+            case data_type::u8:
+                vpmovzxbd(vreg_bias_, bias_addr);
+                break;
+            case data_type::s32:
+            case data_type::f32:
+                vmovups(vreg_bias_, bias_addr);
+                break;
+            default: assert(!"unimplemented");
+            }
+            if (bias_data_type_ != data_type::f32)
+                vcvtdq2ps(vreg_bias(idx), vreg_bias(idx));
+            vaddps(vreg_dst(idx), vreg_dst(idx), vreg_bias(idx));
+        }
+
+        vmulps(vreg_dst(idx), vreg_dst(idx), vreg_scale);
+
+        auto dst_addr = ptr[reg_dst + offset * sizeof(dst_data_t)];
+
+        if (do_sum_)
+        {
+            auto vreg_prev_dst_ = vreg_prev_dst(idx);
+            if (apply_mask)
+                vreg_prev_dst_ = vreg_prev_dst_ | kreg_rem_mask_short;
+            else
+                vreg_prev_dst_ = vreg_prev_dst_ | kreg_rem_mask_vlen;
+
+            switch (dst_type) {
+            case data_type::f32:
+            case data_type::s32: vmovups(vreg_prev_dst_, dst_addr); break;
+            case data_type::s8: vpmovsxbd(vreg_prev_dst_, dst_addr); break;
+            case data_type::u8: vpmovzxbd(vreg_prev_dst_, dst_addr); break;
+            default: assert(!"unsupported data type");
+            }
+            if (dst_type != data_type::f32)
+                vcvtdq2ps(vreg_prev_dst(idx), vreg_prev_dst(idx));
+
+            vfmadd231ps(vreg_dst(idx), vreg_prev_dst(idx), vreg_sum_scale);
+        }
+
+        if (do_relu_) {
+            vcmpps(kreg_relu_cmp, vreg_dst(idx), vreg_zero, _cmp_lt_os);
+            vmulps(vreg_dst(idx) | kreg_relu_cmp, vreg_dst(idx), vreg_nslope);
+        }
+
+        if (dst_type != data_type::f32) {
+            vcvtps2dq(vreg_dst(idx), vreg_dst(idx));
+        }
+
+        if (dst_type == data_type::u8)
+            vpmaxsd(vreg_dst(idx), vreg_dst(idx), vreg_zero);
+
+        switch (dst_type) {
+        case data_type::s8:
+            vpmovsdb(dst_addr, vreg_dst_);
+            break;
+        case data_type::u8:
+            vpmovusdb(dst_addr, vreg_dst_);
+            break;
+        case data_type::f32:
+        case data_type::s32:
+            vmovups(dst_addr, vreg_dst_);
+            break;
+        default: assert(!"unimplemented");
+        }
+    };
+
+    // Advance all pointers by an immediate
+    auto advance_ptrs_imm = [&](size_t offset) {
+        add(reg_dst, offset * sizeof(dst_data_t));
+        add(reg_acc, offset * sizeof(acc_data_t));
+        if (scale_idx_mult_) {
+            assert(scale_idx_mult_ == 1);
+            add(reg_scales, offset * sizeof(float));
+        }
+        if (do_bias_)
+            add(reg_bias, offset * bias_data_type_size_);
+    };
+
+    // Advance all pointers by a value stored in a register
+    auto advance_ptrs_reg = [&](Reg64 offset) {
+        lea(reg_dst, ptr[reg_dst + offset * sizeof(dst_data_t)]);
+        lea(reg_acc, ptr[reg_acc + offset * sizeof(acc_data_t)]);
+        if (scale_idx_mult_) {
+            assert(scale_idx_mult_ == 1);
+            lea(reg_scales, ptr[reg_scales + offset * sizeof(float)]);
+        }
+        if (do_bias_)
+            lea(reg_bias, ptr[reg_bias + offset * bias_data_type_size_]);
+    };
+
+    // Rewind pointers that point to data that is indexed by output channel
+    // (bias or per-oc scaling factors)
+    auto rewind_ptrs = [&]() {
+        if (do_bias_)
+            sub(reg_bias, OC_ * bias_data_type_size_);
+        if (scale_idx_mult_) {
+            assert(scale_idx_mult_ == 1);
+            sub(reg_scales, OC_ * sizeof(float));
+        }
+        add(reg_dst, (dst_os_stride_ - OC_) * sizeof(dst_data_t));
+    };
+
+    //                    <--------- OC --------------->
+    //
+    // ^  ................+..............+-------------+.......................
+    // |  .               : not accessed |Prologue loop|                      .
+    // |  .               +--------------+-------------+                      .
+    //    .               |                            |                      .
+    // O  .               |  Main loop (unrolled)      |                      .
+    // S  .               |                            |                      .
+    //    .               +--------------+-------------+                      .
+    // |  .               | Epilogue loop|not accessed :                      .
+    // v  ................+--------------+.............+.......................
+
+    Label prologue_end;
+    cmp(reg_oc_offset, 0);
+    je(prologue_end, T_NEAR);
+
+    // Prologue loop
+    {
+        mov(reg_tmp, OC_);
+        sub(reg_tmp, reg_oc_offset);
+        cmp(reg_tmp, reg_len);
+        cmovg(reg_tmp, reg_len);
+        sub(reg_len, reg_tmp);
+
+        Label prologue_loop, prologue_loop_tail, prologue_loop_end;
+        cmp(reg_tmp, vlen);
+        jle(prologue_loop_tail, T_NEAR);
+        L(prologue_loop); {
+            compute(0, 0, false);
+            advance_ptrs_imm(vlen);
+            sub(reg_tmp, vlen);
+            cmp(reg_tmp, vlen);
+            jge(prologue_loop, T_NEAR);
+        }
+
+        L(prologue_loop_tail);
+        mov(reg_rem_mask_short, 1);
+        // cl == reg_tmp because reg_tmp <= vlen here
+        shl(reg_rem_mask_short, cl);
+        sub(reg_rem_mask_short, 1);
+        jz(prologue_loop_end, T_NEAR);
+
+        kmovq(kreg_rem_mask_short, reg_rem_mask_short);
+        compute(0, 0, true);
+        advance_ptrs_reg(reg_tmp);
+
+        L(prologue_loop_end);
+        rewind_ptrs();
+    }
+    L(prologue_end);
+
+    // Main loop
+    Label main_loop_end;
+    {
+        cmp(reg_len, OC_);
+        jle(main_loop_end, T_NEAR);
+
+        Label main_loop;
+        L(main_loop); {
+            size_t OC_loop, OC_tail;
+            if (OC_ < max_unroll * vlen) {
+                // Fully unroll small loops
+                OC_loop = 0;
+                OC_tail = OC_;
+            }
+            else {
+                OC_loop = vlen * def_unroll;
+                OC_tail = OC_ % OC_loop;
+            }
+
+            assert(!!OC_loop || !!OC_tail);
+
+            if (OC_tail % vlen) {
+                int vlen_tail = OC_tail % vlen;
+                unsigned tail_mask = (1 << vlen_tail) - 1;
+                mov(reg_tmp, tail_mask);
+                kmovq(kreg_rem_mask_short, reg_tmp);
+            }
+
+            if (OC_loop) {
+                mov(reg_tmp, rnd_dn(OC_, OC_loop));
+                Label oc_loop;
+                L(oc_loop); {
+                    for (size_t offset = 0; offset < OC_loop; offset += vlen)
+                        compute(offset, offset / vlen, false);
+                    advance_ptrs_imm(OC_loop);
+                    sub(reg_tmp, OC_loop);
+                    jnz(oc_loop);
+                }
+            }
+
+            if (OC_tail) {
+                for (size_t offset = 0; offset < OC_tail; offset += vlen) {
+                    bool use_mask = (offset + vlen) > OC_tail;
+                    compute(offset, offset / vlen, use_mask);
+                }
+                advance_ptrs_imm(OC_tail);
+            }
+
+            rewind_ptrs();
+            sub(reg_len, OC_);
+            cmp(reg_len, OC_);
+            jge(main_loop, T_NEAR);
+        }
+    }
+    L(main_loop_end);
+
+    // Epilogue loop
+    Label epilogue_end;
+    {
+        cmp(reg_len, 0);
+        je(epilogue_end, T_NEAR);
+
+        Label epilogue_loop, epilogue_loop_tail;
+        cmp(reg_len, vlen);
+        jle(epilogue_loop_tail, T_NEAR);
+        L(epilogue_loop); {
+            compute(0, 0, false);
+            sub(reg_len, vlen);
+            advance_ptrs_imm(vlen);
+            cmp(reg_len, vlen);
+            jge(epilogue_loop, T_NEAR);
+        }
+
+        L(epilogue_loop_tail);
+        mov(reg_tmp, reg_len); // reg_tmp is rcx, and we need cl for the shift
+        mov(reg_rem_mask_short, 1);
+        shl(reg_rem_mask_short, cl); // reg_tmp == rcx and reg_tail < vlen
+        sub(reg_rem_mask_short, 1);
+        jz(epilogue_end, T_NEAR);
+        kmovq(kreg_rem_mask_short, reg_rem_mask_short);
+        compute(0, 0, true);
+    }
+
+    L(epilogue_end);
+
+    postamble();
+
+    ker_ = getCode<decltype(ker_)>();
+}
+
+template <data_type_t src_type, data_type_t dst_type>
+void _gemm_x8s8s32x_convolution_fwd_t<src_type, dst_type>::pp_ker_t::operator ()
+    (dst_data_t *dst, const acc_data_t *acc, const char *bias,
+        const float *scales, float nslope, float sum_scale, float signed_scale,
+        int g, size_t start, size_t end)
+{
+    using math::get_bias;
+
+    if (end <= start)
+        return;
+
+    if (ker_) {
+        // JIT
+        ker_args args;
+        size_t oc_offset = start % OC_;
+        size_t os_offset = start / OC_;
+        args.acc = acc + start;
+        args.dst = dst + os_offset * dst_os_stride_ + oc_offset;
+        args.bias = bias + (g * jcp_.oc + oc_offset) * bias_data_type_size_;
+        args.scales = scales + scale_idx_mult_ * (g * jcp_.oc + oc_offset);
+        args.nslope = nslope;
+        args.sum_scale = sum_scale;
+        args.signed_scale = signed_scale;
+        args.len = end - start;
+        args.oc_offset = oc_offset;
+        ker_(&args);
+    }
+    else {
+        // Fallback
+        const size_t first_oc = start % OC_;
+        const size_t last_oc = (end - 1) % OC_;
+        const size_t first_os = start / OC_;
+        const size_t last_os = (end - 1) / OC_;
+        for (size_t os = first_os; os <= last_os; os++) {
+            const size_t start_oc = (os == first_os) ? first_oc : 0;
+            const size_t end_oc = (os == last_os) ? last_oc : OC_ - 1;
+            for (size_t oc = start_oc; oc <= end_oc; oc++) {
+                const size_t acc_off = os * jcp_.oc + oc;
+                const size_t dst_off = os * dst_os_stride_ + oc;
+
+                float d = (float)(acc[acc_off]);
+                if (jcp_.signed_input)
+                    d *= signed_scale;
+
+                if (do_bias_)
+                    d += get_bias(bias, g * jcp_.oc + oc,
+                        bias_data_type_);
+
+                d *= scales[(g * jcp_.oc + oc) * scale_idx_mult_];
+                if (do_sum_)
+                    d += sum_scale * dst[dst_off];
+                if (do_relu_ && d < 0)
+                    d *= nslope;
+                dst[dst_off] = qz_a1b0<float, dst_data_t>()(d);
+            }
+        }
+    }
+};
+
+template <data_type_t src_type, data_type_t dst_type>
+void _gemm_x8s8s32x_convolution_fwd_t<src_type, dst_type>::
+execute_forward_thr(const int ithr, const int nthr, const src_data_t *src_base,
+        const wei_data_t *wei_base, const char *bia_base, dst_data_t *dst_base,
+        const memory_tracking::grantor_t &scratchpad) const {
+    const jit_gemm_conv_conf_t &jcp = this->pd()->jcp_;
+
+    const auto src_md = memory_desc_wrapper(pd()->src_md());
+    const size_t src_mb_stride = src_md.blk_off(1);
+    const size_t src_g_stride = src_md.blk_off(0, 1) * jcp.ic;
+
+    const auto wei_md = memory_desc_wrapper(pd()->weights_md(0));
+    const size_t wei_g_stride = pd()->with_groups() ? wei_md.blk_off(1) : 0;
+
+    const auto dst_md = memory_desc_wrapper(pd()->dst_md());
+    const size_t dst_mb_stride = dst_md.blk_off(1);
+    const size_t dst_g_stride = dst_md.blk_off(0, 1) * jcp.oc;
+
+    const float *scales = pd()->attr()->output_scales_.scales_;
+
+    const auto &post_ops = pd()->attr()->post_ops_;
+    const bool do_sum = post_ops.contain(primitive_kind::sum, 0);
+    const float sum_scale = do_sum ? post_ops.entry_[0].sum.scale : 0;
+
+    float nslope = 0;
+    for (int idx = 0; idx < post_ops.len_; ++idx) {
+        const auto &e = post_ops.entry_[idx];
+        if (e.is_relu(true, false)) {
+            nslope = e.eltwise.alpha;
+            break;
+        }
+    }
+
+    auto col = scratchpad.get<uint8_t>(key_conv_gemm_col)
+        + (ptrdiff_t)ithr * jcp.im2col_sz;
+    src_data_t *__restrict imtr = scratchpad.get<src_data_t>(key_conv_gemm_imtr)
+        + (ptrdiff_t)ithr * jcp.is * jcp.ic;
+    auto acc = scratchpad.get<acc_data_t>(key_conv_int_dat_in_acc_dt)
+        + (ptrdiff_t)ithr * jcp.oh_block * jcp.ow_block * jcp.oc;
+
+    const ptrdiff_t offset = (ptrdiff_t)jcp.ngroups * jcp.ks * jcp.ic * jcp.oc;
+    const int32_t *_wei_comp = (const int32_t *)(wei_base + offset);
+
+    int g{ 0 }, n{ 0 }, ohb{ 0 }, owb{ 0 };
+    size_t start = 0, end = 0;
+
+    const int nb_oh = div_up(jcp.oh, jcp.oh_block);
+    const int nb_ow = div_up(jcp.ow, jcp.ow_block);
+    const size_t work_amount = jcp.ngroups * jcp.mb * nb_oh * nb_ow;
+    balance211(work_amount, nthr, ithr, start, end);
+    nd_iterator_init(start, n, jcp.mb, g, jcp.ngroups, ohb,
+                nb_oh, owb, nb_ow);
+
+    for (size_t iwork = start; iwork < end; ++iwork) {
+        int oh = ohb * jcp.oh_block;
+        int ow = owb * jcp.ow_block;
+        const src_data_t *__restrict src = src_base + n * src_mb_stride
+            + g * src_g_stride;
+        const wei_data_t *__restrict wei = wei_base + g * wei_g_stride;
+        dst_data_t *__restrict dst =
+                dst_base + n * dst_mb_stride + g * dst_g_stride;
+        const int32_t *wei_comp = _wei_comp + g * jcp.oc;
+        const int h_step = nstl::min(jcp.oh_block, jcp.oh - oh);
+        const int w_step = nstl::min(jcp.ow_block, jcp.ow - ow);
+
+        if (jcp.im2col_sz)
+            jit_gemm_convolution_utils::im2col_u8<src_data_t>(
+                    jcp, src, imtr, col, oh, h_step, ow, w_step);
+
+        const int M = jcp.oc;
+        const int K = jcp.ks * jcp.ic;
+        const int N = h_step * w_step;
+        const int LDA = M * jcp.ngroups;
+        const int LDB = jcp.im2col_sz ? N : K;
+        const char *BT = jcp.im2col_sz ? "T" : "N";
+        const int8_t off_a = 0, off_b = 0;
+        const int32_t off_c = 0;
+        const float onef = 1.0, zerof = 0.0;
+        gemm_s8x8s32("N", BT, jcp.signed_input ? "C" : "F",
+            &M, &N, &K, &onef, wei, &LDA, &off_a,
+            jcp.im2col_sz ? col : (uint8_t *)src, &LDB, &off_b,
+            &zerof, acc, &M, jcp.signed_input ? wei_comp : &off_c);
+
+        auto wei_adj_scale =
+            (wei_md.extra().flags | memory_extra_flags::scale_adjust)
+            ? wei_md.extra().scale_adjust : 1.f;
+
+        parallel(0, [&](int ithr, int nthr) {
+            size_t start, end;
+            balance211((size_t)N * jcp.oc, nthr, ithr, start, end);
+            (*pp_ker_)(dst + (oh * jcp.ow + ow) * pp_ker_->dst_os_stride_,
+                    acc, bia_base, scales, nslope, sum_scale,
+                    1.f / wei_adj_scale, g, start, end);
+        });
+
+        nd_iterator_step(n, jcp.mb, g, jcp.ngroups, ohb, nb_oh,
+                    owb, nb_ow);
+    }
+}
+
+template <data_type_t dst_type>
+void _gemm_u8s8s32x_convolution_bwd_data_t<dst_type>::
+execute_backward_data(const exec_ctx_t &ctx) const {
+    auto diff_dst_base = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST);
+    auto wei_base = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bia_base = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto diff_src_base = CTX_OUT_MEM(diff_src_data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    auto scratchpad = this->scratchpad(ctx);
+
+    const jit_gemm_conv_conf_t &jcp = this->pd()->jcp_;
+
+    parallel(jcp.nthr, [&](const int ithr, const int nthr) {
+        execute_backward_data_thr(ithr, nthr, diff_dst_base, wei_base,
+                bia_base, diff_src_base, scratchpad);
+    });
+}
+
+template <data_type_t dst_type>
+void _gemm_u8s8s32x_convolution_bwd_data_t<dst_type>::
+execute_backward_data_thr(const int ithr, const int nthr,
+        const diff_dst_data_t *diff_dst_base, const wei_data_t *wei_base,
+        const char *bia_base, diff_src_data_t *diff_src_base,
+        const memory_tracking::grantor_t &scratchpad) const
+{
+    const jit_gemm_conv_conf_t &jcp = this->pd()->jcp_;
+
+    const auto diff_dst_md = memory_desc_wrapper(pd()->diff_dst_md());
+    const size_t diff_dst_mb_stride = diff_dst_md.blk_off(1);
+    const size_t diff_dst_g_stride = diff_dst_md.blk_off(0, 1) * jcp.oc;
+
+    const auto wei_md = memory_desc_wrapper(pd()->weights_md(0));
+    const size_t wei_g_stride = pd()->with_groups() ? wei_md.blk_off(1) : 0;
+
+    const auto diff_src_md = memory_desc_wrapper(pd()->diff_src_md());
+    const size_t diff_src_mb_stride = diff_src_md.blk_off(1);
+    const size_t diff_src_g_stride = diff_src_md.blk_off(0, 1) * jcp.ic;
+    const size_t diff_src_os_stride = diff_src_md.blk_off(0, 0, 0, 1);
+
+    /* scale_idx_mult = 1 for per_oc scales and 0, otherwise */
+    const int scale_idx_mult = pd()->attr()->output_scales_.mask_ == (1 << 1);
+    const float *scales = pd()->attr()->output_scales_.scales_;
+    const size_t work_amount = jcp.ngroups * jcp.mb;
+
+    auto col = scratchpad.get<acc_data_t>(key_conv_gemm_col)
+        + (ptrdiff_t)ithr * jcp.im2col_sz;
+    auto acc = scratchpad.get<acc_data_t>(key_conv_int_dat_in_acc_dt)
+        + (ptrdiff_t)ithr * jcp.is * jcp.ic;
+
+    int n{0}, g{0};
+    size_t start = 0, end = 0;
+
+    balance211(work_amount, nthr, ithr, start, end);
+    nd_iterator_init(start, n, jcp.mb, g, jcp.ngroups);
+
+    for (size_t iwork = start; iwork < end; ++iwork) {
+        const diff_dst_data_t *diff_dst = diff_dst_base
+            + n * diff_dst_mb_stride + g * diff_dst_g_stride;
+        const wei_data_t *wei = wei_base + g * wei_g_stride;
+        diff_src_data_t *diff_src = diff_src_base + n * diff_src_mb_stride
+            + g * diff_src_g_stride;
+
+        const int M = jcp.ks * jcp.ic;
+        const int N = jcp.os;
+        const int K = jcp.oc;
+        const int8_t off_a = 0, off_b = 0;
+        const int32_t off_c = 0;
+        const float onef = 1.0, zerof = 0.0;
+        const int LD = K * jcp.ngroups;
+
+        gemm_s8x8s32("T", "N", "F", &M, &N, &K, &onef,
+                wei, &LD, &off_a, diff_dst, &LD, &off_b,
+                &zerof, jcp.im2col_sz ? col : acc, &M, &off_c);
+
+        if (jcp.im2col_sz)
+            jit_gemm_convolution_utils::col2im_s32(jcp, col, acc);
+
+        parallel_nd(jcp.is, jcp.ic, [&](int is, int ic) {
+            float d = (float)acc[is * jcp.ic + ic];
+            if (jcp.with_bias)
+                d += get_bias(bia_base, g * jcp.ic + ic,
+                        pd()->desc()->bias_desc.data_type);
+            d *= scales[(g * jcp.ic + ic) * scale_idx_mult];
+            const size_t diff_src_off = is * diff_src_os_stride + ic;
+            diff_src[diff_src_off] =
+                qz_a1b0<float, diff_src_data_t>()(d);
+        });
+        nd_iterator_step(n, jcp.mb, g, jcp.ngroups);
+    }
+}
+
+using namespace data_type;
+
+template struct _gemm_x8s8s32x_convolution_fwd_t<u8, f32>;
+template struct _gemm_x8s8s32x_convolution_fwd_t<u8, s32>;
+template struct _gemm_x8s8s32x_convolution_fwd_t<u8, s8>;
+template struct _gemm_x8s8s32x_convolution_fwd_t<u8, u8>;
+
+template struct _gemm_x8s8s32x_convolution_fwd_t<s8, f32>;
+template struct _gemm_x8s8s32x_convolution_fwd_t<s8, s32>;
+template struct _gemm_x8s8s32x_convolution_fwd_t<s8, s8>;
+template struct _gemm_x8s8s32x_convolution_fwd_t<s8, u8>;
+
+template struct _gemm_u8s8s32x_convolution_bwd_data_t<f32>;
+template struct _gemm_u8s8s32x_convolution_bwd_data_t<s32>;
+template struct _gemm_u8s8s32x_convolution_bwd_data_t<s8>;
+template struct _gemm_u8s8s32x_convolution_bwd_data_t<u8>;
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_convolution.hpp
new file mode 100644
index 0000000000..9e77b890d5
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_convolution.hpp
@@ -0,0 +1,266 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef GEMM_X8S8S32X_CONVOLUTION_HPP
+#define GEMM_X8S8S32X_CONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "jit_primitive_conf.hpp"
+#include "jit_generator.hpp"
+#include "gemm_convolution_utils.hpp"
+
+#include "gemm/gemm.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <data_type_t src_type, data_type_t dst_type>
+struct _gemm_x8s8s32x_convolution_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(IGEMM_S8U8S32_IMPL_STR,
+                _gemm_x8s8s32x_convolution_fwd_t<src_type, dst_type>);
+
+        status_t init() {
+            using namespace data_type;
+
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(src_type, s8, data_type::undef, dst_type,
+                        s32)
+                && IMPLICATION(with_bias(), utils::one_of(
+                            desc()->bias_desc.data_type, f32, s32, s8, u8))
+                && !has_zero_dim_memory()
+                && set_default_formats_common(
+                        dat_tag(), format_tag::any, dat_tag())
+                && post_ops_ok()
+                && memory_desc_matches_tag(*src_md(), dat_tag())
+                && memory_desc_matches_tag(*dst_md(), dat_tag())
+                && set_or_check_wei_format();
+            if (!ok) return status::unimplemented;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
+                    *desc(), src_md(), weights_md(0), dst_md(),
+                    mkldnn_get_max_threads());
+        }
+
+        jit_gemm_conv_conf_t jcp_;
+
+    protected:
+        format_tag_t dat_tag() const { return format_tag::nhwc; }
+
+        bool set_or_check_wei_format() {
+            using namespace format_tag;
+
+            const bool is_src_s8 = src_md_.data_type == data_type::s8;
+
+            memory_desc_t want_wei_md = weights_md_;
+            memory_desc_init_by_tag(want_wei_md, with_groups() ? hwigo : hwio);
+
+            if (is_src_s8) {
+                want_wei_md.extra.flags = 0
+                    | memory_extra_flags::compensation_conv_s8s8
+                    | memory_extra_flags::scale_adjust;
+                want_wei_md.extra.compensation_mask = (1 << 0)
+                    + (with_groups() ? (1 << 1) : 0);
+                want_wei_md.extra.scale_adjust =
+                    mayiuse(avx512_core_vnni) ? 1.f : 0.5f;
+            }
+
+            if (weights_md_.format_kind == format_kind::any) {
+                weights_md_ = want_wei_md;
+                return true;
+            }
+
+            return weights_md_ == want_wei_md;
+        }
+
+        bool post_ops_ok() const {
+            using namespace mkldnn::impl::primitive_kind;
+            auto const &po = attr()->post_ops_;
+            auto is_relu = [&](int idx) {
+                return po.entry_[idx].is_relu(true, false); };
+
+            switch (po.len_) {
+            case 0: return true;
+            case 1: return is_relu(0) || po.contain(sum, 0);
+            case 2: return po.contain(sum, 0) && is_relu(1);
+            default: return false;
+            }
+            return false;
+        }
+    };
+
+    _gemm_x8s8s32x_convolution_fwd_t(const pd_t *apd)
+        : cpu_primitive_t(apd, true), pp_ker_(nullptr)
+    { pp_ker_ = new pp_ker_t(pd()); }
+    ~_gemm_x8s8s32x_convolution_fwd_t() { delete pp_ker_; }
+
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<data_type::s8>::type wei_data_t;
+    typedef typename prec_traits<dst_type>::type dst_data_t;
+    typedef typename prec_traits<data_type::s32>::type acc_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    // XXX: this is throwaway code that will become unnecessary when we have a
+    // sufficiently advanced igemm jit generator that supports quantization,
+    // relu, and whatnot
+    class pp_ker_t : jit_generator {
+    public:
+        DECLARE_CPU_JIT_AUX_FUNCTIONS(
+        _gemm_x8s8s32x_convolution_fwd_t::pp_kernel);
+        pp_ker_t(const pd_t *pd);
+
+        void operator()(dst_data_t *dst, const acc_data_t *acc,
+            const char *bias, const float *scales,
+            float nslope, float sum_scale, float signed_scale,
+            int g, size_t start, size_t end);
+
+        size_t dst_os_stride_;
+
+    private:
+        void generate();
+
+        struct ker_args {
+            dst_data_t *dst;
+            const acc_data_t *acc;
+            const char *bias;
+            const float *scales;
+            float nslope;
+            float sum_scale;
+            float signed_scale;
+            size_t len;
+            size_t oc_offset;
+        };
+        void(*ker_)(const ker_args *args);
+
+        const jit_gemm_conv_conf_t &jcp_;
+        size_t OC_;
+        size_t OS_;
+        data_type_t bias_data_type_;
+        size_t bias_data_type_size_;
+        size_t scale_idx_mult_;
+        bool do_bias_;
+        bool do_relu_;
+        bool do_sum_;
+        bool do_signed_scaling_;
+        size_t vlen_;
+    };
+
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    void execute_forward(const exec_ctx_t &ctx) const;
+    void execute_forward_thr(const int ithr, const int nthr,
+            const src_data_t *src_base, const wei_data_t *wei_base,
+            const char *bia_base, dst_data_t *dst_base,
+            const memory_tracking::grantor_t &scratchpad) const;
+
+    int nthr_;
+    pp_ker_t *pp_ker_;
+
+};
+
+template <data_type_t dst_type>
+struct _gemm_u8s8s32x_convolution_bwd_data_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_bwd_data_pd_t{
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc, const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(IGEMM_S8U8S32_IMPL_STR,
+                _gemm_u8s8s32x_convolution_bwd_data_t<dst_type>);
+
+        status_t init() {
+            using namespace data_type;
+
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_data
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(dst_type, s8, data_type::undef, u8, s32)
+                && IMPLICATION(with_bias(), utils::one_of(
+                            desc()->bias_desc.data_type, f32, s32, s8, u8))
+                && !has_zero_dim_memory()
+                && set_default_formats_common(dat_tag(), wei_tag(), dat_tag())
+                && attr()->post_ops_.has_default_values()
+                && memory_desc_matches_tag(*diff_src_md(), dat_tag())
+                && memory_desc_matches_tag(*diff_dst_md(), dat_tag())
+                && memory_desc_matches_tag(*weights_md(), wei_tag());
+            if (!ok) return status::unimplemented;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            return jit_gemm_convolution_utils::init_conf(jcp_, scratchpad,
+                    *desc(), diff_src_md(), weights_md(), diff_dst_md(),
+                    mkldnn_get_max_threads());
+        }
+
+        virtual bool support_bias() const override { return true; }
+
+        jit_gemm_conv_conf_t jcp_;
+
+    protected:
+        format_tag_t dat_tag() const { return format_tag::nhwc; }
+
+        format_tag_t wei_tag() const {
+            return with_groups() ? format_tag::hwigo : format_tag::hwio;
+        }
+    };
+
+    _gemm_u8s8s32x_convolution_bwd_data_t(const pd_t *apd)
+        : cpu_primitive_t(apd, true) {}
+
+    typedef typename prec_traits<data_type::u8>::type diff_dst_data_t;
+    typedef typename prec_traits<data_type::s8>::type wei_data_t;
+    typedef typename prec_traits<dst_type>::type diff_src_data_t;
+    typedef typename prec_traits<data_type::s32>::type acc_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_data(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_data(const exec_ctx_t &ctx) const;
+    void execute_backward_data_thr(const int ithr, const int nthr,
+            const diff_dst_data_t *diff_dst_base, const wei_data_t *wei_base,
+            const char *bia_base, diff_src_data_t *diff_src_base,
+            const memory_tracking::grantor_t &scratchpad) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_inner_product.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_inner_product.cpp
new file mode 100644
index 0000000000..1e435a233a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_inner_product.cpp
@@ -0,0 +1,453 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+#include "simple_q10n.hpp"
+
+#include "gemm/gemm.hpp"
+#include "gemm_x8s8s32x_inner_product.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace math;
+using namespace format_tag;
+using namespace memory_tracking::names;
+
+template<data_type_t src_type, data_type_t dst_type>
+gemm_x8s8s32x_inner_product_fwd_t<src_type, dst_type>::pp_kernel_t::pp_kernel_t(
+        const pd_t *pd, bool dst_is_acc)
+    : ker_(nullptr), OC_(pd->OC())
+    , bias_data_type_(data_type::undef), bias_data_type_size_(0)
+    , scale_idx_mult_(0), do_bias_(false), do_relu_(false)
+{
+    using namespace types;
+
+    scale_idx_mult_ = (pd->attr()->output_scales_.mask_ == (1 << 1));
+
+    auto &post_ops = pd->attr()->post_ops_;
+    do_relu_ = post_ops.len_ == 1;
+    do_bias_ = pd->with_bias();
+    bias_data_type_ = pd->desc()->bias_desc.data_type;
+    if (do_bias_) {
+        assert(bias_data_type_ != data_type::undef);
+        bias_data_type_size_ = data_type_size(bias_data_type_);
+    }
+
+    if (!mayiuse(avx512_core))
+        // use fallback code for older CPUs since they do not have optimized
+        // x8s8s32 GEMM anyways. The configuration variables above are used by
+        // the fallback code.
+        return;
+    else
+        generate();
+}
+
+template<data_type_t src_type, data_type_t dst_type>
+void gemm_x8s8s32x_inner_product_fwd_t<src_type, dst_type>::pp_kernel_t::generate()
+{
+    using namespace Xbyak;
+    using namespace utils;
+
+    // TODO: clean-up
+    Reg64 reg_param = abi_param1;
+    Reg64 reg_dst = rdx;
+    Reg64 reg_acc = rax;
+    Reg64 reg_bias = rbx;
+    Reg64 reg_scales = rsi;
+
+    Reg64 reg_len = r8;
+    Reg64 reg_tmp = rcx; // intentional for shifting purposes
+    Reg64 reg_oc_offset = r9;
+    Reg64 reg_rem_mask = r10;
+    Opmask kreg_rem_mask = k1;
+    Opmask kreg_relu_cmp = k2;
+
+    const size_t vlen = cpu_isa_traits<avx512_common>::vlen / sizeof(float);
+
+    Zmm vreg_zero = Zmm(0);
+    Zmm vreg_scale = Zmm(1);
+    Zmm vreg_nslope = Zmm(2);
+
+    auto vreg_dst = [&](int idx) { return Zmm(3 + idx * 2 + 0); };
+    auto vreg_bias = [&](int idx) { return Zmm(3 + idx * 2 + 1); };
+
+    preamble();
+
+#define PARAM_OFF(x) offsetof(ker_args, x)
+    mov(reg_dst, ptr[reg_param + PARAM_OFF(dst)]);
+    mov(reg_acc, ptr[reg_param + PARAM_OFF(acc)]);
+    mov(reg_bias, ptr[reg_param + PARAM_OFF(bias)]);
+    mov(reg_scales, ptr[reg_param + PARAM_OFF(scales)]);
+    mov(reg_len, ptr[reg_param + PARAM_OFF(len)]);
+    mov(reg_oc_offset, ptr[reg_param + PARAM_OFF(oc_offset)]);
+    vbroadcastss(vreg_nslope, ptr[reg_param + PARAM_OFF(nslope)]);
+    if (scale_idx_mult_ == 0)
+        vbroadcastss(vreg_scale, dword[reg_scales]);
+#undef PARAM_OFF
+
+    if (do_relu_ || dst_type == data_type::u8)
+        vxorps(vreg_zero, vreg_zero, vreg_zero);
+
+    // Load accumulated value, convert to float, apply bias (if any), scaling,
+    // and relu (if any); then convert to destination type and store
+    auto compute = [&](size_t offset, int idx, bool apply_mask) {
+        auto acc_addr = ptr[reg_acc + offset * sizeof(acc_data_t)];
+
+        if (scale_idx_mult_ > 0) {
+            assert(scale_idx_mult_ == 1);
+            auto scale_addr = ptr[reg_scales + offset * sizeof(float)];
+            auto vreg_scale_ = vreg_scale;
+            if (apply_mask)
+                vreg_scale_ = vreg_scale_ | kreg_rem_mask;
+            vmovups(vreg_scale, scale_addr);
+        }
+
+        auto vreg_dst_ = vreg_dst(idx);
+        if (apply_mask)
+            vreg_dst_ = vreg_dst_ | kreg_rem_mask;
+        vcvtdq2ps(vreg_dst_, acc_addr);
+
+        if (do_bias_) {
+            auto bias_addr = ptr[reg_bias + offset * bias_data_type_size_];
+            auto vreg_bias_ = vreg_bias(idx);
+            if (apply_mask)
+                vreg_bias_ = vreg_bias_ | kreg_rem_mask;
+
+            switch (bias_data_type_) {
+            case data_type::s8:
+                vpmovsxbd(vreg_bias_, bias_addr);
+                break;
+            case data_type::u8:
+                vpmovzxbd(vreg_bias_, bias_addr);
+                break;
+            case data_type::s32:
+            case data_type::f32:
+                vmovups(vreg_bias_, bias_addr);
+                break;
+            default: assert(!"unimplemented");
+            }
+            if (bias_data_type_ != data_type::f32)
+                vcvtdq2ps(vreg_bias(idx), vreg_bias(idx));
+            vaddps(vreg_dst(idx), vreg_dst(idx), vreg_bias(idx));
+        }
+
+        vmulps(vreg_dst(idx), vreg_dst(idx), vreg_scale);
+        if (do_relu_) {
+            vcmpps(kreg_relu_cmp, vreg_dst(idx), vreg_zero, _cmp_lt_os);
+            vmulps(vreg_dst(idx) | kreg_relu_cmp, vreg_dst(idx), vreg_nslope);
+        }
+
+        if (dst_type == data_type::u8)
+            vmaxps(vreg_dst(idx), vreg_dst(idx), vreg_zero);
+
+        if (dst_type != data_type::f32) {
+            vcvtps2dq(vreg_dst(idx), vreg_dst(idx));
+        }
+
+        auto dst_addr = ptr[reg_dst + offset * sizeof(dst_data_t)];
+        switch (dst_type) {
+        case data_type::s8:
+            vpmovsdb(dst_addr, vreg_dst_);
+            break;
+        case data_type::u8:
+            vpmovusdb(dst_addr, vreg_dst_);
+            break;
+        case data_type::f32:
+        case data_type::s32:
+            vmovups(dst_addr, vreg_dst_);
+            break;
+        default: assert(!"unimplemented");
+        }
+    };
+
+    // Advance all pointers by an immediate
+    auto advance_ptrs_imm = [&](size_t offset) {
+        add(reg_dst, offset * sizeof(dst_data_t));
+        add(reg_acc, offset * sizeof(acc_data_t));
+        if (scale_idx_mult_) {
+            assert(scale_idx_mult_ == 1);
+            add(reg_scales, offset * sizeof(float));
+        }
+        if (do_bias_)
+            add(reg_bias, offset * bias_data_type_size_);
+    };
+
+    // Advance all pointers by a value stored in a register
+    auto advance_ptrs_reg = [&](Reg64 offset) {
+        lea(reg_dst, ptr[reg_dst + offset * sizeof(dst_data_t)]);
+        lea(reg_acc, ptr[reg_acc + offset * sizeof(acc_data_t)]);
+        if (scale_idx_mult_) {
+            assert(scale_idx_mult_ == 1);
+            lea(reg_scales, ptr[reg_scales + offset * sizeof(float)]);
+        }
+        if (do_bias_)
+            lea(reg_bias, ptr[reg_bias + offset * bias_data_type_size_]);
+    };
+
+    // Rewind pointers that point to data that is indixed by output channel
+    // (bias or per-oc scaling factors)
+    auto rewind_ptrs = [&]() {
+        if (do_bias_)
+            sub(reg_bias, OC_ * bias_data_type_size_);
+        if (scale_idx_mult_) {
+            assert(scale_idx_mult_ == 1);
+            sub(reg_scales, OC_ * sizeof(float));
+        }
+    };
+
+    //      <-------------------- OC ------------------------------->
+    //
+    // ^    +....................+----------------------------------+
+    // |    :   not accessed     |          Prologue loop           |
+    // |    +--------------------+----------------------------------+
+    //      |                                                       |
+    // M    |                 Main loop (unrolled)                  |
+    // B    |                                                       |
+    //      +--------------------------------+----------------------+
+    // |    |       Epilogue loop            |      not accessed    :
+    // v    +--------------------------------+......................+
+
+    Label prologue_end;
+    cmp(reg_oc_offset, 0);
+    je(prologue_end, T_NEAR);
+
+    // Prologue loop
+    {
+        mov(reg_tmp, OC_);
+        sub(reg_tmp, reg_oc_offset);
+        cmp(reg_tmp, reg_len);
+        cmovg(reg_tmp, reg_len);
+        sub(reg_len, reg_tmp);
+
+        Label prologue_loop, prologue_loop_tail, prologue_loop_end;
+        cmp(reg_tmp, vlen);
+        jle(prologue_loop_tail, T_NEAR); // Skips for reg_tmp == 16 too (?)
+        L(prologue_loop); {
+            compute(0, 0, false);
+            advance_ptrs_imm(vlen);
+            sub(reg_tmp, vlen);
+            cmp(reg_tmp, vlen);
+            jge(prologue_loop, T_NEAR);
+        }
+
+        L(prologue_loop_tail);
+        mov(reg_rem_mask, 1);
+        shl(reg_rem_mask, cl); // cl == reg_tmp because reg_tmp <= vlen here
+        sub(reg_rem_mask, 1);
+        jz(prologue_loop_end, T_NEAR);
+
+        kmovq(kreg_rem_mask, reg_rem_mask);
+        compute(0, 0, true);
+        advance_ptrs_reg(reg_tmp);
+
+        L(prologue_loop_end);
+        rewind_ptrs();
+    }
+    L(prologue_end);
+
+    // Main loop
+    Label main_loop_end;
+    {
+        cmp(reg_len, OC_);
+        jle(main_loop_end, T_NEAR);
+
+        Label main_loop;
+        L(main_loop); {
+            size_t def_unroll = 4;
+            size_t max_unroll = 13;
+
+            size_t OC_loop, OC_tail;
+            if (OC_ < max_unroll * vlen) {
+                // Fully unroll small loops
+                OC_loop = 0;
+                OC_tail = OC_;
+            } else {
+                OC_loop = vlen * def_unroll;
+                OC_tail = OC_ % OC_loop;
+            }
+
+            assert(!!OC_loop || !!OC_tail);
+
+            if (OC_tail % vlen) {
+                int vlen_tail = OC_tail % vlen;
+                unsigned tail_mask = (1 << vlen_tail) - 1;
+                mov(reg_tmp, tail_mask);
+                kmovq(kreg_rem_mask, reg_tmp);
+            }
+
+            if (OC_loop) {
+                mov(reg_tmp, rnd_dn(OC_, OC_loop));
+                Label oc_loop;
+                L(oc_loop); {
+                    for (size_t offset = 0; offset < OC_loop; offset += vlen)
+                        compute(offset, offset / vlen, false);
+                    advance_ptrs_imm(OC_loop);
+                    sub(reg_tmp, OC_loop);
+                    jnz(oc_loop);
+                }
+            }
+
+            if (OC_tail) {
+                for (size_t offset = 0; offset < OC_tail; offset += vlen) {
+                    bool use_mask = (offset + vlen) > OC_tail;
+                    compute(offset, offset / vlen, use_mask);
+                }
+                advance_ptrs_imm(OC_tail);
+            }
+
+            rewind_ptrs();
+            sub(reg_len, OC_);
+            cmp(reg_len, OC_);
+            jge(main_loop, T_NEAR);
+        }
+    }
+    L(main_loop_end);
+
+    // Epilogue loop
+    Label epilogue_end;
+    {
+        cmp(reg_len, 0);
+        je(epilogue_end, T_NEAR);
+
+        Label epilogue_loop, epilogue_loop_tail;
+        cmp(reg_len, vlen);
+        jle(epilogue_loop_tail, T_NEAR); // Skips for reg_len == 16 (?)
+        L(epilogue_loop); {
+            compute(0, 0, false);
+            sub(reg_len, vlen);
+            advance_ptrs_imm(vlen);
+            cmp(reg_len, vlen);
+            jge(epilogue_loop, T_NEAR);
+        }
+
+        L(epilogue_loop_tail);
+        mov(reg_tmp, reg_len); // reg_tmp is rcx, and we need cl for the shift
+        mov(reg_rem_mask, 1);
+        shl(reg_rem_mask, cl); // reg_tmp == rcx and reg_tail < vlen == 16
+        sub(reg_rem_mask, 1);
+        jz(epilogue_end, T_NEAR);
+        kmovq(kreg_rem_mask, reg_rem_mask);
+        compute(0, 0, true);
+    }
+
+    L(epilogue_end);
+
+    postamble();
+
+    ker_ = getCode<decltype(ker_)>();
+}
+
+template<data_type_t src_type, data_type_t dst_type>
+void gemm_x8s8s32x_inner_product_fwd_t<src_type, dst_type>::pp_kernel_t::operator ()(
+        dst_data_t *dst, const acc_data_t *acc,
+        const char *bias, const float *scales, float nslope,
+        size_t start, size_t end)
+{
+    using math::get_bias;
+
+    if (end <= start)
+        return;
+
+    if (ker_) {
+        // JIT
+        ker_args args;
+        size_t oc_offset = start % OC_;
+        args.dst = dst + start;
+        args.acc = acc + start;
+        args.bias = bias + oc_offset * bias_data_type_size_;
+        args.scales = scales + scale_idx_mult_ * oc_offset;
+        args.nslope = nslope;
+        args.len = end - start;
+        args.oc_offset = oc_offset;
+        ker_(&args);
+    } else {
+        // Fallback
+        size_t oc = start % OC_;
+        for (size_t i = start; i < end; i++) {
+            float d = (float)acc[i];
+            float b = get_bias(bias, oc, bias_data_type_);
+            d = d + b;
+            d *= scales[oc * scale_idx_mult_];
+            if (do_relu_ && d < 0)
+                d *= nslope;
+            dst[i] = qz_a1b0<float, dst_data_t>()(d);
+            oc = (oc == OC_ - 1) ? 0 : oc + 1;
+        }
+    }
+};
+
+template <data_type_t src_type, data_type_t dst_type>
+void gemm_x8s8s32x_inner_product_fwd_t<src_type, dst_type>::execute_forward(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    const int MB = pd()->MB();
+    const int OC = pd()->OC();
+
+    bool wei_tr = memory_desc_matches_one_of_tag(
+            *pd()->weights_md(), oiw, oihw, oidhw, oi);
+
+    const int M = OC;
+    const int N = MB;
+    const int K = pd()->IC_total_padded();
+    const int8_t off_a = 0, off_b = 0;
+    const int32_t off_c = 0;
+
+    const float *scales = pd()->attr()->output_scales_.scales_;
+
+    const auto &post_ops = pd()->attr()->post_ops_;
+    const bool do_relu = post_ops.len_ == 1;
+    const float nslope = do_relu ? post_ops.entry_[0].eltwise.alpha : 0.f;
+
+    acc_data_t *acc = pd()->dst_is_acc_
+        ? (acc_data_t *)dst
+        : scratchpad(ctx).template get<acc_data_t>(key_iprod_int_dat_in_acc_dt);
+
+    const float onef = 1.0, zerof = 0.0;
+    gemm_s8x8s32(wei_tr ? "T" : "N", "N", "F", &M, &N, &K, &onef, weights,
+            wei_tr ? &K : &M, &off_a, src, &K, &off_b, &zerof, acc, &M, &off_c);
+
+    if (!pd()->attr()->has_default_values() || !pd()->dst_is_acc_
+            || pd()->with_bias()) {
+        const bool force_sequential = MB * OC < 2000;
+        parallel(force_sequential ? 1 : 0, [&](int ithr, int nthr) {
+            size_t start, end;
+            balance211((size_t)OC * MB, nthr, ithr, start, end);
+            (*pp_kernel_)(dst, acc, bias, scales, nslope, start, end);
+        });
+    }
+}
+
+using namespace data_type;
+
+template struct gemm_x8s8s32x_inner_product_fwd_t<u8, f32>;
+template struct gemm_x8s8s32x_inner_product_fwd_t<u8, s32>;
+template struct gemm_x8s8s32x_inner_product_fwd_t<u8, s8>;
+template struct gemm_x8s8s32x_inner_product_fwd_t<u8, u8>;
+template struct gemm_x8s8s32x_inner_product_fwd_t<s8, f32>;
+template struct gemm_x8s8s32x_inner_product_fwd_t<s8, s32>;
+template struct gemm_x8s8s32x_inner_product_fwd_t<s8, s8>;
+template struct gemm_x8s8s32x_inner_product_fwd_t<s8, u8>;
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_inner_product.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_inner_product.hpp
new file mode 100644
index 0000000000..ac6a5c8f85
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/gemm_x8s8s32x_inner_product.hpp
@@ -0,0 +1,166 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef GEMM_X8S8S32X_INNER_PRODUCT_HPP
+#define GEMM_X8S8S32X_INNER_PRODUCT_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "gemm/gemm.hpp"
+#include "jit_generator.hpp"
+
+#include "cpu_inner_product_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t src_type, impl::data_type_t dst_type>
+struct gemm_x8s8s32x_inner_product_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_inner_product_fwd_pd_t {
+        using cpu_inner_product_fwd_pd_t::cpu_inner_product_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T(src_type == data_type::u8
+                ? IGEMM_S8U8S32_IMPL_STR
+                : IGEMM_S8S8S32_IMPL_STR,
+                gemm_x8s8s32x_inner_product_fwd_t);
+
+        status_t init() {
+            using namespace data_type;
+
+            bool ok = true
+                && set_default_params() == status::success
+                && is_fwd()
+                && !has_zero_dim_memory()
+                && src_md()->data_type == src_type
+                && dst_md()->data_type == dst_type
+                && weights_md()->data_type == s8
+                && IMPLICATION(with_bias(), utils::one_of(
+                            weights_md(1)->data_type, f32, s32, s8, u8))
+                && attr()->post_ops_.len_ <= 1
+                && IMPLICATION(attr()->post_ops_.len_,
+                        attr()->post_ops_.entry_[0].is_relu(true, false))
+                && dense_gemm_consitency_check(src_md(), weights_md(),
+                        dst_md());
+            if (!ok) return status::unimplemented;
+
+            dst_is_acc_ = utils::one_of(dst_type, s32, f32);
+
+            init_scratchpad();
+
+            return status::success;
+        }
+
+        bool dst_is_acc_;
+
+    protected:
+        status_t set_default_params() {
+            using namespace format_tag;
+            if (src_md_.format_kind == format_kind::any) {
+                CHECK(memory_desc_init_by_tag(src_md_,
+                            utils::pick(ndims() - 2, nc, nwc, nhwc, ndhwc)));
+            }
+            if (dst_md_.format_kind == format_kind::any)
+                CHECK(memory_desc_init_by_tag(dst_md_, nc));
+            if (weights_md_.format_kind == format_kind::any) {
+                CHECK(memory_desc_init_by_tag(weights_md_,
+                            utils::pick(ndims() - 2, io, wio, hwio, dhwio)));
+            }
+            return inner_product_fwd_pd_t::set_default_params();
+        }
+
+    private:
+        void init_scratchpad() {
+            if (!dst_is_acc_) {
+                auto scratchpad = scratchpad_registry().registrar();
+                scratchpad.book(
+                        memory_tracking::names::key_iprod_int_dat_in_acc_dt,
+                        sizeof(acc_data_t) * MB() * OC());
+            }
+        }
+    };
+
+    gemm_x8s8s32x_inner_product_fwd_t(const pd_t *apd)
+        : cpu_primitive_t(apd, true)
+    { pp_kernel_ = new pp_kernel_t(apd, pd()->dst_is_acc_); }
+    ~gemm_x8s8s32x_inner_product_fwd_t() { delete pp_kernel_; }
+
+    typedef typename prec_traits<dst_type>::type data_t;
+
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<data_type::s8>::type wei_data_t;
+    typedef typename prec_traits<dst_type>::type dst_data_t;
+    typedef typename prec_traits<data_type::s32>::type acc_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    // XXX: this is throwaway code that will become unnecessary when we have a
+    // sufficiently advanced igemm jit generator that supports quantization,
+    // relu, and whatnot
+    class pp_kernel_t: jit_generator {
+    public:
+        DECLARE_CPU_JIT_AUX_FUNCTIONS(
+                gemm_x8s8s32x_inner_product_fwd_t::pp_kernel);
+        pp_kernel_t(const pd_t *pd, bool dst_is_acc);
+
+        void operator()(dst_data_t *dst, const acc_data_t *acc,
+                const char *bias, const float *scales, float nslope,
+                size_t start, size_t end);
+    private:
+        void generate();
+
+        struct ker_args {
+            dst_data_t *dst;
+            const acc_data_t *acc;
+            const char *bias;
+            const float *scales;
+            float nslope;
+            size_t len;
+            size_t oc_offset;
+        };
+        void (*ker_)(const ker_args *args);
+
+        size_t OC_;
+        data_type_t bias_data_type_;
+        size_t bias_data_type_size_;
+        size_t scale_idx_mult_;
+        bool do_bias_;
+        bool do_relu_;
+    };
+
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    pp_kernel_t *pp_kernel_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_conv_kernel_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_conv_kernel_f32.cpp
new file mode 100644
index 0000000000..6fa251d465
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_conv_kernel_f32.cpp
@@ -0,0 +1,674 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+* Copyright 2018 YANDEX LLC
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_memory.hpp"
+
+#include "jit_avx2_1x1_conv_kernel_f32.hpp"
+
+#define GET_OFF(field) offsetof(jit_1x1_conv_call_s, field)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::utils;
+
+using namespace Xbyak;
+
+void jit_avx2_1x1_conv_kernel_f32::generate_bcast_loop(int load_loop_blk)
+{
+    mov(aux1_reg_bcast_data, reg_bcast_data);
+    mov(aux_reg_output_data, reg_output_data);
+    mov(bcast_loop_iter, reg_bcast_loop_work);
+
+    Label bcast_loop, bcast_loop_tail;
+
+    cmp(bcast_loop_iter, jcp.ur);
+    jl(bcast_loop_tail, T_NEAR);
+
+    L(bcast_loop); {
+        assert(jcp.bcast_block % jcp.ur == 0);
+        int num_substeps = jcp.bcast_block / jcp.ur;
+        assert(num_substeps > 0 && num_substeps < 10);
+        for (int i = 0; i < num_substeps; i++) {
+            generate_reduce_loop(load_loop_blk, jcp.ur);
+            if (i < num_substeps - 1) {
+                add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_substep);
+                add(aux_reg_output_data, jcp.bcast_loop_output_substep);
+            } else {
+                add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_step
+                        - (num_substeps - 1) * jcp.bcast_loop_bcast_substep);
+                add(aux_reg_output_data, jcp.bcast_loop_output_step
+                        - (num_substeps - 1) * jcp.bcast_loop_output_substep);
+            }
+        }
+        sub(bcast_loop_iter, jcp.bcast_block);
+        cmp(bcast_loop_iter, jcp.bcast_block);
+        jge(bcast_loop, T_NEAR);
+    }
+
+    L(bcast_loop_tail);
+    if (jcp.ur_tail) {
+        Label bcast_loop_tail_out;
+        cmp(bcast_loop_iter, 0);
+        jz(bcast_loop_tail_out, T_NEAR);
+        generate_reduce_loop(load_loop_blk, jcp.ur_tail);
+        L(bcast_loop_tail_out);
+    }
+}
+
+void jit_avx2_1x1_conv_kernel_f32::generate_reduce_loop(
+        int load_loop_blk, int ur)
+{
+    auto vreg_load = [=](int i) {
+        return Ymm(ur * load_loop_blk + i);
+    };
+
+    auto vreg_accum = [=](int i, int j) {
+        return Ymm(j * load_loop_blk + i);
+    };
+
+    auto bias_ptr = [=](int i) {
+        return ptr[reg_bias_data + sizeof(float) * jcp.oc_block * i];
+    };
+
+    auto bcast_ptr = [=](int u, int j) {
+        assert(j < jcp.ur);
+        assert(u <= jcp.reduce_loop_unroll);
+        size_t offt;
+        if (one_of(jcp.prop_kind,
+                    forward_training, forward_inference, backward_data))
+        {
+            assert(jcp.reduce_loop_unroll == (jcp.prop_kind == backward_data)
+                    ? jcp.oc_block : jcp.ic_block);
+            auto height = (jcp.prop_kind == backward_data) ? jcp.os : jcp.is;
+            offt = (u == jcp.reduce_loop_unroll)
+                ? (height + j) * jcp.reduce_loop_unroll
+                : j * jcp.reduce_loop_unroll + u;
+        } else
+            offt = u * jcp.ic_block + j;
+        return ptr[aux_reg_bcast_data + sizeof(float) * offt];
+    };
+
+    auto load_ptr = [=](int u, int i) {
+        size_t offt;
+        size_t u0 = u % jcp.reduce_loop_unroll;
+        size_t u1 = u / jcp.reduce_loop_unroll;
+        switch (jcp.prop_kind) {
+        case backward_data:
+            offt = (i * jcp.oc_block + u0) * jcp.ic_block;
+            break;
+        case backward_weights:
+            offt = (i * jcp.os + u0) * jcp.oc_block;
+            break;
+        default:
+            offt = (i * jcp.ic + u0) * jcp.oc_block;
+        }
+        return ptr[aux_reg_load_data
+            + u1 * jcp.reduce_loop_load_step + sizeof(float) * offt];
+    };
+
+    auto output_ptr = [=](int i, int j) {
+        switch (jcp.prop_kind) {
+        case backward_data:
+            return ptr[aux_reg_output_data +
+                (i * jcp.is + j) * jcp.ic_block * sizeof(float)];
+        case backward_weights:
+            return ptr[aux_reg_output_data
+                + (i ? reg_output_stride * i : 0) // TODO: Xbyak should allow 0 scale
+                + sizeof(float) * jcp.oc_block * j];
+        default:
+            return ptr[aux_reg_output_data +
+                (i * jcp.os + j) * jcp.oc_block * sizeof(float)];
+        }
+    };
+
+    auto init = [=]() {
+        Label init_done, init_zero;
+
+        if (jcp.with_bias && one_of(jcp.prop_kind, forward_training,
+                    forward_inference)) {
+            test(reg_reduce_pos_flag, FLAG_REDUCE_FIRST);
+            jz(init_zero);
+
+            for (int i = 0; i < load_loop_blk; i++)
+                for (int j = 0; j < ur; ++j)
+                    vmovups(vreg_accum(i, j), bias_ptr(i));
+            jmp(init_done);
+        }
+
+        L(init_zero);
+        for (int i = 0; i < load_loop_blk; ++i)
+            for (int j = 0; j < ur; ++j) {
+                auto r = vreg_accum(i, j);
+                vxorps(r, r, r);
+            }
+
+        L(init_done);
+        for (int i = 0; i < load_loop_blk; ++i)
+            vmovups(vreg_load(i), load_ptr(0, i));
+        vbroadcastss(vreg_bcast, bcast_ptr(0, 0));
+    };
+
+    auto store = [=]() {
+        Label store_noadd;
+
+        if (!jcp.with_sum) {
+            test(reg_reduce_pos_flag, FLAG_REDUCE_FIRST);
+            jnz(store_noadd, T_NEAR);
+        }
+
+        for (int j = 0; j < ur; ++j)
+            for (int i = 0; i < load_loop_blk; ++i) {
+                auto r = vreg_accum(i, j);
+                vaddps(r, r, output_ptr(i, j));
+            }
+
+        L(store_noadd);
+
+        if (jcp.with_eltwise) {
+            assert(ur * load_loop_blk < 14);
+
+            Label store_norelu;
+            test(reg_reduce_pos_flag, FLAG_REDUCE_LAST);
+            jz(store_norelu, T_NEAR);
+
+            eltwise_injector_->compute_vector_range(0, ur * load_loop_blk);
+
+            L(store_norelu);
+        }
+
+        for (int j = 0; j < ur; ++j)
+            for (int i = 0; i < load_loop_blk; ++i) {
+                vmovups(output_ptr(i, j), vreg_accum(i, j));
+            }
+    };
+
+    auto fma_block = [=](bool last_block) {
+        for (int u = 0; u < jcp.reduce_loop_unroll; ++u) {
+            for (int j = 0; j < ur; ++j) {
+                for (int i = 0; i < load_loop_blk; ++i) {
+                    if (mayiuse(avx2))
+                        vfmadd231ps(vreg_accum(i, j), vreg_load(i), vreg_bcast);
+                    else { // Intel(R) Advanced Vector Extensions (Intel(R) AVX) support
+                        vmulps(vtmp, vreg_bcast, vreg_load(i));
+                        vaddps(vreg_accum(i, j), vreg_accum(i, j), vtmp);
+                    }
+                    if (j == ur - 1 && !(last_block
+                                && u == jcp.reduce_loop_unroll - 1))
+                        vmovups(vreg_load(i), load_ptr(u + 1, i));
+                }
+                if (j < ur - 1)
+                    vbroadcastss(vreg_bcast, bcast_ptr(u, j + 1));
+            }
+            if (!last_block || u < jcp.reduce_loop_unroll - 1)
+                vbroadcastss(vreg_bcast, bcast_ptr(u + 1, 0));
+        }
+    };
+
+    Label reduce_loop, reduce_loop_tail;
+
+    mov(aux_reg_load_data, reg_load_data);
+    mov(aux_reg_bcast_data, aux1_reg_bcast_data);
+
+    init();
+
+    mov(reduce_loop_iter, reg_reduce_loop_work);
+    sub(reduce_loop_iter, jcp.reduce_loop_unroll);
+    jle(reduce_loop_tail, T_NEAR);
+
+    L(reduce_loop); {
+        fma_block(false);
+        add(aux_reg_bcast_data, jcp.reduce_loop_bcast_step);
+        add(aux_reg_load_data, jcp.reduce_loop_load_step);
+        sub(reduce_loop_iter, jcp.reduce_loop_unroll);
+        jg(reduce_loop, T_NEAR);
+    }
+
+    L(reduce_loop_tail);
+    fma_block(true);
+
+    store();
+}
+
+void jit_avx2_1x1_conv_kernel_f32::generate_diff_bias_loop(int load_loop_blk)
+{
+    if (!jcp.with_bias || jcp.prop_kind != backward_weights)
+        return;
+
+    Label diff_bias_loop, diff_bias_loop_out, diff_bias_init_out;
+    Label diff_bias_load;
+
+    auto diff_bias_ptr = [=](int i) {
+        return ptr[reg_diff_bias_data + i * jcp.oc_block * sizeof(float)];
+    };
+
+    auto load_ptr = [=](int u, int i) {
+        return ptr[aux_reg_load_data
+            + (i * jcp.os + u) * jcp.oc_block * sizeof(float)];
+    };
+
+    auto diff_bias_reg = [=](int i) { return Ymm(i); };
+
+    mov(reg_diff_bias_data, ptr[rsp + reg_diff_bias_data_stack_offt]);
+    cmp(reg_diff_bias_data, 0);
+    je(diff_bias_loop_out, T_NEAR);
+
+    test(reg_reduce_pos_flag, FLAG_REDUCE_FIRST);
+    jz(diff_bias_load, T_NEAR);
+
+    for (int i = 0; i < load_loop_blk; ++i) {
+        auto r = diff_bias_reg(i);
+        vxorps(r, r, r);
+    }
+    jmp(diff_bias_init_out, T_NEAR);
+
+    L(diff_bias_load);
+    for (int i = 0; i < load_loop_blk; ++i)
+        vmovups(diff_bias_reg(i), diff_bias_ptr(i));
+
+    L(diff_bias_init_out);
+    mov(aux_reg_load_data, reg_load_data);
+    mov(reduce_loop_iter, reg_reduce_loop_work);
+    L(diff_bias_loop); {
+        for(int u = 0; u < jcp.reduce_loop_unroll; ++u)
+            for (int i = 0; i < load_loop_blk; ++i)
+                vaddps(diff_bias_reg(i), diff_bias_reg(i), load_ptr(u, i));
+        assert(jcp.reduce_dim % jcp.reduce_loop_unroll == 0);
+        add(aux_reg_load_data, jcp.reduce_loop_load_step);
+        sub(reduce_loop_iter, jcp.reduce_loop_unroll);
+        jnz(diff_bias_loop, T_NEAR);
+    }
+
+    for (int i = 0; i < load_loop_blk; i++)
+        vmovups(diff_bias_ptr(i), diff_bias_reg(i));
+    add(reg_diff_bias_data, load_loop_blk * jcp.oc_block * sizeof(float));
+    mov(ptr[rsp + reg_diff_bias_data_stack_offt], reg_diff_bias_data);
+
+    L(diff_bias_loop_out);
+}
+
+void jit_avx2_1x1_conv_kernel_f32::generate()
+{
+    preamble();
+
+    mov(reg_bcast_data, ptr[param1 + GET_OFF(bcast_data)]);
+    mov(reg_load_data, ptr[param1 + GET_OFF(load_data)]);
+    mov(reg_output_data, ptr[param1 + GET_OFF(output_data)]);
+    if (jcp.with_bias) {
+        if (jcp.prop_kind == backward_weights) {
+            sub(rsp, stack_space_needed);
+            mov(reg_diff_bias_data, ptr[param1 + GET_OFF(bias_data)]);
+            mov(ptr[rsp + reg_diff_bias_data_stack_offt], reg_diff_bias_data);
+        } else
+            mov(reg_bias_data, ptr[param1 + GET_OFF(bias_data)]);
+    }
+
+    mov(reg_load_loop_work, ptr[param1 + GET_OFF(load_dim)]);
+    mov(reg_bcast_loop_work, ptr[param1 + GET_OFF(bcast_dim)]);
+    mov(reg_reduce_loop_work, ptr[param1 + GET_OFF(reduce_dim)]);
+    mov(reg_reduce_pos_flag, ptr[param1 + GET_OFF(first_last_flag)]);
+    if (jcp.prop_kind == backward_weights)
+        mov(reg_output_stride, ptr[param1 + GET_OFF(output_stride)]);
+
+    auto generate_load_loop_body = [=] (int load_loop_blk) {
+        generate_bcast_loop(load_loop_blk);
+        add(reg_load_data, load_loop_blk * jcp.load_loop_load_step);
+        switch (jcp.prop_kind) {
+        case forward_training:
+        case forward_inference:
+            add(reg_bias_data, load_loop_blk * jcp.oc_block * sizeof(float));
+            add(reg_output_data,
+                    load_loop_blk * jcp.os * jcp.oc_block * sizeof(float));
+            break;
+        case backward_data:
+            add(reg_output_data,
+                    load_loop_blk * jcp.is * jcp.ic_block * sizeof(float));
+            break;
+        case backward_weights:
+            for (int i = 0; i < load_loop_blk; i++)
+                add(reg_output_data, reg_output_stride);
+            break;
+        default:
+            assert(!"invalid prop_kind");
+        }
+        sub(reg_load_loop_work, load_loop_blk * jcp.load_loop_iter_step);
+    };
+
+    Label load_loop_blk_8;
+    Label load_loop_blk_16;
+    Label load_loop_blk_24;
+    Label load_loop_blk_end;
+
+    cmp(reg_load_loop_work, 8);
+    jle(load_loop_blk_8, T_NEAR);
+
+    cmp(reg_load_loop_work, 32);
+    je(load_loop_blk_16, T_NEAR);
+
+    cmp(reg_load_loop_work, 16);
+    jle(load_loop_blk_16, T_NEAR);
+
+    L(load_loop_blk_24); {
+        generate_diff_bias_loop(3);
+        generate_load_loop_body(3);
+        cmp(reg_load_loop_work, 32);
+        je(load_loop_blk_16);
+        cmp(reg_load_loop_work, 24);
+        jge(load_loop_blk_24);
+    }
+
+    cmp(reg_load_loop_work, 8);
+    jle(load_loop_blk_8, T_NEAR);
+
+    L(load_loop_blk_16); {
+        generate_diff_bias_loop(2);
+        generate_load_loop_body(2);
+        cmp(reg_load_loop_work, 16);
+        jge(load_loop_blk_16);
+    }
+
+    L(load_loop_blk_8); {
+        cmp(reg_load_loop_work, 0);
+        je(load_loop_blk_end, T_NEAR);
+        generate_diff_bias_loop(1);
+        generate_load_loop_body(1);
+    }
+
+    L(load_loop_blk_end);
+
+    if (jcp.with_bias && jcp.prop_kind == backward_weights)
+        add(rsp, 8);
+
+    postamble();
+
+    if (jcp.with_eltwise)
+        eltwise_injector_->prepare_table();
+}
+
+bool jit_avx2_1x1_conv_kernel_f32::post_ops_ok(
+        jit_1x1_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    const auto &p = attr.post_ops_;
+
+    auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); };
+    auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); };
+
+    switch (p.len_) {
+    case 0: return true; // no post_ops
+    case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise
+    case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise
+    default: return false;
+    }
+
+    return false;
+}
+
+status_t jit_avx2_1x1_conv_kernel_f32::init_conf(jit_1x1_conv_conf_t &jcp,
+        const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+        const memory_desc_wrapper &weights_d, const memory_desc_wrapper &dst_d,
+        const primitive_attr_t &attr)
+{
+    if (!mayiuse(avx)) return status::unimplemented;
+
+    // TODO (Roma): this code is duplicated from the generic kernel; maybe the
+    // configuration struct could do some stuff below
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+    const int ndims = src_d.ndims();
+
+    jcp.prop_kind = cd.prop_kind;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+
+    jcp.ih = (ndims == 3) ? 1 : src_d.dims()[2];
+    jcp.iw = src_d.dims()[ndims - 1];
+    jcp.oh = (ndims == 3) ? 1 : dst_d.dims()[2];
+    jcp.ow = dst_d.dims()[ndims - 1];
+
+    jcp.kh = (ndims == 3) ? 1 : weights_d.dims()[with_groups + 2];
+    jcp.kw = weights_d.dims()[with_groups + ndims - 1];
+
+    jcp.t_pad = (ndims == 3) ? 0 : cd.padding[0][0];
+    jcp.l_pad = cd.padding[0][ndims - 3];
+
+    jcp.stride_h = (ndims == 3) ? 1 : cd.strides[0];
+    jcp.stride_w = cd.strides[ndims - 3];
+
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+
+    jcp.os = jcp.oh * jcp.ow;
+    jcp.is = jcp.ih * jcp.iw;
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    jcp.with_sum = p.find(primitive_kind::sum) != -1;
+    const int eltwise_ind = p.find(primitive_kind::eltwise);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise) {
+        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+        if (!mayiuse(avx2) && jcp.eltwise.alg != alg_kind::eltwise_relu)
+            return status::unimplemented;
+    }
+
+    const int is_bwd_d = jcp.prop_kind == backward_data;
+
+    format_tag_t dat_tag = ndims == 3 ? nCw8c : nChw8c;
+    format_tag_t wei_tag = with_groups
+        ? utils::pick(2 * ndims - 6 + is_bwd_d, gOIw8i8o, gOIw8o8i, gOIhw8i8o,
+            gOIhw8o8i)
+        : utils::pick(2 * ndims - 6 + is_bwd_d, OIw8i8o, OIw8o8i, OIhw8i8o,
+            OIhw8o8i);
+
+    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    jcp.dst_tag = dst_d.matches_one_of_tag(dat_tag);
+    jcp.wei_tag = weights_d.matches_one_of_tag(wei_tag);
+
+    const int simd_w = 8;
+
+    jcp.oc = rnd_up(jcp.oc, simd_w);
+    jcp.ic = rnd_up(jcp.ic, simd_w);
+
+    bool args_ok = true
+        && jcp.ngroups == 1
+        && jcp.src_tag == dat_tag
+        && jcp.wei_tag == wei_tag
+        && jcp.dst_tag == dat_tag;
+    if (!args_ok) return status::unimplemented;
+
+    args_ok = true
+        && jcp.ih == jcp.oh && jcp.iw == jcp.ow
+        && jcp.oc % simd_w == 0 && jcp.ic % simd_w == 0
+        && jcp.t_pad == 0 && jcp.l_pad == 0
+        && jcp.stride_w == 1 && jcp.stride_h == 1 // TODO: support some strides
+        && jcp.kh == 1 && jcp.kw == 1;
+    if (!args_ok) return status::unimplemented;
+
+    // TODO: remove this restriction
+    // optimized 1x1 bwd_w does not support Intel AVX
+    if (jcp.prop_kind == backward_weights && !mayiuse(avx2))
+        return status::unimplemented;
+
+    jcp.ic_block = jcp.oc_block = simd_w;
+
+    jcp.ur = mayiuse(avx2) ? 4 : 3; // Intel AVX support
+
+    int load_blocking{ 0 };
+    int load_blocking_max{ 0 };
+    int bcast_blocking{ 0 };
+    int bcast_blocking_max{ 0 };
+    int reduce_blocking{ 0 };
+
+    if (one_of(jcp.prop_kind, forward_training, forward_inference)) {
+        jcp.reduce_dim = jcp.ic;
+        jcp.reduce_block = jcp.ic_block;
+
+        jcp.load_dim = jcp.oc;
+        jcp.load_block = jcp.oc_block;
+
+        jcp.bcast_dim = jcp.is;
+        jcp.bcast_block = jcp.ur;
+
+        jcp.reduce_loop_unroll = jcp.reduce_block;
+        jcp.reduce_loop_bcast_step
+            = jcp.reduce_loop_unroll * jcp.is * sizeof(float);
+        jcp.reduce_loop_load_step
+            = jcp.reduce_loop_unroll * jcp.oc_block * sizeof(float);
+
+        jcp.bcast_loop_output_step = jcp.ur * jcp.oc_block * sizeof(float);
+        jcp.bcast_loop_output_substep = -1; // unused
+        jcp.bcast_loop_bcast_step = jcp.ur * jcp.ic_block * sizeof(float);
+        jcp.bcast_loop_bcast_substep = -1; // unused
+
+        jcp.load_loop_load_step = jcp.ic * jcp.oc_block * sizeof(float);
+        jcp.load_loop_iter_step = jcp.oc_block;
+
+        load_blocking = 120; // assumes the kernel is jcp.ur x 3
+        load_blocking_max = 144;
+        bcast_blocking = 128; // affects load balancing across threads
+        bcast_blocking_max = 192;
+        reduce_blocking = 128; // affects L1$ utilization
+    } else if (jcp.prop_kind == backward_data) {
+        jcp.reduce_dim = jcp.oc;
+        jcp.reduce_block = jcp.oc_block;
+
+        jcp.load_dim = jcp.ic;
+        jcp.load_block = jcp.oc_block;
+
+        jcp.bcast_dim = jcp.os;
+        jcp.bcast_block = jcp.ur;
+
+        jcp.reduce_loop_unroll = jcp.reduce_block;
+        jcp.reduce_loop_bcast_step
+            = jcp.reduce_loop_unroll * jcp.os * sizeof(float);
+        jcp.reduce_loop_load_step
+            = jcp.reduce_loop_unroll * jcp.ic * sizeof(float);
+
+        jcp.bcast_loop_output_step = jcp.ur * jcp.ic_block * sizeof(float);
+        jcp.bcast_loop_output_substep = -1; // unused
+        jcp.bcast_loop_bcast_step = jcp.ur * jcp.oc_block * sizeof(float);
+        jcp.bcast_loop_bcast_substep = -1; // unused
+
+        jcp.load_loop_load_step = jcp.oc_block * jcp.ic_block * sizeof(float);
+        jcp.load_loop_iter_step = jcp.ic_block;
+
+        load_blocking = 96; // assumes the kernel is jcp.ur x 3
+        load_blocking_max = 144;
+        bcast_blocking = 128; // affects load balancing across threads
+        bcast_blocking_max = 196;
+        reduce_blocking = 64; // affects L1$ utilization
+    } else if (jcp.prop_kind == backward_weights) {
+        jcp.reduce_dim = jcp.os;
+        jcp.reduce_block = 1;
+
+        jcp.load_dim = jcp.oc;
+        jcp.load_block = jcp.oc_block;
+
+        jcp.bcast_dim = jcp.ic;
+        jcp.bcast_block = jcp.ic_block;
+
+        jcp.reduce_loop_unroll = jcp.reduce_block;
+        jcp.reduce_loop_bcast_step
+            = jcp.reduce_loop_unroll * jcp.ic_block * sizeof(float);
+        jcp.reduce_loop_load_step
+            = jcp.reduce_loop_unroll * jcp.oc_block * sizeof(float);
+
+        jcp.bcast_loop_output_step = jcp.oc_block * jcp.ic_block * sizeof(float);
+        jcp.bcast_loop_output_substep = jcp.oc_block * jcp.ur * sizeof(float);
+        jcp.bcast_loop_bcast_step = jcp.ic_block * jcp.is * sizeof(float);
+        jcp.bcast_loop_bcast_substep = jcp.ur * sizeof(float);
+
+        jcp.load_loop_load_step = jcp.oc_block * jcp.os * sizeof(float);
+        jcp.load_loop_iter_step = jcp.oc_block;
+
+        /* --- */
+
+        load_blocking = div_up(jcp.load_dim, jcp.load_block);
+        while (true) {
+            if (load_blocking <= 32) break;
+            else if (load_blocking % 2 == 0) load_blocking /= 2;
+            else if (load_blocking % 3 == 0) load_blocking /= 3;
+            else break;
+        }
+        load_blocking *= jcp.load_block;
+        load_blocking_max = load_blocking;
+        assert(jcp.load_dim % load_blocking == 0);
+
+        bcast_blocking = div_up(jcp.bcast_dim, jcp.bcast_block);
+        while (true) {
+            if (bcast_blocking <= 9) break;
+            else if (bcast_blocking % 2 == 0) bcast_blocking /= 2;
+            else if (bcast_blocking % 3 == 0) bcast_blocking /= 3;
+            else break;
+        }
+        bcast_blocking *= jcp.bcast_block;
+        bcast_blocking_max = bcast_blocking;
+        assert(jcp.bcast_dim % bcast_blocking == 0);
+
+        reduce_blocking = 128; // affects L1$ utilization
+    } else
+        return status::unimplemented;
+
+    assert(load_blocking);
+    assert(load_blocking_max);
+    assert(bcast_blocking);
+    assert(bcast_blocking_max);
+    assert(reduce_blocking);
+
+    assert(jcp.bcast_block % jcp.ur == 0);
+    jcp.ur_tail = jcp.bcast_dim % jcp.ur;
+
+    jcp.nb_bcast_blocking = bcast_blocking / jcp.bcast_block;
+    jcp.nb_bcast_blocking_max = bcast_blocking_max / jcp.bcast_block;
+    jcp.nb_load_blocking = load_blocking / jcp.load_block;
+    jcp.nb_load_blocking_max = load_blocking_max / jcp.load_block;
+    jcp.nb_reduce_blocking = reduce_blocking / jcp.reduce_block;
+
+    jcp.nb_bcast = div_up(jcp.bcast_dim, jcp.bcast_block);
+    jcp.nb_load = div_up(jcp.load_dim, jcp.load_block);
+    jcp.nb_reduce = div_up(jcp.reduce_dim, jcp.reduce_block);
+
+    return status::success;
+}
+
+void jit_avx2_1x1_conv_kernel_f32::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad,
+        const jit_1x1_conv_conf_t &jcp) {
+    using namespace mkldnn::impl::memory_tracking::names;
+
+    if (jcp.prop_kind != backward_data && jcp.oc != jcp.oc_without_padding)
+        scratchpad.book(key_conv_padded_bias, sizeof(float) * jcp.oc);
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_conv_kernel_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_conv_kernel_f32.hpp
new file mode 100644
index 0000000000..bfdeb2b18d
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_conv_kernel_f32.hpp
@@ -0,0 +1,110 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_AVX2_1x1_CONV_KERNEL_F32_HPP
+#define JIT_AVX2_1x1_CONV_KERNEL_F32_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+
+#include "cpu_memory.hpp"
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+#include "jit_uni_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_avx2_1x1_conv_kernel_f32: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx2_1x1_conv_kernel_f32)
+
+    jit_avx2_1x1_conv_kernel_f32(jit_1x1_conv_conf_t ajcp,
+           const primitive_attr_t &attr)
+        : jcp(ajcp), attr_(attr), eltwise_injector_(nullptr)
+    {
+        if (jcp.with_eltwise)
+            eltwise_injector_ = new jit_uni_eltwise_injector_f32<avx2>(this,
+                    jcp.eltwise);
+
+        this->generate();
+        jit_ker = (void (*)(jit_1x1_conv_call_s *))this->getCode();
+    }
+
+    ~jit_avx2_1x1_conv_kernel_f32() {
+        delete eltwise_injector_;
+    }
+
+    static bool post_ops_ok(jit_1x1_conv_conf_t &jcp,
+            const primitive_attr_t &attr);
+
+    static status_t init_conf(jit_1x1_conv_conf_t &jcp,
+            const convolution_desc_t &cd,
+            const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &dst_d,
+            const primitive_attr_t &attr);
+
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_1x1_conv_conf_t &jcp);
+
+    jit_1x1_conv_conf_t jcp;
+    const primitive_attr_t &attr_;
+    void (*jit_ker)(jit_1x1_conv_call_s *);
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    using ymm_t = const Xbyak::Ymm;
+
+    reg64_t reg_bcast_data = rax;
+    reg64_t reg_load_data = rsi;
+    reg64_t reg_output_data = rbx;
+    reg64_t aux_reg_bcast_data = rdx;
+    reg64_t aux1_reg_bcast_data = abi_not_param1;
+    reg64_t aux_reg_load_data = abi_param1;
+    reg64_t aux_reg_output_data = rbp;
+    reg64_t reg_load_loop_work = r9;
+    reg64_t reg_bcast_loop_work = r10;
+    reg64_t reg_reduce_loop_work = r11;
+    reg64_t load_loop_iter = r13;
+    reg64_t bcast_loop_iter = r14;
+    reg64_t reduce_loop_iter = r15;
+    reg64_t imm_addr64 = reduce_loop_iter;
+    reg64_t reg_reduce_pos_flag = r8;
+    reg64_t reg_output_stride = r12;
+    reg64_t reg_bias_data = r12;
+    reg64_t reg_diff_bias_data = bcast_loop_iter;
+
+    int reg_diff_bias_data_stack_offt = 0;
+    int stack_space_needed = 8;
+
+    ymm_t vreg_bcast = ymm_t(15);
+    ymm_t vtmp = ymm_t(14);
+
+    jit_uni_eltwise_injector_f32<avx2> *eltwise_injector_;
+
+    void generate_bcast_loop(int load_loop_blk);
+    void generate_reduce_loop(int load_loop_blk, int ur);
+    void generate_diff_bias_loop(int load_loop_blk);
+
+    void generate();
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_convolution.cpp
new file mode 100644
index 0000000000..f116ac9056
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_convolution.cpp
@@ -0,0 +1,545 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_generator.hpp"
+
+#include "jit_avx2_1x1_convolution.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+
+#define data_blk_off(f, n, c, h, w) \
+    ((ndims == 3) \
+    ? (f).blk_off(n, c, w) \
+    : (f).blk_off(n, c, h, w))
+
+/* convolution forward */
+
+void jit_avx2_1x1_convolution_fwd_t::execute_forward(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const data_t *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+    auto rtus_space = scratchpad(ctx).get<data_t>(key_conv_rtus_space);
+
+    const int work_amount = jcp.mb * jcp.ngroups * jcp.nb_bcast;
+    const int ndims = dst_d.ndims();
+
+    const int stride_h = (ndims == 3) ? 1 : pd()->desc()->strides[0];
+    const int stride_w = pd()->desc()->strides[ndims - 3];
+    const int pad_t = (ndims == 3) ? 0 : pd()->desc()->padding[0][0];
+    const int pad_l = pd()->desc()->padding[0][ndims - 3];
+
+    auto step = [](int default_step, int remaining, int tail_step) {
+        assert(default_step <= tail_step);
+        return remaining < tail_step ? remaining : default_step;
+    };
+
+    auto ker = [&](const int ithr, const int nthr) {
+        // TODO (Roma): remove this restriction
+        assert(jcp.stride_w == 1 && jcp.stride_h == 1);
+
+        auto p = jit_1x1_conv_call_s();
+        auto rp = rtus_driver_t<avx2>::call_params_t();
+
+        const int nb_oc = jcp.nb_load;
+        const int nb_ic = jcp.nb_reduce;
+        const int nb_ic_blocking = jcp.nb_reduce_blocking;
+        const int os_block = jcp.bcast_block;
+
+        int start{0}, end{0};
+        balance211(work_amount, nthr, ithr, start, end);
+
+        int iwork = start;
+        while (iwork < end) {
+            int n{0}, g{0}, osb{0};
+            nd_iterator_init(iwork, n, jcp.mb, g, jcp.ngroups, osb,
+                    jcp.nb_bcast);
+
+            int bcast_step = step(jcp.nb_bcast_blocking, jcp.nb_bcast - osb,
+                    jcp.nb_bcast_blocking_max);
+            bcast_step = nstl::min(bcast_step, end - iwork);
+
+            const int os = osb * os_block;
+            const int oh = os / jcp.ow;
+            const int ow = os % jcp.ow;
+
+            const int ih = nstl::max(oh * stride_h - pad_t, 0);
+            const int iw = nstl::max(ow * stride_w - pad_l, 0);
+            rp.iw_start = iw;
+
+            p.bcast_dim = this_block_size(os, jcp.os, bcast_step * os_block);
+            rp.os = p.bcast_dim;
+
+            int ocb = 0;
+            while (ocb < jcp.nb_load) {
+                const int load_step = step(jcp.nb_load_blocking,
+                        jcp.nb_load - ocb, jcp.nb_load_blocking_max);
+
+                const int _ocb = g * nb_oc + ocb;
+                p.load_dim = this_block_size(ocb * jcp.oc_block, jcp.oc,
+                        load_step * jcp.oc_block);
+                const size_t dst_off = data_blk_off(dst_d, n, _ocb, oh, ow);
+
+                p.output_data = &dst[dst_off];
+
+                p.bias_data = &bias[_ocb * jcp.oc_block];
+
+                for (int icb = 0; icb < nb_ic; icb += nb_ic_blocking) {
+                    p.first_last_flag = 0
+                        | (icb == 0 ? FLAG_REDUCE_FIRST : 0)
+                        | (icb + nb_ic_blocking >= nb_ic
+                                ? FLAG_REDUCE_LAST : 0);
+
+                    p.reduce_dim = this_block_size(icb * jcp.ic_block, jcp.ic,
+                            nb_ic_blocking * jcp.ic_block);
+                    rp.icb = p.reduce_dim / jcp.reduce_block;
+
+                    p.load_data = &weights[pd()->with_groups()
+                        ? weights_d.blk_off(g, ocb, icb)
+                        : weights_d.blk_off(ocb, icb)];
+
+                    const int _icb = g * nb_ic + icb;
+                    if (pd()->rtus_.reduce_src_) {
+                        rp.ws = rtus_space
+                            + ithr * pd()->rtus_.space_per_thread_
+                            + _icb * jcp.is * jcp.ic_block;
+
+                        if (ocb == 0) {
+                            rp.src = src + data_blk_off(src_d, n, _icb, ih, iw);
+                            rtus_driver_->ker_(&rp);
+                        }
+
+                        p.bcast_data = rp.ws;
+                    } else
+                        p.bcast_data = src + data_blk_off(src_d, n, _icb, ih, iw);
+
+                    kernel_->jit_ker(&p);
+                }
+
+                ocb += load_step;
+            }
+
+            iwork += bcast_step;
+        }
+    };
+
+    if (pd()->wants_padded_bias()) {
+        auto padded_bias = scratchpad(ctx).get<data_t>(key_conv_padded_bias);
+        utils::array_copy(padded_bias, bias, jcp.oc_without_padding);
+        utils::array_set(padded_bias + jcp.oc_without_padding, 0.f,
+                jcp.oc - jcp.oc_without_padding);
+        bias = padded_bias;
+    }
+
+    parallel(0, ker);
+
+    if (pd()->wants_zero_pad_dst())
+        ctx.memory(MKLDNN_ARG_DST)->zero_pad();
+}
+
+/* convolution backward wtr data */
+
+void jit_avx2_1x1_convolution_bwd_data_t::execute_backward_data(
+        const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+
+    const auto &jcp = kernel_->jcp;
+    auto rtus_space = scratchpad(ctx).get<data_t>(key_conv_rtus_space);
+
+    // TODO (Roma): remove this restriction
+    assert(jcp.stride_w == 1 && jcp.stride_h == 1);
+    const int ndims = diff_dst_d.ndims();
+
+    const int stride_h = (ndims == 3) ? 1 : pd()->desc()->strides[0];
+    const int stride_w = pd()->desc()->strides[ndims - 3];
+    const int pad_t = (ndims == 3) ? 0 : pd()->desc()->padding[0][0];
+    const int pad_l = pd()->desc()->padding[0][ndims - 3];
+
+    const int nb_ic = jcp.nb_load;
+    const int nb_oc = jcp.nb_reduce;
+    const int os_block = jcp.bcast_block;
+    const int nb_oc_blocking = jcp.nb_reduce_blocking;
+
+    const int work_amount = jcp.mb * jcp.ngroups * jcp.nb_bcast;
+
+    auto step = [](int default_step, int remaining, int tail_step) {
+        assert(default_step <= tail_step);
+        return remaining < tail_step ? remaining : default_step;
+    };
+
+    auto ker = [&](const int ithr, const int nthr) {
+        auto p = jit_1x1_conv_call_s();
+        auto rp = rtus_driver_t<avx2>::call_params_t();
+
+        int start{0}, end{0};
+        balance211(work_amount, nthr, ithr, start, end);
+
+        int load_step = 0;
+        for (int icb = 0; icb < jcp.nb_load; icb += load_step) {
+            load_step = step(jcp.nb_load_blocking, jcp.nb_load - icb,
+                    jcp.nb_load_blocking_max);
+
+            p.load_dim = this_block_size(icb * jcp.ic_block, jcp.ic,
+                    load_step * jcp.ic_block);
+            rp.icb = p.load_dim / jcp.ic_block;
+
+            int bcast_step;
+            for (int iwork = start; iwork < end; iwork += bcast_step) {
+                int n{0}, g{0}, osb{0};
+                nd_iterator_init(iwork, n, jcp.mb, g, jcp.ngroups, osb,
+                        jcp.nb_bcast);
+
+                bcast_step = step(jcp.nb_bcast_blocking, jcp.nb_bcast - osb,
+                        jcp.nb_bcast_blocking_max);
+                bcast_step = nstl::min(bcast_step, end - iwork);
+
+                const int os = osb * os_block;
+                p.bcast_dim = this_block_size(os, jcp.os,
+                        bcast_step * os_block);
+                rp.os = p.bcast_dim;
+
+                const int oh = os / jcp.ow;
+                const int ow = os % jcp.ow;
+                const int ih = nstl::max(oh * stride_h - pad_t, 0);
+                const int iw = nstl::max(ow * stride_w - pad_l, 0);
+                rp.iw_start = iw;
+
+                const int _icb = g * nb_ic + icb;
+                rp.src = diff_src + data_blk_off(diff_src_d, n, _icb, ih, iw);
+                if (pd()->rtus_.reduce_src_) {
+                    rp.ws = rtus_space
+                        + ithr * pd()->rtus_.space_per_thread_;
+                    p.output_data = rp.ws;
+                } else
+                    p.output_data = rp.src;
+
+                for (int ocb = 0; ocb < jcp.nb_reduce;
+                        ocb += jcp.nb_reduce_blocking) {
+                    const int _ocb = g * nb_oc + ocb;
+                    size_t diff_dst_off = data_blk_off(diff_dst_d, n, _ocb, oh,
+                        ow);
+                    p.bcast_data = &diff_dst[diff_dst_off];
+
+                    p.load_data = &weights[pd()->with_groups()
+                        ? weights_d.blk_off(g, ocb, icb)
+                        : weights_d.blk_off(ocb, icb)];
+
+                    p.first_last_flag = ocb == 0 ? FLAG_REDUCE_FIRST : 0;
+
+                    p.reduce_dim = this_block_size(ocb * jcp.oc_block, jcp.oc,
+                            nb_oc_blocking * jcp.oc_block);
+
+                    kernel_->jit_ker(&p);
+                }
+
+                if (pd()->rtus_.reduce_src_)
+                    rtus_driver_->ker_(&rp);
+            }
+        }
+    };
+
+    parallel(0, ker);
+}
+
+/* convolution backward wtr weights */
+
+jit_avx2_1x1_convolution_bwd_weights_t::jit_avx2_1x1_convolution_bwd_weights_t(
+        const pd_t *apd)
+    : cpu_primitive_t(apd)
+    , kernel_(nullptr)
+    , rtus_driver_(nullptr)
+{
+    kernel_ = new jit_avx2_1x1_conv_kernel_f32(pd()->jcp_, *pd()->attr());
+    reducer_weights_ =
+        new cpu_reducer_2d_t<data_type::f32>(pd()->reducer_wei_conf_);
+    reducer_bias_ = new cpu_reducer_t<data_type::f32>(pd()->reducer_bia_conf_);
+    init_rtus_driver<avx2>(this);
+}
+
+void jit_avx2_1x1_convolution_bwd_weights_t::execute_backward_weights(
+        const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_weights = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
+    auto diff_bias_in = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_BIAS);
+
+    auto scratchpad = this->scratchpad(ctx);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
+    const memory_desc_wrapper diff_bias_d(pd()->diff_weights_md(1));
+
+    const auto &jcp = kernel_->jcp;
+    auto rtus_space = scratchpad.get<data_t>(key_conv_rtus_space);
+
+    data_t *diff_bias = pd()->wants_padded_bias()
+        ? scratchpad.get<data_t>(key_conv_padded_bias) : diff_bias_in;
+
+    auto reducer_bia_scratchpad = memory_tracking::grantor_t(scratchpad,
+            prefix_reducer_bia);
+    auto rb = this->reducer_bias_;
+    rb->init(reducer_bia_scratchpad);
+
+    auto reducer_wei_scratchpad = memory_tracking::grantor_t(scratchpad,
+            prefix_reducer_wei);
+    auto rw = this->reducer_weights_;
+    rw->init(reducer_wei_scratchpad);
+
+    const int ndims = diff_dst_d.ndims();
+    // TODO (Roma): remove this restriction
+    assert(jcp.stride_w == 1 && jcp.stride_h == 1);
+
+    const int nb_ic = jcp.nb_bcast;
+    const int nb_ic_blocking = jcp.nb_bcast_blocking;
+    const int bcast_work = div_up(nb_ic, nb_ic_blocking);
+
+    const int nb_oc = jcp.nb_load;
+    const int nb_oc_blocking = jcp.nb_load_blocking;
+    const int load_work = div_up(nb_oc, nb_oc_blocking);
+
+    const int sp_dim = jcp.reduce_dim;
+    const int mb_sp_work = jcp.mb * sp_dim;
+
+    const int stride_h = (ndims == 3) ? 1 : pd()->desc()->strides[0];
+    const int stride_w = pd()->desc()->strides[ndims - 3];
+    const int pad_t = (ndims == 3) ? 0 : pd()->desc()->padding[0][0];
+    const int pad_l = pd()->desc()->padding[0][ndims - 3];
+
+    auto step = [](int default_step, int remaining, int tail_step) {
+        assert(default_step <= tail_step);
+        return remaining < tail_step ? remaining : default_step;
+    };
+
+    auto oc_ic_sp_loop = [=](int sp_start, int sp_end, bool first_image,
+            data_t *store_to, size_t store_to_ld, const data_t *diff_dst,
+            const data_t *src, int ithr) {
+        auto p = jit_1x1_conv_call_s();
+        auto rp = rtus_driver_t<avx2>::call_params_t();
+
+        p.output_stride = store_to_ld * sizeof(float);
+        const int sp_step_def = jcp.nb_reduce_blocking * jcp.reduce_block;
+
+        int oc_b_step = 0;
+        for (int oc_b = 0; oc_b < nb_oc_blocking; oc_b += oc_b_step) {
+            oc_b_step = step(12, nb_oc_blocking - oc_b, 18);
+            p.load_dim = oc_b_step * jcp.oc_block;
+
+            int ic_b_step = 0;
+            for (int ic_b = 0; ic_b < nb_ic_blocking; ic_b += ic_b_step) {
+                ic_b_step = step(12, nb_ic_blocking - ic_b, 18);
+                p.bcast_dim = ic_b_step * jcp.ic_block;
+                rp.icb = p.bcast_dim / jcp.ic_block;
+
+                p.output_data = store_to + oc_b * store_to_ld
+                    + ic_b * jcp.ic_block * jcp.oc_block;
+
+                /* spatial reduction */
+                int sp_step = 0;
+                for (int sp = sp_start; sp < sp_end; sp += sp_step) {
+                    sp_step = step(sp_step_def, sp_end - sp, 192);
+                    p.reduce_dim = sp_step;
+                    rp.os = p.reduce_dim;
+
+                    p.first_last_flag = sp == sp_start && first_image
+                        ? FLAG_REDUCE_FIRST : 0;
+
+                    p.load_data = diff_dst
+                        + (oc_b * jcp.reduce_dim + sp) * jcp.oc_block;
+
+                    if (pd()->rtus_.reduce_src_) {
+                        const int oh = sp / jcp.ow;
+                        const int ow = sp % jcp.ow;
+
+                        const int ih = nstl::max(oh * stride_h - pad_t, 0);
+                        const int iw = nstl::max(ow * stride_w - pad_l, 0);
+                        rp.iw_start = iw;
+
+                        rp.ws = rtus_space
+                            + ithr * pd()->rtus_.space_per_thread_
+                            + (ic_b * jcp.is + sp) * jcp.ic_block;
+                        if (ndims == 3)
+                            rp.src = src
+                                + iw * src_d.blocking_desc().strides[2];
+                        else
+                            rp.src = src
+                                + ih * src_d.blocking_desc().strides[2]
+                                + iw * src_d.blocking_desc().strides[3];
+
+                        if (oc_b == 0)
+                            rtus_driver_->ker_(&rp);
+
+                        p.bcast_data = rp.ws;
+                    } else
+                        p.bcast_data = src
+                            + (ic_b * jcp.reduce_dim + sp) * jcp.ic_block;
+
+                    kernel_->jit_ker(&p);
+                }
+            }
+        }
+    };
+
+    auto ker = [&](const int ithr, const int nthr) {
+        assert(nthr == rw->balancer().nthr_);
+
+        const int w_njobs = rw->balancer().ithr_njobs(ithr);
+        if (w_njobs == 0) return;
+
+        /* setup: independent work (oc, ic) */
+        const int w_job_start = rw->balancer().ithr_job_off(ithr);
+        int g{0}, load_i{0}, bcast_i{0};
+        nd_iterator_init(w_job_start, g, jcp.ngroups, load_i, load_work,
+                bcast_i, bcast_work);
+
+        /* setup: reduction work (mb, sp) */
+        int mb_sp_start{0}, mb_sp_end{0};
+        balance211(mb_sp_work, rw->balancer().nthr_per_group_,
+                rw->balancer().id_in_group(ithr), mb_sp_start, mb_sp_end);
+        int img_start{0}, sp_start{0};
+        nd_iterator_init(mb_sp_start, img_start, jcp.mb, sp_start, sp_dim);
+
+        /* independent work */
+        for (int iwork = 0; iwork < w_njobs; ++iwork) {
+            const int oc_b = nb_oc_blocking * load_i;
+            const int ic_b = nb_ic_blocking * bcast_i;
+
+            const int _ic_b = g * nb_ic + ic_b;
+            const int _oc_b = g * nb_oc + oc_b;
+
+            data_t *store_to;
+            size_t store_to_ld;
+
+            if (rw->balancer().nthr_per_group_ == 1) {
+                const size_t off = pd()->with_groups()
+                    ? diff_weights_d.blk_off(g, oc_b, ic_b)
+                    : diff_weights_d.blk_off(oc_b, ic_b);
+                store_to = &diff_weights[off];
+                store_to_ld = jcp.ic * jcp.oc_block;
+            } else {
+                const size_t off = iwork * rw->balancer().job_size_;
+                store_to =
+                    rw->get_local_ptr(ithr, reducer_wei_scratchpad) + off;
+                store_to_ld = nb_ic_blocking * jcp.ic_block * jcp.oc_block;
+            }
+
+            /* reduction work */
+            int img = img_start;
+            int sp = sp_start;
+            int sp_step = 0;
+            for (int mb_sp = mb_sp_start; mb_sp < mb_sp_end; mb_sp += sp_step)
+            {
+                sp_step = nstl::min(sp_dim - sp, mb_sp_end - mb_sp);
+
+                const bool first_image = img == img_start;
+                oc_ic_sp_loop(sp, sp + sp_step, first_image, store_to,
+                        store_to_ld, &diff_dst[diff_dst_d.blk_off(img, _oc_b)],
+                        &src[src_d.blk_off(img, _ic_b)], ithr);
+
+                sp = 0;
+                img += 1;
+            }
+
+            nd_iterator_step(g, jcp.ngroups, load_i, load_work, bcast_i,
+                             bcast_work);
+        }
+        rw->reduce(ithr, diff_weights, reducer_wei_scratchpad);
+    };
+
+    auto ker_bias = [&](int ithr, int nthr) {
+        assert(nthr == rb->balancer().nthr_);
+
+        const int b_job_start = rb->balancer().ithr_job_off(ithr);
+        const int b_njobs = rb->balancer().ithr_njobs(ithr);
+
+        if (b_njobs == 0) return;
+
+        /* reduction dimension */
+        int img_start{0}, img_end{0};
+        balance211(jcp.mb, rb->balancer().nthr_per_group_,
+                rb->balancer().id_in_group(ithr), img_start, img_end);
+
+        /* jobs */
+        int g_start{0}, ocb_start{0};
+        nd_iterator_init(b_job_start, g_start, jcp.ngroups, ocb_start, nb_oc);
+
+        for (int img = img_start; img < img_end; ++img) {
+            int g = g_start, ocb = ocb_start;
+            for (int b_job_loc = 0; b_job_loc < b_njobs; ++b_job_loc) {
+                const size_t _oc = g * nb_oc + ocb;
+
+                const data_t *d_dst = &diff_dst[diff_dst_d.blk_off(img, _oc)];
+                data_t *d_bias =
+                    rb->get_local_ptr(ithr, diff_bias, reducer_bia_scratchpad)
+                    + b_job_loc * rb->balancer().job_size_;
+
+                if (img == img_start)
+                    for (int o = 0; o < 8; ++o) d_bias[o] = 0.;
+
+                for (int hw = 0; hw < jcp.oh * jcp.ow; ++hw) {
+                    PRAGMA_OMP_SIMD()
+                    for (int o = 0; o < 8; ++o)
+                        d_bias[o] += d_dst[o];
+                    d_dst += 8;
+                }
+
+                nd_iterator_step(g, jcp.ngroups, ocb, nb_oc);
+            }
+        }
+        rb->reduce(ithr, diff_bias, reducer_bia_scratchpad);
+    };
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        ker(ithr, nthr);
+        if (pd()->with_bias())
+            ker_bias(ithr, nthr);
+    });
+
+    /* TODO: put this in ker_bias */
+    if (pd()->wants_padded_bias()) {
+        assert(jcp.ngroups == 1);
+        for (int oc = 0; oc < jcp.oc_without_padding; ++oc)
+            diff_bias_in[oc] = diff_bias[oc];
+    }
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_convolution.hpp
new file mode 100644
index 0000000000..9762242173
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_1x1_convolution.hpp
@@ -0,0 +1,344 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX2_1x1_CONVOLUTION_HPP
+#define CPU_JIT_AVX2_1x1_CONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+#include "cpu_reducer.hpp"
+
+#include "jit_avx2_1x1_conv_kernel_f32.hpp"
+#include "jit_uni_1x1_conv_utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_avx2_1x1_convolution_fwd_t: public cpu_primitive_t {
+    // TODO: (Roma) Code duplication duplication! Remove with templates
+    //              (maybe...)!
+    struct pd_t: public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_(), rtus_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_1x1:", avx2, ""),
+                jit_avx2_1x1_convolution_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            const convolution_desc_t *conv_d = desc();
+            const memory_desc_t *src_d = src_md();
+            rtus_prepare(this, conv_d, src_d, dst_md());
+
+            status_t status = jit_avx2_1x1_conv_kernel_f32::init_conf(jcp_,
+                    *conv_d, *src_d, *weights_md(), *dst_md(), *attr());
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx2_1x1_conv_kernel_f32::init_scratchpad(scratchpad, jcp_);
+
+            rtus_prepare_space_info(this, scratchpad);
+
+            return status::success;
+        }
+
+        jit_1x1_conv_conf_t jcp_;
+        reduce_to_unit_stride_t rtus_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
+            auto wei_tag = with_groups()
+                ? utils::pick(ndims() - 3, gOIw8i8o, gOIhw8i8o)
+                : utils::pick(ndims() - 3, OIw8i8o, OIhw8i8o);
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    template <cpu_isa_t isa, typename conv_t>
+    friend void init_rtus_driver(conv_t *self);
+
+    jit_avx2_1x1_convolution_fwd_t(const pd_t *apd)
+        : cpu_primitive_t(apd)
+        , kernel_(nullptr), rtus_driver_(nullptr)
+    {
+        kernel_ = new jit_avx2_1x1_conv_kernel_f32(pd()->jcp_, *pd()->attr());
+        init_rtus_driver<avx2>(this);
+    }
+
+    ~jit_avx2_1x1_convolution_fwd_t() {
+        delete kernel_;
+        delete rtus_driver_;
+    }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx2_1x1_conv_kernel_f32 *kernel_;
+    rtus_driver_t<avx2> *rtus_driver_;
+};
+
+struct jit_avx2_1x1_convolution_bwd_data_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_bwd_data_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_(), rtus_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_1x1:", avx2, ""),
+                jit_avx2_1x1_convolution_bwd_data_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_data
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::undef, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            const convolution_desc_t *conv_d = desc();
+            const memory_desc_t *diff_src_d = diff_src_md();
+            rtus_prepare(this, conv_d, diff_src_d, diff_dst_md());
+
+            status_t status = jit_avx2_1x1_conv_kernel_f32::init_conf(jcp_,
+                    *conv_d, *diff_src_d, *weights_md(), *diff_dst_md(),
+                    *attr());
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx2_1x1_conv_kernel_f32::init_scratchpad(scratchpad, jcp_);
+
+            rtus_prepare_space_info(this, scratchpad);
+
+            return status::success;
+        }
+
+        jit_1x1_conv_conf_t jcp_;
+        reduce_to_unit_stride_t rtus_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
+            auto wei_tag = with_groups()
+                ? utils::pick(ndims() - 3, gOIw8o8i, gOIhw8o8i)
+                : utils::pick(ndims() - 3, OIw8o8i, OIhw8o8i);
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    template <cpu_isa_t isa, typename conv_t>
+    friend void init_rtus_driver(conv_t *self);
+
+    jit_avx2_1x1_convolution_bwd_data_t(const pd_t *apd)
+        : cpu_primitive_t(apd)
+        , kernel_(nullptr)
+        , rtus_driver_(nullptr)
+    {
+        kernel_ = new jit_avx2_1x1_conv_kernel_f32(pd()->jcp_, *pd()->attr());
+        init_rtus_driver<avx2>(this);
+    }
+
+    ~jit_avx2_1x1_convolution_bwd_data_t() {
+        delete kernel_;
+        delete rtus_driver_;
+    }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_data(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_data(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx2_1x1_conv_kernel_f32 *kernel_;
+    rtus_driver_t<avx2> *rtus_driver_;
+};
+
+struct jit_avx2_1x1_convolution_bwd_weights_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_bwd_weights_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_weights_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_(), rtus_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_1x1:", avx2, ""),
+                jit_avx2_1x1_convolution_bwd_weights_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_weights
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            const convolution_desc_t *conv_d = desc();
+            const memory_desc_t *src_d = src_md();
+            rtus_prepare(this, conv_d, src_d, diff_dst_md());
+
+            status_t status = jit_avx2_1x1_conv_kernel_f32::init_conf(jcp_,
+                    *conv_d, *src_d, *diff_weights_md(), *diff_dst_md(),
+                    *attr());
+            if (status != status::success) return status;
+
+            init_balancers();
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx2_1x1_conv_kernel_f32::init_scratchpad(scratchpad, jcp_);
+
+            rtus_prepare_space_info(this, scratchpad);
+
+            auto reducer_bia_scratchpad = memory_tracking::registrar_t(
+                    scratchpad, memory_tracking::names::prefix_reducer_bia);
+            reducer_bia_conf_.init_scratchpad(reducer_bia_scratchpad);
+
+            auto reducer_wei_scratchpad = memory_tracking::registrar_t(
+                    scratchpad, memory_tracking::names::prefix_reducer_wei);
+            reducer_wei_conf_.init_scratchpad(reducer_wei_scratchpad);
+
+            return status::success;
+        }
+
+        jit_1x1_conv_conf_t jcp_;
+        cpu_reducer_t<data_type::f32>::conf_t reducer_bia_conf_;
+        cpu_reducer_2d_t<data_type::f32>::conf_t reducer_wei_conf_;
+        reduce_to_unit_stride_t rtus_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
+            auto wei_tag = with_groups()
+                ? utils::pick(ndims() - 3, gOIw8i8o, gOIhw8i8o)
+                : utils::pick(ndims() - 3, OIw8i8o, OIhw8i8o);
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+
+    private:
+        void init_balancers() {
+            const int ic_block = jcp_.bcast_block;
+            const int nb_ic = jcp_.nb_bcast;
+            const int nb_ic_blocking = jcp_.nb_bcast_blocking;
+            const int bcast_work = utils::div_up(nb_ic, nb_ic_blocking);
+
+            const int oc_block = jcp_.load_block;
+            const int nb_oc = jcp_.nb_load;
+            const int nb_oc_blocking = jcp_.nb_load_blocking;
+            const int load_work = utils::div_up(nb_oc, nb_oc_blocking);
+
+            const int job_size
+                = nb_oc_blocking * nb_ic_blocking * ic_block * oc_block;
+            const int njobs_x = bcast_work;
+            const int njobs_y = jcp_.ngroups * load_work;
+
+            const int max_threads = mkldnn_get_max_threads();
+            const size_t max_buffer_size = max_threads * job_size * 8;
+
+            if (with_bias()) {
+                reducer_bia_conf_.init(reduce_balancer_t(max_threads,
+                            oc_block, jcp_.ngroups * jcp_.oc / oc_block,
+                            jcp_.mb, max_buffer_size));
+            }
+
+            reducer_wei_conf_.init(
+                    reduce_balancer_t(max_threads, job_size, njobs_y * njobs_x,
+                        jcp_.mb * jcp_.nb_reduce, max_buffer_size),
+                    job_size / nb_oc_blocking, nb_oc_blocking, ic_block,
+                    nb_ic * ic_block * oc_block, nb_oc);
+        }
+    };
+
+    template <cpu_isa_t isa, typename conv_t>
+    friend void init_rtus_driver(conv_t *self);
+
+    jit_avx2_1x1_convolution_bwd_weights_t(const pd_t *apd);
+
+    ~jit_avx2_1x1_convolution_bwd_weights_t() {
+        delete kernel_;
+        delete rtus_driver_;
+        delete reducer_weights_;
+        delete reducer_bias_;
+    }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_weights(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_weights(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx2_1x1_conv_kernel_f32 *kernel_;
+    cpu_reducer_2d_t<data_type::f32> *reducer_weights_;
+    cpu_reducer_t<data_type::f32> *reducer_bias_;
+    rtus_driver_t<avx2> *rtus_driver_;
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.cpp
new file mode 100644
index 0000000000..e24770a2da
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.cpp
@@ -0,0 +1,1501 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+* Copyright 2018 YANDEX LLC
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+#include "cpu_memory.hpp"
+
+#include "jit_avx2_conv_kernel_f32.hpp"
+
+#define GET_OFF(field) offsetof(jit_conv_call_s, field)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+
+using namespace Xbyak;
+
+void jit_avx2_conv_fwd_kernel_f32::oh_step_unroll_kw(int ur_w,
+        int pad_l, int pad_r, int oc_blocks)
+{
+    int iw = jcp.iw;
+    int ih = jcp.ih;
+    int id = jcp.id;
+    int kw = jcp.kw;
+    int kh = jcp.kh;
+    int kd = jcp.kd;
+    int nb_ic = jcp.nb_ic;
+    int stride_w = jcp.stride_w;
+    int dilate_w = jcp.dilate_w + 1;
+    int ic_blk = jcp.ic_block;
+    int oc_blk = jcp.oc_block;
+
+    for (int ki = 0; ki < kw; ki++) {
+        int jj_start = nstl::max(0, div_up(pad_l - ki * dilate_w, stride_w));
+        int jj_end = ur_w
+            - nstl::max(0, div_up(ki*dilate_w+pad_r-(kw-1)*dilate_w, stride_w));
+        for (int ifm2 = 0; ifm2 < ic_blk; ifm2++) {
+            for (int jj = jj_start; jj < jj_end; jj++) {
+                size_t inp_off;
+                if (one_of(jcp.src_tag, ncw, nchw, ncdhw))
+                    inp_off = sizeof(float)*((size_t)ifm2*id*ih*iw
+                        + (ki*dilate_w + jj*stride_w - pad_l));
+                else
+                    inp_off = sizeof(float)*((ki*dilate_w + jj*stride_w
+                                - pad_l)*ic_blk + ifm2);
+                vbroadcastss(Ymm(oc_blocks * ur_w + jj),
+                        make_safe_addr(aux_reg_input, inp_off, reg_long_offt));
+            }
+
+            for (int ii = 0; ii < oc_blocks; ii++) {
+                int ker_off = ii * nb_ic * kd * kh * kw * ic_blk * oc_blk
+                        + ki * ic_blk * oc_blk + ifm2 * oc_blk;
+                vmovups(ymm15, ptr[aux_reg_kernel + sizeof(float) * ker_off]);
+                for (int jj = jj_start; jj < jj_end; jj++)
+                    if (mayiuse(avx2))
+                        vfmadd231ps(Ymm(ur_w * ii + jj),
+                                Ymm(oc_blocks * ur_w + jj), ymm15);
+                    else { // Intel(R) Advanced Vector Extensions (Intel(R) AVX) support
+                        vmulps(ytmp, ymm15, Ymm(oc_blocks * ur_w + jj));
+                        vaddps(Ymm(ur_w * ii + jj), Ymm(ur_w * ii + jj), ytmp);
+                    }
+            }
+        }
+    }
+}
+
+void jit_avx2_conv_fwd_kernel_f32::oh_step_nopad(int ur_w,
+        int pad_l, int pad_r, char pad_tag,
+        int oc_blocks, char oc_blocks_tag)
+{
+    Label kw_loop;
+
+    int iw = jcp.iw;
+    int ih = jcp.ih;
+    int id = jcp.id;
+    int kw = jcp.kw;
+    int kh = jcp.kh;
+    int kd = jcp.kd;
+    int nb_ic = jcp.nb_ic;
+    int stride_w = jcp.stride_w;
+    int dilate_w = jcp.dilate_w + 1;
+    int ic_blk = jcp.ic_block;
+    int oc_blk = jcp.oc_block;
+
+    xor_(ki_iter, ki_iter);
+    L(kw_loop);
+    {
+        int jj_start = 0;
+        int jj_end = ur_w;
+        for (int ifm2 = 0; ifm2 < ic_blk; ifm2++) {
+            for (int jj = jj_start; jj < jj_end; jj++) {
+                size_t inp_off;
+                if (one_of(jcp.src_tag, ncw, nchw, ncdhw))
+                    inp_off = sizeof(float)*((size_t)ifm2 * id * ih * iw
+                            + (jj * stride_w - pad_l));
+                else
+                    inp_off = sizeof(float)*((jj * stride_w - pad_l) * ic_blk
+                            + ifm2);
+                vbroadcastss(Ymm(oc_blocks * ur_w + jj),
+                    make_safe_addr(aux_reg_input, inp_off, reg_long_offt));
+            }
+            for (int ii = 0; ii < oc_blocks; ii++) {
+                int aux_kernel_offset =
+                    ii * nb_ic * kd * kh * kw * ic_blk * oc_blk + ifm2 * oc_blk;
+                vmovups(ymm15, ptr[aux_reg_kernel
+                        + sizeof(float) * aux_kernel_offset]);
+                for (int jj = jj_start; jj < jj_end; jj++)
+                    if (mayiuse(avx2))
+                        vfmadd231ps(Ymm(ur_w * ii + jj),
+                                Ymm(oc_blocks * ur_w + jj), ymm15);
+                    else { // Intel AVX support
+                        vmulps(ytmp, ymm15, Ymm(oc_blocks * ur_w + jj));
+                        vaddps(Ymm(ur_w * ii + jj), Ymm(ur_w * ii + jj), ytmp);
+                    }
+            }
+        }
+        add(aux_reg_kernel, sizeof(float) * oc_blk * ic_blk);
+        add(aux_reg_input, sizeof(float) * (one_of(jcp.src_tag, ncw, nchw, ncdhw)
+                ? dilate_w : ic_blk * dilate_w));
+
+        inc(ki_iter);
+        cmp(ki_iter, kw);
+        jl(kw_loop, T_NEAR);
+    }
+}
+
+void jit_avx2_conv_fwd_kernel_f32::width_blk_step(int ur_w,
+        int pad_l, int pad_r, char pad_tag,
+        int oc_blocks, char oc_blocks_tag)
+{
+    int iw = jcp.iw;
+    int kw = jcp.kw;
+    int ow = jcp.ow;
+    int oh = jcp.oh;
+    int od = jcp.od;
+    int dilate_h = jcp.dilate_h + 1;
+    int dilate_w = jcp.dilate_w + 1;
+    int ic_blk = jcp.ic_block;
+    int oc_blk = jcp.oc_block;
+    const int inp_mult = one_of(jcp.src_tag, ncw, nchw, ncdhw)
+        ? 1 : ic_blk;
+    const int inp_off = one_of(jcp.src_tag, ncw, nchw, ncdhw)
+        ? dilate_w : ic_blk * dilate_w;
+
+    Label init_done, init_first;
+
+    if (!jcp.with_sum) {
+        test(reg_ci_flag, FLAG_IC_FIRST);
+        jne(init_first, T_NEAR);
+    }
+
+    for (int ii = 0; ii < oc_blocks; ii++) {
+        for (int jj = 0; jj < ur_w; jj++) {
+            size_t offt =
+                sizeof(float) * ((size_t)ii * od * oh * ow + jj) * oc_blk;
+            vmovups(Ymm(ur_w * ii + jj),
+                    make_safe_addr(reg_output, offt, reg_long_offt));
+        }
+    }
+
+    if (jcp.with_sum && jcp.with_bias) {
+        test(reg_ci_flag, FLAG_IC_FIRST);
+        je(init_done, T_NEAR);
+
+        for (int ii = 0; ii < oc_blocks; ii++)
+            for (int jj = 0; jj < ur_w; jj++)
+                vaddps(Ymm(ur_w * ii + jj), Ymm(ur_w * ii + jj),
+                    yword[reg_bias + sizeof(float) * ii * oc_blk]);
+    }
+
+    jmp(init_done);
+
+    L(init_first);
+    if (this->jcp.with_bias) {
+        for (int ii = 0; ii < oc_blocks; ii++)
+            for (int jj = 0; jj < ur_w; jj++)
+                vmovups(Ymm(ur_w * ii + jj),
+                        yword[reg_bias + sizeof(float) * ii * oc_blk]);
+    } else {
+        for (int ii = 0; ii < oc_blocks; ii++)
+            for (int jj = 0; jj < ur_w; jj++)
+                uni_vpxor(Ymm(ur_w * ii + jj), Ymm(ur_w * ii + jj), Ymm(ur_w * ii + jj));
+    }
+
+    L(init_done);
+
+    if (one_of(jcp.ndims, 3, 4)) {
+        mov(aux_reg_input, reg_input);
+        mov(aux_reg_kernel, reg_kernel);
+    }
+
+    Label skip_kh_loop, skip_kd_loop, kd_loop;
+    if (jcp.ndims == 5) {
+        push(reg_output);
+        push(oi_iter);
+
+        mov(reg_ki, ptr[param1 + GET_OFF(kd_padding)]);
+        mov(aux_reg_ker_d, ptr[param1 + GET_OFF(filt)]);
+        mov(aux_reg_inp_d, reg_input);
+
+        if ((jcp.dilate_d >= jcp.id)
+                || (jcp.kd - 1) * (jcp.dilate_d + 1) < jcp.f_pad) {
+            cmp(reg_ki, 0);
+            je(skip_kd_loop, T_NEAR);
+        }
+        L(kd_loop);
+        mov(kj, ptr[param1 + GET_OFF(kh_padding)]);
+    } else {
+        mov(kj, reg_kh);
+    }
+
+    if (jcp.ndims == 5) {
+        mov(aux_reg_input, aux_reg_inp_d);
+        mov(aux_reg_kernel, aux_reg_ker_d);
+    }
+
+    if ((jcp.dilate_h >= jcp.ih)
+            || (jcp.kh - 1) * (jcp.dilate_h + 1) < nstl::max(jcp.t_pad, jcp.b_pad)) {
+        cmp(kj, 0);
+        je(skip_kh_loop, T_NEAR);
+    }
+    Label kh_loop;
+    L(kh_loop);
+    {
+        if (jcp.kw >= 5 && pad_l == 0 && pad_r == 0) {
+            oh_step_nopad(ur_w, pad_l, pad_r, pad_tag, oc_blocks,
+                    oc_blocks_tag);
+            sub(aux_reg_input, sizeof(float) * kw * inp_off);
+            add(aux_reg_input, sizeof(float) * iw * dilate_h * inp_mult);
+        } else {
+            oh_step_unroll_kw(ur_w, pad_l, pad_r, oc_blocks);
+            add(aux_reg_kernel, sizeof(float) * kw * oc_blk * ic_blk);
+            add(aux_reg_input, sizeof(float) * iw * dilate_h * inp_mult);
+        }
+
+        dec(kj);
+        cmp(kj, 0);
+        jg(kh_loop, T_NEAR);
+    }
+
+    L(skip_kh_loop);
+
+    if (jcp.ndims == 5) {
+        add(aux_reg_inp_d,
+            sizeof(float) * (jcp.dilate_d + 1) * jcp.ih * jcp.iw * inp_mult);
+        add(aux_reg_ker_d, sizeof(float) * jcp.kw * jcp.kh * jcp.oc_block
+            * jcp.ic_block);
+
+        dec(reg_ki);
+        cmp(reg_ki, 0);
+        jg(kd_loop, T_NEAR);
+        L(skip_kd_loop);
+
+        pop(oi_iter);
+        pop(reg_output);
+    }
+
+    Label regular_store;
+
+    if (jcp.with_eltwise) {
+        test(reg_ci_flag, FLAG_IC_LAST);
+        je(regular_store, T_NEAR);
+
+        eltwise_injector_->compute_vector_range(0, oc_blocks * ur_w);
+
+        L(regular_store);
+    }
+
+    for (int ii = 0; ii < oc_blocks; ii++) {
+        for (int jj = 0; jj < ur_w; jj++) {
+            const size_t o_off
+                = sizeof(float) * ((size_t)ii * od * oh * ow + jj) * oc_blk;
+            Ymm reg_out = Ymm(ur_w * ii + jj);
+            vmovups(make_safe_addr(reg_output, o_off, reg_long_offt), reg_out);
+        }
+    }
+}
+
+inline void jit_avx2_conv_fwd_kernel_f32::solve_common(
+        int oc_blocks, char oc_blocks_tag)
+{
+    int ur_w = jcp.ur_w;
+    int ur_w_tail = jcp.ur_w_tail;
+    int n_oi = jcp.ow / ur_w;
+    int iw = jcp.iw;
+    int kw = jcp.kw;
+    int ic_blk = jcp.ic_block;
+    int oc_blk = jcp.oc_block;
+    int dilate_w = jcp.dilate_w + 1;
+    int str_w = jcp.stride_w;
+    const int inp_mult = one_of(jcp.src_tag, ncw, nchw, ncdhw) ? 1 : ic_blk;
+
+    int l_pad = jcp.l_pad;
+    int r_pad = nstl::max(0, (int(jcp.ow) - 1) * str_w + (kw - 1) * dilate_w
+            - (iw + l_pad - 1));
+    int r_pad1 = (ur_w * n_oi - 1) * str_w + (kw - 1) * dilate_w
+            - (iw + l_pad - 1);
+    if (r_pad1 > 0) n_oi--;
+
+    if (l_pad > 0) {
+        n_oi--;
+        if (n_oi < 0 && r_pad1 > 0)
+            width_blk_step(ur_w, l_pad, r_pad1,
+                    'l', oc_blocks, oc_blocks_tag); // "lrpad"
+        else
+            width_blk_step(ur_w, l_pad, 0,
+                    'l', oc_blocks, oc_blocks_tag); // "lpad"
+        add(reg_input, sizeof(float) * (ur_w * str_w - l_pad) * inp_mult);
+        add(reg_output, sizeof(float) * ur_w * oc_blk);
+    }
+
+    Label ow_loop;
+    xor_(oi_iter, oi_iter);
+
+    if (n_oi > 0) {
+        L(ow_loop);
+
+        width_blk_step(ur_w, 0, 0,
+                'm', oc_blocks, oc_blocks_tag); // "middle"
+        add(reg_input, sizeof(float) * ur_w * str_w * inp_mult);
+        add(reg_output, sizeof(float) * ur_w * oc_blk);
+
+        inc(oi_iter);
+        cmp(oi_iter, n_oi);
+        jl(ow_loop, T_NEAR);
+    }
+
+    if (r_pad1 > 0 && n_oi >=0) {
+        width_blk_step(ur_w, 0, r_pad1,
+                'r', oc_blocks, oc_blocks_tag); // "rpad"
+        add(reg_input, sizeof(float) * ur_w * str_w * inp_mult);
+        add(reg_output, sizeof(float) * ur_w * oc_blk);
+    }
+
+    if (ur_w_tail != 0)
+        width_blk_step(ur_w_tail, 0, r_pad,
+                't', oc_blocks, oc_blocks_tag); // "tail"
+}
+
+void jit_avx2_conv_fwd_kernel_f32::generate()
+{
+    this->preamble();
+
+    mov(reg_input, ptr[this->param1 + GET_OFF(src)]);
+    mov(reg_output, ptr[this->param1 + GET_OFF(dst)]);
+    mov(reg_kernel, ptr[this->param1 + GET_OFF(filt)]);
+    if (jcp.with_bias)
+        mov(reg_bias, ptr[this->param1 + GET_OFF(bias)]);
+    mov(reg_kh, ptr[this->param1 + GET_OFF(kh_padding)]);
+    mov(reg_ci_flag, ptr[this->param1 + GET_OFF(flags)]);
+    mov(reg_oc_blocks, ptr[this->param1 + GET_OFF(oc_blocks)]);
+
+    int nb_oc_tail = jcp.nb_oc % jcp.nb_oc_blocking;
+    Label tail, exit;
+
+    if (jcp.nb_oc > jcp.nb_oc_blocking) {
+        cmp(reg_oc_blocks, jcp.nb_oc_blocking);
+        jne(nb_oc_tail ? tail : exit, T_NEAR);
+
+        solve_common(jcp.nb_oc_blocking, '0' + jcp.nb_oc_blocking);
+        jmp(exit, T_NEAR);
+
+        if (nb_oc_tail) {
+            L(tail);
+            cmp(reg_oc_blocks, nb_oc_tail);
+            jne(exit, T_NEAR);
+            solve_common(nb_oc_tail, '0' + nb_oc_tail);
+        }
+
+        L(exit);
+    } else if (jcp.nb_oc == jcp.nb_oc_blocking) {
+        solve_common(jcp.nb_oc_blocking, '0' + jcp.nb_oc_blocking);
+    } else {
+        solve_common(nb_oc_tail, '0' + nb_oc_tail);
+    }
+
+    this->postamble();
+
+    if (jcp.with_eltwise)
+        eltwise_injector_->prepare_table();
+}
+
+bool jit_avx2_conv_fwd_kernel_f32::post_ops_ok(
+        jit_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    const auto &p = attr.post_ops_;
+
+    auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); };
+    auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); };
+
+    switch (p.len_) {
+    case 0: return true; // no post_ops
+    case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise
+    case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise
+    default: return false;
+    }
+
+    return false;
+}
+
+status_t jit_avx2_conv_fwd_kernel_f32::init_conf(jit_conv_conf_t &jcp,
+        const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+        const memory_desc_wrapper &weights_d, const memory_desc_wrapper &dst_d,
+        const primitive_attr_t &attr)
+{
+    if (!mayiuse(avx)) return status::unimplemented;
+
+    jcp.prop_kind = cd.prop_kind;
+
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+    int ndims = src_d.ndims();
+    jcp.ndims = ndims;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+
+    jcp.id = (ndims == 5) ? src_d.dims()[2] : 1;
+    jcp.ih = (ndims == 3) ? 1 : src_d.dims()[ndims-2];
+    jcp.iw = src_d.dims()[ndims-1];
+    jcp.od = (ndims == 5) ? dst_d.dims()[2] : 1;
+    jcp.oh = (ndims == 3) ? 1 :dst_d.dims()[ndims-2];
+    jcp.ow = dst_d.dims()[ndims-1];
+    jcp.kd = (ndims == 5) ? weights_d.dims()[with_groups + 2] : 1;
+    jcp.kh = (ndims == 3) ? 1 : weights_d.dims()[with_groups + ndims-2];
+    jcp.kw = weights_d.dims()[with_groups + ndims-1];
+
+    jcp.f_pad = (ndims == 5) ? cd.padding[0][0] : 0;
+    jcp.t_pad = (ndims == 3) ? 0 : cd.padding[0][ndims-4];
+    jcp.l_pad = cd.padding[0][ndims-3];
+    jcp.stride_d = (ndims == 5) ? cd.strides[0] : 1;
+    jcp.stride_h = (ndims == 3) ? 1 :cd.strides[ndims-4];
+    jcp.stride_w = cd.strides[ndims-3];
+
+    jcp.dilate_d = (ndims == 5) ? cd.dilates[0] : 0;
+    jcp.dilate_h = (ndims == 3) ? 0 : cd.dilates[ndims-4];
+    jcp.dilate_w = cd.dilates[ndims-3];
+
+    jcp.b_pad = (jcp.oh - 1) * jcp.stride_h + (jcp.kh - 1) * (jcp.dilate_h + 1)
+            - (jcp.ih + jcp.t_pad - 1);
+
+    if (ndims == 3) {
+        jcp.src_tag = src_d.matches_one_of_tag(ncw, nwc, nCw8c);
+        jcp.wei_tag = weights_d.matches_one_of_tag(
+                Owi8o, gOwi8o, OIw8i8o, gOIw8i8o);
+        jcp.dst_tag = dst_d.matches_one_of_tag(nCw8c);
+    } else if (ndims == 4) {
+        jcp.src_tag = src_d.matches_one_of_tag(nchw, nhwc, nChw8c);
+        jcp.wei_tag = weights_d.matches_one_of_tag(
+                Ohwi8o, gOhwi8o, OIhw8i8o, gOIhw8i8o);
+        jcp.dst_tag = dst_d.matches_one_of_tag(nChw8c);
+    } else if (ndims == 5) {
+        jcp.src_tag = src_d.matches_one_of_tag(ncdhw, ndhwc, nCdhw8c);
+        jcp.wei_tag = weights_d.matches_one_of_tag(
+                Odhwi8o, gOdhwi8o, OIdhw8i8o, gOIdhw8i8o);
+        jcp.dst_tag = dst_d.matches_one_of_tag(nCdhw8c);
+    }
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    jcp.with_sum = p.find(primitive_kind::sum) != -1;
+    const int eltwise_ind = p.find(primitive_kind::eltwise);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise) {
+        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+        if (!mayiuse(avx2) && jcp.eltwise.alg != alg_kind::eltwise_relu)
+            return status::unimplemented;
+    }
+
+    const int simd_w = 8;
+    const bool flat = jcp.ic < simd_w;
+    const bool mimo = !flat;
+
+
+    /* Grouped channel offset to support 'non-blocked data' format for
+     * convolution sizes with '(input_channel / ngroups) < simd' */
+    jcp.nonblk_group_off =
+        one_of(jcp.src_tag, ncw, nchw, ncdhw) && jcp.ngroups > 1 ? jcp.ic : 1;
+
+    bool ok_to_pad_channels = true
+        && jcp.ngroups == 1;
+
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, simd_w);
+        if (mimo)
+            jcp.ic = rnd_up(jcp.ic, simd_w);
+    }
+
+    bool args_ok = true
+        && IMPLICATION(flat, true
+                && one_of(jcp.src_tag, ncw, nwc, nchw, nhwc, ncdhw, ndhwc)
+                && one_of(jcp.wei_tag, Owi8o, gOwi8o, Ohwi8o, gOhwi8o, Odhwi8o,
+                    gOdhwi8o))
+        && IMPLICATION(mimo, true
+                && one_of(jcp.src_tag, nCw8c, nChw8c, nCdhw8c)
+                && one_of(jcp.wei_tag, OIw8i8o, gOIw8i8o, OIhw8i8o, gOIhw8i8o,
+                    OIdhw8i8o, gOIdhw8i8o))
+        && one_of(jcp.dst_tag, nCw8c, nChw8c, nCdhw8c);
+    if (!args_ok) return status::unimplemented;
+
+    jcp.ur_h = 1; /* no code-unrolling by h so far */
+    jcp.ur_w = 3;
+
+    jcp.oc_block = simd_w;
+    jcp.nb_oc = jcp.oc / jcp.oc_block;
+
+    jcp.nb_oc_blocking = 4; /* the optimal value for the kernel */
+
+    // Intel AVX and Intel AVX2 kernels need 2 and 1 temporary YMMs, respectively
+    // Thus, we can only assign 14 or 15 YMMs for data storage
+    const int num_avail_regs = mayiuse(avx2) ? 15 : 14;
+    if (!mayiuse(avx2)) {
+        if ((jcp.nb_oc_blocking + 1) * jcp.ur_w > num_avail_regs) {
+            // current register assignment requires more YMMs than available
+            // adjust one of nb_oc_block, ur_w preserving to ur_w >= l_pad
+            if (jcp.ur_w > jcp.l_pad && jcp.ur_w > 1)
+                jcp.ur_w -= 1;
+            else
+                for (int b = 3; b > 1; b--)
+                    if (jcp.nb_oc % b == 0) {
+                        jcp.nb_oc_blocking = b;
+                        break;
+                    }
+        }
+    }
+
+    if (jcp.ow < jcp.ur_w) jcp.ur_w = jcp.ow;
+    jcp.ur_w_tail = jcp.ow % jcp.ur_w;
+
+    args_ok = true
+        && jcp.oc % simd_w == 0
+        && jcp.l_pad <= jcp.ur_w
+        && IMPLICATION(jcp.kw > 7, (jcp.t_pad == 0 && jcp.l_pad == 0)
+                || (jcp.stride_w == 1 && jcp.stride_h == 1))
+        && IMPLICATION(mimo, jcp.ic % simd_w == 0);
+    if (!args_ok) return status::unimplemented;
+
+    int r_pad_no_tail = nstl::max(0, (jcp.ow - jcp.ur_w_tail - 1) * jcp.stride_w
+        + (jcp.kw - 1) * (jcp.dilate_w + 1) - (jcp.iw + jcp.l_pad - 1));
+
+    if (r_pad_no_tail > jcp.ur_w * jcp.stride_w && jcp.ow / jcp.ur_w > 1) {
+        /* recalculate ur_w, nb_oc_blocking and ur_w_tail */
+        jcp.ur_w = nstl::min(r_pad_no_tail / jcp.stride_w + jcp.ur_w_tail,
+                nstl::min(jcp.ow, num_avail_regs / 2));
+        jcp.nb_oc_blocking = (num_avail_regs - jcp.ur_w) / jcp.ur_w;
+        jcp.ur_w_tail = jcp.ow % jcp.ur_w;
+        /* check again ... */
+        r_pad_no_tail = nstl::max(0, (jcp.ow - jcp.ur_w_tail - 1) * jcp.stride_w
+            + (jcp.kw - 1) * (jcp.dilate_w + 1) - (jcp.iw + jcp.l_pad - 1));
+        if (jcp.ur_w < nstl::max(jcp.l_pad, r_pad_no_tail))
+            return status::unimplemented;
+    }
+    assert(jcp.nb_oc_blocking > 0);
+    assert(jcp.ur_w * (jcp.nb_oc_blocking + 1) <= num_avail_regs);
+
+    jcp.ic_block = (jcp.ic % simd_w != 0) ? jcp.ic : simd_w;
+    jcp.nb_ic = jcp.ic / jcp.ic_block;
+
+    if (one_of(jcp.prop_kind, forward_training, forward_inference)) {
+        jcp.nb_ic_blocking = 12;
+        jcp.nb_ic_blocking_max = 16;
+    } else {
+        jcp.nb_ic_blocking = 1;
+        jcp.nb_ic_blocking_max = jcp.nb_ic_blocking;
+    }
+
+    return status::success;
+}
+
+void jit_avx2_conv_fwd_kernel_f32::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad, const jit_conv_conf_t &jcp) {
+    if (jcp.with_bias && jcp.oc != jcp.oc_without_padding)
+        scratchpad.book(key_conv_padded_bias, sizeof(float) * jcp.oc);
+}
+
+void jit_avx2_conv_bwd_data_kernel_f32::compute_loop(int ur_w, int l_overflow,
+        int r_overflow)
+{
+    int kw = jcp.kw;
+    int kh = jcp.kh;
+    int kd = jcp.kd;
+    int iw = jcp.iw;
+    int ih = jcp.ih;
+    int id = jcp.id;
+    int ow = jcp.ow;
+
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    int nb_ic_block = jcp.nb_ic_blocking;
+    int stride_w = jcp.stride_w;
+    int stride_h = jcp.stride_h;
+
+    Label kd_loop, skip_kd_loop;
+    Label oc_loop, skip_oc_loop;
+
+    for (int ii = 0; ii < nb_ic_block; ii++)
+        for (int jj = 0; jj < ur_w; jj++) {
+            uni_vpxor(Ymm(ur_w * ii + jj), Ymm(ur_w * ii + jj),
+                      Ymm(ur_w * ii + jj));
+        }
+
+    if (one_of(jcp.ndims, 3, 4)) {
+        cmp(reg_channel_work, 0);
+        jle(skip_oc_loop, T_NEAR);
+        xor_(reg_channel, reg_channel);
+
+        mov(aux_reg_ddst_oc_loop, reg_ddst);
+        mov(aux_reg_kernel_oc_loop, reg_kernel);
+
+        L(oc_loop);
+        mov(aux_reg_ddst, aux_reg_ddst_oc_loop);
+        mov(aux_reg_kernel, aux_reg_kernel_oc_loop);
+    }
+
+    if (jcp.ndims == 5) {
+        assert(jcp.nb_oc_blocking == 1);
+        push(oi_iter);
+
+        mov(reg_ki, ptr[this->param1 + GET_OFF(kd_padding)]);
+        mov(aux_reg_dst_d, reg_ddst);
+        mov(aux_reg_ker_d, ptr[this->param1 + GET_OFF(filt)]);
+
+        L(kd_loop);
+        mov(kj, ptr[this->param1 + GET_OFF(kh_padding)]);
+    } else {
+        mov(kj, reg_kh);
+    }
+
+    if (jcp.ndims == 5) {
+        mov(aux_reg_ddst, aux_reg_dst_d);
+        mov(aux_reg_kernel, aux_reg_ker_d);
+    }
+
+    Label kh_loop, skip_kh_loop;
+    cmp(kj, 0);
+    jle(skip_kh_loop, T_NEAR);
+    L(kh_loop); {
+        for (int ki = 0; ki < kw; ki++) {
+            int jj_start = get_iw_start(ki, l_overflow); // 0;
+            int jj_end = get_iw_end(ur_w, ki, r_overflow); // ur_w;
+            for (int ofm2 = 0; ofm2 < jcp.oc_block; ofm2++) {
+
+                for (int jj = jj_start ; jj < jj_end; jj += stride_w) {
+                    int aux_output_offset
+                      = (jj + jcp.l_pad - ki) / stride_w * jcp.oc_block + ofm2;
+                    vbroadcastss(Ymm(nb_ic_block * ur_w + jj / stride_w),
+                            ptr[aux_reg_ddst
+                            + sizeof(float) * aux_output_offset]);
+                }
+
+                for (int ii = 0; ii  < nb_ic_block; ii++) {
+                    int aux_kernel_offset
+                        = ii * kd * kh * kw * jcp.ic_block * jcp.oc_block
+                        + ki * jcp.ic_block * jcp.oc_block
+                        + ofm2 * jcp.ic_block;
+                    vmovups(ymm15,
+                            ptr[aux_reg_kernel
+                            + sizeof(float) * aux_kernel_offset]);
+                    for (int jj = jj_start; jj  < jj_end; jj += stride_w)
+                        vfmadd231ps(Ymm(ur_w * ii + jj),
+                                Ymm(nb_ic_block * ur_w + jj / stride_w), ymm15);
+                }
+            }
+        }
+        add(aux_reg_kernel, sizeof(float) * stride_h * kw  * oc_block
+                                          * ic_block);
+        sub(aux_reg_ddst, sizeof(float) * ow * oc_block);
+
+        dec(kj);
+        cmp(kj, 0);
+        jg(kh_loop, T_NEAR);
+    }
+    L(skip_kh_loop);
+
+    if (jcp.ndims == 5) {
+        sub(aux_reg_dst_d,
+                sizeof(float) * (jcp.dilate_d + 1) * jcp.oh * ow * ic_block);
+        add(aux_reg_ker_d,
+                sizeof(float) * jcp.kw * jcp.kh * oc_block * ic_block);
+
+        dec(reg_ki);
+        cmp(reg_ki, 0);
+        jg(kd_loop, T_NEAR);
+        L(skip_kd_loop);
+
+        pop(oi_iter);
+    }
+
+    if (one_of(jcp.ndims, 3, 4)) {
+        int ddst_oc_shift = sizeof(float) * jcp.od * jcp.oh * jcp.ow
+                          * jcp.oc_block;
+        int kernel_oc_shift = sizeof(float) * jcp.kd * jcp.kh * jcp.kw
+                          * jcp.ic * jcp.oc_block;
+
+        add(aux_reg_ddst_oc_loop, ddst_oc_shift);
+        add(aux_reg_kernel_oc_loop, kernel_oc_shift);
+
+        inc(reg_channel);
+        cmp(reg_channel, reg_channel_work);
+        jl(oc_loop, T_NEAR);
+
+        L(skip_oc_loop);
+        mov(reg_channel, ptr[param1 + GET_OFF(channel)]);
+    }
+
+    Label no_update_label;
+    cmp(reg_channel, 0);
+    je(no_update_label, T_NEAR);
+    for (int ii = 0; ii < nb_ic_block; ii++) {
+        for (int jj = 0; jj < ur_w; jj++) {
+            size_t offt =
+                sizeof(float) * ((size_t)ii * id * ih * iw + jj) * ic_block;
+            vmovups(Ymm(15),
+                    make_safe_addr(reg_dsrc, offt, reg_long_offt));
+            vaddps(Ymm(ur_w * ii + jj), Ymm(ur_w * ii + jj),
+                    Ymm(15));
+
+        }
+    }
+    L(no_update_label);
+
+    for (int ii = 0; ii < nb_ic_block; ii++)
+        for (int jj = 0; jj < ur_w; jj++) {
+            size_t offt =
+                sizeof(float) * ((size_t)ii * id * ih * iw + jj) * ic_block;
+            vmovups(make_safe_addr(reg_dsrc, offt, reg_long_offt),
+                    Ymm(ur_w * ii + jj));
+        }
+}
+
+void jit_avx2_conv_bwd_data_kernel_f32::generate() {
+    preamble();
+
+    mov(reg_dsrc, ptr[this->param1 + GET_OFF(src)]);
+    mov(reg_ddst, ptr[this->param1 + GET_OFF(dst)]);
+    mov(reg_kernel, ptr[this->param1 + GET_OFF(filt)]);
+    mov(reg_kh, ptr[this->param1 + GET_OFF(kh_padding)]);
+    mov(reg_channel, ptr[param1 + GET_OFF(channel)]);
+    mov(reg_channel_work, ptr[param1 + GET_OFF(ch_blocks)]);
+
+    int ddst_shift = sizeof(float) * (jcp.ur_w / jcp.stride_w) * jcp.ic_block;
+    int dsrc_shift = sizeof(float) * jcp.ur_w * jcp.oc_block;
+
+    int l_overflow = nstl::max(0, (jcp.kw - 1 - jcp.l_pad) / jcp.stride_w);
+    int r_overflow = nstl::max(0, (jcp.kw - 1
+                    - nstl::max(0, jcp.r_pad)) / jcp.stride_w);
+    int r_overflow1 = nstl::max(0, (jcp.kw - 1
+                    - nstl::max(0, jcp.r_pad) - jcp.ur_w_tail) / jcp.stride_w);
+
+    int n_oi = jcp.iw / jcp.ur_w;
+    if (r_overflow1 > 0)
+        n_oi--;
+
+    if (jcp.ur_w == jcp.iw) {
+        compute_loop(jcp.ur_w, l_overflow, r_overflow);
+    } else if (n_oi == 0) {
+        compute_loop(jcp.ur_w, l_overflow, r_overflow1);
+        add(reg_dsrc, dsrc_shift);
+        add(reg_ddst, ddst_shift);
+        if (jcp.ur_w_tail != 0)
+            compute_loop(jcp.ur_w_tail, 0, r_overflow);
+    } else {
+        xor_(oi_iter, oi_iter);
+        if (l_overflow > 0) {
+            compute_loop(jcp.ur_w, l_overflow, 0);
+            add(reg_dsrc, dsrc_shift);
+            add(reg_ddst, ddst_shift);
+            inc(oi_iter);
+        }
+
+        if ((l_overflow <= 0 && n_oi > 0) || (l_overflow >  0 && n_oi > 1)) {
+            Label ow_loop;
+            L(ow_loop); {
+                compute_loop(jcp.ur_w, 0, 0);
+                add(reg_dsrc, dsrc_shift);
+                add(reg_ddst, ddst_shift);
+                inc(oi_iter);
+                cmp(oi_iter, n_oi); jl(ow_loop, T_NEAR);
+            }
+        }
+
+        if (r_overflow1 > 0 ) {
+            compute_loop(jcp.ur_w, 0, r_overflow1);
+            add(reg_dsrc, dsrc_shift);
+            add(reg_ddst, ddst_shift);
+        }
+
+        if (jcp.ur_w_tail != 0)
+            compute_loop(jcp.ur_w_tail, 0, r_overflow);
+    }
+
+    this->postamble();
+}
+
+status_t jit_avx2_conv_bwd_data_kernel_f32::init_conf(jit_conv_conf_t &jcp,
+        const convolution_desc_t &cd, const memory_desc_wrapper &diff_src_d,
+        const memory_desc_wrapper &weights_d,
+        const memory_desc_wrapper &diff_dst_d)
+{
+    if (!mayiuse(avx2)) return status::unimplemented;
+
+    const bool with_groups = weights_d.ndims() == diff_src_d.ndims() + 1;
+
+    int ndims = diff_src_d.ndims();
+    jcp.ndims = ndims;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = diff_src_d.dims()[0];
+
+    jcp.oc = diff_dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = diff_src_d.dims()[1] / jcp.ngroups;
+
+    jcp.id = (ndims == 5) ? diff_src_d.dims()[2] : 1;
+    jcp.ih = (ndims == 3) ? 1 : diff_src_d.dims()[ndims-2];
+    jcp.iw = diff_src_d.dims()[ndims-1];
+    jcp.od = (ndims == 5) ? diff_dst_d.dims()[2] : 1;
+    jcp.oh = (ndims == 3) ? 1 : diff_dst_d.dims()[ndims-2];
+    jcp.ow = diff_dst_d.dims()[ndims-1];
+
+    jcp.kd = (ndims == 5) ? weights_d.dims()[with_groups + 2] : 1;
+    jcp.kh = (ndims == 3) ? 1 : weights_d.dims()[with_groups + ndims - 2];
+    jcp.kw = weights_d.dims()[with_groups + ndims - 1];
+
+    jcp.f_pad = (ndims == 5) ? cd.padding[0][0] : 0;
+    jcp.t_pad = (ndims == 3) ? 0 : cd.padding[0][ndims-4];
+    jcp.l_pad = cd.padding[0][ndims-3];
+
+    jcp.stride_d = (ndims == 5) ? cd.strides[0] : 1;
+    jcp.stride_h = (ndims == 3) ? 1 : cd.strides[ndims-4];
+    jcp.stride_w = cd.strides[ndims-3];
+
+    jcp.dilate_d = (ndims == 5) ? cd.dilates[0] : 0;
+    jcp.dilate_h = (ndims == 3) ? 0 : cd.dilates[ndims-4];
+    jcp.dilate_w = cd.dilates[ndims-3];
+
+    const int simd_w = 8;
+
+    /* derivatives */
+    jcp.idp = jcp.id + 2 * jcp.f_pad;
+    jcp.ihp = jcp.ih + 2 * jcp.t_pad;
+    jcp.iwp = jcp.iw + 2 * jcp.l_pad;
+    jcp.ohp = jcp.oh; /* do we really need */
+    jcp.owp = jcp.ow; /* padded output ??? */
+
+    bool ok_to_pad_channels = true
+        && jcp.ngroups == 1;
+
+    /* gemm-based convolution performs better in these cases */
+    if (jcp.ic < simd_w && jcp.kw > 3 && jcp.stride_w > 1)
+        return status::unimplemented;
+
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, simd_w);
+        jcp.ic = rnd_up(jcp.ic, simd_w);
+    }
+
+    jcp.ic_block = (jcp.ic % simd_w) ? 1 : simd_w;
+    jcp.nb_ic = jcp.ic / jcp.ic_block;
+
+    jcp.oc_block = simd_w;
+    if (jcp.oc % jcp.oc_block) return status::unimplemented;
+    jcp.nb_oc = jcp.oc / jcp.oc_block;
+
+    jcp.ur_h = 1; /* no code-unrolling by h so far */
+    jcp.nb_ic_blocking = 1;
+    jcp.nb_oc_blocking = 1;
+    jcp.ur_w = 1;
+
+    if(one_of(ndims, 3, 4) && jcp.ow < 40)
+        jcp.nb_oc_blocking = jcp.ow < 15 ? 4 : 2;
+
+    if (ndims == 3) {
+        jcp.src_tag = diff_src_d.matches_one_of_tag(nCw8c);
+        jcp.wei_tag = weights_d.matches_one_of_tag(OIw8i8o, gOIw8o8i);
+        jcp.dst_tag = diff_dst_d.matches_one_of_tag(nCw8c);
+    } else if (ndims == 4) {
+        jcp.src_tag = diff_src_d.matches_one_of_tag(nChw8c);
+        jcp.wei_tag = weights_d.matches_one_of_tag(OIhw8o8i, gOIhw8o8i);
+        jcp.dst_tag = diff_dst_d.matches_one_of_tag(nChw8c);
+    } else if (ndims == 5) {
+        jcp.src_tag = diff_src_d.matches_one_of_tag(nCdhw8c);
+        jcp.wei_tag = weights_d.matches_one_of_tag(OIdhw8o8i, gOIdhw8o8i);
+        jcp.dst_tag = diff_dst_d.matches_one_of_tag(nCdhw8c);
+    }
+
+    bool args_ok = true
+        && one_of(jcp.src_tag, nCw8c, nChw8c, nCdhw8c)
+        && one_of(jcp.wei_tag, gOIw8o8i, OIw8i8o, gOIhw8o8i, OIhw8o8i,
+                gOIdhw8o8i, OIdhw8o8i)
+        && one_of(jcp.dst_tag, nCw8c, nChw8c, nCdhw8c)
+        && jcp.stride_w == jcp.stride_h
+        && jcp.stride_d == 1
+        && jcp.dilate_d == 0
+        && jcp.dilate_h == 0
+        && jcp.dilate_w == 0
+        && jcp.ic % simd_w == 0
+        && jcp.oc % simd_w == 0
+        && jcp.od == (jcp.idp - jcp.kd) / jcp.stride_d + 1
+        && jcp.oh == (jcp.ihp - jcp.kh) / jcp.stride_h + 1
+        && jcp.ow == (jcp.iwp - jcp.kw) / jcp.stride_w + 1;
+    if (!args_ok) return status::unimplemented;
+    jcp.r_pad = (jcp.ow - 1) * jcp.stride_w + jcp.kw - jcp.iw - jcp.l_pad;
+    jcp.b_pad = (jcp.oh - 1) * jcp.stride_h + jcp.kh - jcp.ih - jcp.t_pad;
+    int l_overflow = nstl::max(0, (jcp.kw - 1 - jcp.l_pad) / jcp.stride_w);
+
+    const int max_regs = 15; /* Maximun number of registers available for
+                                result accumulation and delta dst data.
+                                One additional register is reserved for weights
+                                data. */
+
+    /* Find the best blocking with maximum number of fma instructions
+       per ur_w * nb_ic_blocking compute loops. Number of required registers
+       is num_regs = ur_w * nb_ic_blocking + ur_w / stride_w <= max_regs.
+       ur_w must be divisible by stride_w */
+    if (jcp.stride_w + 1 > max_regs)  /* Minimal possible registers
+                                         distribution exceeds max_regs */
+        return status::unimplemented;
+
+    int best_nfmas = 0;
+    for (int b = 1; b <= 4; b++)
+    {
+        if (jcp.nb_ic % b != 0)
+            continue;
+
+        for (int u = jcp.stride_w;
+             u * b + u / jcp.stride_w <= max_regs && u < jcp.iw + jcp.stride_w;
+             u += jcp.stride_w)
+        {
+            int ur_w = nstl::min(u, jcp.iw);
+            /* maximum 1 step with l_overflow so far */
+            if (l_overflow * jcp.stride_w > ur_w && ur_w != jcp.iw)
+                continue;
+            int nfmas = utils::div_up(ur_w, jcp.stride_w) * b;
+            if (nfmas > best_nfmas
+               || (nfmas == best_nfmas && jcp.ur_w < ur_w)) {
+                jcp.ur_w = ur_w;
+                jcp.nb_ic_blocking = b;
+                best_nfmas = nfmas;
+            }
+        }
+    }
+    if (best_nfmas == 0) /* can't find appropriate blocking */
+        return status::unimplemented;
+
+    jcp.ur_w_tail = jcp.iw % jcp.ur_w;
+
+    int r_overflow_no_tail = nstl::max(0, (jcp.kw - 1 - jcp.ur_w_tail
+                    - nstl::max(0, jcp.r_pad) - jcp.ur_w_tail) / jcp.stride_w);
+    /* maximum 1 ur_w block with r_overflow so far */
+    if (r_overflow_no_tail * jcp.stride_w > jcp.ur_w)
+        return status::unimplemented;
+
+    if ((jcp.iw > jcp.ur_w) && (jcp.ur_w % jcp.stride_w != 0))
+        return status::unimplemented;
+
+    return status::success;
+}
+
+void jit_avx2_conv_bwd_data_kernel_f32::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad, const jit_conv_conf_t &jcp) {
+    UNUSED(scratchpad);
+    UNUSED(jcp);
+}
+
+void jit_avx2_conv_bwd_weights_kernel_f32::generate() {
+    this->preamble();
+
+    mov(reg_input, ptr[this->param1 + GET_OFF(src)]);
+    mov(reg_output, ptr[this->param1 + GET_OFF(dst)]);
+    mov(reg_kernel, ptr[this->param1 + GET_OFF(filt)]);
+    compute_oh_loop_common();
+    this->postamble();
+}
+
+status_t jit_avx2_conv_bwd_weights_kernel_f32::init_conf(jit_conv_conf_t &jcp,
+        const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+        const memory_desc_wrapper &diff_weights_d,
+        const memory_desc_wrapper &diff_dst_d) {
+    if (!mayiuse(avx2)) return status::unimplemented;
+
+    const bool with_groups = diff_weights_d.ndims() == src_d.ndims() + 1;
+    int ndims = src_d.ndims();
+    jcp.ndims = ndims;
+
+    jcp.ngroups = with_groups ? diff_weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+
+    jcp.oc = diff_dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+
+    jcp.id = (ndims == 5) ? src_d.dims()[2] : 1;
+    jcp.ih = (ndims == 3) ? 1 : src_d.dims()[ndims-2];
+    jcp.iw = src_d.dims()[ndims-1];
+    jcp.od = (ndims == 5) ? diff_dst_d.dims()[2] : 1;
+    jcp.oh = (ndims == 3) ? 1 : diff_dst_d.dims()[ndims-2];
+    jcp.ow = diff_dst_d.dims()[ndims-1];
+
+    jcp.kd = (ndims == 5) ? diff_weights_d.dims()[with_groups + 2] : 1;
+    jcp.kh = (ndims == 3) ? 1 : diff_weights_d.dims()[with_groups + ndims-2];
+    jcp.kw = diff_weights_d.dims()[with_groups + ndims-1];
+
+    jcp.f_pad = (ndims == 5) ? cd.padding[0][0] : 0;
+    jcp.t_pad = (ndims == 3) ? 0 : cd.padding[0][ndims-4];
+    jcp.l_pad = cd.padding[0][ndims-3];
+
+    jcp.stride_d = (ndims == 5) ? cd.strides[0] : 1;
+    jcp.stride_h = (ndims == 3) ? 1 : cd.strides[ndims-4];
+    jcp.stride_w = cd.strides[ndims-3];
+
+    jcp.dilate_d = (ndims == 5) ? cd.dilates[0] : 0;
+    jcp.dilate_h = (ndims == 3) ? 0 : cd.dilates[ndims-4];
+    jcp.dilate_w = cd.dilates[ndims-3];
+
+    if (ndims == 3) {
+        jcp.src_tag = src_d.matches_one_of_tag(ncw, nwc, nCw8c);
+        jcp.wei_tag = diff_weights_d.matches_one_of_tag(
+                Owi8o, gOwi8o, OIw8i8o, gOIw8i8o);
+        jcp.dst_tag = diff_dst_d.matches_one_of_tag(nCw8c);
+    } else if (ndims == 4) {
+        jcp.src_tag = src_d.matches_one_of_tag(nchw, nhwc, nChw8c);
+        jcp.wei_tag = diff_weights_d.matches_one_of_tag(
+                Ohwi8o, gOhwi8o, OIhw8i8o, gOIhw8i8o);
+        jcp.dst_tag = diff_dst_d.matches_one_of_tag(nChw8c);
+    } else if (ndims == 5) {
+        jcp.src_tag = src_d.matches_one_of_tag(ncdhw, ndhwc, nCdhw8c);
+        jcp.wei_tag = diff_weights_d.matches_one_of_tag(
+                Odhwi8o, gOdhwi8o, OIdhw8i8o, gOIdhw8i8o);
+        jcp.dst_tag = diff_dst_d.matches_one_of_tag(nCdhw8c);
+    }
+    jcp.with_bias = cd.diff_bias_desc.format_kind != format_kind::undef;
+
+    const bool flat = jcp.ic == 3;
+    const bool mimo = !flat;
+
+    const int simd_w = 8;
+
+    jcp.b_pad = nstl::max(
+            0, (jcp.oh - 1) * jcp.stride_h + jcp.kh - jcp.ih - jcp.t_pad);
+    jcp.r_pad = nstl::max(
+            0, (jcp.ow - 1) * jcp.stride_w + jcp.kw - jcp.iw - jcp.l_pad);
+
+    int back_pad = nstl::max(0, (jcp.od - 1) * jcp.stride_d + jcp.kd - jcp.id
+        - jcp.f_pad);
+    if (ndims == 5)
+        if (jcp.f_pad != 0 || back_pad != 0)
+            return status::unimplemented;
+
+    const int max_h_pad = ((jcp.kh - 1) * (jcp.dilate_h + 1) + 1);
+    const int max_w_pad = ((jcp.kw - 1) * (jcp.dilate_w + 1) + 1);
+    const bool boundaries_ok = true
+        && jcp.t_pad < max_h_pad && jcp.b_pad < max_h_pad
+        && jcp.l_pad < max_w_pad && jcp.r_pad < max_w_pad;
+    if (!boundaries_ok)
+        return status::unimplemented;
+
+    bool ok_to_pad_channels = true
+        && jcp.ngroups == 1;
+
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, simd_w);
+        if (mimo)
+            jcp.ic = rnd_up(jcp.ic, simd_w);
+    }
+
+    bool args_ok = true
+        && IMPLICATION(flat, true
+                && one_of(jcp.src_tag, ncw, nwc, nchw, nhwc, ncdhw, ndhwc)
+                && one_of(jcp.wei_tag, Owi8o, gOwi8o, Ohwi8o, gOhwi8o, Odhwi8o,
+                    gOdhwi8o))
+        && IMPLICATION(mimo, true
+                && one_of(jcp.src_tag, nCw8c, nChw8c, nCdhw8c)
+                && one_of(jcp.wei_tag, OIw8i8o, gOIw8i8o, OIhw8i8o, gOIhw8i8o,
+                    OIdhw8i8o, gOIdhw8i8o))
+        && one_of(jcp.dst_tag, nCw8c, nChw8c, nCdhw8c)
+        && IMPLICATION(mimo, jcp.ic % simd_w == 0)
+        && jcp.oc % simd_w == 0
+        && jcp.kw < 14
+        && jcp.kh <= jcp.t_pad + jcp.ih /* [bwd_w:r1] */
+        && jcp.kh <= jcp.ih /* [bwd_w:r2] */
+        && jcp.kd <= jcp.f_pad + jcp.id
+        && jcp.kd <= jcp.id
+        && jcp.t_pad < jcp.kh /* XXX: must fix the kernel! */
+        && jcp.dilate_d == 0
+        && jcp.dilate_h == 0
+        && jcp.dilate_w == 0;
+    if (!args_ok) return status::unimplemented;
+
+    jcp.ic_block = (jcp.ic % simd_w != 0) ? jcp.ic : simd_w;
+    jcp.nb_ic = jcp.ic / jcp.ic_block;
+
+    jcp.oc_block = simd_w;
+    jcp.nb_oc = jcp.oc / jcp.oc_block;
+    jcp.nb_ic_blocking = jcp.nb_oc_blocking = 1;
+
+    return status::success;
+}
+
+void jit_avx2_conv_bwd_weights_kernel_f32::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad, const jit_conv_conf_t &jcp) {
+    if (jcp.with_bias && jcp.oc != jcp.oc_without_padding)
+        scratchpad.book(key_conv_padded_bias, sizeof(float) * jcp.oc);
+}
+
+inline void jit_avx2_conv_bwd_weights_kernel_f32::od_step_comeback_pointers()
+{
+    Label kd_comeback_loop;
+    mov(kj, jcp.kd); //FIXME (Anton): this works only if f_pad = back_pad = 0
+    L(kd_comeback_loop); {
+        const int inp_mult = one_of(jcp.src_tag, ncw, nchw, ncdhw)
+            ? 1 : jcp.ic_block;
+        sub(aux_reg_input, sizeof(float) * jcp.iw * jcp.ih * inp_mult);
+        sub(aux_reg_kernel, sizeof(float) * jcp.kw * jcp.kh * jcp.ic_block
+                * jcp.oc_block);
+        dec(kj);
+        cmp(kj, 0);
+        jg(kd_comeback_loop, T_NEAR);
+    }
+}
+
+inline void jit_avx2_conv_bwd_weights_kernel_f32::oh_step_comeback_pointers()
+{
+    mov(kj, reg_kh);
+    Label kh_comeback_loop;
+    L(kh_comeback_loop); {
+        const int inp_mult = one_of(jcp.src_tag, ncw, nchw, ncdhw)
+            ? 1 : jcp.ic_block;
+        sub(reg_input, sizeof(float) * jcp.iw * inp_mult);
+        sub(reg_kernel, sizeof(float) * jcp.kw * jcp.ic_block * jcp.oc_block);
+        dec(kj);
+        cmp(kj, 0);
+        jg(kh_comeback_loop, T_NEAR);
+    }
+}
+
+inline void jit_avx2_conv_bwd_weights_kernel_f32::compute_ic_block_step(
+        int ur_w, int pad_l, int pad_r, int ic_block_step, int input_offset,
+        int kernel_offset, int output_offset)
+{
+    const int kw = jcp.kw;
+    const int ic_block = jcp.ic_block;
+    const int oc_block = jcp.oc_block;
+    for (int i_kw = 0; i_kw < kw; i_kw++)
+        for (int i_ic = 0; i_ic < ic_block_step; i_ic++) {
+            size_t off
+                = sizeof(float) * (i_kw * ic_block + i_ic) * jcp.oc_block
+                + kernel_offset;
+            vmovups(Ymm(i_kw * ic_block_step + i_ic), yword[reg_kernel + off]);
+        }
+
+    for (int i_ur = 0; i_ur < ur_w; i_ur++) {
+        vmovups(Ymm(kw * ic_block_step + 0),
+                yword[reg_output
+                + sizeof(float) * i_ur * oc_block + output_offset]);
+
+        for (int i_kw = 0; i_kw < kw; i_kw++) {
+            int i_iw = i_ur * jcp.stride_w + i_kw;
+            if (i_iw - pad_l < 0
+                    || i_iw > (ur_w - 1) * jcp.stride_w + kw - 1 - pad_r)
+                continue;
+            for (int i_ic = 0; i_ic < ic_block_step; i_ic++) {
+                size_t i_off = (size_t)input_offset + sizeof(float)*(
+                    one_of(jcp.src_tag, ncw, nchw, ncdhw)
+                        ? (i_iw - pad_l) + i_ic
+                        * ((size_t)jcp.id * jcp.ih * jcp.iw)
+                        : (i_iw - pad_l) * ic_block + i_ic);
+                vbroadcastss(Ymm(kw * ic_block_step + 1),
+                        make_safe_addr(reg_input, i_off, reg_long_offt));
+                vfmadd231ps(Ymm(i_kw * ic_block_step + i_ic),
+                        Ymm(kw * ic_block_step + 0),
+                        Ymm(kw * ic_block_step + 1));
+            }
+        }
+    }
+
+    for (int i_kw = 0; i_kw < kw; i_kw++)
+        for (int i_ic = 0; i_ic < ic_block_step; i_ic++) {
+            size_t off
+                = sizeof(float) * (i_kw * ic_block + i_ic) * jcp.oc_block
+                + kernel_offset;
+            vmovups(yword[reg_kernel + off],
+                    Ymm(i_kw * ic_block_step + i_ic));
+        }
+}
+
+inline void jit_avx2_conv_bwd_weights_kernel_f32::compute_oh_step_disp()
+{
+    int ic_block_step;
+    if (one_of(jcp.src_tag, ncw, nchw, ncdhw)) {
+        ic_block_step = jcp.kw >= 5 ? 1 : jcp.ic_block;
+    } else {
+        ic_block_step = jcp.kw > 7 ? 1
+        : jcp.kw > 3 ? 2
+        : jcp.kw > 1 ? 4 : 8;
+    }
+
+    const int max_ur_w = jcp.ow > 56 ? 14 : 28;
+
+    if (jcp.ow <= max_ur_w)
+        compute_oh_step_unroll_ow(ic_block_step, max_ur_w);
+    else
+        compute_oh_step_common(ic_block_step, max_ur_w);
+
+    if (jcp.ndims == 5) {
+        od_step_comeback_pointers();
+        mov(reg_input, aux_reg_input);
+        mov(reg_kernel, aux_reg_kernel);
+    } else {
+        oh_step_comeback_pointers();
+    }
+}
+
+inline void jit_avx2_conv_bwd_weights_kernel_f32::compute_oh_step_unroll_ow(
+        int ic_block_step, int max_ur_w)
+{
+    UNUSED(max_ur_w);
+
+    const int ic_block = jcp.ic_block;
+    const int oc_block = jcp.oc_block;
+    int inp_mul = one_of(jcp.src_tag, ncw, nchw, ncdhw) ? 1 : jcp.ic_block;
+    Label kd_loop;
+
+    const int r_pad
+        = nstl::max(0,
+                (jcp.ow - 1) * jcp.stride_w + jcp.kw - jcp.iw - jcp.l_pad);
+
+    if (jcp.ndims == 5) {
+        mov(aux_reg_input, reg_input);
+        mov(aux_reg_kernel, reg_kernel);
+        mov(ki, jcp.kd);
+        L(kd_loop);
+        mov(reg_input, aux_reg_input);
+        mov(reg_kernel, aux_reg_kernel);
+    }
+
+    mov(kj, reg_kh);
+    Label kh_loop;
+    L(kh_loop); {
+        xor_(b_ic, b_ic);
+        Label ic_block_loop;
+        L(ic_block_loop); {
+            compute_ic_block_step(jcp.ow, jcp.l_pad, r_pad, ic_block_step, 0,
+                    0, 0);
+            size_t inp_icblk_stride = sizeof(float) * ic_block_step
+                * (one_of(jcp.src_tag, ncw, nchw, ncdhw)
+                ? jcp.id*jcp.ih*jcp.iw : 1);
+            safe_add(reg_input, inp_icblk_stride, reg_long_offt);
+            add(reg_kernel, sizeof(float) * ic_block_step * oc_block);
+            add(b_ic, ic_block_step);
+            cmp(b_ic, ic_block);
+            jl(ic_block_loop, T_NEAR);
+        }
+        if(one_of(jcp.src_tag, ncw, nchw, ncdhw)) {
+            size_t offt = sizeof(float) * jcp.id * jcp.ih * jcp.iw * ic_block;
+            safe_sub(reg_input, offt, reg_long_offt);
+            add(reg_input, sizeof(float) * jcp.iw);
+        } else {
+            add(reg_input, sizeof(float) * (jcp.iw - 1) * ic_block);
+        }
+        add(reg_kernel, sizeof(float) * (jcp.kw - 1) * ic_block * oc_block);
+        dec(kj);
+        cmp(kj, 0);
+        jg(kh_loop, T_NEAR);
+    }
+
+    if (jcp.ndims == 5) {
+        add(aux_reg_input, sizeof(float) * jcp.ih * jcp.iw * inp_mul);
+        add(aux_reg_kernel, sizeof(float) * jcp.kh * jcp.kw * ic_block
+            * oc_block);
+        dec(ki);
+        cmp(ki, 0);
+        jg(kd_loop, T_NEAR);
+    }
+
+}
+
+inline void jit_avx2_conv_bwd_weights_kernel_f32::compute_oh_step_common(
+        int ic_block_step, int max_ur_w)
+{
+    const int ic_block = jcp.ic_block;
+    const int oc_block = jcp.oc_block;
+    const int stride_w = jcp.stride_w;
+    int inp_mul = one_of(jcp.src_tag, ncw, nchw, ncdhw) ? 1 : jcp.ic_block;
+    Label kd_loop;
+
+    const int r_pad = jcp.r_pad;
+
+    int ur_w = nstl::min(jcp.ow, max_ur_w);
+    int ur_w_trips = jcp.ow / ur_w;
+    int ur_w_tail = jcp.ow % ur_w;
+    if ((ur_w_tail == 0 && r_pad != 0) || r_pad >= ur_w_tail) {
+        if (ur_w_trips > 1) {
+            ur_w_tail += ur_w;
+            ur_w_trips--;
+        } else {
+            ur_w_tail += (ur_w - ur_w / 2);
+            ur_w = ur_w / 2;
+        }
+    }
+    const int inp_mult = one_of(jcp.src_tag, ncw, nchw, ncdhw) ? 1 : ic_block;
+
+    int input_comeback = (ur_w_trips * ur_w * stride_w - jcp.l_pad) * inp_mult;
+    int output_comeback = ur_w_trips * ur_w * oc_block;
+
+    if (jcp.ndims == 5) {
+        mov(aux_reg_input, reg_input);
+        mov(aux_reg_kernel, reg_kernel);
+        mov(ki, jcp.kd);
+        L(kd_loop);
+        mov(reg_input, aux_reg_input);
+        mov(reg_kernel, aux_reg_kernel);
+    }
+
+    mov(kj, reg_kh);
+    Label kh_loop;
+    L(kh_loop); {
+        xor_(b_ic, b_ic);
+        Label ic_block_loop;
+        L(ic_block_loop); {
+            if (jcp.l_pad != 0) {
+                ur_w_trips--;
+                compute_ic_block_step(ur_w,
+                        jcp.l_pad, 0, ic_block_step, 0, 0, 0);
+                add(reg_input, sizeof(float)
+                        * (ur_w * stride_w - jcp.l_pad) * inp_mult);
+                add(reg_output, sizeof(float) * ur_w * oc_block);
+            }
+
+            if (ur_w_trips > 0) {
+                xor_(reg_ur_w_trips, reg_ur_w_trips);
+                Label ow_block_loop;
+                L(ow_block_loop); {
+                    compute_ic_block_step(ur_w, 0, 0, ic_block_step, 0, 0, 0);
+                    add(reg_input, sizeof(float) * ur_w * stride_w * inp_mult);
+                    add(reg_output, sizeof(float) * ur_w * oc_block);
+
+                    inc(reg_ur_w_trips);
+                    cmp(reg_ur_w_trips, ur_w_trips);
+                    jl(ow_block_loop, T_NEAR);
+                }
+            }
+
+            if (ur_w_tail > 0)
+                compute_ic_block_step(ur_w_tail,
+                        0, r_pad, ic_block_step, 0, 0, 0);
+
+            sub(reg_input, sizeof(float) * input_comeback);
+            sub(reg_output, sizeof(float) * output_comeback);
+
+            size_t inp_icblk_stride = sizeof(float) * ic_block_step
+                * (one_of(jcp.src_tag, ncw, nchw, ncdhw)
+                ? jcp.id*jcp.ih*jcp.iw : 1);
+            safe_add(reg_input, inp_icblk_stride, reg_long_offt);
+            add(reg_kernel, sizeof(float) * ic_block_step * oc_block);
+
+            add(b_ic, ic_block_step);
+            cmp(b_ic, jcp.ic_block);
+            jl(ic_block_loop, T_NEAR);
+        }
+        if (one_of(jcp.src_tag, ncw, nchw, ncdhw)) {
+            size_t offt = sizeof(float) * jcp.id * jcp.ih * jcp.iw * ic_block;
+            safe_sub(reg_input, offt, reg_long_offt);
+            add(reg_input, sizeof(float) * jcp.iw);
+        } else {
+            add(reg_input, sizeof(float) * (jcp.iw - 1) * ic_block);
+        }
+        add(reg_kernel, sizeof(float) * (jcp.kw - 1) * ic_block * oc_block);
+        dec(kj);
+        cmp(kj, 0);
+        jg(kh_loop, T_NEAR);
+    }
+
+    if (jcp.ndims == 5) {
+        add(aux_reg_input, sizeof(float) * jcp.ih * jcp.iw * inp_mul);
+        add(aux_reg_kernel, sizeof(float) * jcp.kh * jcp.kw * ic_block
+            * oc_block);
+        dec(ki);
+        cmp(ki, 0);
+        jg(kd_loop, T_NEAR);
+    }
+
+}
+
+inline void jit_avx2_conv_bwd_weights_kernel_f32::compute_oh_loop_common()
+{
+    const int icoc_block = jcp.ic_block * jcp.oc_block;
+    const int t_pad = jcp.t_pad;
+    const int stride_h = jcp.stride_h;
+    const int inp_mult = one_of(jcp.src_tag, ncw, nchw, ncdhw)
+        ? 1 : jcp.ic_block;
+    int b_pad = jcp.b_pad;
+
+    Label oh_tpad_loop, oh_loop, oh_loop_end;
+
+    mov(reg_kh, jcp.kh);
+    xor_(reg_ih_count, reg_ih_count);
+    xor_(reg_oj, reg_oj);
+    if (t_pad > 0) {
+        assert(jcp.kh <= t_pad + jcp.ih); /* [bwd_w:r1] */
+        mov(reg_kh, jcp.kh <= t_pad + jcp.ih ? jcp.kh - t_pad : jcp.ih);
+        add(reg_kernel, sizeof(float) * t_pad * jcp.kw * icoc_block);
+
+        L(oh_tpad_loop); {
+            compute_oh_step_disp();
+            add(reg_output, sizeof(float) * jcp.ow * jcp.oc_block);
+            sub(reg_kernel, sizeof(float) * stride_h * jcp.kw * icoc_block);
+
+            inc(reg_oj);
+            add(reg_ih_count, stride_h);
+            add(reg_kh, stride_h);
+
+            /* the overlap between input and kernel may not reach kernel size.
+             * so far we do not support that (until we put constant here) */
+            const int final_inp_ker_overlap = jcp.kh; /* [bwd_w:r2] */
+            cmp(reg_kh, final_inp_ker_overlap);
+            jl(oh_tpad_loop, T_NEAR);
+        }
+
+        if (t_pad % stride_h != 0) {
+            int inp_corr = stride_h - t_pad % stride_h;
+            add(reg_kernel, sizeof(float) * inp_corr * jcp.kw * icoc_block);
+            add(reg_input, sizeof(float) * inp_corr * jcp.iw * inp_mult);
+        }
+    }
+    cmp(reg_ih_count, jcp.ih + t_pad - jcp.kh + 1);
+    jge(oh_loop_end, T_NEAR);
+    cmp(reg_oj, jcp.oh);
+    jge(oh_loop, T_NEAR);
+
+    mov(reg_kh, jcp.kh);
+    L(oh_loop); {
+        compute_oh_step_disp();
+        add(reg_input, sizeof(float) * stride_h * jcp.iw * inp_mult);
+        add(reg_output, sizeof(float) * jcp.ow * jcp.oc_block);
+
+        inc(reg_oj);
+        add(reg_ih_count, stride_h);
+
+        cmp(reg_ih_count, jcp.ih + t_pad - jcp.kh + 1);
+        jge(oh_loop_end, T_NEAR);
+
+        cmp(reg_oj, jcp.oh);
+        jl(oh_loop, T_NEAR);
+    }
+    L(oh_loop_end);
+    if (b_pad > 0) {
+        Label oh_bpad_loop, oh_bpad_loop_end;
+        cmp(reg_oj, jcp.oh);
+        jge(oh_bpad_loop_end, T_NEAR);
+
+        mov(reg_kh, jcp.ih + t_pad);
+        sub(reg_kh, reg_ih_count);
+        L(oh_bpad_loop); {
+            compute_oh_step_disp();
+            add(reg_input, sizeof(float) * stride_h * jcp.iw * inp_mult);
+            add(reg_output, sizeof(float) * jcp.ow * jcp.oc_block);
+
+            sub(reg_kh, stride_h);
+            cmp(reg_kh, 0);
+            jle(oh_bpad_loop_end, T_NEAR);
+
+            inc(reg_oj);
+            cmp(reg_oj, jcp.oh);
+            jl(oh_bpad_loop, T_NEAR);
+        }
+        L(oh_bpad_loop_end);
+    }
+}
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.hpp
new file mode 100644
index 0000000000..412c50c9ee
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_conv_kernel_f32.hpp
@@ -0,0 +1,225 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_AVX2_CONV_KERNEL_F32_HPP
+#define JIT_AVX2_CONV_KERNEL_F32_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+
+#include "cpu_memory.hpp"
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+#include "jit_uni_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_avx2_conv_fwd_kernel_f32: public jit_generator {
+    jit_avx2_conv_fwd_kernel_f32(jit_conv_conf_t ajcp,
+            const primitive_attr_t &attr)
+        : jcp(ajcp), attr_(attr), eltwise_injector_(nullptr)
+    {
+        if (jcp.with_eltwise)
+            eltwise_injector_ = new jit_uni_eltwise_injector_f32<avx2>(this,
+                    jcp.eltwise);
+
+        this->generate();
+        jit_ker = (void (*)(jit_conv_call_s *))this->getCode();
+    }
+
+    ~jit_avx2_conv_fwd_kernel_f32() {
+        delete eltwise_injector_;
+    }
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx2_conv_fwd_kernel_f32)
+
+    static bool post_ops_ok(jit_conv_conf_t &jcp,
+            const primitive_attr_t &attr);
+    static status_t init_conf(jit_conv_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &dst_d,
+            const primitive_attr_t &attr);
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_conv_conf_t &jcp);
+
+    jit_conv_conf_t jcp;
+    const primitive_attr_t &attr_;
+    void (*jit_ker)(jit_conv_call_s *);
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    reg64_t reg_input = rax;
+    reg64_t aux_reg_input = r8;
+    reg64_t reg_kernel = rdx;
+    reg64_t aux_reg_kernel = r9;
+    reg64_t reg_output = rsi;
+    reg64_t reg_bias = rbx;
+
+    reg64_t aux_reg_inp_d = r11;
+    reg64_t aux_reg_ker_d = abi_not_param1;
+
+    reg64_t reg_ki = rsi;
+    reg64_t kj = r10;
+    reg64_t oi_iter = r11;
+    reg64_t ki_iter = r12;
+    reg64_t reg_kh = abi_not_param1;
+    reg64_t reg_oc_blocks = r14;
+    reg64_t imm_addr64 = r15;
+    reg64_t reg_long_offt = r15;
+    Xbyak::Reg32 reg_ci_flag = r13d;
+
+    Xbyak::Ymm ytmp = Xbyak::Ymm(14);
+
+    jit_uni_eltwise_injector_f32<avx2> *eltwise_injector_;
+
+    inline void oh_step_unroll_kw(int ur_w, int pad_l, int pad_r,
+            int oc_blocks);
+    inline void oh_step_nopad(int ur_w, int pad_l, int pad_r,
+            char pad_label, int oc_blocks, char oc_blocks_label);
+    inline void width_blk_step(int ur_w, int pad_l, int pad_r,
+            char pad_label, int oc_blocks, char oc_blocks_label);
+    inline void solve_common(int oc_blocks, char oc_blocks_label);
+
+    void generate();
+};
+
+struct jit_avx2_conv_bwd_data_kernel_f32: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx2_conv_bwd_data_kernel_f32)
+
+    jit_avx2_conv_bwd_data_kernel_f32(jit_conv_conf_t ajcp): jcp(ajcp)
+    {
+        this->generate();
+        jit_ker = (void (*)(jit_conv_call_s *))this->getCode();
+    }
+
+    static status_t init_conf(jit_conv_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &diff_src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &diff_dst_d);
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_conv_conf_t &jcp);
+
+    jit_conv_conf_t jcp;
+    void (*jit_ker)(jit_conv_call_s *);
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+
+    reg64_t reg_ddst       = rax;
+    reg64_t aux_reg_ddst   = r8;
+    reg64_t reg_kernel     = rdx;
+    reg64_t aux_reg_kernel = r10;
+    reg64_t reg_dsrc       = rsi;
+    reg64_t aux_reg_ddst_oc_loop  = rbx; // used in ndims < 5 case only
+    reg64_t aux_reg_kernel_oc_loop = abi_not_param1; /* used in ndims < 5
+                                                        case only */
+
+    reg64_t aux_reg_dst_d = r12; // used in ndims == 5 case only
+    reg64_t aux_reg_ker_d = r14; // used in ndims == 5 case only
+
+    reg64_t reg_ki  = abi_not_param1; // used in ndims == 5 case only
+    reg64_t kj      = r11;
+    reg64_t oi_iter = r12;
+    reg64_t reg_kh  = r14;
+    reg64_t reg_channel = r13;  // used in ndims < 5 case only
+    reg64_t reg_channel_work = r9;  // used in ndims < 5 case only
+    reg64_t reg_long_offt = r15;
+
+    inline void compute_loop(int ur_w, int l_overflow, int r_overflow);
+
+    void generate();
+
+    inline int get_iw_start(int ki, int l_overflow)
+    {
+        int res = (jcp.iw - 1 + jcp.r_pad) % jcp.stride_w
+                + l_overflow * jcp.stride_w
+                - (jcp.kw - 1 - ki) * (jcp.dilate_w + 1);
+        while (res < 0)
+            res += jcp.stride_w;
+
+        return res;
+    }
+
+    inline int get_iw_end(int ur_w, int ki, int r_overflow)
+    {
+        if (utils::one_of(ur_w, jcp.iw, jcp.ur_w_tail))
+            ur_w += nstl::min(0, jcp.r_pad); // remove negative padding
+        int res = (ur_w - 1 + jcp.l_pad) % jcp.stride_w
+                + r_overflow * jcp.stride_w - ki * (jcp.dilate_w + 1);
+        while (res < 0)
+            res += jcp.stride_w;
+
+        return ur_w - res;
+    }
+};
+
+struct jit_avx2_conv_bwd_weights_kernel_f32: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx2_conv_bwd_weights_kernel_f32)
+
+    jit_avx2_conv_bwd_weights_kernel_f32(jit_conv_conf_t ajcp): jcp(ajcp)
+    {
+        this->generate();
+        jit_ker = (void (*)(jit_conv_call_s *))this->getCode();
+    }
+
+    static status_t init_conf(jit_conv_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &diff_weights_d,
+            const memory_desc_wrapper &diff_dst_d);
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_conv_conf_t &jcp);
+
+    jit_conv_conf_t jcp;
+    void (*jit_ker)(jit_conv_call_s *);
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    reg64_t reg_input = rax;
+    reg64_t reg_kernel = rdx;
+    reg64_t reg_output = rsi;
+    reg64_t b_ic = abi_not_param1;
+    reg64_t kj = r8;
+    reg64_t reg_kh = r9;
+    reg64_t reg_ur_w_trips = r10;
+    reg64_t reg_tmp = r11;
+    reg64_t reg_oj = r15;
+    reg64_t reg_ih_count = rbx;
+    reg64_t aux_reg_input = r12;
+    reg64_t aux_reg_kernel = r13;
+    reg64_t ki = r14;
+    reg64_t reg_long_offt = r11;
+
+    inline void od_step_comeback_pointers();
+    inline void oh_step_comeback_pointers();
+    inline void compute_ic_block_step(int ur_w, int pad_l, int pad_r,
+            int ic_block_step, int input_offset, int kernel_offset,
+            int output_offset);
+    inline void compute_oh_step_disp();
+    inline void compute_oh_step_unroll_ow(int ic_block_step, int max_ur_w);
+    inline void compute_oh_step_common(int ic_block_step, int max_ur_w);
+    inline void compute_oh_loop_common();
+
+    void generate();
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_convolution.cpp
new file mode 100644
index 0000000000..13f61e84fe
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_convolution.cpp
@@ -0,0 +1,410 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_avx2_convolution.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+
+#define src_blk_off(f, n, c, d, h, w) \
+    (pd()->ndims() == 3) \
+    ? (f).blk_off(n, c, w) \
+    : (pd()->ndims() == 4) \
+    ? (f).blk_off(n, c, h, w) \
+    : (f).blk_off(n, c, d, h, w)
+
+#define wht_blk_off_(f, g, ...) \
+    pd()->with_groups() ? (f).blk_off(g, __VA_ARGS__) : (f).blk_off(__VA_ARGS__)
+#define wht_blk_off(f, g, oc, ic, kd, kh, kw) \
+    (pd()->ndims() == 3) \
+    ? wht_blk_off_(f, g, oc, ic, kw) \
+    : (pd()->ndims() == 4) \
+    ? wht_blk_off_(f, g, oc, ic, kh, kw) \
+    : wht_blk_off_(f, g, oc, ic, kd, kh, kw)
+
+void jit_avx2_convolution_fwd_t::execute_forward(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const data_t *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    const auto &jcp = kernel_->jcp;
+
+    int ocb_work = div_up(jcp.nb_oc, jcp.nb_oc_blocking);
+    const size_t work_amount = jcp.mb * jcp.ngroups * ocb_work * jcp.od
+        * jcp.oh;
+
+    auto ker = [&](const int ithr, const int nthr) {
+        size_t start{0}, end{0};
+        balance211(work_amount, nthr, ithr, start, end);
+
+        int icbb = 0;
+        while (icbb < jcp.nb_ic) {
+            int icb_step = jcp.nb_ic_blocking;
+            int icb_step_rem = jcp.nb_ic - icbb;
+            if (icb_step_rem < jcp.nb_ic_blocking_max)
+                icb_step = icb_step_rem;
+
+            size_t n{0}, g{0}, ocbb{0}, oh{0}, od{0};
+            nd_iterator_init(start, n, jcp.mb, g, jcp.ngroups, ocbb, ocb_work,
+                             od, jcp.od, oh, jcp.oh);
+            for (size_t iwork = start; iwork < end; ++iwork) {
+                int ocb = ocbb * jcp.nb_oc_blocking;
+                int ocb_num = jcp.nb_oc_blocking;
+
+                for (int icb = icbb; icb < icbb + icb_step; ++icb) {
+                    auto par_conv = jit_conv_call_s();
+
+                    const int ij = oh * jcp.stride_h;
+                    const int i_t_overflow = nstl::max(0, jcp.t_pad - ij);
+                    const int i_b_overflow = nstl::max(jcp.ih, ij
+                        + (jcp.kh-1) * (jcp.dilate_h+1) - jcp.t_pad+1) - jcp.ih;
+
+                    const int dj = od * jcp.stride_d;
+                    const int d_t_overflow = nstl::max(0, jcp.f_pad - dj);
+                    const int d_b_overflow = nstl::max(jcp.id, dj
+                        + (jcp.kd-1) * (jcp.dilate_d+1) - jcp.f_pad+1) - jcp.id;
+
+                    const size_t _oc = g * jcp.nb_oc + ocb;
+                    const size_t _ic = g * jcp.nb_ic * jcp.nonblk_group_off + icb;
+
+                    const int ih = nstl::max(ij - jcp.t_pad
+                        + div_up(i_t_overflow,
+                                 (jcp.dilate_h+1)) * (jcp.dilate_h + 1), 0);
+
+                    const int id = nstl::max(dj - jcp.f_pad
+                        + div_up(d_t_overflow,
+                                 (jcp.dilate_d+1)) * (jcp.dilate_d + 1), 0);
+
+                    par_conv.src = &src[src_blk_off(src_d, n,
+                        jcp.ic == 3 ? 0 : _ic, id, ih, 0)];
+
+                    par_conv.dst = &dst[src_blk_off(dst_d, n, _oc, od, oh, 0)];
+
+                    const int wh = div_up(i_t_overflow, (jcp.dilate_h + 1));
+                    const int wd = div_up(d_t_overflow, (jcp.dilate_d + 1));
+                    par_conv.filt = &weights[wht_blk_off(weights_d, g, ocb,
+                            jcp.ic == 3 ? 0 : icb, wd, wh, 0)];
+
+                    if (icb == 0) {
+                        if (bias)
+                            par_conv.bias =
+                                    &bias[bias_d.blk_off(_oc * jcp.oc_block)];
+                        par_conv.flags |= FLAG_IC_FIRST;
+                    }
+
+                    if (jcp.with_eltwise && icb + 1 == jcp.nb_ic) {
+                        par_conv.flags |= FLAG_IC_LAST;
+                    }
+
+                    par_conv.oc_blocks =
+                            nstl::min(ocb + ocb_num, jcp.nb_oc) - ocb;
+
+                    par_conv.kw_padding = 0;
+                    const int kh_padding = jcp.kh
+                        - div_up(i_t_overflow, (jcp.dilate_h + 1))
+                        - div_up(i_b_overflow, (jcp.dilate_h + 1));
+                    par_conv.kh_padding = nstl::max(0, kh_padding);
+
+                    const int kd_padding = jcp.kd
+                        - div_up(d_t_overflow, (jcp.dilate_d + 1))
+                        - div_up(d_b_overflow, (jcp.dilate_d + 1));
+                    par_conv.kd_padding = nstl::max(0, kd_padding);
+
+                    kernel_->jit_ker(&par_conv);
+                }
+                nd_iterator_step(n, jcp.mb, g, jcp.ngroups, ocbb, ocb_work,
+                                od, jcp.od, oh, jcp.oh);
+            }
+            icbb += icb_step;
+        }
+    };
+
+    if (pd()->wants_padded_bias()) {
+        auto padded_bias = scratchpad(ctx).get<data_t>(key_conv_padded_bias);
+        utils::array_copy(padded_bias, bias, jcp.oc_without_padding);
+        utils::array_set(padded_bias + jcp.oc_without_padding, 0.f,
+                jcp.oc - jcp.oc_without_padding);
+        bias = padded_bias;
+    }
+
+    parallel(0, ker);
+
+    if (pd()->wants_zero_pad_dst())
+        ctx.memory(MKLDNN_ARG_DST)->zero_pad();
+}
+
+void jit_avx2_convolution_bwd_data_t::execute_backward_data(
+        const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+
+    int icb_work = jcp.nb_ic / jcp.nb_ic_blocking;
+    int ih_block_size = jcp.ih;
+    int num_ih_blocks = utils::div_up(jcp.ih, ih_block_size);
+    size_t work_amount = jcp.mb * jcp.ngroups * icb_work * num_ih_blocks;
+    if (work_amount < (size_t)2 * mkldnn_get_max_threads()) {
+        ih_block_size = 1;
+        num_ih_blocks = utils::div_up(jcp.ih, ih_block_size);
+        work_amount *= num_ih_blocks;
+    }
+
+    auto ker = [&](const int ithr, const int nthr) {
+        size_t start{0}, end{0};
+        balance211(work_amount, nthr, ithr, start, end);
+
+        size_t n{0}, g{0}, icbb{0}, ihb{0};
+        nd_iterator_init(start, n, jcp.mb, g, jcp.ngroups, icbb, icb_work,
+                         ihb, num_ih_blocks);
+        for (size_t iwork = start; iwork < end; ++iwork) {
+            for (int oc = 0; oc < jcp.nb_oc; oc += jcp.nb_oc_blocking)
+            for (int id = 0; id < jcp.id; ++id) {
+                auto par_conv = jit_conv_call_s();
+
+                const int idp = jcp.id + 2 * jcp.f_pad;
+                const int d_t_overflow = nstl::max(0,
+                        jcp.kd - 1 - id - jcp.f_pad);
+                const int back_pad = idp - jcp.id - jcp.f_pad;
+                const int d_b_overflow = nstl::max(0,
+                        jcp.kd - 1 - (jcp.id - 1 - id) - back_pad);
+                const int od = id + jcp.f_pad - d_b_overflow;
+
+                int ih_start = ihb * ih_block_size;
+                int ih_end = nstl::min(jcp.ih, ih_start + ih_block_size);
+                for (int ih = ih_start; ih < ih_end; ++ih) {
+
+                    const int i_t_overflow = nstl::max(0, (jcp.kh - 1
+                                        - ih - jcp.t_pad) / jcp.stride_h);
+                    const int i_b_overflow = nstl::max(0, (jcp.kh - jcp.ih
+                                        + ih - jcp.b_pad) / jcp.stride_h);
+                    int overflow_kh_hi = jcp.kh - 1 - abs((jcp.ih - 1
+                                + jcp.b_pad - ih) % jcp.stride_h);
+                    int overflow_kh_lo = (ih + jcp.t_pad) % jcp.stride_h;
+
+                    par_conv.kd_padding = jcp.kd - d_t_overflow - d_b_overflow;
+                    par_conv.kh_padding = (overflow_kh_hi - overflow_kh_lo)
+                              / jcp.stride_h + 1 - i_t_overflow - i_b_overflow;
+                    par_conv.kw_padding = 0;
+
+                    const int k_lo = overflow_kh_lo
+                                   + i_b_overflow * jcp.stride_h;
+                    const int oh = (ih + jcp.t_pad - k_lo) / jcp.stride_h;
+
+                    par_conv.src = &diff_src[src_blk_off(diff_src_d, n,
+                        /*jcp.ic == 3 ? 0 :*/
+                        g * jcp.nb_ic + jcp.nb_ic_blocking * icbb, id, ih, 0)];
+                    par_conv.dst = &diff_dst[src_blk_off(diff_dst_d,
+                            n, g * jcp.nb_oc + oc, od, oh, 0)];
+                    par_conv.filt = &weights[wht_blk_off(weights_d, g, oc,
+                                jcp.ic == 3 ? 0 : jcp.nb_ic_blocking * icbb,
+                                d_b_overflow, k_lo, 0)];
+
+                    par_conv.src_prf = nullptr;
+                    par_conv.dst_prf = nullptr;
+                    par_conv.filt_prf = nullptr;
+                    par_conv.channel = oc;
+                    par_conv.ch_blocks = nstl::min(jcp.nb_oc - oc,
+                                       jcp.nb_oc_blocking);
+
+                    kernel_->jit_ker(&par_conv);
+                }
+            }
+            nd_iterator_step(n, jcp.mb, g, jcp.ngroups, icbb, icb_work, ihb,
+                             num_ih_blocks);
+        }
+    };
+
+    parallel(0, ker);
+}
+
+void jit_avx2_convolution_bwd_weights_t::execute_backward_weights(
+        const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_weights = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
+    auto diff_bias_in = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_BIAS);
+
+    auto scratchpad = this->scratchpad(ctx);
+
+    data_t *diff_bias = pd()->wants_padded_bias()
+        ? scratchpad.get<data_t>(key_conv_padded_bias) : diff_bias_in;
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+
+    auto reducer_bia_scratchpad = memory_tracking::grantor_t(scratchpad,
+            prefix_reducer_bia);
+    auto rb = this->reducer_bias_;
+    rb->init(reducer_bia_scratchpad);
+
+    auto reducer_wei_scratchpad = memory_tracking::grantor_t(scratchpad,
+            prefix_reducer_wei);
+    auto rw = this->reducer_weights_;
+    rw->init(reducer_wei_scratchpad);
+
+    auto ker = [&](int ithr, int nthr) {
+        assert(nthr == rw->balancer().nthr_);
+
+        const int w_job_start = rw->balancer().ithr_job_off(ithr);
+        const int w_njobs = rw->balancer().ithr_njobs(ithr);
+
+        if (w_njobs == 0) return;
+
+        /* reduction dimension */
+        int img_od_start{0}, img_od_end{0}, img{0}, od_s{0};
+        balance211(jcp.mb * jcp.od, rw->balancer().nthr_per_group_,
+                rw->balancer().id_in_group(ithr), img_od_start, img_od_end);
+
+        int img_start = img_od_start, img_end = img_od_end;
+        nd_iterator_init(img_start, img, jcp.mb, od_s, jcp.od);
+        const int img_first = img;
+
+        /* jobs */
+        int g_start{0}, ocb_start{0}, icb_start{0};
+        nd_iterator_init(w_job_start, g_start, jcp.ngroups, ocb_start,
+                jcp.nb_oc, icb_start, jcp.nb_ic);
+
+        while (img_start < img_end) {
+            int g = g_start, ocb = ocb_start, icb = icb_start;
+
+            const int work_rem = img_end - img_start;
+            const int od_e = od_s + work_rem > jcp.od ? jcp.od : od_s + work_rem;
+            const int id_s = od_s * jcp.stride_d;
+            const int idp = jcp.id + jcp.f_pad + jcp.back_pad;
+
+            if (id_s < idp - jcp.back_pad - jcp.kd + 1)
+            for (int w_job_loc = 0; w_job_loc < w_njobs; ++w_job_loc) {
+                const size_t _oc = g * jcp.nb_oc + ocb;
+                const size_t _ic = g * jcp.nb_ic + icb;
+
+                /* TODO: put dw <-- 0 in kernel */
+                if (img == img_first)
+                    array_set(rw->get_local_ptr(ithr, diff_weights,
+                                reducer_wei_scratchpad) +
+                            w_job_loc * rw->balancer().job_size_, 0,
+                            rw->balancer().job_size_);
+
+                for (int od = od_s; od < od_e; ++od) {
+                    const int id = od * jcp.stride_d;
+                    if (id >= jcp.id - jcp.back_pad - jcp.kd + 1) break;
+
+                    auto par_conv = jit_conv_call_s();
+                    par_conv.src = &src[src_blk_off(src_d, img, _ic, id, 0, 0)];
+                    par_conv.dst =
+                        &diff_dst[src_blk_off(diff_dst_d, img, _oc, od, 0, 0)];
+                    par_conv.filt = rw->get_local_ptr(ithr, diff_weights,
+                            reducer_wei_scratchpad) +
+                        w_job_loc * rw->balancer().job_size_;
+
+                    kernel_->jit_ker(&par_conv);
+                }
+                nd_iterator_step(g, jcp.ngroups, ocb, jcp.nb_oc, icb,
+                        jcp.nb_ic);
+            }
+            nd_iterator_jump(img_start, img_end, img, jcp.mb, od_s, jcp.od);
+        }
+        rw->reduce(ithr, diff_weights, reducer_wei_scratchpad);
+    };
+
+    auto ker_bias = [&](int ithr, int nthr) {
+        assert(nthr == rb->balancer().nthr_);
+
+        const int b_job_start = rb->balancer().ithr_job_off(ithr);
+        const int b_njobs = rb->balancer().ithr_njobs(ithr);
+
+        if (b_njobs == 0) return;
+
+        /* reduction dimension */
+        int img_start{0}, img_end{0};
+        balance211(jcp.mb, rb->balancer().nthr_per_group_,
+                rb->balancer().id_in_group(ithr), img_start, img_end);
+
+        /* jobs */
+        int g_start{0}, ocb_start{0};
+        nd_iterator_init(b_job_start, g_start, jcp.ngroups, ocb_start,
+                jcp.nb_oc);
+
+        for (int img = img_start; img < img_end; ++img) {
+            int g = g_start, ocb = ocb_start;
+            for (int b_job_loc = 0; b_job_loc < b_njobs; ++b_job_loc) {
+                const size_t _oc = g * jcp.nb_oc + ocb;
+
+                const data_t *d_dst = &diff_dst[diff_dst_d.blk_off(img, _oc)];
+                data_t *d_bias = rb->get_local_ptr(ithr, diff_bias,
+                        reducer_bia_scratchpad) +
+                    b_job_loc * rb->balancer().job_size_;
+
+                if (img == img_start)
+                    for (int o = 0; o < 8; ++o)
+                        d_bias[o] = 0.;
+
+                for (int dhw = 0; dhw < jcp.od * jcp.oh * jcp.ow; ++dhw) {
+                    PRAGMA_OMP_SIMD()
+                    for (int o = 0; o < 8; ++o)
+                        d_bias[o] += d_dst[o];
+                    d_dst += 8;
+                }
+
+                nd_iterator_step(g, jcp.ngroups, ocb, jcp.nb_oc);
+            }
+        }
+        rb->reduce(ithr, diff_bias, reducer_bia_scratchpad);
+    };
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        ker(ithr, nthr);
+        if (pd()->with_bias())
+            ker_bias(ithr, nthr);
+    });
+
+    /* TODO: put this in ker_bias */
+    if (pd()->wants_padded_bias()) {
+        assert(jcp.ngroups == 1);
+        for (int oc = 0; oc < jcp.oc_without_padding; ++oc)
+            diff_bias_in[oc] = diff_bias[oc];
+    }
+}
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_convolution.hpp
new file mode 100644
index 0000000000..bb65bce79c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx2_convolution.hpp
@@ -0,0 +1,302 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX2_CONVOLUTION_HPP
+#define CPU_JIT_AVX2_CONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_reducer.hpp"
+
+#include "jit_avx2_conv_kernel_f32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_avx2_convolution_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", avx2, ""),
+                jit_avx2_convolution_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_avx2_conv_fwd_kernel_f32::init_conf(jcp_,
+                    *desc(), src_md(), weights_md(), dst_md(), *attr());
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx2_conv_fwd_kernel_f32::init_scratchpad(scratchpad, jcp_);
+
+            return status::success;
+        }
+
+        jit_conv_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            const bool flat = IC() < 8;
+            auto src_tag = flat
+                ? utils::pick(ndims() - 3, ncw, nchw, ncdhw)
+                : utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
+            auto dst_tag =
+                utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
+            auto wei_tag = with_groups()
+                ? utils::pick(2 * ndims() - 6 + flat, gOIw8i8o, gOwi8o,
+                        gOIhw8i8o, gOhwi8o, gOIdhw8i8o, gOdhwi8o)
+                : utils::pick(2 * ndims() - 6 + flat, OIw8i8o, Owi8o,
+                        OIhw8i8o, Ohwi8o, OIdhw8i8o, Odhwi8o);
+
+            return set_default_formats_common(src_tag, wei_tag, dst_tag);
+        }
+    };
+
+    jit_avx2_convolution_fwd_t(const pd_t *apd): cpu_primitive_t(apd)
+    { kernel_ = new jit_avx2_conv_fwd_kernel_f32(pd()->jcp_, *pd()->attr()); }
+    ~jit_avx2_convolution_fwd_t() { delete kernel_; }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx2_conv_fwd_kernel_f32 *kernel_;
+};
+
+struct jit_avx2_convolution_bwd_data_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_bwd_data_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_()
+        {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", avx2, ""),
+                jit_avx2_convolution_bwd_data_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_data
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::undef, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_avx2_conv_bwd_data_kernel_f32::init_conf(
+                    jcp_, *desc(), *diff_src_md(), *weights_md(),
+                    *diff_dst_md());
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx2_conv_bwd_data_kernel_f32::init_scratchpad(scratchpad,
+                    jcp_);
+
+            return status::success;
+        }
+
+        jit_conv_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
+            auto wei_tag = with_groups()
+                ? utils::pick(ndims() - 3, gOIw8o8i, gOIhw8o8i, gOIdhw8o8i)
+                : utils::pick(ndims() - 3, OIw8o8i, OIhw8o8i, OIdhw8o8i);
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    jit_avx2_convolution_bwd_data_t(const pd_t *apd): cpu_primitive_t(apd)
+    { kernel_ = new jit_avx2_conv_bwd_data_kernel_f32(pd()->jcp_); }
+    ~jit_avx2_convolution_bwd_data_t() { delete kernel_; }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_data(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_data(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx2_conv_bwd_data_kernel_f32 *kernel_;
+};
+
+struct jit_avx2_convolution_bwd_weights_t: public cpu_primitive_t {
+    struct pd_t: public  cpu_convolution_bwd_weights_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_weights_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", avx2, ""),
+                jit_avx2_convolution_bwd_weights_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_weights
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_avx2_conv_bwd_weights_kernel_f32::init_conf(
+                    jcp_, *desc(), *src_md(), *diff_weights_md(),
+                    *diff_dst_md());
+            if (status != status::success) return status;
+
+            init_balancers();
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx2_conv_bwd_weights_kernel_f32::init_scratchpad(scratchpad,
+                    jcp_);
+
+            auto reducer_bia_scratchpad = memory_tracking::registrar_t(
+                    scratchpad, memory_tracking::names::prefix_reducer_bia);
+            reducer_bia_conf_.init_scratchpad(reducer_bia_scratchpad);
+
+            auto reducer_wei_scratchpad = memory_tracking::registrar_t(
+                    scratchpad, memory_tracking::names::prefix_reducer_wei);
+            reducer_wei_conf_.init_scratchpad(reducer_wei_scratchpad);
+
+            return status::success;
+        }
+
+        jit_conv_conf_t jcp_;
+        cpu_reducer_t<data_type::f32>::conf_t reducer_bia_conf_;
+        cpu_reducer_t<data_type::f32>::conf_t reducer_wei_conf_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+            const bool flat = IC() == 3;
+
+            auto src_tag = flat
+                ? utils::pick(ndims() - 3, ncw, nchw, ncdhw)
+                : utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
+            auto dst_tag =
+                utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
+            auto wei_tag = with_groups()
+                ? utils::pick(2 * ndims() - 6 + flat, gOIw8i8o, gOwi8o,
+                        gOIhw8i8o, gOhwi8o, gOIdhw8i8o, gOdhwi8o)
+                : utils::pick(2 * ndims() - 6 + flat, OIw8i8o, Owi8o,
+                        OIhw8i8o, Ohwi8o, OIdhw8i8o, Odhwi8o);
+
+            return set_default_formats_common(src_tag, wei_tag, dst_tag);
+        }
+
+    private:
+        void init_balancers() {
+            const int max_threads = mkldnn_get_max_threads();
+            const size_t max_buffer_size = 1<<21; /* just a heuristic */
+
+            if(with_bias()) {
+                reducer_bia_conf_.init(reduce_balancer_t(max_threads,
+                            jcp_.oc_block, jcp_.ngroups * jcp_.nb_oc, jcp_.mb,
+                            max_buffer_size));
+            }
+
+            reducer_wei_conf_.init(reduce_balancer_t(max_threads,
+                        jcp_.kd * jcp_.kh * jcp_.kw
+                        * jcp_.ic_block * jcp_.oc_block,
+                        jcp_.ngroups * jcp_.nb_ic * jcp_.nb_oc,
+                        jcp_.mb * jcp_.od, max_buffer_size));
+        }
+    };
+
+    jit_avx2_convolution_bwd_weights_t(const pd_t *apd)
+        : cpu_primitive_t(apd)
+        , kernel_(nullptr)
+        , reducer_weights_(nullptr)
+        , reducer_bias_(nullptr)
+    {
+        kernel_ = new jit_avx2_conv_bwd_weights_kernel_f32(pd()->jcp_);
+        reducer_bias_ =
+            new cpu_reducer_t<data_type::f32>(pd()->reducer_bia_conf_);
+        reducer_weights_ =
+            new cpu_reducer_t<data_type::f32>(pd()->reducer_wei_conf_);
+    }
+
+    ~jit_avx2_convolution_bwd_weights_t() {
+        delete kernel_;
+        delete reducer_weights_;
+        delete reducer_bias_;
+    }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_weights(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_weights(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx2_conv_bwd_weights_kernel_f32 *kernel_;
+    cpu_reducer_t<data_type::f32> *reducer_weights_, *reducer_bias_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_conv_kernel.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_conv_kernel.cpp
new file mode 100644
index 0000000000..635b83b2bf
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_conv_kernel.cpp
@@ -0,0 +1,1255 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <float.h>
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_memory.hpp"
+#include "cpu_barrier.hpp"
+
+#include "jit_uni_1x1_conv_utils.hpp"
+#include "jit_avx512_common_1x1_conv_kernel.hpp"
+
+#define GET_OFF(field) offsetof(jit_1x1_conv_call_s, field)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::utils;
+
+using namespace Xbyak;
+
+void jit_avx512_common_1x1_conv_kernel::bcast_loop(int load_loop_blk)
+{
+    mov(aux1_reg_bcast_data, reg_bcast_data);
+    mov(aux_reg_bcast_data, reg_bcast_data);
+
+    mov(aux_reg_output_data, reg_output_data);
+    mov(bcast_loop_iter, EVEX_compress_addr(rsp, bcast_loop_work_offt));
+
+    if (jcp.ver == ver_4fma)
+    {
+        Label bcast_loop;
+        Label bcast_loop_wraparound;
+        Label bcast_loop_out;
+        Label bcast_loop_ur_full;
+
+        cmp(bcast_loop_iter, jcp.ur);
+        jle(bcast_loop_wraparound, T_NEAR);
+
+        L(bcast_loop); {
+            assert(jcp.bcast_block % jcp.ur == 0);
+            int num_substeps = jcp.bcast_block / jcp.ur;
+            assert(num_substeps > 0 && num_substeps < 10);
+            for (int i = 0; i < num_substeps; i++) {
+                reduce_loop(load_loop_blk, jcp.ur, i, false);
+                if (i < num_substeps - 1) {
+                    add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_substep);
+                    add(aux_reg_output_data, jcp.bcast_loop_output_substep);
+                }
+                else {
+                    add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_step
+                        - (num_substeps - 1) * jcp.bcast_loop_bcast_substep);
+                    add(aux_reg_output_data, jcp.bcast_loop_output_step
+                        - (num_substeps - 1) * jcp.bcast_loop_output_substep);
+                }
+            }
+            sub(bcast_loop_iter, jcp.bcast_block);
+            cmp(bcast_loop_iter, jcp.bcast_block);
+            jg(bcast_loop, T_NEAR);
+        }
+
+        L(bcast_loop_wraparound);
+        if (jcp.ur_tail) {
+            je(bcast_loop_ur_full, T_NEAR);
+            reduce_loop(load_loop_blk, jcp.ur_tail, 0, true);
+            jmp(bcast_loop_out, T_NEAR);
+        }
+        L(bcast_loop_ur_full);
+        reduce_loop(load_loop_blk, jcp.ur, 0, true);
+        L(bcast_loop_out);
+    }
+    else
+    {
+        Label bcast_loop;
+        Label bcast_loop_tail;
+
+        cmp(bcast_loop_iter, jcp.ur);
+        jl(bcast_loop_tail, T_NEAR);
+
+        L(bcast_loop); {
+            assert(jcp.bcast_block % jcp.ur == 0);
+            int num_substeps = jcp.bcast_block / jcp.ur;
+            assert(num_substeps > 0 && num_substeps < 10);
+            for (int i = 0; i < num_substeps; i++) {
+                reduce_loop(load_loop_blk, jcp.ur, i, false);
+                if (i < num_substeps - 1) {
+                    add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_substep);
+                    add(aux_reg_output_data, jcp.bcast_loop_output_substep);
+                }
+                else {
+                    add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_step
+                        - (num_substeps - 1) * jcp.bcast_loop_bcast_substep);
+                    add(aux_reg_output_data, jcp.bcast_loop_output_step
+                        - (num_substeps - 1) * jcp.bcast_loop_output_substep);
+                }
+            }
+            sub(bcast_loop_iter, jcp.bcast_block);
+            cmp(bcast_loop_iter, jcp.bcast_block);
+            jge(bcast_loop, T_NEAR);
+        }
+
+        L(bcast_loop_tail);
+        if (jcp.ur_tail) {
+            Label bcast_loop_tail_out;
+            cmp(bcast_loop_iter, 0);
+            jz(bcast_loop_tail_out, T_NEAR);
+            reduce_loop(load_loop_blk, jcp.ur_tail, 0, true);
+            L(bcast_loop_tail_out);
+        }
+    }
+}
+
+void jit_avx512_common_1x1_conv_kernel::reduce_loop(int load_loop_blk,
+         int ur, int substep, bool wraparound)
+{
+    auto vreg_load = [=](int i_load, int i_fma) {
+        return Zmm(utils::rnd_up(ur * load_loop_blk, jcp.fma_step)
+                    + jcp.fma_step * i_load + i_fma);
+    };
+
+    auto vreg_accum = [=](int i_load, int i_ur) {
+        return Zmm(i_ur * load_loop_blk + i_load);
+    };
+
+    auto bias_ptr = [=](int i_load) {
+        return EVEX_compress_addr(reg_bias_data,
+                                  jcp.typesize_out * jcp.oc_block * i_load);
+    };
+
+    auto bcast_ptr = [=](int i_reduce, int i_ur, bool bcast) {
+        assert(i_ur < jcp.ur);
+        assert(i_reduce <= jcp.reduce_loop_unroll);
+        int offt;
+        if (one_of(jcp.prop_kind, forward_training, forward_inference,
+                   backward_data)) {
+            assert(jcp.reduce_loop_unroll == jcp.reduce_block);
+            offt = (i_reduce == jcp.reduce_loop_unroll)
+                    ? (jcp.bcast_dim + i_ur) * jcp.reduce_loop_unroll
+                    : i_ur * jcp.reduce_loop_unroll + i_reduce;
+        } else {
+            if (jcp.transpose_src) {
+                const int reduce_group = i_reduce / 4;
+                const int reduce_shift = i_reduce % 4;
+                offt = 4 * (reduce_group * jcp.ic_block + i_ur) + reduce_shift;
+            }
+            else
+                offt = i_reduce * jcp.ic_block + i_ur;
+        }
+        return EVEX_compress_addr(aux_reg_bcast_data, jcp.typesize_in * offt,
+                                bcast);
+    };
+
+    auto load_ptr = [=](int i_reduce, int i_load) {
+        int offt;
+        int u0 = i_reduce % jcp.reduce_loop_unroll;
+        int u1 = i_reduce / jcp.reduce_loop_unroll;
+        offt = (i_load * jcp.reduce_dim + u0) * jcp.load_block;
+        return EVEX_compress_addr(aux_reg_load_data,
+                                  u1 * jcp.reduce_loop_load_step
+                                  + jcp.typesize_in * offt);
+    };
+
+    auto output_ptr = [=](int i_load, int i_ur) {
+        if (one_of(jcp.prop_kind, forward_training, forward_inference,
+                   backward_data))
+            return EVEX_compress_addr(aux_reg_output_data,
+                    (i_load * jcp.bcast_dim + i_ur) * jcp.load_block
+                    * jcp.typesize_out);
+        else
+            return ptr[aux_reg_output_data +
+                       (i_load
+                            ? reg_output_stride * i_load
+                            : 0) // TODO: Xbyak should allow 0 scale
+                       + jcp.typesize_out * jcp.load_block * i_ur];
+    };
+
+    auto init = [=]() {
+        Label init_done;
+        Label init_zero;
+
+        if (jcp.with_sum) {
+            for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
+                for (int i_ur = 0; i_ur < ur; ++i_ur) {
+                    mic_prefetcht1(output_ptr(i_load, i_ur));
+                }
+            }
+        }
+
+        if (jcp.with_bias
+            && one_of(jcp.prop_kind, forward_training, forward_inference)) {
+            test(reg_reduce_pos_flag, FLAG_REDUCE_FIRST);
+            jz(init_zero, T_NEAR);
+
+            for (int i_load = 0; i_load < load_loop_blk; i_load++)
+                for (int i_ur = 0; i_ur < ur; ++i_ur)
+                    vmovups(vreg_accum(i_load, i_ur), bias_ptr(i_load));
+            jmp(init_done, T_NEAR);
+        }
+
+        L(init_zero);
+        for (int i_load = 0; i_load < load_loop_blk; ++i_load)
+            for (int i_ur = 0; i_ur < ur; ++i_ur) {
+                auto r = vreg_accum(i_load, i_ur);
+                vpxord(r, r, r);
+            }
+        L(init_done);
+    };
+
+    auto store = [=]() {
+        Label store_noadd;
+        if (!jcp.with_sum) {
+            test(reg_reduce_pos_flag, FLAG_REDUCE_FIRST);
+            jnz(store_noadd, T_NEAR);
+        }
+
+        for (int i_ur = 0; i_ur < ur; ++i_ur)
+            for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
+                auto r = vreg_accum(i_load, i_ur);
+                vaddps(r, r, output_ptr(i_load, i_ur));
+            }
+
+        L(store_noadd);
+        if (jcp.with_eltwise) {
+            Label store_noeltwise;
+            test(reg_reduce_pos_flag, FLAG_REDUCE_LAST);
+            jz(store_noeltwise, T_NEAR);
+
+            eltwise_injector_->compute_vector_range(0, ur * load_loop_blk);
+
+            L(store_noeltwise);
+        }
+
+        auto store_output = [=](bool output_is_aligned) {
+            for (int i_ur = 0; i_ur < ur; ++i_ur)
+                for (int i_load = 0; i_load < load_loop_blk; ++i_load)
+                    if (output_is_aligned && jcp.use_vmovntps)
+                        vmovntps(output_ptr(i_load, i_ur),
+                            vreg_accum(i_load, i_ur));
+                    else
+                        vmovups(output_ptr(i_load, i_ur),
+                            vreg_accum(i_load, i_ur));
+        };
+
+        Label unaligned_store, end_store;
+        test(aux_reg_output_data, cpu_isa_traits<avx512_common>::vlen - 1);
+        jnz(unaligned_store, T_NEAR);
+        store_output(true);
+        jmp(end_store, T_NEAR);
+        L(unaligned_store); {
+            store_output(false);
+        }
+        L(end_store);
+    };
+
+    auto prefetch_callback = [=](int ur, int i_reduce, int i_ur, int i_load,
+        bool last_block, bool wraparound, int reduce_step)
+    {
+        bool pf_ker_l1 = true;
+        bool pf_ker_l2 = wraparound;
+        int n_ops = (jcp.reduce_loop_unroll / reduce_step) * ur * load_loop_blk;
+        int i_op = (i_reduce / reduce_step) * ur * load_loop_blk +
+            i_ur * load_loop_blk + i_load;
+
+        int n_pf_ker_l1 = pf_ker_l1 ? jcp.reduce_block : 0;
+        int n_pf_ker_l2 = pf_ker_l2 && wraparound ? jcp.reduce_block : 0;
+        int n_pf_out_l1 = jcp.use_vmovntps ? 0 : ur;
+
+        int pf_inp_ops = n_ops / 2; // # of operations during which to pf input
+        int pf_inp_trigger;
+        if (jcp.prop_kind == backward_weights)
+            pf_inp_trigger = nstl::max(1, pf_inp_ops / jcp.reduce_block);
+        else
+            pf_inp_trigger = nstl::max(1, pf_inp_ops / ur);
+
+        int n_other_pf =
+            load_loop_blk * (n_pf_ker_l1 + n_pf_ker_l2 + n_pf_out_l1);
+        int n_other_pf_ops = n_ops - pf_inp_ops;
+        int other_pf_trigger
+                = n_other_pf ? nstl::max(1, n_other_pf_ops / n_other_pf) : 0;
+
+        if (i_op < pf_inp_ops && i_op % pf_inp_trigger == 0) {
+            // input prefetches have the highest priority b/c the
+            // first iteration of the kernel block touches all the
+            // cache lines
+            int i_pf = i_op / pf_inp_trigger;
+            auto pf_reg = wraparound && last_block
+                                  ? reg_bcast_data
+                                  : (last_block ? aux1_reg_bcast_data
+                                                : aux_reg_bcast_data);
+            int offt = i_pf;
+            if (jcp.prop_kind == backward_weights) {
+                offt += wraparound && last_block
+                                    ? 0
+                                    : (last_block ? jcp.is : jcp.reduce_block);
+                offt *= jcp.bcast_block;
+            } else {
+                offt += wraparound && last_block
+                                    ? 0
+                                    : (last_block ? jcp.ur : jcp.bcast_dim);
+                offt *= jcp.reduce_block;
+            }
+            mic_prefetcht0(ptr[pf_reg + offt * jcp.typesize_in]);
+        } else if (i_op >= pf_inp_ops && n_other_pf) {
+            // remaining prefetches are spread among the rest of the
+            // operations; prefetches for output take priority
+            // TODO: spread L2 prefetches among L1 prefetches
+            i_op -= pf_inp_ops;
+            if (i_op % other_pf_trigger == 0) {
+                int i_pf = i_op / (load_loop_blk * other_pf_trigger);
+                if (i_pf < n_pf_ker_l2) {
+                    int offt = (i_pf + (i_load + 1) * jcp.reduce_dim)
+                        * jcp.load_block;
+                    mic_prefetcht1(ptr[aux_reg_load_data
+                                    + offt * jcp.typesize_in]);
+                } else if (i_pf < n_pf_ker_l2 + n_pf_ker_l1) {
+                    i_pf -= n_pf_ker_l2;
+                    auto pf_reg = last_block ? reg_load_data
+                                             : aux_reg_load_data;
+                    int offt = (i_pf + i_load * jcp.reduce_dim
+                        + (last_block
+                            ? (wraparound ? jcp.reduce_dim : 0)
+                            : jcp.reduce_block))
+                        * jcp.load_block;
+                    mic_prefetcht0(ptr[pf_reg + offt * jcp.typesize_in]);
+                } else if (i_pf < n_pf_ker_l1 + n_pf_ker_l2 + n_pf_out_l1) {
+                    i_pf -= n_pf_ker_l1 + n_pf_ker_l2;
+                    int offt = i_pf * jcp.load_block;
+                    mic_prefetcht0(ptr[aux_reg_output_data
+                                    + offt * jcp.typesize_out]);
+                }
+            }
+        }
+    };
+
+    auto fma_block = [=](bool last_block) {
+        assert(jcp.reduce_loop_unroll % jcp.fma_step == 0);
+
+        int reduce_step = jcp.fma_step;
+
+        for (int i_reduce = 0; i_reduce < jcp.reduce_loop_unroll;
+                i_reduce += reduce_step) {
+            for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
+                // if transposed input data used and if spatial size is
+                // not divided by transpose step (4) then for last reduce step
+                // we should load only needed load_registers data
+                // and clear remaining
+                if (jcp.transpose_src && jcp.is % jcp.fma_step && last_block
+                        && i_reduce == jcp.reduce_loop_unroll - reduce_step) {
+                    Label load_all;
+                    Label load_finish;
+                    test(reg_reduce_pos_flag, FLAG_SP_LAST);
+                    jz(load_all, T_NEAR);
+
+                    const int n_loads = jcp.is % jcp.fma_step;
+                    for (int i_fma = 0; i_fma < jcp.fma_step; i_fma++) {
+                        if (i_fma < n_loads)
+                            vmovups(vreg_load(i_load, i_fma),
+                                    load_ptr(i_reduce + i_fma, i_load));
+                        else
+                            vpxord(vreg_load(i_load, i_fma),
+                                    vreg_load(i_load, i_fma),
+                                    vreg_load(i_load, i_fma));
+                    }
+                    jmp(load_finish);
+
+                    L(load_all);
+                    for (int i_fma = 0; i_fma < jcp.fma_step; i_fma++) {
+                        vmovups(vreg_load(i_load, i_fma),
+                            load_ptr(i_reduce + i_fma, i_load));
+                    }
+                    L(load_finish);
+                } else {
+                    for (int i_fma = 0; i_fma < jcp.fma_step; i_fma++) {
+                        vmovups(vreg_load(i_load, i_fma),
+                            load_ptr(i_reduce + i_fma, i_load));
+                    }
+                }
+            }
+
+            for (int i_ur = 0; i_ur < ur; ++i_ur) {
+                if (jcp.ver == ver_avx512_core && jcp.expl_bcast
+                        && load_loop_blk > 1)
+                    vbroadcastss(vreg_bcast, bcast_ptr(i_reduce, i_ur, false));
+                for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
+                    if (jcp.ver == ver_4fma)
+                        v4fmaddps(vreg_accum(i_load, i_ur),
+                                    vreg_load(i_load, 0),
+                                    bcast_ptr(i_reduce, i_ur, false));
+                    else if (jcp.ver == ver_avx512_core && jcp.expl_bcast
+                            && load_loop_blk > 1)
+                        vfmadd231ps(vreg_accum(i_load, i_ur),
+                                vreg_load(i_load, 0), vreg_bcast);
+                    else
+                        vfmadd231ps(vreg_accum(i_load, i_ur),
+                                vreg_load(i_load, 0),
+                                bcast_ptr(i_reduce, i_ur, true));
+                    prefetch_callback(ur, i_reduce, i_ur, i_load,
+                                    last_block, wraparound, reduce_step);
+                }
+            }
+        }
+    };
+    Label reduce_loop;
+    Label reduce_loop_tail;
+
+    mov(aux_reg_load_data, reg_load_data);
+
+    mov(aux_reg_bcast_data, aux1_reg_bcast_data);
+    init();
+
+    mov(reduce_loop_iter, reg_reduce_loop_work);
+    sub(reduce_loop_iter, jcp.reduce_loop_unroll);
+    jle(reduce_loop_tail, T_NEAR);
+
+    L(reduce_loop); {
+        fma_block(false);
+        add(aux_reg_bcast_data, jcp.reduce_loop_bcast_step);
+        add(aux_reg_load_data, jcp.reduce_loop_load_step);
+        sub(reduce_loop_iter, jcp.reduce_loop_unroll);
+        jg(reduce_loop, T_NEAR);
+    }
+
+    L(reduce_loop_tail);
+    fma_block(true);
+
+    store();
+}
+
+void jit_avx512_common_1x1_conv_kernel::generate()
+{
+    preamble();
+
+    mov(reg_bcast_data, ptr[param1 + GET_OFF(bcast_data)]);
+    mov(reg_load_data, ptr[param1 + GET_OFF(load_data)]);
+    mov(reg_output_data, ptr[param1 + GET_OFF(output_data)]);
+
+    sub(rsp, stack_space_needed);
+
+    if (jcp.with_bias)
+        mov(reg_bias_data, ptr[param1 + GET_OFF(bias_data)]);
+
+    mov(reg_load_loop_work, ptr[param1 + GET_OFF(load_dim)]);
+    mov(reg_bcast_loop_work, ptr[param1 + GET_OFF(bcast_dim)]);
+    mov(EVEX_compress_addr(rsp, bcast_loop_work_offt), reg_bcast_loop_work);
+    mov(reg_reduce_loop_work, ptr[param1 + GET_OFF(reduce_dim)]);
+    mov(reg_reduce_pos_flag, ptr[param1 + GET_OFF(first_last_flag)]);
+    if (one_of(jcp.prop_kind, forward_training, forward_inference))
+        mov(reg_relu_ns, reinterpret_cast<size_t>(&jcp.eltwise.alpha));
+    if (jcp.prop_kind == backward_weights)
+        mov(reg_output_stride, ptr[param1 + GET_OFF(output_stride)]);
+
+    auto load_loop_body = [=](int load_loop_blk) {
+        bcast_loop(load_loop_blk);
+        add(reg_load_data, load_loop_blk * jcp.load_loop_load_step);
+        switch (jcp.prop_kind) {
+        case forward_training:
+        case forward_inference:
+            add(reg_bias_data,
+                load_loop_blk * jcp.load_block * jcp.typesize_out);
+            add(reg_output_data,
+                load_loop_blk * jcp.bcast_dim * jcp.load_block *
+                    jcp.typesize_out);
+            break;
+        case backward_data:
+            add(reg_output_data,
+                load_loop_blk * jcp.bcast_dim * jcp.load_block *
+                    jcp.typesize_out);
+            break;
+        case backward_weights:
+            for (int i_load = 0; i_load < load_loop_blk; i_load++)
+                add(reg_output_data, reg_output_stride);
+            break;
+        default:
+            assert(!"invalid prop_kind");
+        }
+        sub(reg_load_loop_work, load_loop_blk * jcp.load_loop_iter_step);
+    };
+
+    const int simd_w = 16;
+
+    Label load_loop_blk[7];
+
+    static const int ur_cases_fma_embd_bcast[] = { 2, 4, 5, 8, 14, 32 };
+    static const int ur_cases_fma_expl_bcast[] = { 2, 5, 6, 9, 14, 32 };
+    static const int ur_cases_4fma[] = { 2, 4, 6, 12, 32 };
+
+    const int size_ur_cases_fma
+            = (jcp.ver == ver_avx512_core && jcp.expl_bcast) ?
+            sizeof(ur_cases_fma_expl_bcast) :
+            sizeof(ur_cases_fma_embd_bcast);
+    const int size_ur_cases_4fma = sizeof(ur_cases_4fma);
+
+    const int *ur_cases_fma = (jcp.ver == ver_avx512_core && jcp.expl_bcast) ?
+            ur_cases_fma_expl_bcast :
+            ur_cases_fma_embd_bcast;
+    const int *ur_cases = jcp.ver == ver_4fma ? ur_cases_4fma : ur_cases_fma;
+    const int num_ur_cases =
+        (jcp.ver == ver_4fma ?  size_ur_cases_4fma : size_ur_cases_fma)
+        / sizeof(*ur_cases);
+
+    for (int ur_idx = num_ur_cases - 1; ur_idx > 0; ur_idx--) {
+        int label_idx = num_ur_cases - ur_idx - 1;
+        if (jcp.ur <= ur_cases[ur_idx]) {
+            cmp(reg_load_loop_work, simd_w * (label_idx + 1));
+            jle(load_loop_blk[label_idx], T_NEAR);
+        }
+    }
+
+    for (int ur_idx = 0; ur_idx < num_ur_cases; ur_idx++) {
+        if (jcp.ur <= ur_cases[ur_idx]) {
+            int label_idx = num_ur_cases - ur_idx - 1;
+            L(load_loop_blk[label_idx]);
+            {
+                if (label_idx == 0) {
+                    cmp(reg_load_loop_work, 0);
+                    je(load_loop_blk[num_ur_cases], T_NEAR);
+                }
+                load_loop_body(label_idx + 1);
+                if (label_idx - 1 > 0) {
+                    cmp(reg_load_loop_work, 2 * label_idx * simd_w);
+                    je(load_loop_blk[label_idx - 1], T_NEAR);
+                }
+                cmp(reg_load_loop_work, (label_idx + 1) * simd_w);
+                jge(load_loop_blk[label_idx]);
+            }
+            for (int idx = label_idx - 1; idx > 0; --idx) {
+                cmp(reg_load_loop_work, simd_w * (idx + 1));
+                je(load_loop_blk[idx], T_NEAR);
+            }
+            if (ur_idx < num_ur_cases - 2) {
+                cmp(reg_load_loop_work, simd_w);
+                jle(load_loop_blk[0], T_NEAR);
+            }
+        }
+    }
+    L(load_loop_blk[num_ur_cases]);
+
+    add(rsp, stack_space_needed);
+
+    postamble();
+
+    if (jcp.with_eltwise)
+        eltwise_injector_->prepare_table();
+}
+
+bool jit_avx512_common_1x1_conv_kernel::post_ops_ok(
+        jit_1x1_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    const auto &p = attr.post_ops_;
+
+    auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); };
+    auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); };
+
+    switch (p.len_) {
+    case 0: return true; // no post_ops
+    case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise
+    case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise
+    default: return false;
+    }
+
+    return false;
+}
+
+status_t jit_avx512_common_1x1_conv_kernel::init_conf(jit_1x1_conv_conf_t &jcp,
+        const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+        const memory_desc_wrapper &weights_d, const memory_desc_wrapper &dst_d,
+        const primitive_attr_t &attr, int nthreads, bool reduce_src) {
+    if (!mayiuse(avx512_common)) return status::unimplemented;
+
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+    const int simd_w = cpu_isa_traits<avx512_common>::vlen / sizeof(float);
+    const int ndims = src_d.ndims();
+
+    jcp.prop_kind = cd.prop_kind;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+
+    jcp.oc_without_padding = dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+
+    bool ok_to_pad_channels = true
+        && jcp.ngroups == 1
+        && src_d.data_type() == data_type::f32;
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, simd_w);
+        jcp.ic = rnd_up(jcp.ic, simd_w);
+    }
+
+    jcp.ih = (ndims == 3) ? 1 : src_d.dims()[2];
+    jcp.iw = src_d.dims()[ndims - 1];
+    jcp.oh = (ndims == 3) ? 1 : dst_d.dims()[2];
+    jcp.ow = dst_d.dims()[ndims - 1];
+
+    jcp.kh = (ndims == 3) ? 1 : weights_d.dims()[with_groups + 2];
+    jcp.kw = weights_d.dims()[with_groups + ndims - 1];
+
+    jcp.t_pad = (ndims == 3) ? 0 : cd.padding[0][0];
+    jcp.l_pad = cd.padding[0][ndims - 3];
+
+    jcp.stride_h = (ndims == 3) ? 1 : cd.strides[0];
+    jcp.stride_w = cd.strides[ndims - 3];
+
+    jcp.with_bias = pick_by_prop_kind(jcp.prop_kind, cd.bias_desc.format_kind,
+            format_kind::undef, cd.diff_bias_desc.format_kind)
+        != format_kind::undef;
+
+    jcp.os = jcp.oh * jcp.ow;
+    jcp.is = jcp.ih * jcp.iw;
+    jcp.tr_is = rnd_up(jcp.is, 4);
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    jcp.with_sum = p.find(primitive_kind::sum) != -1;
+    const int eltwise_ind = p.find(primitive_kind::eltwise);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise) {
+        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+        if (dst_d.data_type() == data_type::s32) return status::unimplemented;
+    }
+
+    auto dat_tag = pick(ndims - 3, nCw16c, nChw16c);
+    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    jcp.dst_tag = dst_d.matches_one_of_tag(dat_tag);
+
+    bool args_ok = true
+        && jcp.ngroups == 1
+        && jcp.src_tag == dat_tag
+        && jcp.dst_tag == dat_tag;
+    if (!args_ok) return status::unimplemented;
+
+    args_ok = true
+        && jcp.oc % simd_w == 0 && jcp.ic % simd_w == 0
+        && jcp.t_pad == 0 && jcp.l_pad == 0
+        && jcp.stride_w == 1 && jcp.stride_h == 1 // TODO: support some strides
+        && jcp.kh == 1 && jcp.kw == 1;
+    if (!args_ok) return status::unimplemented;
+
+    jcp.ic_block = jcp.oc_block = simd_w;
+    jcp.transpose_src = false;
+
+    if (everyone_is(data_type::f32, src_d.data_type(),
+                            weights_d.data_type(), dst_d.data_type()))
+    {
+        const int is_bwd_d = jcp.prop_kind == backward_data;
+        format_tag_t wei_tag = with_groups
+            ? pick(2 * ndims - 6 + is_bwd_d, gOIw16i16o, gIOw16o16i,
+                gOIhw16i16o, gIOhw16o16i)
+            : pick(2 * ndims - 6 + is_bwd_d, OIw16i16o, IOw16o16i,
+                OIhw16i16o, IOhw16o16i);
+
+        jcp.wei_tag = weights_d.matches_one_of_tag(wei_tag);
+        if (jcp.wei_tag != wei_tag)
+            return status::unimplemented;
+
+        if (jcp.prop_kind != backward_weights && mayiuse(avx512_mic_4ops) &&
+            ((jcp.prop_kind == backward_data) ? jcp.oc_block : jcp.ic_block) % 4
+            == 0) {
+            jcp.ver = ver_4fma;
+            jcp.fma_step = 4;
+        } else if (jcp.prop_kind == backward_weights && mayiuse(avx512_mic_4ops)
+                && !reduce_src
+                /* Heuristic condition for relation of src size to oc. Otherwise
+                   the src transposition overhead exceed the benefit from 4fma
+                */
+                && ((jcp.is * jcp.ic) / jcp.oc <= 2048)
+                && mkldnn_thr_syncable()
+                )
+        {
+            jcp.transpose_src = true;
+            jcp.ver = ver_4fma;
+            jcp.fma_step = 4;
+        } else {
+            jcp.ver = (mayiuse(avx512_core)) ? ver_avx512_core : ver_fma;
+            jcp.fma_step = 1;
+        }
+        jcp.typesize_in = sizeof(prec_traits<data_type::f32>::type);
+        jcp.typesize_out = sizeof(prec_traits<data_type::f32>::type);
+    } else {
+        return status::unimplemented;
+    }
+
+    /* once all the formats are set, check the padding consistency */
+    args_ok = true
+        && jcp.ic <= src_d.padded_dims()[1]
+        && jcp.oc <= dst_d.padded_dims()[1]
+        && jcp.ic <= weights_d.padded_dims()[with_groups + 1]
+        && jcp.oc <= weights_d.padded_dims()[with_groups + 0];
+    if (!args_ok) return status::unimplemented;
+
+    const int SMALL_SPATIAL = 10;
+    const int BIG_SPATIAL = 28;
+    const int BIG_REDUCE_DIM = 1024;
+    const int BIG_LOAD_DIM = 256;
+
+    int load_blocking{ 0 };
+    int load_blocking_max{ 0 };
+    int bcast_blocking{ 0 };
+    int bcast_blocking_max{ 0 };
+    int reduce_blocking{ 0 };
+    int reduce_blocking_max{ 0 };
+
+    jcp.load_grp_count = 1;
+
+    const int L1_capacity = get_cache_size(1, true) / sizeof(float);
+    const int L2_size = get_cache_size(2, true) / sizeof(float);
+    const int L2_capacity = (L2_size * 3) / 4;
+
+    if (one_of(jcp.prop_kind, forward_training, forward_inference,
+                backward_data)) {
+        if (one_of(jcp.prop_kind, forward_training, forward_inference)) {
+            jcp.reduce_dim = jcp.ic;
+            jcp.reduce_block = jcp.ic_block;
+
+            jcp.load_dim = jcp.oc;
+            jcp.load_block = jcp.oc_block;
+
+            jcp.bcast_dim = jcp.is;
+        } else {
+            jcp.reduce_dim = jcp.oc;
+            jcp.reduce_block = jcp.oc_block;
+
+            jcp.load_dim = jcp.ic;
+            jcp.load_block = jcp.ic_block;
+
+            jcp.bcast_dim = jcp.os;
+        }
+        jcp.reduce_loop_unroll = jcp.reduce_block;
+        jcp.reduce_loop_bcast_step
+                = jcp.reduce_loop_unroll * jcp.bcast_dim * jcp.typesize_in;
+
+        jcp.reduce_loop_load_step
+            = jcp.reduce_loop_unroll * jcp.load_block * jcp.typesize_in;
+        jcp.load_loop_load_step
+            = jcp.reduce_dim * jcp.load_block * jcp.typesize_in;
+
+        // adjusting registry blocking
+        int max_regs, min_regs, size_treshold, ur_step;
+        const int spatial
+                = (one_of(jcp.prop_kind, forward_training, forward_inference)) ?
+                jcp.oh :
+                jcp.ih;
+        if (jcp.ver == ver_avx512_core && (8 * jcp.mb) / nthreads >= 1) {
+            max_regs = 9;
+            min_regs = 6;
+            size_treshold = 14;
+            ur_step = 1;
+            jcp.expl_bcast = true;
+
+            if (jcp.load_dim > 128 && jcp.load_dim < BIG_LOAD_DIM
+                    && spatial > SMALL_SPATIAL && spatial < BIG_SPATIAL) {
+                max_regs = 6;
+                min_regs = 5;
+            }
+        } else {
+            max_regs = jcp.ver == ver_4fma ? 28 : 30;
+            min_regs = 9;
+            size_treshold = jcp.ver == ver_4fma ? 28 : 14;
+            ur_step = jcp.ver == ver_4fma ? 4 : 1;
+            jcp.expl_bcast = false;
+            jcp.use_vmovntps = true;
+        }
+        jcp.ur = 1;
+        for (int ur_w = max_regs; ur_w >= min_regs; ur_w -= ur_step) {
+            if ((spatial >= size_treshold && spatial % ur_w == 0)
+                    || (spatial < size_treshold && jcp.os % ur_w == 0)) {
+                jcp.ur = ur_w;
+                break;
+            }
+        }
+        if (jcp.ur == 1) {
+            jcp.ur = nstl::min(max_regs, jcp.os);
+            int os_tail = jcp.os % max_regs;
+            for (int i = max_regs; i >= min_regs; i -= ur_step) {
+                int i_tail = jcp.os % i;
+                if (i_tail > os_tail || i_tail == 0) {
+                    jcp.ur = i;
+                    os_tail = i_tail;
+                    if (i_tail == 0)
+                        break;
+                }
+            }
+        }
+
+        jcp.reduce_loop_unroll = jcp.reduce_block;
+        jcp.reduce_loop_bcast_step
+                = jcp.reduce_loop_unroll * jcp.bcast_dim * jcp.typesize_in;
+
+        jcp.bcast_block = jcp.ur;
+
+        jcp.bcast_loop_output_step = jcp.ur * jcp.load_block * jcp.typesize_out;
+        jcp.bcast_loop_output_substep = -1; // unused
+        jcp.bcast_loop_bcast_step = jcp.ur * jcp.reduce_block * jcp.typesize_in;
+        jcp.bcast_loop_bcast_substep = -1; // unused
+
+        jcp.load_loop_iter_step = jcp.load_block;
+
+        if (jcp.prop_kind == backward_data)
+            jcp.loop_order = loop_lbr;
+        else
+            jcp.loop_order = reduce_src ? loop_blr : loop_lbr;
+
+        int nb_bcast = div_up(jcp.bcast_dim, jcp.bcast_block);
+        int nb_reduce = div_up(jcp.reduce_dim, jcp.reduce_block);
+        int nb_load = div_up(jcp.load_dim, jcp.load_block);
+
+        if (jcp.ver == ver_avx512_core && jcp.expl_bcast) {
+            if (jcp.load_dim <= BIG_LOAD_DIM && spatial > SMALL_SPATIAL
+                    && spatial < BIG_SPATIAL)
+                reduce_blocking = nstl::min(jcp.reduce_dim, 80);
+            else if (spatial > SMALL_SPATIAL)
+                reduce_blocking = nstl::min(jcp.reduce_dim, 512);
+            else
+                reduce_blocking = nstl::min(jcp.reduce_dim, 256);
+
+            if ((jcp.mb > 28 && spatial >= 28)
+                    || (jcp.mb > 112 && spatial >= 17))
+                jcp.use_vmovntps = true;
+            else
+                jcp.use_vmovntps = false;
+        } else {
+
+            reduce_blocking = nb_reduce;
+            if (spatial <= SMALL_SPATIAL && jcp.reduce_dim >= BIG_REDUCE_DIM)
+                reduce_blocking = 16;
+            else if (spatial > SMALL_SPATIAL
+                    && jcp.reduce_dim >= BIG_REDUCE_DIM)
+                reduce_blocking = 8;
+            reduce_blocking = best_divider(nb_reduce, 1, reduce_blocking, true);
+            reduce_blocking *= jcp.reduce_block;
+        }
+
+        // Check input data cache aliasing.
+        // For other ISA constants may be updated.
+        // 64 * 1024 is chosen due to 1MB L2 16-way cache.
+        // 7 is empirical value. It is about half of 16.
+        // So we leave about half of the set for other data - weights, dst
+        int way_size = (64 * 1024) / jcp.typesize_in;
+        int max_hits = 7;
+        if (jcp.bcast_dim * reduce_blocking > way_size * max_hits) {
+            int nrb = reduce_blocking / simd_w;
+            int sp = jcp.bcast_dim;
+            int wl = way_size / simd_w;
+            for (int start_off = 0; start_off < jcp.ur; start_off++) {
+                for (int off = start_off, hits = 0; off < sp * nrb; off += wl) {
+                    if (off % sp >= jcp.ur || ++hits < max_hits)
+                        continue;
+                    int max_r_blocking = simd_w * nstl::max(1, (off + wl) / sp);
+                    reduce_blocking
+                            = nstl::min(reduce_blocking, max_r_blocking);
+                    break;
+                }
+            }
+        }
+
+        if (reduce_blocking < jcp.reduce_dim) {
+            jcp.use_vmovntps = false;
+            if (jcp.prop_kind == backward_data)
+                jcp.loop_order = reduce_src ? loop_lbr : loop_rlb;
+            else
+                jcp.loop_order = reduce_src ? loop_rbl : loop_rlb;
+        }
+        load_blocking = jcp.load_dim;
+
+        int load_size = jcp.load_dim * jcp.reduce_dim;
+        int bcast_size = jcp.mb * jcp.ngroups * jcp.bcast_dim * jcp.reduce_dim;
+
+        if (jcp.ver == ver_avx512_core && nthreads <= 28 && jcp.mb < nthreads
+                && nb_load * nb_bcast > nthreads) {
+            // Some heuristic here
+            float calc_koef = 0.01, best_cost = FLT_MAX;
+            int n_lgc = nthreads;
+            float ratio = (float)load_size / (float)bcast_size;
+            int best_lgc = ratio > 1 ? n_lgc : 1;
+            auto calc_job_cost = [&](int lb, int tg, float mem_k) {
+                int bb_size = jcp.mb * div_up(nb_bcast, tg);
+                float calc_size = (float)(bb_size * jcp.ur)
+                        * (lb * jcp.load_block) * jcp.reduce_dim;
+                float mem_size = (float)(bb_size * jcp.ur + lb * jcp.load_block)
+                        * jcp.reduce_dim;
+                return calc_koef * calc_size + mem_k * mem_size;
+            };
+            for (int lgc, ilgc = 0; ilgc < n_lgc; ilgc++) {
+                lgc = ratio > 1 ? n_lgc - ilgc : ilgc + 1;
+                int min_lb = nb_load / lgc;
+                int max_lb = div_up(nb_load, lgc);
+                int min_tg = nthreads / lgc;
+                int max_tg = div_up(nthreads, lgc);
+                // Some heuristic here
+                float mem_koef = (max_tg == 1) ? 1.f : 1.3f;
+                float job_cost = 0.;
+                if (nthreads % lgc < nb_load % lgc) {
+                    job_cost = calc_job_cost(max_lb, min_tg, mem_koef);
+                } else {
+                    auto job_cost1 = calc_job_cost(max_lb, max_tg, mem_koef);
+                    auto job_cost2 = calc_job_cost(min_lb, min_tg, mem_koef);
+                    job_cost = nstl::max(job_cost1, job_cost2);
+                }
+
+                if (job_cost < best_cost) {
+                    best_lgc = lgc;
+                    best_cost = job_cost;
+                }
+            }
+            jcp.load_grp_count = best_lgc;
+            load_blocking = div_up(nb_load, jcp.load_grp_count) * jcp.load_block;
+        } else {
+            jcp.load_grp_count = div_up(nthreads, jcp.mb * jcp.ngroups * nb_bcast);
+            jcp.load_grp_count = best_divider(
+                nthreads, jcp.load_grp_count, 2 * jcp.load_grp_count, false);
+        }
+
+        if (jcp.ver == ver_avx512_core && jcp.expl_bcast && jcp.bcast_dim <= 64
+                && load_size >= L2_size) {
+            jcp.load_grp_count = nstl::max(jcp.load_grp_count, 4);
+        } else if (jcp.bcast_dim <= 49 && jcp.mb <= nthreads
+                && jcp.load_dim > 512 && jcp.load_dim / jcp.reduce_dim >= 4) {
+            jcp.load_grp_count = nstl::max(jcp.load_grp_count, 2);
+            load_blocking = jcp.load_block;
+        }
+
+        if (jcp.ver == ver_4fma && jcp.bcast_dim * jcp.mb < jcp.load_dim
+                && jcp.oh * jcp.ow > 64
+                && IMPLICATION(reduce_src, jcp.load_dim < 1024)) {
+            /* Looking for best loading dimension blocking
+            * to get the best thread and data read/write efficiency
+            * by finding the optimal 'load_chunk' value
+            * Example:
+            * for 72 threads and convolution with mb=1, ih=iw=7, oc = 512
+            * the 'best' load_chunk value should be 1
+            * TODO: remove heuristic constants in above condition
+            * TODO: check this blocking for other ISA
+            */
+            float best_eff = -1.f;
+            int best_lgc = 1;
+
+            for (int load_chunk = 1; load_chunk <= nb_load; load_chunk++) {
+                int lgc = div_up(nb_load, load_chunk);
+                if (lgc > nthreads)
+                    continue;
+                int thr_per_grp = div_up(nthreads, lgc);
+                int bcast_per_thr = div_up(jcp.mb * nb_bcast, thr_per_grp)
+                        * jcp.bcast_block;
+                int load_per_thr = load_chunk * simd_w;
+                float data_norm = (bcast_per_thr + load_per_thr) / 2.f;
+                float data_eff = (bcast_per_thr * load_per_thr)
+                        / (data_norm * data_norm);
+                float thr_eff_over_grp = (float)nstl::max(1, nthreads / lgc)
+                        / div_up(nthreads, lgc);
+                float thr_eff_in_grp = ((float)jcp.mb * nb_bcast)
+                        / rnd_up(jcp.mb * nb_bcast, thr_per_grp);
+                float thr_eff = thr_eff_over_grp * thr_eff_in_grp;
+                float load_eff = (float)nb_load / rnd_up(nb_load, lgc);
+                float overall_eff = data_eff + thr_eff + load_eff;
+                if (overall_eff > best_eff) {
+                    best_eff = overall_eff;
+                    best_lgc = lgc;
+                }
+            }
+            jcp.load_grp_count = best_lgc;
+            load_blocking
+                    = div_up(nb_load, jcp.load_grp_count) * jcp.load_block;
+        }
+        bcast_blocking = div_up(jcp.mb * jcp.ngroups * nb_bcast,
+                                 div_up(nthreads, jcp.load_grp_count))
+                * jcp.bcast_block;
+        bcast_blocking = nstl::min(jcp.bcast_dim, bcast_blocking);
+        bcast_blocking = rnd_up(bcast_blocking, jcp.bcast_block);
+
+        int space_for_bcast
+                = (L2_capacity - /* kernel_size - */
+                    2 * jcp.load_block * reduce_blocking
+                        - jcp.ur * reduce_blocking - 3 * 1024);
+        if (jcp.reduce_dim * jcp.bcast_dim > L2_capacity)
+            space_for_bcast /= 2;
+
+        int bcast_in_cache
+                = nstl::max(jcp.bcast_block, space_for_bcast / reduce_blocking);
+        bcast_blocking = nstl::min(
+                bcast_blocking, rnd_dn(bcast_in_cache, jcp.bcast_block));
+
+        load_blocking_max = load_blocking;
+        bcast_blocking_max = bcast_blocking * 3 / 2;
+        reduce_blocking_max = reduce_blocking;
+
+    } else if (jcp.prop_kind == backward_weights) {
+
+        jcp.use_vmovntps = false;
+        if (jcp.is > SMALL_SPATIAL * SMALL_SPATIAL && jcp.ver == ver_4fma)
+            jcp.use_vmovntps = true;
+
+        if (jcp.transpose_src)
+            jcp.reduce_dim = jcp.tr_is;
+        else
+            jcp.reduce_dim = jcp.is;
+
+        if (jcp.ver == ver_4fma) {
+            // reduce_block should be divided by fma_step
+            jcp.reduce_block = best_divider(jcp.reduce_dim, 4, 16, true, 4);
+        } else {
+            jcp.reduce_block = best_divider(jcp.reduce_dim, 7, 16, true);
+            if (jcp.reduce_dim % jcp.reduce_block != 0)
+                jcp.reduce_block = best_divider(jcp.iw, 4, jcp.iw, false);
+            if (jcp.reduce_block > 256) {
+                jcp.reduce_block = 1;
+            }
+
+        }
+
+        jcp.load_dim = jcp.oc;
+        jcp.load_block = jcp.oc_block;
+
+        jcp.bcast_dim = jcp.ic;
+        jcp.bcast_block = jcp.ic_block;
+
+        if (jcp.ver == ver_avx512_core && jcp.reduce_block <= 19) {
+            // if reduce_block is big then generated JIT code may be big
+            // for small values of ur because reduce_loop_unroll = reduce_block
+            jcp.ur = jcp.bcast_block / 2;
+            jcp.expl_bcast = true;
+        } else {
+            jcp.ur = jcp.bcast_block;
+            jcp.expl_bcast = false;
+        }
+
+        jcp.reduce_loop_unroll = jcp.reduce_block;
+        jcp.reduce_loop_bcast_step
+            = jcp.reduce_loop_unroll * jcp.ic_block * jcp.typesize_in;
+        jcp.reduce_loop_load_step
+            = jcp.reduce_loop_unroll * jcp.oc_block * jcp.typesize_in;
+
+        jcp.bcast_loop_output_step =
+                                jcp.oc_block * jcp.ic_block * jcp.typesize_out;
+        jcp.bcast_loop_output_substep =
+            jcp.oc_block * jcp.ur * jcp.typesize_out;
+        jcp.bcast_loop_bcast_step =
+                jcp.ic_block * jcp.reduce_dim * jcp.typesize_in;
+        jcp.bcast_loop_bcast_substep = jcp.ur * jcp.typesize_in;
+
+        jcp.load_loop_load_step = jcp.oc_block * jcp.os * jcp.typesize_in;
+        jcp.load_loop_iter_step = jcp.oc_block;
+
+        /* --- */
+        balance(jcp, nthreads);
+
+        load_blocking = div_up(jcp.load_dim, jcp.load_block);
+        load_blocking = best_divider(load_blocking, 16, load_blocking, false);
+        load_blocking *= jcp.load_block;
+
+        load_blocking_max = load_blocking;
+        assert(jcp.load_dim % load_blocking == 0);
+
+        int max_bcast_blocking = div_up(jcp.bcast_dim, jcp.bcast_block);
+        int min_bcast_blocking = 5;
+
+        bcast_blocking = div_up(jcp.bcast_dim, jcp.bcast_block);
+        bcast_blocking = best_divider(
+                bcast_blocking, min_bcast_blocking, max_bcast_blocking, false);
+        bcast_blocking *= jcp.bcast_block;
+        bcast_blocking_max = bcast_blocking;
+        assert(jcp.bcast_dim % bcast_blocking == 0);
+
+        // for reduction balance
+        if (jcp.ver == ver_avx512_core) {
+            int max_reduce_blocking
+                    = nstl::min(L1_capacity / jcp.ur, jcp.reduce_dim);
+            int min_reduce_blocking = nstl::min(
+                    L1_capacity / jcp.ur, nstl::max(jcp.iw, jcp.ih));
+            reduce_blocking = best_divider(jcp.reduce_dim, min_reduce_blocking,
+                    max_reduce_blocking, true);
+            reduce_blocking
+                    = nstl::max(rnd_dn(reduce_blocking, jcp.reduce_block),
+                            jcp.reduce_block);
+        } else {
+            int max_reduce_blocking = L2_capacity
+                    / ((bcast_blocking + load_blocking) * jcp.reduce_block);
+            max_reduce_blocking = nstl::min(max_reduce_blocking,
+                    (L1_capacity / (jcp.bcast_block)) / jcp.reduce_block);
+
+            int num_jobs = div_up(jcp.load_dim, load_blocking)
+                    * div_up(jcp.bcast_dim, bcast_blocking);
+            int threads_per_job = nstl::max(1, nthreads / num_jobs);
+            reduce_blocking = div_up(jcp.mb * jcp.reduce_dim, jcp.reduce_block);
+            reduce_blocking = div_up(reduce_blocking, threads_per_job);
+
+            reduce_blocking = best_divider(reduce_blocking,
+                    max_reduce_blocking - 2, max_reduce_blocking, true);
+            reduce_blocking *= jcp.reduce_block;
+        }
+
+        reduce_blocking_max = rnd_dn(reduce_blocking * 3 / 2, jcp.reduce_block);
+    } else
+        return status::unimplemented;
+
+    assert(load_blocking);
+    assert(load_blocking_max);
+    assert(bcast_blocking);
+    assert(bcast_blocking_max);
+    assert(reduce_blocking);
+    assert(reduce_blocking_max);
+    assert(load_blocking % jcp.load_block == 0);
+    assert(reduce_blocking % jcp.reduce_block == 0);
+    assert(load_blocking_max % jcp.load_block == 0);
+    assert(reduce_blocking_max % jcp.reduce_block == 0);
+    if (jcp.ver == ver_4fma) {
+        assert(jcp.reduce_loop_unroll % jcp.fma_step == 0);
+        assert(jcp.reduce_dim % jcp.reduce_loop_unroll == 0);
+    }
+
+    assert(jcp.bcast_block % jcp.ur == 0);
+    assert(jcp.reduce_dim % jcp.reduce_block == 0);
+
+    jcp.ur_tail = jcp.bcast_dim % jcp.ur;
+
+    jcp.nb_bcast_blocking = bcast_blocking / jcp.bcast_block;
+    jcp.nb_bcast_blocking_max = bcast_blocking_max / jcp.bcast_block;
+    jcp.nb_load_blocking = load_blocking / jcp.load_block;
+    jcp.nb_load_blocking_max = load_blocking_max / jcp.load_block;
+    jcp.nb_reduce_blocking = reduce_blocking / jcp.reduce_block;
+    jcp.nb_reduce_blocking_max = reduce_blocking_max / jcp.reduce_block;
+
+    jcp.nb_bcast = div_up(jcp.bcast_dim, jcp.bcast_block);
+    jcp.nb_load = div_up(jcp.load_dim, jcp.load_block);
+    jcp.nb_reduce = div_up(jcp.reduce_dim, jcp.reduce_block);
+
+    return status::success;
+}
+
+void jit_avx512_common_1x1_conv_kernel::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad,
+        const jit_1x1_conv_conf_t &jcp) {
+    using namespace mkldnn::impl::memory_tracking::names;
+
+    if (jcp.prop_kind != backward_data && jcp.with_bias
+            && jcp.oc != jcp.oc_without_padding)
+        scratchpad.book(key_conv_padded_bias, jcp.typesize_out * jcp.oc);
+
+    if (jcp.prop_kind == backward_weights) {
+        const size_t wei_size = (size_t)jcp.ngroups * jcp.oc * jcp.ic;
+        scratchpad.book(key_conv_wei_reduction,
+                jcp.typesize_out * wei_size * (jcp.nthr_mb - 1));
+    }
+
+    if (jcp.transpose_src) {
+        const size_t tr_src_size =
+            (size_t)jcp.nthr_mb * jcp.ngroups * jcp.ic * jcp.tr_is;
+        scratchpad.book(key_conv_tr_src, jcp.typesize_out * tr_src_size);
+        scratchpad.book(key_conv_tr_src_bctx,
+                sizeof(simple_barrier::ctx_t) * jcp.nthr);
+    }
+}
+
+void jit_avx512_common_1x1_conv_kernel::balance(jit_1x1_conv_conf_t &jcp,
+        int nthreads)
+{
+    // initialize jcp reduction threading properties
+    jcp.nthr = jcp.nthr_mb = jcp.nthr_g = jcp.nthr_oc_b = jcp.nthr_ic_b = 1;
+    if (nthreads < jcp.ngroups) {
+        /* simplification... fortunately it doesn't hurt much */
+        return;
+    }
+    const int nb_bcast = div_up(jcp.bcast_dim, jcp.bcast_block);
+    const int nb_load = div_up(jcp.load_dim, jcp.load_block);
+    const int nb_reduce = div_up(jcp.reduce_dim, jcp.reduce_block);
+
+    jcp.nthr_g = jcp.ngroups;
+    const int nthr = nthreads / jcp.nthr_g;
+
+    auto calc_mem_cost = [=](int nthr_mb, int nthr_oc_b, int nthr_ic_b) {
+        /* calculate per thread memory cost (read/write). high level
+        * optimizer tries to minimize memory consumption. few notes: (n1)
+        * unclear why, but that essentially helps first convolution...
+        *  (n2) assuming the reduction over minibatch is always there:
+        *    - instead of 8 it should be 5 here (write ~= 2 read):
+        *      kernel: temporal workspace 1 write
+        *      reduction: 1 read from workspace and 1 write to the diff_wei
+        *    - but experiments showed 8 works better than 5 or 6... */
+        int bcast_koeff = 1;
+        int load_koeff = 1;
+        int output_koeff = 12;
+        if (jcp.transpose_src) {
+            bcast_koeff = 5;
+            load_koeff = 1;
+            output_koeff = 8;
+        }
+        return 0
+            + (size_t)bcast_koeff * div_up(jcp.mb * nb_reduce, nthr_mb)
+            * div_up(jcp.ngroups, jcp.nthr_g)
+            * div_up(nb_bcast, nthr_ic_b) * jcp.ic_block * jcp.reduce_block
+            / jcp.stride_h / jcp.stride_w /* (n1) */
+            + (size_t)load_koeff * div_up(jcp.mb * nb_reduce, nthr_mb)
+            * div_up(jcp.ngroups, jcp.nthr_g)
+            * div_up(nb_load, nthr_oc_b) * jcp.oc_block * jcp.reduce_block
+            + (size_t)output_koeff /* (n2) */
+            * div_up(jcp.ngroups, jcp.nthr_g) * div_up(nb_load, nthr_oc_b)
+            * div_up(nb_bcast, nthr_ic_b) * jcp.ic_block
+            * jcp.oc_block;
+    };
+
+    int nthr_mb = 1, nthr_oc_b = 1, nthr_ic_b = 1;
+    auto best_mem_cost = calc_mem_cost(nthr_mb, nthr_oc_b, nthr_ic_b);
+
+    /* step 1: find the best thread distribution with lowest memory cost */
+    const int nthr_mb_max = nstl::min(nthr, jcp.mb * nb_reduce);
+    for (nthr_mb = 1; nthr_mb <= nthr_mb_max; ++nthr_mb) {
+        const int nthr_par = nthr / nthr_mb;
+        const int nthr_oc_b_max = nstl::min(nthr_par, nb_load);
+        for (nthr_oc_b = 1; nthr_oc_b <= nthr_oc_b_max; ++nthr_oc_b) {
+            nthr_ic_b = nstl::min(nthr_par / nthr_oc_b, nb_bcast);
+            auto mem_cost = calc_mem_cost(nthr_mb, nthr_oc_b, nthr_ic_b);
+            if (mem_cost <= best_mem_cost) {
+                best_mem_cost = mem_cost;
+                jcp.nthr_mb = nthr_mb;
+                jcp.nthr_oc_b = nthr_oc_b;
+                jcp.nthr_ic_b = nthr_ic_b;
+            }
+        }
+
+        if (!mkldnn_thr_syncable()) { assert(nthr_mb == 1); break; }
+    }
+    if (jcp.nthr_mb > nthreads / 2 && jcp.nthr_mb < nthreads)
+        jcp.nthr_mb = nstl::min(jcp.mb, nthreads);
+
+    jcp.nthr = jcp.nthr_mb * jcp.nthr_g * jcp.nthr_oc_b * jcp.nthr_ic_b;
+    assert(jcp.nthr <= nthreads);
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_conv_kernel.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_conv_kernel.hpp
new file mode 100644
index 0000000000..d2ae017943
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_conv_kernel.hpp
@@ -0,0 +1,108 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_AVX512_COMMON_1x1_CONV_KERNEL_HPP
+#define JIT_AVX512_COMMON_1x1_CONV_KERNEL_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+#include "jit_uni_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_avx512_common_1x1_conv_kernel : public jit_generator {
+    jit_avx512_common_1x1_conv_kernel(jit_1x1_conv_conf_t ajcp,
+            const primitive_attr_t &attr)
+        : jcp(ajcp), attr_(attr), eltwise_injector_(nullptr)
+    {
+        if (jcp.with_eltwise)
+            eltwise_injector_ = new jit_uni_eltwise_injector_f32<avx512_common>(
+                    this, jcp.eltwise);
+
+        this->generate();
+        jit_ker = (void (*)(jit_1x1_conv_call_s *)) this->getCode();
+    }
+
+    ~jit_avx512_common_1x1_conv_kernel() {
+        delete eltwise_injector_;
+    }
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_common_1x1_conv_kernel)
+
+    static bool post_ops_ok(jit_1x1_conv_conf_t &jcp,
+                                const primitive_attr_t &attr);
+
+    static status_t init_conf(jit_1x1_conv_conf_t &jcp,
+            const convolution_desc_t &cd,
+            const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &dst_d,
+            const primitive_attr_t &attr,
+            int nthreads, bool reduce_src);
+
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_1x1_conv_conf_t &jcp);
+
+    jit_1x1_conv_conf_t jcp;
+    const primitive_attr_t &attr_;
+    void (*jit_ker)(jit_1x1_conv_call_s *);
+
+  private:
+    using reg64_t = const Xbyak::Reg64;
+    using zmm_t = const Xbyak::Zmm;
+
+    reg64_t reg_bcast_data = r8;
+    reg64_t reg_load_data = r10;
+    reg64_t reg_output_data = r9;
+    reg64_t aux_reg_bcast_data = r14;
+    reg64_t aux1_reg_bcast_data = rbx;
+    reg64_t aux_reg_load_data = r15;
+    reg64_t imm_addr64 = aux_reg_load_data;
+    reg64_t aux_reg_output_data = abi_not_param1;
+    reg64_t reg_load_loop_work = rsi;
+    reg64_t reg_reduce_loop_work = r11;
+    reg64_t bcast_loop_iter = rdx;
+    reg64_t reduce_loop_iter = abi_param1;
+    reg64_t reg_reduce_pos_flag = rax;
+    reg64_t reg_output_stride = r13;
+    reg64_t reg_bias_data = r12;
+    reg64_t reg_relu_ns = r13;
+    reg64_t reg_bcast_loop_work = aux1_reg_bcast_data;
+
+    Xbyak::Zmm vreg_bcast = Xbyak::Zmm(31);
+
+    jit_uni_eltwise_injector_f32<avx512_common> *eltwise_injector_;
+
+    int bcast_loop_work_offt = 0;
+    int stack_space_needed = 16;
+
+    void bcast_loop(int load_loop_blk);
+    void reduce_loop(int load_loop_blk, int ur, int substep, bool wraparound);
+
+    void generate();
+    static void balance(jit_1x1_conv_conf_t &jcp, int nthreads);
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_convolution.cpp
new file mode 100644
index 0000000000..54d58c8a39
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_convolution.cpp
@@ -0,0 +1,816 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_generator.hpp"
+
+#include "jit_avx512_common_1x1_convolution.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+
+#define data_blk_off(f, n, c, h, w) \
+    ((ndims == 3) \
+    ? (f).blk_off(n, c, w) \
+    : (f).blk_off(n, c, h, w))
+
+
+namespace {
+template <typename T, typename U>
+void balance2D(U nthr, U ithr, T ny, T &ny_start, T &ny_end,
+    T nx, T &nx_start, T &nx_end, T nx_divider)
+{
+    const int grp_count = nstl::min(nx_divider, nthr);
+    const int grp_size_big = nthr / grp_count + 1;
+    const int grp_size_small = nthr / grp_count;
+    const int n_grp_big = nthr % grp_count;
+    const int threads_in_big_groups = n_grp_big * grp_size_big;
+
+    const int ithr_bound_distance = ithr - threads_in_big_groups;
+    T grp, grp_ithr, grp_nthr;
+    if (ithr_bound_distance < 0) { // ithr in first groups
+        grp = ithr / grp_size_big;
+        grp_ithr = ithr % grp_size_big;
+        grp_nthr = grp_size_big;
+    } else { // ithr in last groups
+        grp = n_grp_big + ithr_bound_distance / grp_size_small;
+        grp_ithr = ithr_bound_distance % grp_size_small;
+        grp_nthr = grp_size_small;
+    }
+
+    balance211(nx, grp_count, grp, nx_start, nx_end);
+    balance211(ny, grp_nthr, grp_ithr, ny_start, ny_end);
+}
+}
+/* convolution forward */
+
+template <data_type_t src_type, data_type_t wei_type, data_type_t dst_type>
+void jit_avx512_common_1x1_convolution_fwd_t<src_type, wei_type, dst_type>::
+execute_forward(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const dst_data_t *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    auto scratchpad = this->scratchpad(ctx);
+
+    const auto &jcp = kernel_->jcp;
+    if (pd()->wants_padded_bias()) {
+        auto padded_bias = scratchpad.template get<dst_data_t>(
+                key_conv_padded_bias);
+        utils::array_copy(padded_bias, bias, jcp.oc_without_padding);
+        utils::array_set(padded_bias + jcp.oc_without_padding, 0.f,
+                jcp.oc - jcp.oc_without_padding);
+        bias = padded_bias;
+    }
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        execute_forward_thr(ithr, nthr, src, weights, bias, dst, scratchpad);
+    });
+
+    if (pd()->wants_zero_pad_dst())
+        ctx.memory(MKLDNN_ARG_DST)->zero_pad();
+}
+
+template <data_type_t src_type, data_type_t wei_type, data_type_t dst_type>
+void jit_avx512_common_1x1_convolution_fwd_t<src_type, wei_type, dst_type>::
+execute_forward_thr(const int ithr, const int nthr, const src_data_t *src,
+        const wei_data_t *weights, const dst_data_t *bias, dst_data_t *dst,
+        const memory_tracking::grantor_t &scratchpad) const {
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+    auto rtus_space = scratchpad.get<src_data_t>(key_conv_rtus_space);
+
+    const int ndims = src_d.ndims();
+    const int stride_h = (ndims == 3) ? 1 : pd()->desc()->strides[0];
+    const int stride_w = pd()->desc()->strides[ndims - 3];
+    const int pad_t = (ndims == 3) ? 0 : pd()->desc()->padding[0][0];
+    const int pad_l = pd()->desc()->padding[0][ndims - 3];
+
+    const int work_amount = jcp.mb * jcp.ngroups * jcp.nb_bcast;
+
+    auto step = [](int default_step, int remaining, int tail_step) {
+        assert(default_step <= tail_step);
+        return remaining < tail_step ? remaining : default_step;
+    };
+
+    auto p = jit_1x1_conv_call_s();
+
+    auto rp = rtus_driver_t<avx512_common>::call_params_t();
+
+    const int nb_oc = jcp.nb_load;
+    const int nb_ic = jcp.nb_reduce;
+    const int nb_ic_blocking = jcp.nb_reduce_blocking;
+    const int os_block = jcp.bcast_block;
+
+    int bcast_start{0}, bcast_end{0}, ocb_start{0}, ocb_end{0};
+    balance2D(nthr, ithr, work_amount, bcast_start, bcast_end,
+        jcp.nb_load, ocb_start, ocb_end, jcp.load_grp_count);
+
+    auto init_bcast = [&](int iwork, int &n, int &g, int &bcast_step,
+            int &oh, int &ow, int &ih, int &iw)
+    {
+        int osb{0};
+        nd_iterator_init(iwork, n, jcp.mb, g, jcp.ngroups, osb,
+            jcp.nb_bcast);
+        bcast_step = step(jcp.nb_bcast_blocking, jcp.nb_bcast - osb,
+                jcp.nb_bcast_blocking_max);
+        bcast_step = nstl::min(bcast_step, bcast_end - iwork);
+
+        const int os = osb * os_block;
+        oh = os / jcp.ow;
+        ow = os % jcp.ow;
+
+        ih = nstl::max(oh * stride_h - pad_t, 0);
+        iw = nstl::max(ow * stride_w - pad_l, 0);
+        rp.iw_start = iw;
+
+        p.bcast_dim = this_block_size(os, jcp.os,
+            bcast_step * os_block);
+        rp.os = p.bcast_dim;
+    };
+
+    auto init_load = [&](int ocb, int &load_step)
+    {
+        load_step = step(jcp.nb_load_blocking, ocb_end - ocb,
+            jcp.nb_load_blocking_max);
+        p.load_dim = this_block_size(ocb * jcp.oc_block,
+            ocb_end * jcp.oc_block, load_step * jcp.oc_block);
+    };
+
+    auto init_reduce = [&](int icb)
+    {
+        const int nb_ic_blocking_step =
+            nstl::min(icb + nb_ic_blocking, nb_ic) - icb;
+        p.first_last_flag = 0
+            | (icb == 0 ? FLAG_REDUCE_FIRST : 0)
+            | (icb + nb_ic_blocking_step >= nb_ic
+                    ? FLAG_REDUCE_LAST : 0);
+
+        p.reduce_dim = this_block_size(icb * jcp.ic_block,
+            jcp.ic, nb_ic_blocking_step * jcp.ic_block);
+        rp.icb = p.reduce_dim / jcp.reduce_block;
+    };
+
+    auto inner_ker = [&](int ocb, int icb, int n, int g, int oh, int ow,
+        int ih, int iw)
+    {
+
+        const int _ocb = g * nb_oc + ocb;
+        const size_t dst_off = data_blk_off(dst_d, n, _ocb, oh, ow);
+
+        p.output_data = &dst[dst_off];
+        p.bias_data = &bias[_ocb * jcp.oc_block];
+        p.load_data = &weights[pd()->with_groups()
+            ? weights_d.blk_off(g, ocb, icb)
+            : weights_d.blk_off(ocb, icb)];
+
+        const int _icb = g * nb_ic + icb;
+        if (pd()->rtus_.reduce_src_) {
+            rp.ws = rtus_space + ithr * pd()->rtus_.space_per_thread_
+                + _icb * jcp.is * jcp.ic_block;
+            if (ocb == ocb_start) {
+                rp.src = src + data_blk_off(src_d, n, _icb, ih, iw);
+                rtus_driver_->ker_(&rp);
+            }
+            p.bcast_data = rp.ws;
+        } else
+            p.bcast_data = src + data_blk_off(src_d, n, _icb, ih, iw);
+
+        kernel_->jit_ker(&p);
+    };
+
+    if (jcp.loop_order == loop_rlb) {
+        for (int icb = 0; icb < nb_ic; icb += nb_ic_blocking) {
+            init_reduce(icb);
+            int ocb = ocb_start;
+            while (ocb < ocb_end) {
+                int load_step;
+                init_load(ocb, load_step);
+                int iwork = bcast_start;
+                while (iwork < bcast_end) {
+                    int n, g, bcast_step, oh, ow, ih, iw;
+                    init_bcast(iwork, n, g, bcast_step, oh, ow, ih, iw);
+                    inner_ker(ocb, icb, n, g, oh, ow, ih, iw);
+                    iwork += bcast_step;
+                }
+                ocb += load_step;
+            }
+        }
+    } else if (jcp.loop_order == loop_lbr) {
+        int ocb = ocb_start;
+        while (ocb < ocb_end) {
+            int load_step;
+            init_load(ocb, load_step);
+            int iwork = bcast_start;
+            while (iwork < bcast_end) {
+                int n, g, bcast_step, oh, ow, ih, iw;
+                init_bcast(iwork, n, g, bcast_step, oh, ow, ih, iw);
+                for (int icb = 0; icb < nb_ic; icb += nb_ic_blocking) {
+                    init_reduce(icb);
+                    inner_ker(ocb, icb, n, g, oh, ow, ih, iw);
+                }
+                iwork += bcast_step;
+            }
+            ocb += load_step;
+        }
+    } else if (jcp.loop_order == loop_rbl) {
+        for (int icb = 0; icb < nb_ic; icb += nb_ic_blocking) {
+            init_reduce(icb);
+            int iwork = bcast_start;
+            while (iwork < bcast_end) {
+                int n, g, bcast_step, oh, ow, ih, iw;
+                init_bcast(iwork, n, g, bcast_step, oh, ow, ih, iw);
+                int ocb = ocb_start;
+                while (ocb < ocb_end) {
+                    int load_step;
+                    init_load(ocb, load_step);
+                    inner_ker(ocb, icb, n, g, oh, ow, ih, iw);
+                    ocb += load_step;
+                }
+                iwork += bcast_step;
+            }
+        }
+    } else if (jcp.loop_order == loop_blr) {
+        int iwork = bcast_start;
+        while (iwork < bcast_end) {
+            int n, g, bcast_step, oh, ow, ih, iw;
+            init_bcast(iwork, n, g, bcast_step, oh, ow, ih, iw);
+            int ocb = ocb_start;
+            while (ocb < ocb_end) {
+                int load_step;
+                init_load(ocb, load_step);
+                for (int icb = 0; icb < nb_ic; icb += nb_ic_blocking) {
+                    init_reduce(icb);
+                    inner_ker(ocb, icb, n, g, oh, ow, ih, iw);
+                }
+                ocb += load_step;
+            }
+            iwork += bcast_step;
+        }
+    } else {
+        assert(!"unsupported loop order");
+    }
+}
+
+
+template struct jit_avx512_common_1x1_convolution_fwd_t<data_type::f32>;
+/* convolution backward wtr data */
+
+template <data_type_t diff_dst_type, data_type_t wei_type,
+         data_type_t diff_src_type>
+void jit_avx512_common_1x1_convolution_bwd_data_t<diff_dst_type, wei_type,
+     diff_src_type>::execute_backward_data(const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto diff_src = CTX_OUT_MEM(diff_src_data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+
+    const auto &jcp = kernel_->jcp;
+    auto rtus_space = scratchpad(ctx).template get<diff_src_data_t>(
+            key_conv_rtus_space);
+
+    const int ndims = diff_src_d.ndims();
+
+    // TODO (Roma): remove this restriction
+    assert(jcp.stride_w == 1 && jcp.stride_h == 1);
+
+    const int stride_h = (ndims == 3) ? 1 : pd()->desc()->strides[0];
+    const int stride_w = pd()->desc()->strides[ndims - 3];
+    const int pad_t = (ndims == 3) ? 0 : pd()->desc()->padding[0][0];
+    const int pad_l = pd()->desc()->padding[0][ndims - 3];
+
+    const int nb_ic = jcp.nb_load;
+    const int nb_oc = jcp.nb_reduce;
+    const int os_block = jcp.bcast_block;
+    const int nb_oc_blocking = jcp.nb_reduce_blocking;
+
+    const int work_amount = jcp.mb * jcp.ngroups * jcp.nb_bcast;
+
+    auto step = [](int default_step, int remaining, int tail_step) {
+        assert(default_step <= tail_step);
+        return remaining < tail_step ? remaining : default_step;
+    };
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        auto p = jit_1x1_conv_call_s();
+        auto rp = rtus_driver_t<avx512_common>::call_params_t();
+
+        int bcast_start{0}, bcast_end{0}, icb_start{0}, icb_end{0};
+        balance2D(nthr, ithr, work_amount, bcast_start, bcast_end,
+            jcp.nb_load, icb_start, icb_end, jcp.load_grp_count);
+
+        bool reduce_outer = (jcp.loop_order == loop_rbl
+            || jcp.loop_order == loop_rlb);
+        int nboc_outer = reduce_outer ? nb_oc : 1;
+        int ocb_outer_step = reduce_outer ? nb_oc_blocking : 1;
+
+        int nboc_inner = reduce_outer ? 1 : nb_oc;
+        int ocb_inner_step = reduce_outer ? 1 : nb_oc_blocking;
+
+        for (int ocb_outer = 0; ocb_outer < nboc_outer;
+            ocb_outer += ocb_outer_step) {
+            size_t cur_ocb_outer =
+                nstl::min(ocb_outer + ocb_outer_step, nboc_outer) - ocb_outer;
+
+            int load_step = 0;
+            for (int icb = icb_start; icb < icb_end; icb += load_step) {
+                load_step = step(jcp.nb_load_blocking, jcp.nb_load - icb,
+                        jcp.nb_load_blocking_max);
+
+                p.load_dim = this_block_size(icb * jcp.ic_block,
+                    icb_end * jcp.ic_block, load_step * jcp.ic_block);
+                rp.icb = p.load_dim / jcp.ic_block;
+
+                int bcast_step;
+                for (int iwork = bcast_start; iwork < bcast_end;
+                    iwork += bcast_step)
+                {
+                    int n{0}, g{0}, osb{0};
+                    nd_iterator_init(iwork, n, jcp.mb, g, jcp.ngroups, osb,
+                            jcp.nb_bcast);
+
+                    bcast_step = step(jcp.nb_bcast_blocking, jcp.nb_bcast - osb,
+                            jcp.nb_bcast_blocking_max);
+                    bcast_step = nstl::min(bcast_step, bcast_end - iwork);
+
+                    const int os = osb * os_block;
+                    p.bcast_dim = this_block_size(os, jcp.os,
+                            bcast_step * os_block);
+                    rp.os = p.bcast_dim;
+
+                    const int oh = os / jcp.ow;
+                    const int ow = os % jcp.ow;
+                    const int ih = nstl::max(oh * stride_h - pad_t, 0);
+                    const int iw = nstl::max(ow * stride_w - pad_l, 0);
+                    rp.iw_start = iw;
+
+                    const int _icb = g * nb_ic + icb;
+                    rp.src = diff_src + data_blk_off(diff_src_d, n, _icb, ih, iw);
+                    if (pd()->rtus_.reduce_src_) {
+                        rp.ws = rtus_space
+                            + ithr * pd()->rtus_.space_per_thread_;
+                        p.output_data = rp.ws;
+                    } else
+                        p.output_data = rp.src;
+
+                    for (int ocb_inner = 0; ocb_inner < nboc_inner;
+                        ocb_inner += ocb_inner_step) {
+                        int cur_ocb_inner =
+                            nstl::min(ocb_inner + ocb_inner_step, nboc_inner) -
+                            ocb_inner;
+
+                        int ocb = reduce_outer ? ocb_outer : ocb_inner;
+                        int nb_oc_blocking_step = reduce_outer
+                            ? cur_ocb_outer : cur_ocb_inner;
+                        const int _ocb = g * nb_oc + ocb;
+                        size_t diff_dst_off = data_blk_off(diff_dst_d, n, _ocb, oh, ow);
+                        p.bcast_data = &diff_dst[diff_dst_off];
+
+                        p.load_data = &weights[pd()->with_groups()
+                            ? weights_d.blk_off(g, ocb, icb)
+                            : weights_d.blk_off(ocb, icb)];
+
+                        p.first_last_flag = ocb == 0 ? FLAG_REDUCE_FIRST : 0;
+
+                        p.reduce_dim = this_block_size(ocb * jcp.oc_block,
+                            jcp.oc, nb_oc_blocking_step * jcp.oc_block);
+
+                        kernel_->jit_ker(&p);
+                    }
+                    if (pd()->rtus_.reduce_src_)
+                        rtus_driver_->ker_(&rp);
+                }
+            }
+        }
+    });
+}
+
+template struct jit_avx512_common_1x1_convolution_bwd_data_t<data_type::f32>;
+
+/* convolution backward wtr weights */
+
+#define wht_blk_off(d, g, ...) \
+        (pd()->with_groups() \
+         ? (d).blk_off((g), __VA_ARGS__) \
+         : (d).blk_off(__VA_ARGS__))
+
+jit_avx512_common_1x1_convolution_bwd_weights_t ::
+        jit_avx512_common_1x1_convolution_bwd_weights_t(const pd_t *apd)
+    : cpu_primitive_t(apd)
+    , kernel_(nullptr), acc_ker_(nullptr), reducer_bias_(nullptr)
+    , trans_kernel_(nullptr), rtus_driver_(nullptr)
+{
+    kernel_ = new jit_avx512_common_1x1_conv_kernel(pd()->jcp_, *pd()->attr());
+    acc_ker_ = new cpu_accumulator_1d_t<data_type::f32>();
+    reducer_bias_ = new cpu_reducer_t<data_type::f32>(pd()->reducer_bia_conf_);
+    init_rtus_driver<avx512_common>(this);
+
+    const auto &jcp = kernel_->jcp;
+
+    if (jcp.transpose_src) {
+        auto tp = jit_transpose4x16_src_t();
+        tp.src_pf0_distance = 4;
+        tp.tr_src_pf0_distance = 0;
+        tp.src_pf1 = true;
+        tp.tr_src_pf1 = false;
+        trans_kernel_ = new jit_transpose4x16_src(&jcp, &tp);
+    }
+}
+
+void jit_avx512_common_1x1_convolution_bwd_weights_t::execute_backward_weights(
+        const exec_ctx_t &ctx) const
+{
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_weights = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
+    auto diff_bias_in = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_BIAS);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+
+    const auto scratchpad = this->scratchpad(ctx);
+
+    auto rtus_space = scratchpad.get<data_t>(key_conv_rtus_space);
+    data_t *diff_bias = pd()->wants_padded_bias()
+        ? scratchpad.get<data_t>(key_conv_padded_bias) : diff_bias_in;
+    auto wei_reduction = scratchpad.get<data_t>(key_conv_wei_reduction);
+
+    /* prepare src transposition barriers */
+    auto tr_src = scratchpad.get<data_t>(key_conv_tr_src);
+    auto tr_src_bctx = scratchpad.get<simple_barrier::ctx_t>(
+            key_conv_tr_src_bctx);
+    if (jcp.transpose_src) {
+        for (int i = 0; i < jcp.nthr; ++i)
+            simple_barrier::ctx_init(&tr_src_bctx[i]);
+    }
+
+    const int ndims = src_d.ndims();
+    const int wei_size = jcp.ngroups * jcp.oc * jcp.ic;
+
+    simple_barrier::ctx_t reduction_barrier;
+    simple_barrier::ctx_init(&reduction_barrier);
+
+    const auto reducer_bia_scratchpad = memory_tracking::grantor_t(scratchpad,
+            prefix_reducer_bia);
+    auto rb = this->reducer_bias_;
+    rb->init(reducer_bia_scratchpad);
+
+    // TODO (Roma): remove this restriction
+    assert(jcp.stride_w == 1 && jcp.stride_h == 1);
+
+    const int nb_ic = jcp.nb_bcast;
+    const int nb_ic_blocking = jcp.nb_bcast_blocking;
+
+    const int nb_oc = jcp.nb_load;
+    const int nb_oc_blocking = jcp.nb_load_blocking;
+
+    const int sp_nb = jcp.nb_reduce;
+    const int mb_sp_work = jcp.mb * sp_nb;
+
+    const int stride_h = (ndims == 3) ? 1 : pd()->desc()->strides[0];
+    const int stride_w = pd()->desc()->strides[ndims - 3];
+    const int pad_t = (ndims == 3) ? 0 : pd()->desc()->padding[0][0];
+    const int pad_l = pd()->desc()->padding[0][ndims - 3];
+
+    auto step = [](int default_step, int remaining, int tail_step) {
+        assert(default_step <= tail_step);
+        return remaining < tail_step ? remaining : default_step;
+    };
+
+    // TODO: use memory descriptor with the same fmt as src
+    // (or use a macro :))
+    auto tr_src_off = [&](int img, int icb, int is) {
+        const size_t tr_chn_size = jcp.tr_is * jcp.ic_block;
+        const size_t tr_img_size = tr_chn_size * nb_ic * jcp.ngroups;
+        return img * tr_img_size + icb * tr_chn_size + is * jcp.ic_block;
+    };
+
+    auto uker_trans = [&](int ithr_mb, int img, int sp_b_start, int sp_size,
+        int g_start, int g_work, int ic_b_start, int ic_b_work,
+        int ithr, int nthr, int first_ic_b)
+    {
+        const int work_amount = g_work * ic_b_work;
+
+        int start{ 0 }, end{ 0 };
+        balance211(work_amount, nthr, ithr, start, end);
+
+        int g{ 0 }, ic_b{ 0 };
+        nd_iterator_init(start, g, g_work, ic_b, ic_b_work);
+        g += g_start;
+        const int ic_b_tr = g * nb_ic + first_ic_b + ic_b;
+        ic_b += ic_b_start;
+
+        const int _ic = g * nb_ic + ic_b;
+
+        const int is = sp_b_start * jcp.reduce_block;
+        const int ih = is / jcp.iw;
+        const int iw = is % jcp.iw;
+
+        const int src1_off = data_blk_off(src_d, img, _ic, ih, iw);
+        data_t *src1 = (data_t *)&src[src1_off];
+        data_t *tr_src1 = &tr_src[tr_src_off(ithr_mb, ic_b_tr, is)];
+
+        assert(jcp.ic_block == 16);
+        const int src_stride = jcp.is * jcp.ic_block;
+        const int tr_src_stride = jcp.tr_is * jcp.ic_block;
+
+        const int my_work = end - start;
+        for (int iwork = 0; iwork < my_work; iwork++) {
+            auto par_trans = jit_src_transpose_s();
+            assert(sp_size % 4 == 0 || sp_size % 4 == jcp.is % 4);
+            par_trans.size = sp_size;
+            par_trans.src = src1;
+            par_trans.tr_src = tr_src1;
+            par_trans.src_prf = src1 + 64 * 16;
+            par_trans.tr_src_prf = tr_src1 + 80 * 16;
+            trans_kernel_->jit_ker(&par_trans);
+
+            src1 += src_stride;
+            tr_src1 += tr_src_stride;
+        }
+    };
+
+    auto ker = [&](const int ithr, const int nthr) {
+        assert(nthr == jcp.nthr);
+        assert(IMPLICATION(!mkldnn_thr_syncable(), jcp.nthr_mb == 1));
+
+        const int ithr_ic_b = ithr % jcp.nthr_ic_b;
+        const int ithr_oc_b = ithr / jcp.nthr_ic_b % jcp.nthr_oc_b;
+        const int ithr_g = ithr / jcp.nthr_ic_b / jcp.nthr_oc_b % jcp.nthr_g;
+        const int ithr_mb = ithr / jcp.nthr_ic_b / jcp.nthr_oc_b /
+                            jcp.nthr_g;
+
+        const int ithr_but_oc
+                = (ithr_mb * jcp.nthr_g + ithr_g) * jcp.nthr_ic_b + ithr_ic_b;
+
+        /* reduction dimension */
+        int mb_sp_b_start{ 0 }, mb_sp_b_end{ 0 };
+        if (jcp.transpose_src && jcp.nthr_mb < jcp.mb / 2) {
+            // it's preferable to parallelize by mb if possible
+            int img_start{ 0 }, img_end{ 0 };
+            balance211(jcp.mb, jcp.nthr_mb, ithr_mb, img_start, img_end);
+            mb_sp_b_start = img_start * sp_nb;
+            mb_sp_b_end = img_end * sp_nb;
+        }
+        else {
+            balance211(mb_sp_work, jcp.nthr_mb, ithr_mb, mb_sp_b_start,
+                    mb_sp_b_end);
+        }
+
+        /* independent dimensions */
+        int g_start{ 0 }, oc_b_start{ 0 }, ic_b_start{ 0 };
+        int g_end{ 0 }, oc_b_end{ 0 }, ic_b_end{ 0 };
+
+        balance211(jcp.ngroups, jcp.nthr_g, ithr_g, g_start, g_end);
+        balance211(jcp.nb_load, jcp.nthr_oc_b, ithr_oc_b, oc_b_start,
+                    oc_b_end);
+        balance211(jcp.nb_bcast, jcp.nthr_ic_b, ithr_ic_b, ic_b_start,
+                    ic_b_end);
+
+        const int g_work = g_end - g_start;
+        const int oc_b_work = oc_b_end - oc_b_start;
+        const int ic_b_work = ic_b_end - ic_b_start;
+
+        data_t *diff_wei = ithr_mb == 0
+            ? diff_weights : wei_reduction + (ithr_mb - 1) * wei_size;
+
+        int sp_b_step = 0;
+        for (int mb_sp_b = mb_sp_b_start; mb_sp_b < mb_sp_b_end;
+                mb_sp_b += sp_b_step) {
+            int img{ 0 }, sp_b{ 0 };
+            nd_iterator_init(mb_sp_b, img, jcp.mb, sp_b, sp_nb);
+            sp_b_step = step(jcp.nb_reduce_blocking,
+                    nstl::min(sp_nb - sp_b, mb_sp_b_end - mb_sp_b),
+                    jcp.nb_reduce_blocking_max);
+
+            for (int g = g_start; g < g_end; ++g) {
+                int load_step = 0;
+                int bcast_step = 0;
+                for (int ic_b = ic_b_start; ic_b < ic_b_end;
+                        ic_b += bcast_step) {
+                    bcast_step = step(nb_ic_blocking, ic_b_end - ic_b,
+                            jcp.nb_bcast_blocking_max);
+                    if (jcp.transpose_src) {
+                        if (jcp.nthr_oc_b > 1)
+                            simple_barrier::barrier(
+                                    &tr_src_bctx[ithr_but_oc], jcp.nthr_oc_b);
+                        const int sp_size
+                                = nstl::min(sp_b_step * jcp.reduce_block,
+                                        jcp.is - sp_b * jcp.reduce_block);
+                        uker_trans(ithr_mb, img, sp_b, sp_size, g, 1, ic_b,
+                            bcast_step, ithr_oc_b, jcp.nthr_oc_b, ic_b_start);
+                        if (jcp.nthr_oc_b > 1)
+                            simple_barrier::barrier(
+                                    &tr_src_bctx[ithr_but_oc], jcp.nthr_oc_b);
+                    }
+
+                    for (int oc_b = oc_b_start; oc_b < oc_b_end;
+                            oc_b += load_step) {
+                        load_step = step(nb_oc_blocking, oc_b_end - oc_b,
+                                jcp.nb_load_blocking_max);
+                        const int _ic_b = g * nb_ic + ic_b;
+                        const int _ic_b_tr = g * nb_ic + ic_b_start;
+                        const int _oc_b = g * nb_oc + oc_b;
+
+                        data_t *store_to;
+
+                        const size_t off
+                                = wht_blk_off(diff_weights_d, g, oc_b, ic_b);
+                        store_to = diff_wei + off;
+
+                        const data_t *diff_src = jcp.transpose_src ?
+                                &tr_src[tr_src_off(ithr_mb, _ic_b_tr, 0)] :
+                                &src[src_d.blk_off(img, _ic_b)];
+
+                        int sp_b_end = sp_b + sp_b_step;
+                        const data_t *pdiff_dst
+                                = &diff_dst[diff_dst_d.blk_off(img, _oc_b)];
+                        const data_t *local_src = diff_src;
+
+                        auto p = jit_1x1_conv_call_s();
+                        auto rp = rtus_driver_t<avx512_common>::call_params_t();
+
+                        p.output_stride
+                                = jcp.ic * jcp.oc_block * jcp.typesize_out;
+
+                        p.load_dim = load_step * jcp.oc_block;
+
+                        p.bcast_dim = bcast_step * jcp.ic_block;
+                        rp.icb = bcast_step;
+                        p.output_data = store_to;
+
+                        p.reduce_dim = sp_b_step * jcp.reduce_block;
+                        rp.os = p.reduce_dim;
+
+                        p.first_last_flag = 0
+                            | (mb_sp_b == mb_sp_b_start ? FLAG_REDUCE_FIRST : 0)
+                            | (sp_b_end == sp_nb ? FLAG_SP_LAST : 0);
+
+                        int sp = sp_b * jcp.reduce_block;
+                        p.load_data = pdiff_dst + sp * jcp.oc_block;
+
+                        if (pd()->rtus_.reduce_src_) {
+                            const int oh = sp / jcp.ow;
+                            const int ow = sp % jcp.ow;
+
+                            const int ih = nstl::max(oh * stride_h - pad_t, 0);
+                            const int iw = nstl::max(ow * stride_w - pad_l, 0);
+                            rp.iw_start = iw;
+
+                            rp.ws = rtus_space
+                                + ithr * pd()->rtus_.space_per_thread_
+                                + sp * jcp.ic_block;
+
+                            if (ndims == 3)
+                                rp.src = local_src + iw
+                                    * src_d.blocking_desc().strides[2];
+                            else
+                                rp.src = local_src + ih
+                                    * src_d.blocking_desc().strides[2]
+                                    + iw * src_d.blocking_desc().strides[3];
+                            rtus_driver_->ker_(&rp);
+
+                            p.bcast_data = rp.ws;
+                        } else
+                            p.bcast_data = local_src + sp * jcp.ic_block;
+
+                        kernel_->jit_ker(&p);
+                    }
+                }
+            }
+        }
+
+        /* diff_weights[:] += sum(wei_reduction[thr_mb][:]) */
+        if (jcp.nthr_mb > 1) {
+            simple_barrier::barrier(&reduction_barrier, jcp.nthr);
+            const int work = g_work * oc_b_work * ic_b_work;
+            int start{ 0 }, end{ 0 };
+            balance211(work, jcp.nthr_mb, ithr_mb, start, end);
+            if (start == end)
+                return;
+
+            for (int thr_mb = 1; thr_mb < jcp.nthr_mb; ++thr_mb) {
+                int w = start;
+                int sub_g_start{ 0 }, sub_oc_b_start{ 0 },
+                        sub_ic_b_start{ 0 };
+                nd_iterator_init(w, sub_g_start, g_work, sub_oc_b_start,
+                        oc_b_work, sub_ic_b_start, ic_b_work);
+                while (w < end) {
+                    const int g = g_start + sub_g_start;
+                    const int oc_b = oc_b_start + sub_oc_b_start;
+                    const int ic_b = ic_b_start + sub_ic_b_start;
+
+                    const int acc_size
+                            = nstl::min(end - w, ic_b_work - sub_ic_b_start)
+                            * jcp.ic_block * jcp.oc_block;
+
+                    const size_t off
+                            = wht_blk_off(diff_weights_d, g, oc_b, ic_b);
+                    data_t *d = diff_weights + off;
+                    data_t *s = wei_reduction + (thr_mb - 1) * wei_size + off;
+
+                    acc_ker_->accumulate(d, s, acc_size);
+
+                    nd_iterator_jump(w, end, sub_g_start, g_work,
+                            sub_oc_b_start, oc_b_work, sub_ic_b_start,
+                            ic_b_work);
+                }
+            }
+        }
+    };
+
+    auto ker_bias = [&](int ithr, int nthr) {
+        assert(nthr == rb->balancer().nthr_);
+
+        const int b_job_start = rb->balancer().ithr_job_off(ithr);
+        const int b_njobs = rb->balancer().ithr_njobs(ithr);
+
+        if (b_njobs == 0)
+            return;
+
+        /* reduction dimension */
+        int img_start{ 0 }, img_end{ 0 };
+
+        balance211(jcp.mb, rb->balancer().nthr_per_group_,
+                rb->balancer().id_in_group(ithr), img_start, img_end);
+
+        /* jobs */
+        int g_start{ 0 }, ocb_start{ 0 };
+        nd_iterator_init(
+                b_job_start, g_start, jcp.ngroups, ocb_start, jcp.nb_load);
+
+        for (int img = img_start; img < img_end; ++img) {
+            int g = g_start, ocb = ocb_start;
+            for (int b_job_loc = 0; b_job_loc < b_njobs; ++b_job_loc) {
+                const size_t _oc = g * jcp.nb_load + ocb;
+
+                const data_t *d_dst = &diff_dst[diff_dst_d.blk_off(img, _oc)];
+                data_t *d_bias = rb->get_local_ptr(ithr, diff_bias,
+                        reducer_bia_scratchpad)
+                    + b_job_loc * rb->balancer().job_size_;
+
+                if (img == img_start)
+                    for (int o = 0; o < 16; ++o)
+                        d_bias[o] = 0.;
+
+                for (int hw = 0; hw < jcp.oh * jcp.ow; ++hw) {
+                    PRAGMA_OMP_SIMD()
+                    for (int o = 0; o < 16; ++o)
+                        d_bias[o] += d_dst[o];
+                    d_dst += 16;
+                }
+
+                nd_iterator_step(g, jcp.ngroups, ocb, jcp.nb_load);
+            }
+        }
+        rb->reduce(ithr, diff_bias, reducer_bia_scratchpad);
+    };
+
+    parallel(jcp.nthr, [&](const int ithr, const int nthr) {
+        ker(ithr, jcp.nthr);
+        if (pd()->with_bias())
+            ker_bias(ithr, jcp.nthr);
+    });
+
+    /* TODO: put this in ker_bias */
+    if (pd()->wants_padded_bias()) {
+        assert(jcp.ngroups == 1);
+        utils::array_copy(diff_bias_in, diff_bias, jcp.oc_without_padding);
+    }
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_convolution.hpp
new file mode 100644
index 0000000000..2e9fda76d6
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_1x1_convolution.hpp
@@ -0,0 +1,344 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_COMMON_1x1_CONVOLUTION_HPP
+#define CPU_JIT_AVX512_COMMON_1x1_CONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+#include "cpu_reducer.hpp"
+
+#include "jit_avx512_common_1x1_conv_kernel.hpp"
+#include "jit_uni_1x1_conv_utils.hpp"
+#include "jit_transpose_src_utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t src_type,
+         impl::data_type_t wei_type = src_type,
+         impl::data_type_t dst_type = src_type>
+struct jit_avx512_common_1x1_convolution_fwd_t : public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_(), rtus_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_1x1:", avx512_common, ""),
+                jit_avx512_common_1x1_convolution_fwd_t);
+
+        status_t init() {
+            using namespace utils;
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(src_type, wei_type, dst_type, dst_type,
+                        data_type::undef)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            const convolution_desc_t *conv_d = desc();
+            const memory_desc_t *src_d = src_md();
+            rtus_prepare(this, conv_d, src_d, dst_md());
+
+            status_t status = jit_avx512_common_1x1_conv_kernel::init_conf(
+                    jcp_, *conv_d, *src_d, *weights_md(), *dst_md(), *attr(),
+                    mkldnn_get_max_threads(), rtus_.reduce_src_);
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx512_common_1x1_conv_kernel::init_scratchpad(scratchpad,
+                    jcp_);
+
+            rtus_prepare_space_info(this, scratchpad);
+
+            return status::success;
+        }
+
+        jit_1x1_conv_conf_t jcp_;
+        reduce_to_unit_stride_t rtus_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = utils::pick(ndims() - 3, nCw16c, nChw16c, nCdhw16c);
+            auto wei_tag = utils::pick(2 * ndims() - 6 + with_groups(),
+                    OIw16i16o, gOIw16i16o, OIhw16i16o, gOIhw16i16o);
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    template <cpu_isa_t isa, typename conv_t>
+    friend void init_rtus_driver(conv_t *self);
+
+    jit_avx512_common_1x1_convolution_fwd_t(const pd_t *apd)
+        : cpu_primitive_t(apd)
+        , kernel_(nullptr), rtus_driver_(nullptr)
+    {
+        kernel_ =
+            new jit_avx512_common_1x1_conv_kernel(pd()->jcp_, *pd()->attr());
+        init_rtus_driver<avx512_common>(this);
+    }
+
+    ~jit_avx512_common_1x1_convolution_fwd_t() {
+        delete kernel_;
+        delete rtus_driver_;
+    }
+
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<wei_type>::type wei_data_t;
+    typedef typename prec_traits<dst_type>::type dst_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+  private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    void execute_forward_thr(const int ithr, const int nthr,
+            const src_data_t *src, const wei_data_t *weights,
+            const dst_data_t *bias, dst_data_t *dst,
+            const memory_tracking::grantor_t &scratchpad) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx512_common_1x1_conv_kernel *kernel_;
+    rtus_driver_t<avx512_common> *rtus_driver_;
+};
+
+using jit_avx512_common_1x1_convolution_fwd_f32_t
+        = jit_avx512_common_1x1_convolution_fwd_t<data_type::f32>;
+
+template <impl::data_type_t diff_dst_type,
+          impl::data_type_t wei_type = diff_dst_type,
+          impl::data_type_t diff_src_type = diff_dst_type>
+struct jit_avx512_common_1x1_convolution_bwd_data_t : public cpu_primitive_t {
+    struct pd_t : public cpu_convolution_bwd_data_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_(), rtus_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_1x1:", avx512_common, ""),
+                jit_avx512_common_1x1_convolution_bwd_data_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_data
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(diff_src_type, wei_type, data_type::undef,
+                        diff_dst_type, data_type::undef)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            const convolution_desc_t *conv_d = desc();
+            const memory_desc_t *diff_src_d = diff_src_md();
+            rtus_prepare(this, conv_d, diff_src_d, diff_dst_md());
+
+            status_t status = jit_avx512_common_1x1_conv_kernel::init_conf(
+                    jcp_, *conv_d, *diff_src_d, *weights_md(), *diff_dst_md(),
+                    *attr(), mkldnn_get_max_threads(), rtus_.reduce_src_);
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx512_common_1x1_conv_kernel::init_scratchpad(scratchpad,
+                    jcp_);
+
+            rtus_prepare_space_info(this, scratchpad);
+
+            return status::success;
+        }
+
+        // TODO (Roma): structs conf header cleanup
+        jit_1x1_conv_conf_t jcp_;
+        reduce_to_unit_stride_t rtus_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = utils::pick(ndims() - 3, nCw16c, nChw16c, nCdhw16c);
+            auto wei_tag = utils::pick(2 * ndims() - 6 + with_groups(),
+                    IOw16o16i, gIOw16o16i, IOhw16o16i, gIOhw16o16i);
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    template <cpu_isa_t isa, typename conv_t>
+    friend void init_rtus_driver(conv_t *self);
+
+    jit_avx512_common_1x1_convolution_bwd_data_t(const pd_t *apd)
+        : cpu_primitive_t(apd)
+        , kernel_(nullptr), rtus_driver_(nullptr)
+    {
+        kernel_ = new jit_avx512_common_1x1_conv_kernel(pd()->jcp_,
+                    *pd()->attr());
+        init_rtus_driver<avx512_common>(this);
+    }
+
+    ~jit_avx512_common_1x1_convolution_bwd_data_t() {
+        delete kernel_;
+        delete rtus_driver_;
+    }
+
+    typedef typename prec_traits<diff_dst_type>::type diff_dst_data_t;
+    typedef typename prec_traits<wei_type>::type wei_data_t;
+    typedef typename prec_traits<diff_src_type>::type diff_src_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_data(ctx);
+        return status::success;
+    }
+
+  private:
+    void execute_backward_data(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx512_common_1x1_conv_kernel *kernel_;
+    rtus_driver_t<avx512_common> *rtus_driver_;
+};
+
+using jit_avx512_common_1x1_convolution_bwd_data_f32_t
+        = jit_avx512_common_1x1_convolution_bwd_data_t<data_type::f32>;
+
+struct jit_avx512_common_1x1_convolution_bwd_weights_t : public cpu_primitive_t
+{
+    struct pd_t : public cpu_convolution_bwd_weights_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_weights_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_(), rtus_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_1x1:", avx512_common, ""),
+                jit_avx512_common_1x1_convolution_bwd_weights_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_weights
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            const convolution_desc_t *conv_d = desc();
+            const memory_desc_t *src_d = src_md();
+            rtus_prepare(this, conv_d, src_d, diff_dst_md());
+
+            status_t status = jit_avx512_common_1x1_conv_kernel::init_conf(
+                    jcp_, *conv_d, *src_d, *diff_weights_md(), *diff_dst_md(),
+                    *attr(), mkldnn_get_max_threads(), rtus_.reduce_src_);
+            if (status != status::success) return status;
+
+            init_balancers();
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx512_common_1x1_conv_kernel::init_scratchpad(scratchpad,
+                    jcp_);
+
+            auto reducer_bia_scratchpad = memory_tracking::registrar_t(
+                    scratchpad, memory_tracking::names::prefix_reducer_bia);
+            reducer_bia_conf_.init_scratchpad(reducer_bia_scratchpad);
+
+            rtus_prepare_space_info(this, scratchpad);
+
+            return status::success;
+        }
+
+        // TODO (Roma): structs conf header cleanup
+        jit_1x1_conv_conf_t jcp_;
+        cpu_reducer_t<data_type::f32>::conf_t reducer_bia_conf_;
+        reduce_to_unit_stride_t rtus_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = utils::pick(ndims() - 3, nCw16c, nChw16c, nCdhw16c);
+            auto wei_tag = utils::pick(2 * ndims() - 6 + with_groups(),
+                    OIw16i16o, gOIw16i16o, OIhw16i16o, gOIhw16i16o);
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+
+    private:
+        void init_balancers() {
+            const size_t max_buffer_size = jcp_.nthr * 3 * 5 * 5 * 16 * 16;
+            if (with_bias()) {
+                reducer_bia_conf_.init(reduce_balancer_t(jcp_.nthr,
+                            jcp_.oc_block, jcp_.ngroups * jcp_.nb_load,
+                            jcp_.mb, max_buffer_size));
+            }
+        }
+    };
+
+    template <cpu_isa_t isa, typename conv_t>
+    friend void init_rtus_driver(conv_t *self);
+
+    jit_avx512_common_1x1_convolution_bwd_weights_t(const pd_t *apd);
+
+    ~jit_avx512_common_1x1_convolution_bwd_weights_t() {
+        delete kernel_;
+        delete acc_ker_;
+        delete reducer_bias_;
+        delete rtus_driver_;
+        delete trans_kernel_;
+    }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_weights(ctx);
+        return status::success;
+    }
+
+  private:
+    void execute_backward_weights(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx512_common_1x1_conv_kernel *kernel_;
+    cpu_accumulator_1d_t<data_type::f32> *acc_ker_;
+    cpu_reducer_t<data_type::f32> *reducer_bias_;
+    jit_transpose4x16_src *trans_kernel_;
+    rtus_driver_t<avx512_common> *rtus_driver_;
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_kernel.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_kernel.cpp
new file mode 100644
index 0000000000..235fb02fef
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_kernel.cpp
@@ -0,0 +1,4539 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_barrier.hpp"
+
+#include "jit_avx512_common_conv_kernel.hpp"
+
+#define GET_OFF(field) offsetof(jit_conv_call_s, field)
+#define KNx_L2_EFFECTIVE_CAPACITY ((512-64)*1024)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+using namespace Xbyak;
+
+namespace {
+
+constexpr auto small_spatial = 14;
+unsigned int L1_cache_size = get_cache_size(1, true);
+
+inline void pick_loop_order(jit_conv_conf_t &jcp) {
+    using namespace prop_kind;
+    assert(one_of(jcp.prop_kind,
+                forward_training, forward_inference, backward_data));
+    auto w = (jcp.prop_kind == backward_data) ? jcp.iw : jcp.ow;
+    auto h = (jcp.prop_kind == backward_data) ? jcp.ih : jcp.oh;
+
+    // ow-threading is currently implemented for forward only
+    // TODO: single code for fwd and bwd after ow-thr for bwd
+    // meaningless switch was removed
+    if (jcp.prop_kind == backward_data) {
+        jcp.loop_order = (w <= small_spatial && h <= small_spatial)
+            ? loop_cgn : loop_gnc;
+    } else {
+        jcp.loop_order = (w <= small_spatial && h <= small_spatial)
+            ? loop_cwgn : loop_gncw;
+    }
+}
+
+inline bool is_1stconv(const jit_conv_conf_t &jcp) {
+    if (mayiuse(avx512_core))
+        return (jcp.ic < 16 && jcp.ngroups == 1);
+    else
+        return one_of(jcp.ic, 1, 3);
+}
+
+inline bool is_ow_threading_on(const jit_conv_conf_t &jcp) {
+    return (jcp.nb_ow > 1);
+}
+
+inline bool is_owb_prefetching(const jit_conv_conf_t &jcp) {
+    return (jcp.ver == ver_4fma && is_ow_threading_on(jcp));
+}
+
+}
+
+template<typename Vmm>
+void _jit_avx512_common_conv_fwd_kernel<Vmm>::prepare_output(int ur_w)
+{
+    for (int k = 0; k < jcp.nb_oc_blocking; k++)
+        for (int j = 0; j < ur_w; j++) {
+            Vmm vmm = vmm_out(j, k);
+            vpxord(vmm, vmm, vmm);
+            if (!is_owb_prefetching(jcp)) {
+                size_t aux_output_offset = get_output_offset(j, k);
+                mic_prefetcht1(EVEX_compress_addr_safe(reg_out_prf,
+                            aux_output_offset, reg_out_long_offt));
+            }
+        }
+}
+
+template<typename Vmm>
+void _jit_avx512_common_conv_fwd_kernel<Vmm>::store_output(int ur_w)
+{
+    Label no_update_label, store_label, eltwise_label;
+
+    mov(reg_channel, ptr[param1 + GET_OFF(channel)]);
+    if (jcp.with_bias) {
+        mov(reg_bias, ptr[param1 + GET_OFF(bias)]);
+    }
+
+    if (!jcp.with_sum) {
+        cmp(reg_channel, 0);
+        je(no_update_label, T_NEAR);
+    }
+
+    for (int k = 0; k < jcp.nb_oc_blocking; k++)
+        for (int j = 0; j < ur_w; j++) {
+            Vmm vmm = vmm_out(j, k);
+            size_t aux_output_offset = get_output_offset(j, k);
+            vaddps(vmm,
+                make_safe_addr(reg_out, aux_output_offset, reg_out_long_offt));
+        }
+
+    if (!jcp.with_sum) {
+        jmp(eltwise_label, T_NEAR);
+    } else {
+        cmp(reg_channel, 0);
+        jne(eltwise_label, T_NEAR);
+    }
+
+    L(no_update_label);
+    if (jcp.with_bias) {
+        for (int k = 0; k < jcp.nb_oc_blocking; k++) {
+            int bias_offset = jcp.typesize_out * k * jcp.oc_block;
+            for (int j = 0; j < ur_w; j++) {
+                Vmm vmm = vmm_out(j, k);
+                vaddps(vmm, EVEX_compress_addr(reg_bias, bias_offset));
+            }
+            mic_prefetcht1(EVEX_compress_addr(reg_bias, bias_offset + 64));
+        }
+    }
+
+    L(eltwise_label);
+    if (jcp.with_eltwise) {
+        cmp(reg_channel, jcp.nb_ic - 1);
+        jl(store_label, T_NEAR);
+
+        if (ur_w == jcp.ur_w) {
+            eltwise_injector_->compute_vector_range(0,
+                    jcp.nb_oc_blocking * jcp.ur_w);
+        } else {
+            for (int k = 0; k < jcp.nb_oc_blocking; k++)
+                eltwise_injector_->compute_vector_range(k * jcp.ur_w,
+                        k * jcp.ur_w + ur_w);
+        }
+    }
+
+    L(store_label);
+    for (int k = 0; k < jcp.nb_oc_blocking; k++)
+        for (int j = 0; j < ur_w; j++) {
+            Vmm vmm = vmm_out(j, k);
+            size_t aux_output_offset = (size_t)typesize *
+                ((size_t)k * jcp.od * jcp.oh * jcp.ow + j) * jcp.oc_block;
+            vmovups(EVEX_compress_addr_safe(reg_out, aux_output_offset,
+                        reg_out_long_offt), vmm);
+            if (!is_owb_prefetching(jcp))
+                mic_prefetcht0(EVEX_compress_addr_safe(reg_out_prf,
+                            aux_output_offset, reg_out_long_offt));
+        }
+}
+
+template<typename Vmm>
+void _jit_avx512_common_conv_fwd_kernel<Vmm>::compute_loop_4fma_1st(int ur_w,
+    int pad_l, int pad_r)
+{
+}
+
+template<>
+void _jit_avx512_common_conv_fwd_kernel<Zmm>::compute_loop_4fma_1st(int ur_w,
+        int pad_l, int pad_r)
+{
+    assert(jcp.dilate_d == 0 && jcp.dilate_h == 0 && jcp.dilate_w == 0);
+
+    int iw = jcp.iw;
+    int ih = jcp.ih;
+    int kw = jcp.kw;
+    int stride_w = jcp.stride_w;
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+
+    Label kh_label, kd_label;
+
+    if (one_of(jcp.ndims, 3, 4)) {
+        mov(aux_reg_inp, reg_inp);
+        mov(aux_reg_ker, reg_ker);
+        mov(aux_reg_inp_prf, reg_inp_prf);
+    }
+
+    size_t max_input_offset = (size_t)jcp.typesize_in
+        * ((size_t)(kw + ur_w * stride_w - pad_l)
+                + (size_t)ic_block * iw * ih * jcp.id);
+    assert(reg_inp_prf == reg_long_offt);
+    if (max_input_offset > INT_MAX) push(reg_inp_prf);
+
+    if (jcp.ndims == 5) {
+        push(reg_out_prf);
+        push(reg_out);
+
+        mov(reg_ki, ptr[param1 + GET_OFF(kd_padding)]);
+        mov(aux_reg_ker_d, ptr[param1 + GET_OFF(filt)]);
+        mov(aux_reg_inp_d, reg_inp);
+        mov(aux_reg_inp_d_prf, reg_inp_prf);
+
+        L(kd_label);
+    }
+    mov(reg_kj, reg_kh);
+    if (jcp.ndims == 5) {
+        mov(aux_reg_inp, aux_reg_inp_d);
+        mov(aux_reg_ker, aux_reg_ker_d);
+        mov(aux_reg_inp_prf, aux_reg_inp_d_prf);
+    }
+
+    L(kh_label);
+    for (int ki = 0; ki < kw; ki += 4) {
+        for (int ic = 0; ic < ic_block; ic++) {
+            for (int i = 0; i < 4; i++) {
+                int aux_ker_offset
+                        = jcp.typesize_in
+                        * ((ki + i) * oc_block
+                                  + ic * kw * jcp.kh * jcp.kd * oc_block);
+                if (ki + i < kw)
+                    vmovups(vmm_ker(i),
+                        EVEX_compress_addr(aux_reg_ker, aux_ker_offset));
+                else
+                    vpxord(vmm_ker(i), vmm_ker(i), vmm_ker(i));
+            }
+
+            int j_start = get_ow_start(ki, pad_l);
+            int j_end = get_ow_end(ur_w, ki, pad_r);
+
+            for (int j = j_start, prf_count=0; j < j_end; j++) {
+                size_t aux_input_offset = (size_t)jcp.typesize_in
+                        * ((size_t)(ki + j * stride_w
+                            - pad_l) + (size_t)ic * iw * ih * jcp.id);
+                v4fmaddps(vmm_out(j, 0), vmm_ker(0),
+                        EVEX_compress_addr_safe(aux_reg_inp, aux_input_offset,
+                        reg_long_offt));
+                if (ki + prf_count < kw && prf_count < 4
+                    && ((ki < 2 && j % 4) || j % 2)) {
+                    int aux_ker_offset = jcp.typesize_in
+                        * ((ki + prf_count) * oc_block
+                        + ic * kw * jcp.kh * jcp.kd * oc_block + kw * oc_block);
+                    mic_prefetcht0(EVEX_compress_addr(aux_reg_ker,
+                        aux_ker_offset));
+                    prf_count++;
+                }
+                if (ki == 0
+                    && j % (64 / (stride_w * jcp.typesize_in)) == 0) {
+                    mic_prefetcht0(EVEX_compress_addr_safe(aux_reg_inp_prf,
+                        aux_input_offset, reg_long_offt));
+                }
+                if (ki == 1
+                    && j % (64 / (stride_w * jcp.typesize_in)) == 0) {
+                    mic_prefetcht0(EVEX_compress_addr_safe(aux_reg_inp,
+                        aux_input_offset+jcp.typesize_in * iw, reg_long_offt));
+                }
+            }
+        }
+    }
+    add(aux_reg_ker, jcp.typesize_in * kw * oc_block);
+    add(aux_reg_inp, jcp.typesize_in * iw);
+    add(aux_reg_inp_prf, jcp.typesize_in * iw);
+
+    dec(reg_kj);
+    cmp(reg_kj, 0);
+    jg(kh_label, T_NEAR);
+
+    if (jcp.ndims == 5) {
+        add(aux_reg_inp_d, typesize * jcp.ih * jcp.iw);
+        add(aux_reg_ker_d, typesize * jcp.kw * jcp.kh * oc_block);
+        add(aux_reg_inp_d_prf, typesize * jcp.ih * jcp.iw);
+
+        dec(reg_ki);
+        cmp(reg_ki, 0);
+        jg(kd_label, T_NEAR);
+
+        pop(reg_out);
+        pop(reg_out_prf);
+    }
+
+    if (max_input_offset > INT_MAX) pop(reg_inp_prf);
+}
+
+template<typename Vmm>
+void _jit_avx512_common_conv_fwd_kernel<Vmm>::compute_loop_4fma(int ur_w,
+    int pad_l, int pad_r)
+{
+}
+
+template<>
+void _jit_avx512_common_conv_fwd_kernel<Zmm>::compute_loop_4fma(int ur_w,
+        int pad_l, int pad_r)
+{
+    int stride_w = jcp.stride_w;
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    Label kh_label, last_iter_label, loop_end_label, kd_label;
+    int ker_load_number = 4;
+    int shift_kernel_ptr = typesize * jcp.kw * jcp.oc_block * jcp.ic_block;
+    int shift_input_ptr = typesize * (jcp.dilate_h + 1) * jcp.iw * jcp.ic_block;
+
+    bool check_last_kh = (jcp.kh > 3);
+    bool pref_current_inp = (jcp.iw < 14 || jcp.iw > 28);
+
+    int oi_ipref_t0 = get_ow_start(0, pad_l);
+    int ow_end_ipref = get_ow_end(ur_w, 0, pad_r);
+
+    assert(jcp.oc % jcp.nb_oc_blocking == 0);
+
+    auto kernel_offset = [=](int ocb, int ic, int ki) {
+        int blk_idx = ocb * jcp.nb_ic * jcp.kh * jcp.kw * jcp.kd + ki;
+        int blk_offset = blk_idx * jcp.oc_block * jcp.ic_block;
+        int ic_offset = ic * jcp.oc_block;
+        return typesize * (blk_offset + ic_offset);
+    };
+    auto kernel_loads = [=](int ki, int ic, int kk) {
+        for (int ii = 0; ii < ker_load_number; ii++) {
+            int aux_kernel_offset = kernel_offset(kk, ic + ii, ki);
+            vmovups(vmm_ker(ii),
+                EVEX_compress_addr(aux_reg_ker, aux_kernel_offset));
+        }
+    };
+    auto prefetch_inp_next_kh = [&](int ki, int ki_start, int cnt0, int cnt1) {
+        if (cnt1 >= ker_load_number && cnt0 >= ker_load_number
+            && ki >= ki_start && oi_ipref_t0 < ow_end_ipref) {
+            int aux_inp_offset
+                    = typesize
+                    * ((oi_ipref_t0 * stride_w - pad_l) * ic_block
+                              + (jcp.dilate_h + 1) * jcp.iw * ic_block);
+            prefetcht0(EVEX_compress_addr(aux_reg_inp,
+                    aux_inp_offset));
+            oi_ipref_t0++;
+        }
+    };
+
+    if (one_of(jcp.ndims, 3, 4)) {
+        mov(aux_reg_inp, reg_inp);
+        mov(aux_reg_ker, reg_ker);
+        mov(aux_reg_ker_prf, reg_ker_prf);
+        mov(aux_reg_inp_prf, reg_inp_prf);
+    }
+
+    if (jcp.ndims == 5) {
+        push(reg_out_prf);
+        push(reg_out);
+
+        mov(reg_ki, ptr[param1 + GET_OFF(kd_padding)]);
+        mov(aux_reg_ker_d, ptr[param1 + GET_OFF(filt)]);
+        mov(aux_reg_inp_d, reg_inp);
+        mov(aux_reg_inp_d_prf, reg_inp_prf);
+        mov(aux_reg_ker_d_prf, reg_ker_prf);
+        L(kd_label);
+        mov(reg_kj, ptr[param1 + GET_OFF(kh_padding)]);
+    } else {
+        mov(reg_kj, reg_kh);
+    }
+    if (jcp.ndims == 5) {
+        mov(aux_reg_inp, aux_reg_inp_d);
+        mov(aux_reg_ker, aux_reg_ker_d);
+        mov(aux_reg_ker_prf, aux_reg_ker_d_prf);
+        mov(aux_reg_inp_prf, aux_reg_inp_d_prf);
+    }
+
+    align(16);
+    L(kh_label);
+    int kw = jcp.kw;
+    if (check_last_kh) {
+        for (int ki = 0; ki < kw; ki++)
+            for (int ic = 0; ic < ic_block; ic += 4)
+                for (int kk = 0; kk < jcp.nb_oc_blocking; kk++) {
+                    bool last_kernel_loads = (kk == jcp.nb_oc_blocking - 1
+                        && ki == kw - 1 && (ic + 4) == ic_block);
+
+                    if (last_kernel_loads) {
+                        cmp(reg_kj, 1);
+                        je(last_iter_label, T_NEAR);
+                    }
+
+                    kernel_loads(ki, ic, kk);
+                    for (int oi = get_ow_start(ki, pad_l), prf_count_t1 = 0,
+                             prf_count_t0 = 0;
+                            oi < get_ow_end(ur_w, ki, pad_r); oi++) {
+                        int aux_input_offset = typesize
+                                * ((ki * (jcp.dilate_w + 1) + oi * stride_w
+                                           - pad_l) * ic_block
+                                                       + ic);
+                        v4fmaddps(vmm_out(oi, kk), vmm_ker(0),
+                            EVEX_compress_addr(aux_reg_inp, aux_input_offset));
+
+                        if (oi % 2) {
+                            if (prf_count_t0 < 4) {
+                                int aux_kernel_prf;
+                                if (last_kernel_loads)
+                                    aux_kernel_prf= kernel_offset(0,
+                                        prf_count_t0 + ic + 4
+                                        - ic_block, 0) + typesize * kw
+                                        * oc_block * ic_block;
+                                else
+                                    aux_kernel_prf = kernel_offset(kk, ic + 4
+                                        + prf_count_t0, ki);
+                                mic_prefetcht0(EVEX_compress_addr(aux_reg_ker,
+                                    aux_kernel_prf));
+                                prf_count_t0++;
+                            } else if (prf_count_t1 < 4) {
+                                mic_prefetcht1(EVEX_compress_addr(
+                                    aux_reg_ker_prf, kernel_offset(kk, ic
+                                    + prf_count_t1, ki)));
+                                prf_count_t1++;
+                            }
+                        } else
+                           prefetch_inp_next_kh(ki, 2, prf_count_t0,
+                               prf_count_t1);
+                    }
+
+                    if (last_kernel_loads) {
+                        jmp(loop_end_label, T_NEAR);
+
+                        L(last_iter_label);
+
+                        kernel_loads(ki, ic, kk);
+                        for (int oi = get_ow_start(ki, pad_l), prf_count_t1 = 0,
+                                 prf_count_t0 = 0;
+                                oi < get_ow_end(ur_w, ki, pad_r); oi++) {
+                            int aux_input_offset = typesize
+                                    * ((ki * (jcp.dilate_w + 1) + oi * stride_w
+                                               - pad_l) * ic_block
+                                                           + ic);
+                            v4fmaddps(vmm_out(oi, kk), vmm_ker(0),
+                                EVEX_compress_addr(aux_reg_inp,
+                                    aux_input_offset));
+                            if (oi % 2) {
+                                if (prf_count_t0 < 4) {
+                                    mic_prefetcht0(EVEX_compress_addr(
+                                        aux_reg_ker_prf, kernel_offset(0,
+                                        prf_count_t0, 0)));
+                                    prf_count_t0++;
+                                } else if (prf_count_t1 < 4) {
+                                    mic_prefetcht1(EVEX_compress_addr(
+                                        aux_reg_ker_prf, kernel_offset(kk,
+                                        ic + prf_count_t1, ki)));
+                                    prf_count_t1++;
+                                }
+                            }
+                        }
+                        L(loop_end_label);
+                    }
+                }
+    } else {
+        for (int ki = 0; ki < kw; ki++)
+            for (int ic = 0; ic < ic_block; ic += 4)
+                for (int kk = 0; kk < jcp.nb_oc_blocking; kk++) {
+                    kernel_loads(ki, ic, kk);
+                    for (int oi = get_ow_start(ki, pad_l),
+                            prf_count_t1 = 0, prf_count_t0 = 0;
+                            oi < get_ow_end(ur_w, ki, pad_r); oi++) {
+                        int aux_input_offset = typesize
+                                * ((ki * (jcp.dilate_w + 1) + oi * stride_w
+                                - pad_l) * ic_block + ic);
+                        v4fmaddps(vmm_out(oi, kk), vmm_ker(0),
+                            EVEX_compress_addr(aux_reg_inp,
+                                aux_input_offset));
+
+                        if (!is_owb_prefetching(jcp)) {
+                            if ((oi % 2) && (prf_count_t1 < 4)) {
+                                mic_prefetcht1(EVEX_compress_addr(
+                                    aux_reg_ker_prf, kernel_offset(kk,
+                                    ic + prf_count_t1, ki)));
+                                prf_count_t1++;
+                            }
+                        } else {
+                            if (!(ki == 0 && ic == 0)
+                                && !(ki == kw-1 && ic == 0) &&
+                                (oi % 2) && (prf_count_t1 < 4)
+                                ) {
+                                mic_prefetcht0(EVEX_compress_addr(
+                                    aux_reg_ker, kernel_offset(kk,
+                                    ic + 4 + prf_count_t0, ki)));
+                                prf_count_t0++;
+                            }
+                        }
+                        if (!is_owb_prefetching(jcp)) {
+                            if (pref_current_inp) {
+                                if (ki == 0 && ic == 0 && kk == 0)
+                                    mic_prefetcht0(EVEX_compress_addr(
+                                        aux_reg_inp,
+                                        aux_input_offset + shift_input_ptr));
+                            } else {
+                                if (ki == 1 && ic == 0 && kk == 0)
+                                    mic_prefetcht1(EVEX_compress_addr(
+                                        aux_reg_inp_prf, aux_input_offset));
+                            }
+                        } else {
+                            int inp_mult = jcp.is_1stconv ? 1 : jcp.ic_block;
+                            int inp_shift
+                                = jcp.typesize_in * ur_w * stride_w * inp_mult;
+                            bool kk_pref_slot = kk ? oi % 2 : !(oi % 2);
+                            if (ki == 0 && ic == 0 && kk_pref_slot)
+                                    mic_prefetcht1(EVEX_compress_addr(
+                                        aux_reg_inp,
+                                        aux_input_offset + inp_shift));
+
+                            if (ki == kw - 1 && ic == 0 && kk_pref_slot)
+                                    mic_prefetcht0(EVEX_compress_addr(
+                                        aux_reg_inp,
+                                        aux_input_offset + inp_shift));
+                        }
+                    }
+                }
+    }
+
+    add(aux_reg_ker, shift_kernel_ptr);
+    add(aux_reg_inp, shift_input_ptr);
+    add(aux_reg_ker_prf, shift_kernel_ptr);
+    add(aux_reg_inp_prf, shift_input_ptr);
+
+    dec(reg_kj);
+    cmp(reg_kj, 0);
+    jg(kh_label, T_NEAR);
+
+    if (jcp.ndims == 5) {
+        add(aux_reg_inp_d,
+                typesize * (jcp.dilate_d + 1) * jcp.ih * jcp.iw * jcp.ic_block);
+        add(aux_reg_ker_d, typesize * jcp.kw * jcp.kh * jcp.oc_block
+                * jcp.ic_block);
+        add(aux_reg_inp_d_prf,
+                typesize * (jcp.dilate_d + 1) * jcp.ih * jcp.iw * jcp.ic_block);
+        add(aux_reg_ker_d_prf, typesize * jcp.kw * jcp.kh * jcp.oc_block
+                * jcp.ic_block);
+
+        dec(reg_ki);
+        cmp(reg_ki, 0);
+        jg(kd_label, T_NEAR);
+
+        pop(reg_out);
+        pop(reg_out_prf);
+    }
+}
+
+template<typename Vmm>
+void _jit_avx512_common_conv_fwd_kernel<Vmm>::compute_loop_fma(int ur_w,
+        int pad_l, int pad_r)
+{
+    bool prf_ker = true;
+    bool prf_inp = true;
+    int ih = jcp.ih;
+    int stride_w = jcp.stride_w;
+    int id = jcp.id;
+    int iw = jcp.iw;
+    int kw = jcp.kw;
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    int nb_oc_block = jcp.nb_oc_blocking;
+    Label kh_label, kd_label;
+
+    int ker_pipeline_depth = 4;
+    assert(ker_reg_base_idx + ker_pipeline_depth <= 32);
+    assert(oc_block >= ker_pipeline_depth);
+
+    int num_ker_loads = ic_block * nb_oc_block * kw;
+    int num_ker_prfs = prf_ker ? num_ker_loads : 0;
+    int num_inp_prfs = prf_inp ?
+            ur_w * nstl::min(kw, stride_w) + nstl::max(0, kw - stride_w) :
+            0;
+    if (jcp.is_1stconv && prf_inp) {
+        num_inp_prfs = div_up(num_inp_prfs, jcp.simd_w) * ic_block;
+    }
+    int num_prfs = num_ker_prfs + num_inp_prfs;
+    int num_fmas = num_ker_loads * ur_w;
+    int prf_inst_spacing
+            = (prf_ker || prf_inp) ? nstl::max(1, num_fmas / num_prfs) : 1;
+    int prf_inst_trigger = (num_fmas % prf_inst_spacing) / 2;
+    int inp_mul = !jcp.is_1stconv ? ic_block : 1;
+
+    if (one_of(jcp.ndims, 3, 4)) {
+        mov(aux_reg_inp, reg_inp);
+        mov(aux_reg_ker, reg_ker);
+        mov(aux_reg_inp_prf, reg_inp_prf);
+        mov(aux_reg_ker_prf, reg_ker_prf);
+    }
+
+    size_t max_input_offset = (size_t)jcp.typesize_in * ic_block * iw * ih * id;
+    assert(reg_inp_prf == reg_long_offt);
+    if (max_input_offset > INT_MAX) push(reg_inp_prf);
+
+
+    if (jcp.ndims == 5) {
+        push(reg_out_prf);
+        push(reg_out);
+
+        mov(reg_ki, ptr[param1 + GET_OFF(kd_padding)]);
+        mov(aux_reg_ker_d, ptr[param1 + GET_OFF(filt)]);
+        mov(aux_reg_inp_d, reg_inp);
+        mov(aux_reg_inp_d_prf, reg_inp_prf);
+        mov(aux_reg_ker_d_prf, reg_ker_prf);
+
+        L(kd_label);
+        mov(reg_kj, ptr[param1 + GET_OFF(kh_padding)]);
+    } else {
+        mov(reg_kj, reg_kh);
+    }
+
+    if (jcp.ndims == 5) {
+        mov(aux_reg_inp, aux_reg_inp_d);
+        mov(aux_reg_ker, aux_reg_ker_d);
+        mov(aux_reg_ker_prf, aux_reg_ker_d_prf);
+        mov(aux_reg_inp_prf, aux_reg_inp_d_prf);
+    }
+
+    align(16);
+    L(kh_label);
+    {
+        int step = 0;
+        int ker_prfs = 0;
+        for (int ki = 0; ki < kw; ki++) {
+            for (int ic = 0; ic < ic_block; ic++) {
+                int aux_kernel_offset = 0;
+                if (step == 0) {
+                    for (int i = 0; i < ker_pipeline_depth; i++) {
+                        aux_kernel_offset = get_kernel_offset(ki, ic, 0, i);
+                        vmovups(vmm_ker(i), EVEX_compress_addr(
+                                        aux_reg_ker, aux_kernel_offset));
+                    }
+                } else if (step < num_ker_loads - ker_pipeline_depth + 1) {
+                    int load_offset = ker_pipeline_depth - 1;
+                    int ker_load_reg_idx
+                        = (step + load_offset) % ker_pipeline_depth;
+                    aux_kernel_offset
+                            = get_kernel_offset(ki, ic, 0, load_offset);
+                    vmovups(vmm_ker(ker_load_reg_idx),
+                            EVEX_compress_addr(aux_reg_ker, aux_kernel_offset));
+                }
+
+                bool ker_prf_inserted = false;
+                Vmm vmm_kernel = vmm_ker(step % ker_pipeline_depth);
+                int j_start = get_ow_start(ki, pad_l);
+                int j_end = get_ow_end(ur_w, ki, pad_r);
+                for (int j = j_start; j < j_end; j++) {
+                    size_t aux_input_offset = get_input_offset(ki, ic, j, pad_l);
+                    auto addr = EVEX_compress_addr_safe(aux_reg_inp,
+                            aux_input_offset, reg_long_offt, true);
+                    vfmadd231ps(vmm_out(j, 0), vmm_kernel, addr);
+                    int fma_idx = step * ur_w + j;
+                    int prf_slot_idx = fma_idx / prf_inst_spacing;
+                    if (fma_idx % prf_inst_spacing == prf_inst_trigger) {
+                        if (prf_ker && !ker_prf_inserted
+                                && ker_prfs < num_ker_prfs) {
+                            int ker_prf_offset
+                                    = jcp.typesize_in * ker_prfs * jcp.oc_block;
+                            mic_prefetcht2(EVEX_compress_addr(
+                                    aux_reg_ker_prf, ker_prf_offset));
+                            ker_prf_inserted = true;
+                            ker_prfs++;
+                        } else if (prf_inp) {
+                            int inp_prf_idx = prf_slot_idx - ker_prfs;
+                            if (inp_prf_idx < num_inp_prfs) {
+                                size_t inp_prf_stride = nstl::max(kw, stride_w);
+                                size_t inp_prf_offset;
+                                if (!jcp.is_1stconv) {
+                                    inp_prf_offset
+                                            = ic_block * jcp.typesize_in
+                                            * ((inp_prf_idx / kw)
+                                            * inp_prf_stride
+                                            + (inp_prf_idx % kw));
+                                } else {
+                                    size_t ic_prf_stride =
+                                        (size_t)jcp.typesize_in * iw * ih * id;
+                                    size_t iw_prf_stride
+                                            = jcp.typesize_in * jcp.simd_w;
+                                    inp_prf_offset = ((inp_prf_idx / ic_block)
+                                            * iw_prf_stride
+                                            + (inp_prf_idx % ic_block)
+                                            * ic_prf_stride);
+                                }
+                                mic_prefetcht0(EVEX_compress_addr_safe(
+                                        aux_reg_inp_prf, inp_prf_offset,
+                                        reg_long_offt));
+                            }
+                        }
+                    }
+                }
+                step++;
+            }
+        }
+        add(aux_reg_ker, jcp.typesize_in * kw * oc_block * ic_block);
+        if (prf_ker)
+            add(aux_reg_ker_prf, jcp.typesize_in * kw * oc_block * ic_block);
+        add(aux_reg_inp, jcp.typesize_in * (jcp.dilate_h + 1) * iw * inp_mul);
+        if (prf_inp)
+            add(aux_reg_inp_prf,
+                    jcp.typesize_in * (jcp.dilate_h + 1) * iw * inp_mul);
+        dec(reg_kj);
+        cmp(reg_kj, 0);
+        jg(kh_label, T_NEAR);
+    }
+
+
+    if (jcp.ndims == 5) {
+        add(aux_reg_inp_d,
+                typesize * (jcp.dilate_d + 1) * jcp.ih * jcp.iw * inp_mul);
+        add(aux_reg_ker_d, typesize * jcp.kw * jcp.kh * jcp.oc_block
+                * jcp.ic_block);
+        add(aux_reg_inp_d_prf,
+                typesize * (jcp.dilate_d + 1) * jcp.ih * jcp.iw * inp_mul);
+        add(aux_reg_ker_d_prf, typesize * jcp.kw * jcp.kh * jcp.oc_block
+                * jcp.ic_block);
+
+        dec(reg_ki);
+        cmp(reg_ki, 0);
+        jg(kd_label, T_NEAR);
+
+        pop(reg_out);
+        pop(reg_out_prf);
+    }
+    if (max_input_offset > INT_MAX) pop(reg_inp_prf);
+}
+
+template<typename Vmm>
+void _jit_avx512_common_conv_fwd_kernel<Vmm>::compute_loop_fma_core(int ur_w,
+    int pad_l, int pad_r)
+{
+    int kw = jcp.kw;
+    int stride_w = jcp.stride_w;
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    int nb_oc_block = jcp.nb_oc_blocking;
+    Label kh_label, kd_label;
+    int shift_kernel_ptr = jcp.typesize_in * jcp.kw * jcp.oc_block
+        * jcp.ic_block;
+    int inp_mul = !jcp.is_1stconv ? ic_block : 1;
+    int shift_input_ptr = jcp.typesize_in * (jcp.dilate_h + 1) * jcp.iw
+        * inp_mul;
+
+
+    auto input_offset = [=](int oi, int ic, int ki) {
+        return (size_t)jcp.typesize_in
+                * ((size_t)(ki * (jcp.dilate_w + 1) + oi * stride_w - pad_l)
+                * inp_mul + (size_t)ic
+                * (!jcp.is_1stconv ? 1 : (size_t)jcp.iw * jcp.ih * jcp.id));
+    };
+
+    if (one_of(jcp.ndims, 3, 4)) {
+        mov(aux_reg_inp, reg_inp);
+        mov(aux_reg_ker, reg_ker);
+    }
+
+    if (jcp.ndims == 5) {
+        push(reg_out);
+
+        mov(reg_ki, ptr[param1 + GET_OFF(kd_padding)]);
+        mov(aux_reg_ker_d, ptr[param1 + GET_OFF(filt)]);
+        mov(aux_reg_inp_d, reg_inp);
+
+        L(kd_label);
+        mov(reg_kj, ptr[param1 + GET_OFF(kh_padding)]);
+    } else {
+        mov(reg_kj, reg_kh);
+    }
+
+    if (jcp.ndims == 5) {
+        mov(aux_reg_inp, aux_reg_inp_d);
+        mov(aux_reg_ker, aux_reg_ker_d);
+    }
+
+    L(kh_label);
+    {
+        for (int ki = 0; ki < kw; ki++) {
+            int jj_start = get_ow_start(ki, pad_l);
+            int jj_end = get_ow_end(ur_w, ki, pad_r);
+            for (int ic = 0; ic < ic_block; ic++) {
+                if (jcp.kernel_kind == expl_bcast) {
+                    for (int jj = jj_start; jj < jj_end; jj++) {
+                        size_t aux_input_offset = input_offset(jj, ic, ki);
+                        vbroadcastss(vmm_inp(jj, nb_oc_block),
+                            EVEX_compress_addr_safe(aux_reg_inp,
+                            aux_input_offset, reg_long_offt));
+                    }
+                }
+                for (int ii = 0; ii < nb_oc_block; ii++) {
+                    int aux_kernel_offset = jcp.typesize_in
+                        * (ii * jcp.nb_ic * jcp.kh * jcp.kw * jcp.kd * ic_block
+                        * oc_block + ki * ic_block * oc_block + ic * oc_block);
+                    if (jj_end - jj_start > 0)
+                        vmovups(vmm_wei, EVEX_compress_addr(aux_reg_ker,
+                            aux_kernel_offset));
+                    for (int jj = jj_start; jj < jj_end; jj++)
+                        if (jcp.kernel_kind == expl_bcast)
+                            vfmadd231ps(vmm_out(jj, ii),
+                                vmm_inp(jj, nb_oc_block), vmm_wei);
+                        else {
+                            size_t aux_input_offset = input_offset(jj, ic, ki);
+                            vfmadd231ps(vmm_out(jj, ii), vmm_wei,
+                                EVEX_compress_addr_safe(aux_reg_inp,
+                                aux_input_offset, reg_long_offt, true));
+                        }
+                }
+            }
+        }
+        add(aux_reg_ker, shift_kernel_ptr);
+        add(aux_reg_inp, shift_input_ptr);
+        dec(reg_kj);
+        cmp(reg_kj, 0);
+        jg(kh_label, T_NEAR);
+    }
+
+    if (jcp.ndims == 5) {
+        add(aux_reg_inp_d,
+                typesize * (jcp.dilate_d + 1) * jcp.ih * jcp.iw * inp_mul);
+        add(aux_reg_ker_d, typesize * jcp.kw * jcp.kh * jcp.oc_block
+                * jcp.ic_block);
+
+        dec(reg_ki);
+        cmp(reg_ki, 0);
+        jg(kd_label, T_NEAR);
+
+        pop(reg_out);
+    }
+}
+
+template<typename Vmm>
+void _jit_avx512_common_conv_fwd_kernel<Vmm>::compute_loop(int ur_w,
+        int pad_l, int pad_r)
+{
+    if (jcp.ndims == 5) push(reg_oi);
+
+    prepare_output(ur_w);
+
+    Label skip_compute_loop;
+    if (jcp.ndims == 5) {
+        if ((jcp.dilate_d >= jcp.id)
+                || (jcp.kd - 1) * (jcp.dilate_d + 1) < nstl::max(jcp.f_pad, jcp.back_pad)) {
+            mov(reg_kj, ptr[param1 + GET_OFF(kd_padding)]);
+            cmp(reg_kj, 0);
+            je(skip_compute_loop, T_NEAR);
+        }
+    }
+    if ((jcp.dilate_h >= jcp.ih)
+            || (jcp.kh - 1) * (jcp.dilate_h + 1) < nstl::max(jcp.t_pad, jcp.b_pad)) {
+        mov(reg_kj, ptr[param1 + GET_OFF(kh_padding)]);
+        cmp(reg_kj, 0);
+        je(skip_compute_loop, T_NEAR);
+    }
+
+    if (jcp.ver == ver_4fma)
+        if(jcp.is_1stconv)
+            compute_loop_4fma_1st(ur_w, pad_l, pad_r);
+        else
+            compute_loop_4fma(ur_w, pad_l, pad_r);
+    else if (jcp.ver == ver_fma)
+        if ((jcp.is_1stconv && jcp.kernel_kind != expl_bcast)
+                || mayiuse(avx512_mic))
+            compute_loop_fma(ur_w, pad_l, pad_r);
+        else
+            if (jcp.kernel_kind == embd_bcast && jcp.nb_oc_blocking == 1)
+                compute_loop_fma(ur_w, pad_l, pad_r);
+            else
+                compute_loop_fma_core(ur_w, pad_l, pad_r);
+    else
+        assert(!"unknown convolution version");
+
+    L(skip_compute_loop);
+    store_output(ur_w);
+    if (jcp.ndims == 5) pop(reg_oi);
+}
+
+template<typename Vmm>
+void _jit_avx512_common_conv_fwd_kernel<Vmm>::generate()
+{
+    int iw = jcp.iw;
+    int ow = jcp.ow;
+    int ow_block = jcp.ow_block;
+    int nb_ow = jcp.nb_ow;
+    int kw = jcp.kw;
+    int l_pad = jcp.l_pad;
+    int ur_w = jcp.ur_w;
+    int ur_w_tail = jcp.ur_w_tail;
+    int dilate_w = jcp.dilate_w + 1;
+    int stride_w = jcp.stride_w;
+
+    int inp_mult = jcp.is_1stconv ? 1 : jcp.ic_block;
+    int inp_shift_pad = jcp.typesize_in * (ur_w * stride_w - l_pad) * inp_mult;
+    int inp_shift = jcp.typesize_in * ur_w * stride_w * inp_mult;
+    int inp_shift_pad_second_block = -1 * jcp.typesize_in * l_pad * inp_mult;
+    int out_shift = jcp.typesize_out * ur_w * jcp.oc_block;
+
+    preamble();
+    mov(reg_inp, ptr[param1 + GET_OFF(src)]);
+    mov(reg_out, ptr[param1 + GET_OFF(dst)]);
+    mov(reg_ker, ptr[param1 + GET_OFF(filt)]);
+    mov(reg_ker_prf, ptr[param1 + GET_OFF(filt_prf)]);
+    mov(reg_kh, ptr[param1 + GET_OFF(kh_padding)]);
+
+    int r_pad = nstl::max(
+            0, (ow - 1) * stride_w + (kw - 1) * dilate_w - (iw + l_pad - 1));
+    int n_oi = ow / ur_w;
+    int r_pad1 = (ur_w * n_oi - 1) * stride_w + (kw - 1) * dilate_w
+            - (iw + l_pad - 1);
+
+    if (!is_ow_threading_on(jcp)) {
+        // ow is being processed as a whole - with left and right paddings
+        if (r_pad1 > 0) n_oi--;
+
+        if (ow == ur_w) {
+            mov(reg_inp_prf, ptr[param1 + GET_OFF(src_prf)]);
+            mov(reg_out_prf, ptr[param1 + GET_OFF(dst_prf)]);
+            compute_loop(ur_w, l_pad, r_pad);
+        } else {
+            mov(reg_inp_prf, reg_inp);
+            mov(reg_out_prf, reg_out);
+            if (n_oi == 0) {
+                add(reg_inp_prf, inp_shift_pad);
+                add(reg_out_prf, out_shift);
+                compute_loop(ur_w, l_pad, r_pad1);
+                add(reg_inp, inp_shift_pad);
+                add(reg_out, out_shift);
+                if (ur_w_tail != 0) {
+                    add(reg_inp_prf, inp_shift);
+                    add(reg_out_prf, out_shift);
+                    compute_loop(ur_w_tail, 0, r_pad);
+                }
+            } else {
+                xor_(reg_oi, reg_oi);
+                if (l_pad > 0) {
+                    add(reg_inp_prf, inp_shift_pad);
+                    add(reg_out_prf, out_shift);
+                    compute_loop(ur_w, l_pad, 0);
+                    add(reg_inp, inp_shift_pad);
+                    add(reg_out, out_shift);
+                    inc(reg_oi);
+                }
+                if ((l_pad <= 0 && n_oi > 0) || (l_pad > 0 && n_oi > 1)) {
+                    Label ow_loop_label;
+                    L(ow_loop_label);
+                    {
+                        add(reg_inp_prf, inp_shift);
+                        add(reg_out_prf, out_shift);
+                        compute_loop(ur_w, 0, 0);
+                        add(reg_inp, inp_shift);
+                        add(reg_out, out_shift);
+                        inc(reg_oi);
+                        cmp(reg_oi, n_oi);
+                        jl(ow_loop_label, T_NEAR);
+                    }
+                }
+                if (r_pad1 > 0) {
+                    add(reg_inp_prf, inp_shift);
+                    add(reg_out_prf, out_shift);
+                    compute_loop(ur_w, 0, r_pad1);
+                    add(reg_inp, inp_shift);
+                    add(reg_out, out_shift);
+                }
+                if (ur_w_tail != 0) {
+                    add(reg_inp_prf, inp_shift);
+                    add(reg_out_prf, out_shift);
+                    compute_loop(ur_w_tail, 0, r_pad);
+                }
+            }
+        }
+    } else {
+        // ow block is only processed.
+        // Number of block is passed as parameter owb,
+        // and padding processing depends on this number.
+
+        Label end_label, last_oi_label, middle_ow_blocks_label, tail_label;
+        Label oi_loop_label, oi_loop_start_label, oi_loop_end_label;
+
+        assert(ow_block % ur_w == 0);
+        int n_oi_not_last_ow_block = ow_block / ur_w;
+        // to simplify code (and general regs usage),
+        // size of ow block must be >= 2 * ur_w
+        assert(n_oi_not_last_ow_block > 1);
+        int n_oi_next_last_ow_block = n_oi_not_last_ow_block;
+        int n_oi_first_ow_block = n_oi_not_last_ow_block;
+
+        int n_oi_last_ow_block = (ow - ow_block * (nb_ow-1)) / ur_w;
+
+        // prepare right padding
+        bool next_last_ow_block_padded = r_pad1 > 0 && n_oi_last_ow_block == 0;
+        bool first_ow_block_padded = next_last_ow_block_padded && jcp.nb_ow == 2;
+        bool last_ow_block_padded = r_pad1 > 0 && n_oi_last_ow_block > 0;
+
+        if (last_ow_block_padded) n_oi_last_ow_block--;
+        else if (first_ow_block_padded) n_oi_first_ow_block--;
+        else if (next_last_ow_block_padded) n_oi_next_last_ow_block--;
+
+        mov(reg_owb, ptr[param1 + GET_OFF(owb)]);
+        cmp(reg_owb, 0); // is that the first ow-block ?
+        jg(middle_ow_blocks_label, T_NEAR);
+
+        // the first ow block, compute left padding
+
+        mov(reg_oi, n_oi_first_ow_block);
+        mov(reg_inp_prf, reg_inp);
+        mov(reg_out_prf, reg_out);
+
+        if (l_pad > 0) {
+            mov(reg_ker_prf, ptr[param1 + GET_OFF(filt_prf)]);
+            add(reg_inp_prf, inp_shift_pad);
+            add(reg_out_prf, out_shift);
+            compute_loop(ur_w, l_pad, 0);
+            add(reg_inp, inp_shift_pad);
+            add(reg_out, out_shift);
+            dec(reg_oi);
+        }
+        jmp(oi_loop_label, T_NEAR);
+
+        // middle or last ow block entry
+
+        L(middle_ow_blocks_label);
+
+        if (l_pad > 0) {
+            // just to consider left padding, not compute
+            add(reg_inp, inp_shift_pad_second_block);
+            add(reg_inp_prf, inp_shift_pad_second_block);
+        }
+
+        // set number of iteration for oi-loop
+        cmp(reg_owb, jcp.nb_ow - 1); // last ow-block ?
+        mov(reg_oi, n_oi_last_ow_block);
+        je(oi_loop_label, T_NEAR);
+        cmp(reg_owb, jcp.nb_ow - 2); // next to last ow-block ?
+        mov(reg_oi, n_oi_next_last_ow_block);
+        je(oi_loop_label, T_NEAR);
+        mov(reg_oi, n_oi_not_last_ow_block); // other middle ow-blocks
+
+        // oi loop w/o padding
+        L(oi_loop_label);
+        mov(reg_ker_prf, ptr[param1 + GET_OFF(filt_prf)]);
+        L(oi_loop_start_label);
+            cmp(reg_oi, 0);
+            jle(oi_loop_end_label, T_NEAR);
+
+            add(reg_inp_prf, inp_shift);
+            add(reg_out_prf, out_shift);
+            compute_loop(ur_w, 0, 0);
+            add(reg_inp, inp_shift);
+            add(reg_out, out_shift);
+            dec(reg_oi);
+            jmp(oi_loop_start_label, T_NEAR);
+        L(oi_loop_end_label);
+
+        mov(reg_owb, ptr[param1 + GET_OFF(owb)]);
+
+        cmp(reg_owb, 0); // first ow-block ?
+        if (first_ow_block_padded) {
+            je(last_oi_label, T_NEAR);
+        } else {
+            je(end_label, T_NEAR);
+        }
+        cmp(reg_owb, jcp.nb_ow - 2); // next to last ow-block ?
+        jl(end_label, T_NEAR);
+        if (next_last_ow_block_padded) {
+            je(last_oi_label, T_NEAR);
+        } else {
+            je(end_label, T_NEAR);
+        }
+        // that is last block
+        if (!last_ow_block_padded) {
+            jmp(tail_label, T_NEAR);
+        }
+
+        // last oi block with right padding
+        L(last_oi_label);
+        mov(reg_ker_prf, ptr[param1 + GET_OFF(filt_prf)]);
+        add(reg_inp_prf, inp_shift);
+        add(reg_out_prf, out_shift);
+        compute_loop(ur_w, 0, r_pad1);
+        add(reg_inp, inp_shift);
+        add(reg_out, out_shift);
+
+        mov(reg_owb, ptr[param1 + GET_OFF(owb)]);
+        cmp(reg_owb, jcp.nb_ow - 1); // last ow_block?
+        jl(end_label, T_NEAR);
+
+        L(tail_label);
+        mov(reg_ker_prf, ptr[param1 + GET_OFF(filt_prf)]);
+        if (ur_w_tail != 0) {
+            add(reg_inp_prf, inp_shift);
+            add(reg_out_prf, out_shift);
+            compute_loop(ur_w_tail, 0, r_pad);
+        }
+        L(end_label);
+    }
+    postamble();
+
+    if (jcp.with_eltwise)
+        eltwise_injector_->prepare_table();
+}
+
+bool jit_avx512_common_conv_fwd_kernel::post_ops_ok(
+        jit_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    const auto &p = attr.post_ops_;
+
+    auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); };
+    auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); };
+
+    switch (p.len_) {
+    case 0: return true; // no post_ops
+    case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise
+    case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise
+    default: return false;
+    }
+
+    return false;
+}
+
+status_t jit_avx512_common_conv_fwd_kernel::init_conf(
+            jit_conv_conf_t &jcp, const convolution_desc_t &cd,
+            memory_desc_t &src_md, memory_desc_t &weights_md,
+            memory_desc_t &dst_md, memory_desc_t &bias_md,
+            const primitive_attr_t &attr, int nthreads)
+{
+    using namespace prop_kind;
+
+    if (!mayiuse(avx512_common))
+        return status::unimplemented;
+
+    const memory_desc_wrapper src_d(&src_md);
+    const memory_desc_wrapper weights_d(&weights_md);
+    const memory_desc_wrapper dst_d(&dst_md);
+    const memory_desc_wrapper bias_d(&bias_md);
+
+    const int regs = 28;
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+    int ndims = src_d.ndims();
+
+    jcp = zero<decltype(jcp)>();
+    jcp.ndims = ndims;
+    jcp.prop_kind = cd.prop_kind;
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+    jcp.id = (ndims == 5) ? src_d.dims()[2] : 1;
+    jcp.ih = (ndims == 3) ? 1 : src_d.dims()[ndims-2];
+    jcp.iw = src_d.dims()[ndims-1];
+    jcp.od = (ndims == 5) ? dst_d.dims()[2] : 1;
+    jcp.oh = (ndims == 3) ? 1 : dst_d.dims()[ndims-2];
+    jcp.ow = dst_d.dims()[ndims-1];
+    jcp.kd = (ndims == 5) ? weights_d.dims()[with_groups + 2] : 1;
+    jcp.kh = (ndims == 3) ? 1 : weights_d.dims()[with_groups + ndims-2];
+    jcp.kw = weights_d.dims()[with_groups + ndims-1];
+    jcp.f_pad = (ndims == 5) ? cd.padding[0][0] : 0;
+    jcp.t_pad = (ndims == 3) ? 0 : cd.padding[0][ndims-4];
+    jcp.l_pad = cd.padding[0][ndims-3];
+    jcp.stride_d = (ndims == 5) ? cd.strides[0] : 1;
+    jcp.stride_h = (ndims == 3) ? 1 : cd.strides[ndims-4];
+    jcp.stride_w = cd.strides[ndims-3];
+
+    jcp.dilate_d = (ndims == 5) ? cd.dilates[0] : 0;
+    jcp.dilate_h = (ndims == 3) ? 0 : cd.dilates[ndims-4];
+    jcp.dilate_w = cd.dilates[ndims-3];
+
+    jcp.b_pad = (jcp.oh - 1) * jcp.stride_h + (jcp.kh - 1) * (jcp.dilate_h + 1)
+            - (jcp.ih + jcp.t_pad - 1);
+    jcp.back_pad = (jcp.od - 1) * jcp.stride_d
+            + (jcp.kd - 1) * (jcp.dilate_d + 1) - (jcp.id + jcp.f_pad - 1);
+
+    jcp.is_1stconv = is_1stconv(jcp);
+
+    bool ok_to_pad_channels = true
+        && jcp.ngroups == 1
+        && src_d.data_type() == data_type::f32;
+
+    const int full_simd_w = cpu_isa_traits<avx512_common>::vlen / sizeof(float);
+    jcp.simd_w = full_simd_w;
+    bool ok_to_try_xmm = true
+        && mayiuse(avx512_core)
+        && src_d.data_type() == data_type::f32
+        && !jcp.is_1stconv
+        && !ok_to_pad_channels
+        && (jcp.ic % jcp.simd_w != 0 || jcp.oc % jcp.simd_w != 0)
+        && (jcp.ic % 8 != 0 || jcp.oc % 8 != 0);
+    if (ok_to_try_xmm)
+        jcp.simd_w = 4;
+
+    jcp.oc_block = jcp.simd_w;
+    jcp.ic_block = jcp.is_1stconv ? jcp.ic : jcp.simd_w;
+    jcp.aligned_threads = 0;
+
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, jcp.oc_block);
+        jcp.ic = rnd_up(jcp.ic, jcp.ic_block);
+    }
+    bool args_ok = true
+        && jcp.oc % jcp.oc_block == 0
+        && jcp.ic % jcp.ic_block == 0;
+    if (!args_ok)
+        return status::unimplemented;
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    jcp.with_sum = p.find(primitive_kind::sum) != -1;
+    const int eltwise_ind = p.find(primitive_kind::eltwise);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise) {
+        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+        if (dst_d.data_type() == data_type::s32) return status::unimplemented;
+    }
+
+    auto src_tag = jcp.is_1stconv
+        ? pick(ndims - 3, ncw, nchw, ncdhw)
+        : ((jcp.simd_w == 4)
+            ? pick(ndims - 3, nCw4c, nChw4c, nCdhw4c)
+            : pick(ndims - 3, nCw16c, nChw16c, nCdhw16c));
+    auto dst_tag = (jcp.simd_w == 4)
+        ? pick(ndims - 3, nCw4c, nChw4c, nCdhw4c)
+        : pick(ndims - 3, nCw16c, nChw16c, nCdhw16c);
+    auto wei_tag = with_groups
+        ? ((jcp.simd_w == 4)
+            ? pick(ndims - 3, gOIw4i4o, gOIhw4i4o, gOIdhw4i4o)
+            : pick(ndims - 3, gOIw16i16o, gOIhw16i16o, gOIdhw16i16o))
+        : ((jcp.simd_w == 4)
+            ? pick(ndims - 3, OIw4i4o, OIhw4i4o, OIdhw4i4o)
+            : pick(ndims - 3, OIw16i16o, OIhw16i16o, OIdhw16i16o));
+
+    if (src_d.format_kind() == format_kind::any) {
+        CHECK(memory_desc_init_by_tag(src_md, src_tag));
+        jcp.src_tag = src_tag;
+    } else {
+        jcp.src_tag = src_d.matches_one_of_tag(src_tag);
+    }
+    if (jcp.src_tag != src_tag)
+        return status::unimplemented;
+
+    if (dst_d.format_kind() == format_kind::any) {
+        CHECK(memory_desc_init_by_tag(dst_md, dst_tag));
+        jcp.dst_tag = dst_tag;
+    } else {
+        jcp.dst_tag = dst_d.matches_one_of_tag(dst_tag);
+    }
+    if (jcp.dst_tag != dst_tag)
+        return status::unimplemented;
+
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+    if (jcp.with_bias) {
+        if (bias_d.format_kind() == format_kind::any)
+            CHECK(memory_desc_init_by_tag(bias_md, x));
+    }
+
+    if (mayiuse(avx512_common) &&
+            src_d.data_type() == data_type::f32
+         && weights_d.data_type() == data_type::f32
+         && dst_d.data_type() == data_type::f32) {
+        jcp.ver = ver_fma;
+        jcp.typesize_in = sizeof(float);
+        jcp.typesize_out = sizeof(float);
+        if (mayiuse(avx512_mic_4ops))
+           jcp.ver = ver_4fma;
+
+        if (jcp.is_1stconv) {
+            // TODO: fix & remove constraints below
+            bool not_for_4fma
+                    = IMPLICATION(everyone_is(0, jcp.l_pad, jcp.t_pad),
+                            nstl::max(jcp.kw, jcp.kh) < 7);
+            bool is_dilated
+                    = !everyone_is(0, jcp.dilate_d, jcp.dilate_h, jcp.dilate_w);
+            if (one_of(true, not_for_4fma, is_dilated))
+                jcp.ver = ver_fma;
+            if (jcp.ver == ver_4fma) {
+                wei_tag = with_groups
+                    ? ((jcp.simd_w == 4)
+                        ? pick(ndims - 3, gOiw4o, gOihw4o, gOidhw4o)
+                        : pick(ndims - 3, gOiw16o, gOihw16o, gOidhw16o))
+                    : ((jcp.simd_w == 4)
+                        ? pick(ndims - 3, Oiw4o, Oihw4o, Oidhw4o)
+                        : pick(ndims - 3, Oiw16o, Oihw16o, Oidhw16o));
+            } else {
+                wei_tag = with_groups
+                    ? ((jcp.simd_w == 4)
+                        ? pick(ndims - 3, gOwi4o, gOhwi4o, gOdhwi4o)
+                        : pick(ndims - 3, gOwi16o, gOhwi16o, gOdhwi16o))
+                    : ((jcp.simd_w == 4)
+                        ? pick(ndims - 3, Owi4o, Ohwi4o, Odhwi4o)
+                        : pick(ndims - 3, Owi16o, Ohwi16o, Odhwi16o));
+            }
+        }
+    } else {
+        return status::unimplemented;
+    }
+
+    if (weights_d.format_kind() == format_kind::any) {
+        CHECK(memory_desc_init_by_tag(weights_md, wei_tag));
+        jcp.wei_tag = wei_tag;
+    } else {
+        jcp.wei_tag = weights_d.matches_one_of_tag(wei_tag);
+    }
+    if (jcp.wei_tag != wei_tag)
+        return status::unimplemented;
+
+    if (jcp.is_1stconv) {
+        jcp.ur_w = nstl::min(jcp.ow, regs);
+    } else {
+        // avx512_core guard - just to avoid possible regression for other archs
+        if (jcp.ver == ver_fma && mayiuse(avx512_core)) {
+            jcp.ur_w = nstl::min(jcp.ow, regs);
+        } else {
+            for (int ur_w = regs; ur_w > 0; --ur_w) {
+                if (jcp.ow % ur_w == 0) {
+                    jcp.ur_w = ur_w;
+                    break;
+                }
+            }
+        }
+        if ((ndims == 5 && jcp.ur_w <= 8) || (jcp.ur_w <= 1)) {
+            jcp.ur_w = nstl::min(jcp.ow, regs);
+        }
+    }
+    // TODO (Tanya): currently applied to Segnet convolutions only.
+    // Need to try for other topologies
+    if (jcp.ow > 150 && jcp.ur_w < regs/2)
+        jcp.ur_w = regs;
+
+    int n_oi = (jcp.ow / jcp.ur_w);
+    int r_pad = (jcp.ur_w * n_oi - 1) * jcp.stride_w
+            + (jcp.kw - 1) * (jcp.dilate_w + 1) - (jcp.iw + jcp.l_pad - 1);
+    if (jcp.l_pad > 0 && r_pad > 0)
+        n_oi--;
+
+    bool large_code_size = jcp.ur_w != jcp.ow && jcp.l_pad > 0 && r_pad > 0
+            && ((jcp.l_pad <= 0 && n_oi > 0) || (jcp.l_pad > 0 && n_oi > 1));
+    if (large_code_size) {
+        const int max_code_size = 24 * 1024;
+        const int num_ops_per_reg = 6 + jcp.ic_block * jcp.kw;
+        int mult = 1;
+        if (jcp.l_pad > 0) mult += 1;
+        if (r_pad > 0) mult += 1;
+        for (int ur_w = jcp.ur_w; ur_w > regs/2; --ur_w) {
+            if (ur_w * mult * num_ops_per_reg * 9.0 < max_code_size) {
+                jcp.ur_w = ur_w;
+                break;
+            }
+        }
+    }
+
+    /* Grouped channel offset to support 'non-blocked data' format for
+     * convolution sizes with '(input_channel / ngroups) < simd' */
+    jcp.nonblk_group_off
+            = (jcp.ngroups > 1 && one_of(jcp.src_tag, ncw, nchw, ncdhw)) ?
+            jcp.ic :
+            1;
+
+    jcp.nb_ic = jcp.ic / jcp.ic_block;
+    jcp.nb_oc = jcp.oc / jcp.oc_block;
+    jcp.nb_ic_blocking = jcp.nb_oc_blocking = 1;
+
+    auto is_ow_threading_applicable = [=]() {
+        return (true && !jcp.is_1stconv && one_of(jcp.ndims, 3, 4)
+                && IMPLICATION(mayiuse(avx512_mic),
+                           jcp.ver == ver_4fma
+                                   && IMPLICATION(jcp.mb != 1,
+                                              jcp.ih == 1 && jcp.kh == 1)));
+    };
+
+    if (jcp.ver == ver_4fma && !jcp.is_1stconv) {
+        if ((jcp.kw <= 5 && jcp.kh <= 5 && jcp.kw == jcp.kh && jcp.ow <= 8
+                    && jcp.oh <= 8 && jcp.ow == jcp.oh)
+                || (jcp.stride_h != 1 && jcp.ur_w < jcp.ow)) {
+            if (jcp.nb_oc % 2 == 0) {
+                jcp.nb_oc_blocking = 2;
+                jcp.ur_w = nstl::min(jcp.ow, regs / jcp.nb_oc_blocking);
+            }
+        } else {
+            for (int i = jcp.nb_oc; i > 0; i--)
+                if (i * jcp.ur_w <= regs && jcp.nb_oc % i == 0) {
+                    jcp.nb_oc_blocking = i;
+                    break;
+                }
+        }
+        if (jcp.ver == ver_4fma && is_ow_threading_applicable()) {
+            if (jcp.nb_oc % 2 == 0 && jcp.ur_w < jcp.ow
+                    && jcp.ow != 2 * jcp.ur_w) {
+                jcp.nb_oc_blocking = 2;
+                jcp.ur_w = nstl::min(jcp.ow, regs / jcp.nb_oc_blocking);
+            }
+        }
+    }
+
+    jcp.ow_block = jcp.ow;
+
+    auto get_thr_eff = [=](int nb_oc_blocking, int ow_block) {
+        int nb_ow = div_up(jcp.ow, ow_block);
+        int nb_oc_chunks = div_up(jcp.nb_oc, nb_oc_blocking);
+        int work_amount = jcp.mb * jcp.oh * nb_oc_chunks * nb_ow;
+        float disbalance = (float)jcp.ow / rnd_up(jcp.ow, ow_block);
+        float thr_eff = disbalance * (float)work_amount
+            / rnd_up(work_amount, nthreads);
+        return thr_eff;
+    };
+
+    auto get_ow_block = [=](int nb_oc_blocking, int ur_w, float &eff) {
+        int res_ow_block = jcp.ow;
+        eff = get_thr_eff(nb_oc_blocking, res_ow_block);
+        if (!is_ow_threading_applicable())
+            return res_ow_block;
+
+        int L2_part = (get_cache_size(2) * 7 / 8) / typesize;
+        if (jcp.ver == ver_4fma)
+            L2_part /= 2;
+        int size_src_chunk = jcp.ic_block * ur_w * jcp.kh;
+        int size_dst_chunk = jcp.oc_block * nb_oc_blocking * ur_w;
+        int size_wei_chunk = jcp.oc_block * nb_oc_blocking * jcp.ic_block
+            * jcp.kw * jcp.kh;
+        int nurw_cache = (L2_part - 2 * size_wei_chunk)
+            / (2 * size_dst_chunk + 2 * size_src_chunk);
+        // current design of generate() requires ow_block >= 2 * ur_w
+        int ow_block_cache = ur_w * nstl::max(2, nurw_cache);
+
+        int ow_block_thr = ow_block_cache;
+        eff = get_thr_eff(nb_oc_blocking, ow_block_thr);
+
+        int max_nb_ow = div_up(jcp.ow, 2 * ur_w);
+        int start_nb_ow = div_up(jcp.ow, ow_block_thr);
+        for (int nb_ow = start_nb_ow; nb_ow <= max_nb_ow; nb_ow++) {
+            int ow_block
+                = nstl::min(rnd_up(div_up(jcp.ow, nb_ow), ur_w), jcp.ow);
+            float eff_threshold = (jcp.ver == ver_4fma) ? 0.8f : 0.9f;
+            if (ow_block < nb_oc_blocking * jcp.oc_block && eff > eff_threshold)
+                break;
+            if (div_up(jcp.ow, ow_block) != nb_ow)
+                continue;
+            float thr_eff = get_thr_eff(nb_oc_blocking, ow_block);
+            float eff_step = (jcp.ver == ver_4fma) ? 1.1f : 1.f;
+            if (ow_block >= 2 * ur_w && thr_eff > eff_step * eff) {
+                ow_block_thr = ow_block;
+                eff = thr_eff;
+            }
+            eff_threshold = (jcp.ver == ver_4fma) ? 0.9f : 0.98f;
+            if (eff > eff_threshold)
+                break;
+        }
+        res_ow_block = nstl::min(jcp.ow, nstl::max(2 * ur_w, ow_block_thr));
+        eff = get_thr_eff(nb_oc_blocking, res_ow_block);
+        return res_ow_block;
+    };
+
+
+    if (jcp.ver == ver_fma && mayiuse(avx512_core)) {
+        int try_nb_oc_blocking = 2;
+        unsigned int ker_inp_size = typesize * div_up(jcp.iw, jcp.stride_w)
+            * jcp.ic_block * jcp.kh * jcp.kd;
+        unsigned int ker_out_size = typesize * jcp.ow * jcp.oc_block
+            * try_nb_oc_blocking;
+        unsigned int ker_wei_size = typesize * jcp.kh * jcp.kw * jcp.ic_block
+            * jcp.oc_block * try_nb_oc_blocking * jcp.kd;
+        unsigned int ker_total_size = ker_inp_size + ker_out_size
+            + ker_wei_size;
+
+        bool embd_bcast_condition = true
+            && (jcp.kw == 3 && jcp.ow <= 28 && ker_total_size < L1_cache_size)
+            && !(jcp.kw == 3 && jcp.ow == 13 && jcp.ic >= 192)
+            && !(jcp.kw == 3 && jcp.ow == 28 && jcp.ic >= 512);
+
+        if (jcp.mb == 1) {
+            unsigned int inp_size = jcp.mb * div_up(jcp.ih, jcp.stride_h)
+                    * div_up(jcp.iw, jcp.stride_w) * jcp.ic;
+            unsigned int wei_size = jcp.ic * jcp.oc * jcp.kh * jcp.kw;
+
+            // Estimate whether we need to limit the number of threads
+            // and calculate this number. Includes some heuristic.
+            int oc_chunks = jcp.nb_oc / jcp.nb_oc_blocking;
+            int work_amount = jcp.mb * jcp.ngroups * oc_chunks * jcp.oh;
+            int job_size_min = work_amount / nthreads;
+            int job_size_max = div_up(work_amount, nthreads);
+            int ch_max = rnd_up(jcp.oh, job_size_max);
+            int ch_min = (job_size_min == 0)
+                ? jcp.oh
+                : rnd_up(jcp.oh, job_size_min);
+            bool not_aligned_max = ch_max % jcp.oh != 0 && ch_max / jcp.oh < 2
+                    && (jcp.oh != 8 || ch_max / jcp.oh > 1);
+            bool not_aligned_min = ch_min % jcp.oh != 0 && ch_min / jcp.oh < 2
+                    && (jcp.oh != 8 || ch_min / jcp.oh > 1);
+            bool eligible_case = (jcp.stride_h == 1 && jcp.stride_w == 1)
+                    || nthreads > oc_chunks;
+            if (jcp.loop_order == loop_cgn && oc_chunks > 1 && nthreads > 1
+                && wei_size / inp_size > 24
+                && (not_aligned_max || not_aligned_min)
+                && eligible_case) {
+                // Try to find nthreads > mkldnn_get_max_threads() / 2 such
+                // that oc_chunks is a multiple of nthreads, or nthreads is a
+                // multiple of oc_chunks. Otherwise, keep default value.
+                // TODO: implement a task-based alternative without throttling.
+                jcp.aligned_threads = nthreads;
+                for (int i = nthreads; i > nthreads / 2; i--) {
+                    if (oc_chunks % i == 0 || i % oc_chunks == 0) {
+                        jcp.aligned_threads = i;
+                        break;
+                    }
+                }
+            }
+        }
+
+        if (jcp.kw > 3
+                || (jcp.stride_w == 1 && jcp.stride_h == 1
+                           && embd_bcast_condition)
+                || ((jcp.stride_w != 1 || jcp.stride_h != 1)
+                           && ((jcp.mb <= 16 && (jcp.oc <= 192 || jcp.oh <= 10)
+                                      && embd_bcast_condition)))
+                || (jcp.mb == 1
+                           && (jcp.ur_w >= jcp.ow || jcp.is_1stconv
+                                      || (jcp.ow <= 147 && jcp.oc <= 96)))) {
+            jcp.kernel_kind = embd_bcast;
+            jcp.ur_w = nstl::min(jcp.ow, regs);
+            jcp.nb_ic_blocking = jcp.nb_oc_blocking = 1;
+            if (ker_total_size < L1_cache_size && jcp.ow <= 8 && jcp.kh <= 3
+                    && jcp.kw <= 3 && jcp.nb_oc % try_nb_oc_blocking == 0
+                    && IMPLICATION(jcp.is_1stconv, jcp.mb == 1)
+                    && IMPLICATION(jcp.mb == 1, jcp.ur_w < jcp.ow)) {
+                jcp.nb_oc_blocking = try_nb_oc_blocking;
+                jcp.ur_w = nstl::min(jcp.ow, 31 / (jcp.nb_oc_blocking + 1));
+            }
+        } else {
+            jcp.kernel_kind = expl_bcast;
+            jcp.nb_ic_blocking = 1;
+            if (IMPLICATION(jcp.is_1stconv, jcp.mb > 1)) {
+                float best_thr_eff = 0.f;
+                int best_nb_oc_blocking = 1;
+                for (int i = nstl::min(jcp.nb_oc, 5); i > 0; i--) {
+                    if (jcp.nb_oc % i == 0) {
+                        float thr_eff;
+                        int ur_w = nstl::min(jcp.ow, 31 / (i + 1));
+                        get_ow_block(i, ur_w, thr_eff);
+                        if (thr_eff > 1.05f * best_thr_eff) {
+                            best_nb_oc_blocking = i;
+                            best_thr_eff = thr_eff;
+                        }
+                    }
+                }
+                jcp.nb_oc_blocking = best_nb_oc_blocking;
+                jcp.ur_w = nstl::min(jcp.ow, 31 / (jcp.nb_oc_blocking + 1));
+            }
+        }
+    }
+
+    jcp.ur_w_tail = jcp.ow % jcp.ur_w;
+
+    args_ok = true
+        && jcp.l_pad <= jcp.ur_w
+        && jcp.ic <= src_d.padded_dims()[1]
+        && jcp.oc <= dst_d.padded_dims()[1]
+        && jcp.ic <= weights_d.padded_dims()[with_groups + 1]
+        && jcp.oc <= weights_d.padded_dims()[with_groups + 0];
+    if (!args_ok)
+        return status::unimplemented;
+
+    int r_pad_no_tail = nstl::max(0, (jcp.ow - jcp.ur_w_tail - 1) * jcp.stride_w
+                    + (jcp.kw - 1) * (jcp.dilate_w + 1)
+                    - (jcp.iw + jcp.l_pad - 1));
+    if (r_pad_no_tail > jcp.ur_w)
+        return status::unimplemented;
+
+    pick_loop_order(jcp);
+
+    jcp.nb_ic_L2 = jcp.nb_ic;
+
+    float thr_eff;
+    jcp.ow_block = get_ow_block(jcp.nb_oc_blocking, jcp.ur_w, thr_eff);
+    jcp.nb_ow = div_up(jcp.ow, jcp.ow_block);
+
+    const int L2_size = get_cache_size(2, true) / sizeof(float);
+    // Source and output data needs to fit in L2,
+    // leaving some space for weights and prefetching.
+    int h_L2 = int(((0.6f * L2_size) / jcp.simd_w
+                           - nstl::min(0, jcp.kh - jcp.stride_h) * jcp.iw)
+            / (jcp.stride_h * jcp.iw + jcp.ow));
+    jcp.h_blocking = nstl::max(1, nstl::min(jcp.oh, h_L2));
+
+    if (jcp.ver == ver_4fma) {
+        if (!is_ow_threading_on(jcp)) {
+            for (int divf = 2, temp_nb = jcp.nb_ic_L2; divf <= jcp.nb_ic;
+                  divf++) {
+                size_t l2_src
+                    = (size_t)jcp.iw * jcp.ic_block * jcp.ih * temp_nb * jcp.id;
+                size_t l2_dst = (size_t)jcp.ow * jcp.oc_block * jcp.nb_oc_blocking
+                    * jcp.oh * jcp.od;
+                size_t l2_filt = (size_t)jcp.kw * jcp.oc_block * jcp.ic_block
+                    * jcp.kh * jcp.nb_oc_blocking * temp_nb * jcp.kd;
+                if (4 * (l2_src + l2_dst + l2_filt) > KNx_L2_EFFECTIVE_CAPACITY) {
+                    if (jcp.kh == 3 && jcp.oh == 7) {
+                        jcp.nb_ic_L2 = 1;
+                        break;
+                    }
+                    temp_nb = (jcp.nb_ic_L2 % divf == 0 ? jcp.nb_ic_L2 / divf
+                                    : jcp.nb_ic_L2);
+                } else {
+                    jcp.nb_ic_L2 = temp_nb;
+                    break;
+                }
+            }
+        } else if (jcp.ic > 64) {
+            jcp.nb_ic_L2 = 2; /* according to performance data*/
+        }
+    }
+
+    return status::success;
+}
+
+void jit_avx512_common_conv_fwd_kernel::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad, const jit_conv_conf_t &jcp) {
+    if (jcp.with_bias && jcp.oc != jcp.oc_without_padding)
+        scratchpad.book(key_conv_padded_bias, jcp.typesize_out * jcp.oc);
+}
+
+void jit_avx512_common_conv_bwd_data_kernel_f32::prepare_output(int ur_w)
+{
+    for (int k = 0; k < jcp.nb_ic_blocking; k++) {
+        for (int j = 0; j < ur_w; j++) {
+            Zmm zmm = zmm_out(j, k);
+            vpxord(zmm, zmm, zmm);
+            size_t aux_src_offset
+                = (size_t)typesize * ((size_t)k * jcp.ih * jcp.iw * jcp.id + j)
+                * jcp.ic_block;
+            mic_prefetcht1(EVEX_compress_addr_safe(reg_src_prf, aux_src_offset,
+                        reg_long_offt));
+        }
+    }
+}
+
+void jit_avx512_common_conv_bwd_data_kernel_f32::store_output(int ur_w)
+{
+    Label no_update_label;
+
+    mov(reg_channel, ptr[param + GET_OFF(channel)]);
+    cmp(reg_channel, 0);
+    je(no_update_label, T_NEAR);
+    for (int k = 0; k < jcp.nb_ic_blocking; k++) {
+        for (int j = 0; j < ur_w; j++) {
+            Zmm zmm = zmm_out(j, k);
+            size_t aux_src_offset = (size_t)typesize
+                * ((size_t)k * jcp.ih * jcp.iw * jcp.id + j) * jcp.ic_block;
+            vaddps(zmm, EVEX_compress_addr_safe(reg_src, aux_src_offset,
+                        reg_long_offt));
+        }
+    }
+
+    L(no_update_label);
+    for (int k = 0; k < jcp.nb_ic_blocking; k++) {
+        for (int j = 0; j < ur_w; j++) {
+            Zmm zmm = zmm_out(j, k);
+            size_t aux_src_offset = (size_t)typesize
+                * ((size_t)k * jcp.ih * jcp.iw * jcp.id + j) * jcp.ic_block;
+            vmovups(EVEX_compress_addr_safe(reg_src, aux_src_offset,
+                        reg_long_offt), zmm);
+            mic_prefetcht0(EVEX_compress_addr_safe(reg_src_prf, aux_src_offset,
+                        reg_long_offt));
+        }
+    }
+}
+
+void jit_avx512_common_conv_bwd_data_kernel_f32::compute_loop_4fma(
+        int ur_w, int l_overflow, int r_overflow)
+{
+    int ow = jcp.ow;
+    int kw = jcp.kw;
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    Label kh_label, last_iter_label, loop_end_label, kd_label;
+    int ker_load_number = 4;
+    int shift_ker_ptr = typesize * kw * oc_block * ic_block;
+    int shift_dst_ptr = typesize * ow * oc_block;
+    int ii_dpref_t0 = get_iw_start(0, l_overflow);
+    int iw_end_ipref = get_iw_end(ur_w, 0, r_overflow);
+
+    bool check_last_kh = (jcp.kh > 3);
+    auto kernel_offset = [=](int icb, int oc, int ki) {
+        int blk_idx = icb * jcp.kh * jcp.kw * jcp.kd + ki;
+        int blk_offset = blk_idx * jcp.oc_block * jcp.ic_block;
+        int oc_offset = oc * jcp.oc_block;
+        return typesize * (blk_offset + oc_offset);
+    };
+    auto kernel_loads = [=](int ki, int oc, int kk) {
+        for (int ii = 0; ii < ker_load_number; ii++) {
+            int aux_kernel_offset = kernel_offset(kk, oc + ii, ki);
+            vmovups(zmm_ker(ii),
+                EVEX_compress_addr(aux_reg_ker, aux_kernel_offset));
+        }
+    };
+    auto prefetch_dst_next_kh = [&](int ki, int ki_start, int cnt0, int cnt1) {
+        if (cnt1 >= ker_load_number && cnt0 >= ker_load_number
+            && ki >= ki_start && ii_dpref_t0 < iw_end_ipref) {
+            int aux_dst_offset = typesize * ((ii_dpref_t0
+                + jcp.l_pad) * oc_block + jcp.ow * oc_block);
+            prefetcht0(EVEX_compress_addr(aux_reg_dst, aux_dst_offset));
+            ii_dpref_t0++;
+        }
+    };
+
+    if (one_of(jcp.ndims, 3, 4)) {
+        mov(aux_reg_dst, reg_dst);
+        mov(aux_reg_ker, reg_ker);
+        mov(aux_reg_dst_prf, reg_dst_prf);
+        mov(aux_reg_ker_prf, reg_ker_prf);
+    }
+
+    if (jcp.ndims == 5) {
+        push(reg_src_prf);
+        push(reg_src);
+
+        mov(reg_ki, ptr[param + GET_OFF(kd_padding)]);
+        mov(aux_reg_dst_d, reg_dst);
+        mov(aux_reg_ker_d, ptr[param + GET_OFF(filt)]);
+        mov(aux_reg_dst_d_prf, reg_dst_prf);
+        mov(aux_reg_ker_d_prf, reg_ker_prf);
+
+        L(kd_label);
+        mov(reg_kj, ptr[param + GET_OFF(kh_padding)]);
+    } else {
+        mov(reg_kj, reg_kh);
+    }
+
+    if (jcp.ndims == 5) {
+        mov(aux_reg_dst, aux_reg_dst_d);
+        mov(aux_reg_ker, aux_reg_ker_d);
+        mov(aux_reg_dst_prf, aux_reg_dst_d_prf);
+        mov(aux_reg_ker_prf, aux_reg_ker_d_prf);
+    }
+
+    align(16);
+    L(kh_label);
+    if (check_last_kh) {
+        for (int ki = 0; ki < kw; ki++)
+        for (int oc = 0; oc < oc_block; oc += 4)
+        for (int kk = 0; kk < jcp.nb_ic_blocking; kk++) {
+            bool last_kernel_loads = (kk == jcp.nb_ic_blocking - 1
+                && ki == kw - 1 && (oc + 4) == oc_block);
+
+            if (last_kernel_loads) {
+                cmp(reg_kj, 1);
+                je(last_iter_label, T_NEAR);
+            }
+
+            kernel_loads(ki, oc, kk);
+            for (int ii = get_iw_start(ki, l_overflow),
+                    prf_count_t0 = 0, prf_count_t1 = 0;
+                    ii < get_iw_end(ur_w, ki, r_overflow); ii++) {
+                int aux_dst_offset = typesize
+                    * ((ii + jcp.l_pad - ki) * oc_block + oc);
+                v4fmaddps(zmm_out(ii, kk), zmm_ker(0),
+                    EVEX_compress_addr(aux_reg_dst, aux_dst_offset));
+
+                if (ii % 2) {
+                    if (prf_count_t0 < 4) {
+                        int aux_kernel_prf;
+                        if (last_kernel_loads)
+                            aux_kernel_prf= kernel_offset(0, prf_count_t0
+                                + oc + 4 - oc_block, 0) + typesize * kw
+                                * oc_block * ic_block;
+                        else
+                            aux_kernel_prf = kernel_offset(kk, oc + 4
+                                + prf_count_t0, ki);
+                        mic_prefetcht0(EVEX_compress_addr(aux_reg_ker,
+                            aux_kernel_prf));
+                        prf_count_t0++;
+                    } else if (prf_count_t1 < 4) {
+                        mic_prefetcht1(EVEX_compress_addr(aux_reg_ker_prf,
+                            kernel_offset(kk, oc + prf_count_t1, ki)));
+                        prf_count_t1++;
+                    }
+                } else
+                    prefetch_dst_next_kh(ki, 2, prf_count_t0, prf_count_t1);
+            }
+            if (last_kernel_loads) {
+                jmp(loop_end_label, T_NEAR);
+
+                L(last_iter_label);
+
+                kernel_loads(ki, oc, kk);
+                for (int ii = get_iw_start(ki, l_overflow),
+                        prf_count_t0 = 0, prf_count_t1 = 0;
+                        ii < get_iw_end(ur_w, ki, r_overflow); ii++) {
+                    int aux_dst_offset = typesize
+                        * ((ii + jcp.l_pad - ki) * oc_block + oc);
+                    v4fmaddps(zmm_out(ii, kk), zmm_ker(0),
+                            EVEX_compress_addr(aux_reg_dst, aux_dst_offset));
+                    if (ii % 2) {
+                        if (prf_count_t0 < 4) {
+                            mic_prefetcht0(EVEX_compress_addr(aux_reg_ker_prf,
+                                kernel_offset(0, prf_count_t0, 0)));
+                            prf_count_t0++;
+                        } else if (prf_count_t1 < 4) {
+                            mic_prefetcht1(EVEX_compress_addr(aux_reg_ker_prf,
+                                kernel_offset(kk, oc + prf_count_t1, ki)));
+                            prf_count_t1++;
+                        }
+                    }
+                }
+                L(loop_end_label);
+            }
+        }
+    } else {
+        for (int ki = 0; ki < kw; ki++)
+        for (int oc = 0; oc < oc_block; oc += 4)
+        for (int kk = 0; kk < jcp.nb_ic_blocking; kk++) {
+            kernel_loads(ki, oc, kk);
+
+            for (int ii = get_iw_start(ki, l_overflow), prf_count_t1 = 0;
+                    ii < get_iw_end(ur_w, ki, r_overflow); ii++) {
+                int aux_dst_offset = typesize
+                    * ((ii + jcp.l_pad - ki) * oc_block + oc);
+                v4fmaddps(zmm_out(ii, kk), zmm_ker(0),
+                    EVEX_compress_addr(aux_reg_dst, aux_dst_offset));
+                if ((ii % 2) && (prf_count_t1 < 4)) {
+                    mic_prefetcht1(EVEX_compress_addr(
+                        aux_reg_ker_prf, kernel_offset(kk,
+                        oc + prf_count_t1, ki)));
+                    prf_count_t1++;
+                }
+                if ( ki == 1 && oc == 0 && kk == 0)
+                    mic_prefetcht1(EVEX_compress_addr(
+                        aux_reg_dst_prf, aux_dst_offset));
+            }
+        }
+    }
+
+    add(aux_reg_ker, shift_ker_ptr);
+    sub(aux_reg_dst, shift_dst_ptr);
+    add(aux_reg_ker_prf, shift_ker_ptr);
+    sub(aux_reg_dst_prf, shift_dst_ptr);
+
+    dec(reg_kj);
+    cmp(reg_kj, 0);
+    jg(kh_label, T_NEAR);
+
+    if (jcp.ndims == 5) {
+        sub(aux_reg_dst_d, typesize * (jcp.oh * ow) * ic_block);
+        add(aux_reg_ker_d, typesize * jcp.kw * jcp.kh * oc_block * ic_block);
+        sub(aux_reg_dst_d_prf, typesize * (jcp.oh * ow) * ic_block);
+        add(aux_reg_ker_d_prf, typesize * jcp.kw * jcp.kh *oc_block * ic_block);
+
+        dec(reg_ki);
+        cmp(reg_ki, 0);
+        jg(kd_label, T_NEAR);
+
+        pop(reg_src);
+        pop(reg_src_prf);
+    }
+}
+
+void jit_avx512_common_conv_bwd_data_kernel_f32::compute_loop_fma(
+        int ur_w, int l_overflow, int r_overflow)
+{
+    Label kh_label, kd_label;
+    int kw = jcp.kw;
+    int ow = jcp.ow;
+
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    int l_pad = jcp.l_pad;
+    int dilate_w = jcp.dilate_w + 1;
+    int stride_w = jcp.stride_w;
+    int stride_h = jcp.stride_h;
+
+    int ker_pipeline_depth = 4;
+    assert(ker_reg_base_idx + ker_pipeline_depth <= 32);
+    assert(oc_block >= ker_pipeline_depth);
+
+    int num_ker_loads = oc_block * kw;
+    int num_inp_prfs = ur_w * nstl::min(kw, stride_w)
+                       + nstl::max(0, kw - stride_w);
+    int num_prfs = num_ker_loads + num_inp_prfs;
+    int num_fmas = num_ker_loads * ur_w / stride_w;
+    int prf_inst_spacing = nstl::max(1, num_fmas / num_prfs);
+    int prf_inst_trigger = (num_fmas % prf_inst_spacing) / 2;
+
+    if (one_of(jcp.ndims, 3, 4)) {
+        mov(aux_reg_dst, reg_dst);
+        mov(aux_reg_ker, reg_ker);
+
+        mov(aux_reg_dst_prf, reg_dst_prf);
+        mov(aux_reg_ker_prf, reg_ker_prf);
+    }
+
+    if (jcp.ndims == 5) {
+        push(reg_src_prf);
+        push(reg_src);
+
+        mov(reg_ki, ptr[param + GET_OFF(kd_padding)]);
+        mov(aux_reg_dst_d, reg_dst);
+        mov(aux_reg_ker_d, ptr[param + GET_OFF(filt)]);
+        mov(aux_reg_dst_d_prf, reg_dst_prf);
+        mov(aux_reg_ker_d_prf, reg_ker_prf);
+
+        L(kd_label);
+        mov(reg_kj, ptr[param + GET_OFF(kh_padding)]);
+    } else {
+        mov(reg_kj, reg_kh);
+    }
+
+    if (jcp.ndims == 5) {
+        mov(aux_reg_dst, aux_reg_dst_d);
+        mov(aux_reg_ker, aux_reg_ker_d);
+        mov(aux_reg_dst_prf, aux_reg_dst_d_prf);
+        mov(aux_reg_ker_prf, aux_reg_ker_d_prf);
+    }
+
+    L(kh_label); {
+        int step = 0;
+        int ker_prfs = 0;
+        for (int ki = 0; ki < kw; ki++) {
+            for (int oc = 0; oc < oc_block; oc++) {
+                if (step == 0) {
+                    for (int i = 0; i < ker_pipeline_depth; i++) {
+                        int aux_kernel_offset = typesize * ((oc + i) * oc_block
+                                + ki * ic_block * oc_block);
+                        vmovups(zmm_ker(i), EVEX_compress_addr(
+                                    aux_reg_ker, aux_kernel_offset));
+                    }
+                } else if (step < num_ker_loads - ker_pipeline_depth + 1) {
+                    int load_offset = ker_pipeline_depth - 1;
+                    int ker_load_reg_idx
+                        = (step + load_offset) % ker_pipeline_depth;
+                    int aux_kernel_offset = typesize * ((oc + load_offset)
+                            * oc_block + ki * ic_block * oc_block);
+                    vmovups(zmm_ker(ker_load_reg_idx),
+                            EVEX_compress_addr(aux_reg_ker, aux_kernel_offset));
+                }
+
+                bool ker_prf_inserted = false;
+                auto zmm_kernel = zmm_ker(step % ker_pipeline_depth);
+
+                int jj_start = get_iw_start(ki, l_overflow);
+                int jj_end = get_iw_end(ur_w, ki, r_overflow);
+                assert(stride_w != 1
+                        || jj_start == nstl::max(0,
+                            l_overflow - (kw - 1 - ki) * dilate_w));
+                assert(stride_w != 1
+                        || jj_end == ur_w - nstl::max(0,
+                            r_overflow - ki * dilate_w));
+
+                for (int jj = jj_start; jj < jj_end; jj += stride_w) {
+                    assert((jj + l_pad - ki * dilate_w) % stride_w == 0);
+                    int aux_dst_offset = typesize *
+                        (((jj + l_pad - ki * dilate_w)
+                                / stride_w) * jcp.oc_block + oc);
+                    vfmadd231ps(zmm_out(jj, 0), zmm_kernel,
+                        EVEX_compress_addr(aux_reg_dst, aux_dst_offset, true));
+
+                    int fma_idx = (step * ur_w + jj) / stride_w;
+                    int prf_slot_idx = fma_idx / prf_inst_spacing;
+                    if (fma_idx % prf_inst_spacing == prf_inst_trigger) {
+                        if (!ker_prf_inserted && ker_prfs < num_ker_loads) {
+                            int ker_prf_offset = typesize
+                                * ker_prfs * jcp.oc_block;
+                            mic_prefetcht1(EVEX_compress_addr(
+                                        aux_reg_ker_prf, ker_prf_offset));
+                            ker_prf_inserted = true;
+                            ker_prfs++;
+                        } else {
+                            int inp_prf_idx = prf_slot_idx - ker_prfs;
+                            if (inp_prf_idx < num_inp_prfs) {
+                                int inp_prf_offset
+                                    = ic_block * typesize
+                                    * ((inp_prf_idx / kw) * kw
+                                            + (inp_prf_idx % kw));
+                                mic_prefetcht0(EVEX_compress_addr(
+                                            aux_reg_dst_prf, inp_prf_offset));
+                            }
+                        }
+                    }
+                }
+                step++;
+            }
+        }
+
+        add(aux_reg_ker, typesize * stride_h * kw * oc_block * ic_block);
+        sub(aux_reg_dst, typesize * (jcp.dilate_h + 1) * ow * oc_block);
+        add(aux_reg_ker_prf, typesize * stride_h * kw * oc_block * ic_block);
+        sub(aux_reg_dst_prf, typesize * (jcp.dilate_h + 1) * ow * oc_block);
+
+        dec(reg_kj);
+        cmp(reg_kj, 0);
+        jg(kh_label, T_NEAR);
+    }
+    if (jcp.ndims == 5) {
+        sub(aux_reg_dst_d,
+                typesize * (jcp.dilate_d + 1) * jcp.oh * ow * ic_block);
+        add(aux_reg_ker_d, typesize * jcp.stride_d * jcp.kw * jcp.kh
+                * oc_block * ic_block);
+        sub(aux_reg_dst_d_prf,
+                typesize * (jcp.dilate_d + 1) * jcp.oh * ow * ic_block);
+        add(aux_reg_ker_d_prf, typesize * jcp.stride_d * jcp.kw * jcp.kh
+                * oc_block * ic_block);
+
+        dec(reg_ki);
+        cmp(reg_ki, 0);
+        jg(kd_label, T_NEAR);
+    }
+
+    if (jcp.ndims == 5)
+    {
+        pop(reg_src);
+        pop(reg_src_prf);
+    }
+}
+
+void jit_avx512_common_conv_bwd_data_kernel_f32::compute_loop_fma_core(
+        int ur_w, int l_overflow, int r_overflow)
+{
+    int kw = jcp.kw;
+    int ow = jcp.ow;
+    int dilate_w = jcp.dilate_w + 1;
+    int stride_w = jcp.stride_w;
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    int nb_ic_block = jcp.nb_ic_blocking;
+    Label kh_label, kd_label;
+
+    int shift_ker_ptr = typesize * kw * oc_block * ic_block;
+    int shift_dst_ptr = typesize * (jcp.dilate_h + 1) * ow * oc_block;
+
+    auto output_offset = [=](int oi, int oc, int ki) {
+        return typesize *
+            (((oi + jcp.l_pad - ki * dilate_w) / stride_w) * oc_block + oc);
+    };
+    auto kernel_offset = [=](int icb, int oc, int ki) {
+        int blk_idx = icb * jcp.kh * jcp.kw * jcp.kd + ki;
+        int blk_offset = blk_idx * jcp.oc_block * jcp.ic_block;
+        int oc_offset = oc * jcp.oc_block;
+        return typesize * (blk_offset + oc_offset);
+    };
+
+    if (one_of(jcp.ndims, 3, 4)) {
+        mov(aux_reg_dst, reg_dst);
+        mov(aux_reg_ker, reg_ker);
+    }
+
+    if (jcp.ndims == 5) {
+        push(reg_src_prf);
+        push(reg_src);
+
+        mov(reg_ki, ptr[param + GET_OFF(kd_padding)]);
+        mov(aux_reg_dst_d, reg_dst);
+        mov(aux_reg_ker_d, ptr[param + GET_OFF(filt)]);
+
+        L(kd_label);
+        mov(reg_kj, ptr[param + GET_OFF(kh_padding)]);
+    } else {
+        mov(reg_kj, reg_kh);
+    }
+
+    if (jcp.ndims == 5) {
+        mov(aux_reg_dst, aux_reg_dst_d);
+        mov(aux_reg_ker, aux_reg_ker_d);
+    }
+
+    L(kh_label);
+    {
+        for (int ki = 0; ki < kw; ki++) {
+            int jj_start = get_iw_start(ki, l_overflow);
+            int jj_end = get_iw_end(ur_w, ki, r_overflow);
+            for (int oc = 0; oc < oc_block; oc++) {
+                if (jcp.kernel_kind == expl_bcast) {
+                    for (int jj = jj_start; jj < jj_end; jj++) {
+                        int aux_output_offset = output_offset(jj, oc, ki);
+                        vbroadcastss(zmm_inp(jj, nb_ic_block),
+                            ptr[aux_reg_dst + aux_output_offset]);
+                    }
+                }
+                for (int ii = 0; ii < nb_ic_block; ii++) {
+                    int aux_kernel_offset = kernel_offset(ii, oc, ki);
+                    if (jj_end - jj_start > 0)
+                        vmovups(zmm_wei, EVEX_compress_addr(aux_reg_ker,
+                            aux_kernel_offset));
+                    for (int jj = jj_start; jj < jj_end; jj += stride_w)
+                        if (jcp.kernel_kind == expl_bcast)
+                            vfmadd231ps(zmm_out(jj, ii),
+                                zmm_inp(jj, nb_ic_block), zmm_wei);
+                        else
+                            vfmadd231ps(zmm_out(jj, ii), zmm_wei,
+                                EVEX_compress_addr(aux_reg_dst,
+                                output_offset(jj, oc, ki), true));
+                }
+            }
+        }
+        add(aux_reg_ker, shift_ker_ptr);
+        sub(aux_reg_dst, shift_dst_ptr);
+        dec(reg_kj);
+        cmp(reg_kj, 0);
+        jg(kh_label, T_NEAR);
+    }
+
+    if (jcp.ndims == 5) {
+        sub(aux_reg_dst_d,
+                typesize * (jcp.dilate_d + 1) * jcp.oh * ow * ic_block);
+        add(aux_reg_ker_d, typesize * jcp.kw * jcp.kh * oc_block * ic_block);
+
+        dec(reg_ki);
+        cmp(reg_ki, 0);
+        jg(kd_label, T_NEAR);
+
+        pop(reg_src);
+        pop(reg_src_prf);
+    }
+}
+
+inline void jit_avx512_common_conv_bwd_data_kernel_f32::compute_loop(
+        int ur_w, int l_overflow, int r_overflow)
+{
+    if (jcp.ndims == 5) push(reg_oi);
+
+    prepare_output(ur_w);
+
+    Label skip_compute_loop;
+    if (jcp.ndims == 5) {
+        mov(reg_kj, ptr[param + GET_OFF(kd_padding)]);
+        cmp(reg_kj, 0);
+        je(skip_compute_loop, T_NEAR);
+    }
+    mov(reg_kj, ptr[param + GET_OFF(kh_padding)]);
+    cmp(reg_kj, 0);
+    je(skip_compute_loop, T_NEAR);
+
+    if (jcp.ver == ver_4fma)
+        compute_loop_4fma(ur_w, l_overflow, r_overflow);
+    else if (jcp.ver == ver_fma)
+        if (mayiuse(avx512_mic))
+            compute_loop_fma(ur_w, l_overflow, r_overflow);
+        else
+          if (jcp.kernel_kind == embd_bcast && jcp.nb_ic_blocking == 1)
+              compute_loop_fma(ur_w, l_overflow, r_overflow);
+          else
+              compute_loop_fma_core(ur_w, l_overflow, r_overflow);
+    else
+        assert("!unknown convolution version");
+
+    L(skip_compute_loop);
+    store_output(ur_w);
+    if (jcp.ndims == 5) pop(reg_oi);
+}
+
+void jit_avx512_common_conv_bwd_data_kernel_f32::generate()
+{
+    int iw = jcp.iw;
+    int kw = jcp.kw;
+    int ur_w = jcp.ur_w;
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    int ur_w_tail = jcp.ur_w_tail;
+    int dilate_w = jcp.dilate_w + 1;
+    int stride_w = jcp.stride_w;
+
+    int dst_shift = jcp.typesize_in * (ur_w / stride_w) * ic_block;
+    int src_shift = jcp.typesize_out * ur_w * oc_block;
+
+    preamble();
+
+    mov(reg_src, ptr[param + GET_OFF(src)]);
+    mov(reg_dst, ptr[param + GET_OFF(dst)]);
+    mov(reg_ker, ptr[param + GET_OFF(filt)]);
+
+    mov(reg_kh, ptr[param + GET_OFF(kh_padding)]);
+    mov(reg_src_prf, ptr[param + GET_OFF(src_prf)]);
+    mov(reg_dst_prf, ptr[param + GET_OFF(dst_prf)]);
+    mov(reg_ker_prf, ptr[param + GET_OFF(filt_prf)]);
+
+    int l_overflow = nstl::max(0, ((kw - 1) * dilate_w - jcp.l_pad) / stride_w);
+    int r_overflow = nstl::max(0, ((kw - 1) * dilate_w
+                    - nstl::max(0, jcp.r_pad)) / stride_w);
+    int r_overflow1 = nstl::max(0, ((kw - 1) * dilate_w
+                    - nstl::max(0, jcp.r_pad) - ur_w_tail) / stride_w);
+
+    int n_oi = iw / ur_w;
+    if (r_overflow1 > 0) n_oi--;
+
+    if (ur_w == iw) {
+        compute_loop(ur_w, l_overflow, r_overflow);
+    } else if (n_oi == 0) {
+        compute_loop(ur_w, l_overflow, r_overflow1);
+        add(reg_src, src_shift);
+        add(reg_dst, dst_shift);
+        add(reg_src_prf, src_shift);
+        add(reg_dst_prf, dst_shift);
+        if (ur_w_tail != 0)
+            compute_loop(ur_w_tail, 0, r_overflow);
+    } else {
+        xor_(reg_oi, reg_oi);
+        if (l_overflow > 0) {
+            compute_loop(ur_w, l_overflow, 0);
+            add(reg_src, src_shift);
+            add(reg_dst, dst_shift);
+            add(reg_src_prf, src_shift);
+            add(reg_dst_prf, dst_shift);
+
+            inc(reg_oi);
+        }
+        if ((l_overflow <= 0 && n_oi > 0)
+            || (l_overflow > 0 && n_oi > 1)) {
+            Label ow_loop_label;
+            L(ow_loop_label); {
+                compute_loop(ur_w, 0, 0);
+                add(reg_src, src_shift);
+                add(reg_dst, dst_shift);
+                add(reg_src_prf, src_shift);
+                add(reg_dst_prf, dst_shift);
+
+                inc(reg_oi);
+                cmp(reg_oi, n_oi);
+                jl(ow_loop_label, T_NEAR);
+            }
+        }
+        if (r_overflow1 > 0) {
+            compute_loop(ur_w, 0, r_overflow1);
+            add(reg_src, src_shift);
+            add(reg_dst, dst_shift);
+            add(reg_src_prf, src_shift);
+            add(reg_dst_prf, dst_shift);
+        }
+        if (ur_w_tail != 0) {
+            compute_loop(ur_w_tail, 0, r_overflow);
+        }
+    }
+
+    postamble();
+}
+
+status_t jit_avx512_common_conv_bwd_data_kernel_f32::init_conf(
+        jit_conv_conf_t &jcp,
+        const convolution_desc_t &cd,
+        const memory_desc_wrapper &diff_src_d,
+        const memory_desc_wrapper &weights_d,
+        const memory_desc_wrapper &diff_dst_d)
+{
+    if (!mayiuse(avx512_common)) return status::unimplemented;
+
+    jcp = zero<decltype(jcp)>();
+
+    jcp.simd_w = cpu_isa_traits<avx512_common>::vlen / sizeof(float);
+    const bool with_groups = weights_d.ndims() == diff_src_d.ndims() + 1;
+    int ndims = diff_src_d.ndims();
+
+    jcp.ndims = ndims;
+    jcp.prop_kind = cd.prop_kind;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = diff_src_d.dims()[0];
+
+    jcp.oc = diff_dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = diff_src_d.dims()[1] / jcp.ngroups;
+
+    jcp.id = (ndims == 5) ? diff_src_d.dims()[2] : 1;
+    jcp.ih = (ndims == 3) ? 1 : diff_src_d.dims()[ndims-2];
+    jcp.iw = diff_src_d.dims()[ndims-1];
+    jcp.od = (ndims == 5) ? diff_dst_d.dims()[2] : 1;
+    jcp.oh = (ndims == 3) ? 1 : diff_dst_d.dims()[ndims-2];
+    jcp.ow = diff_dst_d.dims()[ndims-1];
+
+    jcp.kd = (ndims == 5) ? weights_d.dims()[with_groups + 2] : 1;
+    jcp.kh = (ndims == 3) ? 1 : weights_d.dims()[with_groups + ndims - 2];
+    jcp.kw = weights_d.dims()[with_groups + ndims - 1];
+
+    jcp.f_pad = (ndims == 5) ? cd.padding[0][0] : 0;
+    jcp.t_pad = (ndims == 3) ? 0 : cd.padding[0][ndims-4];
+    jcp.l_pad = cd.padding[0][ndims-3];
+
+    jcp.stride_d = (ndims == 5) ? cd.strides[0] : 1;
+    jcp.stride_h = (ndims == 3) ? 1 : cd.strides[ndims-4];
+    jcp.stride_w = cd.strides[ndims-3];
+
+    jcp.dilate_d = (ndims == 5) ? cd.dilates[0] : 0;
+    jcp.dilate_h = (ndims == 3) ? 0 : cd.dilates[ndims-4];
+    jcp.dilate_w = cd.dilates[ndims-3];
+    if ((jcp.dilate_w != 0 && jcp.stride_w != 1)
+            || (jcp.dilate_d != 0 && jcp.stride_d != 1)
+            || (jcp.dilate_h != 0 && jcp.stride_h != 1))
+        return status::unimplemented;
+
+    jcp.r_pad = (jcp.ow - 1) * jcp.stride_w + (jcp.kw - 1) * (jcp.dilate_w + 1)
+            - (jcp.iw + jcp.l_pad - 1);
+    jcp.b_pad = (jcp.oh - 1) * jcp.stride_h + (jcp.kh - 1) * (jcp.dilate_h + 1)
+            - (jcp.ih + jcp.t_pad - 1);
+    jcp.back_pad = (jcp.od - 1) * jcp.stride_d
+            + (jcp.kd - 1) * (jcp.dilate_d + 1) - (jcp.id + jcp.f_pad - 1);
+
+    jcp.aligned_threads = 0;
+
+    jcp.is_1stconv = false;
+
+    jcp.oc_block = jcp.simd_w;
+    jcp.ic_block = jcp.is_1stconv ? jcp.ic : jcp.simd_w;
+
+    bool ok_to_pad_channels = true
+        && jcp.ngroups == 1
+        && diff_src_d.data_type() == data_type::f32;
+
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, jcp.oc_block);
+        jcp.ic = rnd_up(jcp.ic, jcp.ic_block);
+    }
+
+    auto dat_tag = pick(ndims - 3, nCw16c, nChw16c, nCdhw16c);
+    auto wei_tag = with_groups
+        ? pick(ndims - 3, gOIw16o16i, gOIhw16o16i, gOIdhw16o16i)
+        : pick(ndims - 3, OIw16o16i, OIhw16o16i, OIdhw16o16i);
+    jcp.src_tag = diff_src_d.matches_one_of_tag(dat_tag);
+    jcp.dst_tag = diff_dst_d.matches_one_of_tag(dat_tag);
+
+    bool args_ok = true
+        && jcp.oc % jcp.oc_block == 0
+        && jcp.ic % jcp.ic_block == 0
+        && jcp.src_tag == dat_tag
+        && jcp.dst_tag == dat_tag;
+    if (!args_ok)
+        return status::unimplemented;
+
+    jcp.nb_ic = jcp.ic / jcp.ic_block;
+    jcp.nb_oc = jcp.oc / jcp.oc_block;
+
+    jcp.ur_w = jcp.stride_w;
+
+    int regs = 28;
+    if (jcp.iw <= regs)
+        jcp.ur_w = jcp.iw;
+    else {
+        for (int ur_w = regs; ur_w > 0; --ur_w)
+            if (ur_w % jcp.stride_w == 0) {
+                jcp.ur_w = ur_w;
+                break;
+            }
+    }
+    int l_overflow = nstl::max(0, ((jcp.kw - 1) * (jcp.dilate_w + 1)
+                    - jcp.l_pad) / jcp.stride_w);
+    int r_overflow1 = nstl::max(0, ((jcp.kw - 1) * (jcp.dilate_w + 1)
+                    - nstl::max(0, jcp.r_pad) - jcp.iw % jcp.ur_w) / jcp.stride_w);
+    int n_oi = jcp.iw / jcp.ur_w;
+    if (r_overflow1 > 0) n_oi--;
+
+    if (mayiuse(avx512_common)
+         && diff_dst_d.data_type() == data_type::f32
+         && weights_d.data_type() == data_type::f32
+         && diff_src_d.data_type() == data_type::f32) {
+        jcp.ver = ver_fma;
+        jcp.typesize_in = sizeof(float);
+        jcp.typesize_out = sizeof(float);
+        if (mayiuse(avx512_mic_4ops)
+            && jcp.stride_w == 1 && jcp.stride_h == 1 && jcp.stride_d == 1) {
+                jcp.ver = ver_4fma;
+            }
+    } else {
+        return status::unimplemented;
+    }
+
+    jcp.wei_tag = weights_d.matches_one_of_tag(wei_tag);
+    if (jcp.wei_tag != wei_tag)
+        return status::unimplemented;
+
+    if (!utils::everyone_is(0, jcp.dilate_d, jcp.dilate_h, jcp.dilate_w)
+            && jcp.ver != ver_fma)
+        return status::unimplemented;
+
+    jcp.nb_ic_blocking = jcp.nb_oc_blocking = 1;
+    if (jcp.ver == ver_4fma) {
+        if (jcp.kw == 3 && jcp.kh == 3 && jcp.iw == 7 && jcp.ih == 7) {
+            jcp.nb_ic_blocking = 2;
+        } else {
+            for (int i = jcp.nb_ic; i > 0; i--)
+                if (i * jcp.ur_w <= regs && jcp.nb_ic % i == 0) {
+                    jcp.nb_ic_blocking = i;
+                    break;
+                }
+        }
+    }
+
+    jcp.loop_order = loop_gnc;
+
+    bool large_code_size = (jcp.ur_w != jcp.ow)
+         && ((l_overflow <= 0 && n_oi > 0) ||(l_overflow > 0 && n_oi > 1))
+         && (r_overflow1 > 0) && (l_overflow > 0);
+    if (large_code_size) {
+        const int max_code_size = 24 * 1024;
+        const int num_ops_per_reg = 6 + jcp.oc_block * jcp.kw;
+        int mult = 1;
+        if (l_overflow > 0) mult += 1;
+        if (r_overflow1 > 0) mult += 1;
+        for (int ur_w = jcp.ur_w; ur_w > regs/2; --ur_w) {
+            if ((ur_w / jcp.stride_w) * mult * num_ops_per_reg * 9.2
+                    < max_code_size) {
+                if (ur_w % jcp.stride_w == 0) {
+                    jcp.ur_w = ur_w;
+                    break;
+                }
+            }
+        }
+    }
+
+    if (jcp.ver == ver_fma && mayiuse(avx512_core)) {
+        int try_nb_ic_blocking = 2;
+        unsigned int ker_inp_size = typesize * jcp.iw * jcp.ic_block
+            * try_nb_ic_blocking * jcp.kh;
+        unsigned int ker_out_size = typesize * jcp.ow * jcp.oc_block;
+        unsigned int ker_wei_size = typesize * jcp.kh * jcp.kw * jcp.ic_block
+            * jcp.oc_block * try_nb_ic_blocking;
+        unsigned int ker_total_size = ker_inp_size + ker_out_size
+            + ker_wei_size;
+        if (!(jcp.kw == 1 || (jcp.kw == 5 && jcp.iw < 8)
+            || (jcp.kw < 5 && ((jcp.iw <= 5 || (jcp.iw > 8 && jcp.iw <= 13))
+            || ker_total_size > L1_cache_size )))
+                || jcp.stride_h > 1 || jcp.stride_d > 1) {
+            jcp.kernel_kind = embd_bcast;
+            jcp.ur_w = nstl::min(jcp.iw, regs);
+            jcp.nb_ic_blocking = jcp.nb_oc_blocking = 1;
+            if (!(jcp.kw > 3 || (jcp.kw == 3 && ker_total_size < L1_cache_size
+                && jcp.ow > 8)) && jcp.stride_h == 1)
+                if (jcp.nb_ic % try_nb_ic_blocking == 0) {
+                    jcp.nb_ic_blocking = try_nb_ic_blocking;
+                    jcp.ur_w = 31 / (jcp.nb_ic_blocking + 1);
+                    if (jcp.iw < jcp.ur_w) jcp.ur_w = jcp.iw;
+                }
+         } else {
+            jcp.kernel_kind = expl_bcast;
+            jcp.nb_oc_blocking = 1;
+            jcp.nb_ic_blocking = 4;
+            if (jcp.nb_ic < jcp.nb_ic_blocking) jcp.nb_ic_blocking = jcp.nb_ic;
+            if (jcp.nb_ic % jcp.nb_ic_blocking != 0)
+                for (int i = jcp.nb_ic_blocking; i > 0; i--)
+                    if (jcp.nb_ic % i == 0) {
+                        jcp.nb_ic_blocking = i;
+                        break;
+                    }
+            jcp.ur_w = 31 / (jcp.nb_ic_blocking + 1);
+            if (jcp.iw < jcp.ur_w) jcp.ur_w = jcp.iw;
+        }
+    }
+    jcp.ur_w_tail = jcp.iw % jcp.ur_w;
+
+    if (l_overflow * jcp.stride_w > jcp.ur_w)
+        return status::unimplemented;
+    int r_overflow_no_tail = nstl::max(0, ((jcp.kw - 1) * (jcp.dilate_w + 1)
+                    - nstl::max(0, jcp.r_pad) - jcp.ur_w_tail) / jcp.stride_w);
+    if (r_overflow_no_tail * jcp.stride_w > jcp.ur_w)
+        return status::unimplemented;
+    if ((jcp.iw > jcp.ur_w) && (jcp.ur_w % jcp.stride_w != 0))
+        return status::unimplemented;
+
+    pick_loop_order(jcp);
+
+    jcp.nb_oc_L2 = jcp.nb_oc;
+    if (jcp.ver == ver_4fma && (jcp.kh < 5 && jcp.kw < 5)) {
+        for (int divf = 2, temp_nb = jcp.nb_oc_L2; divf <= jcp.nb_oc;
+              divf++) {
+            size_t l2_src = jcp.iw * jcp.ic_block * jcp.nb_ic_blocking * jcp.ih
+                * jcp.id;
+            size_t l2_dst = jcp.ow * jcp.oc_block * temp_nb * jcp.oh * jcp.od;
+            size_t l2_filt = jcp.kw * jcp.oc_block * jcp.ic_block * jcp.kh
+                * jcp.kd * jcp.nb_ic_blocking * temp_nb;
+            if (4 * (l2_src + l2_dst + l2_filt) > KNx_L2_EFFECTIVE_CAPACITY) {
+                if (jcp.kh == 3 && jcp.ih == 7) {
+                    jcp.nb_oc_L2 = 1;
+                    break;
+                }
+                temp_nb = (jcp.nb_oc_L2 % divf == 0 ? jcp.nb_oc_L2 / divf
+                                : jcp.nb_oc_L2);
+            } else {
+                jcp.nb_oc_L2 = temp_nb;
+                break;
+            }
+        }
+    }
+
+    args_ok = true
+        && jcp.ic <= diff_src_d.padded_dims()[1]
+        && jcp.oc <= diff_dst_d.padded_dims()[1]
+        && jcp.ic <= weights_d.padded_dims()[with_groups + 1]
+        && jcp.oc <= weights_d.padded_dims()[with_groups + 0];
+    if (!args_ok) return status::unimplemented;
+
+    return status::success;
+}
+
+void jit_avx512_common_conv_bwd_data_kernel_f32::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad, const jit_conv_conf_t &jcp) {
+    UNUSED(scratchpad);
+    UNUSED(jcp);
+}
+
+const int jit_avx512_common_conv_bwd_weights_kernel_f32::max_ur_w = 28;
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::od_step_comeback_pointers()
+{
+    Label kd_comeback_label;
+
+    /* 'depth' loop count bound by 'kd_work_size' */
+    mov(kj, reg_kd_count);
+    L(kd_comeback_label); {
+        int inp_mult = jcp.is_1stconv ? 1 : jcp.ic_block;
+        int iw = jcp.ver == ver_4fma ? jcp.tr_iw : jcp.iw;
+        sub(reg_input,
+                jcp.typesize_in * (jcp.dilate_d + 1) * jcp.ih * iw * inp_mult);
+        sub(reg_kernel,
+            jcp.typesize_out * jcp.kh * jcp.kw * jcp.ic_block * jcp.oc_block);
+        dec(kj);
+        cmp(kj, 0);
+        jg(kd_comeback_label, T_NEAR);
+    }
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::oh_step_comeback_pointers()
+{
+    Label kh_comeback_label, kd_comeback_label;
+    mov(kj, reg_kh);
+    L(kh_comeback_label); {
+        int inp_mult = jcp.is_1stconv ? 1 : jcp.ic_block;
+        int iw = jcp.ver == ver_4fma ? jcp.tr_iw : jcp.iw;
+        sub(reg_input, jcp.typesize_in * (jcp.dilate_h + 1) * iw * inp_mult);
+        sub(reg_kernel,
+            jcp.typesize_out * jcp.kw * jcp.ic_block * jcp.oc_block);
+        dec(kj);
+        cmp(kj, 0);
+        jg(kh_comeback_label, T_NEAR);
+    }
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::compute_ic_block_step_fma(
+    int ur_w, int pad_l, int pad_r,
+    int ic_block_step, int input_offset, int kernel_offset,
+    int output_offset, bool input_wraparound)
+{
+
+    int kw = jcp.kw;
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    for (int i_kw = 0; i_kw < kw; i_kw++)
+        for (int i_ic = 0; i_ic < ic_block_step; i_ic++)
+            vmovups(Zmm(i_kw * ic_block_step + i_ic),
+                EVEX_compress_addr(reg_kernel, typesize * (i_kw * ic_block
+                + i_ic) * jcp.oc_block + kernel_offset));
+
+    for (int i_ur = 0; i_ur < ur_w; i_ur++) {
+        if (i_ur == 0) {
+            vmovups(Zmm(kw * ic_block_step + (i_ur + 0) % 4),
+                EVEX_compress_addr(reg_output, typesize * (i_ur + 0)
+                * oc_block + output_offset));
+            if (ur_w > 1) vmovups(Zmm(kw * ic_block_step + (i_ur + 1) % 4),
+                EVEX_compress_addr(reg_output, typesize * (i_ur + 1) * oc_block
+                + output_offset));
+            if (ur_w > 2) vmovups(Zmm(kw * ic_block_step + (i_ur + 2) % 4),
+                EVEX_compress_addr(reg_output, typesize * (i_ur + 2) * oc_block
+                + output_offset));
+            if (ur_w > 3) vmovups(Zmm(kw * ic_block_step + (i_ur + 3) % 4),
+                EVEX_compress_addr(reg_output, typesize * (i_ur + 3) * oc_block
+                + output_offset));
+        } else if (i_ur + 3 < ur_w)
+            vmovups(Zmm(kw * ic_block_step + (i_ur + 3) % 4),
+                EVEX_compress_addr(reg_output, typesize * (i_ur + 3) * oc_block
+                + output_offset));
+
+        for (int i_kw = 0; i_kw < kw; i_kw++) {
+            int i_iw = i_ur * jcp.stride_w + i_kw * (jcp.dilate_w + 1);
+            if (i_iw - pad_l < 0 || i_iw > (ur_w - 1) * jcp.stride_w +
+                    (kw - 1) * (jcp.dilate_w + 1) - pad_r) continue;
+            for (int i_ic = 0; i_ic < ic_block_step; i_ic++) {
+                const size_t i_offset = (size_t)input_offset
+                    + (size_t)typesize * (jcp.ver == ver_4fma
+                            ? (i_iw - pad_l + i_ic * jcp.tr_iw)
+                            : (jcp.is_1stconv
+                                ? (i_iw - pad_l) + (size_t)i_ic
+                                    * ((size_t)jcp.ih*jcp.iw*jcp.id)
+                                : (i_iw - pad_l) * ic_block + i_ic));
+                vfmadd231ps(Zmm(i_kw * ic_block_step + i_ic),
+                    Zmm(kw * ic_block_step + i_ur % 4),
+                    EVEX_compress_addr_safe(reg_input, i_offset, reg_long_offt,
+                        true));
+            }
+        }
+    }
+
+    for (int i_kw = 0; i_kw < kw; i_kw++)
+        for (int i_ic = 0; i_ic < ic_block_step; i_ic++)
+            vmovups(EVEX_compress_addr(reg_kernel, typesize
+                * (i_kw * ic_block + i_ic) * jcp.oc_block + kernel_offset),
+                Zmm(i_kw * ic_block_step + i_ic));
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::compute_ic_block_step_4fma(
+    int ur_w, int pad_l, int pad_r,
+    int ic_block_step, int input_offset, int kernel_offset,
+    int output_offset, bool input_wraparound)
+{
+    // TODO: add prefetches to fma version as well
+
+    assert(jcp.ver == ver_4fma);
+
+    int kw = jcp.kw;
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+
+    auto zmm_ker = [=](int i_kw, int i_ic) {
+        return Zmm(i_kw * ic_block_step + i_ic);
+    };
+
+    auto ker_addr = [=](int i_kw, int i_ic) {
+        size_t local_offset
+            = jcp.typesize_out * (i_kw * ic_block + i_ic) * jcp.oc_block;
+        return EVEX_compress_addr(reg_kernel, local_offset + kernel_offset);
+    };
+
+    auto inp_addr = [=](int i_iw, int i_ic, ptrdiff_t extra_offset = 0) {
+        int stride = jcp.tr_iw * (jcp.is_1stconv ? jcp.ih : 1);
+        int local_offset = jcp.typesize_in * (i_iw + i_ic * stride);
+        return EVEX_compress_addr(reg_input,
+                local_offset + input_offset + extra_offset);
+    };
+
+    auto zmm_out = [=](int i_iw) {
+        // TODO: move reg calc to global member funcs
+        const int out_zmm_base_idx = 28;
+        return Zmm(out_zmm_base_idx + i_iw % 4);
+    };
+
+    auto out_addr = [=](int i_ur) {
+        return EVEX_compress_addr(reg_output,
+                jcp.typesize_in * i_ur * oc_block + output_offset);
+    };
+
+    auto pf_callback = [=](int i_ur, int i_kw, int i_ic) {
+        assert(i_ur % 4 == 0);
+        if (i_ur == 0)
+            prefetcht1(ker_addr(i_kw, i_ic));
+        if (i_ur + 4 >= ur_w)
+            prefetcht0(ker_addr(i_kw, i_ic));
+
+        const ptrdiff_t next_input_block_offset
+            = jcp.typesize_in * ic_block_step * jcp.tr_iw;
+        if (i_ur % 16 == 4 && i_kw == 0) {
+            if (i_ur + 16 < ur_w)
+                prefetcht0(inp_addr(i_ur + 16, i_ic));
+            else
+                prefetcht0(inp_addr(0, i_ic, next_input_block_offset));
+        }
+        if (i_ur % 16 == 4 && i_kw == 1) {
+            if (input_wraparound)
+                prefetcht1(inp_addr(i_ur, i_ic, -input_offset));
+            else
+                prefetcht1(inp_addr(i_ur, i_ic, next_input_block_offset));
+        }
+    };
+
+    for (int i_kw = 0; i_kw < kw; i_kw++)
+        for (int i_ic = 0; i_ic < ic_block_step; i_ic++) {
+            auto zmm = zmm_ker(i_kw, i_ic);
+            vpxord(zmm, zmm, zmm);
+        }
+
+    for (int i_ur = 0; i_ur < ur_w; i_ur += 4) {
+
+        for (int i = 0; i < 4; i++) {
+            auto zmm = zmm_out(i_ur + i);
+            if (i_ur + i < ur_w)
+                vmovups(zmm, out_addr(i_ur + i));
+            else
+                vpxord(zmm, zmm, zmm);
+            prefetcht0(out_addr(i_ur + i + 4));
+        }
+
+        for (int i_kw = 0; i_kw < kw; i_kw++)
+            for (int i_ic = 0; i_ic < ic_block_step; i_ic++) {
+                int i_iw = i_ur + i_kw;
+                v4fmaddps(zmm_ker(i_kw, i_ic),
+                        zmm_out(i_ur), inp_addr(i_iw, i_ic));
+                pf_callback(i_ur, i_kw, i_ic);
+            }
+    }
+
+    for (int i_kw = 0; i_kw < kw; i_kw++)
+        for (int i_ic = 0; i_ic < ic_block_step; i_ic++) {
+            auto addr = ker_addr(i_kw, i_ic);
+            auto zmm = zmm_ker(i_kw, i_ic);
+            vaddps(zmm, zmm, addr);
+            vmovups(addr, zmm);
+        }
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::compute_ic_block_step(
+    int ur_w, int pad_l, int pad_r,
+    int ic_block_step, int input_offset, int kernel_offset,
+    int output_offset, bool input_wraparound)
+{
+    if (jcp.ver == ver_4fma)
+        compute_ic_block_step_4fma(ur_w, pad_l, pad_r,
+                ic_block_step, input_offset, kernel_offset, output_offset,
+                input_wraparound);
+    else if (jcp.ver == ver_fma)
+        compute_ic_block_step_fma(ur_w, pad_l, pad_r,
+                ic_block_step, input_offset, kernel_offset, output_offset,
+                input_wraparound);
+    else
+        assert(!"unknown convolution version");
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32
+    ::compute_oh_step_unroll_ow_icblock(
+    int ic_block_step, int max_ur_w)
+{
+    UNUSED(max_ur_w);
+
+    Label kh_label, kd_label;
+
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    int inp_mul = !jcp.is_1stconv ? ic_block : 1;
+    int iw = jcp.ver == ver_4fma ? jcp.tr_iw : jcp.iw;
+    int ow = jcp.ow;
+
+    int r_pad = nstl::max(0, (ow - 1) * jcp.stride_w
+            + (jcp.kw - 1) * (jcp.dilate_w + 1) - (jcp.iw + jcp.l_pad - 1));
+    int l_pad = jcp.l_pad;
+
+    if (jcp.ndims == 5) {
+        L(kd_label);
+        mov(reg_input, aux_reg_input);
+        mov(reg_kernel, aux_reg_kernel);
+    }
+
+    mov(kj, reg_kh);
+    L(kh_label);
+    {
+        for (int i_b_ic = 0; i_b_ic < jcp.ic_block; i_b_ic += ic_block_step) {
+            const int input_offset = jcp.typesize_in
+                * (jcp.ver == ver_4fma ? i_b_ic * iw : i_b_ic);
+            compute_ic_block_step(jcp.ur_w, l_pad, r_pad, ic_block_step,
+                input_offset, jcp.typesize_out * i_b_ic * jcp.oc_block, 0,
+                i_b_ic + ic_block_step >= jcp.ic_block);
+        }
+        add(reg_input, jcp.typesize_in * (jcp.dilate_h + 1) * iw * inp_mul);
+        add(reg_kernel, jcp.typesize_out * jcp.kw * ic_block * oc_block);
+        dec(kj);
+        cmp(kj, 0);
+        jg(kh_label, T_NEAR);
+    }
+
+    if (jcp.ndims == 5) {
+        add(aux_reg_input,
+                jcp.typesize_in * (jcp.dilate_d + 1) * jcp.ih * iw * inp_mul);
+        add(aux_reg_kernel, jcp.typesize_out * jcp.kh * jcp.kw * ic_block
+            * oc_block);
+        dec(ki);
+        cmp(ki, 0);
+        jg(kd_label, T_NEAR);
+    }
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32
+    ::compute_oh_step_unroll_ow(
+    int ic_block_step, int max_ur_w)
+{
+    Label kh_label, ic_block_label, kd_label;
+
+    UNUSED(max_ur_w);
+
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+
+    int ow = jcp.ow;
+
+    int r_pad = nstl::max(0,
+        (ow - 1) * jcp.stride_w + (jcp.kw - 1) * (jcp.dilate_w + 1)
+        - (jcp.iw + jcp.l_pad - 1));
+    int l_pad = jcp.l_pad;
+
+    if (jcp.ndims == 5) {
+        L(kd_label);
+        mov(reg_input, aux_reg_input);
+        mov(reg_kernel, aux_reg_kernel);
+    }
+
+    mov(kj, reg_kh);
+    L(kh_label);
+    {
+        xor_(b_ic, b_ic);
+        L(ic_block_label); {
+            compute_ic_block_step(ow, l_pad, r_pad, ic_block_step,
+                0, 0, 0);
+            size_t inp_icblk_stride = jcp.is_1stconv
+                ? (size_t)jcp.ih * jcp.iw * jcp.id
+                : (jcp.ver == ver_4fma ? jcp.tr_iw : 1);
+            size_t input_offset
+                = inp_icblk_stride * jcp.typesize_in * ic_block_step;
+            safe_add(reg_input, input_offset, reg_long_offt);
+            add(reg_kernel, jcp.typesize_out * ic_block_step * oc_block);
+            add(b_ic, ic_block_step);
+            cmp(b_ic, jcp.ic_block);
+            jl(ic_block_label, T_NEAR);
+        }
+
+        if (jcp.is_1stconv) {
+            size_t input_offset
+                = (size_t)jcp.typesize_in * jcp.id * jcp.ih * jcp.iw * ic_block;
+            safe_sub(reg_input, input_offset, reg_long_offt);
+            add(reg_input, jcp.typesize_in * (jcp.dilate_h + 1) * jcp.iw);
+        } else if (jcp.ver != ver_4fma) {
+            add(reg_input, jcp.typesize_in
+                    * ((jcp.dilate_h + 1) * jcp.iw - 1) * ic_block);
+        }
+        add(reg_kernel, jcp.typesize_out * (jcp.kw - 1) * ic_block * oc_block);
+        dec(kj);
+        cmp(kj, 0);
+        jg(kh_label, T_NEAR);
+    }
+    if (jcp.ndims == 5) {
+        add(aux_reg_input, jcp.typesize_in * (jcp.dilate_d + 1) * jcp.ih
+                * jcp.iw * (jcp.is_1stconv ? 1 : ic_block));
+        add(aux_reg_kernel, jcp.typesize_out * jcp.kh * jcp.kw * ic_block
+            * oc_block);
+        dec(ki);
+        cmp(ki, 0);
+        jg(kd_label, T_NEAR);
+    }
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32
+    ::compute_oh_step_common(
+    int ic_block_step, int max_ur_w)
+{
+    Label kh_label, ic_block_label, ow_block_label, kd_label;
+
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+
+    int ow = jcp.ow;
+    int r_pad = nstl::max(0, (ow - 1) * jcp.stride_w
+            + (jcp.kw - 1) * (jcp.dilate_w + 1) - (jcp.iw + jcp.l_pad - 1));
+    int l_pad = jcp.ver == ver_4fma ? 0 : jcp.l_pad;
+
+    int ur_w = nstl::min(ow, max_ur_w);
+    int ur_w_trips = ow / ur_w;
+    int ur_w_tail = ow % ur_w;
+    if ((ur_w_tail == 0 && r_pad != 0)
+        || r_pad >= ur_w_tail) {
+        if (ur_w_trips > 1) {
+            ur_w_tail += ur_w;
+            ur_w_trips--;
+        } else {
+            ur_w_tail += (ur_w - ur_w / 2);
+            ur_w = ur_w / 2;
+        }
+    }
+
+    int inp_mult = (jcp.is_1stconv || jcp.ver == ver_4fma) ? 1 : ic_block;
+    int input_comeback = (ur_w_trips * ur_w * jcp.stride_w - l_pad) * inp_mult;
+    int output_comeback = ur_w_trips * ur_w * oc_block;
+
+    if (jcp.ndims == 5) {
+        L(kd_label);
+        mov(reg_input, aux_reg_input);
+        mov(reg_kernel, aux_reg_kernel);
+    }
+
+    mov(kj, reg_kh);
+    L(kh_label); {
+        xor_(b_ic, b_ic);
+        L(ic_block_label); {
+            if (l_pad != 0) {
+                ur_w_trips--;
+                compute_ic_block_step(ur_w, l_pad, 0, ic_block_step, 0, 0, 0);
+                add(reg_input, jcp.typesize_in * (ur_w * jcp.stride_w - l_pad)
+                    * inp_mult);
+                add(reg_output, jcp.typesize_in * ur_w * oc_block);
+            }
+
+            if (ur_w_trips > 0) {
+                xor_(reg_ur_w_trips, reg_ur_w_trips);
+                L(ow_block_label); {
+                    compute_ic_block_step(ur_w, 0, 0, ic_block_step, 0, 0, 0);
+                    add(reg_input, jcp.typesize_in * ur_w * jcp.stride_w
+                        * inp_mult);
+                    add(reg_output, jcp.typesize_in * ur_w * oc_block);
+
+                    inc(reg_ur_w_trips);
+                    cmp(reg_ur_w_trips, ur_w_trips);
+                    jl(ow_block_label, T_NEAR);
+                }
+            }
+
+            if (ur_w_tail > 0) compute_ic_block_step(ur_w_tail, 0, r_pad,
+                ic_block_step, 0, 0, 0);
+
+            sub(reg_input, jcp.typesize_in * input_comeback);
+            sub(reg_output, jcp.typesize_in * output_comeback);
+            int inp_icblk_stride = jcp.is_1stconv
+                ? jcp.ih * jcp.iw * jcp.id
+                : (jcp.ver == ver_4fma ? jcp.tr_iw : 1);
+            size_t input_offset
+                = inp_icblk_stride * jcp.typesize_in * ic_block_step;
+            safe_add(reg_input, input_offset, reg_long_offt);
+            add(reg_kernel, jcp.typesize_out * ic_block_step * oc_block);
+
+            add(b_ic, ic_block_step);
+            cmp(b_ic, jcp.ic_block);
+            jl(ic_block_label, T_NEAR);
+        }
+        if (jcp.is_1stconv) {
+            size_t input_offset
+                = (size_t)jcp.typesize_in * jcp.id * jcp.ih * jcp.iw * ic_block;
+            safe_sub(reg_input, input_offset, reg_long_offt);
+            add(reg_input, jcp.typesize_in * (jcp.dilate_h + 1) * jcp.iw);
+        } else if (jcp.ver != ver_4fma) {
+            add(reg_input, jcp.typesize_in
+                    * ((jcp.dilate_h + 1 ) * jcp.iw - 1) * ic_block);
+        }
+        add(reg_kernel, jcp.typesize_out * (jcp.kw - 1) * ic_block * oc_block);
+        dec(kj);
+        cmp(kj, 0);
+        jg(kh_label, T_NEAR);
+    }
+    if (jcp.ndims == 5) {
+        add(aux_reg_input, jcp.typesize_in * (jcp.dilate_d + 1) * jcp.ih
+                * jcp.iw * (jcp.is_1stconv ? 1 : ic_block));
+        add(aux_reg_kernel, jcp.typesize_out * jcp.kh * jcp.kw * ic_block
+            * oc_block);
+        dec(ki);
+        cmp(ki, 0);
+        jg(kd_label, T_NEAR);
+    }
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32
+    ::compute_oh_step_disp()
+{
+    int ic_block_step = jcp.kw <= 3 ? 8 : (jcp.kw <= 7 ? 4 : 2);
+    if (jcp.is_1stconv) {
+        bool large_code = jcp.kw >= 7 && (jcp.l_pad > 0 || jcp.t_pad > 0);
+        ic_block_step
+            = (jcp.kw * jcp.ic_block <= 28 && !large_code) ? jcp.ic_block : 1;
+    }
+
+    bool too_large_to_unroll
+        = (jcp.kw > 1 || jcp.kh > 1 || jcp.kd > 1)
+        && (jcp.stride_w > 1 || jcp.stride_h > 1 || jcp.stride_d > 1);
+
+    int ow = jcp.ow;
+    if (jcp.ndims == 5) {
+        /* NOTE: reg_kd_count = aux_reg_input = r12. The following order of
+         * 'movs' must be guaranteed. */
+        mov(ki, reg_kd_count);
+        push(reg_kd_count);
+        mov(aux_reg_input, reg_input);
+        mov(aux_reg_kernel, reg_kernel);
+    }
+
+    if (jcp.kw <= 3 && ow <= 16 && !too_large_to_unroll)
+        compute_oh_step_unroll_ow_icblock(ic_block_step, max_ur_w);
+    else if (ow <= max_ur_w)
+        compute_oh_step_unroll_ow(ic_block_step, max_ur_w);
+    else
+        compute_oh_step_common(ic_block_step, max_ur_w);
+
+    if (jcp.ndims == 5) {
+        mov(reg_input, aux_reg_input);
+        mov(reg_kernel, aux_reg_kernel);
+        pop(reg_kd_count);
+        od_step_comeback_pointers();
+    } else {
+        oh_step_comeback_pointers();
+    }
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::maybe_zero_kernel()
+{
+    Label skip_zeroing, zeroing_loop;
+
+    mov(reg_tmp, ptr[param + GET_OFF(channel)]);
+    cmp(reg_tmp, 0);
+    jz(skip_zeroing, T_NEAR);
+
+    Zmm zero = Zmm(0);
+    vpxord(zero, zero, zero);
+    xor_(reg_tmp, reg_tmp);
+    L(zeroing_loop); {
+        assert(jcp.oc_block * jcp.typesize_out
+            == cpu_isa_traits<avx512_common>::vlen);
+        for (int ic1 = 0; ic1 < jcp.ic_block; ic1++)
+            vmovups(ptr[reg_kernel + reg_tmp + ic1 * jcp.oc_block
+                * jcp.typesize_out], zero);
+        add(reg_tmp, jcp.ic_block * jcp.oc_block * jcp.typesize_out);
+        cmp(reg_tmp, jcp.ic_block * jcp.oc_block * jcp.kw * jcp.kh * jcp.kd
+            * jcp.typesize_out);
+        jnz(zeroing_loop);
+    }
+
+    L(skip_zeroing);
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::bias_kernel()
+{
+    Label skip_bias, bias_loop, skip_load_bias;
+
+    mov(reg_tmp, ptr[param + GET_OFF(flags)]);
+    test(reg_tmp,reg_tmp);
+    jne(skip_bias, T_NEAR);
+
+    mov(reg_bias, ptr[param + GET_OFF(bias)]);
+    mov(reg_output, ptr[param + GET_OFF(dst)]);
+    vpxord(Zmm(1), Zmm(1), Zmm(1));
+
+    mov(reg_tmp, ptr[param + GET_OFF(channel)]);
+    cmp(reg_tmp, 0);
+    jne(skip_load_bias, T_NEAR);
+    vmovups(Zmm(1), ptr[reg_bias]);
+
+    L(skip_load_bias);
+
+    mov(reg_oi, ptr[param + GET_OFF(d_worksize)]);
+    sub(reg_oi, ptr[param + GET_OFF(d_index)]);
+    mov(reg_tmp, jcp.oc_block * jcp.ow * jcp.oh * jcp.typesize_out);
+    imul(reg_oi, reg_tmp);
+
+    xor_(reg_tmp, reg_tmp);
+    L(bias_loop); {
+        vmovups(Zmm(0), ptr[reg_output + reg_tmp]);
+        vaddps(Zmm(1), Zmm(1), Zmm(0));
+        add(reg_tmp, jcp.oc_block * jcp.typesize_out);
+        cmp(reg_tmp, reg_oi);
+        jl(bias_loop);
+    }
+    vmovups(EVEX_compress_addr(reg_bias,0), Zmm(1));
+
+    L(skip_bias);
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32
+    ::compute_oh_loop_common()
+{
+    int b_pad = jcp.b_pad;
+    int t_pad = jcp.t_pad;
+    bool is_dilated = jcp.dilate_h != 0;
+    int dilate_h = jcp.dilate_h + 1;
+    int stride_h = jcp.stride_h;
+    const int inp_mult = jcp.is_1stconv ? 1 : jcp.ic_block;
+    int iw = jcp.ver == ver_4fma ? jcp.tr_iw : jcp.iw;
+    Label oh_label, oh_label_end, oh_tpad_label, oh_tpad_tail_label,
+            oh_bpad_label, oh_bpad_label_end, od_label, od_label_end,
+            oh_dilate_label_shift, oh_dilate_label_noshift, oh_dilate_label_end;
+
+    int ow = jcp.ow;
+
+    mov(reg_kh, jcp.kh);
+    xor_(reg_ih_count, reg_ih_count);
+    xor_(reg_oj, reg_oj);
+    /* Compute 'top' edge */
+    if (t_pad > 0) {
+        const int kh_range = 1 + (jcp.kh - 1) * dilate_h;
+        const int overflow
+            = nstl::max(0, jcp.kh - div_up(t_pad + jcp.ih, dilate_h));
+        const int underflow = div_up(t_pad, dilate_h);
+        const int initial_inp_ker_overlap = jcp.kh - overflow - underflow;
+        mov(reg_kh, initial_inp_ker_overlap);
+        add(reg_kernel, jcp.typesize_out * underflow * jcp.kw * jcp.ic_block
+            * jcp.oc_block);
+        // generate loop to process kernel while it remains within t_pad + ih
+        if (kh_range < t_pad + jcp.ih) {
+            if (is_dilated) {
+                const int tail = t_pad % dilate_h;
+                const int shift = tail == 0 ? 0 : dilate_h - tail;
+                mov(reg_tmp, shift);
+                if (tail != 0)
+                    add(reg_input, jcp.typesize_in * shift * iw * inp_mult);
+            }
+            L(oh_tpad_label); {
+                compute_oh_step_disp();
+                add(reg_output, jcp.typesize_in * ow * jcp.oc_block);
+                if (is_dilated) {
+                    inc(reg_tmp);
+                    cmp(reg_tmp, dilate_h);
+                    jl(oh_dilate_label_shift, T_NEAR);
+                    // unshift input as new kernel element enters
+                    sub(reg_input, jcp.typesize_in * (dilate_h - 1) * iw * inp_mult);
+                    xor_(reg_tmp, reg_tmp);
+                }
+                // kernel overlap only changes when (t_pad + oj) % dilate_h == 0
+                sub(reg_kernel, jcp.typesize_out * stride_h * jcp.kw
+                                * jcp.ic_block * jcp.oc_block);
+                add(reg_kh, stride_h);
+                if (is_dilated) {
+                    jmp(oh_dilate_label_noshift, T_NEAR);
+                    L(oh_dilate_label_shift);
+                    // shift input as old kernel element progresses
+                    add(reg_input, jcp.typesize_in * stride_h * iw * inp_mult);
+                    L(oh_dilate_label_noshift);
+                }
+                inc(reg_oj);
+                add(reg_ih_count, stride_h);
+
+                // final number of kernel elements that overlap with input
+                const int final_inp_ker_overlap
+                    = nstl::min(jcp.kh, div_up(jcp.ih, dilate_h));
+                cmp(reg_kh, final_inp_ker_overlap);
+                jl(oh_tpad_label, T_NEAR);
+            }
+        }
+        // need second loop to process kernel if it is larger than the input
+        // (does not apply to dilations as they must have unit stride)
+        if (kh_range >= jcp.ih + (t_pad % stride_h == 0 ? stride_h :
+                                                        t_pad % stride_h)) {
+            assert(!is_dilated);
+            mov(reg_kh, jcp.ih);
+            L(oh_tpad_tail_label); {
+                compute_oh_step_disp();
+                add(reg_output, jcp.typesize_in * ow * jcp.oc_block);
+                sub(reg_kernel, jcp.typesize_out * stride_h * jcp.kw
+                                * jcp.ic_block * jcp.oc_block);
+
+                inc(reg_oj);
+                add(reg_ih_count, stride_h);
+
+                cmp(reg_ih_count, nstl::min(t_pad, jcp.oh * stride_h));
+                jl(oh_tpad_tail_label, T_NEAR);
+            }
+        }
+        // correct any excess shifts to kernel and input
+        // (does not apply to dilations as they must have unit stride,
+        //  kernel must fit inside input, and padding is smaller than input)
+        if (t_pad <= jcp.oh * stride_h) {
+            // kernel has moved beyond padding (adjust for stride effects)
+            if (t_pad % stride_h != 0) {
+                assert(!is_dilated);
+                int inp_corr = stride_h - t_pad % stride_h;
+                add(reg_kernel, jcp.typesize_out * inp_corr * jcp.kw
+                                * jcp.ic_block * jcp.oc_block);
+                add(reg_input, jcp.typesize_in * inp_corr * iw * inp_mult);
+            }
+        } else {
+            // kernel still overlaps padding (complete reset)
+            assert(!is_dilated);
+            sub(reg_kernel, jcp.typesize_out * (t_pad - jcp.oh * stride_h)
+                            * jcp.kw * jcp.ic_block * jcp.oc_block);
+        }
+    }
+
+    cmp(reg_ih_count, jcp.ihp - b_pad - (jcp.kh - 1) * dilate_h);
+    jge(oh_label_end, T_NEAR);
+    cmp(reg_oj, jcp.oh);
+    jge(oh_label, T_NEAR);
+
+    /* Compute middle block(s) */
+    mov(reg_kh, jcp.kh);
+    L(oh_label); {
+        compute_oh_step_disp();
+        add(reg_input, jcp.typesize_in * stride_h * iw * inp_mult);
+        add(reg_output, jcp.typesize_in * ow * jcp.oc_block);
+
+        inc(reg_oj);
+        add(reg_ih_count, stride_h);
+
+        cmp(reg_ih_count, jcp.ihp - b_pad - (jcp.kh - 1) * dilate_h);
+        jge(oh_label_end, T_NEAR);
+
+        cmp(reg_oj, jcp.oh);
+        jl(oh_label, T_NEAR);
+    }
+    L(oh_label_end);
+
+    /* Compute bottom edge */
+    if (b_pad > 0) {
+        cmp(reg_oj, jcp.oh);
+        jge(oh_bpad_label_end, T_NEAR);
+
+        if (is_dilated) {
+            mov(reg_kh, jcp.kh - 1); // assumes unit stride for dilations
+            mov(reg_tmp, 0);
+        } else {
+            mov(reg_kh, jcp.ihp - b_pad);
+            sub(reg_kh, reg_ih_count);
+        }
+        L(oh_bpad_label);
+        {
+            compute_oh_step_disp();
+            add(reg_input, jcp.typesize_in * stride_h * iw * inp_mult);
+            add(reg_output, jcp.typesize_in * ow * jcp.oc_block);
+            if (is_dilated) {
+                inc(reg_tmp);
+                cmp(reg_tmp, dilate_h);
+                jl(oh_dilate_label_end, T_NEAR);
+                xor_(reg_tmp, reg_tmp);
+            }
+            sub(reg_kh, stride_h);
+            cmp(reg_kh, 0);
+            jle(oh_bpad_label_end, T_NEAR);
+            if (is_dilated)
+                L(oh_dilate_label_end);
+
+            inc(reg_oj);
+            cmp(reg_oj, jcp.oh);
+            jl(oh_bpad_label, T_NEAR);
+        }
+        L(oh_bpad_label_end);
+    }
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::compute_d_loop_common() {
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    const int inp_mult = jcp.is_1stconv ? 1 : jcp.ic_block;
+    int iw = jcp.ver == ver_4fma ? jcp.tr_iw : jcp.iw;
+    int ow = jcp.ow;
+    const int input_backpad_overlap
+            = div_up(jcp.id + jcp.f_pad - (jcp.kd - 1), jcp.stride_d);
+
+    const size_t filter_shift
+            = jcp.typesize_out * jcp.kh * jcp.kw * ic_block * oc_block;
+    const size_t input_shift = jcp.typesize_in * jcp.ih * iw * inp_mult;
+    const size_t output_shift = jcp.typesize_in * jcp.oh * ow * jcp.oc_block;
+
+    Label d_loop_label, loop_end_label, common_block_label, fpad_end_label,
+            backpad_end_label, backpad_label;
+
+    if (jcp.with_bias) bias_kernel();
+
+    /* initially offset 'kd' by f_pad */
+    add(reg_kernel, ptr[param + GET_OFF(kd_offset)]);
+
+    mov(reg_input_d, ptr[param + GET_OFF(src)]);
+    mov(reg_output_d, ptr[param + GET_OFF(dst)]);
+    mov(reg_d_index, ptr[param + GET_OFF(d_index)]);
+    mov(reg_kd_count, ptr[param + GET_OFF(kd_padding)]);
+
+    cmp(reg_d_index, ptr[param + GET_OFF(d_worksize)]);
+    jge(loop_end_label, T_NEAR);
+
+    L(d_loop_label);
+
+    mov(reg_input, reg_input_d);
+    mov(reg_output, reg_output_d);
+
+    push(reg_input_d);
+    push(reg_output_d);
+    push(reg_d_index);
+
+    compute_oh_loop_common();
+
+    pop(reg_d_index);
+    pop(reg_output_d);
+    pop(reg_input_d);
+
+    /* Compute 'front' edge */
+    if (jcp.f_pad > 0) {
+
+        /* Check if within fpad region */
+        cmp(reg_d_index, div_up(jcp.f_pad, jcp.stride_d));
+        jge(fpad_end_label, T_NEAR);
+
+        /* Fpad steps */
+        sub(reg_kernel, filter_shift * jcp.stride_d);
+        add(reg_kd_count, jcp.stride_d);
+
+        /* Final number of kernel elements that overlap with input */
+        const int inp_ker_overlap = nstl::min(jcp.kd, jcp.id);
+        cmp(reg_kd_count, inp_ker_overlap);
+        jl(common_block_label, T_NEAR);
+
+        /* Correct any excess shifts to kernel and input */
+        if (jcp.f_pad <= jcp.od * jcp.stride_d) {
+            /* Filter has moved beyond padding (adjust for stride effects) */
+            if (jcp.f_pad % jcp.stride_d != 0) {
+                int inp_corr = jcp.stride_d - jcp.f_pad % jcp.stride_d;
+                add(reg_kernel, filter_shift * inp_corr);
+                add(reg_input_d, input_shift * inp_corr);
+            }
+        } else {
+            /* Filter still overlaps padding (complete reset) */
+            sub(reg_kernel, (jcp.f_pad - jcp.od * jcp.stride_d) * filter_shift);
+        }
+
+        /* Apply correction */
+        mov(reg_kd_count, jcp.kd);
+        jmp(common_block_label);
+
+        L(fpad_end_label);
+    }
+
+    /* Compute bottom edge */
+    if (jcp.back_pad > 0) {
+
+        /* Check if within back_pad region */
+        cmp(reg_d_index, input_backpad_overlap - 1);
+        jl(backpad_end_label, T_NEAR);
+        jg(backpad_label, T_NEAR);
+
+        /* Execute overlap correction between the filter and the initial
+         * back_pad region. */
+        mov(reg_kd_count,
+                jcp.id + jcp.f_pad - input_backpad_overlap * jcp.stride_d);
+        jmp(backpad_end_label, T_NEAR);
+
+        L(backpad_label);
+        sub(reg_kd_count, jcp.stride_d);
+        cmp(reg_kd_count, 0);
+        jle(loop_end_label, T_NEAR);
+
+        L(backpad_end_label);
+    }
+
+    /* Compute middle block */
+    add(reg_input_d, input_shift * jcp.stride_d);
+
+    /* Execute common block and loop */
+    L(common_block_label);
+    add(reg_output_d, output_shift);
+    inc(reg_d_index);
+    cmp(reg_d_index, ptr[param + GET_OFF(d_worksize)]);
+    jl(d_loop_label, T_NEAR);
+
+    L(loop_end_label);
+}
+
+bool jit_avx512_common_conv_bwd_weights_kernel_f32::compute_full_spat_loop() {
+    // FIXME: use register mapping from the class declaration
+    bool ok = jcp.ver == ver_4fma
+        && everyone_is(0, jcp.dilate_h, jcp.dilate_w)
+        && everyone_is(1, jcp.stride_h, jcp.stride_w);
+    if (!ok) return false;
+    if (jcp.l_pad != jcp.kw / 2 || jcp.t_pad != jcp.kh / 2)
+        return false;
+
+    // General code layout:
+    //
+    // Blocking over OH -- top level
+    // (Reduces L2 pressure; not very useful right now)
+    //  Loop over all KHxKW kernel -- emit_kh_kw_loop()
+    //    Loop over OH block -- emit_h_loop()
+    //      Loop over OW blocks -- emit_fma_block()
+    //      (Supports both fully unrolled and partially unrolled versions to
+    //      reduce code size)
+    //          Loop over OW block -- emit_fma_step()
+
+    int max_working_set_size = 128 * 1024;
+    int pad_ow = jcp.ow;
+
+    int inp_row_size = jcp.ic_block * jcp.tr_iw * jcp.typesize_in;
+    int out_row_size = jcp.oc_block * pad_ow * jcp.typesize_in;
+    int row_size = inp_row_size + out_row_size;
+
+    int h_block_size = jcp.oh;
+    int working_set_size = row_size * h_block_size;
+
+    if (working_set_size > max_working_set_size) {
+        int opt_working_set_size = 48 * 1024;
+        assert(opt_working_set_size < max_working_set_size);
+
+        while (working_set_size > opt_working_set_size) {
+            for (int i = 2; i <= h_block_size; i++)
+                if (i == h_block_size)
+                    h_block_size = h_block_size / 2;
+                else if (h_block_size % i == 0) {
+                    h_block_size = h_block_size / i;
+                    break;
+                }
+            working_set_size = row_size * h_block_size;
+
+            if (h_block_size == 1 && working_set_size > opt_working_set_size)
+                return false;
+        }
+    }
+
+    // NB1: t_pad <= oh_block_size and b_pad <= last_oh_block_size (see below)
+    if (h_block_size < nstl::max(1, jcp.t_pad)
+            || jcp.b_pad > (jcp.oh % h_block_size == 0 ? h_block_size
+                                                       : jcp.oh % h_block_size))
+        return false;
+
+    // check that we can use simple arithmetic for prefetch address
+    // calculations
+    // TODO: we need some traits for this check (Roma)
+    int cache_line_size = 64;
+    assert(jcp.ic_block * typesize == 64);
+    assert(jcp.oc_block * typesize == 64);
+
+    int num_inp_l2_pfs = jcp.tr_iw * h_block_size;
+    int avg_h_loop_len = h_block_size;
+    int num_inp_l2_pfs_per_fma_block
+        = div_up(num_inp_l2_pfs, avg_h_loop_len * jcp.kw * jcp.kh);
+    int num_out_l2_pfs = pad_ow * h_block_size;
+    int num_out_l2_pfs_per_fma_block
+        = div_up(num_out_l2_pfs, avg_h_loop_len * jcp.kw * jcp.kh);
+
+    Opmask reg_h_block = k1; // 32-bit only on Intel(R) Xeon Phi(TM) processors
+    Reg64 reg_kh = rax;
+    Reg64 reg_kw = rbx;
+    Reg64 reg_tmp = abi_not_param1;
+    Reg32 reg_tmp_w = reg_tmp.cvt32();
+    Reg64 reg_ohs = rdx;
+    Reg64 reg_ihs = rsi;
+    Reg64 reg_h = r8;
+    Reg64 reg_i = r9;
+    Reg64 reg_j = r10;
+
+    Reg64 reg_inp = r13;
+    Reg64 reg_out = r14;
+    Reg64 reg_ker = r15;
+
+    Reg64 reg_inp_pf_l1 = rbp;
+
+    Reg64 reg_inp_pf_l2 = r11;
+    Reg64 reg_out_pf_l2 = r12;
+
+    Xmm reg_inp_pf_save = xmm17;
+    Xmm reg_out_pf_save = xmm18;
+
+    Reg64 reg_inp_save = abi_param1;
+    Reg64 reg_out_save = reg_tmp;
+
+    auto zmm_out = [&](int oi) { return Zmm(24 + oi % 8); };
+    auto zmm_ker = [&](int ic1) { return Zmm(ic1); };
+    auto inp_addr = [&](int oi, int ic1) {
+        return ptr[reg_inp + (ic1 * jcp.tr_iw + oi) * jcp.typesize_in];
+    };
+    auto out_addr = [&](int oi, int oj = 0) {
+        assert(jcp.ver == ver_4fma);
+        return ptr[reg_out
+            + ((oi + oj * jcp.ow) * jcp.oc_block) * jcp.typesize_in];
+    };
+    auto ker_addr = [&](int ic1) {
+        return ptr[reg_ker + ic1 * jcp.oc_block * jcp.typesize_out];
+    };
+
+    auto emit_block = [&](int h_block_size,
+            bool is_last_block, bool is_last_kh_kw_iter, bool is_last_row)
+    {
+        // TODO: add an fma version (Roma)
+        auto pad_ow = jcp.ow;
+
+        int ow4u = rnd_up(pad_ow, 4);
+        int def_step_size = 16;
+
+        bool has_w_tail = (pad_ow % def_step_size != 0
+                || pad_ow % 4 != 0);
+        bool full_w_unroll = pad_ow / def_step_size < 2 + has_w_tail;
+
+        auto emit_step = [&](int ur_ow,
+                int num_inp_l1_pfs_per_fma_step,
+                int num_inp_l2_pfs_per_fma_step,
+                int num_out_l2_pfs_per_fma_step, bool is_w_tail)
+        {
+            bool block_wraparound = is_w_tail && is_last_row;
+
+            assert(ur_ow % 4 == 0);
+            int tail_size = ow4u % ur_ow;
+            int this_ur_ow
+                = (is_w_tail && tail_size) ? tail_size : ur_ow;
+            int ow_last_chunk4 = pad_ow % 4;
+            int ow_zero_tail4 = ow_last_chunk4
+                ? 4 - ow_last_chunk4 : 0;
+
+            auto emit_out_pf = [&](int oi) {
+#if 1
+                if (oi + def_step_size < ur_ow || !block_wraparound)
+                    mic_prefetcht0(ptr[reg_out
+                            + ((def_step_size + oi)
+                                * jcp.oc_block * jcp.typesize_in)]);
+                else {
+                    assert(block_wraparound);
+                    assert(oi + def_step_size >= ur_ow);
+                    mic_prefetcht0(ptr[reg_out_save
+                            + ((oi + def_step_size - ur_ow)
+                                * jcp.oc_block * jcp.typesize_in)]);
+                }
+#else
+                // XXX: This is an alternative prefetching strategy that
+                // always prefetches the next row. Keeping it here for
+                // future experiments (Roma)
+                if (!block_wraparound)
+                    mic_prefetcht0(ptr[reg_out
+                            + (jcp.ow + oi) * jcp.oc_block * jcp.typesize_in]);
+                else
+                    mic_prefetcht0(ptr[reg_out + reg_ohs
+                            - ((h_block_size - 1) * jcp.ow
+                                - oi) * jcp.oc_block * jcp.typesize_in]);
+#endif
+                if (oi < num_out_l2_pfs_per_fma_step)
+                    mic_prefetcht1(ptr[reg_out_pf_l2
+                            + oi * jcp.oc_block * jcp.typesize_in]);
+            };
+
+            auto emit_inp_pf = [&](int oi4, int ic1) {
+                int pf_slot_idx = ic1 + oi4 / 4 * jcp.ic_block;
+                int num_pf_slots = jcp.ic_block * ur_ow / 4;
+
+                int num_pfs = num_inp_l1_pfs_per_fma_step
+                    + num_inp_l2_pfs_per_fma_step;
+                int pf_freq = nstl::max(1, num_pf_slots / num_pfs);
+
+                if (pf_slot_idx % pf_freq)
+                    return;
+
+                int pf_idx = pf_slot_idx / pf_freq;
+
+                if (pf_idx < num_inp_l2_pfs_per_fma_step)
+                    mic_prefetcht1(ptr[reg_inp_pf_l2
+                            + pf_idx * jcp.ic_block * jcp.typesize_in]);
+                else {
+                    pf_idx -= num_inp_l2_pfs_per_fma_step;
+                    // prefetch the 'tail' of the cache line because most of
+                    // the accesses are not aligned
+                    mic_prefetcht0(ptr[reg_inp_pf_l1
+                            + pf_idx * jcp.ic_block * jcp.typesize_in
+                            + cache_line_size - jcp.typesize_in]);
+                }
+            };
+
+            auto numloads = 4;
+
+            int steps = this_ur_ow;
+            for (int oi4 = 0; oi4 < steps; oi4 += numloads) {
+                for (int oi1 = 0; oi1 < numloads; oi1++) {
+                    int oi = oi4 + oi1;
+                    if (!is_w_tail || oi < (this_ur_ow - ow_zero_tail4)) {
+                        vmovups(zmm_out(oi), out_addr(oi));
+                        emit_out_pf(oi);
+                    } else {
+                        auto zmm = zmm_out(oi);
+                        vpxord(zmm, zmm, zmm);
+                    }
+                }
+
+                for (int ic1 = 0; ic1 < jcp.ic_block; ic1++) {
+                    if (jcp.ver == ver_4fma) {
+                        v4fmaddps(zmm_ker(ic1),
+                                zmm_out(oi4), inp_addr(oi4, ic1));
+                    } else {
+                        assert(!"unknown convolution version");
+                    }
+                        emit_inp_pf(oi4, ic1);
+                }
+            }
+        };
+
+        // Input is transposed and padded but we only access about jcp.iw
+        // elements so use that to compute the # of cache lines in each 'row'
+        int num_inp_l1_pfs
+            = div_up(jcp.iw * jcp.typesize_in, cache_line_size) * jcp.ic_block;
+
+        if (full_w_unroll) {
+            emit_step(ow4u, num_inp_l1_pfs,
+                    num_inp_l2_pfs_per_fma_block,
+                    num_out_l2_pfs_per_fma_block, true);
+            add(reg_inp_pf_l2, num_inp_l2_pfs_per_fma_block * cache_line_size);
+            add(reg_out_pf_l2, num_out_l2_pfs_per_fma_block * cache_line_size);
+        } else {
+            Label w_loop;
+            int num_w_iters = pad_ow / def_step_size;
+            int num_w_iters_full = num_w_iters + has_w_tail;
+            int num_inp_l1_pfs_per_fma_step
+                = div_up(num_inp_l1_pfs, num_w_iters_full);
+            int num_inp_l2_pfs_per_fma_step
+                = div_up(num_inp_l2_pfs_per_fma_block, num_w_iters_full);
+            int num_out_l2_pfs_per_fma_step
+                = div_up(num_out_l2_pfs_per_fma_block, num_w_iters_full);
+            mov(reg_i, num_w_iters);
+            L(w_loop); {
+                emit_step(def_step_size, num_inp_l1_pfs_per_fma_step,
+                        num_inp_l2_pfs_per_fma_step,
+                        num_out_l2_pfs_per_fma_step, false);
+                add(reg_inp, def_step_size * jcp.typesize_in);
+                add(reg_out, def_step_size * jcp.oc_block * jcp.typesize_in);
+                add(reg_inp_pf_l1,
+                        num_inp_l1_pfs_per_fma_step * cache_line_size);
+                add(reg_inp_pf_l2,
+                        num_inp_l2_pfs_per_fma_step * cache_line_size);
+                add(reg_out_pf_l2,
+                        num_out_l2_pfs_per_fma_step * cache_line_size);
+                sub(reg_i, 1);
+                jnz(w_loop);
+            }
+            if (has_w_tail) {
+                emit_step(def_step_size, num_inp_l1_pfs_per_fma_step,
+                        num_inp_l2_pfs_per_fma_step,
+                        num_out_l2_pfs_per_fma_step, true);
+                add(reg_inp_pf_l2,
+                        num_inp_l2_pfs_per_fma_step * cache_line_size);
+                add(reg_out_pf_l2,
+                        num_out_l2_pfs_per_fma_step * cache_line_size);
+            }
+            // reset reg_inp and reg_out because emit_h_loop expects
+            // unmodified pointers
+            int w_offset = num_w_iters * def_step_size;
+            sub(reg_inp, w_offset * jcp.typesize_in);
+            sub(reg_out, w_offset * jcp.oc_block * jcp.typesize_in);
+        }
+    };
+
+    auto emit_h_loop = [&](int h_block_size,
+            bool is_last_block, bool is_last_kh_kw_iter)
+    {
+        Label h_loop, skip_h_loop;
+        mov(reg_j, 1);
+        cmp(reg_j, reg_h);
+        je(skip_h_loop, T_NEAR);
+        L(h_loop); {
+
+            lea(reg_inp_pf_l1,
+                    ptr[reg_inp + jcp.tr_iw * jcp.ic_block * jcp.typesize_in]);
+            emit_block(h_block_size,
+                    is_last_block, is_last_kh_kw_iter, false);
+
+            add(reg_inp, jcp.tr_iw * jcp.ic_block * jcp.typesize_in);
+            add(reg_out, pad_ow * jcp.oc_block * jcp.typesize_in);
+            add(reg_j, 1);
+            cmp(reg_j, reg_h);
+            jb(h_loop);
+        }
+
+        L(skip_h_loop);
+
+        for (int ic1 = 0; ic1 < jcp.ic_block; ic1++)
+            mic_prefetcht0(ker_addr(ic1));
+
+        lea(reg_inp_pf_l1, ptr[reg_inp_save + reg_kw * jcp.typesize_in]);
+        emit_block(h_block_size, is_last_block, is_last_kh_kw_iter, true);
+    };
+
+    auto emit_kh_kw_loop = [&](bool is_first_block, bool is_last_block,
+            int h_block_size)
+    {
+        xor_(reg_kh, reg_kh);
+        Label kh_loop, kh_loop_end;
+
+        int last_oh_block_size
+            = jcp.oh - rnd_up(jcp.oh - h_block_size, h_block_size);
+        int oh_block_size = (is_last_block) ? last_oh_block_size : h_block_size;
+        // NB1: t_pad <= oh_block_size and b_pad <= last_oh_block_size
+        int ih_block_size = oh_block_size - 1 + jcp.kh
+                - is_first_block * jcp.t_pad - is_last_block * jcp.b_pad;
+
+        L(kh_loop); {
+            // determine starting indices for this block
+            if (is_first_block) {
+                xor_(reg_tmp, reg_tmp);
+                mov(reg_ohs, jcp.t_pad);
+                sub(reg_ohs, reg_kh);
+                cmovb(reg_ohs, reg_tmp);
+
+                mov(reg_ihs, reg_ohs);
+                sub(reg_ihs, jcp.t_pad);
+                add(reg_ihs, reg_kh);
+            } else {
+                xor_(reg_ohs, reg_ohs);
+                mov(reg_ihs, reg_kh);
+            }
+
+            // determine effective size of block based on padding
+            mov(reg_tmp, oh_block_size);
+            sub(reg_tmp, reg_ohs);
+            mov(reg_h, ih_block_size);
+            sub(reg_h, reg_ihs);
+            cmp(reg_tmp, reg_h);
+            cmovb(reg_h, reg_tmp);
+
+            Label kh_loop_work;
+            cmp(reg_h, 0);
+            jg(kh_loop_work, T_NEAR);
+
+            // empty h loop for this jcp.kh:
+            // - set the output to 0 if necessary
+            // - move ker pt
+            // - jump to the end
+            sub(reg_h, 1);
+            Label skip_ker_zeroing;
+
+            // The reg_ker ptr has highest bit set if the output needs to be
+            // zeroed. Those who have byte-aligned their data will suffer the
+            // consiquences :(
+            // TODO: move the flag to a mask register? (Roma)
+            test(reg_ker, 1);
+            jz(skip_ker_zeroing, T_NEAR);
+
+            Label zeroing_loop;
+            vpxord(zmm0, zmm0, zmm0);
+            and_(reg_ker, ~1); // temporarily clear the zeroing flag
+            mov(reg_tmp, jcp.kw);
+            L(zeroing_loop); {
+                for (int ic1 = 0; ic1 < jcp.ic_block; ic1++)
+                    vmovups(ker_addr(ic1), zmm0);
+                add(reg_ker, jcp.oc_block * jcp.ic_block * jcp.typesize_out);
+                sub(reg_tmp, 1);
+                jnz(zeroing_loop, T_NEAR);
+            }
+            // restore the zeroing flag (it will be cleared after the end of
+            // emit_kh_kw_loop, but we may need it until then)
+            or_(reg_ker, 1);
+            jmp(kh_loop_end, T_NEAR);
+
+            L(skip_ker_zeroing);
+            add(reg_ker, jcp.oc_block * jcp.ic_block * jcp.kw
+                * jcp.typesize_out);
+            jmp(kh_loop_end, T_NEAR);
+
+            L(kh_loop_work);
+
+            mul_by_const(reg_ihs, reg_tmp,
+                    jcp.tr_iw * jcp.ic_block * jcp.typesize_in);
+            mul_by_const(reg_ohs, reg_tmp,
+                    pad_ow * jcp.oc_block * jcp.typesize_in);
+
+            add(reg_inp, reg_ihs);
+            add(reg_out, reg_ohs);
+
+            Label kw_loop;
+            xor_(reg_kw, reg_kw);
+            L(kw_loop); {
+                for (int ic1 = 0; ic1 < jcp.ic_block; ic1++) {
+                    auto zmm = zmm_ker(ic1);
+                    vpxord(zmm, zmm, zmm);
+                    mic_prefetcht1(ker_addr(ic1));
+                }
+
+                mov(reg_out_save, reg_out);
+                mov(reg_inp_save, reg_inp);
+                lea(reg_inp, ptr[reg_inp + reg_kw * jcp.typesize_in]);
+
+#if 0
+                // XXX: Generate code with special prefetches when switching
+                // blocks or at the end of the last block. Disabled to reduce
+                // code size and because there's no performance benefit (Roma)
+                Label regular_h_loop, end_h_loop;
+                cmp(reg_kw, jcp.kw - 1);
+                jne(regular_h_loop, T_NEAR);
+                cmp(reg_kh, jcp.kh - 1);
+                jne(regular_h_loop, T_NEAR);
+
+                emit_h_loop(oh_block_size, is_last_block, true);
+                jmp(end_h_loop, T_NEAR);
+
+                L(regular_h_loop);
+                emit_h_loop(oh_block_size, is_last_block, false);
+
+                L(end_h_loop);
+#else
+                emit_h_loop(oh_block_size, is_last_block, false);
+#endif
+
+                mov(reg_out, reg_out_save);
+                mov(reg_inp, reg_inp_save);
+
+                Label do_store;
+                // The reg_ker ptr has highest bit set if the output needs to
+                // be zeroed. Those who have byte-aligned their data will
+                // suffer the consiquences :(
+                mov(reg_tmp, reg_ker);
+                and_(reg_ker, ~1);
+                test(reg_tmp, 1);
+                jnz(do_store, T_NEAR);
+
+                for (int ic1 = 0; ic1 < jcp.ic_block; ic1++) {
+                    auto zmm = zmm_ker(ic1);
+                    if (jcp.ver == ver_4fma) {
+                        vaddps(zmm, ker_addr(ic1));
+                    } else {
+                        assert(!"unknown convolution version");
+                    }
+                }
+
+                L(do_store);
+                for (int ic1 = 0; ic1 < jcp.ic_block; ic1++) {
+                    auto zmm = zmm_ker(ic1);
+                    vmovups(ker_addr(ic1), zmm);
+                }
+
+                mov(reg_ker, reg_tmp);
+                add(reg_ker, jcp.ic_block * jcp.oc_block * jcp.typesize_out);
+                add(reg_kw, 1);
+                cmp(reg_kw, jcp.kw);
+                jl(kw_loop);
+            }
+
+            sub(reg_inp, reg_ihs);
+            sub(reg_out, reg_ohs);
+
+
+            L(kh_loop_end);
+            add(reg_kh, 1);
+            cmp(reg_kh, jcp.kh);
+            jl(kh_loop);
+        }
+    };
+
+    mov(reg_inp, ptr[param + GET_OFF(src)]);
+    mov(reg_out, ptr[param + GET_OFF(dst)]);
+    mov(reg_ker, ptr[param + GET_OFF(filt)]);
+    mov(reg_inp_pf_l2, ptr[param + GET_OFF(src_prf)]);
+    mov(reg_out_pf_l2, ptr[param + GET_OFF(dst_prf)]);
+    mov(reg_tmp, ptr[param + GET_OFF(channel)]);
+    or_(reg_ker, reg_tmp);
+
+    bool single_kh_kw_loop = (h_block_size == jcp.oh);
+
+    size_t inp_row_step = jcp.tr_iw * jcp.ic_block * jcp.typesize_in;
+    size_t first_inp_block_step = inp_row_step * (h_block_size - jcp.t_pad);
+    size_t inp_block_step = inp_row_step * h_block_size;
+    size_t out_block_step = pad_ow * jcp.oc_block * jcp.typesize_in
+        * h_block_size;
+
+    if (!single_kh_kw_loop) {
+        // Save the original prefetch pointers from the OpenMP driver
+        vmovq(reg_inp_pf_save, reg_inp_pf_l2);
+        vmovq(reg_out_pf_save, reg_out_pf_l2);
+        mov(reg_inp_pf_l2, reg_inp);
+        add(reg_inp_pf_l2, first_inp_block_step);
+        mov(reg_out_pf_l2, reg_out);
+        add(reg_out_pf_l2, out_block_step);
+    }
+    emit_kh_kw_loop(true, single_kh_kw_loop, h_block_size);
+
+    if (!single_kh_kw_loop) {
+        size_t ker_reset_offset
+            = jcp.oc_block * jcp.ic_block * jcp.typesize_out * jcp.kw * jcp.kh;
+        sub(reg_ker, ker_reset_offset);
+        and_(reg_ker, ~1); // Clear the zeroing flag for subsequent updates
+
+        add(reg_inp, first_inp_block_step);
+        add(reg_out, out_block_step);
+        mov(reg_inp_pf_l2, reg_inp);
+        add(reg_inp_pf_l2, inp_block_step);
+        mov(reg_out_pf_l2, reg_out);
+        add(reg_out_pf_l2, out_block_step);
+
+        int num_innermost_iters = div_up(jcp.oh, h_block_size) - 2;
+        if (num_innermost_iters > 0) {
+            Label h_block_loop;
+
+            mov(reg_tmp_w, num_innermost_iters);
+            kmovw(reg_h_block, reg_tmp_w);
+            L(h_block_loop); {
+                emit_kh_kw_loop(false, false, h_block_size);
+                sub(reg_ker, ker_reset_offset);
+                add(reg_inp, inp_row_step * h_block_size);
+                add(reg_out, out_block_step);
+                mov(reg_inp_pf_l2, reg_inp);
+                add(reg_inp_pf_l2, inp_block_step);
+                mov(reg_out_pf_l2, reg_out);
+                add(reg_out_pf_l2, out_block_step);
+                kmovw(reg_tmp_w, reg_h_block);
+                sub(reg_tmp_w, 1);
+                kmovw(reg_h_block, reg_tmp_w);
+                jnz(h_block_loop);
+            }
+        }
+
+        // Restore the original prefetch pointers that came from the OpenMP
+        // driver
+        vmovq(reg_inp_pf_l2, reg_inp_pf_save);
+        vmovq(reg_out_pf_l2, reg_out_pf_save);
+        emit_kh_kw_loop(false, true, h_block_size);
+    }
+
+    return true;
+}
+
+bool jit_avx512_common_conv_bwd_weights_kernel_f32
+    ::flat_4ops_compute() {
+    const auto &j = jcp;
+    const bool ok = j.ver == ver_4fma && j.is_1stconv
+        && everyone_is(0, j.dilate_h, j.dilate_w);
+    if (!ok) return false;
+
+    Reg64 reg_ptr_tr_src = r8;
+    Reg64 reg_ptr_dst = r9;
+    Reg64 reg_ptr_wei = r10;
+    Reg64 reg_ptr_bia = r11;
+
+    Reg64 reg_kh_step = rax;
+    Reg64 reg_oh = abi_not_param1;
+    Reg64 reg_kh = rdx;
+
+    Reg32 reg_flag_save = ebx;
+    Reg32 reg_flag = esi;
+
+    Zmm vbia(31);
+
+    auto zmm_wei = [&](int kh, int kw) {
+        return Zmm(8 + kh * j.kw + kw);
+    };
+    auto zmm_dst = [&](int ow) {
+        return Zmm(ow % 8);
+    };
+
+    auto addr_tr_src = [&](int kh, int iw) {
+        return ptr[reg_ptr_tr_src
+            + (kh * j.stride_w * j.tr_ld + iw) * jcp.typesize_in];
+    };
+    auto addr_dst = [&](int ow) {
+        return ptr[reg_ptr_dst + ow * jcp.oc_block * jcp.typesize_in];
+    };
+    auto addr_wei = [&](int kh, int kw) {
+        return ptr[reg_ptr_wei + (kh * j.kw + kw) * j.oc_block
+            * jcp.typesize_out];
+    };
+
+    auto emit_fma_block = [&](int kh_step) {
+        for (int kh = 0; kh < kh_step; ++kh) {
+            for (int kw = 0; kw < j.kw; ++kw) {
+                auto vwei = zmm_wei(kh, kw);
+                vpxord(vwei, vwei, vwei);
+            }
+        }
+
+        for (int ow = 0; ow < j.ow; ow += 4) {
+            for (int _ow = ow; _ow < ow + 4; ++_ow) {
+                auto vdst = zmm_dst(_ow);
+                if (_ow < j.ow)
+                    vmovups(vdst, addr_dst(_ow));
+                else
+                    vpxord(vdst, vdst, vdst);
+            }
+
+            for (int kh = 0; kh < kh_step; ++kh) {
+                for (int kw = 0; kw < j.kw; ++kw) {
+                    const int iw = ow + (kw % j.stride_w) * j.tr_ld
+                        + (kw / j.stride_w);
+                    v4fmaddps(zmm_wei(kh, kw), zmm_dst(ow),
+                            addr_tr_src(kh, iw));
+                    if (1 && kh == 0 && kw < 4) {
+                        prefetcht1(ptr[reg_ptr_dst
+                            + (j.ow + ow + kw) * jcp.oc_block
+                            * jcp.typesize_in]);
+                    }
+                    if (j.with_bias && kh_step == 1) { /* [bwd_w:b:r1] */
+                        const int off = kw + 4 - j.kw;
+                        if (off >= 0 && ow + off < j.ow)
+                            vaddps(vbia, vbia, zmm_dst(ow + off));
+                    }
+                }
+            }
+        }
+
+        Label l_store;
+        test(reg_flag, FLAG_MB_FIRST);
+        jnz(l_store, T_NEAR);
+        for (int kh = 0; kh < kh_step; ++kh) {
+            for (int kw = 0; kw < j.kw; ++kw)
+                vaddps(zmm_wei(kh, kw), addr_wei(kh, kw));
+        }
+        L(l_store);
+        for (int kh = 0; kh < kh_step; ++kh) {
+            for (int kw = 0; kw < j.kw; ++kw)
+                vmovups(addr_wei(kh, kw), zmm_wei(kh, kw));
+        }
+    };
+
+    auto emit_kh_loop = [&]() {
+        const int kh_step_rem = j.kh % j.kh_step;
+        xor_(reg_kh, reg_kh);
+        mov(reg_kh_step, j.kh_step);
+
+        Label l_kh_loop;
+        L(l_kh_loop); {
+            Label l_done;
+
+            if (kh_step_rem != 0) {
+                Label l_keep_kh_step;
+                cmp(reg_kh, j.kh - j.kh_step);
+                jle(l_keep_kh_step, T_NEAR);
+
+                mov(reg_kh_step, kh_step_rem);
+                emit_fma_block(kh_step_rem);
+                jmp(l_done, T_NEAR);
+
+                L(l_keep_kh_step);
+            }
+
+            emit_fma_block(j.kh_step);
+
+            L(l_done);
+
+            add(reg_ptr_tr_src, j.kh_step * j.stride_w * j.tr_ld
+                * jcp.typesize_in);
+            add(reg_ptr_wei, j.kh_step * j.kw * j.oc_block * jcp.typesize_out);
+            add(reg_kh, j.kh_step);
+
+            cmp(reg_kh, j.kh);
+            jl(l_kh_loop, T_NEAR);
+        }
+
+        const int kh_steps = rnd_up(j.kh, j.kh_step);
+        sub(reg_ptr_tr_src, kh_steps * j.stride_w * j.tr_ld * jcp.typesize_in);
+        sub(reg_ptr_wei, kh_steps * j.kw * j.oc_block * jcp.typesize_out);
+    };
+
+    auto emit_oh_loop = [&]() {
+        mov(reg_oh, j.oh);
+
+        Label l_oh_loop;
+        L(l_oh_loop); {
+            Label l_restore_mb_flag, l_jump;
+
+            cmp(reg_oh, j.oh);
+            je(l_restore_mb_flag, T_NEAR);
+
+            and_(reg_flag, ~FLAG_MB_FIRST);
+            jmp(l_jump, T_NEAR);
+
+            L(l_restore_mb_flag);
+            mov(reg_flag, reg_flag_save);
+
+            L(l_jump);
+
+            emit_kh_loop();
+
+            add(reg_ptr_tr_src, j.stride_h * j.stride_w * j.tr_ld
+                * jcp.typesize_in);
+            add(reg_ptr_dst, j.ow * j.oc_block * jcp.typesize_in);
+
+            dec(reg_oh);
+            jnz(l_oh_loop, T_NEAR);
+        }
+    };
+
+    auto emit_bia_store = [&]() {
+        if (!j.with_bias) return;
+
+        Label l_bia_store, l_bia_skip;
+        test(reg_flag, FLAG_IC_FIRST);
+        jz(l_bia_skip);
+
+        test(reg_flag, FLAG_MB_FIRST);
+        jnz(l_bia_store, T_NEAR);
+        vaddps(vbia, ptr[reg_ptr_bia]);
+        L(l_bia_store);
+        vmovups(ptr[reg_ptr_bia], vbia);
+        L(l_bia_skip);
+    };
+
+    mov(reg_ptr_tr_src, ptr[param + GET_OFF(src)]);
+    mov(reg_ptr_dst, ptr[param + GET_OFF(dst)]);
+    mov(reg_ptr_wei, ptr[param + GET_OFF(filt)]);
+    mov(reg_ptr_bia, ptr[param + GET_OFF(bias)]);
+    mov(reg_flag_save, ptr[param + GET_OFF(flags)]);
+
+    vpxord(vbia, vbia, vbia);
+    emit_oh_loop();
+    emit_bia_store();
+
+    return true;
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::compute_loop()
+{
+    if (flat_4ops_compute())
+        return;
+    if (compute_full_spat_loop())
+        return;
+
+    maybe_zero_kernel();
+
+    if (jcp.ndims == 5) compute_d_loop_common();
+    else compute_oh_loop_common();
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::generate()
+{
+    preamble();
+
+    mov(reg_input, ptr[param + GET_OFF(src)]);
+    mov(reg_output, ptr[param + GET_OFF(dst)]);
+    mov(reg_kernel, ptr[param + GET_OFF(filt)]);
+
+    compute_loop();
+
+    postamble();
+}
+
+status_t jit_avx512_common_conv_bwd_weights_kernel_f32::init_conf(
+    jit_conv_conf_t &jcp, const convolution_desc_t &cd,
+    memory_desc_t &src_md, memory_desc_t &diff_weights_md,
+    memory_desc_t &diff_bias_md, memory_desc_t &diff_dst_md) {
+    if (!mayiuse(avx512_common))
+        return status::unimplemented;
+
+    const memory_desc_wrapper src_d(&src_md);
+    const memory_desc_wrapper diff_weights_d(&diff_weights_md);
+    const memory_desc_wrapper diff_bias_d(&diff_bias_md);
+    const memory_desc_wrapper diff_dst_d(&diff_dst_md);
+
+    const bool with_groups = diff_weights_d.ndims() == src_d.ndims() + 1;
+    int ndims = src_d.ndims();
+
+    jcp = zero<decltype(jcp)>();
+
+    jcp.simd_w = cpu_isa_traits<avx512_common>::vlen / sizeof(float);
+    jcp.ndims = ndims;
+    jcp.prop_kind = cd.prop_kind;
+
+    jcp.ngroups = with_groups ? diff_weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+
+    jcp.oc = diff_dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+
+    jcp.id = (ndims == 5) ? src_d.dims()[2] : 1;
+    jcp.ih = (ndims == 3) ? 1 : src_d.dims()[ndims-2];
+    jcp.iw = src_d.dims()[ndims-1];
+    jcp.od = (ndims == 5) ? diff_dst_d.dims()[2] : 1;
+    jcp.oh = (ndims == 3) ? 1 : diff_dst_d.dims()[ndims-2];
+    jcp.ow = diff_dst_d.dims()[ndims-1];
+
+    jcp.kd = (ndims == 5) ? diff_weights_d.dims()[with_groups + 2] : 1;
+    jcp.kh = (ndims == 3) ? 1 : diff_weights_d.dims()[with_groups + ndims-2];
+    jcp.kw = diff_weights_d.dims()[with_groups + ndims-1];
+
+    jcp.f_pad = (ndims == 5) ? cd.padding[0][0] : 0;
+    jcp.t_pad = (ndims == 3) ? 0 : cd.padding[0][ndims-4];
+    jcp.l_pad = cd.padding[0][ndims-3];
+
+    jcp.stride_d = (ndims == 5) ? cd.strides[0] : 1;
+    jcp.stride_h = (ndims == 3) ? 1 : cd.strides[ndims-4];
+    jcp.stride_w = cd.strides[ndims-3];
+
+    jcp.dilate_d = (ndims == 5) ? cd.dilates[0] : 0;
+    jcp.dilate_h = (ndims == 3) ? 0 : cd.dilates[ndims-4];
+    jcp.dilate_w = cd.dilates[ndims-3];
+
+    const int kh_range = 1 + (jcp.kh - 1) * (jcp.dilate_h + 1);
+    bool ok = true
+        // general condition to simplify dilations
+        && IMPLICATION(jcp.dilate_d != 0, jcp.stride_d == 1)
+        && IMPLICATION(jcp.dilate_h != 0, jcp.stride_h == 1)
+        && IMPLICATION(jcp.dilate_w != 0, jcp.stride_w == 1)
+        // special condition to simplify dilations in compute_oh_loop_common
+        && IMPLICATION(jcp.dilate_h != 0, kh_range <= jcp.ih);
+    if (!ok)
+        return status::unimplemented;
+
+    jcp.r_pad = nstl::max(0, (jcp.ow - 1) * jcp.stride_w
+            + (jcp.kw - 1) * (jcp.dilate_w + 1) - (jcp.iw + jcp.l_pad - 1));
+    jcp.b_pad = nstl::max(0, (jcp.oh - 1) * jcp.stride_h
+            + (jcp.kh - 1) * (jcp.dilate_h + 1) - (jcp.ih + jcp.t_pad - 1));
+    jcp.back_pad = nstl::max(0, (jcp.od - 1) * jcp.stride_d
+            + (jcp.kd - 1) * (jcp.dilate_d + 1) - (jcp.id + jcp.f_pad - 1));
+
+    /* XXX: currently, does not support dilation_d > 0 */
+    if (ndims == 5)
+        if (jcp.dilate_d > 0)
+            return status::unimplemented;
+
+    jcp.ihp = jcp.ih + jcp.t_pad + jcp.b_pad;
+    jcp.iwp = jcp.iw + jcp.l_pad + jcp.r_pad;
+    jcp.ohp = jcp.oh;
+    jcp.owp = jcp.ow;
+    jcp.aligned_threads = 0;
+
+    /* check for the 1st convolution */
+    jcp.is_1stconv = is_1stconv(jcp);
+
+    jcp.oc_block = jcp.simd_w;
+
+    bool ok_to_pad_channels = true
+        && jcp.ngroups == 1
+        && src_d.data_type() == data_type::f32;
+
+    if (ok_to_pad_channels)
+        jcp.oc = rnd_up(jcp.oc, jcp.simd_w);
+
+    if (jcp.oc % jcp.oc_block)
+        return status::unimplemented;
+
+    auto dst_tag = pick(ndims - 3, nCw16c, nChw16c, nCdhw16c);
+    auto wei_tag = with_groups
+        ? pick(ndims - 3, gOIw16i16o, gOIhw16i16o, gOIdhw16i16o)
+        : pick(ndims - 3, OIw16i16o, OIhw16i16o, OIdhw16i16o);
+
+    if (diff_dst_d.format_kind() == format_kind::any) {
+        CHECK(memory_desc_init_by_tag(diff_dst_md, dst_tag));
+        jcp.dst_tag = dst_tag;
+    } else {
+        jcp.dst_tag = diff_dst_d.matches_one_of_tag(dst_tag);
+    }
+    if (jcp.dst_tag != dst_tag)
+        return status::unimplemented;
+
+    /* conditions on bias memory */
+    jcp.with_bias = cd.diff_bias_desc.format_kind != format_kind::undef;
+    if (jcp.with_bias) {
+        if (diff_bias_d.format_kind() == format_kind::any)
+            CHECK(memory_desc_init_by_tag(diff_bias_md, x));
+    }
+
+    jcp.nb_oc = jcp.oc / jcp.oc_block;
+
+    /* kernel applicability check wrt boundaries
+     * the conditions are quite general across the kernels we have,
+     * but ideally the check should belong to a specific kernel... */
+    const int max_pad = ((jcp.kh - 1) * (jcp.dilate_h + 1) + 1) / 2;
+    const bool boundaries_ok = true
+        && jcp.t_pad <= max_pad
+        && jcp.b_pad <= max_pad
+        && IMPLICATION(jcp.f_pad > 0, jcp.kd < jcp.id + jcp.f_pad)
+        && jcp.f_pad < jcp.kd;
+    if (!boundaries_ok)
+        return status::unimplemented;
+
+    /* yet another common check */
+    if (jcp.kw > 14)
+        return status::unimplemented;
+
+    /* setting register strategy */
+    for (int ur_w = nstl::min(max_ur_w, jcp.ow); ur_w > 0; --ur_w) {
+        if (jcp.ow % ur_w == 0) { jcp.ur_w = ur_w; break; }
+    }
+
+    if (jcp.is_1stconv) {
+        auto src_tag = pick(ndims - 3, ncw, nchw, ncdhw);
+        if (src_d.format_kind() == format_kind::any) {
+            CHECK(memory_desc_init_by_tag(src_md, src_tag));
+            jcp.src_tag = src_tag;
+        } else {
+            jcp.src_tag = src_d.matches_one_of_tag(src_tag);
+            if (jcp.ic == 1 && jcp.src_tag != src_tag)
+                jcp.src_tag = src_d.matches_one_of_tag(
+                        pick(ndims - 3, nwc, nhwc, ndhwc));
+        }
+        if (jcp.src_tag == format_tag::undef)
+            return status::unimplemented;
+
+        const bool src_ok = true
+            && utils::everyone_is(data_type::f32,
+                src_d.data_type(), diff_weights_d.data_type(),
+                diff_dst_d.data_type())
+            && one_of(jcp.ic, 1, 2, 3)
+            && jcp.ngroups == 1;
+        if (!src_ok)
+            return status::unimplemented;
+
+        const int tr_ld = rnd_up(div_up(jcp.iw + jcp.l_pad + jcp.r_pad,
+                    jcp.stride_w), 16);
+        const int kh_step = nstl::max((28 - jcp.with_bias) / jcp.kw, 1);
+        const int kh_step_rem = jcp.kh % kh_step;
+
+        const auto wei_4fma_tag = with_groups
+            ? pick(ndims - 3, gOiw16o, gOihw16o, gOidhw16o)
+            : pick(ndims - 3, Oiw16o, Oihw16o, Oidhw16o);
+
+        auto current_wei_tag = format_tag::undef;
+        if (diff_weights_d.format_kind() != format_kind::any)
+            current_wei_tag = diff_weights_d.matches_one_of_tag(wei_4fma_tag);
+
+        const bool use_4fma = true
+            && one_of(ndims, 3, 4)
+            && mayiuse(avx512_mic_4ops)
+            && mkldnn_thr_syncable()
+            && everyone_is(0, jcp.dilate_d, jcp.dilate_h, jcp.dilate_w)
+            && everyone_is(0, jcp.l_pad, jcp.r_pad, jcp.t_pad, jcp.b_pad)
+            && jcp.kw <= 28 - jcp.with_bias
+            && jcp.stride_w == 4
+            && tr_ld / jcp.simd_w <= 4 /* [bwd_w:tr_src:r1] */
+            && IMPLICATION(jcp.with_bias, kh_step_rem == 1) /* [bwd_w:b:r1] */
+            && IMPLICATION(diff_weights_d.format_kind() != format_kind::any,
+                    current_wei_tag == wei_4fma_tag);
+
+        if (use_4fma) {
+            jcp.ver = ver_4fma;
+            jcp.kh_step = kh_step;
+            jcp.tr_ld = tr_ld;
+            jcp.ic_block = 1;
+            if (diff_weights_d.format_kind() == format_kind::any)
+                CHECK(memory_desc_init_by_tag(diff_weights_md, wei_4fma_tag));
+            jcp.wei_tag = wei_4fma_tag;
+        } else {
+            jcp.ver = ver_fma;
+            jcp.ic_block = jcp.ic;
+
+            wei_tag = with_groups
+                ? pick(ndims - 3, gOwi16o, gOhwi16o, gOdhwi16o)
+                : pick(ndims - 3, Owi16o, Ohwi16o, Odhwi16o);
+
+            if (diff_weights_d.format_kind() == format_kind::any) {
+                CHECK(memory_desc_init_by_tag(diff_weights_md, wei_tag));
+                jcp.wei_tag = wei_tag;
+            } else {
+                jcp.wei_tag = diff_weights_d.matches_one_of_tag(wei_tag);
+            }
+            if (jcp.wei_tag != wei_tag)
+                return status::unimplemented;
+        }
+
+        jcp.nb_ic = jcp.ic / jcp.ic_block;
+    } else {
+        auto src_tag = pick(ndims - 3, nCw16c, nChw16c, nCdhw16c);
+        if (src_d.format_kind() == format_kind::any) {
+            CHECK(memory_desc_init_by_tag(src_md, src_tag));
+            jcp.src_tag = src_tag;
+        } else {
+            jcp.src_tag = src_d.matches_one_of_tag(src_tag);
+        }
+        if (jcp.src_tag != src_tag)
+            return status::unimplemented;
+
+        if (diff_weights_d.format_kind() == format_kind::any) {
+            CHECK(memory_desc_init_by_tag(diff_weights_md, wei_tag));
+            jcp.wei_tag = wei_tag;
+        } else {
+            jcp.wei_tag = diff_weights_d.matches_one_of_tag(wei_tag);
+        }
+        if (jcp.wei_tag != wei_tag)
+            return status::unimplemented;
+
+        jcp.ic_block = jcp.simd_w;
+        if (ok_to_pad_channels)
+            jcp.ic = rnd_up(jcp.ic, jcp.ic_block);
+        jcp.nb_ic = jcp.ic / jcp.ic_block;
+        if ((mayiuse(avx512_mic) || mayiuse(avx512_core))
+                && utils::everyone_is(data_type::f32,
+                    src_d.data_type(), diff_weights_d.data_type(),
+                    diff_dst_d.data_type())) {
+            jcp.ver = ver_fma;
+            if (one_of(ndims, 3, 4) && mayiuse(avx512_mic_4ops) && jcp.stride_w == 1 &&
+                    everyone_is(0, jcp.dilate_d, jcp.dilate_h, jcp.dilate_w) &&
+                    mkldnn_thr_syncable()) {
+                jcp.ver = ver_4fma;
+            }
+        } else {
+            return status::unimplemented;
+        }
+        if (jcp.ver == ver_4fma) {
+            jcp.ur_w = jcp.ow;
+            // XXX, BUGBUGBUG, but not a FIXME: this assumes that it's OK to
+            // cross the right boundary. The only requirement is not to have
+            // NaNs there because another multiplicand is always guaranteed to
+            // be zero. This also may require the top-level driver to allocate
+            // four extra guarding elements at the very end of the buffer.
+            // I'm not proud of this hack, but it improves performance by
+            // about 5-10% depending on the dimensions (Roma)
+
+            const int tr_round = 4;
+
+            jcp.tr_iw = rnd_up(jcp.iw + jcp.kw - 1, tr_round);
+            jcp.tr_src_num_guard_elems = tr_round; // upper bound
+        }
+    }
+
+    if (utils::one_of(jcp.ver, ver_4fma, ver_fma)) {
+        jcp.typesize_in = sizeof(float);
+        jcp.typesize_out = sizeof(float);
+    } else
+        return status::unimplemented;
+
+    bool args_ok = true
+        && jcp.ic % jcp.ic_block == 0
+        && jcp.oc % jcp.oc_block == 0
+        && jcp.ic <= src_d.padded_dims()[1]
+        && jcp.oc <= diff_dst_d.padded_dims()[1]
+        && jcp.ic <= diff_weights_d.padded_dims()[with_groups + 1]
+        && jcp.oc <= diff_weights_d.padded_dims()[with_groups + 0];
+    if (!args_ok) return status::unimplemented;
+
+    {   // balancing
+        int nthr, nthr_mb, nthr_g, nthr_oc_b, nthr_ic_b;
+        balance(jcp, nthr, nthr_mb, nthr_g, nthr_oc_b, nthr_ic_b);
+        jcp.nthr = nthr;
+        jcp.nthr_mb = nthr_mb;
+        jcp.nthr_g = nthr_g;
+        jcp.nthr_oc_b = nthr_oc_b;
+        jcp.nthr_ic_b = nthr_ic_b;
+    }
+
+    return status::success;
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad, const jit_conv_conf_t &jcp) {
+    if (jcp.ver == ver_4fma) {
+        if (jcp.is_1stconv) {
+            const size_t tr_src_size =
+                jcp.nthr / jcp.nthr_oc_b * jcp.ih * jcp.stride_w * jcp.tr_ld;
+            scratchpad.book(key_conv_tr_src, jcp.typesize_in * tr_src_size);
+        } else {
+            // XXX: See the comment about tr_iw and guarding elements in
+            // jit_avx512_common_conv_bwd_weights_kernel_f32::init_conf()
+            const size_t max_nthr = jcp.nthr_mb * jcp.ngroups * jcp.nb_ic;
+            const size_t min_tr_src_size_per_thr
+                = jcp.ih * jcp.ic_block * jcp.tr_iw;
+            const size_t tr_src_size = max_nthr * min_tr_src_size_per_thr
+                + jcp.tr_src_num_guard_elems;
+            scratchpad.book(key_conv_tr_src, jcp.typesize_in * tr_src_size);
+        }
+
+        /* prepare synchronization contexts */
+        if (jcp.nthr_oc_b > 1) {
+            const int tr_src_bctx_size = jcp.nthr / jcp.nthr_oc_b;
+            scratchpad.book(key_conv_tr_src_bctx,
+                    sizeof(simple_barrier::ctx_t) * tr_src_bctx_size);
+        }
+    }
+
+    if (jcp.nthr_mb > 1) {
+        const int wei_size = jcp.ngroups * jcp.oc * jcp.ic
+            * jcp.kh * jcp.kw * jcp.kd;
+        const int bia_size = jcp.ngroups * jcp.oc;
+        const size_t wei_bia_reduction_size = wei_size + bia_size;
+
+        scratchpad.book(key_conv_wei_bia_reduction,
+                jcp.typesize_out * wei_bia_reduction_size * (jcp.nthr_mb - 1));
+        scratchpad.book(key_conv_wei_bia_reduction_bctx,
+                sizeof(simple_barrier::ctx_t));
+    }
+
+    if (jcp.with_bias && jcp.oc != jcp.oc_without_padding)
+        scratchpad.book(key_conv_padded_bias, jcp.typesize_out * jcp.oc);
+}
+
+void jit_avx512_common_conv_bwd_weights_kernel_f32::balance(
+        const jit_conv_conf_t &j, int &nthr_, int &nthr_mb_, int &nthr_g_,
+        int &nthr_oc_b_, int &nthr_ic_b_)
+{
+    nthr_ = nthr_mb_ = nthr_g_ = nthr_oc_b_ = nthr_ic_b_ = 1;
+
+    const int max_threads = mkldnn_get_max_threads();
+
+    if (max_threads < j.ngroups) {
+        /* simplification... fortunately it doesn't hurt much */
+        return;
+    }
+
+    if (!mkldnn_thr_syncable() && j.ver == ver_4fma) {
+        // should not happen -- the driver is not ready
+        // for TBB-like non-synchronous threading yet
+        return;
+    }
+
+    if (j.ver == ver_4fma && j.is_1stconv) {
+        nthr_g_ = 1;
+        nthr_oc_b_ = 1;
+        nthr_ic_b_ = nstl::min(j.nb_ic, max_threads);
+        nthr_mb_ = nstl::min(max_threads / nthr_ic_b_, j.mb);
+        nthr_ = nthr_mb_ * nthr_oc_b_ * nthr_ic_b_ * nthr_g_;
+        return;
+    }
+
+    nthr_g_ = j.ngroups;
+    const int nthr = max_threads / nthr_g_;
+
+    auto calc_mem_cost = [=](int nthr_mb, int nthr_oc_b, int nthr_ic_b) {
+        /* calculate per thread memory cost (read/write). high level optimizer
+         * tries to minimize memory consumption. few notes:
+         *  (n1) unclear why, but that essentially helps first convolution...
+         *  (n2) assuming the reduction over minibatch is always there:
+         *    - instead of 8 it should be 5 here (write ~= 2 read):
+         *      kernel: temporal workspace 1 write
+         *      reduction: 1 read from workspace and 1 write to the diff_wei
+         *    - but experiments showed 8 works better than 5 or 6... */
+
+        const int src_coef = j.ver == ver_4fma ? 4 : 1;
+        const int dst_coef = 1;
+        const int wei_coef = 8;
+
+        return 0
+            + src_coef
+            * div_up(j.mb, nthr_mb) * div_up(j.ngroups, nthr_g_)
+            * div_up(j.nb_ic, nthr_ic_b) * j.ic_block * j.ih * j.iw * j.id
+            / j.stride_d / j.stride_h / j.stride_w /* (n1) */
+            + dst_coef
+            * div_up(j.mb, nthr_mb) * div_up(j.ngroups, nthr_g_)
+            * div_up(j.nb_oc, nthr_oc_b) * j.oc_block * j.oh * j.ow * j.od
+            + wei_coef /* (n2) */
+            * div_up(j.ngroups, nthr_g_)
+            * div_up(j.nb_oc, nthr_oc_b) * div_up(j.nb_ic, nthr_ic_b)
+            * j.kh * j.kw * j.kd * j.ic_block * j.oc_block;
+    };
+
+    int best_mem_cost = calc_mem_cost(nthr_mb_, nthr_oc_b_, nthr_ic_b_);
+
+    /* step 1: find the best thread distribution with lowest memory cost */
+    const int nthr_mb_max = nstl::min(nthr, j.mb * j.od);
+    for (int nthr_mb = 1; nthr_mb <= nthr_mb_max; ++nthr_mb) {
+        const int nthr_par = nthr / nthr_mb;
+        const int nthr_oc_b_max = nstl::min(nthr_par, j.nb_oc);
+        for (int nthr_oc_b = 1; nthr_oc_b <= nthr_oc_b_max; ++nthr_oc_b) {
+            int nthr_ic_b = nstl::min(nthr_par / nthr_oc_b, j.nb_ic);
+
+            int mem_cost = calc_mem_cost(nthr_mb, nthr_oc_b, nthr_ic_b);
+            if (mem_cost <= best_mem_cost) {
+                best_mem_cost = mem_cost;
+                nthr_mb_ = nthr_mb;
+                nthr_oc_b_ = nthr_oc_b;
+                nthr_ic_b_ = nthr_ic_b;
+            }
+        }
+
+        if (!mkldnn_thr_syncable()) { assert(nthr_mb == 1); break; }
+    }
+
+    if (!mayiuse(avx512_mic)) {
+        auto calc_comp_cost = [=](int nthr_mb, int nthr_oc_b, int nthr_ic_b) {
+            return 1
+                * div_up(j.mb, nthr_mb)
+                * div_up(j.ngroups, nthr_g_)
+                * div_up(j.nb_oc, nthr_oc_b)
+                * div_up(j.nb_ic, nthr_ic_b);
+        };
+
+        /* step 2: search for a thread distribution with lower compute cost.
+         * the constrains:
+         *  - memory cost cannot exceed 110% of the best found in the step 1
+         *  - unless compute cost is 133% lower than the current best case
+         * note: both constants were found empirically */
+        int best_comp_cost = calc_comp_cost(nthr_mb_, nthr_oc_b_, nthr_ic_b_);
+        for (int nthr_mb = 1; nthr_mb <= nthr_mb_max; ++nthr_mb) {
+            const int nthr_par = nthr / nthr_mb;
+            const int nthr_oc_b_max = nstl::min(nthr_par, j.nb_oc);
+            for (int nthr_oc_b = 1; nthr_oc_b <= nthr_oc_b_max; ++nthr_oc_b) {
+                int nthr_ic_b = nstl::min(nthr_par / nthr_oc_b, j.nb_ic);
+                int mem_cost = calc_mem_cost(nthr_mb, nthr_oc_b, nthr_ic_b);
+                int comp_cost = calc_comp_cost(nthr_mb, nthr_oc_b, nthr_ic_b);
+
+                const bool opt1 = comp_cost <= best_comp_cost
+                    && mem_cost < 1.1 * best_mem_cost;
+                const bool opt2 = 4 * comp_cost <= 3 * best_comp_cost;
+
+                if (opt1 || opt2) {
+                    best_comp_cost = comp_cost;
+                    nthr_mb_ = nthr_mb;
+                    nthr_oc_b_ = nthr_oc_b;
+                    nthr_ic_b_ = nthr_ic_b;
+                }
+            }
+
+            if (!mkldnn_thr_syncable()) { assert(nthr_mb == 1); break; }
+        }
+    }
+
+    if (nthr_mb_ > max_threads/2 && nthr_mb_ < max_threads)
+        nthr_mb_ = nstl::min(j.mb * j.od, max_threads);
+    nthr_ = nthr_mb_ * nthr_g_ * nthr_oc_b_ * nthr_ic_b_;
+
+    assert(nthr_ <= max_threads);
+    assert(IMPLICATION(!mkldnn_thr_syncable(), nthr_mb_ == 1));
+}
+
+template struct  _jit_avx512_common_conv_fwd_kernel<Zmm>;
+template struct  _jit_avx512_common_conv_fwd_kernel<Xmm>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_kernel.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_kernel.hpp
new file mode 100644
index 0000000000..f76770797a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_kernel.hpp
@@ -0,0 +1,423 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_AVX512_COMMON_CONV_KERNEL_F32_HPP
+#define JIT_AVX512_COMMON_CONV_KERNEL_F32_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+#include "jit_uni_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template<typename Vmm>
+struct _jit_avx512_common_conv_fwd_kernel : public jit_generator {
+
+    _jit_avx512_common_conv_fwd_kernel(jit_conv_conf_t ajcp,
+            const primitive_attr_t &attr)
+        : jcp(ajcp), attr_(attr), eltwise_injector_(nullptr)
+    {
+        if (jcp.with_eltwise)
+            eltwise_injector_ = new jit_uni_eltwise_injector_f32<avx512_common>(
+                    this, jcp.eltwise);
+
+        generate();
+        jit_ker_ = (void (*)(jit_conv_call_s *))getCode();
+    }
+
+    ~_jit_avx512_common_conv_fwd_kernel() {
+        delete eltwise_injector_;
+    }
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(_jit_avx512_common_conv_fwd_kernel)
+
+    jit_conv_conf_t jcp;
+    const primitive_attr_t &attr_;
+    void (*jit_ker_)(jit_conv_call_s *);
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    enum {
+        typesize = sizeof(float),
+        ker_reg_base_idx = 28,
+    };
+
+    reg64_t param = abi_param1;
+    reg64_t reg_inp = r8;
+    reg64_t reg_ker = r9;
+    reg64_t reg_out = r10;
+
+    reg64_t reg_inp_prf = r11;
+    reg64_t reg_ker_prf = r12;
+    reg64_t reg_out_prf = r13;
+    reg64_t reg_owb = r12;
+
+    reg64_t aux_reg_inp = r14;
+    reg64_t aux_reg_ker = r15;
+
+    reg64_t aux_reg_inp_prf = rsi;
+    reg64_t aux_reg_ker_prf = rdx;
+
+    reg64_t reg_channel = rsi;
+    reg64_t reg_bias = rdx;
+
+    reg64_t aux_reg_ker_d = r9;
+    reg64_t aux_reg_inp_d = rbx;
+    reg64_t aux_reg_inp_d_prf = r13;
+    reg64_t aux_reg_ker_d_prf = abi_not_param1;
+    reg64_t reg_ki = r10;
+
+    reg64_t reg_kj = rax;
+    reg64_t reg_relu_ns = rax;
+    reg64_t reg_oi = rbx;
+    reg64_t reg_kh = abi_not_param1;
+
+    reg64_t reg_tmp = rbp;
+
+    reg64_t reg_ic_loop = rdx;
+    reg64_t reg_inp_loop = rsi;
+
+    reg64_t reg_init_flag = r13;
+    reg64_t reg_bias_ptr = param;
+
+    reg64_t aux_reg_ic = r12;
+    reg64_t reg_binp = rax;
+    reg64_t reg_bout = r11;
+    reg64_t aux1_reg_inp = rbx;
+    reg64_t aux_reg_out = abi_not_param1;
+
+    reg64_t reg_long_offt = r11;
+    reg64_t reg_out_long_offt = r14;
+
+    inline Vmm vmm_ker(int i_ic) {
+        assert(i_ic < 4);
+        return Vmm(ker_reg_base_idx + i_ic);
+    }
+
+    inline Vmm vmm_out(int i_ur, int i_oc) {
+        int idx = i_ur + i_oc * jcp.ur_w;
+        assert(idx < ker_reg_base_idx);
+        return Vmm(idx);
+    }
+
+    inline Vmm vmm_inp(int i_ic, int nb_x_blocking) {
+        int idx = i_ic + nb_x_blocking * jcp.ur_w;
+        assert(idx < 31);
+        return Vmm(idx);
+    }
+
+    Xbyak::Reg64 imm_addr64 = r15;
+    Vmm vmm_wei = Vmm(31);
+
+    jit_uni_eltwise_injector_f32<avx512_common> *eltwise_injector_;
+
+    inline void prepare_output(int ur_w);
+    inline void store_output(int ur_w);
+    inline void compute_loop_fma(int ur_w, int pad_l, int pad_r);
+    inline void compute_loop_fma_core(int ur_w, int pad_l, int pad_r);
+    inline void compute_loop_4fma(int ur_w, int pad_l, int pad_r);
+    inline void compute_loop_4fma_1st(int ur_w, int pad_l, int pad_r);
+    inline void compute_loop(int ur_w, int pad_l, int pad_r);
+
+    void generate();
+
+    inline size_t get_output_offset(int oi, int n_oc_block) {
+        return (size_t)jcp.typesize_out * ((size_t)n_oc_block * jcp.oh
+            * jcp.ow * jcp.od + oi) * jcp.oc_block;
+    }
+
+    inline size_t get_input_offset(int ki, int ic, int oi, int pad_l) {
+        size_t iw_str = !jcp.is_1stconv ? jcp.ic_block : 1;
+        size_t ic_str = !jcp.is_1stconv ? 1 : (size_t)jcp.iw * jcp.ih * jcp.id;
+        return (size_t)jcp.typesize_in * ((size_t)(ki * (jcp.dilate_w + 1)
+                    + oi * jcp.stride_w - pad_l) * iw_str + ic * ic_str);
+    }
+
+    inline int get_kernel_offset(int ki,int ic,int n_oc_block,int ker_number) {
+        return jcp.typesize_in * jcp.oc_block
+            * (n_oc_block * jcp.nb_ic * jcp.ic_block * jcp.kh * jcp.kw * jcp.kd
+                    + (ic + ker_number) + ki * jcp.ic_block);
+    }
+
+    inline int get_ow_start(int ki, int pad_l) {
+        return nstl::max(0,
+                utils::div_up(pad_l - ki * (jcp.dilate_w + 1), jcp.stride_w));
+    }
+
+    inline int get_ow_end(int ur_w, int ki, int pad_r) {
+        return ur_w - nstl::max(0, utils::div_up(pad_r
+                                                   - (jcp.kw - 1 - ki)
+                                                           * (jcp.dilate_w + 1),
+                                           jcp.stride_w));
+    }
+};
+
+struct jit_avx512_common_conv_fwd_kernel {
+
+    jit_avx512_common_conv_fwd_kernel(jit_conv_conf_t ajcp,
+        const primitive_attr_t &attr) :
+        jit_ker(nullptr),
+        zmm_kernel_(nullptr),
+        xmm_kernel_(nullptr) {
+        int ch_block = ajcp.is_depthwise ? ajcp.ch_block : ajcp.oc_block;
+        switch (ch_block) {
+        case 16:
+            zmm_kernel_ =
+                new _jit_avx512_common_conv_fwd_kernel<Xbyak::Zmm>(
+                    ajcp, attr);
+            jit_ker = zmm_kernel_->jit_ker_;
+            return;
+        case 4:
+            xmm_kernel_ =
+                new _jit_avx512_common_conv_fwd_kernel<Xbyak::Xmm>(
+                    ajcp, attr);
+            jit_ker = xmm_kernel_->jit_ker_;
+            return;
+        default:
+            assert(!"invalid channel blocking");
+        }
+    }
+
+    ~jit_avx512_common_conv_fwd_kernel() {
+        delete xmm_kernel_;
+        delete zmm_kernel_;
+    }
+
+    enum {
+        typesize = sizeof(float)
+    };
+
+    static bool post_ops_ok(jit_conv_conf_t &jcp,
+        const primitive_attr_t &attr);
+    static status_t init_conf(jit_conv_conf_t &jcp,
+        const convolution_desc_t &cd,
+        memory_desc_t &src_pd,
+        memory_desc_t &weights_pd,
+        memory_desc_t &dst_pd,
+        memory_desc_t &bias_pd,
+        const primitive_attr_t &attr,
+        int nthreads);
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+        const jit_conv_conf_t &jcp);
+
+    void(*jit_ker)(jit_conv_call_s *);
+    _jit_avx512_common_conv_fwd_kernel<Xbyak::Zmm> *zmm_kernel_;
+    _jit_avx512_common_conv_fwd_kernel<Xbyak::Xmm> *xmm_kernel_;
+};
+
+struct jit_avx512_common_conv_bwd_data_kernel_f32: public jit_generator {
+
+    jit_avx512_common_conv_bwd_data_kernel_f32(jit_conv_conf_t ajcp): jcp(ajcp)
+    {
+        generate();
+        jit_ker = (void (*)(jit_conv_call_s *))getCode();
+    }
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_common_conv_bwd_data_kernel_f32)
+
+    static status_t init_conf(jit_conv_conf_t &jcp,
+            const convolution_desc_t &cd,
+            const memory_desc_wrapper &diff_src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &diff_dst_d);
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_conv_conf_t &jcp);
+
+    jit_conv_conf_t jcp;
+    void (*jit_ker)(jit_conv_call_s *);
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    enum {
+        typesize = sizeof(float),
+        ker_reg_base_idx = 28,
+    };
+
+    reg64_t param = abi_param1;
+    reg64_t reg_dst = r8;
+    reg64_t reg_ker = r9;
+    reg64_t reg_src = r10;
+
+    reg64_t reg_dst_prf = r11;
+    reg64_t reg_ker_prf = r12;
+    reg64_t reg_src_prf = r13;
+
+    reg64_t aux_reg_dst = r14;
+    reg64_t aux_reg_ker = r15;
+
+    reg64_t aux_reg_dst_prf = rsi;
+    reg64_t aux_reg_ker_prf = rdx;
+
+    reg64_t aux_reg_dst_d_prf = r13;
+    reg64_t aux_reg_dst_d = rbx;
+    reg64_t aux_reg_ker_d_prf = abi_not_param1;
+    reg64_t aux_reg_ker_d = r9;
+    reg64_t reg_ki = r10;
+
+    reg64_t reg_kj = rax;
+    reg64_t reg_oi = rbx;
+    reg64_t reg_kh = abi_not_param1;
+
+    reg64_t reg_channel = rsi;
+
+    reg64_t reg_tmp = rbp;
+    reg64_t reg_long_offt = r14;
+
+    inline Xbyak::Zmm zmm_ker(int i_ic) {
+        assert(i_ic < 4);
+        return Xbyak::Zmm(ker_reg_base_idx + i_ic);
+    }
+    inline Xbyak::Zmm zmm_inp(int i_ic, int nb_x_blocking) {
+        int idx = i_ic + nb_x_blocking * jcp.ur_w;
+        assert(idx < 31);
+        return Xbyak::Zmm(idx);
+    }
+    inline Xbyak::Zmm zmm_out(int i_ur, int i_oc) {
+        int idx = i_ur + i_oc * jcp.ur_w;
+        assert(idx < ker_reg_base_idx);
+        return Xbyak::Zmm(idx);
+    }
+
+    Xbyak::Zmm zmm_wei = Xbyak::Zmm(31);
+
+    inline void prepare_output(int ur_w);
+    inline void store_output(int ur_w);
+    inline void compute_loop_4fma(int ur_w, int l_overflow, int r_overflow);
+    inline void compute_loop_fma(int ur_w, int l_overflow, int r_overflow);
+    inline void compute_loop_fma_core(int ur_w, int l_overflow, int r_overflow);
+    inline void compute_loop(int ur_w, int l_overflow, int r_overflow);
+    void generate();
+
+    inline int get_iw_start(int ki, int l_overflow)
+    {
+        int res = (jcp.iw - 1 + jcp.r_pad) % jcp.stride_w
+                + l_overflow * jcp.stride_w
+                - (jcp.kw - 1 - ki) * (jcp.dilate_w + 1);
+        while (res < 0)
+            res += jcp.stride_w;
+
+        return res;
+    }
+
+    inline int get_iw_end(int ur_w, int ki, int r_overflow)
+    {
+        if (utils::one_of(ur_w, jcp.iw, jcp.ur_w_tail))
+            ur_w += nstl::min(0, jcp.r_pad); // remove negative padding
+        int res = (ur_w - 1 + jcp.l_pad) % jcp.stride_w
+                + r_overflow * jcp.stride_w - ki * (jcp.dilate_w + 1);
+        while (res < 0)
+            res += jcp.stride_w;
+
+        return ur_w - res;
+    }
+};
+
+struct jit_avx512_common_conv_bwd_weights_kernel_f32 : public jit_generator {
+
+    jit_avx512_common_conv_bwd_weights_kernel_f32(jit_conv_conf_t ajcp)
+        : jcp(ajcp)
+    {
+        generate();
+        jit_ker = (void (*)(jit_conv_call_s *))getCode();
+    }
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_common_conv_bwd_weights_kernel_f32)
+
+    static status_t init_conf(jit_conv_conf_t &jcp,
+            const convolution_desc_t &cd,
+            memory_desc_t &src_md,
+            memory_desc_t &diff_weights_md,
+            memory_desc_t &diff_bias_md,
+            memory_desc_t &diff_dst_md);
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_conv_conf_t &jcp);
+
+    jit_conv_conf_t jcp;
+    void (*jit_ker)(jit_conv_call_s *);
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    enum {typesize = sizeof(float)};
+    static const int max_ur_w;
+
+    reg64_t param = abi_param1;
+    reg64_t reg_input = rax;
+    reg64_t reg_kernel = rdx;
+    reg64_t reg_output = rsi;
+    reg64_t b_ic = abi_not_param1;
+    reg64_t kj = r8;
+    reg64_t reg_kh = r9;
+    reg64_t reg_ur_w_trips = r10;
+    reg64_t reg_oj = r15;
+    reg64_t reg_ih_count = rbx;
+    reg64_t reg_tmp = r14;
+    reg64_t reg_long_offt = r14;
+
+    reg64_t ki = r11;
+    reg64_t reg_kd_count = r12;
+    reg64_t reg_oi = r12;
+    reg64_t reg_d_index = r13;
+    reg64_t reg_input_d = r15;
+    reg64_t reg_output_d = rbx;
+    reg64_t aux_reg_input = r12;
+    reg64_t aux_reg_kernel = r13;
+    reg64_t reg_bias = rbx;
+
+    inline void bias_kernel();
+    inline void maybe_zero_kernel();
+    inline void compute_oh_step_unroll_ow_icblock(int ic_block_step,
+            int max_ur_w);
+    inline void od_step_comeback_pointers();
+    inline void oh_step_comeback_pointers();
+    inline void compute_oh_step_unroll_ow(int ic_block_step, int max_ur_w);
+    inline void compute_ic_block_step(int ur_w,
+            int pad_l, int pad_r, int ic_block_step,
+            int input_offset, int kernel_offset, int output_offset,
+            bool input_wraparound = false);
+    inline void compute_ic_block_step_fma(int ur_w,
+            int pad_l, int pad_r, int ic_block_step,
+            int input_offset, int kernel_offset, int output_offset,
+            bool input_wraparound);
+    inline void compute_ic_block_step_4fma(int ur_w,
+            int pad_l, int pad_r, int ic_block_step,
+            int input_offset, int kernel_offset, int output_offset,
+            bool input_wraparound);
+    inline void compute_oh_step_common(int ic_block_step, int max_ur_w);
+    inline void compute_oh_step_disp();
+    inline void compute_oh_loop_common();
+    inline void compute_d_loop_common();
+
+    inline bool compute_full_spat_loop();
+    inline bool flat_4ops_compute();
+
+    inline void compute_loop();
+
+    void generate();
+
+    static void balance(const jit_conv_conf_t &j, int &nthr, int &nthr_mb,
+            int &nthr_g, int &nthr_oc_b, int &nthr_ic_b);
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_winograd_kernel_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_winograd_kernel_f32.cpp
new file mode 100644
index 0000000000..1bdcd0d6a8
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_winograd_kernel_f32.cpp
@@ -0,0 +1,1163 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+#include "cpu_memory.hpp"
+
+#include <math.h>
+
+#include "jit_avx512_common_conv_winograd_kernel_f32.hpp"
+
+#ifndef KERNEL_SIZE_THRESHOLD
+#define KERNEL_SIZE_THRESHOLD 16
+#endif
+
+#define MIN_REQUIRED_DIMN_REG_BLOCK 14
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace {
+
+using namespace mkldnn::impl::utils;
+
+unsigned int L1_cache_size = get_cache_size(1, true);
+unsigned int L2_cache_size = get_cache_size(2, true);
+unsigned int LLC_data_size = get_cache_size(3, false);
+
+// the test funtion takes jcp, the candidate and the current best.
+// it  returns true if the new candidate is better
+int get_divisor_satisfying_cond(jit_conv_winograd_conf_t &jcp, int number,
+        int default_best, bool (*test)(jit_conv_winograd_conf_t &, int, int))
+{
+    int best_divisor = default_best;
+    auto test_num
+            = [&best_divisor, test](jit_conv_winograd_conf_t &jcp, int num) {
+                  if (test(jcp, num, best_divisor)) {
+                      best_divisor = num;
+                  }
+              };
+
+    for (int divisor = 1; divisor <= ::sqrt(number); divisor++) {
+        if (number % divisor == 0) {
+            test_num(jcp, divisor);
+            test_num(jcp, number / divisor);
+        }
+    }
+
+    return best_divisor;
+}
+
+namespace {
+bool is_winograd_faster_than_direct(const jit_conv_winograd_conf_t &jcp) {
+    if (jcp.ver == ver_4fma)
+        return jcp.mb >= 32;
+    else
+        return jcp.mb >= 16;
+}
+}
+
+/* assumes 512 bits registers */
+/* TODO: add support for strides */
+/* TODO: handle the prefetch distance automatically */
+typedef enum cache_t_ { L1, L2, L3 } cache_t;
+
+template <typename data_t>
+struct prefetcher_t {
+    prefetcher_t(jit_generator *generator, Xbyak::Reg64 reg_base_addr,
+            cache_t cache_type, size_t block_size, /* in number of elements*/
+            int nb_instructions_in_block, int fma_ipc)
+        : cg_(generator)
+        , reg_base_addr_(reg_base_addr)
+        , cache_type_(cache_type)
+        , cache_block_size_(block_size)
+    {
+        nb_cache_lines_to_prefetch_ = cache_block_size_ / (64 / sizeof(data_t));
+        prefetch_spread_
+                = div_up(nb_instructions_in_block, nb_cache_lines_to_prefetch_);
+        prefetch_blk_
+                = div_up(nb_cache_lines_to_prefetch_, nb_instructions_in_block);
+
+        /* assumption: when fetch in Li, data is already in L(i+1) */
+        int cache_latency;
+        switch (cache_type_) {
+        case L1: cache_latency = 14; break;
+        case L2:
+        case L3:
+        default: cache_latency = 250; break;
+        }
+
+        prefetch_distance_ = div_up(cache_latency, nb_cache_lines_to_prefetch_);
+    }
+
+    void prefetch(int instruction_number)
+    {
+        if (instruction_number % prefetch_spread_ == 0) {
+            for (int i = 0; (i < prefetch_blk_)
+                    && (prefetches_issued_ < nb_cache_lines_to_prefetch_);
+                    i++, prefetches_issued_++) {
+                prefetch_inst_(cg_->EVEX_compress_addr(
+                        reg_base_addr_, (cache_block_size_ * prefetch_distance_)
+                                        * sizeof(data_t)
+                                + (prefetches_issued_ * 64)));
+            }
+        }
+    }
+
+private:
+    void prefetch_inst_(const Xbyak::Address &addr)
+    {
+        switch (cache_type_) {
+        case L1: cg_->prefetcht0(addr); break;
+        case L2: cg_->prefetcht1(addr); break;
+        case L3: cg_->prefetcht2(addr); break;
+        default:
+            break; // TODO: raise an exception or put an assert
+        }
+    }
+
+    jit_generator *cg_;
+    Xbyak::Reg64 reg_base_addr_;
+    cache_t cache_type_;
+    int cache_block_size_ = 0;
+    int nb_cache_lines_to_prefetch_ = 0;
+    int prefetches_issued_ = 0;
+    int prefetch_spread_ = 0;
+    int prefetch_blk_ = 0;
+    int prefetch_distance_ = 0;
+};
+
+// utilities to support kernel parameter selection
+bool check_cond1(int dimN_reg_block, int dimK_block, int dimK_reg_block,
+        int dimM_block, int dimM_simd_block, float C)
+{
+    float lhs = (dimM_block * dimN_reg_block * dimM_simd_block
+                        + dimM_block * dimK_block * dimK_reg_block
+                                * dimM_simd_block
+                        + dimK_block * dimN_reg_block * dimK_reg_block)
+            * (float)sizeof(float);
+    float rhs = C * L1_cache_size;
+    return (lhs < rhs);
+}
+
+bool check_cond1_bis(int dimN_reg_block, int dimK_block, int dimK_reg_block,
+        int dimM_block, int dimM_simd_block, float C)
+{
+    float lhs = (dimM_block * dimK_block * dimK_reg_block * dimM_simd_block
+                        + dimK_block * dimN_reg_block * dimK_reg_block)
+            * (float)sizeof(float);
+    float rhs = C * L1_cache_size;
+    return (lhs < rhs);
+}
+
+bool check_cond2(int nb_dimN_reg_block, int dimN_reg_block, int dimK_nb_block,
+        int dimK_block, int dimK_reg_block, int dimM_block, int dimM_simd_block,
+        float C)
+{
+    float lhs = (nb_dimN_reg_block * dimM_block * dimN_reg_block * dimM_simd_block
+                      + dimK_nb_block * dimM_block * dimK_block * dimK_reg_block
+                              * dimM_simd_block
+                      + nb_dimN_reg_block * dimK_nb_block * dimK_block
+                              * dimN_reg_block * dimK_reg_block)
+            * (float)sizeof(float);
+    float rhs = C * L2_cache_size;
+    return (lhs < rhs);
+}
+}
+
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::utils;
+using namespace Xbyak;
+
+void _jit_avx512_common_conv_winograd_data_kernel_f32::gemm_loop_generate(
+        bool is_beta_zero)
+{
+    // const int dimK_simd_block = jcp.dimK_reg_block;
+
+    // for (int dimM_block =0; dimM_block < jcp.dimM_block; dimM_block++)
+    //     for (int dimK_block = 0; dimK_block < jcp.dimK_block; dimK_block++)
+    //         for (int dimK_reg_block= 0; dimK_reg_block < jcp.dimK_reg_block;
+    //         dimK_reg_block++)
+    //                 for (int tile =0; tile < jcp.dimN_reg_block; tile++)
+    //                     C[dimM_block][tile] +=
+    //                     A[dimM_block][dimK_block][dimK_reg_block] *
+    //                     broadcast(B[dimK_block][tile][dimK_reg_block]);
+    // 1) We do register blocking on A[dimM_block][dimK_block][dimK_reg_block],
+    // so we load it before the loop on tile
+    // 2) the loop on tile must be fully unrolled. Don't know about the one on
+    // dimK_reg_block. I think it should be
+
+    auto inner_loops = [=]() {
+        Label dimM_block_loop, dimK_block_loop;
+        const int inc_dimK_reg_block = jcp.ver == ver_4fma ? 4 : 1;
+        const int fma_ipc = jcp.ver == ver_4fma ? 1 : 2;
+
+        prefetcher_t<float> L1_pf(this, reg_srcB, L1,
+                jcp.dimN_reg_block * jcp.dimK_reg_block,
+                jcp.dimK_reg_block * jcp.dimN_reg_block / inc_dimK_reg_block,
+                fma_ipc);
+        prefetcher_t<float> L2_pf(this, reg_srcB, L2,
+                jcp.dimN_reg_block * jcp.dimK_reg_block,
+                jcp.dimK_reg_block * jcp.dimN_reg_block / inc_dimK_reg_block,
+                fma_ipc);
+
+        if (jcp.dimM_block > 1) {
+            mov(reg_dimM_block_loop_cnt, jcp.dimM_block);
+            L(dimM_block_loop);
+        }
+        {
+            // First, we zero the accumulators if first nb_ic iteration,
+            // otherwise we load them
+            for (int tile = 0; tile < jcp.dimN_reg_block; tile++) {
+                Zmm zmm(jcp.zmm_start + tile);
+                if (is_beta_zero)
+                    vpxord(zmm, zmm, zmm);
+                else
+                    vmovups(zmm, zword[reg_dstC + 64 * tile]);
+            }
+
+            if (jcp.dimK_block > 1) {
+                mov(reg_dimK_block_loop_cnt, jcp.dimK_block);
+                L(dimK_block_loop);
+            }
+            {
+                auto load_A = [=](int reg_idx, int offset) {
+                    for (int i = 0; i < inc_dimK_reg_block; i++)
+                        vmovups(Zmm(reg_idx + i),
+                                zword[reg_srcA + 64 * (offset + i)]);
+                };
+
+                // Used when doing double buffering
+                int next = 0;
+                if (jcp.double_buffering) {
+                    load_A(next, 0);
+                }
+                for (int dimK_reg_block = 0;
+                        dimK_reg_block < jcp.dimK_reg_block;
+                        dimK_reg_block += inc_dimK_reg_block) {
+                    int current;
+                    /* Loading the next vector from A */
+                    current = next;
+                    if (jcp.double_buffering) {
+                        next = (dimK_reg_block + inc_dimK_reg_block)
+                                % (2 * inc_dimK_reg_block);
+                        load_A(next, dimK_reg_block + inc_dimK_reg_block);
+                    } else {
+                        next = 0;
+                        load_A(next, dimK_reg_block);
+                    }
+                    /* Performing the fmas */
+                    for (int tile = 0; tile < jcp.dimN_reg_block; tile++) {
+                        Zmm zmm(jcp.zmm_start + tile);
+                        if (jcp.ver != ver_avx512_core)
+                            L1_pf.prefetch(
+                                    dimK_reg_block * jcp.dimN_reg_block + tile);
+                        if (jcp.ver == ver_4fma)
+                            v4fmaddps(zmm, Zmm(current),
+                                    EVEX_compress_addr(reg_srcB,
+                                              64 * tile + dimK_reg_block * 4));
+                        else
+                            vfmadd231ps(zmm, Zmm(current),
+                                    EVEX_compress_addr(reg_srcB,
+                                                64 * tile + dimK_reg_block * 4,
+                                                true));
+                        if (jcp.ver != ver_avx512_core)
+                            L2_pf.prefetch(
+                                    dimK_reg_block * jcp.dimN_reg_block + tile);
+                    }
+                }
+
+                add(reg_srcA, jcp.dimK_reg_block * 64);
+                add(reg_srcB, jcp.dimN_reg_block * 64);
+                if (jcp.dimK_block > 1) {
+                    sub(reg_dimK_block_loop_cnt, 1);
+                    jnz(dimK_block_loop);
+                }
+            }
+
+
+            auto store_output = [=](bool output_is_aligned) {
+                for (int tile = 0; tile < jcp.dimN_reg_block; tile++) {
+                    Zmm zmm(jcp.zmm_start + tile);
+                    if (output_is_aligned
+                        && jcp.dimK_nb_block == 1
+                        && (jcp.dimN * jcp.dimM * alpha * alpha
+                            * sizeof(float) > 2 * LLC_data_size))
+                        vmovntps(zword[reg_dstC + 64 * tile], zmm);
+                    else
+                        vmovups(zword[reg_dstC + 64 * tile], zmm);
+                }
+            };
+
+            Label unaligned_store, end_store;
+            test(reg_dstC, cpu_isa_traits<avx512_common>::vlen - 1);
+            jnz(unaligned_store, T_NEAR);
+            store_output(true);
+            jmp(end_store, T_NEAR);
+            L(unaligned_store); {
+                store_output(false);
+            }
+            L(end_store);
+
+            if (jcp.dimM_block > 1) {
+                sub(reg_srcB, jcp.dimK_block * jcp.dimN_reg_block * 64);
+                add(reg_dstC, jcp.dimN_reg_block * 64);
+                sub(reg_dimM_block_loop_cnt, 1);
+                jnz(dimM_block_loop);
+            }
+        }
+    };
+
+    /* Preamble */
+    preamble();
+
+    /* kernel */
+    inner_loops();
+
+    /* Postamble */
+    postamble();
+    ret();
+}
+
+status_t _jit_avx512_common_conv_winograd_data_kernel_f32::init_conf_common(
+        jit_conv_winograd_conf_t &jcp, const convolution_desc_t &cd,
+        const memory_desc_wrapper &src_d, const memory_desc_wrapper &weights_d,
+        const memory_desc_wrapper &dst_d)
+{
+
+    if (mayiuse(avx512_core))
+        return status::unimplemented;
+    else if (!mayiuse(avx512_common))
+        return status::unimplemented;
+    else if (mayiuse(avx512_mic_4ops))
+        jcp.ver = ver_4fma;
+    else
+        jcp.ver = ver_fma;
+
+    jcp.nthr = mkldnn_get_max_threads();
+
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+    jcp.ih = src_d.dims()[2];
+    jcp.iw = src_d.dims()[3];
+    jcp.oh = dst_d.dims()[2];
+    jcp.ow = dst_d.dims()[3];
+    jcp.kh = weights_d.dims()[with_groups + 2];
+    jcp.kw = weights_d.dims()[with_groups + 3];
+    jcp.t_pad = cd.padding[0][0];
+    jcp.l_pad = cd.padding[0][1];
+    jcp.stride_h = cd.strides[0];
+    jcp.stride_w = cd.strides[1];
+    jcp.dilate_h = cd.dilates[0];
+    jcp.dilate_w = cd.dilates[1];
+    jcp.r_pad = nstl::max(
+            0, (jcp.ow - 1) * jcp.stride_w + jcp.kw - jcp.iw - jcp.l_pad);
+    jcp.b_pad = nstl::max(
+            0, (jcp.oh - 1) * jcp.stride_h + jcp.kh - jcp.ih - jcp.t_pad);
+    jcp.ihp = jcp.ih + jcp.t_pad + jcp.b_pad;
+    jcp.iwp = jcp.iw + jcp.l_pad + jcp.r_pad;
+    jcp.ohp = jcp.oh;
+    jcp.owp = jcp.ow;
+
+    bool ok_to_pad_channels = jcp.ngroups == 1;
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, simd_w);
+        jcp.ic = rnd_up(jcp.ic, simd_w);
+    }
+
+    if (!IMPLICATION(cd.alg_kind == alg_kind::convolution_auto,
+                is_winograd_faster_than_direct(jcp)))
+        return status::unimplemented;
+
+    // Checking conditions not supported by these kernels
+    if (jcp.ngroups != 1)
+        return status::unimplemented;
+    if ((jcp.kh != 3) || (jcp.kw != 3))
+        return status::unimplemented;
+    if ((jcp.dilate_h != 0) || (jcp.dilate_w != 0))
+        return status::unimplemented;
+    if ((jcp.stride_h != 1) || (jcp.stride_w != 1))
+        return status::unimplemented;
+    if ((jcp.ic % simd_w) != 0 || (jcp.oc % simd_w) != 0)
+        return status::unimplemented;
+
+    format_tag_t dat_tag = nChw16c;
+    format_tag_t wei_tag = with_groups ? gOIhw16i16o : OIhw16i16o;
+    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    jcp.wei_tag = weights_d.matches_one_of_tag(wei_tag);
+    jcp.dst_tag = dst_d.matches_one_of_tag(dat_tag);
+
+    if (jcp.src_tag != dat_tag) return status::unimplemented;
+    if (jcp.wei_tag != wei_tag) return status::unimplemented;
+    if (jcp.dst_tag != dat_tag) return status::unimplemented;
+
+    bool layout_consistency = true
+        && jcp.ic <= src_d.padded_dims()[1]
+        && jcp.oc <= dst_d.padded_dims()[1]
+        && jcp.ic <= weights_d.padded_dims()[with_groups + 1]
+        && jcp.oc <= weights_d.padded_dims()[with_groups + 0];
+    if (!layout_consistency) return status::unimplemented;
+
+    return status::success;
+}
+
+
+status_t set_wsched_DATA_W_S_G_D_avx512_common(jit_conv_winograd_conf_t &jcp) {
+
+    auto test_cond_dimN_reg_block = [](jit_conv_winograd_conf_t &jcp,
+            int dimN_reg_block, int current_best) {
+        return (dimN_reg_block >= MIN_REQUIRED_DIMN_REG_BLOCK)
+                && (dimN_reg_block < jcp.nb_reg)
+                && (dimN_reg_block < current_best);
+    };
+    jcp.dimN_reg_block = get_divisor_satisfying_cond(
+            jcp, jcp.dimN, jcp.dimN, test_cond_dimN_reg_block);
+
+    if (jcp.dimN_reg_block >= jcp.nb_reg) {
+        auto test_cond_dimN_reg_block = [](jit_conv_winograd_conf_t &jcp,
+                int dimN_reg_block, int current_best) {
+            return (dimN_reg_block < jcp.nb_reg)
+                    && (dimN_reg_block > current_best);
+        };
+
+        jcp.dimN_reg_block = get_divisor_satisfying_cond(
+                jcp, jcp.dimN, 1, test_cond_dimN_reg_block);
+    }
+
+    //********************* Choosing dimK_block **********************//
+    auto test_cond1_dimK_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimK_block, int current_best) {
+        return check_cond1(jcp.dimN_reg_block, dimK_block, jcp.dimK_reg_block,
+                       1, jcp.dimM_simd_block, .75f)
+                && (dimK_block > current_best);
+    };
+
+    auto test_cond1_bis_dimK_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimK_block, int current_best) {
+        return check_cond1_bis(jcp.dimN_reg_block, dimK_block,
+                       jcp.dimK_reg_block, 1, jcp.dimM_simd_block, .9f)
+                && (dimK_block > current_best);
+    };
+
+    jcp.dimK_block = get_divisor_satisfying_cond(
+            jcp, jcp.dimK / jcp.dimK_reg_block, 1, test_cond1_bis_dimK_block);
+    // If we are not able to use streams, we fall back to condition [1]
+    if (jcp.dimK_block < jcp.dimK / jcp.dimK_reg_block)
+        jcp.dimK_block = get_divisor_satisfying_cond(
+                jcp, jcp.dimK / jcp.dimK_reg_block, 1, test_cond1_dimK_block);
+    jcp.dimK_nb_block = (jcp.dimK / jcp.dimK_reg_block) / jcp.dimK_block;
+
+    //********************* Choosing dimM_block **********************//
+    jcp.dimM_simd_block = 16;
+    /*XXX: Why C=0.5 here but C=0.75 for dimK_block?*/
+    auto test_cond1_dimM_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimM_block, int current_best) {
+        return check_cond1(jcp.dimN_reg_block, jcp.dimK_block,
+                       jcp.dimK_reg_block, dimM_block, jcp.dimM_simd_block, .5f)
+                && (dimM_block > current_best);
+    };
+
+    auto test_cond1_bis_dimM_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimM_block, int current_best) {
+        return check_cond1_bis(jcp.dimN_reg_block, jcp.dimK_block,
+                       jcp.dimK_reg_block, dimM_block, jcp.dimM_simd_block, .3f)
+                && (dimM_block > current_best);
+    };
+
+    if (jcp.dimK_block < jcp.dimK / jcp.dimK_reg_block)
+        jcp.dimM_block = get_divisor_satisfying_cond(
+                jcp, jcp.dimM / jcp.dimM_simd_block, 1, test_cond1_dimM_block);
+    else
+        jcp.dimM_block = get_divisor_satisfying_cond(jcp,
+                jcp.dimM / jcp.dimM_simd_block, 1, test_cond1_bis_dimM_block);
+    jcp.dimM_nb_block = (jcp.dimM / jcp.dimM_simd_block) / jcp.dimM_block;
+
+    //******************* Choosing dimN_block *******************//
+    auto test_cond2_dimN_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimN_block, int current_best) {
+        return check_cond2(dimN_block, jcp.dimN_reg_block, jcp.dimK_nb_block,
+                       jcp.dimK_block, jcp.dimK_reg_block, jcp.dimM_block,
+                       jcp.dimM_simd_block, .5f)
+                && (dimN_block > current_best);
+    };
+
+    jcp.dimN_block = get_divisor_satisfying_cond(
+            jcp, jcp.dimN / jcp.dimN_reg_block, 1, test_cond2_dimN_block);
+    jcp.dimN_nb_block = jcp.dimN / (jcp.dimN_reg_block * jcp.dimN_block);
+    jcp.sched_policy = WSCHED_DATA_W_S_G_D;
+    return status::success;
+}
+
+status_t _jit_avx512_common_conv_winograd_data_kernel_f32::init_conf_kernel(
+        jit_conv_winograd_conf_t &jcp, int dimM, int dimN, int dimK)
+{
+    jcp.dimK_reg_block = 16;
+    jcp.dimM_simd_block = 16;
+
+    // TODO: replace double buffering with nuple buffering to maximize register
+    // usage.
+    // the choice of the number of buffers will then come after choosing
+    // dimN_reg_block
+    jcp.double_buffering = true;
+    if (jcp.double_buffering)
+        jcp.zmm_start = 2 * ((jcp.ver == ver_4fma) ? 4 : 2);
+    else
+        jcp.zmm_start = 1;
+    jcp.nb_reg = 32 - jcp.zmm_start;
+
+    jcp.dimN = dimN;
+    jcp.dimK = dimK;
+    jcp.dimM = dimM;
+
+    jcp.sched_policy = WSCHED_INVALID;
+    set_wsched_DATA_W_S_G_D_avx512_common(jcp);
+
+    assert(jcp.sched_policy == WSCHED_DATA_W_S_G_D);
+    return status::success;
+}
+
+bool jit_avx512_common_conv_winograd_fwd_kernel_f32::post_ops_ok(
+        jit_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    const auto &p = attr.post_ops_;
+
+    auto is_relu = [&](int idx) { return p.entry_[idx].is_relu(); };
+    auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); };
+
+    switch (p.len_) {
+    case 0: return true; // no post_ops
+    case 1: return is_relu(0) || is_sum(0); // relu or sum
+    case 2: return (is_sum(0) && is_relu(1)) ||
+                       (is_relu(0) && is_sum(1)); // sum->relu or relu->sum
+    case 3: return is_relu(0) && is_sum(1) && is_relu(2); // relu->sum->relu
+    default: return false;
+    }
+
+    return false;
+}
+
+status_t jit_avx512_common_conv_winograd_fwd_kernel_f32::init_conf(
+        jit_conv_winograd_conf_t &jcp, const convolution_desc_t &cd,
+        const memory_desc_wrapper &src_d, const memory_desc_wrapper &weights_d,
+        const memory_desc_wrapper &dst_d, const primitive_attr_t &attr) {
+    status_t st = init_conf_common(jcp, cd, src_d, weights_d, dst_d);
+
+    if (st != status::success)
+        return st;
+
+    // Winograd specific initialization
+    jcp.itiles = (jcp.ow + tile_size - 1) / tile_size;
+    jcp.jtiles = (jcp.oh + tile_size - 1) / tile_size;
+    jcp.ntiles = jcp.mb * jcp.itiles * jcp.jtiles;
+
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    const int eltwise_ind = p.find(primitive_kind::eltwise, 0, 1);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise) jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+    jcp.with_sum = p.find(primitive_kind::sum, 0) != -1;
+
+    status_t res = init_conf_kernel(jcp, jcp.oc, jcp.ntiles, jcp.ic);
+    jcp.ic_simd_block = jcp.dimK_reg_block;
+    jcp.ic_block = jcp.dimK_block;
+    jcp.nb_ic = jcp.dimK_nb_block;
+    jcp.oc_simd_block = jcp.dimM_simd_block;
+    jcp.oc_block = jcp.dimM_block;
+    jcp.nb_oc = jcp.dimM_nb_block;
+    jcp.tile_block_ur = jcp.dimN_reg_block;
+    jcp.nb_tile_block_ur = jcp.dimN_block;
+    jcp.tile_block = jcp.dimN_nb_block;
+    jcp.tile_4fma_padding = 0; // only relevant for backward weights
+
+    return res;
+}
+
+status_t jit_avx512_common_conv_winograd_bwd_data_kernel_f32::init_conf(
+        jit_conv_winograd_conf_t &jcp, const convolution_desc_t &cd,
+        const memory_desc_wrapper &diff_src_d,
+        const memory_desc_wrapper &weights_d,
+        const memory_desc_wrapper &diff_dst_d)
+{
+    status_t st = init_conf_common(jcp, cd, diff_src_d, weights_d, diff_dst_d);
+
+    if (st != status::success)
+        return st;
+
+    jcp.itiles = (jcp.iw + tile_size - 1) / tile_size;
+    jcp.jtiles = (jcp.ih + tile_size - 1) / tile_size;
+    jcp.ntiles = jcp.mb * jcp.itiles * jcp.jtiles;
+
+    status_t res = init_conf_kernel(jcp, jcp.ic, jcp.ntiles, jcp.oc);
+    jcp.oc_simd_block = jcp.dimK_reg_block;
+    jcp.oc_block = jcp.dimK_block;
+    jcp.nb_oc = jcp.dimK_nb_block;
+    jcp.ic_simd_block = jcp.dimM_simd_block;
+    jcp.ic_block = jcp.dimM_block;
+    jcp.nb_ic = jcp.dimM_nb_block;
+    jcp.tile_block_ur = jcp.dimN_reg_block;
+    jcp.nb_tile_block_ur = jcp.dimN_block;
+    jcp.tile_block = jcp.dimN_nb_block;
+    jcp.tile_4fma_padding = 0; // only relevant for backward weights
+
+    return res;
+}
+
+void jit_avx512_common_conv_winograd_bwd_weights_kernel_f32::transpose_ker_generate()
+{
+    auto load_B = [=](int reg_idx, int offset) {
+        for (int i = 0; i < 4; i++) {
+            vmovups(Zmm(reg_idx + i), zword[reg_origB + (offset + i) * jcp.dimN_reg_block * sizeof(float)]);
+        }
+    };
+
+    preamble();
+    int curr = 0;
+    for (int j = 0; j < alpha; j++) {
+        for (int i = 0; i < alpha; i++) {
+            int origB_offset = (j * alpha + i) * jcp.dimK_4fma;
+            size_t transB_offset = (size_t)(j * alpha + i) * jcp.dimK_nb_block *
+                jcp.dimN_block * jcp.dimK_block * jcp.dimK_reg_block *
+                jcp.dimK_4fma * jcp.dimN_reg_block * sizeof(float);
+            mov(reg_transB_idx, transB_offset);
+            for (int tb = 0; tb < jcp.dimK_4fma; tb+=4) {
+                /*double buffering to hide load latencies*/
+                int next = (curr + 4) % 8;
+                if (i == 0 && tb == 0) {
+                    load_B(0, origB_offset);
+                }
+                if (tb + 4 < (jcp.dimK_4fma -1)) {
+                    load_B(next, origB_offset + 4);
+                } else if (i < alpha - 1) {
+                    load_B(next, origB_offset + jcp.dimK_4fma);
+                }
+
+                vunpcklps(Zmm(8), Zmm(curr), Zmm(curr + 1));
+                vunpcklps(Zmm(9), Zmm(curr + 2), Zmm(curr + 3));
+                vunpckhps(Zmm(curr), Zmm(curr), Zmm(curr + 1));
+                vunpckhps(Zmm(curr + 1), Zmm(curr + 2), Zmm(curr + 3));
+
+                vunpcklpd(Zmm(curr + 2), Zmm(8), Zmm(9));
+                vunpckhpd(Zmm(curr + 3), Zmm(8), Zmm(9));
+
+                vunpcklpd(Zmm(8), Zmm(curr), Zmm(curr + 1));
+                vunpckhpd(Zmm(9), Zmm(curr), Zmm(curr + 1));
+
+                vmovntps(zword[reg_transB + reg_transB_idx
+                        + sizeof(float) * tb * jcp.dimN_reg_block],
+                        Zmm(curr+2));
+                vmovntps(zword[reg_transB + reg_transB_idx
+                        + sizeof(float) * (tb + 1) * jcp.dimN_reg_block],
+                        Zmm(curr+3));
+                vmovntps(zword[reg_transB + reg_transB_idx
+                        + sizeof(float) * (tb + 2) * jcp.dimN_reg_block],
+                        Zmm(8));
+                vmovntps(zword[reg_transB + reg_transB_idx
+                        + sizeof(float) * (tb + 3) * jcp.dimN_reg_block],
+                        Zmm(9));
+                curr = next;
+
+            }
+        }
+    }
+    postamble();
+    ret();
+}
+void jit_avx512_common_conv_winograd_bwd_weights_kernel_f32::gemm_loop_generate(
+        bool is_first_tile)
+{
+    // for (int ofm2 = 0; ofm2 < jcp.oc_block; ofm2++)
+    //     for (int ifm2 = 0; ifm2 < jcp.ic_block; ifm2++)
+    //             for (int nb_tile_block_ur = 0; nb_tile_block_ur <
+    //             jcp.nb_tile_block_ur; nb_tile_block_ur++)
+    //                 for (int tile_block_ur = 0; tile_block_ur <
+    //                 jcp.tile_block_ur; tile_block_ur++)
+    //                     for (int ifm3 = 0; ifm3 < jcp.ic_reg_block; ++ifm3)
+    //                         U[ofm2][ifm2][ofm3][ifm3][0:oc_simd_block] +=
+    //                             M[ofm2][ofm3][nb_tile_block_ur][tile_block_ur][0:oc_simd_block]
+    //                              *
+    //                              broadcast(V[ifm2][nb_tile_block_ur][ifm3][tile_block_ur])
+    auto inner_loops = [=]() {
+        int inc_fma = jcp.ver == ver_4fma ? 4 : 1;
+        const int fma_ipc = jcp.ver == ver_4fma ? 1 : 2;
+        prefetcher_t<float> L1_pf(this, reg_srcB, L1,
+                jcp.dimK_reg_block * jcp.dimN_reg_block * jcp.dimK_4fma,
+                jcp.dimK_reg_block * jcp.dimN_reg_block * jcp.dimK_4fma
+                        / inc_fma,
+                fma_ipc);
+        prefetcher_t<float> L2_pf(this, reg_srcB, L2,
+                jcp.dimK_reg_block * jcp.dimN_reg_block * jcp.dimK_4fma,
+                jcp.dimK_reg_block * jcp.dimN_reg_block * jcp.dimK_4fma
+                        / inc_fma,
+                fma_ipc);
+
+        auto load_A = [=](int reg_idx, int offset) {
+            for (int i = 0; i < inc_fma; i++) {
+                vmovups(Zmm(reg_idx + i),
+                        zword[reg_srcA +
+                        sizeof(float) * jcp.dimM_simd_block * (offset + i)]);
+            }
+        };
+
+        Label dimM_block_loop, dimK_block_loop, dimN_block_loop;
+        if (jcp.dimM_block > 1) {
+            mov(reg_dimM_block_loop_cnt, jcp.dimM_block);
+            L(dimM_block_loop);
+        }
+        { /************* OC_block (M) loop ***********/
+            if (jcp.dimN_block > 1) {
+                mov(reg_dimN_block_loop_cnt, jcp.dimN_block);
+                L(dimN_block_loop);
+            }
+            { /*************** IC_block (N) loop *********/
+                for (int dimN_reg_block = 0;
+                        dimN_reg_block < jcp.dimN_reg_block; ++dimN_reg_block) {
+                    Zmm zmm(jcp.zmm_start + dimN_reg_block);
+                    if (is_first_tile)
+                        vpxord(zmm, zmm, zmm);
+                    else
+                        vmovups(zmm, zword[reg_dstC +
+                                dimN_reg_block * jcp.dimM_simd_block *
+                                sizeof(float)]);
+                }
+
+                if (jcp.dimK_block > 1) {
+                    mov(reg_dimK_block_loop_cnt, jcp.dimK_block);
+                    L(dimK_block_loop);
+                }
+                { /************* nb_tile_ur(K) loop ********/
+                    int next = 0;
+                    if (jcp.double_buffering) {
+                        load_A(next, 0);
+                    }
+                    for (int dimK_reg_block = 0;
+                            dimK_reg_block < jcp.dimK_reg_block;
+                            dimK_reg_block++) {
+                        int srcB_offset = dimK_reg_block * jcp.dimK_4fma
+                                * jcp.dimN_reg_block;
+                        for (int dimK_4fma = 0; dimK_4fma < jcp.dimK_4fma;
+                                dimK_4fma += inc_fma) {
+                            int current = next;
+                            if (jcp.double_buffering) {
+                                next = (dimK_reg_block * jcp.dimK_4fma
+                                               + dimK_4fma + inc_fma)
+                                        % (2 * inc_fma);
+                                load_A(next, dimK_reg_block * jcp.dimK_4fma
+                                                + dimK_4fma + inc_fma);
+                            } else {
+                                next = 0;
+                                load_A(next, dimK_reg_block * jcp.dimK_4fma
+                                                + dimK_4fma);
+                            }
+                            for (int dimN_reg_block = 0;
+                                    dimN_reg_block < jcp.dimN_reg_block;
+                                    ++dimN_reg_block) {
+                                L1_pf.prefetch(srcB_offset / inc_fma
+                                        + dimK_4fma / inc_fma
+                                                * jcp.dimN_reg_block
+                                        + dimN_reg_block);
+                                L2_pf.prefetch(srcB_offset / inc_fma
+                                        + dimK_4fma / inc_fma
+                                                * jcp.dimN_reg_block
+                                        + dimN_reg_block);
+                                if (jcp.ver == ver_4fma) {
+                                    int srcB_trans_offset = (dimK_4fma / 4) * 64
+                                            + dimK_4fma % 4;
+                                    v4fmaddps(
+                                            Zmm(jcp.zmm_start + dimN_reg_block),
+                                            Zmm(current),
+                                            EVEX_compress_addr(reg_srcB,
+                                                    sizeof(float) * (
+                                                        srcB_offset +
+                                                        srcB_trans_offset +
+                                                        (dimN_reg_block % 4) * 16 +
+                                                        (dimN_reg_block / 4) * 4)));
+                                } else {
+                                    vfmadd231ps(
+                                            Zmm(jcp.zmm_start + dimN_reg_block),
+                                            Zmm(current),
+                                            EVEX_compress_addr(reg_srcB,
+                                                sizeof(float) * (srcB_offset + dimN_reg_block),
+                                                    true));
+                                }
+                            }
+                        }
+                    }
+                }
+
+                add(reg_srcA, jcp.dimK_reg_block * jcp.dimK_4fma
+                                * jcp.dimM_simd_block * sizeof(float));
+                add(reg_srcB, jcp.dimK_reg_block * jcp.dimN_reg_block
+                                * jcp.dimK_4fma * sizeof(float));
+                if (jcp.dimK_block > 1) {
+                    sub(reg_dimK_block_loop_cnt, 1);
+                    jnz(dimK_block_loop);
+                }
+
+                /******** Write C back to memory *******/
+                for (int dimN_reg_block = 0;
+                        dimN_reg_block < jcp.dimN_reg_block; ++dimN_reg_block) {
+                    Zmm zmm(jcp.zmm_start + dimN_reg_block);
+                    vmovups(zword[reg_dstC +
+                            dimN_reg_block * jcp.dimM_simd_block * sizeof(float)],
+                            zmm);
+                }
+
+                sub(reg_srcA, jcp.dimK_block * jcp.dimK_reg_block *
+                        jcp.dimK_4fma * jcp.dimM_simd_block * sizeof(float));
+                add(reg_dstC, jcp.dimN_reg_block * jcp.dimM_simd_block
+                        * sizeof(float));
+                if (jcp.dimN_block > 1) {
+                    sub(reg_dimN_block_loop_cnt, 1);
+                    jnz(dimN_block_loop);
+                }
+            }
+
+            if (jcp.dimM_block > 1) {
+                sub(reg_srcB, jcp.dimN_block * jcp.dimK_block
+                                * jcp.dimK_reg_block * jcp.dimN_reg_block
+                                * jcp.dimK_4fma * sizeof(float));
+                add(reg_srcA, jcp.dimK_block * jcp.dimK_reg_block
+                                * jcp.dimK_4fma * jcp.dimM_simd_block * sizeof(float));
+                sub(reg_dimM_block_loop_cnt, 1);
+                jnz(dimM_block_loop);
+            }
+        }
+    };
+
+    /* Preamble */
+    // register used to handle long fma encoding
+    preamble();
+    mov(reg_srcA, reg_srcA_const);
+    inner_loops();
+
+    /* Postamble */
+    postamble();
+    ret();
+}
+
+namespace {
+bool check_cond1_wu(int dimM_block, int dimM_simdw, int dimK_block,
+        int dimK_reg_block, int dimK_4fma, int dimN_reg_block, float C)
+{
+    float lhs = 1.0f * dimM_block * dimN_reg_block * dimM_simdw;
+    lhs += dimM_block * dimK_block * dimK_reg_block * dimK_4fma * dimM_simdw;
+    lhs += dimK_block * dimN_reg_block * dimK_reg_block * dimK_4fma;
+    lhs *= sizeof(float);
+    float rhs = C * L1_cache_size;
+    return (lhs <= rhs);
+}
+
+bool check_cond1bis_wu(int dimM_block, int dimM_simdw, int dimK_block,
+        int dimK_reg_block, int dimK_4fma, int dimN_reg_block, float C)
+{
+    float lhs = 1.0f * dimM_block * dimK_block * dimK_reg_block * dimK_4fma
+            * dimM_simdw;
+    lhs += dimK_block * dimN_reg_block * dimK_reg_block * dimK_4fma;
+    lhs *= sizeof(float);
+    float rhs = C * L1_cache_size;
+    return (lhs <= rhs);
+}
+
+bool check_cond2bis_wu(int dimM_block, int dimM_simdw, int dimK_block,
+        int dimK_reg_block, int dimK_4fma, int dimN_block, int dimN_reg_block,
+        float C)
+{
+    float lhs = 1.0f * dimM_block * dimM_simdw * dimK_block * dimK_reg_block
+            * dimK_4fma;
+    lhs += dimK_block * dimK_reg_block * dimK_4fma * dimN_block
+            * dimN_reg_block;
+    lhs *= sizeof(float);
+    float rhs = C * L2_cache_size;
+    return (lhs <= rhs);
+}
+
+bool check_cond2_wu(int dimM_block, int dimM_simdw, int dimK_block,
+        int dimK_reg_block, int dimK_4fma, int dimN_block, int dimN_reg_block,
+        float C)
+{
+    float lhs = 1.0f * dimM_block * dimM_simdw * dimN_block * dimN_reg_block;
+    lhs += dimM_block * dimM_simdw * dimK_block * dimK_reg_block * dimK_4fma;
+    lhs += dimK_block * dimK_reg_block * dimK_4fma * dimN_block
+            * dimN_reg_block;
+    lhs *= sizeof(float);
+    float rhs = C * L2_cache_size;
+    return (lhs <= rhs);
+}
+} // namespace
+
+status_t set_wsched_WEI_S_D_G_W_avx512_common(jit_conv_winograd_conf_t &jcp)
+{
+    /*************** Choose dimN_reg_block (ic_simd_block)
+     * *******************************/
+    jcp.dimN = jcp.ic;
+    /*Hardcoded to 16 because N = ic for bwd weights and
+     innermost dimension for ic is assumed 16 in src transforms. This
+     choice covers load latencies while maintaining simplicity of kernel
+     for POR topologies. FIXME in future??: Will not work for future topologies
+     when ic%16 != 0*/
+    jcp.dimN_reg_block = jcp.ic_simd_block;
+
+    /****************************** Choose dimK_block
+     * **************************/
+    // No freedom for choosing dimM_simd_block because ic_simd_block
+    // is determined by input data format
+    jcp.dimM_simd_block = jcp.oc_simd_block;
+
+    auto test_cond1bis_dimK_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimK_block, int current_best) {
+        return check_cond1bis_wu(1, jcp.dimM_simd_block, dimK_block, 1,
+                       jcp.dimK_4fma, jcp.dimN_reg_block, 0.4f)
+                && (dimK_block > current_best);
+    };
+
+    auto test_cond1_dimK_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimK_block, int current_best) {
+        return check_cond1_wu(1, jcp.dimM_simd_block, dimK_block, 1,
+                       jcp.dimK_4fma, jcp.dimN_reg_block, 0.4f)
+                && (dimK_block > current_best);
+    };
+
+    auto test_cond2bis_dimK_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimK_block, int current_best) {
+        return check_cond2bis_wu(1, jcp.dimM_simd_block, dimK_block, 1,
+                       jcp.dimK_4fma, 1, jcp.dimN_reg_block, 0.5f)
+                && (dimK_block > current_best);
+    };
+
+    auto test_cond2_dimK_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimK_block, int current_best) {
+        return check_cond2_wu(1, jcp.dimM_simd_block, dimK_block, 1,
+                       jcp.dimK_4fma, 1, jcp.dimN_reg_block, 0.1f)
+                && (dimK_block > current_best);
+    };
+
+    jcp.dimK_block = get_divisor_satisfying_cond(
+            jcp, jcp.dimK / jcp.dimK_4fma, 1, test_cond2bis_dimK_block);
+    if (jcp.dimK_block < jcp.dimK / jcp.dimK_4fma)
+        jcp.dimK_block = get_divisor_satisfying_cond(
+                jcp, jcp.dimK / jcp.dimK_4fma, 1, test_cond2_dimK_block);
+
+    jcp.dimK_reg_block = get_divisor_satisfying_cond(
+            jcp, jcp.dimK_block, 1, test_cond1bis_dimK_block);
+    if (jcp.dimK_reg_block < jcp.dimK_block) {
+        jcp.dimK_reg_block = get_divisor_satisfying_cond(
+                jcp, jcp.dimK_block, 1, test_cond1_dimK_block);
+    }
+    jcp.dimK_block /= jcp.dimK_reg_block;
+    jcp.dimK_nb_block
+            = jcp.dimK / jcp.dimK_4fma / jcp.dimK_reg_block / jcp.dimK_block;
+    jcp.tile_block_ur = jcp.dimK_reg_block;
+    jcp.nb_tile_block_ur = jcp.dimK_block;
+    jcp.tile_block = jcp.dimK_nb_block;
+
+    /***************************** Chose dimN block
+     * ****************************/
+    auto test_cond2_dimN_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimN_block, int current_best) {
+        return check_cond2_wu(1, jcp.dimM_simd_block, jcp.dimK_block,
+                       jcp.dimK_reg_block, jcp.dimK_4fma, dimN_block,
+                       jcp.dimN_reg_block, 0.5f)
+                && (dimN_block > current_best);
+    };
+
+    jcp.dimN_block = get_divisor_satisfying_cond(
+            jcp, jcp.dimN / jcp.dimN_reg_block, 1, test_cond2_dimN_block);
+    jcp.ic_block = jcp.dimN_block;
+    jcp.dimN_nb_block = jcp.dimN / jcp.dimN_reg_block / jcp.dimN_block;
+    jcp.nb_ic = jcp.dimN_nb_block;
+
+    /********************************* Choose dimM block
+     * ************************/
+    jcp.dimM = jcp.oc;
+
+    auto test_cond1_dimM_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimM_block, int current_best) {
+        return check_cond1_wu(dimM_block, jcp.dimM_simd_block, 1,
+                       jcp.dimK_reg_block, jcp.dimK_4fma, jcp.dimN_reg_block,
+                       1.0f)
+                && (dimM_block > current_best)
+                && (jcp.dimM / jcp.dimM_simd_block / dimM_block) >= 2;
+    };
+
+    jcp.dimM_block = get_divisor_satisfying_cond(
+            jcp, jcp.dimM / jcp.dimM_simd_block, 1, test_cond1_dimM_block);
+    jcp.dimM_nb_block = (jcp.dimM / jcp.dimM_simd_block) / jcp.dimM_block;
+
+    jcp.sched_policy = WSCHED_WEI_S_D_G_W;
+    return status::success;
+}
+
+status_t jit_avx512_common_conv_winograd_bwd_weights_kernel_f32::init_conf(
+        jit_conv_winograd_conf_t &jcp, const convolution_desc_t &cd,
+        const memory_desc_wrapper &src_d, const memory_desc_wrapper &diff_dst_d,
+        const memory_desc_wrapper &diff_weights_d)
+{
+    jcp.nthr = mkldnn_get_max_threads();
+
+    const bool with_groups = diff_weights_d.ndims() == src_d.ndims() + 1;
+
+    jcp.ngroups = with_groups ? diff_weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+    jcp.oc = diff_dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+    jcp.ih = src_d.dims()[2];
+    jcp.iw = src_d.dims()[3];
+    jcp.oh = diff_dst_d.dims()[2];
+    jcp.ow = diff_dst_d.dims()[3];
+    jcp.kh = diff_weights_d.dims()[with_groups + 2];
+    jcp.kw = diff_weights_d.dims()[with_groups + 3];
+    jcp.t_pad = cd.padding[0][0];
+    jcp.l_pad = cd.padding[0][1];
+    jcp.stride_h = cd.strides[0];
+    jcp.stride_w = cd.strides[1];
+    jcp.r_pad = nstl::max(
+            0, (jcp.ow - 1) * jcp.stride_w + jcp.kw - jcp.iw - jcp.l_pad);
+    jcp.b_pad = nstl::max(
+            0, (jcp.oh - 1) * jcp.stride_h + jcp.kh - jcp.ih - jcp.t_pad);
+    jcp.ihp = jcp.ih + jcp.t_pad + jcp.b_pad;
+    jcp.iwp = jcp.iw + jcp.l_pad + jcp.r_pad;
+    jcp.ohp = jcp.oh;
+    jcp.owp = jcp.ow;
+    jcp.with_bias = (cd.diff_bias_desc.format_kind != format_kind::undef);
+    jcp.dilate_h = cd.dilates[0];
+    jcp.dilate_w = cd.dilates[1];
+
+    bool ok_to_pad_channels = jcp.ngroups == 1;
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, simd_w);
+        jcp.ic = rnd_up(jcp.ic, simd_w);
+    }
+
+    if (mayiuse(avx512_core))
+        return status::unimplemented;
+    if (!mayiuse(avx512_common))
+        return status::unimplemented;
+    else if (mayiuse(avx512_mic_4ops))
+        jcp.ver = ver_4fma;
+    else
+        jcp.ver = ver_fma;
+
+    if (!IMPLICATION(cd.alg_kind == alg_kind::convolution_auto,
+                is_winograd_faster_than_direct(jcp)))
+        return status::unimplemented;
+    // Winograd specific initialization
+    jcp.itiles = (jcp.ow + tile_size - 1) / tile_size;
+    jcp.jtiles = (jcp.oh + tile_size - 1) / tile_size;
+    jcp.ntiles = jcp.mb * jcp.itiles * jcp.jtiles;
+
+    // Winograd kernel works only for 3x3 convolution with stride 1
+    if (jcp.ngroups != 1)
+        return status::unimplemented;
+    if ((jcp.kh != 3) || (jcp.kw != 3))
+        return status::unimplemented;
+    if ((jcp.dilate_h != 0) || (jcp.dilate_w != 0))
+        return status::unimplemented;
+    if ((jcp.stride_h != 1) || (jcp.stride_w != 1))
+        return status::unimplemented;
+    if ((jcp.ic % simd_w) != 0 || (jcp.oc % simd_w) != 0)
+        return status::unimplemented;
+
+    format_tag_t dat_tag = nChw16c;
+    format_tag_t wei_tag = with_groups ? gOIhw16i16o : OIhw16i16o;
+    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    jcp.wei_tag = diff_weights_d.matches_one_of_tag(wei_tag);
+    jcp.dst_tag = diff_dst_d.matches_one_of_tag(dat_tag);
+
+    if (jcp.src_tag != dat_tag) return status::unimplemented;
+    if (jcp.wei_tag != wei_tag) return status::unimplemented;
+    if (jcp.dst_tag != dat_tag) return status::unimplemented;
+
+    bool layout_consistency = true
+        && jcp.ic <= src_d.padded_dims()[1]
+        && jcp.oc <= diff_dst_d.padded_dims()[1]
+        && jcp.ic <= diff_weights_d.padded_dims()[with_groups + 1]
+        && jcp.oc <= diff_weights_d.padded_dims()[with_groups + 0];
+    if (!layout_consistency) return status::unimplemented;
+
+    /*************************** New Kernel Parameters
+     * *****************************/
+    jcp.ic_simd_block = simd_w;
+    jcp.oc_simd_block = simd_w;
+    jcp.dimK_4fma = 1;
+    jcp.tile_4fma_padding = 0;
+
+#define MAX_4FMA_UR 8
+    if (jcp.ver == ver_4fma) {
+        auto test_cond_4fma = [](jit_conv_winograd_conf_t &jcp, int dimK_4fma,
+                                      int current_best) {
+            return (dimK_4fma % 4 == 0) && (dimK_4fma <= MAX_4FMA_UR)
+                    && (dimK_4fma > current_best);
+        };
+        jcp.dimK_4fma = get_divisor_satisfying_cond(
+                jcp, jcp.itiles * jcp.jtiles, 4, test_cond_4fma);
+        if (jcp.dimK_4fma == 1)
+            jcp.dimK_4fma = 4;
+        if ((jcp.itiles * jcp.jtiles) % jcp.dimK_4fma != 0)
+            jcp.tile_4fma_padding = jcp.dimK_4fma
+                    - ((jcp.itiles * jcp.jtiles) % jcp.dimK_4fma);
+    }
+
+    jcp.tile_4fma = jcp.dimK_4fma;
+    /*NOTE: When (itiles * jtiles) % dimK_4fma != 0, transpose in diff_src
+     * transform
+     * will not work correctly, this is solved by applying padding.*/
+    jcp.dimK = jcp.mb * (jcp.itiles * jcp.jtiles + jcp.tile_4fma_padding);
+    jcp.dimN = jcp.ic;
+    jcp.dimM = jcp.oc;
+
+    jcp.double_buffering = true;
+    if (jcp.double_buffering)
+        jcp.zmm_start = jcp.ver == ver_4fma ? 8 : 2;
+    else
+        jcp.zmm_start = jcp.ver == ver_4fma ? 4 : 1;
+    jcp.nb_reg = 32 - jcp.zmm_start;
+
+    jcp.sched_policy = WSCHED_INVALID;
+    status_t res = set_wsched_WEI_S_D_G_W_avx512_common(jcp);
+    assert(jcp.sched_policy == WSCHED_WEI_S_D_G_W);
+
+    jcp.tile_block_ur = jcp.dimK_reg_block;
+    jcp.nb_tile_block_ur = jcp.dimK_block;
+    jcp.tile_block = jcp.dimK_nb_block;
+
+    jcp.ic_block = jcp.dimN_block;
+    jcp.nb_ic = jcp.dimN_nb_block;
+
+    jcp.oc_block = jcp.dimM_block;
+    jcp.nb_oc = jcp.dimM_nb_block;
+
+    return res;
+
+}
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_winograd_kernel_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_winograd_kernel_f32.hpp
new file mode 100644
index 0000000000..6c117143f5
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_conv_winograd_kernel_f32.hpp
@@ -0,0 +1,179 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_AVX512_COMMON_CONV_WINOGRAD_KERNEL_F32_HPP
+#define JIT_AVX512_COMMON_CONV_WINOGRAD_KERNEL_F32_HPP
+
+#include "c_types_map.hpp"
+#include "cpu_memory.hpp"
+
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+//alpha determines the output tile_size
+constexpr int alpha = 6;
+constexpr int tile_size = 4;
+//simd length used for vectorization
+constexpr int simd_w = 16;
+
+struct _jit_avx512_common_conv_winograd_data_kernel_f32 : public jit_generator {
+    _jit_avx512_common_conv_winograd_data_kernel_f32(
+            jit_conv_winograd_conf_t ajcp)
+        : jcp(ajcp)
+    {
+        //******************* First iter kernel ********************//
+        this->gemm_loop_generate(true);
+        gemm_loop_ker_first_iter
+                = (decltype(gemm_loop_ker_first_iter)) this->getCode();
+
+        //************** Subsequent iterations kernel **************//
+        if (jcp.dimK_nb_block > 1) {
+            align();
+            const Xbyak::uint8 *addr = getCurr();
+            this->gemm_loop_generate(false);
+            gemm_loop_ker = (decltype(gemm_loop_ker))addr;
+        }
+    }
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(_jit_avx512_common_conv_winograd_data_kernel_f32)
+
+    static status_t init_conf_common(jit_conv_winograd_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &dst_d);
+
+    static status_t init_conf_kernel(
+            jit_conv_winograd_conf_t &jcp, int dimM, int dimN, int dimK);
+
+    jit_conv_winograd_conf_t jcp;
+    void (*gemm_loop_ker)(float *, const float *, const float *);
+    void (*gemm_loop_ker_first_iter)(float *, const float *, const float *);
+
+protected:
+    using reg64_t = const Xbyak::Reg64;
+    enum { typesize = sizeof(float) };
+
+    void gemm_loop_generate(bool is_beta_zero);
+
+    /* registers used for GEMM */
+    reg64_t reg_dstC = abi_param1;
+    reg64_t reg_srcA = abi_param2;
+    reg64_t reg_srcB = abi_param3;
+
+    reg64_t reg_dimM_block_loop_cnt = r10;
+    reg64_t reg_dimK_block_loop_cnt = r11;
+};
+
+struct jit_avx512_common_conv_winograd_fwd_kernel_f32
+        : _jit_avx512_common_conv_winograd_data_kernel_f32 {
+    using _jit_avx512_common_conv_winograd_data_kernel_f32::
+            _jit_avx512_common_conv_winograd_data_kernel_f32;
+
+    static bool post_ops_ok(jit_conv_conf_t &jcp, const primitive_attr_t &attr);
+
+    static status_t init_conf(jit_conv_winograd_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &dst_d, const primitive_attr_t &attr);
+};
+
+struct jit_avx512_common_conv_winograd_bwd_data_kernel_f32
+        : public _jit_avx512_common_conv_winograd_data_kernel_f32 {
+    using _jit_avx512_common_conv_winograd_data_kernel_f32::
+            _jit_avx512_common_conv_winograd_data_kernel_f32;
+
+    static status_t init_conf(jit_conv_winograd_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &diff_src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &diff_dst_d);
+};
+
+struct jit_avx512_common_conv_winograd_bwd_weights_kernel_f32
+        : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(_jit_avx512_common_conv_winograd_bwd_weights_kernel_f32)
+
+    jit_avx512_common_conv_winograd_bwd_weights_kernel_f32(
+            jit_conv_winograd_conf_t ajcp)
+        : jcp(ajcp)
+    {
+
+        //******************* First iter kernel ********************//
+        {
+            align();
+            const Xbyak::uint8 *addr = getCurr();
+            this->gemm_loop_generate(true);
+            gemm_loop_ker_first_iter = (decltype(gemm_loop_ker_first_iter))addr;
+        }
+
+        if (jcp.tile_block > 1) {
+            align();
+            const Xbyak::uint8 *addr = getCurr();
+            this->gemm_loop_generate(false);
+            gemm_loop_ker = (decltype(gemm_loop_ker))addr;
+        }
+
+        if (jcp.ver == ver_4fma) {
+            align();
+            const Xbyak::uint8 *addr = getCurr();
+            this->transpose_ker_generate();
+            transpose_4fma_ker = (decltype(transpose_4fma_ker))addr;
+        }
+    }
+
+    static status_t init_conf(jit_conv_winograd_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &diff_dst_d,
+            const memory_desc_wrapper &diff_weights_d);
+
+    jit_conv_winograd_conf_t jcp;
+    void (*gemm_loop_ker)(float *, const float *, const float *);
+    void (*gemm_loop_ker_first_iter)(float *, const float *, const float *);
+    void (*transpose_4fma_ker)(float *, float *);
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    enum { typesize = sizeof(float) };
+
+    void gemm_loop_generate(bool is_first_tile);
+    void transpose_ker_generate();
+
+    reg64_t reg_origB = abi_param2;
+    reg64_t reg_transB = abi_param1;
+
+    reg64_t reg_dstC = abi_param1;
+    reg64_t reg_srcA_const = abi_param2;
+    reg64_t reg_srcB = abi_param3;
+
+    reg64_t reg_sp = rsp;
+    reg64_t reg_srcA = r9;
+    reg64_t reg_nb_ic = r10;
+    reg64_t reg_loop_cpt = r11;
+    reg64_t reg_transB_idx = r13;
+
+    /* Registers used by new kernel */
+    reg64_t reg_dimM_block_loop_cnt = r10;
+    reg64_t reg_dimK_block_loop_cnt = r12;
+    reg64_t reg_dimN_block_loop_cnt = r11;
+};
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution.cpp
new file mode 100644
index 0000000000..abddc19221
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution.cpp
@@ -0,0 +1,1526 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_avx512_common_convolution.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+
+using namespace nstl;
+
+using jit_conv_ker_t = void (*)(jit_conv_call_s *);
+
+#define PIPELINE(field) \
+    do { \
+        p.field = p.field ## _prf; \
+        p.field ## _prf = field; \
+    } while (0)
+
+inline void jit_conv_ker_pipeline(jit_conv_ker_t ker, jit_conv_call_s &p,
+        const void *src, const void *dst, const void *filt, const void *bias,
+        int channel, int kh_padding)
+{
+    PIPELINE(src);
+    PIPELINE(dst);
+    PIPELINE(filt);
+    PIPELINE(bias);
+    PIPELINE(channel);
+    PIPELINE(kh_padding);
+
+    if (p.src)
+        ker(&p);
+}
+// The special case for the driver with ow-parallelization (FWD)
+// TODO: implement it for BWD_D and BWD_W too
+inline void jit_conv_ker_pipeline_ow_thr(jit_conv_ker_t ker, jit_conv_call_s &p,
+        const void *src, const void *dst, const void *filt, const void *bias,
+        int channel, int kh_padding, int owb)
+{
+    PIPELINE(src);
+    PIPELINE(dst);
+    PIPELINE(filt);
+    PIPELINE(bias);
+    PIPELINE(channel);
+    PIPELINE(kh_padding);
+    PIPELINE(owb);
+
+    if (p.src)
+        ker(&p);
+}
+
+inline void jit_conv_3d_ker_pipeline(jit_conv_ker_t ker, jit_conv_call_s &p,
+        const void *src, const void *dst, const void *filt, const void *bias,
+        int channel, int kh_padding, int kd_padding)
+{
+    PIPELINE(src);
+    PIPELINE(dst);
+    PIPELINE(filt);
+    PIPELINE(bias);
+    PIPELINE(channel);
+    PIPELINE(kh_padding);
+    PIPELINE(kd_padding);
+
+    if (p.src)
+        ker(&p);
+}
+// The special case for the driver with ow-parallelization (FWD)
+// TODO: implement it for BWD_D and BWD_W too
+inline void jit_conv_3d_ker_pipeline_ow_thr(jit_conv_ker_t ker,
+        jit_conv_call_s &p, const void *src, const void *dst, const void *filt,
+        const void *bias, int channel, int kh_padding, int kd_padding, int owb)
+{
+    PIPELINE(src);
+    PIPELINE(dst);
+    PIPELINE(filt);
+    PIPELINE(bias);
+    PIPELINE(channel);
+    PIPELINE(kh_padding);
+    PIPELINE(kd_padding);
+    PIPELINE(owb);
+
+    if (p.src)
+        ker(&p);
+}
+
+void jit_conv_3d_ker_bwd_w_pipeline(jit_conv_ker_t ker, jit_conv_call_s &p,
+        const void *src, const void *dst, const void *filt, const void *bias,
+        int channel, int d_index, int d_worksize,
+        int kd_padding /* kd_work_size */, size_t kd_offset) {
+    PIPELINE(src);
+    PIPELINE(dst);
+    PIPELINE(filt);
+    PIPELINE(bias);
+    PIPELINE(channel);
+    PIPELINE(kd_padding);
+    PIPELINE(d_worksize);
+    PIPELINE(d_index);
+    PIPELINE(kd_offset);
+
+    if (p.src)
+        ker(&p);
+}
+#define wht_blk_off(d, g, ...) \
+        (pd()->with_groups() \
+         ? (d).blk_off((g), __VA_ARGS__) \
+         : (d).blk_off(__VA_ARGS__))
+
+template <data_type_t src_type, data_type_t wei_type, data_type_t dst_type>
+void jit_avx512_common_convolution_fwd_t<src_type, wei_type,
+        dst_type>::prepare_padded_bias(const dst_data_t *&bias,
+        const memory_tracking::grantor_t &scratchpad) const {
+    if (!pd()->wants_padded_bias()) return;
+
+    auto padded_bias = scratchpad.template get<dst_data_t>(
+            key_conv_padded_bias);
+    utils::array_copy(padded_bias, bias, pd()->jcp_.oc_without_padding);
+    utils::array_set(padded_bias + pd()->jcp_.oc_without_padding,
+            (dst_data_t)0, pd()->jcp_.oc - pd()->jcp_.oc_without_padding);
+    bias = padded_bias;
+}
+
+template <data_type_t src_type, data_type_t wei_type,
+          data_type_t dst_type>
+void jit_avx512_common_convolution_fwd_t<src_type, wei_type, dst_type>::
+execute_forward_1d(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const dst_data_t *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    prepare_padded_bias(bias, this->scratchpad(ctx));
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+
+    const auto &jcp = pd()->jcp_;
+    assert(jcp.nb_oc % jcp.nb_oc_blocking == 0);
+
+    int oc_chunks = jcp.nb_oc / jcp.nb_oc_blocking;
+    int work_amount = jcp.mb * jcp.ngroups * oc_chunks * jcp.nb_ow;
+
+    int nthr;
+    if (jcp.aligned_threads)
+        nthr = jcp.aligned_threads;
+    else
+        nthr = mkldnn_get_max_threads();
+
+    parallel(nthr, [&](const int ithr, const int nthr) {
+        int start{0}, end{0}, start_copy;
+        balance211(work_amount, nthr, ithr, start, end);
+        start_copy = start;
+
+        auto par_conv = jit_conv_call_s();
+        size_t src_c_stride = src_d.blk_off(0, 1);
+        size_t wht_ic_stride = wht_blk_off(weights_d, 0, 0, 1);
+
+        for (int icb_l2 = 0 ; icb_l2 < jcp.nb_ic; icb_l2 += jcp.nb_ic_L2) {
+            start = start_copy;
+            int n{0}, g{0}, occ{0}, owb{0};
+
+            if (jcp.loop_order == loop_cwgn) {
+                int dummy{0};
+                nd_iterator_init(start, occ, oc_chunks, owb, jcp.nb_ow,
+                        g, jcp.ngroups, n, jcp.mb, dummy, 1);
+            } else if (jcp.loop_order == loop_gncw) {
+                int dummy{0};
+                nd_iterator_init(start, g, jcp.ngroups, n, jcp.mb, occ,
+                        oc_chunks, owb, jcp.nb_ow, dummy, 1);
+            } else {
+                assert(!"unsupported loop order");
+            }
+
+            while (start < end) {
+                int ocb = occ * jcp.nb_oc_blocking;
+                int g_ocb = g * jcp.nb_oc + ocb;
+                int g_oc = g_ocb * jcp.oc_block;
+                int g_icb = g * jcp.nb_ic * jcp.nonblk_group_off;
+
+                int ow_s =  owb * jcp.ow_block;
+                int iw_s =  ow_s * jcp.stride_w;
+                auto bias_w = bias ? bias + g_oc : nullptr;
+                auto dst_w = dst + dst_d.blk_off(n, g_ocb, ow_s);
+                auto src_w = src + src_d.blk_off(n, g_icb + icb_l2, iw_s);
+                auto wht_w = weights + wht_blk_off(weights_d, g, ocb, icb_l2);
+
+                for (int icb = icb_l2;
+                     icb < min(jcp.nb_ic, icb_l2 + jcp.nb_ic_L2); ++icb) {
+                     jit_conv_ker_pipeline_ow_thr(kernel_->jit_ker, par_conv,
+                        src_w, dst_w, wht_w, bias_w, icb, 1, owb);
+
+                    src_w += src_c_stride;
+                    wht_w += wht_ic_stride;
+                }
+                if (jcp.loop_order == loop_cwgn) {
+                    int dummy{0};
+                    nd_iterator_jump(start, end, occ, oc_chunks, owb, jcp.nb_ow,
+                            g, jcp.ngroups, n, jcp.mb, dummy, 1);
+                } else if (jcp.loop_order == loop_gncw) {
+                    int dummy{0};
+                    nd_iterator_jump(start, end, g, jcp.ngroups, n, jcp.mb,
+                            occ, oc_chunks, owb, jcp.nb_ow, dummy, 1);
+                } else {
+                    assert(!"unsupported loop order");
+                }
+            }
+        }
+        jit_conv_ker_pipeline_ow_thr(kernel_->jit_ker, par_conv,
+                src, dst, weights, bias, 0, 0, 0);
+    });
+}
+
+template <data_type_t src_type, data_type_t wei_type,
+          data_type_t dst_type>
+void jit_avx512_common_convolution_fwd_t<src_type, wei_type, dst_type>::
+execute_forward_2d(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const dst_data_t *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    prepare_padded_bias(bias, this->scratchpad(ctx));
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+
+    const auto &jcp = pd()->jcp_;
+    assert(jcp.nb_oc % jcp.nb_oc_blocking == 0);
+
+    int oc_chunks = jcp.nb_oc / jcp.nb_oc_blocking;
+    int work_amount = jcp.mb * jcp.ngroups * oc_chunks * jcp.oh * jcp.nb_ow;
+
+    int nthr;
+    if (jcp.aligned_threads)
+        nthr = jcp.aligned_threads;
+    else
+        nthr = mkldnn_get_max_threads();
+
+    parallel(nthr, [&](const int ithr, const int nthr) {
+        int start{0}, end{0}, start_copy;
+        balance211(work_amount, nthr, ithr, start, end);
+        start_copy = start;
+
+        auto par_conv = jit_conv_call_s();
+        size_t src_h_stride = src_d.blk_off(0, 0, 1);
+        size_t src_c_stride = src_d.blk_off(0, 1);
+        size_t dst_h_stride = dst_d.blk_off(0, 0, 1);
+        size_t wht_h_stride = wht_blk_off(weights_d, 0, 0, 0, 1);
+        size_t wht_ic_stride = wht_blk_off(weights_d, 0, 0, 1);
+
+        for (int icb_l2 = 0 ; icb_l2 < jcp.nb_ic; icb_l2 += jcp.nb_ic_L2) {
+            start = start_copy;
+            int n{0}, g{0}, occ{0}, oh_s{0}, owb{0};
+
+            if (jcp.loop_order == loop_cwgn)
+                nd_iterator_init(start, occ, oc_chunks, owb, jcp.nb_ow,
+                    g, jcp.ngroups, n, jcp.mb, oh_s, jcp.oh);
+            else if (jcp.loop_order == loop_gncw)
+                nd_iterator_init(start, g, jcp.ngroups, n, jcp.mb,
+                    occ, oc_chunks, owb, jcp.nb_ow, oh_s, jcp.oh);
+            else
+                assert(!"unsupported loop order");
+
+            while (start < end) {
+                int ocb = occ * jcp.nb_oc_blocking;
+                int g_ocb = g * jcp.nb_oc + ocb;
+                int g_oc = g_ocb * jcp.oc_block;
+                int g_icb = g * jcp.nb_ic * jcp.nonblk_group_off;
+
+                int work_rem = end - start;
+
+                int ow_s =  owb * jcp.ow_block;
+                int iw_s =  ow_s * jcp.stride_w;
+                int oh_e = oh_s + work_rem > jcp.oh ? jcp.oh : oh_s + work_rem;
+                auto bias_w = bias ? bias + g_oc : nullptr;
+
+                for (int oh_b = oh_s; oh_b < oh_e; oh_b += jcp.h_blocking) {
+                    int ih_b = -jcp.t_pad + oh_b * jcp.stride_h;
+
+                    auto dst_w = dst + dst_d.blk_off(n, g_ocb, oh_b, ow_s);
+                    auto src_w
+                        = src + src_d.blk_off(n, g_icb + icb_l2, ih_b, iw_s);
+                    auto wht_w
+                            = weights + wht_blk_off(weights_d, g, ocb, icb_l2);
+
+                    for (int icb = icb_l2;
+                            icb < min(jcp.nb_ic, icb_l2 + jcp.nb_ic_L2);
+                            ++icb) {
+                        auto src_c = src_w;
+                        auto dst_c = dst_w;
+                        for (int oj = oh_b, ij = ih_b;
+                                oj < min(oh_e, oh_b + jcp.h_blocking);
+                                ++oj, ij += jcp.stride_h) {
+                            int dilate_h = jcp.dilate_h + 1;
+                            int i_t_overflow = div_up(max(0, -ij), dilate_h);
+                            int i_b_overflow = div_up(max(0, ij - jcp.ih
+                                + (jcp.kh - 1) * dilate_h + 1), dilate_h);
+                            int kh_padding = nstl::max(
+                                    0, jcp.kh - i_t_overflow - i_b_overflow);
+
+                            auto aux_src = src_c
+                                    + i_t_overflow * dilate_h * src_h_stride;
+                            auto aux_wht = wht_w + i_t_overflow * wht_h_stride;
+
+                            jit_conv_ker_pipeline_ow_thr(kernel_->jit_ker,
+                                par_conv, aux_src, dst_c, aux_wht, bias_w, icb,
+                                kh_padding, owb);
+
+                            src_c += src_h_stride * jcp.stride_h;
+                            dst_c += dst_h_stride;
+                        }
+                        src_w += src_c_stride;
+                        wht_w += wht_ic_stride;
+                    }
+                }
+
+                if (jcp.loop_order == loop_cwgn)
+                    nd_iterator_jump(start, end, occ, oc_chunks, owb, jcp.nb_ow,
+                        g, jcp.ngroups, n, jcp.mb, oh_s, jcp.oh);
+                else if (jcp.loop_order == loop_gncw)
+                    nd_iterator_jump(start, end, g, jcp.ngroups, n, jcp.mb, occ,
+                        oc_chunks, owb, jcp.nb_ow, oh_s, jcp.oh);
+                else
+                    assert(!"unsupported loop order");
+            }
+        }
+
+        jit_conv_ker_pipeline_ow_thr(kernel_->jit_ker, par_conv,
+                src, dst, weights, bias, 0, 0, 0);
+    });
+}
+
+template <data_type_t src_type, data_type_t wei_type,
+          data_type_t dst_type>
+void jit_avx512_common_convolution_fwd_t<src_type, wei_type, dst_type>::
+execute_forward_3d(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const dst_data_t *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    prepare_padded_bias(bias, this->scratchpad(ctx));
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    const auto &jcp = pd()->jcp_;
+    assert(jcp.nb_oc % jcp.nb_oc_blocking == 0);
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        int oc_chunks = jcp.nb_oc / jcp.nb_oc_blocking;
+        int start{0}, end{0}, start_copy;
+        int work_amount = jcp.mb * jcp.ngroups * oc_chunks * jcp.od * jcp.oh
+            * jcp.nb_ow;
+        balance211(work_amount, nthr, ithr, start, end);
+        start_copy = start;
+
+        auto par_conv = jit_conv_call_s();
+        size_t src_d_stride = src_d.blk_off(0, 0, 1);
+        size_t src_h_stride = src_d.blk_off(0, 0, 0, 1);
+        size_t src_c_stride = src_d.blk_off(0, 1);
+        size_t dst_h_stride = dst_d.blk_off(0, 0, 0, 1);
+        size_t wht_d_stride = wht_blk_off(weights_d, 0, 0, 0, 1);
+        size_t wht_h_stride = wht_blk_off(weights_d, 0, 0, 0, 0, 1);
+        size_t wht_ic_stride = wht_blk_off(weights_d, 0, 0, 1);
+
+        for (int icb_l2 = 0 ; icb_l2 < jcp.nb_ic; icb_l2 += jcp.nb_ic_L2) {
+            start = start_copy;
+            int n{0}, g{0}, occ{0}, oh_s{0}, od_s{0}, owb{0};
+
+            if (jcp.loop_order == loop_cwgn)
+                nd_iterator_init(start,
+                    occ, oc_chunks, owb, jcp.nb_ow, g, jcp.ngroups, n, jcp.mb,
+                    od_s, jcp.od, oh_s, jcp.oh);
+            else if (jcp.loop_order == loop_gncw)
+                nd_iterator_init(start,
+                    g, jcp.ngroups, n, jcp.mb, occ, oc_chunks, owb, jcp.nb_ow,
+                    od_s, jcp.od, oh_s, jcp.oh);
+            else
+                assert(!"unsupported loop order");
+
+            while (start < end) {
+                int ocb = occ * jcp.nb_oc_blocking;
+                int g_ocb = g * jcp.nb_oc + ocb;
+                int g_oc = g_ocb * jcp.oc_block;
+                int g_icb = g * jcp.nb_ic * jcp.nonblk_group_off;
+
+                int work_rem = end - start;
+                int ih_s = -jcp.t_pad + oh_s * jcp.stride_h;
+                int ow_s =  owb * jcp.ow_block;
+                int iw_s =  ow_s * jcp.stride_w;
+                int oh_e = oh_s + work_rem > jcp.oh ? jcp.oh : oh_s + work_rem;
+
+                int id_s = -jcp.f_pad + od_s * jcp.stride_d;
+
+                int dilate_d = jcp.dilate_d + 1;
+                int d_t_overflow = div_up(max(0, -id_s), dilate_d);
+                int d_b_overflow = div_up(
+                        max(0, id_s - jcp.id + (jcp.kd - 1) * dilate_d + 1),
+                        dilate_d);
+                int kd_padding = nstl::max(0,
+                    jcp.kd - d_t_overflow - d_b_overflow);
+
+                auto bias_w = bias ? bias + bias_d.blk_off(g_oc) : 0;
+                auto dst_w = dst + dst_d.blk_off(n, g_ocb, od_s, oh_s, ow_s);
+                auto src_w = src + src_d.blk_off(n, g_icb + icb_l2, id_s, ih_s,
+                    iw_s) + d_t_overflow * dilate_d * src_d_stride;
+                auto wht_w = weights + wht_blk_off(weights_d, g, ocb, icb_l2)
+                    + d_t_overflow * wht_d_stride;
+
+                for (int icb = icb_l2;
+                     icb < min(jcp.nb_ic, icb_l2 + jcp.nb_ic_L2); ++icb) {
+                    auto src_c = src_w;
+                    auto dst_c = dst_w;
+                    for (int oj = oh_s, ij = ih_s;
+                            oj < oh_e; ++oj, ij += jcp.stride_h)
+                    {
+                        int dilate_h = jcp.dilate_h + 1;
+                        int i_t_overflow = div_up(max(0, -ij), dilate_h);
+                        int i_b_overflow = div_up(
+                                max(0, ij - jcp.ih + (jcp.kh - 1) * dilate_h
+                                                + 1),
+                                dilate_h);
+                        int kh_padding = nstl::max(0,
+                            jcp.kh - i_t_overflow - i_b_overflow);
+                        jit_conv_3d_ker_pipeline_ow_thr(kernel_->jit_ker,
+                            par_conv,
+                            src_c + i_t_overflow * dilate_h * src_h_stride,
+                            dst_c, wht_w + i_t_overflow * wht_h_stride,
+                            bias_w, icb, kh_padding, kd_padding, owb);
+
+                        src_c += src_h_stride * jcp.stride_h;
+                        dst_c += dst_h_stride;
+                    }
+                    src_w += src_c_stride;
+                    wht_w += wht_ic_stride;
+                }
+
+                if (jcp.loop_order == loop_cwgn)
+                    nd_iterator_jump(start, end,
+                      occ, oc_chunks, owb, jcp.nb_ow, g, jcp.ngroups, n, jcp.mb,
+                      od_s, jcp.od, oh_s, jcp.oh);
+                else if (jcp.loop_order == loop_gncw)
+                    nd_iterator_jump(start, end,
+                      g, jcp.ngroups, n, jcp.mb, occ, oc_chunks, owb, jcp.nb_ow,
+                      od_s, jcp.od, oh_s, jcp.oh);
+                else
+                    assert(!"unsupported loop order");
+            }
+        }
+        jit_conv_3d_ker_pipeline(kernel_->jit_ker, par_conv,
+                src, dst, weights, bias, 0, 0, 0);
+    });
+}
+
+template struct jit_avx512_common_convolution_fwd_t<data_type::f32>;
+
+template <data_type_t diff_dst_type, data_type_t wei_type,
+          data_type_t diff_src_type>
+void jit_avx512_common_convolution_bwd_data_t<diff_dst_type, wei_type,
+          diff_src_type>::execute_backward_data_1d(const exec_ctx_t &ctx) const
+{
+    auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto diff_src = CTX_OUT_MEM(diff_src_data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        int start{0}, end{0}, start_copy;
+        int ic_chunks = jcp.nb_ic / jcp.nb_ic_blocking;
+        int work_amount = jcp.ngroups * jcp.mb * ic_chunks * jcp.ih;
+        balance211(work_amount, nthr, ithr, start, end);
+        start_copy = start;
+
+        auto par_conv = jit_conv_call_s();
+        size_t diff_dst_c_stride = diff_dst_d.blk_off(0, 1);
+        size_t wht_oc_stride = wht_blk_off(weights_d, 0, 1);
+
+        for (int ocb_l2 = 0; ocb_l2 < jcp.nb_oc; ocb_l2 += jcp.nb_oc_L2) {
+            start = start_copy;
+            int n{0}, g{0}, icc{0};
+            if (jcp.loop_order == loop_cgn) {
+                int dummy{0};
+                nd_iterator_init(start, icc, ic_chunks, g, jcp.ngroups, n,
+                        jcp.mb, dummy, 1);
+            } else if (jcp.loop_order == loop_gnc) {
+                int dummy{0};
+                nd_iterator_init(start, g, jcp.ngroups, n, jcp.mb, icc,
+                        ic_chunks, dummy, 1);
+            } else {
+                assert(!"unsupported loop order");
+            }
+
+            while (start < end) {
+                int icb = icc * jcp.nb_ic_blocking;
+                int g_icb = g * jcp.nb_ic + icb;
+                int g_ocb = g * jcp.nb_oc;
+
+                auto diff_src_w = diff_src + diff_src_d.blk_off(n, g_icb);
+                auto diff_dst_w = diff_dst
+                    + diff_dst_d.blk_off(n, g_ocb + ocb_l2);
+                auto wht_w = weights + wht_blk_off(weights_d, g, ocb_l2, icb);
+
+                for (int ocb = ocb_l2;
+                      ocb < min(jcp.nb_oc, ocb_l2 + jcp.nb_oc_L2); ++ocb) {
+                    jit_conv_ker_pipeline(kernel_->jit_ker, par_conv,
+                            diff_src_w, diff_dst_w, wht_w, 0, ocb, 1);
+                    diff_dst_w += diff_dst_c_stride;
+                    wht_w += wht_oc_stride;
+                }
+
+                if (jcp.loop_order == loop_cgn) {
+                    int dummy{0};
+                    nd_iterator_jump(start, end, icc, ic_chunks, g, jcp.ngroups,
+                            n, jcp.mb, dummy, 1);
+                } else if (jcp.loop_order == loop_gnc) {
+                    int dummy{0};
+                    nd_iterator_jump(start, end, g, jcp.ngroups, n, jcp.mb, icc,
+                            ic_chunks, dummy, 1);
+                } else {
+                    assert(!"unsupported loop order");
+                }
+            }
+        }
+
+        jit_conv_ker_pipeline(kernel_->jit_ker, par_conv,
+                diff_src, diff_dst, weights, 0, 0, 1);
+    });
+}
+
+template <data_type_t diff_dst_type, data_type_t wei_type,
+          data_type_t diff_src_type>
+void jit_avx512_common_convolution_bwd_data_t<diff_dst_type, wei_type,
+          diff_src_type>::execute_backward_data_2d(const exec_ctx_t &ctx) const
+{
+    auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto diff_src = CTX_OUT_MEM(diff_src_data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        int start{0}, end{0}, start_copy;
+        int ic_chunks = jcp.nb_ic / jcp.nb_ic_blocking;
+        int work_amount = jcp.ngroups * jcp.mb * ic_chunks * jcp.ih;
+        balance211(work_amount, nthr, ithr, start, end);
+        start_copy = start;
+
+        auto par_conv = jit_conv_call_s();
+        size_t diff_src_h_stride = diff_src_d.blk_off(0, 0, 1);
+        size_t diff_dst_h_stride = diff_dst_d.blk_off(0, 0, 1);
+        size_t diff_dst_c_stride = diff_dst_d.blk_off(0, 1);
+        size_t wht_h_stride = wht_blk_off(weights_d, 0, 0, 0, 1);
+        size_t wht_oc_stride = wht_blk_off(weights_d, 0, 1);
+
+        bool is_fast_path = jcp.dilate_h == 0 && jcp.stride_h == 1;
+
+        for (int ocb_l2 = 0; ocb_l2 < jcp.nb_oc; ocb_l2 += jcp.nb_oc_L2) {
+            start = start_copy;
+            int n{0}, g{0}, icc{0}, ih_s{0};
+            if (jcp.loop_order == loop_cgn)
+                nd_iterator_init(start,
+                    icc, ic_chunks, g, jcp.ngroups, n, jcp.mb, ih_s, jcp.ih);
+            else if (jcp.loop_order == loop_gnc)
+                nd_iterator_init(start,
+                    g, jcp.ngroups, n, jcp.mb, icc, ic_chunks, ih_s, jcp.ih);
+            else
+                assert(!"unsupported loop order");
+
+            while (start < end) {
+                int icb = icc * jcp.nb_ic_blocking;
+                int g_icb = g * jcp.nb_ic + icb;
+                int g_ocb = g * jcp.nb_oc;
+
+                int work_rem = end - start;
+                int ih_e = ih_s + work_rem > jcp.ih ? jcp.ih : ih_s + work_rem;
+
+                auto diff_src_w = diff_src + diff_src_d.blk_off(n, g_icb);
+                auto diff_dst_w = diff_dst
+                    + diff_dst_d.blk_off(n, g_ocb + ocb_l2);
+                auto wht_w = weights + wht_blk_off(weights_d, g, ocb_l2, icb);
+
+                for (int ocb = ocb_l2;
+                      ocb < min(jcp.nb_oc, ocb_l2 + jcp.nb_oc_L2); ++ocb) {
+                    for (int ij = ih_s; ij < ih_e; ++ij) {
+                        int oj, k_len, k_lo;
+                        if (is_fast_path) { // dilate == 0 && stride == 1
+                            int i_t_overflow = max(0, jcp.kh - 1 - ij
+                                - jcp.t_pad);
+                            int i_b_overflow = max(0, jcp.kh - jcp.ih + ij
+                                - jcp.b_pad);
+                            k_len = jcp.kh - i_t_overflow - i_b_overflow;
+                            k_lo = i_b_overflow;
+                            oj = ij + jcp.t_pad - i_b_overflow;
+                        } else if (jcp.dilate_h != 0) { // stride == 1
+                            int dilate_h = jcp.dilate_h + 1;
+                            // Note: use div_up to account for "holes" in filter
+                            int i_t_overflow
+                                = div_up(max(0, (jcp.kh - 1) * dilate_h
+                                        - ij - jcp.t_pad), dilate_h);
+                            int i_b_overflow
+                                = div_up(max(0, (jcp.kh - 1) * dilate_h + 1
+                                        - jcp.ih + ij - jcp.b_pad), dilate_h);
+                            k_len = jcp.kh - i_t_overflow - i_b_overflow;
+                            k_lo = i_b_overflow;
+                            oj = ij + jcp.t_pad - i_b_overflow * dilate_h;
+                        } else { // dilate == 0
+                            int i_t_overflow = max(0, (jcp.kh - 1 - ij
+                                - jcp.t_pad) / jcp.stride_h);
+                            int i_b_overflow = max(0, (jcp.kh - jcp.ih + ij
+                                - jcp.b_pad) / jcp.stride_h);
+                            int overflow_kh_hi = jcp.kh - 1 - abs((jcp.ih - 1
+                                + jcp.b_pad - ij) % jcp.stride_h);
+                            int overflow_kh_lo = (ij + jcp.t_pad)
+                                % jcp.stride_h;
+
+                            k_len = (overflow_kh_hi - overflow_kh_lo)
+                                / jcp.stride_h + 1 - i_t_overflow
+                                - i_b_overflow;
+                            k_lo = overflow_kh_lo + i_b_overflow * jcp.stride_h;
+                            oj = (ij + jcp.t_pad - k_lo) / jcp.stride_h;
+                        }
+                        assert(k_len >= 0);
+
+                        jit_conv_ker_pipeline(kernel_->jit_ker, par_conv,
+                                diff_src_w + ij * diff_src_h_stride,
+                                diff_dst_w + oj * diff_dst_h_stride,
+                                wht_w + k_lo * wht_h_stride,
+                                0, ocb, k_len);
+                    }
+                    diff_dst_w += diff_dst_c_stride;
+                    wht_w += wht_oc_stride;
+                }
+
+                if (jcp.loop_order == loop_cgn)
+                    nd_iterator_jump(start, end,
+                      icc, ic_chunks, g, jcp.ngroups, n, jcp.mb, ih_s, jcp.ih);
+                else if (jcp.loop_order == loop_gnc)
+                    nd_iterator_jump(start, end,
+                      g, jcp.ngroups, n, jcp.mb, icc, ic_chunks, ih_s, jcp.ih);
+                else
+                    assert(!"unsupported loop order");
+            }
+        }
+
+        jit_conv_ker_pipeline(kernel_->jit_ker, par_conv,
+                diff_src, diff_dst, weights, 0, 0, 1);
+    });
+}
+
+template <data_type_t diff_dst_type, data_type_t wei_type,
+          data_type_t diff_src_type>
+void jit_avx512_common_convolution_bwd_data_t<diff_dst_type, wei_type,
+          diff_src_type>::execute_backward_data_3d(const exec_ctx_t &ctx) const
+{
+    auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto diff_src = CTX_OUT_MEM(diff_src_data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        int start{0}, end{0}, start_copy;
+        int ic_chunks = jcp.nb_ic / jcp.nb_ic_blocking;
+        int work_amount = jcp.ngroups * jcp.mb * ic_chunks * jcp.id * jcp.ih;
+        balance211(work_amount, nthr, ithr, start, end);
+        start_copy = start;
+
+        auto par_conv = jit_conv_call_s();
+        size_t diff_src_h_stride = diff_src_d.blk_off(0, 0, 0, 1);
+        size_t diff_src_d_stride = diff_src_d.blk_off(0, 0, 1);
+        size_t diff_dst_h_stride = diff_dst_d.blk_off(0, 0, 0, 1);
+        size_t diff_dst_d_stride = diff_dst_d.blk_off(0, 0, 1);
+        size_t diff_dst_c_stride = diff_dst_d.blk_off(0, 1);
+        size_t wht_h_stride = wht_blk_off(weights_d, 0, 0, 0, 0, 1);
+        size_t wht_d_stride = wht_blk_off(weights_d, 0, 0, 0, 1);
+        size_t wht_oc_stride = wht_blk_off(weights_d, 0, 1);
+
+        bool is_fast_path_d = jcp.dilate_d == 0 && jcp.stride_d == 1;
+        bool is_fast_path_h = jcp.dilate_h == 0 && jcp.stride_h == 1;
+
+        for (int ocb_l2 = 0; ocb_l2 < jcp.nb_oc; ocb_l2 += jcp.nb_oc_L2) {
+            start = start_copy;
+            int n{0}, g{0}, icc{0}, ih_s{0}, id_s{0};
+            if (jcp.loop_order == loop_cgn)
+                nd_iterator_init(start,
+                    icc, ic_chunks, g, jcp.ngroups, n, jcp.mb, id_s, jcp.id,
+                    ih_s, jcp.ih);
+            else if (jcp.loop_order == loop_gnc)
+                nd_iterator_init(start,
+                    g, jcp.ngroups, n, jcp.mb, icc, ic_chunks, id_s, jcp.id,
+                    ih_s, jcp.ih);
+            else
+                assert(!"unsupported loop order");
+
+            while (start < end) {
+                int icb = icc * jcp.nb_ic_blocking;
+                int g_icb = g * jcp.nb_ic + icb;
+                int g_ocb = g * jcp.nb_oc;
+
+                int work_rem = end - start;
+                int ih_e = ih_s + work_rem > jcp.ih ? jcp.ih : ih_s + work_rem;
+                int d_len = 0, d_lo = 0, d_oj = 0;
+                if (is_fast_path_d) { // dilate == 0 && stride == 1
+                    int d_t_overflow = max(0, jcp.kd - 1 - id_s
+                            - jcp.f_pad);
+                    int d_b_overflow = max(0, jcp.kd - jcp.id + id_s
+                            - jcp.back_pad);
+                    d_len = jcp.kd - d_t_overflow - d_b_overflow;
+                    d_lo = d_b_overflow;
+                    d_oj = id_s + jcp.f_pad - d_b_overflow;
+                } else if (jcp.dilate_d != 0) { // stride == 1
+                    int dilate_d = jcp.dilate_d + 1;
+                    // Note: use div_up to account for "holes" in filter
+                    int d_t_overflow = div_up(max(0, (jcp.kd - 1) * dilate_d
+                                - id_s - jcp.f_pad), dilate_d);
+                    int d_b_overflow = div_up(max(0, (jcp.kd - 1) * dilate_d + 1
+                                - jcp.id + id_s - jcp.back_pad), dilate_d);
+                    d_len = jcp.kd - d_t_overflow - d_b_overflow;
+                    d_lo = d_b_overflow;
+                    d_oj = id_s + jcp.f_pad - d_b_overflow * dilate_d;
+                } else { // dilate == 0
+                    int d_t_overflow = max(0, (jcp.kd - 1 - id_s
+                                - jcp.f_pad) / jcp.stride_d);
+                    int d_b_overflow = max(0, (jcp.kd - jcp.id + id_s
+                                - jcp.back_pad) / jcp.stride_d);
+                    int overflow_kd_hi = jcp.kd - 1 - abs((jcp.id - 1
+                                + jcp.back_pad - id_s) % jcp.stride_d);
+                    int overflow_kd_lo = (id_s + jcp.f_pad)
+                        % jcp.stride_d;
+
+                    d_len = (overflow_kd_hi - overflow_kd_lo)
+                        / jcp.stride_d + 1 - d_t_overflow
+                        - d_b_overflow;
+                    d_lo = overflow_kd_lo + d_b_overflow * jcp.stride_d;
+                    d_oj = (id_s + jcp.f_pad - d_lo) / jcp.stride_d;
+                }
+                assert(d_len >= 0);
+
+                auto diff_src_w = diff_src + diff_src_d.blk_off(n, g_icb)
+                    + id_s * diff_src_d_stride;
+                auto diff_dst_w = diff_dst
+                    + diff_dst_d.blk_off(n, g_ocb + ocb_l2)
+                    + d_oj * diff_dst_d_stride;
+                auto wht_w = weights + wht_blk_off(weights_d, g, ocb_l2, icb)
+                    + d_lo * wht_d_stride;
+
+                for (int ocb = ocb_l2;
+                      ocb < min(jcp.nb_oc, ocb_l2 + jcp.nb_oc_L2); ++ocb) {
+                    for (int ij = ih_s; ij < ih_e; ++ij) {
+                        int oj, k_len, k_lo;
+                        if (is_fast_path_h) { // dilate == 0 && stride == 1
+                            int i_t_overflow = max(0, jcp.kh - 1 - ij
+                                - jcp.t_pad);
+                            int i_b_overflow = max(0, jcp.kh - jcp.ih + ij
+                                - jcp.b_pad);
+                            k_len = jcp.kh - i_t_overflow - i_b_overflow;
+                            k_lo = i_b_overflow;
+                            oj = ij + jcp.t_pad - i_b_overflow;
+                        } else if (jcp.dilate_h != 0) { // stride == 1
+                            int dilate_h = jcp.dilate_h + 1;
+                            // Note: use div_up to account for "holes" in filter
+                            int i_t_overflow
+                                = div_up(max(0, (jcp.kh - 1) * dilate_h
+                                        - ij - jcp.t_pad), dilate_h);
+                            int i_b_overflow
+                                = div_up(max(0, (jcp.kh - 1) * dilate_h + 1
+                                        - jcp.ih + ij - jcp.b_pad), dilate_h);
+                            k_len = jcp.kh - i_t_overflow - i_b_overflow;
+                            k_lo = i_b_overflow;
+                            oj = ij + jcp.t_pad - i_b_overflow * dilate_h;
+                        } else { // dilate == 0
+                            int i_t_overflow = max(0, (jcp.kh - 1 - ij
+                                - jcp.t_pad) / jcp.stride_h);
+                            int i_b_overflow = max(0, (jcp.kh - jcp.ih + ij
+                                - jcp.b_pad) / jcp.stride_h);
+                            int overflow_kh_hi = jcp.kh - 1 - abs((jcp.ih - 1
+                                + jcp.b_pad - ij) % jcp.stride_h);
+                            int overflow_kh_lo = (ij + jcp.t_pad)
+                                % jcp.stride_h;
+
+                            k_len = (overflow_kh_hi - overflow_kh_lo)
+                                / jcp.stride_h + 1 - i_t_overflow
+                                - i_b_overflow;
+                            k_lo = overflow_kh_lo + i_b_overflow * jcp.stride_h;
+                            oj = (ij + jcp.t_pad - k_lo) / jcp.stride_h;
+                        }
+                        assert(k_len >= 0);
+
+                        jit_conv_3d_ker_pipeline(kernel_->jit_ker, par_conv,
+                                diff_src_w + ij * diff_src_h_stride,
+                                diff_dst_w + oj * diff_dst_h_stride,
+                                wht_w + k_lo * wht_h_stride,
+                                0, ocb, k_len, d_len);
+                    }
+                    diff_dst_w += diff_dst_c_stride;
+                    wht_w += wht_oc_stride;
+                }
+
+                if (jcp.loop_order == loop_cgn)
+                    nd_iterator_jump(start, end,
+                      icc, ic_chunks, g, jcp.ngroups, n, jcp.mb, id_s, jcp.id,
+                      ih_s, jcp.ih);
+                else if (jcp.loop_order == loop_gnc)
+                    nd_iterator_jump(start, end,
+                      g, jcp.ngroups, n, jcp.mb, icc, ic_chunks, id_s, jcp.id,
+                      ih_s, jcp.ih);
+                else
+                    assert(!"unsupported loop order");
+            }
+        }
+
+        jit_conv_3d_ker_pipeline(kernel_->jit_ker, par_conv,
+                diff_src, diff_dst, weights, 0, 0, 1, 1);
+    });
+}
+
+template struct jit_avx512_common_convolution_bwd_data_t<data_type::f32>;
+
+template <data_type_t src_type, data_type_t diff_dst_type,
+          data_type_t diff_weights_type>
+jit_avx512_common_convolution_bwd_weights_t<src_type, diff_dst_type,
+          diff_weights_type>::
+jit_avx512_common_convolution_bwd_weights_t(const pd_t *apd)
+    : cpu_primitive_t(apd), kernel_(nullptr)
+    , trans_kernel_(nullptr), acc_ker_(nullptr), reducer_bias_(nullptr)
+{
+    const auto &j = pd()->jcp_;
+
+    nthr_ = j.nthr;
+    nthr_mb_ = j.nthr_mb;
+    nthr_g_ = j.nthr_g;
+    nthr_oc_b_ = j.nthr_oc_b;
+    nthr_ic_b_ = j.nthr_ic_b;
+
+    kernel_ = new jit_avx512_common_conv_bwd_weights_kernel_f32(j);
+
+    if (j.ver == ver_4fma)
+        trans_kernel_ = create_trans_src(&j);
+
+    if (nthr_mb_ > 1)
+        acc_ker_ = new cpu_accumulator_1d_t<diff_weights_type>();
+
+    reducer_bias_ =
+        new cpu_reducer_t<diff_weights_type>(pd()->reducer_bia_conf_);
+}
+
+template <data_type_t src_type, data_type_t diff_dst_type,
+          data_type_t diff_weights_type>
+struct jit_avx512_common_convolution_bwd_weights_t<src_type, diff_dst_type,
+    diff_weights_type>::thread_info_t {
+    const src_data_t *src;
+    const diff_dst_data_t *diff_dst;
+    const diff_weights_data_t *diff_weights;
+    diff_weights_data_t *diff_bias;
+
+    const memory_tracking::grantor_t scratchpad;
+
+    src_data_t *tr_src;
+    simple_barrier::ctx_t *tr_src_bctx;
+
+    diff_dst_data_t *tr_diff_dst;
+    simple_barrier::ctx_t *tr_diff_dst_bctx;
+
+    diff_weights_data_t *wei_bia_reduction;
+    simple_barrier::ctx_t *wei_bia_reduction_bctx;
+
+    int ithr;
+    int ithr_ic_b, ithr_oc_b, ithr_g, ithr_mb;
+    int ithr_but_oc;
+    int ithr_but_ic;
+
+    int img_start = 0, img_end = 0, img_work;
+    int g_start = 0, g_end = 0, g_work;
+    int oc_b_start = 0, oc_b_end = 0, oc_b_work;
+    int ic_b_start = 0, ic_b_end = 0, ic_b_work;
+
+    thread_info_t(const jit_avx512_common_convolution_bwd_weights_t *self,
+            const exec_ctx_t &ctx, int ithr)
+        : scratchpad(self->scratchpad(ctx)), ithr(ithr)
+    {
+        diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST);
+        src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+        diff_weights = CTX_OUT_MEM(diff_weights_data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
+        diff_bias = self->pd()->wants_padded_bias()
+            ? scratchpad.template get<diff_weights_data_t>(
+                    key_conv_padded_bias)
+            : CTX_OUT_MEM(diff_weights_data_t *, MKLDNN_ARG_DIFF_BIAS);
+
+        tr_src = scratchpad.template get<src_data_t>(key_conv_tr_src);
+        tr_src_bctx = scratchpad.template get<simple_barrier::ctx_t>(
+                key_conv_tr_src_bctx);
+
+        tr_diff_dst = scratchpad.template get<diff_dst_data_t>(
+                key_conv_tr_diff_dst);
+        tr_diff_dst_bctx = scratchpad.template get<simple_barrier::ctx_t>(
+                key_conv_tr_diff_dst_bctx);
+
+        wei_bia_reduction = scratchpad.template get<diff_weights_data_t>(
+                key_conv_wei_bia_reduction);
+        wei_bia_reduction_bctx = scratchpad.template get<simple_barrier::ctx_t>(
+                key_conv_wei_bia_reduction_bctx);
+
+        ithr_ic_b = ithr % self->nthr_ic_b_;
+        ithr_oc_b = ithr / self->nthr_ic_b_ % self->nthr_oc_b_;
+        ithr_g = ithr / self->nthr_ic_b_ / self->nthr_oc_b_ % self->nthr_g_;
+        ithr_mb = ithr / self->nthr_ic_b_ / self->nthr_oc_b_ / self->nthr_g_;
+
+        ithr_but_oc = (ithr_mb * self->nthr_g_ + ithr_g) * self->nthr_ic_b_
+            + ithr_ic_b;
+
+        ithr_but_ic = (ithr_mb * self->nthr_g_ + ithr_g) * self->nthr_oc_b_
+            + ithr_oc_b;
+
+        const auto &jcp = self->kernel_->jcp;
+
+        /* reduction dimension */
+        balance211(jcp.mb*jcp.od, self->nthr_mb_, ithr_mb, img_start, img_end);
+        img_work = img_end - img_start;
+
+        /* independent dimensions */
+        balance211(jcp.ngroups, self->nthr_g_, ithr_g, g_start, g_end);
+        g_work = g_end - g_start;
+
+        balance211(jcp.nb_oc, self->nthr_oc_b_, ithr_oc_b, oc_b_start,
+                oc_b_end);
+        oc_b_work = oc_b_end - oc_b_start;
+
+        balance211(jcp.nb_ic, self->nthr_ic_b_, ithr_ic_b, ic_b_start,
+                ic_b_end);
+        ic_b_work = ic_b_end - ic_b_start;
+    }
+};
+
+template <data_type_t src_type, data_type_t diff_dst_type,
+          data_type_t diff_weights_type>
+void jit_avx512_common_convolution_bwd_weights_t<src_type, diff_dst_type,
+    diff_weights_type>::compute_diff_weights(const thread_info_t *ti) const {
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+    const int wei_size = jcp.ngroups * jcp.oc * jcp.ic * jcp.kh*jcp.kw*jcp.kd;
+
+    diff_weights_data_t *diff_wei = ti->ithr_mb == 0
+        ? (diff_weights_data_t*)ti->diff_weights
+        : ti->wei_bia_reduction + (ti->ithr_mb - 1) * wei_size;
+    diff_weights_data_t *diff_bia = ti->ithr_mb == 0
+        ? (diff_weights_data_t*)ti->diff_bias
+        : ti->wei_bia_reduction + (nthr_mb_ - 1) * wei_size
+          + (ti->ithr_mb - 1) * jcp.ngroups * jcp.oc;
+
+    // TODO: use memory descriptor with the same fmt as src (or use a macro :))
+    auto tr_src_off = [&](int ithr_mb, int ic, int ij) {
+        const size_t tr_row_size = jcp.tr_iw * jcp.ic_block;
+        const size_t tr_chn_size = tr_row_size * jcp.ih;
+        const size_t tr_img_size = tr_chn_size * jcp.nb_ic * jcp.ngroups;
+
+        return ti->ithr_mb * tr_img_size + ic * tr_chn_size + ij * tr_row_size;
+    };
+
+    auto uker_trans = [&](int img) {
+        const int work_amount = ti->g_work * ti->ic_b_work * jcp.ih;
+
+        int start{0}, end{0};
+        balance211(work_amount, nthr_oc_b_, ti->ithr_oc_b, start, end);
+        const int my_work = end - start;
+
+        int g{0}, ic_b{0}, j{0};
+        nd_iterator_init(start, g, ti->g_work, ic_b, ti->ic_b_work, j, jcp.ih);
+        g += ti->g_start;
+        ic_b += ti->ic_b_start;
+
+        const int _ic = g * jcp.nb_ic + ic_b;
+        src_data_t *src1 = (src_data_t*)&ti->src[src_d.blk_off(img, _ic, j)];
+        src_data_t *tr_src1 = &ti->tr_src[tr_src_off(ti->ithr_mb, _ic, j)];
+
+        assert(jcp.ic_block == 16);
+        const int src_stride = jcp.iw * jcp.ic_block;
+        const int tr_src_stride = jcp.tr_iw * jcp.ic_block;
+
+        const int pf_depth = 2;
+        struct { src_data_t *src, *tr_src; } pf_circ_buf[pf_depth];
+
+        for (int iwork = 0; iwork < my_work + pf_depth - 1; iwork++) {
+            pf_circ_buf[iwork % pf_depth] = {src1, tr_src1};
+
+            if (iwork >= pf_depth - 1) {
+                int old_idx = (iwork - pf_depth + 1) % pf_depth;
+                auto ctx = jit_trans_src_t::ctx_t();
+                ctx.src = pf_circ_buf[old_idx].src;
+                ctx.tr_src = pf_circ_buf[old_idx].tr_src;
+                ctx.src_prf = src1;
+                ctx.tr_src_prf = tr_src1;
+                (*trans_kernel_)(&ctx);
+            }
+            src1 += src_stride;
+            tr_src1 += tr_src_stride;
+        }
+#if 0
+        // reference transposition
+        const int l_pad = jcp.l_pad;
+        const int iwlp = l_pad + jcp.iw;
+        const int tr_iw = jcp.tr_iw;
+
+        for (size_t iwork = start; iwork < end; iwork++) {
+            PRAGMA_OMP_SIMD()
+#           pragma unroll
+            for (int i = 0; i < l_pad; i++)
+                for (int j = 0; j < jcp.ic_block; j++)
+                    tr_src1[j * jcp.tr_iw + i] = (src_data_t)0.0;
+
+            PRAGMA_OMP_SIMD()
+#           pragma unroll
+            for (int i = l_pad; i < iwlp; i++)
+                for (int j = 0; j < jcp.ic_block; j++)
+                    tr_src1[j * jcp.tr_iw + i]
+                        = (src_data_t)src1[(i - l_pad) * 16 + j];
+
+            PRAGMA_OMP_SIMD()
+#           pragma unroll
+            for (int i = iwlp; i < tr_iw; i++)
+                for (int j = 0; j < jcp.ic_block; j++)
+                    tr_src1[j * jcp.tr_iw + i] = (src_data_t)0.0;
+
+             src1 += src_stride;
+             tr_src1 += tr_src_stride;
+         }
+#endif
+    };
+
+    if (jcp.is_1stconv && jcp.ver == ver_4fma) {
+        /* prepare contexts */
+        auto tr_ctx = jit_trans_src_t::ctx_t();
+        tr_ctx.tr_src = ti->tr_src
+            + ti->ithr_but_oc * jcp.ih * jcp.stride_w * jcp.tr_ld;
+
+        assert(IMPLICATION(!mkldnn_thr_syncable(), nthr_oc_b_ == 1));
+        tr_ctx.nthr_oc_b = nthr_oc_b_;
+        int ih_start{0}, ih_end{0};
+        balance211(jcp.ih, nthr_oc_b_, ti->ithr_oc_b, ih_start, ih_end);
+        tr_ctx.tr_src_ih_start = ih_start;
+        tr_ctx.tr_src_ih_end = ih_end;
+        tr_ctx.tr_src_bctx = ti->tr_src_bctx + ti->ithr_but_oc;
+
+        auto p = jit_conv_call_s();
+        p.src = tr_ctx.tr_src;
+
+        /* zero diff_bias if applicable */
+        if (jcp.with_bias && ti->ithr_ic_b == 0) {
+            assert(jcp.oc_block == 16);
+            for (int oc_b = ti->ic_b_start; oc_b < ti->oc_b_end; ++oc_b) {
+                diff_weights_data_t *db = &diff_bia[oc_b * 16];
+                for (int o = 0; o < 16; ++o)
+                    db[o] = 0;
+            }
+        }
+
+        for (int img = ti->img_start; img < ti->img_end; ++img) {
+            p.flags = (img == ti->img_start) * FLAG_MB_FIRST;
+
+            for (int g = ti->g_start; g < ti->g_end; ++g) {
+            for (int ic_b = ti->ic_b_start; ic_b < ti->ic_b_end; ++ic_b) {
+                const int _ic = g * jcp.nb_ic + ic_b;
+                tr_ctx.src = &ti->src[src_d.blk_off(img, _ic)];
+
+                (*trans_kernel_)(&tr_ctx);
+
+                if (ic_b == 0)
+                    p.flags |= FLAG_IC_FIRST;
+                else
+                    p.flags &= ~FLAG_IC_FIRST;
+
+                for (int oc_b = ti->oc_b_start; oc_b < ti->oc_b_end; ++oc_b) {
+                    const int _oc = g * jcp.nb_oc + oc_b;
+                    p.dst = &ti->diff_dst[diff_dst_d.blk_off(img, _oc)];
+
+                    const size_t off =
+                        wht_blk_off(diff_weights_d, g, oc_b, ic_b);
+                    p.filt = diff_wei + off;
+                    p.bias = diff_bia + _oc * jcp.oc_block;
+
+                    kernel_->jit_ker(&p);
+                }
+            }
+            }
+        }
+    } else {
+        for (int img = ti->img_start; img < ti->img_end; ++img) {
+            auto p = jit_conv_call_s();
+
+            if (jcp.ver == ver_4fma) {
+                /* tr_src[nb_ic][ih][16][~iw~] <- src[nb_ic][ih][iw][16] */
+                using simple_barrier::barrier;
+                if (nthr_oc_b_ > 1)
+                    barrier(&ti->tr_src_bctx[ti->ithr_but_oc], nthr_oc_b_);
+                uker_trans(img);
+                if (nthr_oc_b_ > 1)
+                    barrier(&ti->tr_src_bctx[ti->ithr_but_oc], nthr_oc_b_);
+            }
+
+            for (int g = ti->g_start; g < ti->g_end; ++g) {
+            for (int oc_b = ti->oc_b_start; oc_b < ti->oc_b_end; ++oc_b) {
+            for (int ic_b = ti->ic_b_start; ic_b < ti->ic_b_end; ++ic_b) {
+                const int _oc = g * jcp.nb_oc + oc_b;
+                const int _ic = g * jcp.nb_ic + ic_b;
+
+                jit_conv_ker_pipeline(kernel_->jit_ker, p,
+                        jcp.ver == ver_4fma
+                        ? &ti->tr_src[tr_src_off(ti->ithr_mb, _ic, 0)]
+                        : &ti->src[src_d.blk_off(img, _ic)],
+                        &ti->diff_dst[diff_dst_d.blk_off(img, _oc)],
+                        diff_wei + wht_blk_off(diff_weights_d, g, oc_b, ic_b),
+                        0, (img == ti->img_start), 0);
+
+            }
+            }
+            }
+
+            const int _oc = ti->g_start * jcp.nb_oc + ti->oc_b_start;
+            const int _ic = ti->g_start * jcp.nb_ic + ti->ic_b_start;
+            jit_conv_ker_pipeline(kernel_->jit_ker, p,
+                    jcp.ver == ver_4fma
+                     ? &ti->tr_src[tr_src_off(ti->ithr_mb, _ic, 0)]
+                     : &ti->src[src_d.blk_off(img + 1, _ic)],
+                    &ti->diff_dst[diff_dst_d.blk_off(img + 1, _oc)],
+                    diff_wei + wht_blk_off(
+                        diff_weights_d, ti->g_start,
+                        ti->oc_b_start, ti->ic_b_start),
+                    0, 0, 0);
+        }
+    }
+}
+
+template <data_type_t src_type, data_type_t diff_dst_type,
+          data_type_t diff_weights_type>
+void jit_avx512_common_convolution_bwd_weights_t<src_type, diff_dst_type,
+    diff_weights_type>::compute_diff_weights_3d(const thread_info_t *ti) const
+{
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+    const int wei_size
+            = jcp.ngroups * jcp.oc * jcp.ic * jcp.kh * jcp.kw * jcp.kd;
+
+    diff_weights_data_t *diff_wei = ti->ithr_mb == 0
+        ? (diff_weights_data_t*)ti->diff_weights
+        : ti->wei_bia_reduction + (ti->ithr_mb - 1) * wei_size;
+    diff_weights_data_t *diff_bia = ti->ithr_mb == 0
+        ? (diff_weights_data_t*)ti->diff_bias
+        : ti->wei_bia_reduction + (nthr_mb_ - 1) * wei_size
+          + (ti->ithr_mb - 1) * jcp.ngroups * jcp.oc;
+
+    const int inp_mult = jcp.is_1stconv ? 1 : jcp.ic_block;
+    const int input_step = jcp.ih * jcp.iw * inp_mult;
+    const int output_step = jcp.ow * jcp.oh * jcp.oc_block;
+    int img{0}, od_s{0};
+    int img_start = ti->img_start, img_end = ti->img_end;
+    nd_iterator_init(img_start, img, jcp.mb, od_s, jcp.od);
+    const int img_first = img;
+
+    while (img_start < img_end) {
+        auto p = jit_conv_call_s();
+
+        int work_rem = img_end - img_start;
+        const int od_e = od_s + work_rem > jcp.od ? jcp.od : od_s + work_rem;
+        const int id_s = od_s * jcp.stride_d;
+        const int ik_overlap = nstl::max(0, id_s - jcp.f_pad);
+        const int kd_front_pad = nstl::max(0, jcp.f_pad - id_s);
+        const int kd_back_pad
+                = nstl::max(0, id_s - jcp.f_pad - jcp.id + jcp.kd);
+        int kd_pad_off = nstl::min(jcp.kd - 1, kd_front_pad) * jcp.kh * jcp.kw
+                * jcp.ic_block * jcp.oc_block * jcp.typesize_out;
+
+        for (int g = ti->g_start; g < ti->g_end; ++g) {
+        for (int oc_b = ti->oc_b_start; oc_b < ti->oc_b_end; ++oc_b) {
+        for (int ic_b = ti->ic_b_start; ic_b < ti->ic_b_end; ++ic_b) {
+            const int _oc = g * jcp.nb_oc + oc_b;
+            const int _ic = g * jcp.nb_ic + ic_b;
+
+            auto src = &ti->src[src_d.blk_off(img, _ic)
+                    + ik_overlap * input_step];
+            auto dst = &ti->diff_dst[diff_dst_d.blk_off(img, _oc)
+                    + od_s * output_step];
+
+            jit_conv_3d_ker_bwd_w_pipeline(kernel_->jit_ker, p, src, dst,
+                    diff_wei + wht_blk_off(diff_weights_d, g, oc_b, ic_b),
+                    diff_bia + _oc * 16, (img == img_first), od_s, od_e,
+                    jcp.kd - kd_front_pad - kd_back_pad, kd_pad_off);
+
+            if (ic_b == 0) p.flags = 0;
+            else p.flags = 1;
+        }
+        }
+        }
+
+        const int _oc = ti->g_start * jcp.nb_oc + ti->oc_b_start;
+        const int _ic = ti->g_start * jcp.nb_ic + ti->ic_b_start;
+        jit_conv_3d_ker_bwd_w_pipeline(kernel_->jit_ker, p,
+                &ti->src[src_d.blk_off(img + 1, _ic)],
+                &ti->diff_dst[diff_dst_d.blk_off(img + 1, _oc)],
+                diff_wei + wht_blk_off(diff_weights_d, ti->g_start,
+                    ti->oc_b_start, ti->ic_b_start),
+                diff_bia, 0, 0, 0, 0, 0);
+        nd_iterator_jump(img_start, img_end, img, jcp.mb, od_s, jcp.od);
+    }
+}
+
+template <data_type_t src_type, data_type_t diff_dst_type,
+          data_type_t diff_weights_type>
+void jit_avx512_common_convolution_bwd_weights_t<src_type, diff_dst_type,
+    diff_weights_type>::reduce_diff_weights(const thread_info_t *ti) const {
+    const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+    const int wei_size = jcp.ngroups * jcp.oc * jcp.ic * jcp.kh * jcp.kw;
+    const int bia_size = jcp.ngroups * jcp.oc;
+    const diff_weights_data_t *diff_bias_ws
+        = ti->wei_bia_reduction + (nthr_mb_ - 1) * wei_size;
+
+    /* diff_weights[:] += sum(wei_reduction_[thr_mb][:]) */
+    simple_barrier::barrier(ti->wei_bia_reduction_bctx, nthr_);
+
+    const int ic_b_kh_work = ti->ic_b_work * jcp.kh;
+    const int work = ti->g_work * ti->oc_b_work * ic_b_kh_work;
+
+    int start{0}, end{0};
+    balance211(work, nthr_mb_, ti->ithr_mb, start, end);
+    if (start == end) return;
+
+    for (int thr_mb = 1; thr_mb < nthr_mb_; ++thr_mb) {
+        int w = start;
+        int sub_g_start{0}, sub_oc_b_start{0}, sub_ic_b_kh_start{0};
+        nd_iterator_init(w, sub_g_start, ti->g_work, sub_oc_b_start,
+                ti->oc_b_work, sub_ic_b_kh_start, ic_b_kh_work);
+        while (w < end) {
+            const int g = ti->g_start + sub_g_start;
+            const int oc_b = ti->oc_b_start + sub_oc_b_start;
+            const int ic_b = ti->ic_b_start + sub_ic_b_kh_start / jcp.kh;
+            const int kh = sub_ic_b_kh_start % jcp.kh;
+
+            const int acc_size
+                = nstl::min(end - w, ic_b_kh_work - sub_ic_b_kh_start)
+                * jcp.kw * jcp.ic_block * jcp.oc_block;
+
+            const size_t off
+                = wht_blk_off(diff_weights_d, g, oc_b, ic_b, kh);
+
+            diff_weights_data_t *d
+                = (diff_weights_data_t *)ti->diff_weights + off;
+            diff_weights_data_t *s
+                = ti->wei_bia_reduction + (thr_mb - 1) * wei_size + off;
+
+            acc_ker_->accumulate(d, s, acc_size);
+
+            nd_iterator_jump(w, end, sub_g_start, ti->g_work, sub_oc_b_start,
+                    ti->oc_b_work, sub_ic_b_kh_start, ic_b_kh_work);
+        }
+
+        if (jcp.with_bias && jcp.is_1stconv && jcp.ver == ver_4fma) {
+            if (ti->ithr == 0)
+                acc_ker_->accumulate((diff_weights_data_t *)ti->diff_bias,
+                    diff_bias_ws, bia_size);
+            diff_bias_ws += bia_size;
+        }
+    }
+}
+
+template <data_type_t src_type, data_type_t diff_dst_type,
+          data_type_t diff_weights_type>
+void jit_avx512_common_convolution_bwd_weights_t<src_type, diff_dst_type,
+    diff_weights_type>::reduce_diff_weights_3d(const thread_info_t *ti) const {
+    const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+    const int wei_size = jcp.ngroups * jcp.oc * jcp.ic * jcp.kh * jcp.kw
+        * jcp.kd;
+
+    /* diff_weights[:] += sum(wei_reduction_[thr_mb][:]) */
+    simple_barrier::barrier(ti->wei_bia_reduction_bctx, nthr_);
+
+    const int ic_b_kh_work = ti->ic_b_work * jcp.kd;
+    const int work = ti->g_work * ti->oc_b_work * ic_b_kh_work;
+
+    int start{0}, end{0};
+    balance211(work, nthr_mb_, ti->ithr_mb, start, end);
+    if (start == end) return;
+
+    for (int thr_mb = 1; thr_mb < nthr_mb_; ++thr_mb) {
+        int w = start;
+        int sub_g_start{0}, sub_oc_b_start{0}, sub_ic_b_kh_start{0};
+        nd_iterator_init(w, sub_g_start, ti->g_work, sub_oc_b_start,
+                ti->oc_b_work, sub_ic_b_kh_start, ic_b_kh_work);
+        while (w < end) {
+            const int g = ti->g_start + sub_g_start;
+            const int oc_b = ti->oc_b_start + sub_oc_b_start;
+            const int ic_b = ti->ic_b_start + sub_ic_b_kh_start / jcp.kd;
+            const int kd = sub_ic_b_kh_start % jcp.kd;
+
+            const int acc_size
+                = nstl::min(end - w, ic_b_kh_work - sub_ic_b_kh_start)
+                * jcp.kw * jcp.ic_block * jcp.oc_block * jcp.kh;
+
+            const size_t off
+                = wht_blk_off(diff_weights_d, g, oc_b, ic_b, kd);
+            diff_weights_data_t *d
+                = (diff_weights_data_t *)ti->diff_weights + off;
+            diff_weights_data_t *s
+                = ti->wei_bia_reduction + (thr_mb - 1) * wei_size + off;
+            acc_ker_->accumulate(d, s, acc_size);
+
+            nd_iterator_jump(w, end, sub_g_start, ti->g_work, sub_oc_b_start,
+                    ti->oc_b_work, sub_ic_b_kh_start, ic_b_kh_work);
+        }
+    }
+}
+
+template <data_type_t src_type, data_type_t diff_dst_type,
+          data_type_t diff_weights_type>
+void jit_avx512_common_convolution_bwd_weights_t<src_type, diff_dst_type,
+    diff_weights_type>::compute_diff_bias(const thread_info_t *ti) const {
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+
+    auto rb = this->reducer_bias_;
+    assert(nthr_ == rb->balancer().nthr_);
+
+    const auto reducer_bia_scratchpad = memory_tracking::grantor_t(
+            ti->scratchpad, prefix_reducer_bia);
+
+    const auto &jcp = kernel_->jcp;
+
+    if (jcp.with_bias && jcp.is_1stconv && jcp.ver == ver_4fma) return;
+
+    const int b_job_start = rb->balancer().ithr_job_off(ti->ithr);
+    const int b_njobs = rb->balancer().ithr_njobs(ti->ithr);
+
+    if (b_njobs == 0) return;
+
+    /* reduction dimension */
+    int img_start{0}, img_end{0};
+    balance211(jcp.mb, rb->balancer().nthr_per_group_,
+            rb->balancer().id_in_group(ti->ithr), img_start, img_end);
+
+    /* jobs */
+    int g_start{0}, ocb_start{0};
+    nd_iterator_init(b_job_start, g_start, jcp.ngroups, ocb_start, jcp.nb_oc);
+    for (int img = img_start; img < img_end; ++img) {
+        int g = g_start, ocb = ocb_start;
+        for (int b_job_loc = 0; b_job_loc < b_njobs; ++b_job_loc) {
+            const size_t _oc = g * jcp.nb_oc + ocb;
+
+            const diff_dst_data_t *d_dst
+                = &ti->diff_dst[diff_dst_d.blk_off(img, _oc)];
+            diff_weights_data_t *d_bias = rb->get_local_ptr(ti->ithr,
+                    ti->diff_bias, reducer_bia_scratchpad)
+                + b_job_loc * rb->balancer().job_size_;
+
+            if (img == img_start)
+                for (int o = 0; o < 16; ++o)
+                    d_bias[o] = 0;
+            for (int hw = 0; hw < jcp.oh * jcp.ow * jcp.od; ++hw) {
+                PRAGMA_OMP_SIMD()
+                for (int o = 0; o < 16; ++o)
+                    d_bias[o] += d_dst[o];
+                d_dst += 16;
+            }
+
+            nd_iterator_step(g, jcp.ngroups, ocb, jcp.nb_oc);
+        }
+    }
+
+    rb->reduce(ti->ithr, ti->diff_bias, reducer_bia_scratchpad);
+}
+
+template <data_type_t src_type, data_type_t diff_dst_type,
+          data_type_t diff_weights_type>
+void jit_avx512_common_convolution_bwd_weights_t<src_type, diff_dst_type,
+    diff_weights_type>::compute_diff_bias_3d(const thread_info_t *ti) const {
+
+    const auto &jcp = kernel_->jcp;
+
+    const size_t wei_size = (size_t)jcp.ngroups * jcp.oc * jcp.ic * jcp.kh
+        * jcp.kw * jcp.kd;
+    const int bia_size = jcp.ngroups * jcp.oc;
+    const diff_weights_data_t *diff_bias_ws
+            = ti->wei_bia_reduction + (size_t)(nthr_mb_ - 1) * wei_size;
+
+    if (nthr_mb_ > 1) mkldnn_thr_barrier();
+
+    if (ti->ithr == 0)
+    {
+        for (int thr_mb = 1; thr_mb < nthr_mb_; ++thr_mb) {
+            acc_ker_->accumulate(ti->diff_bias, diff_bias_ws, bia_size);
+            diff_bias_ws += bia_size;
+        }
+    }
+}
+
+template <data_type_t src_type, data_type_t diff_dst_type,
+          data_type_t diff_weights_type>
+void jit_avx512_common_convolution_bwd_weights_t<src_type, diff_dst_type,
+    diff_weights_type>::prepare_scratchpad_data(const exec_ctx_t &ctx) const
+{
+    const auto &j = pd()->jcp_;
+    auto scratchpad = this->scratchpad(ctx);
+
+    if (j.ver == ver_4fma) {
+        if (!j.is_1stconv) {
+            // XXX: See the comment about tr_iw and guarding elements in
+            // jit_avx512_common_conv_bwd_weights_kernel_f32::init_conf()
+            const int max_nthr = j.nthr_mb * j.ngroups * j.nb_ic;
+            const int min_tr_src_size_per_thr = j.ih * j.ic_block * j.tr_iw;
+
+            auto tr_src = scratchpad.template get<src_data_t>(key_conv_tr_src);
+            /* to avoid NaNs in computations we zero tail num_guard_elems for
+             * each possible thread group */
+
+            for (int ithr = 1; ithr <= max_nthr; ++ithr) {
+                src_data_t *ts = &tr_src[ithr * min_tr_src_size_per_thr];
+                for (int i = 0; i < j.tr_src_num_guard_elems; ++i)
+                    ts[i] = 0;
+            }
+        }
+
+        if (j.nthr_oc_b > 1) {
+            const int tr_src_bctx_size = j.nthr / j.nthr_oc_b;
+            auto tr_src_bctx = scratchpad.template get<simple_barrier::ctx_t>(
+                    key_conv_tr_src_bctx);
+            for (int i = 0; i < tr_src_bctx_size; ++i)
+                simple_barrier::ctx_init(&tr_src_bctx[i]);
+        }
+    }
+
+    if (nthr_mb_ > 1) {
+        simple_barrier::ctx_init(scratchpad.template get<simple_barrier::ctx_t>(
+                    key_conv_wei_bia_reduction_bctx));
+    }
+
+    const auto reducer_bia_scratchpad = memory_tracking::grantor_t(scratchpad,
+            prefix_reducer_bia);
+    auto rb = this->reducer_bias_;
+    rb->init(reducer_bia_scratchpad);
+}
+
+template <data_type_t src_type, data_type_t diff_dst_type,
+          data_type_t diff_weights_type>
+void jit_avx512_common_convolution_bwd_weights_t<src_type, diff_dst_type,
+    diff_weights_type>::execute_backward_weights(const exec_ctx_t &ctx) const {
+    prepare_scratchpad_data(ctx);
+
+    parallel(nthr_, [&](const int ithr, const int nthr) {
+        assert(nthr_ == nthr);
+
+        thread_info_t thread_info(this, ctx, ithr);
+
+        if (utils::one_of(pd()->ndims(), 3, 4)) {
+            compute_diff_weights(&thread_info);
+            if (nthr_mb_ > 1) reduce_diff_weights(&thread_info);
+            if (pd()->with_bias()) compute_diff_bias(&thread_info);
+        } else if (pd()->ndims() == 5) {
+            compute_diff_weights_3d(&thread_info);
+            if (nthr_mb_ > 1) reduce_diff_weights_3d(&thread_info);
+            if (pd()->with_bias()) compute_diff_bias_3d(&thread_info);
+        } else {
+            assert(false);
+        }
+    });
+
+    /* TODO: put that into compute_diff_bias() */
+    if (pd()->wants_padded_bias()) {
+        auto diff_bias = scratchpad(ctx).template get<const diff_weights_data_t>(
+                key_conv_padded_bias);
+        auto diff_bias_in = CTX_OUT_MEM(diff_weights_data_t *, MKLDNN_ARG_DIFF_BIAS);
+        for (int oc = 0; oc < pd()->jcp_.oc_without_padding; ++oc)
+            diff_bias_in[oc] = diff_bias[oc];
+    }
+}
+
+template struct jit_avx512_common_convolution_bwd_weights_t<data_type::f32>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution.hpp
new file mode 100644
index 0000000000..3341c3ebe0
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution.hpp
@@ -0,0 +1,302 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_COMMON_CONVOLUTION_HPP
+#define CPU_JIT_AVX512_COMMON_CONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "cpu_barrier.hpp"
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+#include "cpu_reducer.hpp"
+
+#include "jit_transpose_src_utils.hpp"
+#include "jit_avx512_common_conv_kernel.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t src_type,
+         impl::data_type_t wei_type = src_type,
+         impl::data_type_t dst_type = src_type>
+struct jit_avx512_common_convolution_fwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_()
+        {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", avx512_common, ""),
+                jit_avx512_common_convolution_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(src_type, wei_type, dst_type, dst_type,
+                        data_type::undef)
+                && !has_zero_dim_memory();
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_avx512_common_conv_fwd_kernel::init_conf(
+                    jcp_, *desc(), src_md_, weights_md_, dst_md_, bias_md_,
+                    *attr(), mkldnn_get_max_threads());
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx512_common_conv_fwd_kernel::init_scratchpad(scratchpad,
+                    jcp_);
+
+            return status;
+        }
+
+        jit_conv_conf_t jcp_;
+    };
+
+    jit_avx512_common_convolution_fwd_t(const pd_t *apd)
+        : cpu_primitive_t(apd)
+    {
+        kernel_ = new jit_avx512_common_conv_fwd_kernel(pd()->jcp_,
+                *pd()->attr());
+    }
+    ~jit_avx512_common_convolution_fwd_t() { delete kernel_; }
+
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<wei_type>::type wei_data_t;
+    typedef typename prec_traits<dst_type>::type dst_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        if (pd()->ndims() == 3)
+            execute_forward_1d(ctx);
+        else if (pd()->ndims() == 4)
+            execute_forward_2d(ctx);
+        else if (pd()->ndims() == 5)
+            execute_forward_3d(ctx);
+        else
+            assert(false);
+
+        if (pd()->wants_zero_pad_dst())
+            ctx.memory(MKLDNN_ARG_DST)->zero_pad();
+
+        return status::success;
+    }
+
+private:
+    void prepare_padded_bias(const dst_data_t *&bias,
+            const memory_tracking::grantor_t &scratchpad) const;
+    void execute_forward_1d(const exec_ctx_t &ctx) const;
+    void execute_forward_2d(const exec_ctx_t &ctx) const;
+    void execute_forward_3d(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx512_common_conv_fwd_kernel *kernel_;
+};
+
+template <impl::data_type_t diff_dst_type,
+          impl::data_type_t wei_type = diff_dst_type,
+          impl::data_type_t diff_src_type = diff_dst_type>
+struct jit_avx512_common_convolution_bwd_data_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_bwd_data_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_()
+        {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", avx512_common, ""),
+                jit_avx512_common_convolution_bwd_data_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_data
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(diff_src_type, wei_type,
+                        data_type::undef, diff_dst_type, data_type::undef)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            status_t status =
+                jit_avx512_common_conv_bwd_data_kernel_f32::init_conf(jcp_,
+                        *desc(), *diff_src_md(), *weights_md(), *diff_dst_md());
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx512_common_conv_bwd_data_kernel_f32::init_scratchpad(
+                    scratchpad, jcp_);
+
+            return status::success;
+        }
+
+        jit_conv_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = utils::pick(ndims() - 3, nCw16c, nChw16c, nCdhw16c);
+            auto wei_tag = utils::pick(2 * ndims() - 6 + with_groups(),
+                    OIw16o16i, gOIw16o16i, OIhw16o16i, gOIhw16o16i,
+                    OIdhw16o16i, gOIdhw16o16i);
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    jit_avx512_common_convolution_bwd_data_t(const pd_t *apd)
+        : cpu_primitive_t(apd)
+    { kernel_ = new jit_avx512_common_conv_bwd_data_kernel_f32(pd()->jcp_); }
+    ~jit_avx512_common_convolution_bwd_data_t() { delete kernel_; };
+
+    typedef typename prec_traits<diff_dst_type>::type diff_dst_data_t;
+    typedef typename prec_traits<wei_type>::type wei_data_t;
+    typedef typename prec_traits<diff_src_type>::type diff_src_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        if (pd()->ndims() == 3)
+            execute_backward_data_1d(ctx);
+        else if (pd()->ndims() == 4)
+            execute_backward_data_2d(ctx);
+        else if (pd()->ndims() == 5)
+            execute_backward_data_3d(ctx);
+        else
+            assert(false);
+        return status::success;
+    }
+
+private:
+    void execute_backward_data_1d(const exec_ctx_t &ctx) const;
+    void execute_backward_data_2d(const exec_ctx_t &ctx) const;
+    void execute_backward_data_3d(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx512_common_conv_bwd_data_kernel_f32 *kernel_;
+};
+
+template <impl::data_type_t src_type,
+          impl::data_type_t diff_dst_type = src_type,
+          impl::data_type_t diff_weights_type = src_type>
+struct jit_avx512_common_convolution_bwd_weights_t: public cpu_primitive_t {
+    struct pd_t: public  cpu_convolution_bwd_weights_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_weights_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", avx512_common, ""),
+                jit_avx512_common_convolution_bwd_weights_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_weights
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(src_type, diff_weights_type,
+                        diff_weights_type, diff_dst_type, data_type::undef)
+                && !has_zero_dim_memory();
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_avx512_common_conv_bwd_weights_kernel_f32::
+                init_conf(jcp_, *desc(), src_md_, diff_weights_md_,
+                        diff_bias_md_, diff_dst_md_);
+            if (status != status::success) return status;
+
+            init_balancers();
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx512_common_conv_bwd_weights_kernel_f32::init_scratchpad(
+                    scratchpad, jcp_);
+
+            auto reducer_bia_scratchpad = memory_tracking::registrar_t(
+                    scratchpad, memory_tracking::names::prefix_reducer_bia);
+            reducer_bia_conf_.init_scratchpad(reducer_bia_scratchpad);
+
+            return status;
+        }
+
+        jit_conv_conf_t jcp_;
+        typename cpu_reducer_t<diff_weights_type>::conf_t reducer_bia_conf_;
+
+    private:
+        void init_balancers() {
+            const size_t max_buffer_size = jcp_.nthr * 3 * 5 * 5 * 16 * 16;
+            if (with_bias()) {
+                reducer_bia_conf_.init(reduce_balancer_t(jcp_.nthr,
+                            jcp_.oc_block, jcp_.ngroups * jcp_.nb_oc, jcp_.mb,
+                            max_buffer_size));
+            }
+        }
+    };
+
+    jit_avx512_common_convolution_bwd_weights_t(const pd_t *apd);
+    ~jit_avx512_common_convolution_bwd_weights_t() {
+        delete kernel_;
+        if (trans_kernel_)
+            delete trans_kernel_;
+        if (acc_ker_)
+            delete acc_ker_;
+        delete reducer_bias_;
+    }
+
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<diff_dst_type>::type diff_dst_data_t;
+    typedef typename prec_traits<diff_weights_type>::type diff_weights_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_weights(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_weights(const exec_ctx_t &ctx) const;
+    void prepare_scratchpad_data(const exec_ctx_t &ctx) const;
+    struct thread_info_t;
+    void compute_diff_weights(const thread_info_t *) const;
+    void compute_diff_weights_3d(const thread_info_t *) const;
+    void reduce_diff_weights(const thread_info_t *) const;
+    void reduce_diff_weights_3d(const thread_info_t *) const;
+    void compute_diff_bias(const thread_info_t *) const;
+    void compute_diff_bias_3d(const thread_info_t *) const;
+
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    int nthr_, nthr_mb_, nthr_g_, nthr_oc_b_, nthr_ic_b_;
+
+    jit_avx512_common_conv_bwd_weights_kernel_f32 *kernel_;
+    jit_trans_src_t *trans_kernel_;
+    cpu_accumulator_1d_t<diff_weights_type> *acc_ker_;
+    cpu_reducer_t<diff_weights_type> *reducer_bias_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution_winograd.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution_winograd.cpp
new file mode 100644
index 0000000000..62247c0264
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution_winograd.cpp
@@ -0,0 +1,1215 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifdef __INTEL_COMPILER
+#include <immintrin.h>
+#endif
+
+#include "mkldnn_types.h"
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_avx512_common_convolution_winograd.hpp"
+
+#ifndef _MSC_VER
+#define pragma_unroll _Pragma("unroll")
+#else
+#define pragma_unroll
+#endif
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace memory_tracking::names;
+
+namespace {
+
+unsigned int LLC_cache_size = get_cache_size(3, false);
+
+void inline load_ps(float *dest, const float *src_mem) {
+#ifdef __INTEL_COMPILER
+    __m512 *Iv512 = (__m512 *)dest;
+    Iv512[0] = _mm512_load_ps(src_mem);
+#else
+    PRAGMA_OMP_SIMD()
+    for (int v = 0; v < simd_w; v++) dest[v] = src_mem[v];
+#endif
+}
+
+void inline store_output(float *dest, const float *data, bool streamout) {
+#ifdef __INTEL_COMPILER
+    if (streamout)
+        _mm512_stream_ps(dest, *((__m512 *)data));
+    else
+        _mm512_store_ps(dest, *((__m512 *)data));
+#else
+    PRAGMA_OMP_SIMD()
+    for (int v = 0; v < simd_w; v++)
+        dest[v] = data[v];
+#endif
+}
+
+void inline accum_output(
+        float *dest, float *data, bool streamout, bool with_relu_postsum) {
+#ifdef __INTEL_COMPILER
+    __m512 _data = _mm512_loadu_ps(data);
+    __m512 _dest = _mm512_loadu_ps(dest);
+    _data = _mm512_add_ps(_data, _dest);
+    if (with_relu_postsum)
+        _data = _mm512_max_ps(_data, _mm512_setzero_ps());
+    if (streamout)
+        _mm512_stream_ps(dest, _data);
+    else
+        _mm512_store_ps(dest, _data);
+#else
+    PRAGMA_OMP_SIMD()
+    for (int v = 0; v < simd_w; v++)
+        data[v] += dest[v];
+
+    if (with_relu_postsum) {
+        PRAGMA_OMP_SIMD()
+        for (int v = 0; v < simd_w; v++)
+            if (data[v] < 0.f)
+                data[v] = 0.f;
+    }
+
+    PRAGMA_OMP_SIMD()
+    for (int v = 0; v < simd_w; v++)
+        dest[v] = data[v];
+#endif
+}
+}
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::utils;
+
+void trans_W_4x4_3x3(float Fw_[6][6][16][16], float F[3][3][16][16]) {
+    float Fw[6][16];
+    float T[6][3][16];
+    float t0[16];
+    float t1[16];
+    float t2[16];
+
+    for (int j = 0; j < 16; j++) {
+#pragma unroll
+        for (int i = 0; i < 3; i++) {
+            PRAGMA_OMP_SIMD()
+            for (int k = 0; k < 16; k++) {
+                t0[k] = 0.26890756302521f * F[2][i][j][k];
+                t1[k] = -t0[k] - 0.688403361344538f * F[0][i][j][k];
+                t2[k] = t0[k] + 0.119514472455649f * F[0][i][j][k];
+
+                T[0][i][k] = 1.13777777777778f * F[0][i][j][k];
+                T[1][i][k] = t1[k] - 0.430252100840336f * F[1][i][j][k];
+                T[2][i][k] = t1[k] + 0.430252100840336f * F[1][i][j][k];
+                T[3][i][k] = t2[k] + 0.179271708683473f * F[1][i][j][k];
+                T[4][i][k] = t2[k] - 0.179271708683473f * F[1][i][j][k];
+                T[5][i][k] = F[2][i][j][k];
+            }
+        }
+#pragma unroll
+        for (int i = 0; i < 6; i++) {
+            PRAGMA_OMP_SIMD()
+            for (int k = 0; k < 16; k++) {
+                t0[k] = 0.26890756302521f * T[i][2][k];
+                t1[k] = -t0[k] - 0.688403361344538f * T[i][0][k];
+                t2[k] = t0[k] + 0.119514472455649f * T[i][0][k];
+
+                Fw[0][k] = 1.13777777777778f * T[i][0][k];
+                Fw[1][k] = t1[k] - 0.430252100840336f * T[i][1][k];
+                Fw[2][k] = t1[k] + 0.430252100840336f * T[i][1][k];
+                Fw[3][k] = t2[k] + 0.179271708683473f * T[i][1][k];
+                Fw[4][k] = t2[k] - 0.179271708683473f * T[i][1][k];
+                Fw[5][k] = T[i][2][k];
+#pragma unroll
+                for (int l = 0; l < 6; l++) {
+                    Fw_[i][l][j][k] = Fw[l][k];
+                }
+            }
+        }
+    }
+}
+
+void trans_O_4x4_3x3(float Mw[6][6][16], float O[4][4][16]) {
+    float T[4][6][16];
+    float t0[16];
+    float t1[16];
+    float t2[16];
+    float t3[16];
+
+#pragma unroll
+    for (int i = 0; i < 6; i++) {
+        PRAGMA_OMP_SIMD()
+        for (int v = 0; v < 16; v++) {
+            t0[v] = Mw[1][i][v] + Mw[2][i][v];
+            t1[v] = Mw[3][i][v] + Mw[4][i][v];
+            t2[v] = Mw[1][i][v] - Mw[2][i][v];
+            t3[v] = Mw[3][i][v] - Mw[4][i][v];
+
+            T[0][i][v] = t0[v] + t1[v] + Mw[0][i][v];
+            T[1][i][v] = t2[v] * 0.625f + t3[v] * 1.5f;
+            T[2][i][v] = t0[v] * 0.390625f + t1[v] * 2.25f;
+            T[3][i][v] = t2[v] * 0.244140625f + t3[v] * 3.375f + Mw[5][i][v];
+        }
+    }
+#pragma unroll
+    for (int i = 0; i < 4; i++) {
+        PRAGMA_OMP_SIMD()
+        for (int v = 0; v < 16; v++) {
+            t0[v] = T[i][1][v] + T[i][2][v];
+            t1[v] = T[i][3][v] + T[i][4][v];
+            t2[v] = T[i][1][v] - T[i][2][v];
+            t3[v] = T[i][3][v] - T[i][4][v];
+
+            O[i][0][v] = t0[v] + t1[v] + T[i][0][v];
+            O[i][1][v] = t2[v] * 0.625f + t3[v] * 1.5f;
+            O[i][2][v] = t0[v] * 0.390625f + t1[v] * 2.25f;
+            O[i][3][v] = t2[v] * 0.244140625f + t3[v] * 3.375f + T[i][5][v];
+        }
+    }
+}
+
+
+void trans_W_3x3_4x4(float Fw[6][6][16], float F[4][6][16])
+{
+    const float rcp3 = 1.0f / 3.0f;
+    const float rcp4 = 1.0f / 4.0f;
+    const float rcp6 = 1.0f / 6.0f;
+    const float rcp12 = 1.0f / 12.0f;
+    const float rcp24 = 1.0f / 24.0f;
+    float t0[16];
+    float t1[16];
+    float t2[16];
+    float t3[16];
+    float t4[16];
+    float T[6][4][16];
+
+pragma_unroll
+    for (int i = 0; i < 4; i++) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < 16; j++) {
+            t0[j] = F[2][i][j] * rcp6;
+            t1[j] = F[0][i][j] * -rcp6 - t0[j];
+            t2[j] = F[0][i][j] * rcp24 + t0[j];
+            t3[j] = (F[1][i][j] + F[3][i][j]) * rcp6;
+            t4[j] = F[1][i][j] * rcp12 + F[3][i][j] * rcp3;
+
+            T[0][i][j] = F[0][i][j] * rcp4;
+            T[1][i][j] = t1[j] - t3[j];
+            T[2][i][j] = t1[j] + t3[j];
+            T[3][i][j] = t2[j] + t4[j];
+            T[4][i][j] = t2[j] - t4[j];
+            T[5][i][j] = F[3][i][j];
+        }
+    }
+pragma_unroll
+    for (int i = 0; i < 6; i++) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < 16; j++) {
+            t0[j] = T[i][2][j] * rcp6;
+            t1[j] = T[i][0][j] * -rcp6 - t0[j];
+            t2[j] = T[i][0][j] * rcp24 + t0[j];
+            t3[j] = (T[i][1][j] + T[i][3][j]) * rcp6;
+            t4[j] = T[i][1][j] * rcp12 + T[i][3][j] * rcp3;
+
+            Fw[i][0][j] = T[i][0][j] * rcp4;
+            Fw[i][1][j] = t1[j] - t3[j];
+            Fw[i][2][j] = t1[j] + t3[j];
+            Fw[i][3][j] = t2[j] + t4[j];
+            Fw[i][4][j] = t2[j] - t4[j];
+            Fw[i][5][j] = T[i][3][j];
+        }
+    }
+}
+
+void trans_O_3x3_4x4(float Mw[6][6][16][16], float M[3][3][16][16])
+{
+    float T[4][6][16];
+    float M_[3][16];
+    float t0[16];
+    float t1[16];
+    float t2[16];
+
+    for (int j = 0; j < 16; j++) {
+pragma_unroll
+        for (int i = 0; i < 6; i++) {
+            PRAGMA_OMP_SIMD()
+            for (int l = 0; l < 16; l++) {
+                t0[l] = Mw[1][i][j][l] + Mw[2][i][j][l];
+                t1[l] = Mw[3][i][j][l] + Mw[4][i][j][l];
+                t2[l] = t1[l] * 4.0f + Mw[5][i][j][l];
+
+                T[0][i][l] = Mw[0][i][j][l] + t0[l] + t1[l];
+                T[1][i][l] = (Mw[1][i][j][l] - Mw[2][i][j][l]) +
+                             2.0f * (Mw[3][i][j][l] - Mw[4][i][j][l]);
+                T[2][i][l] = t0[l] + t2[l];
+            }
+        }
+pragma_unroll
+        for (int i = 0; i < 3; i++) {
+            PRAGMA_OMP_SIMD()
+            for (int l = 0; l < 16; l++) {
+                t0[l] = T[i][1][l] + T[i][2][l];
+                t1[l] = T[i][3][l] + T[i][4][l];
+                t2[l] = t1[l] * 4.0f + T[i][5][l];
+
+                M_[0][l] = T[i][0][l] + t0[l] + t1[l];
+                M_[1][l] = (T[i][1][l] - T[i][2][l]) +
+                           2.0f * (T[i][3][l] - T[i][4][l]);
+                M_[2][l] = t0[l] + t2[l];
+
+                for (int k = 0; k < 3; k++) {
+                    M[i][k][j][l] = M_[k][l];
+                }
+            }
+        }
+    }
+}
+
+void trans_I_4x4_3x3(float Iw[6][6][16], float I[6][6][16])
+{
+    float T[6][6][16];
+    float t0[16];
+    float t1[16];
+    float t2[16];
+    float t3[16];
+    float t4[16];
+    float t5[16];
+
+pragma_unroll
+    for (int i = 0; i < 6; i++) {
+        PRAGMA_OMP_SIMD()
+        for (int v = 0; v < 16; v++) {
+            t0[v] = I[2][i][v] * -2.25f + I[4][i][v];
+            t1[v] = I[1][i][v] * -2.25f + I[3][i][v];
+            t2[v] = I[2][i][v] * -0.390625f + I[4][i][v];
+            t3[v] = I[1][i][v] * -0.390625f + I[3][i][v];
+            t4[v] = I[0][i][v] * 0.87890625f + I[4][i][v];
+            t5[v] = I[1][i][v] * 0.87890625f + I[5][i][v];
+
+            T[0][i][v] = I[2][i][v] * -2.640625f + t4[v];
+            T[1][i][v] = t1[v] * 0.625f + t0[v];
+            T[2][i][v] = t1[v] * -0.625f + t0[v];
+            T[3][i][v] = t3[v] * 1.5f + t2[v];
+            T[4][i][v] = t3[v] * -1.5f + t2[v];
+            T[5][i][v] = I[3][i][v] * -2.640625f + t5[v];
+        }
+    }
+
+pragma_unroll
+    for (int i = 0; i < 6; i++) {
+        PRAGMA_OMP_SIMD()
+        for (int v = 0; v < 16; v++) {
+            t0[v] = T[i][2][v] * -2.25f + T[i][4][v];
+            t1[v] = T[i][1][v] * -2.25f + T[i][3][v];
+            t2[v] = T[i][2][v] * -0.390625f + T[i][4][v];
+            t3[v] = T[i][1][v] * -0.390625f + T[i][3][v];
+            t4[v] = T[i][0][v] * 0.87890625f + T[i][4][v];
+            t5[v] = T[i][1][v] * 0.87890625f + T[i][5][v];
+
+            Iw[i][0][v] = T[i][2][v] * -2.640625f + t4[v];
+            Iw[i][1][v] = t1[v] * 0.625f + t0[v];
+            Iw[i][2][v] = t1[v] * -0.625f + t0[v];
+            Iw[i][3][v] = t3[v] * 1.5f + t2[v];
+            Iw[i][4][v] = t3[v] * -1.5f + t2[v];
+            Iw[i][5][v] = T[i][3][v] * -2.640625f + t5[v];
+        }
+    }
+}
+
+void trans_W_3x3_4x4_wu(float Fw[6][6][16], float F[4][6][16])
+{
+    float T[6][4][16];
+    float t0[16];
+    float t1[16];
+    float t2[16];
+    float t3[16];
+    float t4[16];
+
+pragma_unroll
+    for (int i = 0; i < 4; i++) {
+        PRAGMA_OMP_SIMD()
+        for (int v = 0; v < 16; v++) {
+            t0[v] = F[2][i][v] * 0.26890756302521f;
+            t1[v] = F[0][i][v] * -0.688403361344538f - t0[v];
+            t2[v] = F[0][i][v] * 0.119514472455649f + t0[v];
+            t3[v] = F[1][i][v] * 0.430252100840336f +
+                    F[3][i][v] * 0.168067226890756f;
+            t4[v] = F[1][i][v] * 0.179271708683473f +
+                    F[3][i][v] * 0.403361344537815f;
+
+            T[0][i][v] = F[0][i][v] * 1.13777777777778f;
+            T[1][i][v] = t1[v] - t3[v];
+            T[2][i][v] = t1[v] + t3[v];
+            T[3][i][v] = t2[v] + t4[v];
+            T[4][i][v] = t2[v] - t4[v];
+            T[5][i][v] = F[3][i][v];
+        }
+    }
+pragma_unroll
+    for (int i = 0; i < 6; i++) {
+        for (int v = 0; v < 16; v++) {
+            t0[v] = T[i][2][v] * 0.26890756302521f;
+            t1[v] = T[i][0][v] * -0.688403361344538f - t0[v];
+            t2[v] = T[i][0][v] * 0.119514472455649f + t0[v];
+            t3[v] = T[i][1][v] * 0.430252100840336f +
+                    T[i][3][v] * 0.168067226890756f;
+            t4[v] = T[i][1][v] * 0.179271708683473f +
+                    T[i][3][v] * 0.403361344537815f;
+
+            Fw[i][0][v] = T[i][0][v] * 1.13777777777778f;
+            Fw[i][1][v] = t1[v] - t3[v];
+            Fw[i][2][v] = t1[v] + t3[v];
+            Fw[i][3][v] = t2[v] + t4[v];
+            Fw[i][4][v] = t2[v] - t4[v];
+            Fw[i][5][v] = T[i][3][v];
+        }
+    }
+}
+
+void trans_O_3x3_4x4_wu(float Mw[6][6][16][16], float M[3][3][16][16])
+{
+    float T[3][6][16];
+    float t0[16];
+    float t1[16];
+    float t2[16];
+    float M_[3][16];
+
+    for (int j = 0; j < 16; j++) {
+pragma_unroll
+        for (int i = 0; i < 6; i++) {
+            PRAGMA_OMP_SIMD()
+            for (int v = 0; v < 16; v++) {
+                t0[v] = Mw[1][i][j][v] + Mw[2][i][j][v];
+                t1[v] = Mw[3][i][j][v] + Mw[4][i][j][v];
+                t2[v] = t1[v] * 2.25f + Mw[5][i][j][v];
+
+                T[0][i][v] = Mw[0][i][j][v] + t0[v] + t1[v];
+                T[1][i][v] = 0.625f * (Mw[1][i][j][v] - Mw[2][i][j][v]) +
+                             1.5f * (Mw[3][i][j][v] - Mw[4][i][j][v]);
+                T[2][i][v] = t0[v] * 0.390625f + t2[v];
+            }
+        }
+pragma_unroll
+        for (int i = 0; i < 3; i++) {
+            PRAGMA_OMP_SIMD()
+            for (int v = 0; v < 16; v++) {
+                t0[v] = T[i][1][v] + T[i][2][v];
+                t1[v] = T[i][3][v] + T[i][4][v];
+                t2[v] = t1[v] * 2.25f + T[i][5][v];
+
+                M_[0][v] = T[i][0][v] + t0[v] + t1[v];
+                M_[1][v] = 0.625f * (T[i][1][v] - T[i][2][v]) +
+                           1.5f * (T[i][3][v] - T[i][4][v]);
+                M_[2][v] = t0[v] * 0.390625f + t2[v];
+            }
+
+pragma_unroll
+            for (int k = 0; k < 3; k++) {
+                PRAGMA_OMP_SIMD()
+                for (int v = 0; v < 16; v++) {
+                    M[i][k][j][v] = M_[k][v];
+                }
+            }
+        }
+    }
+}
+
+template <bool is_fwd>
+void input_transform_data(int image, const jit_conv_winograd_conf_t &jcp,
+        float *inp, float *tinp, bool streamout = true)
+{
+    const int inpw = is_fwd ? jcp.iw : jcp.ow;
+    const int inph = is_fwd ? jcp.ih : jcp.oh;
+    const int l_pad = is_fwd ? jcp.l_pad : jcp.iw + jcp.r_pad - jcp.ow;
+    const int t_pad = is_fwd ? jcp.t_pad : jcp.ih + jcp.t_pad - jcp.oh;
+    const int wp_max = inpw + l_pad;
+    const int hp_max = inph + t_pad;
+    float Iw[alpha][alpha][simd_w];
+    float I[alpha][alpha][simd_w];
+
+    array_offset_calculator<float, 5> input(inp,
+            jcp.mb, jcp.dimK/simd_w, inph, inpw,
+            simd_w);
+    array_offset_calculator<float, 8> output(tinp,
+            jcp.dimN_nb_block, alpha, alpha,
+            jcp.dimN_block, jcp.dimK_nb_block, jcp.dimK_block,
+            jcp.dimN_reg_block, jcp.dimK_reg_block);
+
+    int tile_base_index = image * jcp.itiles * jcp.jtiles;
+    int tile_block_ur = tile_base_index % jcp.tile_block_ur;
+    int nb_tile_block_ur =
+        (tile_base_index / jcp.tile_block_ur) % jcp.nb_tile_block_ur;
+    int tile_block =
+        (tile_base_index / jcp.tile_block_ur) / jcp.nb_tile_block_ur;
+
+    for (int tj = 0; tj < jcp.jtiles; tj++) {
+        for (int ti = 0; ti < jcp.itiles; ti++) {
+            for (int j = 0; j < alpha; j++) {
+                int ydim = tj * tile_size + j;
+                if ((t_pad <= ydim) && (ydim < hp_max)) {
+                    float *pinp_j = inp + (ydim - t_pad) * inpw * 16 ;
+                    for (int i = 0; i < alpha; i++) {
+                        int xdim = ti * tile_size + i;
+                        if ((l_pad <= xdim) && (xdim < wp_max)) {
+                            float *pinp_i = pinp_j + (xdim - l_pad) * 16;
+                            load_ps(I[j][i], pinp_i);
+                        } else {
+                            PRAGMA_OMP_SIMD()
+                            for (int v = 0; v < simd_w; v++) {
+                                I[j][i][v] = 0.0f;
+                            }
+                        }
+                    }
+                } else {
+                    for (int i = 0; i < alpha; i++) {
+                        PRAGMA_OMP_SIMD()
+                        for (int v = 0; v < simd_w; v++) {
+                            I[j][i][v] = 0.0f;
+                        }
+                    }
+                }
+            }
+
+            trans_I_4x4_3x3(Iw, I);
+
+            for (int j = 0; j < alpha; j++) {
+                for (int i = 0; i < alpha; i++) {
+                    store_output(&(output(tile_block, j, i,
+                                    nb_tile_block_ur, 0, 0,
+                                    tile_block_ur, 0)),
+                                 Iw[j][i], streamout);
+                }
+            }
+            tile_block_ur++;
+            if (tile_block_ur >= jcp.tile_block_ur) {
+                tile_block_ur = 0;
+                nb_tile_block_ur++;
+            }
+            if (nb_tile_block_ur >= jcp.nb_tile_block_ur) {
+                nb_tile_block_ur = 0;
+                tile_block++;
+            }
+        }
+    }
+}
+
+template <bool is_fwd>
+void weight_transform_data(const jit_conv_winograd_conf_t &jcp,
+        float *wp, float *twp)
+{
+    const int kh = 3;
+    const int kw = 3;
+    array_offset_calculator<float, 6> input(wp,
+            jcp.oc/jcp.oc_simd_block,
+            jcp.ic/jcp.ic_simd_block,
+            jcp.kh, jcp.kw,
+            simd_w, simd_w);
+    array_offset_calculator<float, 8> output(twp,
+            jcp.dimM_nb_block,
+            alpha, alpha,
+            jcp.dimK_nb_block,
+            jcp.dimM_block, jcp.dimK_block,
+            simd_w, simd_w);
+    float Fw[alpha][alpha][simd_w][simd_w];
+    float F[kh][kw][simd_w][simd_w];
+
+    for (int j = 0; j < kh; j++) {
+        for (int i = 0; i < kw; i++) {
+            for (int v1 = 0; v1 < simd_w; v1++) {
+                float *base_inp = is_fwd
+                                ? &(input(0, 0, j, i, v1, 0))
+                                : &(input(0, 0, 2 - j, 2 - i, v1, 0));
+                PRAGMA_OMP_SIMD()
+                for (int v2 = 0; v2 < simd_w; v2++) {
+                    if (is_fwd)
+                        F[j][i][v1][v2] = *(base_inp + v2);
+                    else
+                        F[j][i][v2][v1] = *(base_inp + v2);
+                }
+            }
+        }
+    }
+
+    trans_W_4x4_3x3(Fw, F);
+
+    for (int j = 0; j < alpha; j++) {
+        for (int i = 0; i < alpha; i++) {
+            for (int v1 = 0; v1 < simd_w; v1++) {
+                PRAGMA_OMP_SIMD()
+                for (int v2 = 0; v2 < simd_w; v2++) {
+                    output(0, j, i, 0, 0, 0, v1, v2) = Fw[j][i][v1][v2];
+                }
+            }
+        }
+    }
+}
+
+template <bool is_fwd, bool with_bias, bool with_relu_presum, bool with_sum>
+void output_transform_data(int image, const jit_conv_winograd_conf_t &jcp,
+        const post_ops_t &p_ops, float *toutp, float *pout_b, float *bias,
+        bool streamout = true) {
+    float Ow[alpha][alpha][simd_w];
+    float O[tile_size][tile_size][simd_w];
+    int outw = is_fwd ? jcp.ow : jcp.iw;
+    int outh = is_fwd ? jcp.oh : jcp.ih;
+
+    /* Prepare for PostOps */
+    bool with_relu_postsum = p_ops.find(primitive_kind::eltwise, 1) != -1;
+
+    array_offset_calculator<float, 8> input(toutp,
+            jcp.dimN_nb_block, jcp.dimM_nb_block,
+            alpha, alpha,
+            jcp.dimN_block, jcp.dimM_block,
+            jcp.dimN_reg_block, jcp.dimM_simd_block);
+
+    int tile_base_index = image * jcp.itiles * jcp.jtiles;
+    int tile_block_ur = tile_base_index % jcp.tile_block_ur;
+    int nb_tile_block_ur =
+        (tile_base_index / jcp.tile_block_ur) % jcp.nb_tile_block_ur;
+    int tile_block =
+        (tile_base_index / jcp.tile_block_ur) / jcp.nb_tile_block_ur;
+
+    for (int tj = 0; tj < jcp.jtiles; tj++) {
+        for (int ti = 0; ti < jcp.itiles; ti++) {
+            for (int j = 0; j < alpha; j++) {
+                for (int i = 0; i < alpha; i++) {
+                    PRAGMA_OMP_SIMD()
+                    for (int v = 0; v < simd_w; v++) {
+                        Ow[j][i][v] = input(tile_block, 0,
+                                j, i,
+                                nb_tile_block_ur, 0,
+                                tile_block_ur, v);
+                    }
+                }
+            }
+
+            trans_O_4x4_3x3(Ow, O);
+
+            for (int j = 0; j < tile_size; j++) {
+                int ydim = tj * tile_size + j;
+                if (ydim < outh) {
+                    float *pout_j = pout_b + ydim * outw * simd_w;
+                    for (int i = 0; i < tile_size; i++) {
+                        int xdim = ti * tile_size + i;
+                        if (xdim < outw) {
+                            float *pout_i = pout_j + xdim * simd_w;
+                            if (is_fwd) {
+                                PRAGMA_OMP_SIMD()
+                                for (int v = 0; v < simd_w; v++) {
+                                    O[j][i][v] += with_bias ? bias[v] : 0.f;
+                                    O[j][i][v] = true
+                                        && with_relu_presum && O[j][i][v] < 0.f
+                                                ? O[j][i][v]
+                                                * jcp.eltwise.alpha
+                                                : O[j][i][v];
+                                }
+                            }
+                            if (with_sum)
+                                accum_output(pout_i, O[j][i], streamout,
+                                        with_relu_postsum);
+                            else
+                                store_output(pout_i, O[j][i], streamout);
+                        }
+                    }
+                }
+            }
+            tile_block_ur++;
+            if (tile_block_ur >= jcp.tile_block_ur) {
+                tile_block_ur = 0;
+                nb_tile_block_ur++;
+            }
+            if (nb_tile_block_ur >= jcp.nb_tile_block_ur) {
+                nb_tile_block_ur = 0;
+                tile_block++;
+            }
+        }
+    }
+}
+
+template <bool ver_4fma>
+void diff_src_transform_bwd_weights(int image, jit_conv_winograd_conf_t conv,
+        float *inp, float *tinp, float *Iw_temp,
+        void (*transpose_4fma_ker)(float *, float *))
+{
+
+    const int ifwp = conv.iw + conv.l_pad;
+    const int ifhp = conv.ih + conv.t_pad;
+    float I[alpha][alpha][simd_w];
+    float Iw[alpha][alpha][simd_w];
+
+    array_offset_calculator<float, 4> Iw_trans_temp(Iw_temp,
+            alpha, alpha, conv.tile_4fma, simd_w);
+    array_offset_calculator<float, 5> input(inp,
+            conv.mb, conv.ic/simd_w, conv.ih, conv.iw, simd_w);
+    array_offset_calculator<float, 8> output(tinp,
+            conv.nb_ic, alpha, alpha,
+            conv.tile_block, conv.ic_block,
+            conv.nb_tile_block_ur, conv.tile_block_ur,
+            conv.ic_simd_block * conv.tile_4fma);
+
+    int tile_base_index =
+        image * (conv.itiles * conv.jtiles + conv.tile_4fma_padding);
+    int tile_4fma = 0;
+    int tile_block_ur = (tile_base_index / conv.tile_4fma) % conv.tile_block_ur;
+    int nb_tile_block_ur =
+        (tile_base_index / conv.tile_4fma / conv.tile_block_ur)
+        % conv.nb_tile_block_ur;
+    int tile_block = (tile_base_index / conv.tile_4fma / conv.tile_block_ur)
+            / conv.nb_tile_block_ur;
+
+    for (int tj = 0; tj < conv.jtiles; tj++) {
+        for (int ti = 0; ti < conv.itiles; ti++) {
+            for (int j = 0; j < alpha; j++) {
+                int ydim = tj * tile_size + j;
+                if ((conv.t_pad <= ydim) && ydim < ifhp) {
+                    for (int i = 0; i < alpha; i++) {
+                        int xdim = ti * tile_size + i;
+                        if ((conv.l_pad <= xdim) && xdim < ifwp) {
+                            PRAGMA_OMP_SIMD()
+                            for (int v = 0; v < simd_w; v++) {
+                                I[j][i][v] = input(0, 0,
+                                        ydim - conv.t_pad,
+                                        xdim - conv.l_pad, v);
+                            }
+                        } else {
+                            PRAGMA_OMP_SIMD()
+                            for (int v = 0; v < simd_w; v++) {
+                                I[j][i][v] = 0.0f;
+                            }
+                        }
+                    }
+                } else {
+                    for (int i = 0; i < alpha; i++) {
+                        PRAGMA_OMP_SIMD()
+                        for (int v = 0; v < simd_w; v++) {
+                            I[j][i][v] = 0.0f;
+                        }
+                    }
+                }
+            }
+            trans_I_4x4_3x3(Iw, I);
+
+            if (ver_4fma) {
+                for (int j = 0; j < alpha; j++) {
+                    for (int i = 0; i < alpha; i++) {
+                        float *Iw_temp_base = &(Iw_trans_temp(j, i,
+                                                        tile_4fma, 0));
+                        PRAGMA_OMP_SIMD()
+                        for (int v = 0; v < simd_w; v++) {
+                            Iw_temp_base[v] = Iw[j][i][v];
+                        }
+                    }
+                }
+                tile_4fma++;
+                if (tile_4fma == conv.tile_4fma) {
+                    float *outp = &(output(0, 0, 0,
+                                tile_block, 0,
+                                nb_tile_block_ur, tile_block_ur, 0));
+                    transpose_4fma_ker(outp, (float *)Iw_temp);
+                    tile_4fma = 0;
+                    tile_block_ur++;
+                }
+            } else {
+                for (int j = 0; j < alpha; j++) {
+                    for (int i = 0; i < alpha; i++) {
+                        store_output(&(output(0, j, i,
+                                        tile_block, 0,
+                                        nb_tile_block_ur, tile_block_ur, 0)),
+                                     Iw[j][i], true);
+                    }
+                }
+                tile_block_ur++;
+            }
+
+            if (tile_block_ur == conv.tile_block_ur) {
+                tile_block_ur = 0;
+                ++nb_tile_block_ur;
+            }
+            if (nb_tile_block_ur == conv.nb_tile_block_ur) {
+                nb_tile_block_ur = 0;
+                tile_block++;
+            }
+        }
+    }
+
+    if (ver_4fma && tile_4fma < conv.tile_4fma && conv.tile_4fma_padding != 0) {
+
+        for (int j = 0; j < alpha; j++) {
+            for (int i = 0; i < alpha; i++) {
+                for (int tb = tile_4fma; tb < conv.tile_4fma; tb++) {
+                    float *Iw_temp_base = &(Iw_trans_temp(j, i, tb, 0));
+                    PRAGMA_OMP_SIMD()
+                    for (int v = 0; v < simd_w; v++) {
+                        Iw_temp_base[v] = 0;
+                    }
+                }
+            }
+        }
+        float *outp = &(output(0, 0, 0,
+                    tile_block, 0,
+                    nb_tile_block_ur, tile_block_ur, 0));
+        transpose_4fma_ker(outp, (float *)Iw_temp);
+    }
+}
+
+template <bool with_bias>
+void diff_dst_transform_bwd_weights(int image, jit_conv_winograd_conf_t conv,
+        float *inp, float *tinp, float *dbias)
+{
+
+    const int total_tiles = conv.itiles * conv.jtiles + conv.tile_4fma_padding;
+    float I[alpha][alpha][simd_w];
+    float Iw[alpha][alpha][simd_w];
+
+    array_offset_calculator<float, 5> input(inp,
+            conv.mb, conv.oc/simd_w, conv.oh, conv.ow, conv.oc_simd_block);
+    array_offset_calculator<float, 8> output(tinp,
+            conv.nb_oc, alpha, alpha,
+            conv.tile_block, conv.oc_block,
+            conv.nb_tile_block_ur,
+            conv.tile_block_ur * conv.tile_4fma, conv.oc_simd_block);
+
+    int tile_base_index = image * total_tiles;
+    int tile_block_ur = tile_base_index % (conv.tile_block_ur * conv.tile_4fma);
+    int nb_tile_block_ur =
+        (tile_base_index / conv.tile_block_ur / conv.tile_4fma)
+            % conv.nb_tile_block_ur;
+    int tile_block = (tile_base_index / conv.tile_block_ur / conv.tile_4fma)
+            / conv.nb_tile_block_ur;
+
+    for (int tj = 0; tj < conv.jtiles; tj++) {
+        for (int ti = 0; ti < conv.itiles; ti++) {
+            for (int j = 0; j < alpha; j++) {
+                int ydim = tj * tile_size + j;
+                if (ydim < conv.oh) {
+                    for (int i = 0; i < alpha; i++) {
+                        int xdim = ti * tile_size + i;
+                        if (xdim < conv.ow) {
+                            float *input_base = &(input(0, 0, ydim, xdim, 0));
+
+                            PRAGMA_OMP_SIMD()
+                            for (int v = 0; v < simd_w; v++) {
+                                I[j][i][v] = input_base[v];
+                            }
+                            if (with_bias && j < tile_size && i < tile_size) {
+                                PRAGMA_OMP_SIMD()
+                                for (int v = 0; v < simd_w; v++) {
+                                    dbias[v] += input_base[v];
+                                }
+                            }
+                        } else {
+                            PRAGMA_OMP_SIMD()
+                            for (int v = 0; v < simd_w; v++) {
+                                I[j][i][v] = 0.0f;
+                            }
+                        }
+                    }
+                } else {
+                    for (int i = 0; i < alpha; i++) {
+                        PRAGMA_OMP_SIMD()
+                        for (int v = 0; v < simd_w; v++) {
+                            I[j][i][v] = 0.0f;
+                        }
+                    }
+                }
+            }
+
+            trans_W_3x3_4x4_wu(Iw, I);
+
+            for (int j = 0; j < alpha; j++) {
+                for (int i = 0; i < alpha; i++) {
+                    store_output(&(output(0, j, i,
+                                    tile_block, 0,
+                                    nb_tile_block_ur,
+                                    tile_block_ur, 0)),
+                                 Iw[j][i], true);
+                }
+            }
+            tile_block_ur++;
+            if (tile_block_ur >= conv.tile_block_ur * conv.tile_4fma) {
+                tile_block_ur = 0;
+                nb_tile_block_ur++;
+            }
+            if (nb_tile_block_ur >= conv.nb_tile_block_ur) {
+                nb_tile_block_ur = 0;
+                tile_block++;
+            }
+        }
+    }
+}
+
+void diff_weights_transform_bwd_weights(jit_conv_winograd_conf_t conv,
+        float *wp, float *twp)
+{
+    const int kh = 3;
+    const int kw = 3;
+    float Fw[alpha][alpha][simd_w][simd_w];
+    float F[kh][kw][simd_w][simd_w];
+
+    array_offset_calculator<float, 8> input(twp,
+            conv.nb_ic, conv.nb_oc,
+            alpha, alpha,
+            conv.oc_block, conv.ic_block,
+            conv.ic_simd_block, conv.oc_simd_block);
+    array_offset_calculator<float, 6> output(wp,
+            conv.oc/simd_w, conv.ic/simd_w,
+            conv.kh, conv.kw,
+            conv.ic_simd_block, conv.oc_simd_block);
+
+    for (int j = 0; j < alpha; j++) {
+        for (int i = 0; i < alpha; i++) {
+            for (int v = 0; v < conv.ic_simd_block; v++) {
+                PRAGMA_OMP_SIMD()
+                for (int k = 0; k < conv.oc_simd_block; k++) {
+                    Fw[j][i][v][k] = input(0, 0, j, i, 0, 0, v, k);
+                }
+            }
+        }
+    }
+
+    trans_O_3x3_4x4_wu(Fw, F);
+
+    for (int j = 0; j < kh; j++) {
+        for (int i = 0; i < kw; i++) {
+            for (int v = 0; v < conv.ic_simd_block; v++) {
+                store_output(&(output(0, 0, j, i, v, 0)),
+                             F[j][i][v], true);
+            }
+        }
+    }
+}
+
+template <bool is_fwd>
+void _jit_avx512_common_convolution_winograd_t<is_fwd>::_execute_data_W_S_G_D(
+        float *inp_ptr, float *out_ptr, float *wei_ptr, float *bias_ptr,
+        const memory_tracking::grantor_t &scratchpad) const {
+    const auto &jcp = kernel_->jcp;
+    const auto &p_ops = attr_->post_ops_;
+
+    const int inph = is_fwd ? jcp.ih : jcp.oh;
+    const int inpw = is_fwd ? jcp.iw : jcp.ow;
+    const int outh = is_fwd ? jcp.oh : jcp.ih;
+    const int outw = is_fwd ? jcp.ow : jcp.iw;
+
+    /* Note that jcp.with_eltwise is true for both fused conv+relu primitive
+     * and conv primitive with PostOps with relu before sum
+     * (PostOps relu after sum is handled later) */
+    auto output_transform = jcp.with_bias
+            ? (jcp.with_eltwise
+                ? (jcp.with_sum
+                    ? output_transform_data<is_fwd, true, true, true>
+                    : output_transform_data<is_fwd, true, true, false>)
+                : (jcp.with_sum
+                    ? output_transform_data<is_fwd, true, false, true>
+                    : output_transform_data<is_fwd, true, false, false>))
+            : (jcp.with_eltwise
+                ? (jcp.with_sum
+                    ? output_transform_data<is_fwd, false, true, true>
+                    : output_transform_data<is_fwd, false, true, false>)
+                : (jcp.with_sum
+                    ? output_transform_data<is_fwd, false, false, true>
+                    : output_transform_data<is_fwd, false, false, false>));
+
+    /* Notation:
+       FWD: dimM:oc, dimN:ntiles, dimK:ic,
+       BWD: dimM:ic, dimN:ntiles, dimK:oc,
+       FWD/BWD: V: src/diff_dst transform, U:weight transform,
+                M:dst/diff_src transform  */
+    array_offset_calculator<float, 5> input(inp_ptr,
+            jcp.mb, jcp.dimK/jcp.dimK_reg_block, inph, inpw,
+            jcp.dimK_reg_block);
+    array_offset_calculator<float, 5> output(out_ptr,
+            jcp.mb, jcp.dimM/jcp.dimM_simd_block, outh, outw,
+            jcp.dimM_simd_block);
+    array_offset_calculator<float, 6> weights(wei_ptr,
+            jcp.oc/jcp.oc_simd_block, jcp.ic/jcp.ic_simd_block, jcp.kh, jcp.kw,
+            jcp.ic_simd_block, jcp.oc_simd_block);
+    array_offset_calculator<float, 2> bias(bias_ptr,
+            jcp.dimM/jcp.dimM_simd_block, jcp.dimM_simd_block);
+
+    array_offset_calculator<float, 8> M(is_fwd
+            ? scratchpad.template get<float>(key_wino_M)
+            : scratchpad.template get<float>(key_wino_V),
+            jcp.dimN_nb_block, jcp.dimM_nb_block,
+            alpha, alpha,
+            jcp.dimN_block, jcp.dimM_block,
+            jcp.dimN_reg_block, jcp.dimM_simd_block);
+    array_offset_calculator<float, 8> U(
+            scratchpad.template get<float>(key_wino_U),
+            jcp.dimM_nb_block,
+            alpha, alpha,
+            jcp.dimK_nb_block,
+            jcp.dimM_block, jcp.dimK_block,
+            jcp.dimK_reg_block, jcp.dimM_simd_block);
+    array_offset_calculator<float, 8> V(is_fwd
+            ? scratchpad.template get<float>(key_wino_V)
+            : scratchpad.template get<float>(key_wino_M),
+            jcp.dimN_nb_block, alpha, alpha,
+            jcp.dimN_block, jcp.dimK_nb_block,
+            jcp.dimK_block, jcp.dimN_reg_block, jcp.dimK_reg_block);
+
+    bool V_streamout = jcp.dimN * jcp.dimK * alpha * alpha * sizeof(float)
+        > 2 * LLC_cache_size ? true : false;
+
+    const bool output_is_aligned = ((size_t)out_ptr & (64 - 1)) == 0;
+
+    const bool wants_padded_bias = jcp.with_bias
+        && jcp.oc_without_padding != jcp.oc;
+    float last_slice_bias[simd_w] = {0};
+    if (wants_padded_bias) {
+        for (int oc = 0; oc < jcp.oc_without_padding % jcp.oc_simd_block; ++oc)
+            last_slice_bias[oc] = bias(jcp.dimM / jcp.dimM_simd_block - 1, oc);
+    }
+
+    {
+        parallel_nd(jcp.mb, jcp.dimK_nb_block, jcp.dimK_block,
+            [&](int img, int K_blk1, int K_blk2) {
+            input_transform_data<is_fwd>(img, jcp,
+                &(input(img, K_blk1 * jcp.dimK_block + K_blk2, 0, 0, 0)),
+                &(V(0, 0, 0, 0, K_blk1, K_blk2, 0, 0)), V_streamout);
+        });
+
+        parallel_nd(jcp.nb_oc, jcp.nb_ic, jcp.oc_block, jcp.ic_block,
+            [&](int ofm1, int ifm1, int ofm2, int ifm2) {
+            float *U_base_ptr = is_fwd
+                ? &(U(ofm1, 0, 0, ifm1, ofm2, ifm2, 0, 0))
+                : &(U(ifm1, 0, 0, ofm1, ifm2, ofm2, 0, 0));
+            weight_transform_data<is_fwd>(jcp,
+                &(weights(ofm1 * jcp.oc_block + ofm2,
+                ifm1 * jcp.ic_block + ifm2, 0, 0, 0, 0)), U_base_ptr);
+        });
+
+        parallel_nd(jcp.dimN_nb_block, alpha, alpha, jcp.dimM_nb_block, jcp.dimN_block,
+            [&](int N_blk1, int oj, int oi, int M_blk1, int N_blk2) {
+
+            kernel_->gemm_loop_ker_first_iter(
+                    (float *)&(M(N_blk1, M_blk1, oj, oi,
+                            N_blk2, 0, 0, 0)),
+                    (const float *)&(U(M_blk1, oj, oi,
+                            0, 0, 0, 0, 0)),
+                    (const float *)&(V(N_blk1, oj, oi,
+                            N_blk2, 0, 0, 0, 0)));
+            for (int K_blk1 = 1; K_blk1 < jcp.dimK_nb_block; K_blk1++) {
+                kernel_->gemm_loop_ker(
+                        (float *)&(M(N_blk1, M_blk1, oj, oi,
+                                N_blk2, 0, 0, 0)),
+                        (const float *)&(U(M_blk1, oj, oi,
+                                K_blk1, 0, 0, 0, 0)),
+                        (const float *)&(V(N_blk1, oj, oi,
+                                N_blk2, K_blk1,
+                                0, 0, 0)));
+            }
+
+        });
+
+        parallel_nd(jcp.mb, jcp.dimM_nb_block, jcp.dimM_block,
+                    [&](int img, int M_blk1, int M_blk2) {
+
+            const int M_blk = M_blk1 * jcp.dimM_block + M_blk2;
+
+            float *bias_ptr = wants_padded_bias
+                && M_blk == jcp.dimM / jcp.dimM_simd_block - 1
+                ? last_slice_bias : &bias(M_blk, 0);
+
+            output_transform(img, jcp, p_ops,
+                    &(M(0, M_blk1, 0, 0, 0, M_blk2, 0, 0)),
+                    &(output(img, M_blk, 0, 0, 0)),
+                    bias_ptr, output_is_aligned);
+
+       });
+
+    }
+}
+
+template struct _jit_avx512_common_convolution_winograd_t<true>;
+template struct _jit_avx512_common_convolution_winograd_t<false>;
+
+void jit_avx512_common_convolution_winograd_bwd_weights_t::
+_maybe_execute_diff_bias_copy(float *diff_bias,
+        const memory_tracking::grantor_t &scratchpad) const {
+    if (pd()->wants_padded_bias()) {
+        auto padded_bias = scratchpad.get<float>(key_conv_padded_bias);
+        for (int oc = 0; oc < pd()->jcp_.oc_without_padding; ++oc)
+            diff_bias[oc] = padded_bias[oc];
+    }
+}
+
+void jit_avx512_common_convolution_winograd_bwd_weights_t::
+_execute_backward_weights_S_D_G_W(const exec_ctx_t &ctx,
+        const memory_tracking::grantor_t &scratchpad) const {
+    auto ptr_diff_dst = CTX_IN_MEM(const float *, MKLDNN_ARG_DIFF_DST);
+    auto ptr_src = CTX_IN_MEM(const float *, MKLDNN_ARG_SRC);
+    auto ptr_diff_weights = CTX_OUT_MEM(float *, MKLDNN_ARG_DIFF_WEIGHTS);
+    auto ptr_diff_bias = CTX_OUT_MEM(float *, MKLDNN_ARG_DIFF_BIAS);
+
+    const auto &jcp = kernel_->jcp;
+    const int nthreads = jcp.nthr;
+
+    auto diff_src_transform_bwd_weights_ver = jcp.ver == ver_4fma ?
+            diff_src_transform_bwd_weights<true> :
+            diff_src_transform_bwd_weights<false>;
+    auto diff_dst_transform_bwd_weights_ver = jcp.with_bias
+                                            ? diff_dst_transform_bwd_weights<true>
+                                            : diff_dst_transform_bwd_weights<false>;
+
+    array_offset_calculator<float, 5> src((float *)ptr_src,
+            jcp.mb, jcp.ic/simd_w, jcp.ih, jcp.iw, simd_w);
+    array_offset_calculator<float, 5> diff_dst((float *)ptr_diff_dst,
+            jcp.mb, jcp.oc/simd_w, jcp.oh, jcp.ow, simd_w);
+    array_offset_calculator<float, 6> diff_weights(ptr_diff_weights,
+            jcp.oc/simd_w, jcp.ic/simd_w, jcp.kh, jcp.kw, simd_w, simd_w);
+    array_offset_calculator<float, 2> diff_bias(pd()->wants_padded_bias()
+            ? scratchpad.get<float>(key_conv_padded_bias) : ptr_diff_bias,
+            jcp.oc/simd_w, simd_w);
+
+    array_offset_calculator<float, 8> U(
+            scratchpad.get<float>(key_wino_U),
+            jcp.nb_ic, jcp.nb_oc,
+            alpha, alpha,
+            jcp.oc_block, jcp.ic_block,
+            jcp.ic_simd_block, jcp.oc_simd_block);
+
+    array_offset_calculator<float, 8> M(
+            scratchpad.get<float>(key_wino_M),
+            jcp.nb_oc, alpha, alpha,
+            jcp.tile_block, jcp.oc_block,
+            jcp.nb_tile_block_ur, jcp.tile_block_ur * jcp.tile_4fma,
+            jcp.oc_simd_block);
+    array_offset_calculator<float, 8> V(
+            scratchpad.get<float>(key_wino_V),
+            jcp.nb_ic, alpha, alpha,
+            jcp.tile_block, jcp.ic_block,
+            jcp.nb_tile_block_ur, jcp.tile_block_ur,
+            jcp.ic_simd_block * jcp.tile_4fma);
+
+    const int trans_buffer_size = alpha * alpha * jcp.tile_4fma
+                                * jcp.ic_simd_block;
+    array_offset_calculator<float, 2> trans_buffer(
+            scratchpad.get<float>(key_conv_tr_src),
+            nthreads,
+            trans_buffer_size);
+
+    array_offset_calculator<float, 2> diff_bias_prv(
+            scratchpad.get<float>(key_conv_bia_reduction),
+            nthreads,
+            jcp.oc);
+
+PRAGMA_OMP(parallel num_threads(nthreads))
+    {
+        if (jcp.with_bias) {
+            parallel_nd_in_omp(nthreads, jcp.oc, [&](int ithr, int ofm) {
+                diff_bias_prv(ithr, ofm) = 0.0f;
+            });
+
+PRAGMA_OMP(for nowait)
+            for (int bofm = 0; bofm < jcp.oc / simd_w; bofm++) {
+                PRAGMA_OMP_SIMD()
+                for (int v = 0; v < simd_w; v++)
+                    diff_bias(bofm, v) = 0.0f;
+            }
+        }
+
+        const int ithread = mkldnn_get_thread_num();
+
+        parallel_nd_in_omp(jcp.mb, jcp.nb_ic, jcp.ic_block,
+            [&](int img, int ifm1, int ifm2) {
+            float *transb = jcp.ver == ver_4fma
+               ? &(trans_buffer(ithread, 0))
+               : NULL;
+            diff_src_transform_bwd_weights_ver(img, jcp,
+               &(src(img, ifm1 * jcp.ic_block + ifm2,
+                       0, 0, 0)),
+               &(V(ifm1, 0, 0, 0, ifm2, 0, 0, 0)),
+               transb,
+               kernel_->transpose_4fma_ker);
+        });
+
+        parallel_nd_in_omp(jcp.mb, jcp.nb_oc, jcp.oc_block,
+            [&](int img, int ofm1, int ofm2) {
+            float *dbias = jcp.with_bias
+                   ? &(diff_bias_prv(ithread,
+                               simd_w * (ofm1 * jcp.oc_block + ofm2)))
+                   : NULL;
+            diff_dst_transform_bwd_weights_ver(img, jcp,
+                    &(diff_dst(img, ofm1 * jcp.oc_block + ofm2,
+                            0, 0, 0)),
+                    &(M(ofm1, 0, 0, 0, ofm2, 0, 0, 0)),
+                    dbias);
+        });
+
+PRAGMA_OMP(barrier)
+
+        for (int ifm1 = 0; ifm1 < jcp.nb_ic; ifm1++) {
+            parallel_nd_in_omp(alpha, alpha, jcp.nb_oc,
+                [&](int oj, int oi, int ofm1) {
+                kernel_->gemm_loop_ker_first_iter(
+                    (float *)&(U(ifm1, ofm1, oj, oi,
+                            0, 0, 0, 0)),
+                    (const float *)&(M(ofm1, oj, oi,
+                            0, 0, 0, 0, 0)),
+                    (const float *)&(V(ifm1, oj, oi,
+                            0, 0, 0, 0, 0)));
+                for (int tile_block = 1; tile_block < jcp.tile_block;
+                     tile_block++) {
+                    kernel_->gemm_loop_ker((float *)&(U(ifm1, ofm1,
+                                oj, oi,
+                                0, 0, 0, 0)),
+                        (const float *)&(M(ofm1, oj, oi, tile_block,
+                                0, 0, 0, 0)),
+                        (const float *)&(V(ifm1, oj, oi, tile_block,
+                                0, 0, 0, 0)));
+                }
+            });
+        }
+
+PRAGMA_OMP(barrier)
+
+        parallel_nd_in_omp(jcp.nb_ic, jcp.nb_oc, jcp.oc_block, jcp.ic_block,
+            [&](int ifm1, int ofm1, int ofm2, int ifm2) {
+            diff_weights_transform_bwd_weights(jcp,
+                    &(diff_weights(ofm1 * jcp.oc_block + ofm2,
+                            ifm1 * jcp.ic_block + ifm2, 0, 0, 0, 0)),
+                    &(U(ifm1, ofm1, 0, 0, ofm2, ifm2, 0, 0)));
+        });
+
+        if (jcp.with_bias) {
+PRAGMA_OMP(for)
+            for (int ofm1 = 0; ofm1 < jcp.oc / simd_w; ofm1++) {
+                for (int ithr = 0; ithr < nthreads; ithr++) {
+                    float* base_bias_ptr = &(diff_bias(ofm1, 0));
+                    float* base_bias_prv_ptr = &(diff_bias_prv(
+                                ithr * jcp.oc + ofm1 * simd_w));
+                    PRAGMA_OMP_SIMD()
+                    for (int ofm2 = 0; ofm2 < simd_w; ofm2++) {
+                        base_bias_ptr[ofm2] += base_bias_prv_ptr[ofm2];
+                    }
+                }
+            }
+        }
+    }
+
+    _maybe_execute_diff_bias_copy(ptr_diff_bias, scratchpad);
+}
+
+}
+}
+}
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution_winograd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution_winograd.hpp
new file mode 100644
index 0000000000..6c76f37c72
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_convolution_winograd.hpp
@@ -0,0 +1,318 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_COMMON_CONVOLUTION_WINOGRAD_HPP
+#define CPU_JIT_AVX512_COMMON_CONVOLUTION_WINOGRAD_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "jit_avx512_common_conv_winograd_kernel_f32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace winograd_avx512_common {
+inline void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+        const jit_conv_winograd_conf_t &jcp) {
+    using namespace memory_tracking::names;
+
+    size_t U_sz = (size_t)alpha * alpha * jcp.ic * jcp.oc;
+    size_t V_sz = (size_t)alpha * alpha * jcp.mb * jcp.ic
+        * (jcp.itiles * jcp.jtiles + jcp.tile_4fma_padding);
+    size_t M_sz = (size_t)alpha * alpha * jcp.mb * jcp.oc
+        * (jcp.itiles * jcp.jtiles + jcp.tile_4fma_padding);
+
+    scratchpad.book(key_wino_U, sizeof(float) * U_sz, PAGE_2M);
+    scratchpad.book(key_wino_V, sizeof(float) * V_sz, PAGE_2M);
+    scratchpad.book(key_wino_M, sizeof(float) * M_sz, PAGE_2M);
+
+    if (jcp.sched_policy == WSCHED_WEI_S_D_G_W) {
+        const int nthr = mkldnn_get_max_threads();
+
+        size_t tr_src_sz = jcp.ver != ver_4fma ? 0 : (size_t)nthr
+            * alpha * alpha * jcp.tile_4fma * jcp.ic_simd_block;
+        scratchpad.book(key_conv_tr_src, sizeof(float) * tr_src_sz, PAGE_2M);
+
+        size_t br_sz = jcp.with_bias ? nthr * jcp.oc : 0;
+        scratchpad.book(key_conv_bia_reduction, sizeof(float) * br_sz, PAGE_2M);
+
+        size_t padded_bias_sz =
+            jcp.with_bias && jcp.oc_without_padding != jcp.oc ? jcp.oc : 0;
+        scratchpad.book(key_conv_padded_bias, sizeof(float) * padded_bias_sz);
+    }
+}
+}
+
+template <bool is_fwd>
+struct _jit_avx512_common_convolution_winograd_t {
+    _jit_avx512_common_convolution_winograd_t(
+            const jit_conv_winograd_conf_t &jcp, const primitive_attr_t *attr)
+        : kernel_(nullptr), attr_(attr) {
+        kernel_ = new _jit_avx512_common_conv_winograd_data_kernel_f32(jcp);
+    }
+
+    ~_jit_avx512_common_convolution_winograd_t() { delete kernel_; }
+
+    protected:
+        void _execute_data_W_S_G_D(float *inp_ptr, float *out_ptr,
+                float *wei_ptr, float *bias_ptr,
+                const memory_tracking::grantor_t &scratchpad) const;
+        _jit_avx512_common_conv_winograd_data_kernel_f32 *kernel_;
+        const primitive_attr_t *attr_;
+};
+
+struct jit_avx512_common_convolution_winograd_fwd_t
+     : _jit_avx512_common_convolution_winograd_t<true>
+     , public cpu_primitive_t
+    {
+    struct pd_t : public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_wino:", avx512_common, ""),
+                jit_avx512_common_convolution_winograd_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && utils::one_of(desc()->alg_kind,
+                        alg_kind::convolution_auto,
+                        alg_kind::convolution_winograd)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_avx512_common_conv_winograd_fwd_kernel_f32::
+                init_conf(jcp_, *desc(), *src_md(), *weights_md(), *dst_md(),
+                        *attr());
+            if (status != status::success) return status;
+            set_default_alg_kind(alg_kind::convolution_winograd);
+
+            auto scratchpad = scratchpad_registry().registrar();
+            winograd_avx512_common::init_scratchpad(scratchpad, jcp_);
+
+            return status;
+        }
+
+        jit_conv_winograd_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+            auto wei_tag = with_groups() ? gOIhw16i16o : OIhw16i16o;
+            return set_default_formats_common(nChw16c, wei_tag, nChw16c);
+        }
+    };
+
+    jit_avx512_common_convolution_winograd_fwd_t(const pd_t *apd)
+        : _jit_avx512_common_convolution_winograd_t<true>(apd->jcp_, apd->attr())
+        , cpu_primitive_t(apd, true) {}
+
+    ~jit_avx512_common_convolution_winograd_fwd_t(){};
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override
+    {
+        auto src = CTX_IN_MEM(const float *, MKLDNN_ARG_SRC);
+        auto weights = CTX_IN_MEM(const float *, MKLDNN_ARG_WEIGHTS);
+        auto bias = CTX_IN_MEM(const float *, MKLDNN_ARG_BIAS);
+        auto dst = CTX_OUT_MEM(float *, MKLDNN_ARG_DST);
+        this->_execute_data_W_S_G_D((float *)src, dst, (float *)weights,
+                (float *)bias, this->scratchpad(ctx));
+        return status::success;
+    }
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+struct jit_avx512_common_convolution_winograd_bwd_data_t
+        : _jit_avx512_common_convolution_winograd_t<false>,
+        public cpu_primitive_t {
+    struct pd_t : public cpu_convolution_bwd_data_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_wino:", avx512_common, ""),
+                jit_avx512_common_convolution_winograd_bwd_data_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_data
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::undef, data_type::f32, data_type::f32)
+                && utils::one_of(desc()->alg_kind,
+                        alg_kind::convolution_auto,
+                        alg_kind::convolution_winograd)
+                && !has_zero_dim_memory()
+                && set_default_formats()
+                && mkldnn_thr_syncable();
+            if (!ok) return status::unimplemented;
+
+            status_t status =
+                jit_avx512_common_conv_winograd_bwd_data_kernel_f32::init_conf(
+                        jcp_, *desc(), *diff_src_md(), *weights_md(),
+                        *diff_dst_md());
+            if (status != status::success) return status;
+            set_default_alg_kind(alg_kind::convolution_winograd);
+
+            auto scratchpad = scratchpad_registry().registrar();
+            winograd_avx512_common::init_scratchpad(scratchpad, jcp_);
+
+            return status;
+        }
+
+        jit_conv_winograd_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+            auto wei_tag = with_groups() ? gOIhw16i16o : OIhw16i16o;
+            return set_default_formats_common(nChw16c, wei_tag, nChw16c);
+        }
+    };
+
+    jit_avx512_common_convolution_winograd_bwd_data_t(const pd_t *apd)
+        : _jit_avx512_common_convolution_winograd_t<false>(apd->jcp_, apd->attr())
+        , cpu_primitive_t(apd, true) {}
+
+    ~jit_avx512_common_convolution_winograd_bwd_data_t(){};
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        auto diff_dst = CTX_IN_MEM(const float *, MKLDNN_ARG_DIFF_DST);
+        auto weights = CTX_IN_MEM(const float *, MKLDNN_ARG_WEIGHTS);
+        auto diff_src = CTX_OUT_MEM(float *, MKLDNN_ARG_DIFF_SRC);
+        this->_execute_data_W_S_G_D((float *)diff_dst, diff_src,
+                (float *)weights, nullptr, this->scratchpad(ctx));
+        return status::success;
+    }
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+struct jit_avx512_common_convolution_winograd_bwd_weights_t
+        : public cpu_primitive_t {
+    struct pd_t : public cpu_convolution_bwd_weights_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_weights_pd_t(engine, adesc, attr,
+                    hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_wino:", avx512_common, ""),
+                jit_avx512_common_convolution_winograd_bwd_weights_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_weights
+                && utils::one_of(desc()->alg_kind,
+                        alg_kind::convolution_auto,
+                        alg_kind::convolution_winograd)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats()
+                && mkldnn_thr_syncable();
+            if (!ok) return status::unimplemented;
+
+            status_t status =
+                jit_avx512_common_conv_winograd_bwd_weights_kernel_f32::
+                init_conf(jcp_, *desc(), *src_md(), *diff_dst_md(),
+                        *diff_weights_md());
+            if (status != status::success) return status;
+            set_default_alg_kind(alg_kind::convolution_winograd);
+
+            auto scratchpad = scratchpad_registry().registrar();
+            winograd_avx512_common::init_scratchpad(scratchpad, jcp_);
+
+            return status;
+        }
+
+        jit_conv_winograd_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+            auto wei_tag = with_groups() ? gOIhw16i16o : OIhw16i16o;
+            return set_default_formats_common(nChw16c, wei_tag, nChw16c);
+        }
+    };
+
+    jit_avx512_common_convolution_winograd_bwd_weights_t(const pd_t *apd)
+        : cpu_primitive_t(apd, true), kernel_(nullptr)
+    {
+        kernel_ = new jit_avx512_common_conv_winograd_bwd_weights_kernel_f32(
+                pd()->jcp_);
+    }
+
+    ~jit_avx512_common_convolution_winograd_bwd_weights_t()
+    { delete kernel_; }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override
+    {
+        _execute_backward_weights_S_D_G_W(ctx, scratchpad(ctx));
+        return status::success;
+    }
+
+private:
+    void _execute_backward_weights_S_D_G_W(const exec_ctx_t &ctx,
+            const memory_tracking::grantor_t &scratchpad) const;
+    void _maybe_execute_diff_bias_copy(float *diff_bias,
+            const memory_tracking::grantor_t &scratchpad) const;
+
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    jit_avx512_common_conv_winograd_bwd_weights_kernel_f32 *kernel_;
+};
+
+void trans_W_4x4_3x3(float Fw_[6][6][16][16], float F[3][3][16][16]);
+void trans_O_4x4_3x3(float Mw[6][6][16], float O[4][4][16]);
+void trans_W_3x3_4x4(float Fw[6][6][16], float F[4][6][16]);
+void trans_O_3x3_4x4(float Mw[6][6][16][16], float M[3][3][16][16]);
+void trans_I_4x4_3x3(float Iw[6][6][16], float I[6][6][16]);
+void trans_W_3x3_4x4_wu(float Fw[6][6][16], float F[4][6][16]);
+void trans_O_3x3_4x4_wu(float Mw[6][6][16][16], float M[3][3][16][16]);
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_lrn.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_lrn.cpp
new file mode 100644
index 0000000000..d4a451c021
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_lrn.cpp
@@ -0,0 +1,853 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_avx512_common_lrn.hpp"
+
+#include "jit_generator.hpp"
+
+#define FWD_RBC 4
+#define BWD_RBC 3
+
+#define XMM_SIZE (4*sizeof(float))
+#define ZMM_SIZE (vlen)
+#define BUFFER_BLOCK (XMM_SIZE + ZMM_SIZE + XMM_SIZE)
+#define BUFFER_NEXT_OFFSET (XMM_SIZE + ZMM_SIZE)
+#define SRC_PREV_OFFSET (vlen - XMM_SIZE)
+
+#define IRB_LOOP(statement) for(int irb = 0; irb < loop_size; irb++) { \
+    statement;\
+}
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::utils;
+
+using namespace Xbyak;
+
+enum params { vsize = 16, vlen = 64};
+
+typedef struct {
+    const float *src;
+    float *dst, *ws0, *ws1;
+} jit_args_fwd_t;
+
+typedef struct {
+    const float *src, *diff_dst, *ws0, *ws1;
+    float *diff_src;
+} jit_args_bwd_t;
+
+struct nChw16c_across {
+/*  version:
+ *  -1: channels 0..15,
+ *   1: channels C-16 .. C-1,
+ *   0: other channels
+ *   3: channels only for this kernel(without prev and next)
+ */
+    int H, W, version;
+    nChw16c_across(int h, int w, int v) : H(h), W(w), version(v) {}
+};
+
+struct jit_avx512_common_lrn_fwd_t::jit_avx512_common_lrn_kernel_f32:
+       public jit_generator {
+    int HW, W;
+    bool is_first;
+    bool is_last;
+    bool is_single;
+
+    Reg64 src = rax;
+    Reg64 dst = r8;
+    Reg64 scratch0 = rdx;
+    Reg64 scratch1 = rsi;
+    Reg64 imm_addr64 = rbx;
+
+    Zmm zalpha = zmm0;
+    Xmm xalpha = xmm0;
+    Zmm zk = zmm1;
+    Xmm xk = xmm1;
+
+    Reg64 param = abi_param1;
+    Reg64 t = rsp;
+    Reg64 hw = r9;
+
+    int xsrc_prev = 2;
+    int zsrc = 7;
+    int xsrc_next = 3;
+    int zc = 7;
+
+    int za = 2;
+    int zb = 3;
+    int zd = 5;
+    int ze = 6;
+    int zsum = 4;
+    int zdst = 2;
+    int zbase = 3;
+    int zsum2 = 5;
+
+    prop_kind_t pk;
+    int use_h_parallelism;
+
+    float alpha, k;
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_common_lrn_kernel_f32)
+
+    void (*ker)(jit_args_fwd_t *);
+    void operator()(jit_args_fwd_t *arg) { ker(arg); }
+
+    enum {
+        prf0_offt = 1*FWD_RBC,
+        prf2_offt = 8*FWD_RBC
+    };
+
+    inline void compute_loop(int loop_size_param)
+    {
+        // loop_size - param for IRB_LOOP macro
+        int loop_size = FWD_RBC;
+
+        auto xreg = [=](int irb, int i) {
+            return Xmm(irb*3 + i);
+        };
+
+        auto zreg = [=](int irb, int i) {
+            return Zmm(irb*7 + i);
+        };
+
+        if (!is_first && !is_single) {
+            IRB_LOOP(mic_prefetcht0(ptr[src + (irb + prf0_offt - HW)*vlen]));
+            IRB_LOOP(mic_prefetcht2(ptr[src + (irb + prf2_offt - HW)*vlen]));
+        }
+        IRB_LOOP(mic_prefetcht0(EVEX_compress_addr(src, (irb + prf0_offt)*vlen)));
+        IRB_LOOP(mic_prefetcht2(EVEX_compress_addr(src, (irb + prf2_offt)*vlen)));
+        if (!is_last && !is_single) {
+            IRB_LOOP(mic_prefetcht0(ptr[src + (irb + prf0_offt + HW)*vlen]));
+            IRB_LOOP(mic_prefetcht2(ptr[src + (irb + prf2_offt + HW)*vlen]));
+        }
+        if (pk != prop_kind::forward_inference) {
+            IRB_LOOP(mic_prefetcht0(EVEX_compress_addr(scratch0,
+                       (irb + prf0_offt)*vlen)));
+            IRB_LOOP(mic_prefetcht2(EVEX_compress_addr(scratch0,
+                       (irb + prf2_offt)*vlen)));
+        }
+        IRB_LOOP(mic_prefetcht0(EVEX_compress_addr(dst, (irb + prf0_offt)*vlen)));
+        IRB_LOOP(mic_prefetcht2(EVEX_compress_addr(dst, (irb + prf2_offt)*vlen)));
+        if (pk != prop_kind::forward_inference) {
+            IRB_LOOP(mic_prefetcht0(EVEX_compress_addr(scratch1,
+                         (irb + prf0_offt) * vlen)));
+            IRB_LOOP(mic_prefetcht2(EVEX_compress_addr(scratch1,
+                         (irb + prf2_offt) * vlen)));
+        }
+
+        loop_size = loop_size_param;
+        if (loop_size == 0)
+            return;
+        if (!is_first && !is_single) {
+            IRB_LOOP(vmovups(xreg(irb, xsrc_prev),
+                        ptr[src + (irb - HW) * vlen + SRC_PREV_OFFSET]));
+        }
+        IRB_LOOP(vmovups(zreg(irb, zsrc), EVEX_compress_addr(src,irb*vlen)));
+        if (!is_last && !is_single) {
+            IRB_LOOP(vmovups(xreg(irb, xsrc_next),
+                        ptr[src + (irb + HW) * vlen]));
+        }
+
+        if (!is_first && !is_single) {
+            IRB_LOOP(vmovups(ptr[t + irb*BUFFER_BLOCK],
+                        xreg(irb, xsrc_prev)));
+        }
+        IRB_LOOP(vmovups(EVEX_compress_addr(t, irb*BUFFER_BLOCK + XMM_SIZE),
+                    zreg(irb, zsrc)));
+        if (!is_last && !is_single) {
+            IRB_LOOP(vmovups(ptr[t + irb*BUFFER_BLOCK + BUFFER_NEXT_OFFSET],
+                    xreg(irb, xsrc_next)));
+        }
+
+        IRB_LOOP(vmovups(zreg(irb, za), EVEX_compress_addr(t, irb*BUFFER_BLOCK
+                        + XMM_SIZE - 2*sizeof(float))));
+        IRB_LOOP(vmovups(zreg(irb, zb), EVEX_compress_addr(t, irb*BUFFER_BLOCK
+                        + XMM_SIZE - sizeof(float))));
+        IRB_LOOP(vmovups(zreg(irb, zd), EVEX_compress_addr(t, irb*BUFFER_BLOCK
+                        + XMM_SIZE + sizeof(float))));
+        IRB_LOOP(vmovups(zreg(irb, ze), EVEX_compress_addr(t, irb*BUFFER_BLOCK
+                        + XMM_SIZE + 2*sizeof(float))));
+
+        assert(zc == zsrc);
+        IRB_LOOP(vmulps(zreg(irb, zsum), zreg(irb, zc), zreg(irb, zc)));
+
+        IRB_LOOP(vfmadd231ps(zreg(irb, zsum), zreg(irb, za), zreg(irb, za)));
+        IRB_LOOP(vfmadd231ps(zreg(irb, zsum), zreg(irb, zb), zreg(irb, zb)));
+        IRB_LOOP(vfmadd231ps(zreg(irb, zsum), zreg(irb, zd), zreg(irb, zd)));
+        IRB_LOOP(vfmadd231ps(zreg(irb, zsum), zreg(irb, ze), zreg(irb, ze)));
+
+        IRB_LOOP(vfmadd132ps(zreg(irb, zsum), zk, zalpha));
+
+        IRB_LOOP(vmovaps(zreg(irb, zbase), zreg(irb, zsum)));
+
+        IRB_LOOP(vmulps(zreg(irb, zsum2), zreg(irb, zsum), zreg(irb, zsum)));
+        IRB_LOOP(vmulps(zreg(irb, zsum), zreg(irb, zsum), zreg(irb, zsum2)));
+
+        IRB_LOOP(vsqrtps(zreg(irb, zsum), zreg(irb, zsum)));
+        IRB_LOOP(vsqrtps(zreg(irb, zsum), zreg(irb, zsum)));
+
+        if (pk != prop_kind::forward_inference) {
+            IRB_LOOP(vmovups(EVEX_compress_addr(scratch0, irb*vlen),
+                        zreg(irb, zsum)));
+        }
+        IRB_LOOP(vdivps(zreg(irb, zdst), zreg(irb, zsrc), zreg(irb, zsum)));
+        IRB_LOOP(vmovups(EVEX_compress_addr(dst, irb*vlen), zreg(irb, zdst)));
+        if (pk != prop_kind::forward_inference) {
+            /* ws1 = zdst / zbase = zsrc / (zbase^1.75) */
+            IRB_LOOP(vdivps(zreg(irb, zsum), zreg(irb, zdst), zreg(irb, zbase)));
+            IRB_LOOP(vmovups(EVEX_compress_addr(scratch1, irb*vlen),
+                        zreg(irb, zsum)));
+        }
+    }
+
+    jit_avx512_common_lrn_kernel_f32(
+        const struct nChw16c_across &J,
+        prop_kind_t prop_kind,
+        int use_h_parallel,
+        float A,
+        float K,
+        void *code_ptr = nullptr,
+        size_t code_size = 2 * Xbyak::DEFAULT_MAX_CODE_SIZE)
+        : jit_generator(code_ptr, code_size)
+        , pk(prop_kind)
+        , use_h_parallelism(use_h_parallel)
+        , alpha(A)
+        , k(K)
+    {
+        this->preamble();
+
+        mov(src, ptr[param + 0]);
+        mov(dst, ptr[param + 8]);
+        if (pk != prop_kind::forward_inference)
+        {
+            mov(scratch0, ptr[param + 16]);
+            mov(scratch1, ptr[param + 24]);
+        }
+        is_first = J.version == -1 || J.version == -2;
+        is_last  = J.version == +1 || J.version == -2;
+        is_single = J.version == 3;
+
+        W = J.W;
+        HW = J.W*J.H;
+        int LSB = use_h_parallelism ? W : HW;
+
+        sub(t, FWD_RBC*BUFFER_BLOCK);
+        mov(imm_addr64, float2int(this->alpha));
+        movq(xalpha, imm_addr64);
+        vbroadcastss(zalpha, xalpha);
+
+        mov(imm_addr64, float2int(this->k));
+        movq(xk, imm_addr64);
+        vbroadcastss(zk, xk);
+
+        if (is_first || is_single) {
+            vxorps(xmm2, xmm2, xmm2);
+            for(int irb = 0; irb < FWD_RBC; irb++) {
+                vmovups(ptr[t + irb*BUFFER_BLOCK], xmm2);
+            }
+        }
+        if (is_last || is_single) {
+            vxorps(xmm2, xmm2, xmm2);
+            for(int irb = 0; irb < FWD_RBC; irb++) {
+                vmovups(ptr[t + irb*BUFFER_BLOCK + BUFFER_NEXT_OFFSET],
+                    xmm2);
+            }
+        }
+
+        int LSREST = LSB % FWD_RBC;
+        int LS = LSB - LSREST;
+
+        Label lrn_loop;
+
+        if (LS > 0) {
+            mov(hw, LS);
+
+            L(lrn_loop);
+            {
+                compute_loop(FWD_RBC);
+
+                add(src, FWD_RBC*vlen);
+                add(dst, FWD_RBC*vlen);
+                if (pk != prop_kind::forward_inference)
+                {
+                    add(scratch0, FWD_RBC*vlen);
+                    add(scratch1, FWD_RBC*vlen);
+                }
+
+                for(int irb = 0; irb < FWD_RBC; irb++)
+                    dec(hw);
+                cmp(hw, 0);
+                jne(lrn_loop, T_NEAR);
+            }
+        }
+
+        compute_loop(LSREST);
+
+        add(t, FWD_RBC*BUFFER_BLOCK);
+        this->postamble();
+
+        ker = reinterpret_cast<decltype(ker)>(const_cast<uint8_t*>(
+                    this->getCode()));
+    }
+};
+
+status_t jit_avx512_common_lrn_fwd_t::pd_t::init() {
+    using namespace prop_kind;
+    using namespace alg_kind;
+
+    const memory_desc_wrapper data_d(src_md());
+    bool ok = true
+        && mayiuse(avx512_common)
+        && is_fwd()
+        && !has_zero_dim_memory()
+        && everyone_is(data_type::f32, data_d.data_type())
+        && data_d.ndims() == 4
+        && data_d.dims()[1] % vsize == 0
+        && attr()->has_default_values();
+    if (!ok) return unimplemented;
+
+    if (desc()->prop_kind == forward_training) {
+        dims_t ws_dims = { MB(), C(), H(), 2*W() };
+        mkldnn_memory_desc_init_by_tag(&ws_md_, 4, ws_dims, data_type::f32,
+                format_tag::nChw16c);
+    }
+
+    bool args_ok_across = true
+        && desc()->alg_kind == lrn_across_channels
+        && desc()->local_size == 5
+        && desc()->lrn_beta == 0.75
+        && data_d.matches_tag(format_tag::nChw16c);
+
+    return args_ok_across ? success : unimplemented;
+}
+
+jit_avx512_common_lrn_fwd_t::jit_avx512_common_lrn_fwd_t(const pd_t *apd)
+    : cpu_primitive_t(apd)
+    , use_h_parallelism(0), ker_(nullptr), ker_first_(nullptr)
+    , ker_last_(nullptr) {
+    using namespace alg_kind;
+    const int C = pd()->C();
+    const int H = pd()->H();
+    const int W = pd()->W();
+    const int ls = pd()->desc()->local_size;
+    const float alpha = pd()->desc()->lrn_alpha / ls;
+    const float k = pd()->desc()->lrn_k;
+
+    auto pk = pd()->desc()->prop_kind;
+
+    use_h_parallelism = H > 28 ? 1 : 0;
+
+    if (C / vsize == 1) {
+        ker_ = new jit_avx512_common_lrn_kernel_f32(nChw16c_across(H, W, 3), pk,
+            use_h_parallelism, alpha, k);
+    } else {
+        ker_ = new jit_avx512_common_lrn_kernel_f32(nChw16c_across(H, W, 0), pk,
+            use_h_parallelism, alpha, k);
+        ker_first_ = new jit_avx512_common_lrn_kernel_f32(
+            nChw16c_across(H, W, -1), pk, use_h_parallelism, alpha, k);
+        ker_last_ = new jit_avx512_common_lrn_kernel_f32(
+            nChw16c_across(H, W, +1), pk, use_h_parallelism, alpha, k);
+    }
+}
+
+jit_avx512_common_lrn_fwd_t::~jit_avx512_common_lrn_fwd_t()
+{ delete ker_; delete ker_first_; delete ker_last_; }
+
+void jit_avx512_common_lrn_fwd_t::execute_forward(const exec_ctx_t &ctx) const
+{
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+    auto ws = CTX_OUT_MEM(data_t *, MKLDNN_ARG_WORKSPACE);
+
+    const int N = pd()->MB();
+    const int C = pd()->C();
+    const int H = pd()->H();
+    const int W = pd()->W();
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        size_t start{0}, end{0};
+        const int C16 = C / vsize;
+        const size_t work_amount = use_h_parallelism ? N*C16*H : N*C16;
+
+        balance211(work_amount, nthr, ithr, start, end);
+        if (use_h_parallelism) {
+            int n{0}, c16{0}, h{0};
+            nd_iterator_init(start, n, N, c16, C16, h, H);
+            for (size_t iwork = start; iwork < end; ++iwork) {
+                auto offset = n*C*H*W + c16*H*W*vsize
+                    + h*W*vsize;
+                auto ws_offset0 = n*C*H*2*W + c16*H*2*W*vsize
+                    + h*2*W*vsize;
+                auto ws_offset1 = ws_offset0 + W*vsize;
+
+                jit_args_fwd_t args;
+                args.src = &src[offset];
+                args.dst = &dst[offset];
+                args.ws0 = &ws[ws_offset0];
+                args.ws1 = &ws[ws_offset1];
+
+                if (C16 == 1)
+                    (*ker_)(&args);
+                else if (c16 == 0)
+                    (*ker_first_)(&args);
+                else if (c16 == C16 - 1)
+                    (*ker_last_)(&args);
+                else
+                    (*ker_)(&args);
+                nd_iterator_step(n, N, c16, C16, h, H);
+            }
+        } else {
+            int n{0}, c16{0};
+            nd_iterator_init(start, n, N, c16, C16);
+            for (size_t iwork = start; iwork < end; ++iwork) {
+                auto offset = n*C*H*W + c16*H*W*vsize;
+                auto ws_offset0 = n*C*H*2*W + c16*H*2*W*vsize;
+                auto ws_offset1 = ws_offset0 + H*W*vsize;
+
+                jit_args_fwd_t args;
+                args.src = &src[offset];
+                args.dst = &dst[offset];
+                args.ws0 = &ws[ws_offset0];
+                args.ws1 = &ws[ws_offset1];
+
+                if (C16 == 1)
+                    (*ker_)(&args);
+                else if (c16 == 0)
+                    (*ker_first_)(&args);
+                else if (c16 == C16 - 1)
+                    (*ker_last_)(&args);
+                else
+                    (*ker_)(&args);
+
+                nd_iterator_step(n, N, c16, C16);
+            }
+        }
+    });
+}
+
+struct jit_avx512_common_lrn_bwd_t::jit_avx512_common_lrn_kernel_f32:
+    public jit_generator {
+    int HW, W;
+    bool is_first;
+    bool is_last;
+    bool is_single;
+
+    Reg64 src = rax;
+    Reg64 diffsrc = r8;
+    Reg64 diffdst = r9;
+    Reg64 workspace0 = rdx;
+    Reg64 workspace1 = rsi;
+    Reg64 imm_addr64 = rbx;
+
+    Zmm znalphabeta = zmm0;
+    Xmm xnalphabeta = xmm0;
+
+    Reg64 param = abi_param1;
+    Reg64 t = rsp;
+    Reg64 hw = r10;
+
+    int xws1_prev = 1;
+    int xdiffdst_prev = 2;
+    int zws1 = 1;
+
+    int zsrc = 1;
+    int zdiffdst = 5;
+    int zdiffsrc = 6;
+
+    int xws1_next = 1;
+    int xdiffdst_next = 3;
+
+    int za = 1;
+    int zb = 2;
+    int zd = 3;
+    int ze = 4;
+    int zws0 = 2;
+
+    float nalphabeta;
+
+    int use_h_parallelism;
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_common_lrn_kernel_f32)
+
+    void (*ker)(jit_args_bwd_t *);
+    void operator()(jit_args_bwd_t *arg) { ker(arg); }
+
+    enum {
+        prf0_offt = 1*BWD_RBC,
+        prf2_offt = 8*BWD_RBC
+    };
+
+    inline void compute_loop(int loop_size_param, int prefetchL1,
+            int prefetchL2)
+    {
+        // loop_size - param for IRB_LOOP macro
+        int loop_size = loop_size_param;
+
+        auto xreg = [=](int irb, int i) {
+            return Xmm(irb*6 + i);
+        };
+
+        auto zreg = [=](int irb, int i) {
+            return Zmm(irb*6 + i);
+        };
+
+// ---- prefetching -------------------------------------------
+        if (!is_first && !is_single) {
+            if (prefetchL1)
+                IRB_LOOP(mic_prefetcht0(ptr[workspace1 + (irb + prf0_offt
+                        - 2 * HW) * vlen]));
+            if (prefetchL1)
+                IRB_LOOP(mic_prefetcht0(ptr[diffdst    + (irb + prf0_offt
+                        - HW) * vlen]));
+        }
+
+        if (prefetchL1)
+            IRB_LOOP(mic_prefetcht0(ptr[src + (irb + prf0_offt)*vlen]));
+        if (prefetchL2)
+            IRB_LOOP(mic_prefetcht2(ptr[src + (irb + prf2_offt)*vlen]));
+
+        if (prefetchL1)
+            IRB_LOOP(mic_prefetcht0(ptr[workspace1 + (irb + prf0_offt)*vlen]));
+
+        if (prefetchL1)
+            IRB_LOOP(mic_prefetcht0(ptr[diffdst + (irb + prf0_offt)*vlen]));
+
+        if (!is_last && !is_single) {
+            if (prefetchL1)
+                IRB_LOOP(mic_prefetcht0(ptr[workspace1 + (irb + prf0_offt
+                        + 2 * HW) * vlen]));
+            if (prefetchL2)
+                IRB_LOOP(mic_prefetcht2(ptr[workspace1 + (irb + prf2_offt
+                        + 2 * HW) * vlen]));
+
+            if (prefetchL1)
+                IRB_LOOP(mic_prefetcht0(ptr[diffdst +  (irb + prf0_offt
+                          + HW) * vlen]));
+            if (prefetchL2)
+                IRB_LOOP(mic_prefetcht2(ptr[diffdst +  (irb + prf2_offt
+                        + HW) * vlen]));
+        }
+        if (prefetchL1)
+            IRB_LOOP(mic_prefetcht0(ptr[workspace0 + (irb + prf0_offt)*vlen]));
+        if (prefetchL2)
+            IRB_LOOP(mic_prefetcht2(ptr[workspace0 + (irb + prf2_offt)*vlen]));
+// -----------------------------------------------------------
+
+        if (loop_size_param == 0)
+            return;
+
+        if (!is_first && !is_single) {
+            IRB_LOOP(vmovups(xreg(irb, xws1_prev), ptr[workspace1 + (irb
+                    - 2 * HW) * vlen + SRC_PREV_OFFSET]));
+            IRB_LOOP(vmovups(xreg(irb, xdiffdst_prev), ptr[diffdst + (irb
+                    - HW) * vlen + SRC_PREV_OFFSET]));
+            IRB_LOOP(vmulps(xreg(irb, xdiffdst_prev), xreg(irb, xdiffdst_prev),
+                    xreg(irb, xws1_prev)));
+        }
+
+        IRB_LOOP(vmovups(zreg(irb, zws1),
+                EVEX_compress_addr(workspace1, irb*vlen)));
+        IRB_LOOP(vmovups(zreg(irb, zdiffdst),
+                EVEX_compress_addr(diffdst, irb*vlen)));
+        IRB_LOOP(vmulps(zreg(irb, zdiffsrc), zreg(irb, zdiffdst),
+                zreg(irb, zws1)));
+
+        if (!is_last && !is_single) {
+            IRB_LOOP(vmovups(xreg(irb, xws1_next), ptr[workspace1 + (irb
+                    + 2 * HW) * vlen]));
+            IRB_LOOP(vmovups(xreg(irb, xdiffdst_next), ptr[diffdst +  (irb
+                    + HW) * vlen]));
+            IRB_LOOP(vmulps(xreg(irb, xdiffdst_next), xreg(irb, xdiffdst_next),
+                    xreg(irb, xws1_next)));
+        }
+
+        if (!is_first && !is_single) {
+            IRB_LOOP(vmovups(ptr[t + irb*BUFFER_BLOCK],
+                    xreg(irb, xdiffdst_prev)));
+        }
+        IRB_LOOP(vmovups(EVEX_compress_addr(t, irb*BUFFER_BLOCK + XMM_SIZE),
+                 zreg(irb, zdiffsrc)));
+        if (!is_last && !is_single) {
+            IRB_LOOP(vmovups(ptr[t + irb*BUFFER_BLOCK + BUFFER_NEXT_OFFSET],
+                 xreg(irb, xdiffdst_next)));
+        }
+
+        IRB_LOOP(vmovups(zreg(irb, za), EVEX_compress_addr(t, irb*BUFFER_BLOCK
+                + XMM_SIZE - 2*sizeof(float))));
+        IRB_LOOP(vmovups(zreg(irb, zb), EVEX_compress_addr(t, irb*BUFFER_BLOCK
+                + XMM_SIZE - 1*sizeof(float))));
+        IRB_LOOP(vmovups(zreg(irb, zd), EVEX_compress_addr(t, irb*BUFFER_BLOCK
+                + XMM_SIZE + 1*sizeof(float))));
+        IRB_LOOP(vmovups(zreg(irb, ze), EVEX_compress_addr(t, irb*BUFFER_BLOCK
+                + XMM_SIZE + 2*sizeof(float))));
+        IRB_LOOP(vaddps(zreg(irb, zdiffsrc), zreg(irb, zdiffsrc),
+                zreg(irb, za)));
+        assert(zsrc == za);
+        IRB_LOOP(vmovups(zreg(irb, zsrc), EVEX_compress_addr(src, irb*vlen)));
+        IRB_LOOP(vaddps(zreg(irb, zdiffsrc), zreg(irb, zdiffsrc),
+                zreg(irb, zb)));
+        IRB_LOOP(vaddps(zreg(irb, zdiffsrc), zreg(irb, zdiffsrc),
+                zreg(irb, zd)));
+        IRB_LOOP(vaddps(zreg(irb, zdiffsrc), zreg(irb, zdiffsrc),
+                zreg(irb, ze)));
+        IRB_LOOP(vmulps(zreg(irb, zsrc), zreg(irb, zsrc), znalphabeta));
+
+        IRB_LOOP(vmovups(zreg(irb, zws0),
+                 EVEX_compress_addr(workspace0, irb*vlen)));
+        IRB_LOOP(vdivps(zreg(irb, zdiffdst), zreg(irb, zdiffdst),
+                 zreg(irb, zws0)));
+        IRB_LOOP(vfmadd213ps(zreg(irb, zdiffsrc), zreg(irb, zsrc),
+                 zreg(irb, zdiffdst)));
+
+        Label unaligned_store, end_store;
+        test(diffsrc, vlen - 1);
+        jnz(unaligned_store, T_NEAR);
+        IRB_LOOP(uni_vmovntps(EVEX_compress_addr(diffsrc, irb*vlen),
+                 zreg(irb, zdiffsrc)));
+        jmp(end_store, T_NEAR);
+        L(unaligned_store); {
+            IRB_LOOP(uni_vmovups(EVEX_compress_addr(diffsrc, irb*vlen),
+                     zreg(irb, zdiffsrc)));
+        }
+        L(end_store);
+    }
+
+    jit_avx512_common_lrn_kernel_f32(
+        const struct nChw16c_across &J,
+        float A,
+        float B,
+        int use_h_parallel,
+        void *code_ptr = nullptr,
+        size_t code_size = 1 * Xbyak::DEFAULT_MAX_CODE_SIZE)
+        : jit_generator(code_ptr, code_size)
+        , nalphabeta(-2*A*B)
+        , use_h_parallelism(use_h_parallel)
+    {
+        this->preamble();
+
+        mov(src, ptr[param + 0]);
+        mov(diffdst, ptr[param + 8]);
+        mov(workspace0, ptr[param + 16]);
+        mov(workspace1, ptr[param + 24]);
+        mov(diffsrc, ptr[param + 32]);
+
+        W = J.W;
+        HW = J.H*J.W;
+        int LSB = this->use_h_parallelism ? W : HW;
+
+        sub(t, BWD_RBC*BUFFER_BLOCK);
+        mov(imm_addr64, float2int(this->nalphabeta));
+        movq(xnalphabeta, imm_addr64);
+        vbroadcastss(znalphabeta, xnalphabeta);
+
+        is_first = J.version == -1 || J.version == -2;
+        is_last  = J.version == +1 || J.version == +2;
+        is_single = J.version == 3;
+
+        if (is_first || is_single) {
+            vxorps(xmm1, xmm1, xmm1);
+            for(int irb = 0; irb < BWD_RBC; irb++) {
+                vmovups(ptr[t + irb*BUFFER_BLOCK], xmm1);
+            }
+        }
+        if (is_last || is_single) {
+            vxorps(xmm1, xmm1, xmm1);
+            for(int irb = 0; irb < BWD_RBC; irb++) {
+                vmovups(ptr[t + irb*BUFFER_BLOCK + BUFFER_NEXT_OFFSET], xmm1);
+            }
+        }
+
+        int LSREST = LSB % BWD_RBC;
+        int LS = LSB - LSREST;
+
+        Label lrn_loop;
+
+        if (LS > 0) {
+            mov(hw, LS);
+
+            L(lrn_loop);
+            {
+                compute_loop(BWD_RBC, 1, 1);
+
+                add(src, BWD_RBC*vlen);
+                add(diffsrc, BWD_RBC*vlen);
+                add(diffdst, BWD_RBC*vlen);
+                add(workspace0, BWD_RBC*vlen);
+                add(workspace1, BWD_RBC*vlen);
+
+                for(int irb = 0; irb < BWD_RBC; irb++)
+                    dec(hw);
+                cmp(hw, 0);
+                jne(lrn_loop, T_NEAR);
+            }
+        }
+
+        compute_loop(LSREST, 1, this->use_h_parallelism ? 0 : 1);
+
+        add(t, BWD_RBC*BUFFER_BLOCK);
+        this->postamble();
+
+        ker = reinterpret_cast<decltype(ker)>(const_cast<uint8_t*>(
+                    this->getCode()));
+    }
+
+};
+
+status_t jit_avx512_common_lrn_bwd_t::pd_t::init() {
+    using namespace alg_kind;
+
+    const memory_desc_wrapper data_d(src_md());
+    bool ok = true
+        && mayiuse(avx512_common)
+        && !is_fwd()
+        && utils::everyone_is(data_type::f32, data_d.data_type())
+        && !has_zero_dim_memory()
+        && data_d.ndims() == 4
+        && data_d.dims()[1] % vsize == 0
+        && attr()->has_default_values();
+    if (!ok) return unimplemented;
+
+    dims_t ws_dims = { MB(), C(), H(), 2*W() };
+    mkldnn_memory_desc_init_by_tag(&ws_md_, 4, ws_dims, data_type::f32,
+            format_tag::nChw16c);
+
+    if (!compare_ws(hint_fwd_pd_)) return unimplemented;
+
+    bool args_ok_across = true
+        && desc()->alg_kind == lrn_across_channels
+        && desc()->local_size == 5
+        && desc()->lrn_beta == 0.75
+        && data_d.matches_tag(format_tag::nChw16c);
+
+    return args_ok_across ? success : unimplemented;
+}
+
+jit_avx512_common_lrn_bwd_t::jit_avx512_common_lrn_bwd_t(const pd_t *apd)
+    : cpu_primitive_t(apd)
+    , use_h_parallelism(0),  ker_(nullptr), ker_first_(nullptr)
+    , ker_last_(nullptr) {
+    const int C = pd()->C();
+    const int H = pd()->H();
+    const int W = pd()->W();
+    const int ls = pd()->desc()->local_size;
+    const float alpha = pd()->desc()->lrn_alpha / ls;
+    const float beta = pd()->desc()->lrn_beta;
+
+    use_h_parallelism = H > 28 ? 1 : 0;
+
+    if (C / vsize == 1) {
+        ker_ = new jit_avx512_common_lrn_kernel_f32(nChw16c_across(H, W, 3),
+        alpha, beta, use_h_parallelism);
+    } else {
+        ker_ = new jit_avx512_common_lrn_kernel_f32(nChw16c_across(H, W, 0),
+            alpha, beta, use_h_parallelism);
+        ker_first_ = new jit_avx512_common_lrn_kernel_f32(
+            nChw16c_across(H, W, -1), alpha, beta, use_h_parallelism);
+        ker_last_ = new jit_avx512_common_lrn_kernel_f32(
+            nChw16c_across(H, W, +1), alpha, beta, use_h_parallelism);
+    }
+}
+
+jit_avx512_common_lrn_bwd_t::~jit_avx512_common_lrn_bwd_t()
+{ delete ker_; delete ker_first_; delete ker_last_; }
+
+void jit_avx512_common_lrn_bwd_t::execute_backward(const exec_ctx_t &ctx) const
+{
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto ws = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WORKSPACE);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const int N = pd()->MB();
+    const int C = pd()->C();
+    const int H = pd()->H();
+    const int W = pd()->W();
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        size_t start{0}, end{0};
+        const int C16 = C / vsize;
+        const size_t work_amount = use_h_parallelism ? N*C16*H : N*C16;
+
+        balance211(work_amount, nthr, ithr, start, end);
+        if (use_h_parallelism) {
+            int n{0}, c16{0}, h{0};
+            nd_iterator_init(start, n, N,  h, H, c16, C16);
+            for (size_t iwork = start; iwork < end; ++iwork) {
+                auto offset = n*C*H*W + c16*H*W*vsize
+                    + h*W*vsize;
+                auto ws_offset0 = n*C*H*2*W + c16*H*2*W*vsize
+                    + h*2*W*vsize;
+                auto ws_offset1 = ws_offset0 + W*vsize;
+
+                jit_args_bwd_t args;
+                args.src = &src[offset];
+                args.diff_dst = &diff_dst[offset];
+                args.ws0 = &ws[ws_offset0];
+                args.ws1 = &ws[ws_offset1];
+                args.diff_src = &diff_src[offset];
+
+                if (C16 == 1)
+                    (*ker_)(&args);
+                else if (c16 == 0)
+                    (*ker_first_)(&args);
+                else if (c16 == C16 - 1)
+                    (*ker_last_)(&args);
+                else
+                    (*ker_)(&args);
+                nd_iterator_step(n, N, h, H, c16, C16);
+            }
+        } else {
+            int n{0}, c16{0};
+            nd_iterator_init(start, n, N, c16, C16);
+            for (size_t iwork = start; iwork < end; ++iwork) {
+                auto offset = n*C*H*W + c16*H*W*vsize;
+                auto ws_offset0 = n*C*H*2*W + c16*H*2*W*vsize;
+                auto ws_offset1 = ws_offset0 + H*W*vsize;
+
+                jit_args_bwd_t args;
+                args.src = &src[offset];
+                args.diff_dst = &diff_dst[offset];
+                args.ws0 = &ws[ws_offset0];
+                args.ws1 = &ws[ws_offset1];
+                args.diff_src = &diff_src[offset];
+
+                if (C16 == 1)
+                    (*ker_)(&args);
+                else if (c16 == 0)
+                    (*ker_first_)(&args);
+                else if (c16 == C16 - 1)
+                    (*ker_last_)(&args);
+                else
+                    (*ker_)(&args);
+
+                nd_iterator_step(n, N, c16, C16);
+            }
+        }
+    });
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_lrn.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_lrn.hpp
new file mode 100644
index 0000000000..37fbb9b3e5
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_common_lrn.hpp
@@ -0,0 +1,96 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_COMMON_LRN_HPP
+#define CPU_JIT_AVX512_COMMON_LRN_HPP
+
+#include "c_types_map.hpp"
+
+#include "cpu_isa_traits.hpp"
+#include "cpu_lrn_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_avx512_common_lrn_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_lrn_fwd_pd_t {
+        using cpu_lrn_fwd_pd_t::cpu_lrn_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", avx512_common, ""),
+                jit_avx512_common_lrn_fwd_t);
+
+        status_t init();
+    };
+
+    jit_avx512_common_lrn_fwd_t(const pd_t *apd);
+    ~jit_avx512_common_lrn_fwd_t();
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    int use_h_parallelism;
+    struct jit_avx512_common_lrn_kernel_f32;
+    jit_avx512_common_lrn_kernel_f32 *ker_, *ker_first_, *ker_last_;
+};
+
+struct jit_avx512_common_lrn_bwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_lrn_bwd_pd_t {
+        using cpu_lrn_bwd_pd_t::cpu_lrn_bwd_pd_t;
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", avx512_common, ""),
+                jit_avx512_common_lrn_bwd_t);
+
+        status_t init();
+    };
+
+    jit_avx512_common_lrn_bwd_t(const pd_t *apd);
+    ~jit_avx512_common_lrn_bwd_t();
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    int use_h_parallelism;
+    struct jit_avx512_common_lrn_kernel_f32;
+    jit_avx512_common_lrn_kernel_f32 *ker_, *ker_first_, *ker_last_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_2x3.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_2x3.cpp
new file mode 100644
index 0000000000..c58d3fa0a6
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_2x3.cpp
@@ -0,0 +1,1103 @@
+/*******************************************************************************
+ * Copyright 2018 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *******************************************************************************/
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_avx512_core_fp32_wino_conv_2x3.hpp"
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::format_kind;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+using namespace Xbyak;
+
+/// SRC TRANSFORMS /////////////////////////////////////////////////////////////
+struct jit_avx512_core_fp32_wino_conv_2x3_src_trans_t: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(
+            jit_avx512_core_fp32_wino_conv_2x3_src_trans_t)
+
+    jit_conv_conf_2x3_wino_t jcp;
+
+    struct call_params_t {
+        const void *src;
+        const void *wino_src;
+        const void *v_y_masks;
+        const void *v_x_masks;
+    };
+    void (*ker_)(const call_params_t *);
+
+    jit_avx512_core_fp32_wino_conv_2x3_src_trans_t(
+        jit_conv_conf_2x3_wino_t ajcp, const primitive_attr_t &attr)
+        : jcp(ajcp) {
+        generate();
+        ker_ =
+            reinterpret_cast<decltype(ker_)>(const_cast<uint8_t*>(getCode()));
+    }
+
+    void generate();
+
+    Zmm vreg_inp(int i) {
+        assert(i < jcp.alpha * jcp.alpha);
+        return Zmm(31 - i);
+    }
+
+    Zmm vreg_tmp(int i) {
+        assert(i < jcp.alpha * jcp.alpha);
+        return Zmm(15 - i);
+    }
+
+    Zmm vreg_out(int i) {
+        assert(i < jcp.alpha * jcp.alpha);
+        return Zmm(31 - i);
+    }
+
+    Opmask y_mask = Opmask(1);
+    Opmask r_mask = Opmask(2);
+    Opmask x_mask(int id) {
+        assert (id < 4);
+        return Opmask(3 + id);
+    }
+
+    Reg64 reg_ptr_v_y_masks = r12;
+    Reg64 reg_ptr_v_x_masks = r11;
+
+    Reg64 reg_aux_ptr_src = r10;
+    Reg64 reg_aux_ptr_dst = r9;
+
+    Reg64 reg_ic_block = r8;
+
+};
+
+void jit_avx512_core_fp32_wino_conv_2x3_src_trans_t::generate() {
+    Label ic_block_label;
+
+    const int load_block = 16;
+    int out_offset = 0, inp_offset = 0;
+    preamble();
+
+#define READ_PARAM(reg, field) \
+        mov(reg, ptr[abi_param1 + offsetof(call_params_t, field)])
+    READ_PARAM(reg_aux_ptr_src, src);
+    READ_PARAM(reg_aux_ptr_dst, wino_src);
+    READ_PARAM(reg_ptr_v_y_masks, v_y_masks);
+    READ_PARAM(reg_ptr_v_x_masks, v_x_masks);
+#undef READ_PARAM
+
+    for (int i = 0; i < jcp.alpha; i++) {
+        kmovw(x_mask(i), ptr[reg_ptr_v_x_masks + sizeof(int16_t) * i]);
+    }
+    mov(reg_ic_block, jcp.ic / load_block);
+    L(ic_block_label);
+    {
+        for (int y = 0; y < jcp.alpha; y++) {
+            kmovw(y_mask, ptr[reg_ptr_v_y_masks + sizeof(int16_t) * y]);
+            for (int x = 0; x < jcp.alpha; x++) {
+                Zmm zmm = vreg_inp(y * jcp.alpha + x);
+
+                vxorps(zmm, zmm, zmm);
+                kandw(r_mask, y_mask, x_mask(x));
+                inp_offset = sizeof(float)
+                        * ((-jcp.t_pad + y) * jcp.iw * load_block
+                                  + (-jcp.l_pad + x) * load_block);
+                vmovups(zmm | r_mask,
+                        EVEX_compress_addr(reg_aux_ptr_src, inp_offset));
+            }
+        }
+        for (int y = 0; y < jcp.alpha; y++) {
+            vsubps(vreg_tmp(y * jcp.alpha + 0), vreg_inp(y * jcp.alpha + 0),
+                    vreg_inp(y * jcp.alpha + 2));
+            vaddps(vreg_tmp(y * jcp.alpha + 1), vreg_inp(y * jcp.alpha + 1),
+                    vreg_inp(y * jcp.alpha + 2));
+            vsubps(vreg_tmp(y * jcp.alpha + 2), vreg_inp(y * jcp.alpha + 2),
+                    vreg_inp(y * jcp.alpha + 1));
+            vsubps(vreg_tmp(y * jcp.alpha + 3), vreg_inp(y * jcp.alpha + 1),
+                    vreg_inp(y * jcp.alpha + 3));
+        }
+        for (int x = 0; x < jcp.alpha; x++) {
+            vsubps(vreg_out(x + 0 * jcp.alpha), vreg_tmp(x + jcp.alpha * 0),
+                    vreg_tmp(x + jcp.alpha * 2));
+            vaddps(vreg_out(x + 1 * jcp.alpha), vreg_tmp(x + jcp.alpha * 1),
+                    vreg_tmp(x + jcp.alpha * 2));
+            vsubps(vreg_out(x + 2 * jcp.alpha), vreg_tmp(x + jcp.alpha * 2),
+                    vreg_tmp(x + jcp.alpha * 1));
+            vsubps(vreg_out(x + 3 * jcp.alpha), vreg_tmp(x + jcp.alpha * 1),
+                    vreg_tmp(x + jcp.alpha * 3));
+        }
+
+        for (int i = 0; i < 16; i++) {
+            out_offset = sizeof(float) * (jcp.inp_stride * i);
+            vmovups(EVEX_compress_addr(reg_aux_ptr_dst, out_offset),
+                    vreg_out(i));
+        }
+
+        add(reg_aux_ptr_src, sizeof(float) * jcp.ih * jcp.iw * load_block);
+        add(reg_aux_ptr_dst, sizeof(float) * load_block);
+    }
+    dec(reg_ic_block);
+    cmp(reg_ic_block, 0);
+    jg(ic_block_label, T_NEAR);
+    postamble();
+}
+
+/// DST TRANSFORMS /////////////////////////////////////////////////////////////
+struct jit_avx512_core_fp32_wino_conv_2x3_dst_trans_t: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(
+            jit_avx512_core_fp32_wino_conv_2x3_dst_trans_t)
+
+    jit_conv_conf_2x3_wino_t jcp;
+    const primitive_attr_t &attr_;
+
+    struct call_params_t {
+        const void *wino_dst;
+        const void *dst;
+        const void *v_y_masks;
+        const void *v_x_masks;
+
+        const void *bias;
+        const void *scales;
+    };
+    void (*ker_)(const call_params_t *);
+
+    jit_avx512_core_fp32_wino_conv_2x3_dst_trans_t(
+            jit_conv_conf_2x3_wino_t ajcp, const primitive_attr_t &attr)
+        : jcp(ajcp), attr_(attr) {
+        generate();
+        ker_ = reinterpret_cast<decltype(ker_)>(
+                const_cast<uint8_t *>(getCode()));
+    }
+
+    void generate();
+    bool maybe_relu(int position);
+
+    Zmm vreg_inp(int i) { // 16
+        assert(i < jcp.alpha * jcp.alpha);
+        return Zmm(31 - i);
+    }
+
+    Zmm vreg_stg(int id) { // 8
+        const int id_reg_stg = jcp.alpha * jcp.alpha + id;
+        assert(id_reg_stg < jcp.alpha * jcp.alpha + 8);
+        return Zmm(31 - id_reg_stg);
+    }
+
+    Zmm vreg_out(int id) { // 4
+        const int id_reg_out = jcp.alpha * jcp.alpha + 8 + id;
+        assert(id_reg_out < jcp.alpha * jcp.alpha + 12);
+        return Zmm(31 - id_reg_out);
+    }
+
+    Zmm vreg_tmp(int id) { // 2
+        const int id_reg_tmp = jcp.alpha * jcp.alpha + 12 + id;
+        assert(id_reg_tmp < jcp.alpha * jcp.alpha + 14);
+        return Zmm(31 - id_reg_tmp);
+    }
+
+    Zmm vreg_zero = Zmm(0);
+    Zmm vreg_prev_dst = Zmm(0);
+    Zmm vreg_bias = Zmm(2);
+
+    Opmask y_mask = Opmask(1);
+    Opmask r_mask = Opmask(2);
+    Opmask x_mask(int id) {
+        assert (id < 4);
+        return Opmask(3 + id);
+    }
+
+    Reg64 reg_ptr_v_y_masks = r12;
+    Reg64 reg_ptr_v_x_masks = r11;
+
+    Reg64 reg_aux_ptr_src = r10;
+    Reg64 reg_aux_ptr_dst = r9;
+
+    Reg64 reg_oc_block = r8;
+
+    Reg64 reg_ptr_bias = rbx;
+    Reg64 reg_ptr_scales = abi_not_param1;
+    Reg64 reg_ptr_sum_scale = rdx;
+};
+
+bool jit_avx512_core_fp32_wino_conv_2x3_dst_trans_t::maybe_relu(int position) {
+    using namespace primitive_kind;
+    const auto &p = attr_.post_ops_;
+
+    if (position == 0) {
+        /* relu before sum */
+        return false
+            || p.contain(eltwise, 0);
+    } else if (position == 1) {
+        /* relu after sum */
+        const int sum_idx = p.contain(sum, 0)
+            ? 0 : (p.contain(sum, 1) ? 1 : -1);
+        if (sum_idx == -1)
+            return false;
+
+        return false
+            || p.contain(eltwise, sum_idx + 1);
+    }
+
+    return false;
+}
+
+void jit_avx512_core_fp32_wino_conv_2x3_dst_trans_t::generate() {
+    Label oc_block_label;
+
+    const int load_block = 16;
+
+    auto loop_body = [=]() {
+        const auto &p = attr_.post_ops_;
+        const int sum_idx = p.find(primitive_kind::sum);
+        const float *p_sum_scale = (sum_idx != -1)
+                ? &p.entry_[sum_idx].sum.scale
+                : nullptr;
+        if (p_sum_scale && *p_sum_scale != 1.f)
+            mov(reg_ptr_sum_scale, (size_t)p_sum_scale);
+
+        for (int i = 0; i < 16; i++) {
+            int internal_offset = sizeof(float) * jcp.out_stride * i;
+            vmovups(vreg_inp(i),
+                EVEX_compress_addr(reg_aux_ptr_src, internal_offset));
+        }
+        for (int y = 0; y < jcp.alpha; y++) {
+            vaddps(vreg_tmp(0), vreg_inp(y * 4 + 0), vreg_inp(y * 4 + 1));
+            vaddps(vreg_stg(y * 2), vreg_tmp(0), vreg_inp(y * 4 + 2));
+
+            vsubps(vreg_tmp(1), vreg_inp(y * 4 + 1), vreg_inp(y * 4 + 2));
+            vsubps(vreg_stg(y * 2+1), vreg_tmp(1), vreg_inp(y * 4 + 3));
+        }
+        for (int x = 0; x < jcp.m; x++) {
+            vaddps(vreg_tmp(0), vreg_stg(x), vreg_stg(x+2 * 1));
+            vaddps(vreg_out(x), vreg_tmp(0), vreg_stg(x+2 * 2));
+
+            vsubps(vreg_tmp(1), vreg_stg(x+2 * 1), vreg_stg(x+2 * 2));
+            vsubps(vreg_out(x+2), vreg_tmp(1), vreg_stg(x+2 * 3));
+        }
+
+
+        if (jcp.with_bias) {
+            auto bias_addr = ptr [ reg_ptr_bias ];
+            vmovups(vreg_bias, bias_addr);
+        }
+        for (int y = 0; y < jcp.m; y++) {
+            kmovw(y_mask, ptr[ reg_ptr_v_y_masks + sizeof(int16_t) * y ]);
+            for (int x = 0; x < jcp.m; x++) {
+                kandw(r_mask, y_mask, x_mask(x));
+
+                int i = y * jcp.m + x;
+                int offset = sizeof(float) *
+                    (y * jcp.ow * jcp.oc_block + x * jcp.oc_block);
+                Address addr = EVEX_compress_addr(reg_aux_ptr_dst, offset);
+
+                Zmm zmm = vreg_out(i);
+                if (jcp.with_bias)
+                    vaddps(zmm, zmm, vreg_bias);
+                vmulps(zmm, zmm, ptr [reg_ptr_scales]);
+
+                if (maybe_relu(0)) {
+                    vxorps(vreg_zero, vreg_zero, vreg_zero);
+                    vmaxps(zmm, vreg_zero, zmm);
+                }
+                if (p_sum_scale) { // post_op: sum
+                    vxorps(vreg_prev_dst, vreg_prev_dst, vreg_prev_dst);
+                    vmovups(vreg_prev_dst | r_mask, addr);
+                    if (*p_sum_scale == 1.f)
+                        vaddps(zmm, vreg_prev_dst);
+                    else
+                        vfmadd231ps(zmm, vreg_prev_dst,
+                            zword_b[reg_ptr_sum_scale]);
+                }
+                if (maybe_relu(1)) {
+                    vxorps(vreg_zero, vreg_zero, vreg_zero);
+                    vmaxps(zmm, vreg_zero, zmm);
+                }
+
+                vmovups(addr, zmm | r_mask);
+            }
+        }
+    };
+
+    preamble();
+
+#define READ_PARAM(reg, field) \
+        mov(reg, ptr[abi_param1 + offsetof(call_params_t, field)])
+    READ_PARAM(reg_aux_ptr_src, wino_dst);
+    READ_PARAM(reg_aux_ptr_dst, dst);
+    READ_PARAM(reg_ptr_v_y_masks, v_y_masks);
+    READ_PARAM(reg_ptr_v_x_masks, v_x_masks);
+    READ_PARAM(reg_ptr_bias, bias);
+    READ_PARAM(reg_ptr_scales, scales);
+#undef READ_PARAM
+
+    for (int i = 0; i < jcp.alpha * jcp.alpha; i++)
+        vxorps(vreg_inp(i), vreg_inp(i), vreg_inp(i));
+
+    for (int i = 0; i < jcp.alpha; i++)
+        kmovw(x_mask(i), ptr[reg_ptr_v_x_masks + sizeof(int16_t) * i]);
+
+    int oc_blocks = 1;
+    oc_blocks = jcp.oc / load_block;
+    mov(reg_oc_block, oc_blocks);
+    L(oc_block_label);
+    {
+        loop_body();
+        add(reg_aux_ptr_src, sizeof(float) * load_block);
+        add(reg_aux_ptr_dst, sizeof(float) * jcp.oh * jcp.ow * load_block);
+
+        add(reg_ptr_scales, jcp.is_oc_scale * sizeof(float) * load_block);
+        add(reg_ptr_bias, jcp.typesize_bia * load_block);
+    }
+    dec(reg_oc_block);
+    cmp(reg_oc_block, 0);
+    jg(oc_block_label, T_NEAR);
+
+    sub(reg_ptr_scales, jcp.is_oc_scale * sizeof(float) * load_block);
+    sub(reg_ptr_bias, oc_blocks * jcp.typesize_bia * load_block);
+
+    postamble();
+
+}
+
+/// GEMM kernel ////////////////////////////////////////////////////////////////
+struct jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t)
+    jit_conv_conf_2x3_wino_t jcp;
+
+    struct call_params_t {
+        const void *src;
+        const void *dst;
+        const void *wei;
+        const void *dst_b;
+    };
+    void (*ker_)(const call_params_t *);
+
+    void generate();
+    static bool post_ops_ok(jit_conv_conf_2x3_wino_t &jcp,
+                            const primitive_attr_t &attr);
+
+    jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t(
+            jit_conv_conf_2x3_wino_t ajcp, const primitive_attr_t &attr)
+        : jcp(ajcp) {
+        generate();
+        ker_ = reinterpret_cast<decltype(ker_)>(
+                const_cast<uint8_t *>(getCode()));
+    }
+
+    static status_t init_conf(
+            jit_conv_conf_2x3_wino_t &jcp, const convolution_desc_t &cd,
+            memory_desc_t &src_md, memory_desc_t &weights_md,
+            memory_desc_t &dst_md, memory_desc_t &bias_md,
+            const primitive_attr_t &attr,
+            memory_desc_t& expect_wei_md);
+
+    Zmm vreg_out(int n, int m) {
+        const int id_reg_out = n * jcp.m_block + m;
+        assert(id_reg_out < jcp.n2_block * jcp.m_block);
+        return Zmm(31 - id_reg_out);
+    }
+    Zmm vreg_wei(int i) {
+        assert (31 - jcp.n2_block * jcp.m_block - i > 1);
+        return Zmm(31 - jcp.n2_block * jcp.m_block - i);
+    }
+
+    Zmm vreg_src = Zmm(0);
+    Zmm vreg_one = Zmm(1);
+    Zmm vreg_tmp = Zmm(2);
+
+    Reg64 reg_ptr_src = r15;
+
+    Reg64 reg_aux_dst = r12;
+    Reg64 reg_aux_dst2 = r11;
+    Reg64 reg_aux_wei = r10;
+    Reg64 reg_aux_wei2 = r9;
+    Reg64 reg_aux_src = r8;
+    Reg64 reg_aux_src2 = rax;
+
+    Reg64 reg_mb = rbx;
+    Reg64 reg_nnb = rdx;
+    Reg64 reg_K = rsi;
+
+};
+
+bool jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t::post_ops_ok(
+        jit_conv_conf_2x3_wino_t &jcp, const primitive_attr_t &attr) {
+    using namespace primitive_kind;
+    const auto &p = attr.post_ops_;
+
+    auto is_relu = [&](int idx) { return p.entry_[idx].is_relu(); };
+
+    switch (p.len_) {
+    case 0: return true;
+    case 1: return is_relu(0) || p.contain(sum, 0);
+    case 2: return (p.contain(sum, 0) && is_relu(1)) ||
+                       (p.contain(sum, 1) && is_relu(0));
+    case 3: return is_relu(0) && p.contain(sum, 1) && is_relu(2);
+    default: return false;
+    }
+
+    return false;
+}
+
+void jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t::generate() {
+    Label nnb_loop_label, K_loop_label, mb_loop_label;
+
+    preamble();
+#define READ_PARAM(reg, field) \
+    mov(reg, ptr[abi_param1 + offsetof(call_params_t, field)])
+    READ_PARAM(reg_ptr_src, src);
+    READ_PARAM(reg_aux_dst, dst);
+    READ_PARAM(reg_aux_wei, wei);
+#undef READ_PARAM
+
+    if (!jcp.small_mb) {
+        mov(reg_nnb, jcp.n_chunks);
+        L(nnb_loop_label);
+    }
+    mov(reg_aux_dst2, reg_aux_dst);
+    mov(reg_aux_src, reg_ptr_src);
+    mov(reg_mb, jcp.M / jcp.m_block);
+    L(mb_loop_label);
+    {
+        int nb2 = 0;
+        for (nb2 = 0; nb2 < jcp.n2_block; nb2++) {
+            for (int m = 0; m < jcp.m_block; m++) {
+                vxorps(vreg_out(nb2, m), vreg_out(nb2, m), vreg_out(nb2, m));
+            }
+        }
+        mov(reg_aux_src2, reg_aux_src);
+        mov(reg_aux_wei2, reg_aux_wei);
+
+        mov(reg_K, jcp.k_chunks);
+        L(K_loop_label); {
+            int wei_offset = 0;
+            for (int _i = 0; _i < jcp.k2_block; _i++) {
+                for (int nb2 = 0; nb2 < jcp.n2_block; nb2++) {
+                    if (jcp.small_mb) {
+                        int wei_offset = sizeof(float)
+                                * ((nb2 * jcp.nb_ic * jcp.ic_block
+                                           * jcp.oc_block)
+                                          + _i * jcp.oc_block);
+                        vmovups(vreg_wei(nb2),
+                                EVEX_compress_addr(reg_aux_wei2, wei_offset));
+                    } else {
+                        vmovups(vreg_wei(nb2),
+                                EVEX_compress_addr(reg_aux_wei2,
+                                        sizeof(float) * wei_offset));
+                        wei_offset += jcp.oc_block;
+                    }
+                }
+                for (int m = 0; m < jcp.m_block; m++) {
+                    int inp_offset = sizeof(float) * (m * jcp.K + _i);
+                    if (jcp.n2_block > 1) {
+                        vbroadcastss(vreg_src,
+                            EVEX_compress_addr(reg_aux_src2, inp_offset));
+                        for (int nb2 = 0; nb2 < jcp.n2_block; nb2++)
+                            vfmadd231ps(vreg_out(nb2, m), vreg_wei(nb2),
+                                vreg_src);
+                    } else {
+                        vfmadd231ps(vreg_out(0, m), vreg_wei(0),
+                            EVEX_compress_addr(reg_aux_src2, inp_offset, true));
+                    }
+                }
+            }
+            add(reg_aux_src2, sizeof(float) * jcp.ic_block);
+            if (jcp.small_mb)
+                add(reg_aux_wei2, sizeof(float) * jcp.oc_block * jcp.ic_block);
+            else
+                add(reg_aux_wei2,
+                        sizeof(float) * jcp.k2_block * jcp.n2_block
+                                * jcp.oc_block);
+        }
+        dec(reg_K);
+        cmp(reg_K, 0);
+        jg(K_loop_label, T_NEAR);
+
+        for (int m = 0; m < jcp.m_block; m++) {
+            int nb2 = 0;
+            for (nb2 = 0; nb2 < jcp.n2_block; nb2++) {
+                int offset = sizeof(float) *
+                    (m * jcp.N + nb2 * jcp.oc_block);
+                vmovups(EVEX_compress_addr(reg_aux_dst2,offset),
+                            vreg_out(nb2, m));
+            }
+        }
+        add(reg_aux_src, sizeof(float) * jcp.m_block * jcp.K);
+        add(reg_aux_dst2, sizeof(float) * jcp.m_block * jcp.N);
+    }
+    dec(reg_mb);
+    cmp(reg_mb, 0);
+    jg(mb_loop_label, T_NEAR);
+
+    if (!jcp.small_mb) {
+        add(reg_aux_dst, sizeof(float) * jcp.n2_block * jcp.oc_block);
+        add(reg_aux_wei,
+                sizeof(float) * jcp.k_chunks * jcp.ic_block * jcp.n2_block
+                        * jcp.oc_block);
+
+        dec(reg_nnb);
+        cmp(reg_nnb, 0);
+        jg(nnb_loop_label, T_NEAR);
+    }
+    postamble();
+}
+
+namespace {
+bool is_winograd_faster_than_direct(const jit_conv_conf_2x3_wino_t &jcp) {
+    return jcp.mb >= 4;
+}
+}
+
+status_t jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t ::init_conf(
+        jit_conv_conf_2x3_wino_t &jcp, const convolution_desc_t &cd,
+        memory_desc_t &src_md, memory_desc_t &wei_md,
+        memory_desc_t &dst_md, memory_desc_t &bias_md,
+        const primitive_attr_t &attr, memory_desc_t &expect_wei_md) {
+    const memory_desc_wrapper src_d(&src_md);
+    const memory_desc_wrapper wei_d(&wei_md);
+    const memory_desc_wrapper dst_d(&dst_md);
+    const memory_desc_wrapper bias_d(&bias_md);
+
+    const bool with_groups = wei_d.ndims() == src_d.ndims() + 1;
+
+    jcp.nthr = mkldnn_get_max_threads();
+
+    jcp.ngroups = with_groups ? wei_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+    jcp.ih = src_d.dims()[2];
+    jcp.iw = src_d.dims()[3];
+    jcp.oh = dst_d.dims()[2];
+    jcp.ow = dst_d.dims()[3];
+    jcp.kh = wei_d.dims()[with_groups + 2];
+    jcp.kw = wei_d.dims()[with_groups + 3];
+    jcp.t_pad = cd.padding[0][0];
+    jcp.b_pad = cd.padding[1][0];
+    jcp.l_pad = cd.padding[0][1];
+    jcp.r_pad = cd.padding[1][1];
+    jcp.stride_h = cd.strides[0];
+    jcp.stride_w = cd.strides[1];
+    jcp.dilate_h = cd.dilates[0];
+    jcp.dilate_w = cd.dilates[1];
+
+    jcp.m = 2;
+    jcp.r = 3;
+    jcp.alpha = jcp.m + jcp.r - 1;
+    int simdw = 16;
+
+    format_tag_t dat_tag = format_tag::nChw16c;
+    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    jcp.dst_tag = dst_d.matches_one_of_tag(dat_tag);
+
+    if (jcp.src_tag != dat_tag) return status::unimplemented;
+    if (jcp.dst_tag != dat_tag) return status::unimplemented;
+
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    bool ok_to_pad_channels = jcp.ngroups == 1;
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, simdw);
+        jcp.ic = rnd_up(jcp.ic, simdw);
+    }
+
+    jcp.ver = ver_avx512_core;
+    if (!(mayiuse(avx512_core)))
+        return status::unimplemented;
+
+    if (!IMPLICATION(cd.alg_kind == alg_kind::convolution_auto,
+               is_winograd_faster_than_direct(jcp)))
+        return status::unimplemented;
+
+    if (src_d.data_type() != data_type::f32)
+        return status::unimplemented;
+    if (wei_d.data_type() != data_type::f32)
+        return status::unimplemented;
+    if (dst_d.data_type() != data_type::f32)
+        return status::unimplemented;
+
+    jcp.ic_block = simdw;
+    jcp.oc_block = simdw;
+
+    bool ok = true && jcp.kh == 3 && jcp.kw == 3 && jcp.ngroups == 1
+            && jcp.oc % jcp.oc_block == 0 && jcp.ic % jcp.ic_block == 0
+            && jcp.stride_h == 1 && jcp.stride_w == 1 && jcp.dilate_h == 0
+            && jcp.dilate_w == 0 && jcp.t_pad == jcp.b_pad
+            && jcp.l_pad == jcp.r_pad && jcp.t_pad < 2 && jcp.t_pad >= 0
+            && jcp.l_pad < 2 && jcp.l_pad >= 0;
+    if (!ok)
+        return status::unimplemented;
+
+    const int L2_cap = get_cache_size(2, true) / sizeof(float);
+    const int L3_capacity = get_cache_size(3, false) / sizeof(float);
+    int a = jcp.alpha;
+    int aa = a * a;
+    int mb = jcp.mb;
+    int ic = jcp.ic;
+    int oc = jcp.oc;
+    int ih = jcp.ih;
+    int iw = jcp.iw;
+    auto wei_sz = (float)aa * ic * oc;
+    auto inp_sz = (float)mb * ih * iw * ic;
+    auto sp_sz = (float)mb * ih * iw;
+
+    /* Heuristics here. Numbers '28','196' is an observation from data. */
+    if (wei_sz / inp_sz > 5)
+        jcp.small_mb = true;
+    else
+        jcp.small_mb = false;
+
+    if (mb > nstl::min(jcp.nthr, 28)
+        || (!jcp.small_mb
+            && (wei_sz >= 0.9f * L2_cap
+                || inp_sz > L2_cap * jcp.nthr + L3_capacity))
+        || (jcp.small_mb && sp_sz > 196))
+        return status::unimplemented;
+
+    jcp.bia_dt = jcp.with_bias ? cd.bias_desc.data_type : data_type::undef;
+    jcp.dst_dt = cd.dst_desc.data_type;
+
+    jcp.typesize_bia
+            = jcp.with_bias ? types::data_type_size(bias_d.data_type()) : 0;
+
+    jcp.nb_oc = jcp.oc / jcp.oc_block;
+    jcp.nb_ic = jcp.ic / jcp.ic_block;
+
+    const int skx_free_regs = 30;
+
+    auto find_m_n2_blocks = [=](int xb, int yb, int &M, int &m_block,
+                                    int &n2_block, float &reg_eff) {
+        M = (xb * yb) / jcp.alpha;
+        int max_m_block = m_block = nstl::min(M, skx_free_regs);
+        int max_n2_block = n2_block = nstl::min(jcp.nb_oc, skx_free_regs);
+        reg_eff = 0;
+        for (int im = max_m_block; im > 0; im--) {
+            for (int in2 = max_n2_block; in2 > 0; in2--) {
+                int used_regs = in2 * im + in2;
+                float cur_reg_eff = ((float)in2 * im) / (im + in2) / 2.5f;
+                if (M % im || jcp.nb_oc % in2 || used_regs > skx_free_regs
+                        || cur_reg_eff <= reg_eff)
+                    continue;
+                reg_eff = cur_reg_eff;
+                m_block = im;
+                n2_block = in2;
+            }
+        }
+    };
+
+    int oh = jcp.oh;
+    int ow = jcp.ow;
+    int nb_oc = jcp.nb_oc;
+    int Z = ic + oc;
+    int Y = ic * oc;
+    const int L3_cap_per_core = get_cache_size(3, true) / sizeof(float);
+
+    /* Selecting xb and yb blocking */
+    int min_yb = jcp.alpha;
+    int min_xb = jcp.alpha;
+    int max_yb = nstl::max(min_yb, rnd_up(ih, 2));
+    int max_xb = nstl::max(min_xb, rnd_up(iw, 2));
+    float best_eff = 0.f;
+    for (int ix = max_xb; ix >= min_xb; ix -= 2) {
+        if (rnd_up(ow, ix) < iw - 2)
+            continue;
+        for (int iy = max_yb; iy >= min_yb; iy -= 2) {
+            if (rnd_up(oh, iy) < ih - 2)
+                continue;
+            int ex_y = rnd_up(oh, iy);
+            int ex_x = rnd_up(ow, ix);
+            float work_eff = (float)(ih * iw) / (ex_y * ex_x);
+
+            int M, m_block, n2_b;
+            float reg_eff, thr_eff, par_eff, mem_eff, req_mem;
+
+            find_m_n2_blocks(ix, iy, M, m_block, n2_b, reg_eff);
+
+            /* outer parallelization */
+            int nblocks = mb * div_up(ih, iy) * div_up(iw, ix);
+            thr_eff = (float)nblocks / rnd_up(nblocks, jcp.nthr);
+
+            mem_eff = 1.f;
+            req_mem = (((float)ix + 2) * (iy + 2) + aa * M) * Z + aa * Y;
+            if (req_mem > L2_cap / 2) {
+                if (req_mem > ((L2_cap + L3_cap_per_core) * 4) / 7)
+                    mem_eff /= (n2_b + 1) / 2.f;
+                else
+                    mem_eff /= (n2_b + 1) / 3.f;
+            }
+
+            float outer_eff = thr_eff + work_eff + reg_eff + mem_eff;
+
+            /* inner parallelization */
+            int bsz = iy * ix / a;
+            int gemmw = aa * (nb_oc / n2_b);
+            int bsz_r = rnd_up(bsz, jcp.nthr);
+            int gemmw_r = rnd_up(gemmw, jcp.nthr);
+            thr_eff = ((float)Z * bsz / bsz_r + Y * gemmw / gemmw_r) / (Z + Y);
+
+            req_mem = (float)ix * iy * (ic + simdw * n2_b) + simdw * n2_b * ic;
+            mem_eff = nstl::min(1.f, L2_cap / req_mem);
+            int M_per_thr = nstl::max(2, div_up(aa, jcp.nthr));
+            int oc_per_thr =
+                nstl::min(oc, div_up(aa * (nb_oc / n2_b), jcp.nthr));
+            req_mem = (float)aa * oc_per_thr * ic + M_per_thr * M * Z;
+            if (req_mem > L2_cap)
+                mem_eff = 0.1f;
+            par_eff = 1 / (2.f * nblocks);
+
+            float inner_eff = thr_eff + work_eff + mem_eff + par_eff;
+
+            float eff = jcp.small_mb ? inner_eff : outer_eff;
+            if (eff > best_eff) {
+                best_eff = eff;
+                jcp.yb = iy;
+                jcp.xb = ix;
+                jcp.M = M;
+                jcp.m_block = m_block;
+                jcp.n2_block = n2_b;
+            }
+        }
+    }
+
+    assert(jcp.xb % 2 == 0 && jcp.yb % 2 == 0);
+
+    jcp.inp_stride = jcp.M * jcp.ic;
+    jcp.out_stride = jcp.M * jcp.oc;
+    jcp.wei_stride = jcp.ic * jcp.oc;
+    jcp.bia_stride = jcp.oc;
+
+    jcp.N = jcp.oc;
+    jcp.K = jcp.ic;
+
+    jcp.n_block = jcp.oc_block;
+    jcp.k_block = jcp.ic_block;
+
+    assert(jcp.M % jcp.m_block == 0);
+    assert(jcp.nb_oc % jcp.n2_block == 0);
+
+    jcp.n_chunks = jcp.nb_oc / jcp.n2_block;
+    jcp.k2_block = jcp.ic_block;
+    jcp.k_chunks = jcp.K / jcp.k2_block;
+
+    const auto &oscales = attr.output_scales_;
+    jcp.is_oc_scale = oscales.mask_ == 1 << 1;
+    assert(IMPLICATION(!jcp.is_oc_scale, oscales.mask_ == 0));
+
+    /* re-create weights primitive descriptor
+                                    and set weights wino_blocking */
+    expect_wei_md.format_kind = format_kind::wino;
+    expect_wei_md.data_type = data_type::f32;
+    mkldnn_wino_desc_t &wd = expect_wei_md.format_desc.wino_desc;
+    wd.wino_format
+            = jcp.small_mb ? mkldnn_wino_wei_aaOio : mkldnn_wino_wei_aaOBiOo;
+    wd.r = jcp.r;
+    wd.alpha = jcp.alpha;
+    wd.ic = jcp.ic;
+    wd.oc = jcp.oc;
+    wd.ic_block = jcp.ic_block;
+    wd.oc_block = jcp.oc_block;
+    wd.oc2_block = jcp.n2_block;
+    wd.ic2_block = 1;
+    wd.adj_scale = 1.f;
+    size_t max_size = sizeof(float) * jcp.alpha * jcp.alpha * jcp.ic * jcp.oc;
+    wd.size = max_size;
+
+    return status::success;
+}
+////////////////////////////////////////////////////////////////////////////////
+
+status_t jit_avx512_core_fp32_wino_conv_2x3_fwd_t
+    ::pd_t::jit_conf(memory_desc_t& expect_wei_md) {
+    return jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t::init_conf(
+            jcp_, *this->desc(), this->src_md_, this->weights_md_,
+            this->dst_md_,this->bias_md_, *this->attr(), expect_wei_md);
+}
+
+jit_avx512_core_fp32_wino_conv_2x3_fwd_t::
+        jit_avx512_core_fp32_wino_conv_2x3_fwd_t(const pd_t *apd)
+    : cpu_primitive_t(apd)
+{
+    kernel_ = new jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t(
+            pd()->jcp_, *pd()->attr());
+    src_trans_ = new jit_avx512_core_fp32_wino_conv_2x3_src_trans_t(
+            pd()->jcp_, *pd()->attr());
+    dst_trans_ = new jit_avx512_core_fp32_wino_conv_2x3_dst_trans_t(
+            pd()->jcp_, *pd()->attr());
+}
+
+jit_avx512_core_fp32_wino_conv_2x3_fwd_t
+    ::~jit_avx512_core_fp32_wino_conv_2x3_fwd_t() {
+    delete kernel_;
+    delete src_trans_;
+    delete dst_trans_;
+}
+
+void jit_avx512_core_fp32_wino_conv_2x3_fwd_t::execute_forward_mbN(
+        const float *src, const float *wei, const float *bia, float *dst,
+        const memory_tracking::grantor_t &scratchpad) const
+{
+    const auto &jcp = kernel_->jcp;
+    const auto &oscales = pd()->attr()->output_scales_;
+
+    const size_t wino_size_offset =
+        (size_t)(pd()->jcp_.yb / 2) * (pd()->jcp_.xb / 2) + (pd()->jcp_.xb);
+    const size_t size_wino_src = wino_size_offset * pd()->jcp_.ic * 16;
+    const size_t size_wino_dst = wino_size_offset * pd()->jcp_.oc * 16;
+
+    if (pd()->wants_padded_bias()) {
+        auto padded_bias = scratchpad.get<float>(key_conv_padded_bias);
+        utils::array_copy(padded_bias, bia, jcp.oc_without_padding);
+        utils::array_set(padded_bias + jcp.oc_without_padding, 0.f,
+                jcp.oc - jcp.oc_without_padding);
+        bia = padded_bias;
+    }
+
+    auto ptr_V = scratchpad.get<float>(key_wino_V);
+    auto ptr_M = scratchpad.get<float>(key_wino_M);
+
+    parallel_nd(jcp.mb, div_up(jcp.oh,jcp.yb), div_up(jcp.ow, jcp.xb),
+        [&](int mb, int tile_y_b, int tile_x_b) {
+        int tile_y = tile_y_b * jcp.yb;
+        int tile_x = tile_x_b * jcp.xb;
+
+        int ithr = mkldnn_get_thread_num();
+        auto wino_src = ptr_V + size_wino_src * ithr;
+        auto wino_dst = ptr_M + size_wino_dst * ithr;
+
+        auto src_trans_p =
+            jit_avx512_core_fp32_wino_conv_2x3_src_trans_t
+                ::call_params_t();
+        auto dst_trans_p =
+            jit_avx512_core_fp32_wino_conv_2x3_dst_trans_t
+                ::call_params_t();
+        auto gemm_p = jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t ::
+                call_params_t();
+
+        /* transformation of input tensor to winograd domain */
+        for (int y_in_block = 0; y_in_block < jcp.yb; y_in_block += 2) {
+            for (int x_in_block = 0; x_in_block < jcp.xb;
+                    x_in_block += 2) {
+
+                unsigned short v_y_masks[4], v_x_masks[4];
+
+                int y = y_in_block + tile_y;
+                int x = x_in_block + tile_x;
+                int m = (y_in_block / 2) * (jcp.xb / 2)
+                        + (x_in_block / 2);
+
+                int v_ys = nstl::max(0, jcp.t_pad - y);
+                int v_ye = nstl::min(jcp.alpha,
+                        nstl::max(0, jcp.ih + jcp.t_pad - y));
+
+                int v_xs = nstl::max(0, jcp.l_pad - x);
+                int v_xe = nstl::min(jcp.alpha,
+                        nstl::max(0, jcp.iw + jcp.l_pad - x));
+
+#pragma unroll(4)
+                for (int i = 0; i < jcp.alpha; i++) {
+                    v_y_masks[i] = (i < v_ys || i >= v_ye) ? 0 : 0xffff;
+                    v_x_masks[i] = (i < v_xs || i >= v_xe) ? 0 : 0xffff;
+                }
+                auto local_s = src
+                        + mb * jcp.nb_ic * jcp.ih * jcp.iw
+                                * jcp.ic_block
+                        + y * jcp.iw * jcp.ic_block + x * jcp.ic_block;
+                auto local_w = wino_src + m * jcp.ic;
+
+                src_trans_p.src = local_s;
+                src_trans_p.wino_src = local_w;
+                src_trans_p.v_y_masks = v_y_masks;
+                src_trans_p.v_x_masks = v_x_masks;
+
+                src_trans_->ker_(&src_trans_p);
+            }
+        }
+        /* gemms */
+        for (int tile_ij = 0; tile_ij < 16; tile_ij++) {
+            int offset = (tile_ij + ithr) % 16;
+            gemm_p.src = wino_src + jcp.inp_stride * offset;
+            gemm_p.dst = wino_dst + jcp.out_stride * offset;
+            gemm_p.wei = wei + jcp.wei_stride * offset;
+
+            kernel_->ker_(&gemm_p);
+        }
+
+        /* transformation from winograd domain to output tensor */
+        for (int y_in_block = 0; y_in_block < jcp.yb; y_in_block += 2) {
+            for (int x_in_block = 0; x_in_block < jcp.xb;
+                    x_in_block += 2) {
+                unsigned short v_y_masks[2], v_x_masks[2];
+
+                int y = y_in_block + tile_y;
+                int x = x_in_block + tile_x;
+                int m = (y_in_block / 2) * (jcp.xb / 2)
+                        + (x_in_block / 2);
+
+#pragma unroll(2)
+                for (int i = 0; i < jcp.m; i++) {
+                    v_x_masks[i] = (x + i < jcp.ow) ? 0xffff : 0;
+                    v_y_masks[i] = (y + i < jcp.oh) ? 0xffff : 0;
+                }
+                auto local_d = dst
+                        + mb * jcp.nb_oc * jcp.oh * jcp.ow
+                                * jcp.oc_block
+                        + y * jcp.ow * jcp.oc_block + x * jcp.oc_block;
+                auto local_w = wino_dst + m * jcp.oc;
+
+                auto scales = oscales.scales_;
+                dst_trans_p.dst = local_d;
+                dst_trans_p.wino_dst = local_w;
+                dst_trans_p.v_y_masks = v_y_masks;
+                dst_trans_p.v_x_masks = v_x_masks;
+
+                dst_trans_p.scales = scales;
+                dst_trans_p.bias = bia;
+
+                dst_trans_->ker_(&dst_trans_p);
+            }
+        }
+    });
+}
+
+void jit_avx512_core_fp32_wino_conv_2x3_fwd_t::execute_forward_small_mb(
+        const float *src, const float *wei, const float *bia, float *dst,
+        const memory_tracking::grantor_t &scratchpad) const
+{
+    const auto &jcp = kernel_->jcp;
+    const auto &oscales = pd()->attr()->output_scales_;
+
+    if (pd()->wants_padded_bias()) {
+        auto padded_bias = scratchpad.get<float>(key_conv_padded_bias);
+        utils::array_copy(padded_bias, bia, jcp.oc_without_padding);
+        utils::array_set(padded_bias + jcp.oc_without_padding, 0.f,
+                jcp.oc - jcp.oc_without_padding);
+        bia = padded_bias;
+    }
+
+    auto ptr_V = scratchpad.get<float>(key_wino_V);
+    auto ptr_M = scratchpad.get<float>(key_wino_M);
+
+    for (int mb = 0; mb < jcp.mb; mb++) {
+    for (int tile_y = 0; tile_y < jcp.oh; tile_y += jcp.yb) {
+    for (int tile_x = 0; tile_x < jcp.ow; tile_x += jcp.xb) {
+        /* transformation of input tensor to winograd domain */
+        parallel_nd(div_up(jcp.yb, 2), div_up(jcp.xb, 2),
+            [&](int y_in_block_b, int x_in_block_b) {
+            int y_in_block = y_in_block_b * 2;
+            int x_in_block = x_in_block_b * 2;
+
+            auto src_trans_p = jit_avx512_core_fp32_wino_conv_2x3_src_trans_t ::
+                    call_params_t();
+
+            unsigned short v_y_masks[4], v_x_masks[4];
+
+            int y = y_in_block + tile_y;
+            int x = x_in_block + tile_x;
+            int m = (y_in_block / 2) * (jcp.xb / 2) + (x_in_block / 2);
+
+            int v_ys = nstl::max(0, jcp.t_pad - y);
+            int v_ye = nstl::min(
+                    jcp.alpha, nstl::max(0, jcp.ih + jcp.t_pad - y));
+
+            int v_xs = nstl::max(0, jcp.l_pad - x);
+            int v_xe = nstl::min(
+                    jcp.alpha, nstl::max(0, jcp.iw + jcp.l_pad - x));
+
+#pragma unroll(4)
+            for (int i = 0; i < jcp.alpha; i++) {
+                v_y_masks[i] = (i < v_ys || i >= v_ye) ? 0 : 0xffff;
+                v_x_masks[i] = (i < v_xs || i >= v_xe) ? 0 : 0xffff;
+            }
+            auto local_s = src
+                    + mb * jcp.nb_ic * jcp.ih * jcp.iw * jcp.ic_block
+                    + y * jcp.iw * jcp.ic_block + x * jcp.ic_block;
+            auto local_w = ptr_V + m * jcp.ic;
+
+            src_trans_p.src = local_s;
+            src_trans_p.wino_src = local_w;
+            src_trans_p.v_y_masks = v_y_masks;
+            src_trans_p.v_x_masks = v_x_masks;
+
+            src_trans_->ker_(&src_trans_p);
+        });
+
+        /* gemms */
+        parallel_nd(16, jcp.n_chunks, [&](int tile_ij, int nnb) {
+            auto gemm_p = jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t ::
+                    call_params_t();
+
+            gemm_p.src = ptr_V + jcp.inp_stride * tile_ij;
+            gemm_p.dst = ptr_M + jcp.out_stride * tile_ij
+                    + nnb * jcp.n2_block * jcp.n_block;
+            gemm_p.wei = wei + jcp.wei_stride * tile_ij
+                    + nnb * jcp.n2_block * jcp.n_block * jcp.K;
+
+            kernel_->ker_(&gemm_p);
+        });
+
+        /* transformation from winograd domain to output tensor */
+
+        parallel_nd(div_up(jcp.yb, 2), div_up(jcp.xb, 2),
+            [&](int y_in_block_b, int x_in_block_b) {
+            int y_in_block = y_in_block_b * 2;
+            int x_in_block = x_in_block_b * 2;
+
+            auto dst_trans_p = jit_avx512_core_fp32_wino_conv_2x3_dst_trans_t ::
+                    call_params_t();
+
+            unsigned short v_y_masks[2], v_x_masks[2];
+
+            int y = y_in_block + tile_y;
+            int x = x_in_block + tile_x;
+            int m = (y_in_block / 2) * (jcp.xb / 2) + (x_in_block / 2);
+
+#pragma unroll(2)
+            for (int i = 0; i < jcp.m; i++) {
+                v_x_masks[i] = (x + i < jcp.ow) ? 0xffff : 0;
+                v_y_masks[i] = (y + i < jcp.oh) ? 0xffff : 0;
+            }
+            auto local_d = dst
+                    + mb * jcp.nb_oc * jcp.oh * jcp.ow * jcp.oc_block
+                    + y * jcp.ow * jcp.oc_block + x * jcp.oc_block;
+            auto local_w = ptr_M + m * jcp.oc;
+
+            auto scales = oscales.scales_;
+            dst_trans_p.dst = local_d;
+            dst_trans_p.wino_dst = local_w;
+            dst_trans_p.v_y_masks = v_y_masks;
+            dst_trans_p.v_x_masks = v_x_masks;
+
+            dst_trans_p.scales = scales;
+            dst_trans_p.bias = bia;
+
+            dst_trans_->ker_(&dst_trans_p);
+        });
+    }}}
+}
+
+} // namespace cpu
+} // namespace impl
+} // namespace mkldnn
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_2x3.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_2x3.hpp
new file mode 100644
index 0000000000..7e38b07f5a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_2x3.hpp
@@ -0,0 +1,144 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_CORE_FP32_WINO_CONV_2x3_HPP
+#define CPU_JIT_AVX512_CORE_FP32_WINO_CONV_2x3_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "jit_primitive_conf.hpp"
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t;
+struct jit_avx512_core_fp32_wino_conv_2x3_src_trans_t;
+struct jit_avx512_core_fp32_wino_conv_2x3_dst_trans_t;
+
+struct jit_avx512_core_fp32_wino_conv_2x3_fwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_fp32_wino_2x3:", avx512_core, ""),
+                jit_avx512_core_fp32_wino_conv_2x3_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::forward_inference
+                && utils::one_of(desc()->alg_kind,
+                        alg_kind::convolution_auto,
+                        alg_kind::convolution_winograd)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            memory_desc_t expect_wei_md = *weights_md();
+            status_t jit_conf_result = jit_conf(expect_wei_md);
+            if (jit_conf_result != status::success) return jit_conf_result;
+            set_default_alg_kind(alg_kind::convolution_winograd);
+
+            if (weights_md_.format_kind == format_kind::any)
+                weights_md_ = expect_wei_md;
+            if (weights_md_ != expect_wei_md)
+                return status::unimplemented;
+
+            init_scratchpad();
+
+            return status::success;
+        }
+
+        jit_conv_conf_2x3_wino_t jcp_;
+
+    protected:
+        status_t jit_conf(memory_desc_t& expect_wei_md);
+
+        void init_scratchpad() {
+            using namespace memory_tracking::names;
+
+            auto scratchpad = scratchpad_registry().registrar();
+
+            int wino_size_offset = (jcp_.yb / 2) * (jcp_.xb / 2) + jcp_.xb;
+
+            size_t V_sz = (size_t)jcp_.ic * 16 * wino_size_offset * jcp_.nthr;
+            scratchpad.book(key_wino_V, sizeof(float) * V_sz, PAGE_4K);
+
+            size_t M_sz = (size_t)jcp_.oc * 16 * wino_size_offset * jcp_.nthr;
+            scratchpad.book(key_wino_M, sizeof(float) * M_sz, PAGE_4K);
+
+            if (wants_padded_bias()) {
+                assert(jcp_.ngroups == 1);
+                scratchpad.book(key_conv_padded_bias, sizeof(float) * jcp_.oc);
+            }
+        }
+
+        bool set_default_formats() {
+            using namespace format_tag;
+            return set_default_formats_common(nChw16c, any, nChw16c);
+        }
+    };
+
+    jit_avx512_core_fp32_wino_conv_2x3_fwd_t(const pd_t *apd);
+    ~jit_avx512_core_fp32_wino_conv_2x3_fwd_t();
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        auto src = CTX_IN_MEM(const float *, MKLDNN_ARG_SRC);
+        auto wei = CTX_IN_MEM(const float *, MKLDNN_ARG_WEIGHTS);
+        auto bia = CTX_IN_MEM(const float *, MKLDNN_ARG_BIAS);
+        auto dst = CTX_OUT_MEM(float *, MKLDNN_ARG_DST);
+
+        if (pd()->jcp_.small_mb)
+            execute_forward_small_mb(src, wei, bia, dst, this->scratchpad(ctx));
+        else
+            execute_forward_mbN(src, wei, bia, dst, this->scratchpad(ctx));
+
+        return status::success;
+    }
+
+private:
+    void execute_forward_small_mb(const float *src, const float *wei,
+            const float *bia, float *dst,
+            const memory_tracking::grantor_t &scratchpad) const;
+    void execute_forward_mbN(const float *src, const float *wei,
+            const float *bia, float *dst,
+            const memory_tracking::grantor_t &scratchpad) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx512_core_fp32_wino_conv_2x3_fwd_ker_t *kernel_;
+    jit_avx512_core_fp32_wino_conv_2x3_src_trans_t *src_trans_;
+    jit_avx512_core_fp32_wino_conv_2x3_dst_trans_t *dst_trans_;
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3.cpp
new file mode 100644
index 0000000000..96325e3ade
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3.cpp
@@ -0,0 +1,1020 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifdef __INTEL_COMPILER
+#include <immintrin.h>
+#endif
+
+#include "mkldnn_types.h"
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_avx512_core_fp32_wino_conv_4x3.hpp"
+
+#ifndef _MSC_VER
+#define pragma_unroll _Pragma("unroll")
+#else
+#define pragma_unroll
+#endif
+
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+
+template <bool is_fwd>
+void _jit_avx512_core_fp32_wino_conv_4x3_t<is_fwd>
+::weight_transform_data(const jit_conv_winograd_conf_t &jcp,
+        float *wp, float *twp) const
+{
+    float G[] = {0.26890756302521f, 0.688403361344538f, 0.119514472455649f,
+                 1.13777777777778f, 0.430252100840336f, 0.179271708683473f};
+    const int kh = 3;
+    const int kw = 3;
+    float Fw[alpha][alpha][simd_w][simd_w];
+    float F[kh][kw][simd_w][simd_w];
+    float T[alpha][3][simd_w];
+    auto p = jit_wino_transform_call_s();
+
+    p.src = wp;
+    p.dst = twp;
+    p.G = G;
+    p.M = F;
+    p.Mw = Fw;
+    p.T = T;
+
+    kernel_->weights_transform_data_ker(&p);
+}
+
+template<bool is_fwd>
+void _jit_avx512_core_fp32_wino_conv_4x3_t<is_fwd>::output_transform_data
+(int image, const jit_conv_winograd_conf_t &jcp,
+    const post_ops_t &p_ops, float *toutp, float *pout_b, float *bias) const {
+
+    float G[] = {0.625f, 1.5f, 0.390625f, 2.25f, 0.244140625f, 3.375f};
+    float Ow[alpha][alpha][simd_w];
+    float O[tile_size][tile_size][simd_w];
+    float T[tile_size][alpha][simd_w];
+
+    auto p = jit_wino_transform_call_s();
+    p.src = toutp;
+    p.dst = pout_b;
+    p.G = G;
+    p.M = O;
+    p.Mw = Ow;
+    p.T = T;
+    p.bias = bias;
+
+    int tile_base_index = image * jcp.itiles * jcp.jtiles;
+    int tile_block_ur = tile_base_index % jcp.tile_block_ur;
+    int nb_tile_block_ur =
+        (tile_base_index / jcp.tile_block_ur) % jcp.nb_tile_block_ur;
+    int tile_block =
+        (tile_base_index / jcp.tile_block_ur) / jcp.nb_tile_block_ur;
+
+    for (int tj = 0; tj < jcp.jtiles; tj++) {
+        for (int ti = 0; ti < jcp.itiles; ti++) {
+
+            p.tile_block_ur = tile_block_ur;
+            p.nb_tile_block_ur = nb_tile_block_ur;
+            p.tile_block = tile_block;
+            p.tj = tj;
+            p.ti = ti;
+
+            kernel_->output_transform_data_ker(&p);
+
+            tile_block_ur++;
+            if (tile_block_ur >= jcp.tile_block_ur) {
+                tile_block_ur = 0;
+                nb_tile_block_ur++;
+            }
+            if (nb_tile_block_ur >= jcp.nb_tile_block_ur) {
+                nb_tile_block_ur = 0;
+                tile_block++;
+            }
+        }
+    }
+}
+
+template<bool is_fwd>
+void _jit_avx512_core_fp32_wino_conv_4x3_t<is_fwd>
+::output_transform_tileblock_data(int tile_block,
+    const jit_conv_winograd_conf_t &jcp, const post_ops_t &p_ops,
+    float *toutp, float *outp, float *bias) const {
+
+    float G[] = {0.625f, 1.5f, 0.390625f, 2.25f, 0.244140625f, 3.375f};
+    float Ow[alpha][alpha][simd_w];
+    float O[tile_size][tile_size][simd_w];
+    float T[tile_size][alpha][simd_w];
+
+    auto p = jit_wino_transform_call_s();
+    p.src = toutp;
+    p.dst = outp;
+    p.G = G;
+    p.M = O;
+    p.Mw = Ow;
+    p.T = T;
+    p.bias = bias;
+
+    int outw = is_fwd ? jcp.ow : jcp.iw;
+    int outh = is_fwd ? jcp.oh : jcp.ih;
+
+    int tile_index = tile_block * jcp.nb_tile_block_ur * jcp.tile_block_ur;
+
+    for (int nb_tile_block_ur = 0;
+        nb_tile_block_ur < jcp.nb_tile_block_ur;
+        nb_tile_block_ur++) {
+
+        for (int tile_block_ur = 0; tile_block_ur < jcp.tile_block_ur;
+            tile_block_ur++) {
+            int img = tile_index / (jcp.jtiles * jcp.itiles);
+            int ti = tile_index % jcp.itiles;
+            int tj = (tile_index / jcp.itiles) % jcp.jtiles;
+
+            p.tile_block_ur = tile_block_ur;
+            p.nb_tile_block_ur = nb_tile_block_ur;
+            p.tile_block = tile_block;
+            p.tj = tj;
+            p.ti = ti;
+            p.dst = outp + img * (jcp.dimM / jcp.dimM_simd_block)
+                               * outh * outw * jcp.dimM_simd_block;
+
+            kernel_->output_transform_data_ker(&p);
+
+            tile_index++;
+        }
+    }
+}
+
+
+template<bool is_fwd>
+void _jit_avx512_core_fp32_wino_conv_4x3_t<is_fwd>
+    ::input_transform_data(int image, const jit_conv_winograd_conf_t &jcp,
+        float *inp, float *tinp) const
+{
+    float G[] = {-2.25f, -0.390625f, 0.87890625f, -2.640625f,
+                 0.625f, -0.625f, 1.5f, -1.5f, -2.640625f};
+
+    float Iw[alpha][alpha][simd_w];
+    float I[alpha][alpha][simd_w];
+    float T[alpha][alpha][simd_w];
+
+    auto p = jit_wino_transform_call_s();
+
+    p.src = inp;
+    p.dst = tinp;
+    p.G = G;
+    p.M = I;
+    p.Mw = Iw;
+    p.T = T;
+
+    int tile_base_index = image * jcp.itiles * jcp.jtiles;
+    int tile_block_ur = tile_base_index % jcp.tile_block_ur;
+    int nb_tile_block_ur =
+        (tile_base_index / jcp.tile_block_ur) % jcp.nb_tile_block_ur;
+    int tile_block =
+        (tile_base_index / jcp.tile_block_ur) / jcp.nb_tile_block_ur;
+
+    for (int tj = 0; tj < jcp.jtiles; tj++) {
+        for (int ti = 0; ti < jcp.itiles; ti++) {
+
+            p.tile_block_ur = tile_block_ur;
+            p.nb_tile_block_ur = nb_tile_block_ur;
+            p.tile_block = tile_block;
+            p.tj = tj;
+            p.ti = ti;
+
+            kernel_->input_transform_data_ker(&p);
+
+            tile_block_ur++;
+            if (tile_block_ur >= jcp.tile_block_ur) {
+                tile_block_ur = 0;
+                nb_tile_block_ur++;
+            }
+            if (nb_tile_block_ur >= jcp.nb_tile_block_ur) {
+                nb_tile_block_ur = 0;
+                tile_block++;
+            }
+        }
+    }
+}
+
+template <bool is_fwd>
+void _jit_avx512_core_fp32_wino_conv_4x3_t<is_fwd>
+    ::input_transform_tileblock_data(int tile_block,
+        const jit_conv_winograd_conf_t &jcp,
+        float *inp, float *tinp) const
+{
+    float G[] = {-2.25f, -0.390625f, 0.87890625f, -2.640625f,
+               0.625f, -0.625f, 1.5f, -1.5f, -2.640625f};
+    float Iw[alpha][alpha][simd_w];
+    float I[alpha][alpha][simd_w];
+    float T[alpha][alpha][simd_w];
+
+    const int inph = is_fwd ? jcp.ih : jcp.oh;
+    const int inpw = is_fwd ? jcp.iw : jcp.ow;
+
+    array_offset_calculator<float, 5> input(inp,
+        jcp.mb, jcp.dimK / simd_w, inph, inpw, simd_w);
+    array_offset_calculator<float, 7> output(tinp,
+        alpha, alpha,
+        jcp.dimN_block, jcp.dimK_nb_block, jcp.dimK_block,
+        jcp.dimN_reg_block, jcp.dimK_reg_block);
+
+    auto p = jit_wino_transform_call_s();
+
+    p.dst = tinp;
+    p.G = G;
+    p.M = I;
+    p.Mw = Iw;
+    p.T = T;
+
+
+    int tile_index = tile_block * jcp.nb_tile_block_ur * jcp.tile_block_ur;
+
+    for (int nb_tile_block_ur = 0;
+            nb_tile_block_ur < jcp.nb_tile_block_ur;
+            nb_tile_block_ur++) {
+
+        for (int tile_block_ur = 0; tile_block_ur < jcp.tile_block_ur;
+                tile_block_ur++) {
+
+            int img = tile_index / (jcp.jtiles * jcp.itiles);
+            int ti = tile_index % jcp.itiles;
+            int tj = (tile_index / jcp.itiles) % jcp.jtiles;
+            float *pinp_b = &(input(img, 0, 0, 0, 0));
+
+            p.src = pinp_b;
+            p.tile_block_ur = tile_block_ur;
+            p.nb_tile_block_ur = nb_tile_block_ur;
+            p.tj = tj;
+            p.ti = ti;
+
+            kernel_->input_transform_data_ker(&p);
+
+            tile_index++;
+        }
+    }
+}
+
+template <bool is_fwd>
+void _jit_avx512_core_fp32_wino_conv_4x3_t<is_fwd>::_execute_data_W_S_G_D(
+        float *inp_ptr, float *out_ptr, float *wei_ptr, float *bias_ptr,
+        const memory_tracking::grantor_t &scratchpad) const {
+    const auto &jcp = kernel_->jcp;
+    const auto &p_ops = attr_->post_ops_;
+
+    const int inph = is_fwd ? jcp.ih : jcp.oh;
+    const int inpw = is_fwd ? jcp.iw : jcp.ow;
+    const int outh = is_fwd ? jcp.oh : jcp.ih;
+    const int outw = is_fwd ? jcp.ow : jcp.iw;
+
+    /* Notation:
+       FWD: dimM:oc, dimN:ntiles, dimK:ic,
+       BWD: dimM:ic, dimN:ntiles, dimK:oc,
+       FWD/BWD: V: src/diff_dst transform, U:weight transform,
+                M:dst/diff_src transform  */
+    array_offset_calculator<float, 5> input(inp_ptr,
+            jcp.mb, jcp.dimK/jcp.dimK_reg_block, inph, inpw,
+            jcp.dimK_reg_block);
+    array_offset_calculator<float, 5> output(out_ptr,
+            jcp.mb, jcp.dimM/jcp.dimM_simd_block, outh, outw,
+            jcp.dimM_simd_block);
+    array_offset_calculator<float, 6> weights(wei_ptr,
+            jcp.oc/jcp.oc_simd_block, jcp.ic/jcp.ic_simd_block, jcp.kh, jcp.kw,
+            jcp.ic_simd_block, jcp.oc_simd_block);
+    array_offset_calculator<float, 2> bias(bias_ptr,
+            jcp.dimM/jcp.dimM_simd_block, jcp.dimM_simd_block);
+
+    array_offset_calculator<float, 8> M(is_fwd
+            ? scratchpad.template get<float>(key_wino_M)
+            : scratchpad.template get<float>(key_wino_V),
+            jcp.dimN_nb_block, jcp.dimM_nb_block,
+            alpha, alpha,
+            jcp.dimN_block, jcp.dimM_block * jcp.dimM_reg_block,
+            jcp.dimN_reg_block, jcp.dimM_simd_block);
+
+    auto wino_wei = (jcp.prop_kind == prop_kind::forward_inference)
+            ? wei_ptr
+            : scratchpad.template get<float>(key_wino_U);
+
+    array_offset_calculator<float, 8> U(wino_wei,
+            jcp.dimM_nb_block,
+            alpha, alpha,
+            jcp.dimK_nb_block,
+            jcp.dimM_block * jcp.dimM_reg_block, jcp.dimK_block,
+            jcp.dimK_reg_block, jcp.dimM_simd_block);
+    array_offset_calculator<float, 8> V(is_fwd
+            ? scratchpad.template get<float>(key_wino_V)
+            : scratchpad.template get<float>(key_wino_M),
+            jcp.dimN_nb_block, alpha, alpha,
+            jcp.dimN_block, jcp.dimK_nb_block,
+            jcp.dimK_block, jcp.dimN_reg_block, jcp.dimK_reg_block);
+
+    const bool wants_padded_bias = jcp.with_bias
+        && jcp.oc_without_padding != jcp.oc;
+    float last_slice_bias[simd_w] = {0};
+    if (wants_padded_bias) {
+        for (int oc = 0; oc < jcp.oc_without_padding % jcp.oc_simd_block; ++oc)
+            last_slice_bias[oc] = bias(jcp.dimM / jcp.dimM_simd_block - 1, oc);
+    }
+
+    {
+
+        parallel_nd(jcp.mb, jcp.dimK_nb_block, jcp.dimK_block,
+                [&](int img, int K_blk1, int K_blk2) {
+                input_transform_data(img, jcp,
+                    &(input(img, K_blk1 * jcp.dimK_block + K_blk2,
+                            0, 0, 0)),
+                        &(V(0, 0, 0, 0, K_blk1, K_blk2, 0, 0)));
+                });
+
+        if (jcp.prop_kind != prop_kind::forward_inference) {
+            parallel_nd(jcp.nb_oc, jcp.nb_ic, (jcp.oc_block * jcp.oc_reg_block),
+                (jcp.ic_block * jcp.ic_reg_block),
+                [&](int ofm1, int ifm1, int ofm2, int ifm2) {
+                    float *U_base_ptr = is_fwd
+                        ? &(U(ofm1, 0, 0, ifm1, ofm2, ifm2, 0, 0))
+                        : &(U(ifm1, 0, 0, ofm1, ifm2, ofm2, 0, 0));
+                    weight_transform_data(jcp,
+                        &(weights(
+                                ofm1 * jcp.oc_block * jcp.oc_reg_block + ofm2,
+                                ifm1 * jcp.ic_block * jcp.ic_reg_block + ifm2,
+                                0, 0, 0, 0)),
+                        U_base_ptr);
+            });
+        }
+
+        parallel_nd(jcp.dimN_nb_block, alpha, alpha, jcp.dimM_nb_block,
+            [&](int N_blk1, int oj, int oi, int M_blk1) {
+            for (int K_blk1 = 0; K_blk1 < jcp.dimK_nb_block;
+                 K_blk1++)
+            for (int N_blk2 = 0; N_blk2 < jcp.dimN_block; N_blk2++)
+                kernel_->gemm_loop_ker(
+                        (float *)&(M(N_blk1, M_blk1, oj, oi,
+                            N_blk2, 0, 0, 0)),
+                        (const float *)&(U(M_blk1, oj, oi,
+                            K_blk1, 0, 0, 0, 0)),
+                        (const float *)&(V(N_blk1, oj, oi,
+                            N_blk2, K_blk1, 0, 0, 0)), K_blk1);
+        });
+
+        parallel_nd(jcp.mb, jcp.dimM_nb_block, (jcp.dimM_block * jcp.dimM_reg_block),
+                    [&](int img, int M_blk1, int M_blk2) {
+            const int M_blk =
+                M_blk1 * jcp.dimM_block  * jcp.dimM_reg_block + M_blk2;
+
+            float *bias_ptr = wants_padded_bias
+                && M_blk == jcp.dimM / jcp.dimM_simd_block - 1
+                ? last_slice_bias : &bias(M_blk, 0);
+            output_transform_data(img, jcp, p_ops,
+                    &(M(0, M_blk1, 0, 0, 0, M_blk2, 0, 0)),
+                    &(output(img, M_blk, 0, 0, 0)), bias_ptr);
+        });
+
+    }
+}
+
+template <bool is_fwd>
+void _jit_avx512_core_fp32_wino_conv_4x3_t<is_fwd>::_execute_data_W_SGD(
+        float *inp_ptr, float *out_ptr, float *wei_ptr, float *bias_ptr,
+        const memory_tracking::grantor_t &scratchpad) const {
+    const auto &jcp = kernel_->jcp;
+    const auto &p_ops = attr_->post_ops_;
+
+    const int inph = is_fwd ? jcp.ih : jcp.oh;
+    const int inpw = is_fwd ? jcp.iw : jcp.ow;
+    const int outh = is_fwd ? jcp.oh : jcp.ih;
+    const int outw = is_fwd ? jcp.ow : jcp.iw;
+
+    array_offset_calculator<float, 5> input(inp_ptr,
+        jcp.mb, jcp.dimK/jcp.dimK_reg_block, inph, inpw, jcp.dimK_reg_block);
+    array_offset_calculator<float, 5> output(out_ptr,
+        jcp.mb, jcp.dimM/jcp.dimM_simd_block, outh, outw, jcp.dimM_simd_block);
+    array_offset_calculator<float, 6> weights(wei_ptr,
+        jcp.oc/jcp.oc_simd_block, jcp.ic/jcp.ic_simd_block, jcp.kh, jcp.kw,
+        jcp.ic_simd_block, jcp.oc_simd_block);
+    array_offset_calculator<float, 2> bias(bias_ptr,
+        jcp.oc/jcp.oc_simd_block, jcp.oc_simd_block);
+
+    auto wino_wei = (jcp.prop_kind == prop_kind::forward_inference)
+                ? wei_ptr
+                : scratchpad.template get<float>(key_wino_U);
+
+    array_offset_calculator<float, 8> U(wino_wei,
+            jcp.dimM_nb_block,
+            alpha, alpha,
+            jcp.dimK_nb_block,
+            jcp.dimM_block  * jcp.dimM_reg_block, jcp.dimK_block,
+            jcp.dimK_reg_block, jcp.dimM_simd_block);
+
+    array_offset_calculator<float, 8> M(is_fwd
+            ? scratchpad.template get<float>(key_wino_M)
+            : scratchpad.template get<float>(key_wino_V),
+            0, jcp.dimM_nb_block, alpha, alpha,
+            jcp.dimN_block, jcp.dimM_block * jcp.dimM_reg_block,
+            jcp.dimN_reg_block, jcp.dimM_simd_block);
+    array_offset_calculator<float, 8> V(is_fwd
+            ? scratchpad.template get<float>(key_wino_V)
+            : scratchpad.template get<float>(key_wino_M),
+            0, alpha, alpha, jcp.dimN_block,
+            jcp.dimK_nb_block, jcp.dimK_block,
+            jcp.dimN_reg_block, jcp.dimK_reg_block);
+
+    const bool wants_padded_bias = jcp.with_bias
+        && jcp.oc_without_padding != jcp.oc;
+    float last_slice_bias[simd_w] = {0};
+    if (wants_padded_bias) {
+        for (int oc = 0; oc < jcp.oc_without_padding % jcp.oc_simd_block; ++oc)
+            last_slice_bias[oc] = bias(jcp.dimM / jcp.dimM_simd_block - 1, oc);
+    }
+
+    if (jcp.prop_kind != prop_kind::forward_inference) {
+
+        parallel_nd(jcp.nb_oc, jcp.nb_ic, (jcp.oc_block * jcp.oc_reg_block), (jcp.ic_block * jcp.ic_reg_block),
+                    [&](int ofm1, int ifm1, int ofm2, int ifm2) {
+            float *U_base_ptr = is_fwd
+                              ? &(U(ofm1, 0, 0, ifm1, ofm2, ifm2, 0, 0))
+                              : &(U(ifm1, 0, 0, ofm1, ifm2, ofm2, 0, 0));
+            weight_transform_data(jcp,
+                    &(weights(
+                        ofm1 * jcp.oc_block * jcp.oc_reg_block + ofm2,
+                        ifm1 * jcp.ic_block * jcp.ic_reg_block + ifm2,
+                        0, 0, 0, 0)),
+                    U_base_ptr);
+        });
+    }
+
+    parallel_nd(jcp.tile_block, [&](int tile_block) {
+        int ithr = mkldnn_get_thread_num();
+
+        for (int K_blk1 = 0; K_blk1 < jcp.dimK_nb_block; K_blk1++) {
+            for (int K_blk2 = 0; K_blk2 < jcp.dimK_block; K_blk2++) {
+
+                input_transform_tileblock_data(
+                        tile_block, jcp,
+                        &(input(0, K_blk1 * jcp.dimK_block + K_blk2, 0, 0, 0)),
+                        &(V(ithr, 0, 0, 0, K_blk1, K_blk2, 0, 0)));
+            }
+        }
+
+        for (int oj = 0; oj < alpha; oj++) {
+            for (int oi = 0; oi < alpha; oi++) {
+                for (int M_blk1 = 0; M_blk1 < jcp.dimM_nb_block; M_blk1++)
+                for (int K_blk1 = 0; K_blk1 < jcp.dimK_nb_block; K_blk1++)
+                for (int N_blk = 0; N_blk < jcp.dimN_block; N_blk++)
+                    kernel_->gemm_loop_ker(
+                            (float *)&(M(ithr, M_blk1, oj, oi,
+                                    N_blk, 0, 0, 0)),
+                            (const float *)&(U(M_blk1, oj, oi, K_blk1,
+                                    0, 0, 0, 0)),
+                            (const float *)&(V(ithr, oj, oi,
+                                    N_blk, K_blk1, 0, 0, 0)), K_blk1);
+            }
+        }
+
+        for (int M_blk1 = 0; M_blk1 < jcp.dimM_nb_block; M_blk1++) {
+            for (int M_blk2 = 0; M_blk2 < jcp.dimM_block * jcp.dimM_reg_block;
+                  M_blk2++) {
+                const int M_blk =
+                    M_blk1 * jcp.dimM_block  * jcp.dimM_reg_block + M_blk2;
+
+                float *bias_ptr = wants_padded_bias
+                    && M_blk == jcp.dimM / jcp.dimM_simd_block - 1
+                    ? last_slice_bias : &bias(M_blk, 0);
+
+                output_transform_tileblock_data(tile_block, jcp, p_ops,
+                        &(M(ithr, M_blk1, 0, 0, 0, M_blk2, 0, 0)),
+                        &(output(0, M_blk, 0, 0, 0)), bias_ptr);
+            }
+        }
+    });
+}
+
+template struct _jit_avx512_core_fp32_wino_conv_4x3_t<true>;
+template struct _jit_avx512_core_fp32_wino_conv_4x3_t<false>;
+
+namespace {
+
+void subarray_sum(size_t num_arrs, float *output, size_t nelems,
+        float *input_ptrs[], size_t input_starts[], size_t input_ends[]) {
+    using namespace nstl;
+    const size_t block_size = 16 * 1024 / sizeof(float);
+    const size_t blocks_number = nelems / block_size;
+    const size_t tail = nelems % block_size;
+
+PRAGMA_OMP(parallel)
+    {
+        const int ithr = mkldnn_get_thread_num();
+        const int nthr = mkldnn_get_num_threads();
+        size_t start{ 0 }, end{ 0 };
+        balance211(blocks_number, nthr, ithr, start, end);
+
+        for (size_t nb = start; nb < end; ++nb) {
+            size_t start_e = nb * block_size;
+            size_t end_e = start_e + block_size;
+            size_t input_start = max(start_e, min(input_starts[0], end_e));
+            size_t input_end = max(start_e, min(input_ends[0], end_e));
+
+            PRAGMA_OMP_SIMD()
+            for (size_t e = start_e; e < input_start; e++) {
+                output[e] = 0.f;
+            }
+
+            PRAGMA_OMP_SIMD()
+            for (size_t e = input_start; e < input_end; e++) {
+                output[e] = input_ptrs[0][e];
+            }
+
+            PRAGMA_OMP_SIMD()
+            for (size_t e = input_end; e < end_e; e++) {
+                output[e] = 0.f;
+            }
+
+            for (size_t a = 1; a < num_arrs; a++) {
+                input_start = max(start_e, input_starts[a]);
+                input_end = min(input_ends[a], end_e);
+
+                PRAGMA_OMP_SIMD()
+                for (size_t e = input_start; e < input_end; e++) {
+                    output[e] += input_ptrs[a][e];
+                }
+            }
+        }
+
+        if (tail != 0 && ithr == nthr - 1) {
+            size_t start_e = nelems - tail;
+            size_t end_e = nelems;
+            size_t input_start = max(start_e, min(input_starts[0], end_e));
+            size_t input_end = max(start_e, min(input_ends[0], end_e));
+
+            PRAGMA_OMP_SIMD()
+            for (size_t e = start_e; e < input_start; e++) {
+                output[e] = 0.f;
+            }
+
+            PRAGMA_OMP_SIMD()
+            for (size_t e = input_start; e < input_end; e++) {
+                output[e] = input_ptrs[0][e];
+            }
+
+            PRAGMA_OMP_SIMD()
+            for (size_t e = input_end; e < end_e; e++) {
+                output[e] = 0.f;
+            }
+
+            for (size_t a = 1; a < num_arrs; a++) {
+                input_start = max(start_e, input_starts[a]);
+                input_end = min(input_ends[a], end_e);
+
+                PRAGMA_OMP_SIMD()
+                for (size_t e = input_start; e < input_end; e++) {
+                    output[e] += input_ptrs[a][e];
+                }
+            }
+        }
+    }
+}
+
+const int max_threads_number = 1024;
+
+// Sum to the first buffer array
+void array_sum(size_t num_arrs, float *output,
+    size_t nelems, float *input_ptrs[], bool reduce_to_first = true) {
+    const size_t block_size = 16 * 1024 / sizeof(float);
+    const size_t blocks_number = nelems / block_size;
+    const size_t tail = nelems % block_size;
+
+PRAGMA_OMP(parallel)
+    {
+        const size_t ithr = mkldnn_get_thread_num();
+        const size_t nthr = mkldnn_get_num_threads();
+        size_t start{ 0 }, end{ 0 };
+        balance211(blocks_number, nthr, ithr, start, end);
+
+        for (size_t nb = start; nb < end; ++nb) {
+            size_t start_e = nb * block_size;
+            size_t end_e = start_e + block_size;
+            if (!reduce_to_first) {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start_e; e < end_e; e++) {
+                    output[e] = input_ptrs[0][e];
+                }
+            }
+            for (size_t a = 1; a < num_arrs; a++) {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start_e; e < end_e; e++) {
+                    output[e] += input_ptrs[a][e];
+                }
+            }
+        }
+
+        if (tail != 0 && ithr == nthr - 1) {
+            size_t start_e = nelems - tail;
+            size_t end_e = nelems;
+            if (!reduce_to_first) {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start_e; e < end_e; e++) {
+                    output[e] = input_ptrs[0][e];
+                }
+            }
+            for (size_t a = 1; a < num_arrs; a++) {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start_e; e < end_e; e++) {
+                    output[e] += input_ptrs[a][e];
+                }
+            }
+        }
+    }
+}
+} //bwdw namespace
+
+void jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_t::
+_execute_backward_weights_SDGtWo(const float *ptr_src,
+        const float *ptr_diff_dst, float *ptr_diff_weights,
+        float *ptr_diff_bias,
+        const memory_tracking::grantor_t &scratchpad) const {
+    const auto &jcp = kernel_->jcp;
+    const int nthreads = jcp.nthr;
+
+    array_offset_calculator<float, 5> src((float *)ptr_src,
+            jcp.mb, jcp.ic / simd_w, jcp.ih, jcp.iw, simd_w);
+    array_offset_calculator<float, 5> diff_dst((float *)ptr_diff_dst,
+            jcp.mb, jcp.oc / simd_w, jcp.oh, jcp.ow, simd_w);
+    array_offset_calculator<float, 6> diff_weights(ptr_diff_weights,
+            jcp.oc / simd_w, jcp.ic / simd_w, jcp.kh, jcp.kw, simd_w, simd_w);
+
+    array_offset_calculator<float, 8> Us(scratchpad.get<float>(key_wino_U),
+            0, alpha, alpha,
+            jcp.oc_block, jcp.ic_block,
+            jcp.ic_simd_block,
+            jcp.oc_reg_block,
+            jcp.oc_simd_block);
+
+    const int U_sz = nthreads * alpha * alpha * jcp.oc / jcp.nb_oc
+        * jcp.ic / jcp.nb_ic;
+    array_offset_calculator<float, 7>diff_weights_prv(
+            scratchpad.get<float>(key_wino_U) + U_sz,
+            0, jcp.oc / simd_w, jcp.ic / simd_w, jcp.kh, jcp.kw, simd_w, simd_w);
+
+    array_offset_calculator<float, 8> M(scratchpad.get<float>(key_wino_M),
+            0, alpha, alpha,
+            jcp.oc_block,
+            jcp.nb_tile_block_ur,
+            jcp.tile_block_ur,
+            jcp.oc_reg_block,
+            jcp.oc_simd_block);
+
+    array_offset_calculator<float, 7> V(scratchpad.get<float>(key_wino_V),
+            0, alpha, alpha,
+            jcp.ic_block,
+            jcp.nb_tile_block_ur,
+            jcp.tile_block_ur,
+            jcp.ic_simd_block);
+
+    array_offset_calculator<float, 2> diff_bias_prv(
+            scratchpad.get<float>(key_conv_bia_reduction), nthreads, jcp.oc);
+
+    auto trans_ker_p = jit_wino_transform_call_s();
+    float I[alpha][alpha][simd_w];
+    float T[alpha][alpha][simd_w];
+    float G_I_3x3_4x4[9] = {-2.25f, -0.390625f, 0.87890625f, -2.640625f,
+               0.625f, -0.625f, 1.5f, -1.5f, -2.640625f};
+    float G_W_3x3_4x4[8] = {0.26890756302521f, -0.688403361344538f, 0.119514472455649f,
+       0.430252100840336f, 0.168067226890756f, 0.179271708683473f, 0.403361344537815f,
+       1.13777777777778f};
+    float G_O_3x3_4x4[4] = {2.25f, 0.625f, 1.5f, 0.390625f};
+
+PRAGMA_OMP(parallel num_threads(nthreads) firstprivate(trans_ker_p, I, T))
+{
+    if (jcp.with_bias) {
+        parallel_nd_in_omp(nthreads, jcp.oc / simd_w,
+            [&](int ithr, int ofm){
+                float *pdbias = &(diff_bias_prv(ithr, ofm * simd_w));
+                PRAGMA_OMP_SIMD()
+                for (int v = 0; v < simd_w; v++) {
+                    pdbias[v] = 0.0f;
+                }
+        });
+    }
+
+    int ithr = mkldnn_get_thread_num();
+    for (int ifm1 = 0; ifm1 < jcp.nb_ic; ++ifm1) {
+        int first_tblk = 0;
+PRAGMA_OMP(for)
+        for (int tblk1 = 0; tblk1 < jcp.tile_block; ++tblk1) {
+            int tile_index = tblk1 * jcp.nb_tile_block_ur * jcp.tile_block_ur;
+            int img = tile_index / (jcp.itiles * jcp.jtiles);
+            trans_ker_p.ti = tile_index % jcp.itiles;
+            trans_ker_p.tj = (tile_index / jcp.itiles) % jcp.jtiles;
+            trans_ker_p.M = I;
+            trans_ker_p.T = T;
+            trans_ker_p.G = G_I_3x3_4x4;
+            for (int ifm2 = 0; ifm2 < jcp.ic_block; ++ifm2) {
+                int ifm = ifm1 * jcp.ic_block + ifm2;
+                trans_ker_p.src = (float *)&(src(img, ifm, 0, 0, 0));
+                trans_ker_p.dst = (float *)&(V(ithr, 0, 0, ifm2, 0, 0, 0));
+                kernel_->src_transform(&trans_ker_p);
+            }
+
+            for (int ofm1 = 0; ofm1 < jcp.nb_oc; ++ofm1) {
+                trans_ker_p.G = G_W_3x3_4x4;
+                for (int ofm2 = 0; ofm2 < jcp.oc_block; ++ofm2) {
+                    int ofm = (ofm1 * jcp.oc_block + ofm2) * jcp.oc_reg_block;
+                    trans_ker_p.src = (float *)&(diff_dst(img, ofm, 0, 0, 0));
+                    trans_ker_p.dst = (float *)&(M(ithr, 0, 0, ofm2, 0, 0, 0, 0));
+                    if (jcp.with_bias && ifm1 == 0) {
+                        trans_ker_p.bias = (float *)&(diff_bias_prv(ithr, ofm * simd_w));
+                        kernel_->diff_dst_transform_wbias(&trans_ker_p);
+                    } else {
+                        kernel_->diff_dst_transform(&trans_ker_p);
+                    }
+                }
+
+                for (int oj = 0; oj < alpha; ++oj) {
+                    for (int oi = 0; oi < alpha; ++oi) {
+                        kernel_->gemm_loop_ker_first_iter(
+                                &(Us(ithr, oj, oi, 0, 0, 0, 0, 0)),
+                                &(M(ithr, oj, oi, 0, 0, 0, 0, 0)),
+                                &(V(ithr, oj, oi, 0, 0, 0, 0)));
+                    }
+                }
+                trans_ker_p.G = G_O_3x3_4x4;
+                for (int ofm2 = 0; ofm2 < jcp.oc_block; ++ofm2) {
+                    for (int ofm3 = 0; ofm3 < jcp.oc_reg_block; ++ofm3) {
+                        int ofm = (ofm1 * jcp.oc_block + ofm2) * jcp.oc_reg_block
+                                + ofm3;
+                        for (int ifm2 = 0; ifm2 < jcp.ic_block; ++ifm2) {
+                            int ifm = ifm1 * jcp.ic_block + ifm2;
+                            trans_ker_p.src = (float *)&(Us(ithr, 0, 0,
+                                        ofm2, ifm2, 0, ofm3, 0));
+                            trans_ker_p.dst = (float *)&(diff_weights_prv(ithr,
+                                        ofm, ifm, 0, 0, 0, 0));
+                            if (first_tblk == 0) {
+                                kernel_->diff_weights_transform(&trans_ker_p);
+                            } else {
+                                kernel_->diff_weights_transform_accum(&trans_ker_p);
+                            }
+                        }
+                    }
+                }
+            }
+            ++first_tblk;
+        }
+    }
+}
+
+    // Reduce diff-weights
+    {
+        float *output = ptr_diff_weights;
+        float *input_base = scratchpad.get<float>(key_wino_U) + U_sz;
+        int nelems = jcp.oc * jcp.ic * jcp.kh * jcp.kw;
+        float *input_ptrs[max_threads_number];
+        for (int i = 0; i < nthreads; ++i) {
+            input_ptrs[i] = input_base + nelems * i;
+        }
+        array_sum(nthreads, output, nelems, input_ptrs, false);
+
+        if (jcp.with_bias) {
+            output = ptr_diff_bias;
+            input_base = scratchpad.get<float>(key_conv_bia_reduction);
+            for (int i = 0; i < nthreads; ++i) {
+                input_ptrs[i] = input_base + jcp.oc * i;
+            }
+            array_sum(nthreads, output, jcp.oc_without_padding, input_ptrs,
+                    false);
+        }
+    }
+}
+
+void jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_t::
+_execute_backward_weights_S_D_Giot_W(const float *ptr_src,
+        const float *ptr_diff_dst, float *ptr_diff_weights,
+        float *ptr_diff_bias,
+        const memory_tracking::grantor_t &scratchpad) const {
+    const auto &jcp = kernel_->jcp;
+    const int nthreads = jcp.nthr;
+
+    array_offset_calculator<float, 5> src((float *)ptr_src,
+            jcp.mb, jcp.ic / simd_w, jcp.ih, jcp.iw, simd_w);
+    array_offset_calculator<float, 5> diff_dst((float *)ptr_diff_dst,
+            jcp.mb, jcp.oc / simd_w, jcp.oh, jcp.ow, simd_w);
+    array_offset_calculator<float, 6> diff_weights((float *)ptr_diff_weights,
+            jcp.oc / simd_w, jcp.ic / simd_w, jcp.kh, jcp.kw, simd_w, simd_w);
+    array_offset_calculator<float, 1> diff_bias((float *)ptr_diff_bias, jcp.oc);
+
+    array_offset_calculator<float, 9> U(scratchpad.get<float>(key_wino_U),
+            jcp.nb_ic, jcp.nb_oc,
+            alpha, alpha,
+            jcp.oc_block, jcp.ic_block,
+            jcp.ic_simd_block,
+            jcp.oc_reg_block,
+            jcp.oc_simd_block);
+
+    const int U_size = jcp.oc * jcp.ic * alpha * alpha;
+    array_offset_calculator<float, 10> Us(
+            scratchpad.get<float>(key_wino_U) + U_size,
+            0, jcp.nb_ic, jcp.nb_oc,
+            alpha, alpha,
+            jcp.oc_block, jcp.ic_block,
+            jcp.ic_simd_block,
+            jcp.oc_reg_block,
+            jcp.oc_simd_block);
+
+    array_offset_calculator<float, 9> M(scratchpad.get<float>(key_wino_M),
+            jcp.nb_oc,
+            jcp.tile_block,
+            alpha, alpha,
+            jcp.oc_block,
+            jcp.nb_tile_block_ur,
+            jcp.tile_block_ur ,
+            jcp.oc_reg_block,
+            jcp.oc_simd_block);
+
+    array_offset_calculator<float, 8> V(scratchpad.get<float>(key_wino_V),
+            jcp.nb_ic,
+            jcp.tile_block,
+            alpha, alpha,
+            jcp.ic_block,
+            jcp.nb_tile_block_ur, jcp.tile_block_ur,
+            jcp.ic_simd_block);
+
+    array_offset_calculator<float, 2> diff_bias_prv(
+            scratchpad.get<float>(key_conv_bia_reduction), nthreads, jcp.oc);
+
+    size_t input_starts[max_threads_number] = {0};
+    size_t input_ends[max_threads_number] = {0};
+    size_t first_tblk = 0;
+
+    auto trans_ker_p = jit_wino_transform_call_s();
+    float G_I_3x3_4x4[9] = {-2.25f, -0.390625f, 0.87890625f, -2.640625f,
+               0.625f, -0.625f, 1.5f, -1.5f, -2.640625f};
+    float G_W_3x3_4x4[8] = {0.26890756302521f, -0.688403361344538f,
+        0.119514472455649f, 0.430252100840336f, 0.168067226890756f,
+        0.179271708683473f, 0.403361344537815f, 1.13777777777778f};
+    float G_O_3x3_4x4[4] = {2.25f, 0.625f, 1.5f, 0.390625f};
+    float I[alpha][alpha][simd_w];
+    float T[alpha][alpha][simd_w];
+
+PRAGMA_OMP(parallel firstprivate(first_tblk, trans_ker_p, I, T))
+{
+    if (jcp.with_bias) {
+        parallel_nd_in_omp(nthreads, jcp.oc, [&](int ithr, int ofm) {
+            diff_bias_prv(ithr, ofm) = 0.0f;
+        });
+    }
+
+    trans_ker_p.G = G_I_3x3_4x4;
+    trans_ker_p.M = I;
+    trans_ker_p.T = T;
+
+    parallel_nd_in_omp(jcp.nb_ic, jcp.ic_block, jcp.mb,
+        [&](int ifm1, int ifm2, int img){
+         size_t ifm = ifm1 * jcp.ic_block + ifm2;
+         size_t tile_base_index = img * (jcp.itiles * jcp.jtiles);
+         size_t tblk3 = tile_base_index  % jcp.tile_block_ur;
+         size_t tblk2 = (tile_base_index / jcp.tile_block_ur)
+             % jcp.nb_tile_block_ur;
+         size_t tblk1 = (tile_base_index / jcp.tile_block_ur)
+             / jcp.nb_tile_block_ur;
+         trans_ker_p.tile_count = tblk2 * jcp.tile_block_ur + tblk3;
+         trans_ker_p.src = (float *)&(src(img, ifm, 0, 0, 0));
+         trans_ker_p.dst = (float *)&(V(ifm1, tblk1, 0, 0, ifm2, 0, 0, 0));
+         kernel_->src_transform(&trans_ker_p);
+    });
+
+    int ithr = mkldnn_get_thread_num();
+    trans_ker_p.G = G_W_3x3_4x4;
+    parallel_nd_in_omp(jcp.nb_oc, jcp.oc_block, jcp.mb,
+        [&](int ofm1, int ofm2, int img){
+        int ofm = (ofm1 * jcp.oc_block + ofm2) * jcp.oc_reg_block;
+        size_t tile_base_index = img * (jcp.itiles * jcp.jtiles);
+        size_t tblk3 = tile_base_index  % jcp.tile_block_ur;
+        size_t tblk2 = (tile_base_index / jcp.tile_block_ur)
+            % jcp.nb_tile_block_ur;
+        size_t tblk1 = (tile_base_index / jcp.tile_block_ur)
+            / jcp.nb_tile_block_ur;
+        trans_ker_p.tile_count = tblk2 * jcp.tile_block_ur + tblk3;
+        trans_ker_p.src = (float *)&(diff_dst(img, ofm, 0, 0, 0));
+        trans_ker_p.dst = (float *)&(M(ofm1, tblk1, 0, 0, ofm2, 0, 0, 0, 0));
+        if (jcp.with_bias) {
+            trans_ker_p.bias = (float *)&(diff_bias_prv(ithr, ofm * simd_w));
+            kernel_->diff_dst_transform_wbias(&trans_ker_p);
+        } else {
+            kernel_->diff_dst_transform(&trans_ker_p);
+        }
+    });
+
+    PRAGMA_OMP(barrier)
+
+    parallel_nd_in_omp(jcp.nb_ic, jcp.nb_oc, alpha, alpha, jcp.tile_block,
+        [&](int ifm1, int ofm1, int oj, int oi, int tblk1){
+        if (first_tblk == 0) {
+            input_starts[ithr] =
+                (float *)&(Us(ithr, ifm1, ofm1, oj, oi, 0, 0, 0,
+                            0, 0))
+                - (float *)&(Us(ithr, 0, 0, 0, 0, 0, 0,
+                            0, 0, 0));
+            input_ends[ithr] = input_starts[ithr]
+                    + jcp.oc_block * jcp.ic_block
+                      * jcp.ic_simd_block * jcp.oc_reg_block
+                      * jcp.oc_simd_block;
+        }
+        else if (tblk1 == 0) {
+            input_ends[ithr] += jcp.oc_block * jcp.ic_block
+                * jcp.ic_simd_block * jcp.oc_reg_block
+                * jcp.oc_simd_block;
+        }
+
+        if (first_tblk == 0 || tblk1 == 0) {
+            kernel_->gemm_loop_ker_first_iter(
+                    &(Us(ithr, ifm1, ofm1, oj, oi,
+                            0, 0, 0, 0, 0)),
+                    &(M(ofm1, tblk1, oj, oi, 0, 0, 0, 0, 0)),
+                    &(V(ifm1, tblk1, oj, oi, 0, 0, 0, 0)));
+        } else {
+            kernel_->gemm_loop_ker(
+                    &(Us(ithr, ifm1, ofm1, oj, oi,
+                            0, 0, 0, 0, 0)),
+                    &(M(ofm1, tblk1, oj, oi, 0, 0, 0, 0, 0)),
+                    &(V(ifm1, tblk1, oj, oi, 0, 0, 0, 0)));
+        }
+        ++first_tblk;
+    });
+}
+
+    // Reduce diff-weights
+    {
+        float *output = &(U(0, 0, 0, 0, 0, 0, 0, 0, 0));
+        size_t nelems = jcp.ic * jcp.oc * alpha * alpha;
+        float *input_ptrs[max_threads_number];
+        for (int i = 0; i < nthreads; ++i)
+            input_ptrs[i] = output + nelems * (i + 1);
+        subarray_sum(nthreads, output, nelems, input_ptrs,
+                input_starts, input_ends);
+    }
+
+    trans_ker_p.G = G_O_3x3_4x4;
+PRAGMA_OMP(parallel firstprivate(trans_ker_p))
+    {
+        parallel_nd_in_omp(jcp.nb_ic, jcp.nb_oc, jcp.oc_block, jcp.ic_block, jcp.oc_reg_block,
+            [&](int ifm1, int ofm1, int ofm2, int ifm2, int ofm3){
+            int ofm = (ofm1 * jcp.oc_block + ofm2)
+                * jcp.oc_reg_block + ofm3;
+            int ifm = ifm1 * jcp.ic_block + ifm2;
+            trans_ker_p.src = (float *)&(U(ifm1, ofm1, 0, 0,
+                        ofm2, ifm2, 0, ofm3, 0));
+            trans_ker_p.dst = (float *)&(diff_weights(ofm, ifm,
+                        0, 0, 0, 0));
+            kernel_->diff_weights_transform(&trans_ker_p);
+        });
+    }
+
+    if (jcp.with_bias) {
+        parallel_nd(jcp.oc / simd_w, [&](int ofm1) {
+            float* pbias = &(diff_bias(ofm1 * simd_w));
+            float *pbias_prv = &(diff_bias_prv(0, ofm1 * simd_w));
+
+            const int blk_sz = ofm1 == jcp.oc / simd_w - 1
+                ? jcp.oc_without_padding - ofm1 * simd_w : simd_w;
+
+            PRAGMA_OMP_SIMD()
+            for (int ofm2 = 0; ofm2 < blk_sz; ++ofm2) {
+                pbias[ofm2] = pbias_prv[ofm2];
+            }
+
+            for (int ithr = 1; ithr < nthreads; ++ithr) {
+                pbias_prv = &(diff_bias_prv(ithr, ofm1 * simd_w));
+                PRAGMA_OMP_SIMD()
+                for (int ofm2 = 0; ofm2 < blk_sz; ++ofm2) {
+                    pbias[ofm2] += pbias_prv[ofm2];
+                }
+            }
+        });
+    }
+}
+
+}
+}
+}
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3.hpp
new file mode 100644
index 0000000000..f1a56aac70
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3.hpp
@@ -0,0 +1,386 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_CORE_FP32_WINO_CONV_4x3_HPP
+#define CPU_JIT_AVX512_CORE_FP32_WINO_CONV_4x3_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "jit_avx512_core_fp32_wino_conv_4x3_kernel.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace winograd_avx512_core {
+inline void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+        const jit_conv_winograd_conf_t &jcp) {
+    using namespace utils;
+    using namespace memory_tracking::names;
+
+    size_t U_sz = (size_t)alpha * alpha * jcp.ic * jcp.oc;
+    size_t V_sz = (size_t)alpha * alpha * jcp.mb * jcp.ic * jcp.itiles
+        * jcp.jtiles;
+    size_t M_sz = (size_t)alpha * alpha * jcp.mb * jcp.oc * jcp.itiles
+        * jcp.jtiles;
+
+    switch (jcp.sched_policy) {
+    case WSCHED_DATA_W_SGD:
+        V_sz = (size_t)jcp.nthr * alpha * alpha * jcp.nb_tile_block_ur
+            * jcp.tile_block_ur * jcp.ic;
+        M_sz = (size_t)jcp.nthr * alpha * alpha * jcp.nb_tile_block_ur
+            * jcp.tile_block_ur * jcp.oc;
+        break;
+    case WSCHED_WEI_SDGtWo:
+        U_sz = (size_t)jcp.nthr * (alpha * alpha * jcp.oc
+                * (jcp.ic / jcp.nb_ic) + jcp.ic * jcp.oc * jcp.kh * jcp.kw);
+        M_sz = (size_t)jcp.nthr * alpha * alpha * (jcp.ntiles / jcp.tile_block)
+            * (jcp.oc / jcp.nb_oc);
+        V_sz = (size_t)jcp.nthr * alpha * alpha * (jcp.ntiles / jcp.tile_block)
+            * (jcp.ic / jcp.nb_ic);
+        break;
+    case WSCHED_WEI_S_D_Giot_W:
+        U_sz = (size_t)(jcp.nthr + 1) * alpha * alpha * jcp.ic * jcp.oc;
+        M_sz = (size_t)alpha * alpha * jcp.oc * jcp.ntiles;
+        V_sz = (size_t)alpha * alpha * jcp.ic * jcp.ntiles;
+        break;
+    default: break;
+    }
+
+    scratchpad.book(key_wino_U, sizeof(float) * U_sz, PAGE_2M);
+    scratchpad.book(key_wino_V, sizeof(float) * V_sz, PAGE_2M);
+    scratchpad.book(key_wino_M, sizeof(float) * M_sz, PAGE_2M);
+
+    if (one_of(jcp.sched_policy, WSCHED_WEI_SDGtWo, WSCHED_WEI_S_D_Giot_W)) {
+        size_t br_sz = (size_t)jcp.nthr * jcp.oc;
+        scratchpad.book(key_conv_bia_reduction, sizeof(float) * br_sz, PAGE_2M);
+    }
+}
+}
+
+template <bool is_fwd>
+struct _jit_avx512_core_fp32_wino_conv_4x3_t {
+
+    _jit_avx512_core_fp32_wino_conv_4x3_t(
+            const jit_conv_winograd_conf_t &jcp, const primitive_attr_t *attr)
+        : kernel_(nullptr), attr_(attr) {
+            kernel_ =  new _jit_avx512_core_fp32_wino_conv_4x3_data_kernel(jcp);
+        }
+
+    ~_jit_avx512_core_fp32_wino_conv_4x3_t() { delete kernel_; }
+
+    protected:
+        void weight_transform_data(const jit_conv_winograd_conf_t &jcp,
+            float *wp, float *twp) const;
+        void input_transform_data(int image,
+            const jit_conv_winograd_conf_t &jcp,
+            float *inp, float *tinp) const;
+        void input_transform_tileblock_data(int tile_block,
+            const jit_conv_winograd_conf_t &jcp,
+            float *inp, float *tinp) const;
+        void output_transform_data(int image,
+            const jit_conv_winograd_conf_t &jcp,
+            const post_ops_t &p_ops, float *toutp, float *pout_b,
+            float *bias) const;
+        void output_transform_tileblock_data(int tile_block,
+            const jit_conv_winograd_conf_t &jcp, const post_ops_t &p_ops,
+            float *toutp, float *outp, float *bias) const;
+        void _execute_data_W_S_G_D(float *inp_ptr, float *out_ptr,
+                float *wei_ptr, float *bias_ptr,
+                const memory_tracking::grantor_t &scratchpad) const;
+        void _execute_data_W_SGD(float *inp_ptr, float *out_ptr,
+                float *wei_ptr, float *bias_ptr,
+                const memory_tracking::grantor_t &scratchpad) const;
+        _jit_avx512_core_fp32_wino_conv_4x3_data_kernel *kernel_;
+        const primitive_attr_t *attr_;
+};
+
+struct jit_avx512_core_fp32_wino_conv_4x3_fwd_t
+     : _jit_avx512_core_fp32_wino_conv_4x3_t<true>
+     , public cpu_primitive_t
+    {
+    struct pd_t : public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_wino_4x3:", avx512_core, ""),
+                jit_avx512_core_fp32_wino_conv_4x3_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && utils::one_of(desc()->alg_kind,
+                        alg_kind::convolution_auto,
+                        alg_kind::convolution_winograd)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_avx512_core_fp32_wino_conv_4x3_fwd_kernel::
+                init_conf(jcp_, *desc(), src_md_, weights_md_, dst_md_,
+                        *attr());
+            if (status != status::success) return status;
+            set_default_alg_kind(alg_kind::convolution_winograd);
+
+            auto scratchpad = scratchpad_registry().registrar();
+            winograd_avx512_core::init_scratchpad(scratchpad, jcp_);
+
+            return status;
+        }
+
+        jit_conv_winograd_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+            auto wei_fmt = desc()->prop_kind == prop_kind::forward_training
+                ? (with_groups() ? gOIhw16i16o : OIhw16i16o) : any;
+            return set_default_formats_common(nChw16c, wei_fmt, nChw16c);
+        }
+    };
+
+    jit_avx512_core_fp32_wino_conv_4x3_fwd_t(const pd_t *apd)
+        : _jit_avx512_core_fp32_wino_conv_4x3_t<true>(apd->jcp_, apd->attr())
+        , cpu_primitive_t(apd, true)
+         {}
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        auto src = CTX_IN_MEM(const float *, MKLDNN_ARG_SRC);
+        auto weights = CTX_IN_MEM(const float *, MKLDNN_ARG_WEIGHTS);
+        auto bias = CTX_IN_MEM(const float *, MKLDNN_ARG_BIAS);
+        auto dst = CTX_OUT_MEM(float *, MKLDNN_ARG_DST);
+
+        auto scratchpad = this->scratchpad(ctx);
+
+        switch ((pd()->jcp_).sched_policy) {
+        case WSCHED_DATA_W_S_G_D:
+            this->_execute_data_W_S_G_D((float *)src, dst, (float *)weights,
+                    (float *)bias, scratchpad);
+            break;
+        case WSCHED_DATA_W_SGD:
+            this->_execute_data_W_SGD((float *)src, dst, (float *)weights,
+                    (float *)bias, scratchpad);
+            break;
+        default:
+            break;
+        }
+        return status::success;
+    }
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+struct jit_avx512_core_fp32_wino_conv_4x3_bwd_data_t
+        : _jit_avx512_core_fp32_wino_conv_4x3_t<false>,
+        public cpu_primitive_t {
+    struct pd_t : public cpu_convolution_bwd_data_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_wino_4x3:", avx512_core, ""),
+                jit_avx512_core_fp32_wino_conv_4x3_bwd_data_t);
+
+        status_t init() {
+            bool ok = true
+                && mkldnn_thr_syncable()
+                && desc()->prop_kind == prop_kind::backward_data
+                && utils::one_of(desc()->alg_kind,
+                        alg_kind::convolution_auto,
+                        alg_kind::convolution_winograd)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::undef, data_type::f32, data_type::f32)
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_avx512_core_fp32_wino_conv_4x3_bwd_data_kernel
+                ::init_conf(jcp_, *desc(), *diff_src_md(), *weights_md(),
+                        *diff_dst_md());
+            if (status != status::success) return status;
+            set_default_alg_kind(alg_kind::convolution_winograd);
+
+            auto scratchpad = scratchpad_registry().registrar();
+            winograd_avx512_core::init_scratchpad(scratchpad, jcp_);
+
+            return status;
+        }
+
+        jit_conv_winograd_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+            auto wei_fmt = with_groups() ? gOIhw16i16o : OIhw16i16o;
+            return set_default_formats_common(nChw16c, wei_fmt, nChw16c);
+        }
+    };
+
+    jit_avx512_core_fp32_wino_conv_4x3_bwd_data_t(const pd_t *apd)
+        : _jit_avx512_core_fp32_wino_conv_4x3_t<false>(apd->jcp_, apd->attr())
+        , cpu_primitive_t(apd, true)
+         {}
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        auto diff_dst = CTX_IN_MEM(const float *, MKLDNN_ARG_DIFF_DST);
+        auto weights = CTX_IN_MEM(const float *, MKLDNN_ARG_WEIGHTS);
+        auto diff_src = CTX_OUT_MEM(float *, MKLDNN_ARG_DIFF_SRC);
+
+        auto scratchpad = this->scratchpad(ctx);
+
+        switch ((pd()->jcp_).sched_policy) {
+        case WSCHED_DATA_W_S_G_D:
+            this->_execute_data_W_S_G_D((float *)diff_dst, diff_src,
+                    (float *)weights, NULL, scratchpad);
+            break;
+
+        case WSCHED_DATA_W_SGD:
+            this->_execute_data_W_SGD((float *)diff_dst, diff_src,
+                    (float *)weights, NULL, scratchpad);
+            break;
+
+        default:
+            break;
+        }
+
+        return status::success;
+    }
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+struct jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_t
+        : public cpu_primitive_t {
+    struct pd_t : public cpu_convolution_bwd_weights_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_weights_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_wino_4x3:", avx512_core, ""),
+                jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_t);
+
+        status_t init() {
+            bool ok = true
+                && mkldnn_thr_syncable()
+                && desc()->prop_kind == prop_kind::backward_weights
+                && utils::one_of(desc()->alg_kind,
+                        alg_kind::convolution_auto,
+                        alg_kind::convolution_winograd)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && set_default_formats();
+            if (!ok)
+                return status::unimplemented;
+
+            status_t status =
+                jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_kernel::
+                init_conf(jcp_, *desc(), *src_md(), *diff_dst_md(),
+                        *diff_weights_md());
+            if (status != status::success) return status;
+            set_default_alg_kind(alg_kind::convolution_winograd);
+
+            auto scratchpad = scratchpad_registry().registrar();
+            winograd_avx512_core::init_scratchpad(scratchpad, jcp_);
+
+            return status;
+        }
+
+        jit_conv_winograd_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+            auto wei_fmt = with_groups() ? gOIhw16i16o : OIhw16i16o;
+            return set_default_formats_common(nChw16c, wei_fmt, nChw16c);
+        }
+    };
+
+    jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_t(const pd_t *apd)
+        : cpu_primitive_t(apd, true)
+        , kernel_(nullptr)
+    {
+        kernel_ = new jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_kernel(
+                pd()->jcp_);
+    }
+
+    ~jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_t()
+    {
+        delete kernel_;
+    }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        auto diff_dst = CTX_IN_MEM(const float *, MKLDNN_ARG_DIFF_DST);
+        auto src = CTX_IN_MEM(const float *, MKLDNN_ARG_SRC);
+        auto diff_weights = CTX_OUT_MEM(float *, MKLDNN_ARG_DIFF_WEIGHTS);
+        auto diff_bias = CTX_OUT_MEM(float *, MKLDNN_ARG_DIFF_BIAS);
+
+        switch (kernel_->jcp.sched_policy) {
+        case WSCHED_WEI_SDGtWo:
+            _execute_backward_weights_SDGtWo(src, diff_dst, diff_weights,
+                    diff_bias, scratchpad(ctx));
+            break;
+        case WSCHED_WEI_S_D_Giot_W:
+            _execute_backward_weights_S_D_Giot_W(src, diff_dst, diff_weights,
+                    diff_bias, scratchpad(ctx));
+            break;
+        default:
+            assert(kernel_->jcp.sched_policy != WSCHED_INVALID);
+            break;
+        }
+        return status::success;
+    }
+
+private:
+    void _execute_backward_weights_SDGtWo(const float *src,
+            const float *diff_dst, float *diff_weights, float *diff_bias,
+            const memory_tracking::grantor_t &scratchpad) const;
+    void _execute_backward_weights_S_D_Giot_W(const float *src,
+            const float *diff_dst, float *diff_weights, float *diff_bias,
+            const memory_tracking::grantor_t &scratchpad) const;
+
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_kernel *kernel_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3_kernel.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3_kernel.cpp
new file mode 100644
index 0000000000..0d64a2d13a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3_kernel.cpp
@@ -0,0 +1,2596 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include <math.h>
+
+#include "jit_avx512_core_fp32_wino_conv_4x3_kernel.hpp"
+
+#define GET_OFF(field) offsetof(jit_wino_transform_call_s, field)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace {
+
+using namespace mkldnn::impl::utils;
+
+unsigned int L1_cache_size = get_cache_size(1, true);
+unsigned int L2_cache_size = get_cache_size(2, true);
+unsigned int LLC_data_size = get_cache_size(3, false);
+
+// the test funtion takes jcp, the candidate and the current best.
+// it  returns true if the new candidate is better
+int get_divisor_satisfying_cond(jit_conv_winograd_conf_t &jcp, int number,
+        int default_best, bool (*test)(jit_conv_winograd_conf_t &, int, int))
+{
+    int best_divisor = default_best;
+    auto test_num
+            = [&best_divisor, test](jit_conv_winograd_conf_t &jcp, int num) {
+                  if (test(jcp, num, best_divisor)) {
+                      best_divisor = num;
+                  }
+              };
+
+    for (int divisor = 1; divisor <= ::sqrt(number); divisor++) {
+        if (number % divisor == 0) {
+            test_num(jcp, divisor);
+            test_num(jcp, number / divisor);
+        }
+    }
+
+    return best_divisor;
+}
+
+namespace {
+bool is_winograd_faster_than_direct(const jit_conv_winograd_conf_t &jcp) {
+    /* Determines if current winograd implementation is faster than direct.
+       Following conditions are empirical and based on performance data */
+    unsigned int ncores_per_socket =
+        cpu.getNumCores(Xbyak::util::IntelCpuTopologyLevel::CoreLevel);
+    unsigned int nthreads = mkldnn_get_max_threads();
+
+    if (jcp.prop_kind == prop_kind::forward_inference) {
+        return jcp.mb >= 4;
+    } else if (nthreads > ncores_per_socket) {
+        double src_dst_transforms_per_core = alpha * alpha
+            * (jcp.ic + jcp.oc)
+            * jcp.mb * ((jcp.oh + tile_size - 1) / tile_size)
+            * ((jcp.ow + tile_size - 1) / tile_size)
+            * sizeof(float) / 1024. / 1024. / nthreads;
+        double wei_transform = alpha * alpha
+            * jcp.ic * jcp.oc * sizeof(float) /1024. / 1024.;
+
+        if (jcp.prop_kind == prop_kind::backward_weights) {
+            if (src_dst_transforms_per_core < 0.3
+                    || (src_dst_transforms_per_core <= 28 && wei_transform < 4))
+                return false;
+            else
+                return true;
+        } else {
+            if (src_dst_transforms_per_core < 2.0 || wei_transform < 0.02)
+                return false;
+        }
+    }
+
+    return jcp.mb > 8;
+}
+}
+
+/* assumes 512 bits registers */
+/* TODO: add support for strides */
+/* TODO: handle the prefetch distance automatically */
+typedef enum cache_t_ { L1, L2, L3 } cache_t;
+
+template <typename data_t>
+struct prefetcher_t {
+    prefetcher_t(jit_generator *generator, Xbyak::Reg64 reg_base_addr,
+            cache_t cache_type, size_t block_size, /* in number of elements*/
+            int nb_instructions_in_block, int fma_ipc)
+        : cg_(generator)
+        , reg_base_addr_(reg_base_addr)
+        , cache_type_(cache_type)
+        , cache_block_size_(block_size)
+    {
+        nb_cache_lines_to_prefetch_ = cache_block_size_ / (64 / sizeof(data_t));
+        prefetch_spread_
+                = div_up(nb_instructions_in_block, nb_cache_lines_to_prefetch_);
+        prefetch_blk_
+                = div_up(nb_cache_lines_to_prefetch_, nb_instructions_in_block);
+
+        /* assumption: when fetch in Li, data is already in L(i+1) */
+        int cache_latency;
+        switch (cache_type_) {
+        case L1: cache_latency = 14; break;
+        case L2: cache_latency = 250; break;
+        case L3: cache_latency = 250; break;
+        }
+
+        prefetch_distance_ = div_up(cache_latency, nb_cache_lines_to_prefetch_);
+    }
+
+    void prefetch(int instruction_number)
+    {
+        if (instruction_number % prefetch_spread_ == 0) {
+            for (int i = 0; (i < prefetch_blk_)
+                    && (prefetches_issued_ < nb_cache_lines_to_prefetch_);
+                    i++, prefetches_issued_++) {
+                prefetch_inst_(cg_->EVEX_compress_addr(
+                        reg_base_addr_, (cache_block_size_ * prefetch_distance_)
+                                        * sizeof(data_t)
+                                + (prefetches_issued_ * 64)));
+            }
+        }
+    }
+
+private:
+    void prefetch_inst_(const Xbyak::Address &addr)
+    {
+        switch (cache_type_) {
+        case L1: cg_->prefetcht0(addr); break;
+        case L2: cg_->prefetcht1(addr); break;
+        case L3: cg_->prefetcht2(addr); break;
+        default:
+            break; // TODO: raise an exception or put an assert
+        }
+    }
+
+    jit_generator *cg_;
+    Xbyak::Reg64 reg_base_addr_;
+    cache_t cache_type_;
+    int cache_block_size_ = 0;
+    int nb_cache_lines_to_prefetch_ = 0;
+    int prefetches_issued_ = 0;
+    int prefetch_spread_ = 0;
+    int prefetch_blk_ = 0;
+    int prefetch_distance_ = 0;
+};
+
+// utilities to support kernel parameter selection
+bool check_L2_block_per_thread(jit_conv_winograd_conf_t &jcp,
+        int dimN_block, float C2_min, float C2_max) {
+    float block_size = alpha * alpha * (2*(jcp.oc + jcp.ic)
+        * dimN_block * jcp.dimN_reg_block
+        + div_up(jcp.ic * jcp.oc,mkldnn_get_max_threads())) * (float)sizeof(float);
+    float L2_lb = C2_min * L2_cache_size;
+    float L2_ub = C2_max * L2_cache_size;
+    return (block_size > L2_lb && block_size < L2_ub);
+}
+
+bool check_L1_block_gemm(jit_conv_winograd_conf_t &jcp, int dimK_block,
+        int dimM_block, float C1_min, float C1_max) {
+    float gemm_block_size = (dimM_block * jcp.dimM_simd_block * dimK_block
+                             * jcp.dimK_reg_block * jcp.dimM_reg_block
+                     + dimK_block * jcp.dimK_reg_block * jcp.dimN_reg_block
+                     + dimM_block * jcp.dimM_simd_block * jcp.dimN_reg_block)
+                     * (float)sizeof(float);
+    float L1_lb = C1_min * L1_cache_size;
+    float L1_ub = C1_max * L1_cache_size;
+    return (gemm_block_size > L1_lb && gemm_block_size < L1_ub);
+}
+bool check_cond1(int dimN_reg_block, int dimK_block, int dimK_reg_block,
+        int dimM_block, int dimM_reg_block, int dimM_simd_block, float C)
+{
+    float lhs = (dimM_block * dimN_reg_block * dimM_simd_block * dimM_reg_block
+                        + dimM_block * dimK_block * dimK_reg_block
+                                * dimM_simd_block * dimM_reg_block
+                        + dimK_block * dimN_reg_block * dimK_reg_block)
+            * (float)sizeof(float);
+    float rhs = C * L1_cache_size;
+    return (lhs < rhs);
+}
+bool check_cond1_bis(int dimN_reg_block, int dimK_block, int dimK_reg_block,
+        int dimM_block, int dimM_reg_block, int dimM_simd_block, float C)
+{
+    float lhs = (dimM_block * dimM_reg_block * dimK_block * dimK_reg_block
+            * dimM_simd_block + dimK_block * dimN_reg_block * dimK_reg_block)
+            * (float)sizeof(float);
+    float rhs = C * L1_cache_size;
+    return (lhs < rhs);
+}
+bool check_cond2(int nb_dimN_reg_block, int dimN_reg_block, int dimK_nb_block,
+        int dimK_block, int dimK_reg_block, int dimM_block, int dimM_reg_block,
+        int dimM_simd_block, float C)
+{
+    float lhs = (nb_dimN_reg_block * dimM_block * dimN_reg_block
+                              * dimM_simd_block * dimM_reg_block
+                      + dimK_nb_block * dimM_block * dimK_block * dimK_reg_block
+                              * dimM_simd_block * dimM_reg_block
+                      + nb_dimN_reg_block * dimK_nb_block * dimK_block
+                              * dimN_reg_block * dimK_reg_block)
+            * (float)sizeof(float);
+    float rhs = C * L2_cache_size;
+    return (lhs < rhs);
+}
+
+bool check_kernel_cond(int dimM_block, int dimM_reg_block, int dimM_simd_block,
+        int dimN_block, int dimN_reg_block, int dimK, float C1, float C2)
+{
+    float A_size = dimM_block * dimM_reg_block * dimM_simd_block * dimK
+        * (float)sizeof(float);
+    float B_size = dimN_block * dimN_reg_block * dimK
+        * (float)sizeof(float);
+    return (A_size > C1 * L2_cache_size && B_size > C2 * L2_cache_size);
+}
+}
+
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::utils;
+using namespace Xbyak;
+
+void _jit_avx512_core_fp32_wino_conv_4x3_data_kernel::gemm_loop_generate()
+{
+    // for (int dimM_block =0; dimM_block < jcp.dimM_block; dimM_block++)
+    // for (int dimM_reg_block =0; dimM_reg_block < jcp.dimM_reg_block;
+    //      dimM_reg_block++) // unrolled
+    //     for (int dimK_block = 0; dimK_block < jcp.dimK_block; dimK_block++)
+    //         for (int dimK_reg_block= 0; dimK_reg_block < jcp.dimK_reg_block;
+    //              dimK_reg_block++) // unrolled
+    //             for (int tile =0; tile < jcp.dimN_reg_block; tile++)
+    //                 C[dimM_block][dimM_reg_block][tile] +=
+    //                 A[dimM_block][dimM_reg_block][dimK_block][dimK_reg_block]
+    //                 * broadcast(B[dimK_block][tile][dimK_reg_block]);
+    // Notes:
+    // jcp.kernel_kind defines embedded or explicit broadcast
+    // dimM_reg_block=1 for embedded bcast kernel
+
+    auto zmm_srcA = [=]() {
+        return Xbyak::Zmm(0);
+    };
+    auto zmm_srcB = [=](int tile) {
+        int idx = 1 + tile;
+        assert(idx < 1 + jcp.dimN_reg_block);
+        return Xbyak::Zmm(idx);
+    };
+    auto zmm_dstC = [=](int dimM_reg_block, int tile) {
+        int idx{0};
+        if (jcp.kernel_kind == embd_bcast)
+            idx = 1 + tile;
+        else
+            idx = 1 + jcp.dimN_reg_block
+                  + dimM_reg_block * jcp.dimN_reg_block + tile;
+        assert(idx < 32);
+        return Xbyak::Zmm(idx);
+    };
+
+    auto prepare_output = [=]() {
+        for (int dimM_reg_block = 0; dimM_reg_block < jcp.dimM_reg_block;
+              dimM_reg_block++) {
+            for (int tile = 0; tile < jcp.dimN_reg_block; tile++) {
+                Zmm zmm = zmm_dstC(dimM_reg_block, tile);
+                vpxord(zmm, zmm, zmm);
+            }
+        }
+    };
+    auto store_output = [=](bool output_is_aligned) {
+        Label save;
+        cmp(reg_is_beta_zero, 0);
+        je(save, T_NEAR);
+
+        for (int dimM_reg_block = 0; dimM_reg_block < jcp.dimM_reg_block;
+              dimM_reg_block++) {
+            for (int tile = 0; tile < jcp.dimN_reg_block; tile++) {
+                Zmm zmm = zmm_dstC(dimM_reg_block,tile);
+                int output_offset
+                    = jcp.dimN_reg_block * dimM_reg_block * 64 + tile * 64;
+                vaddps(zmm, zmm, EVEX_compress_addr(reg_dstC, output_offset));
+            }
+        }
+
+        L(save);
+        for (int dimM_reg_block = 0; dimM_reg_block < jcp.dimM_reg_block;
+              dimM_reg_block++) {
+            for (int tile = 0; tile < jcp.dimN_reg_block; tile++) {
+                Zmm zmm = zmm_dstC(dimM_reg_block,tile);
+                int output_offset
+                    = jcp.dimN_reg_block * dimM_reg_block * 64 + tile * 64;
+
+                // In W_SGD, output will be reused.
+                if (output_is_aligned
+                    && jcp.dimK_nb_block == 1
+                    && jcp.sched_policy == WSCHED_DATA_W_S_G_D
+                    && (jcp.dimN * jcp.dimM * alpha * alpha
+                        * sizeof(float) > 2 * LLC_data_size))
+                    vmovntps(EVEX_compress_addr(reg_dstC, output_offset), zmm);
+                else vmovups(EVEX_compress_addr(reg_dstC, output_offset), zmm);
+            }
+        }
+    };
+
+    auto inner_loops = [=]() {
+        Label dimM_block_loop, dimK_block_loop;
+
+        if (jcp.dimM_block > 1) {
+            mov(reg_dimM_block_loop_cnt, jcp.dimM_block);
+            L(dimM_block_loop);
+        }
+
+        prepare_output();
+
+        if (jcp.dimK_block > 1) {
+            mov(reg_dimK_block_loop_cnt, jcp.dimK_block);
+            L(dimK_block_loop);
+        }
+
+        for (int dimK_reg_block = 0;
+                dimK_reg_block < jcp.dimK_reg_block;
+                dimK_reg_block ++) {
+
+            if (jcp.kernel_kind == expl_bcast) {
+                for (int tile = 0; tile < jcp.dimN_reg_block; tile++) {
+                    vbroadcastss(zmm_srcB(tile),
+                        ptr[reg_srcB + 64 * tile + dimK_reg_block * 4]);
+                }
+            }
+
+            /* Performing the fmas */
+
+            for (int dimM_reg_block = 0; dimM_reg_block < jcp.dimM_reg_block;
+                dimM_reg_block++) {
+
+                vmovups(zmm_srcA(),
+                    zword[reg_srcA
+                            + jcp.dimK_reg_block * jcp.dimK_block * 64
+                              * dimM_reg_block
+                            + dimK_reg_block * 64]
+                    );
+
+                for (int tile = 0; tile < jcp.dimN_reg_block; tile++) {
+                    if (jcp.kernel_kind == expl_bcast)
+                        vfmadd231ps(zmm_dstC(dimM_reg_block, tile), zmm_srcA(),
+                            zmm_srcB(tile));
+                    else
+                        vfmadd231ps(zmm_dstC(dimM_reg_block, tile), zmm_srcA(),
+                            EVEX_compress_addr(reg_srcB,
+                                64 * tile + dimK_reg_block * 4, true));
+                }
+            }
+        }
+        add(reg_srcA, jcp.dimK_reg_block * 64);
+        add(reg_srcB, jcp.dimN_reg_block * 64);
+        if (jcp.dimK_block > 1) {
+            sub(reg_dimK_block_loop_cnt, 1);
+            jnz(dimK_block_loop);
+        }
+
+        Label unaligned_store, end_store;
+        test(reg_dstC, cpu_isa_traits<avx512_core>::vlen - 1);
+        jnz(unaligned_store, T_NEAR);
+        store_output(true);
+        jmp(end_store, T_NEAR);
+        L(unaligned_store); {
+            store_output(false);
+        }
+        L(end_store);
+
+        if (jcp.dimM_block > 1) {
+            sub(reg_srcB, jcp.dimK_block * jcp.dimN_reg_block * 64);
+            add(reg_dstC, jcp.dimM_reg_block * jcp.dimN_reg_block * 64);
+            if (jcp.kernel_kind == expl_bcast) {
+                add(reg_srcA,
+                     (jcp.dimM_reg_block-1) * jcp.dimK_reg_block * 64
+                      * jcp.dimK_block);
+            }
+            sub(reg_dimM_block_loop_cnt, 1);
+            jnz(dimM_block_loop);
+        }
+    };
+
+    /* Preamble */
+    preamble();
+
+    /* kernel */
+    inner_loops();
+
+    /* Postamble */
+    postamble();
+    ret();
+}
+
+void _jit_avx512_core_fp32_wino_conv_4x3_data_kernel
+    ::weights_transform_data_ker_generate()
+{
+    bool is_fwd = one_of(jcp.prop_kind,
+        mkldnn_forward_training, mkldnn_forward_inference);
+    int kh = jcp.kh;
+    int kw = jcp.kw;
+
+    auto zmm_temp = Xbyak::Zmm(31);
+    auto zmm_zero = Xbyak::Zmm(30);
+
+    auto zmm_M = [=](int i) {
+        return Xbyak::Zmm(i);
+    };
+    auto zmm_MT = [=](int i) {
+        return Xbyak::Zmm(i + simd_w);
+    };
+
+    auto zmm_G = [=](int i) {
+        return Xbyak::Zmm(i);
+    };
+    auto zmm_F = [=](int i) {
+        return Xbyak::Zmm(alpha + i);
+    };
+    auto zmm_T = [=](int i) {
+        return Xbyak::Zmm(alpha + 3 + i);
+    };
+    auto zmm_t = [=](int i) {
+        return Xbyak::Zmm(2 * alpha + 3 + i);
+    };
+
+    auto zmm_load = [=](int i) {
+        return Xbyak::Zmm(i);
+    };
+
+    auto init_G = [=]() {
+        mov(wreg_temp, ptr[param1 + GET_OFF(G)]);
+        for (int i = 0; i < alpha; i++) {
+            vbroadcastss(zmm_G(i), ptr[wreg_temp + i * typesize]);
+        }
+        vpxord(zmm_zero, zmm_zero, zmm_zero);
+    };
+
+    auto trans16x16 = [=]() {
+        for (int i = 0; i < simd_w; i+=2 ) {
+            vmovups(zmm_M(i), ptr[wreg_M + i * simd_w * 4]);
+            vmovups(zmm_M(i+1), ptr[wreg_M + (i + 1) * simd_w * 4]);
+            vunpcklps(zmm_MT(i), zmm_M(i), zmm_M(i+1));
+            vunpckhps(zmm_MT(i+1), zmm_M(i), zmm_M(i+1));
+        }
+        for (int i = 0; i < simd_w; i+=4 ) {
+            vunpcklpd(zmm_M(i), zmm_MT(i), zmm_MT(i+2));
+            vunpckhpd(zmm_M(i+1), zmm_MT(i), zmm_MT(i+2));
+            vunpcklpd(zmm_M(i+2), zmm_MT(i+1), zmm_MT(i+3));
+            vunpckhpd(zmm_M(i+3), zmm_MT(i+1), zmm_MT(i+3));
+        }
+        for (int i = 0; i < simd_w; i += 8) {
+            vshuff32x4(zmm_MT(i), zmm_M(i), zmm_M(i + 4), 0x88);
+            vshuff32x4(zmm_MT(i+1), zmm_M(i+1), zmm_M(i + 5), 0x88);
+            vshuff32x4(zmm_MT(i+2), zmm_M(i+2), zmm_M(i + 6), 0x88);
+            vshuff32x4(zmm_MT(i+3), zmm_M(i+3), zmm_M(i + 7), 0x88);
+            vshuff32x4(zmm_MT(i+4), zmm_M(i), zmm_M(i + 4), 0xdd);
+            vshuff32x4(zmm_MT(i+5), zmm_M(i+1), zmm_M(i + 5), 0xdd);
+            vshuff32x4(zmm_MT(i+6), zmm_M(i+2), zmm_M(i + 6), 0xdd);
+            vshuff32x4(zmm_MT(i+7), zmm_M(i+3), zmm_M(i + 7), 0xdd);
+        }
+        {
+            int i = 0;
+            int mask = 0x88;
+            vshuff32x4(zmm_M(0), zmm_MT(i), zmm_MT(i + 8), mask);
+            vmovups(ptr[wreg_MT + 0 * 16 * 4], zmm_M(0));
+            vshuff32x4(zmm_M(1), zmm_MT(i + 1), zmm_MT(i + 9), mask);
+            vmovups(ptr[wreg_MT + 1 * 16 * 4], zmm_M(1));
+            vshuff32x4(zmm_M(2), zmm_MT(i + 2), zmm_MT(i + 10), mask);
+            vmovups(ptr[wreg_MT + 2 * 16 * 4], zmm_M(2));
+            vshuff32x4(zmm_M(3), zmm_MT(i + 3), zmm_MT(i + 11), mask);
+            vmovups(ptr[wreg_MT + 3 * 16 * 4], zmm_M(3));
+            vshuff32x4(zmm_M(4), zmm_MT(i + 4), zmm_MT(i + 12), mask);
+            vmovups(ptr[wreg_MT + 4 * 16 * 4], zmm_M(4));
+            vshuff32x4(zmm_M(5), zmm_MT(i + 5), zmm_MT(i + 13), mask);
+            vmovups(ptr[wreg_MT + 5 * 16 * 4], zmm_M(5));
+            vshuff32x4(zmm_M(6), zmm_MT(i + 6), zmm_MT(i + 14), mask);
+            vmovups(ptr[wreg_MT + 6 * 16 * 4], zmm_M(6));
+            vshuff32x4(zmm_M(7), zmm_MT(i + 7), zmm_MT(i + 15), mask);
+            vmovups(ptr[wreg_MT + 7 * 16 * 4], zmm_M(7));
+            mask = 0xdd;
+            vshuff32x4(zmm_M(8), zmm_MT(i), zmm_MT(i + 8), mask);
+            vmovups(ptr[wreg_MT + 8 * 16 * 4], zmm_M(8));
+            vshuff32x4(zmm_M(9), zmm_MT(i + 1), zmm_MT(i + 9), mask);
+            vmovups(ptr[wreg_MT + 9 * 16 * 4], zmm_M(9));
+            vshuff32x4(zmm_M(10), zmm_MT(i + 2), zmm_MT(i + 10), mask);
+            vmovups(ptr[wreg_MT + 10 * 16 * 4], zmm_M(10));
+            vshuff32x4(zmm_M(11), zmm_MT(i + 3), zmm_MT(i + 11), mask);
+            vmovups(ptr[wreg_MT + 11 * 16 * 4], zmm_M(11));
+            vshuff32x4(zmm_M(12), zmm_MT(i + 4), zmm_MT(i + 12), mask);
+            vmovups(ptr[wreg_MT + 12 * 16 * 4], zmm_M(12));
+            vshuff32x4(zmm_M(13), zmm_MT(i + 5), zmm_MT(i + 13), mask);
+            vmovups(ptr[wreg_MT + 13 * 16 * 4], zmm_M(13));
+            vshuff32x4(zmm_M(14), zmm_MT(i + 6), zmm_MT(i + 14), mask);
+            vmovups(ptr[wreg_MT + 14 * 16 * 4], zmm_M(14));
+            vshuff32x4(zmm_M(15), zmm_MT(i + 7), zmm_MT(i + 15), mask);
+            vmovups(ptr[wreg_MT + 15 * 16 * 4], zmm_M(15));
+        }
+    };
+
+    auto load_src = [=]() {
+        mov(wreg_src, ptr[param1 + GET_OFF(src)]);
+        mov(wreg_F, ptr[param1 + GET_OFF(M)]);
+        for (int j = 0; j < kh; j++) {
+            for (int i = 0; i < kw; i++) {
+                if (is_fwd) {
+                    for (int v1 = 0; v1 < simd_w; v1++) {
+                        int offset_src = (j * kw * simd_w * simd_w
+                            + i * simd_w * simd_w + v1 * simd_w) * typesize;
+                        int offset_F = (j * kw * simd_w * simd_w
+                            + i * simd_w * simd_w  + v1 * simd_w) * typesize;
+                        vmovups(zmm_temp, ptr[wreg_src + offset_src]);
+                        vmovups(ptr[wreg_F + offset_F], zmm_temp);
+                    }
+                } else {
+                    int offset_src = ((2 - j) * kw * simd_w * simd_w
+                        + (2 - i) * simd_w * simd_w) * typesize;
+                    int offset_F = (j * kw * simd_w * simd_w
+                        + i * simd_w * simd_w) * typesize;
+                    lea(wreg_M, ptr[wreg_src + offset_src]);
+                    lea(wreg_MT, ptr[wreg_F + offset_F]);
+                    trans16x16();
+                }
+            }
+        }
+    };
+
+    auto store_dst = [=]() {
+        mov(wreg_dst, ptr[param1 + GET_OFF(dst)]);
+        mov(wreg_Fw, ptr[param1 + GET_OFF(Mw)]);
+
+        Label Loop_j;
+        mov(wreg_cnt_j, 0);
+        mov(wreg_dst_aux, wreg_dst);
+        mov(wreg_Fw_aux, wreg_Fw);
+
+        int dim5 = jcp.dimK_nb_block * (jcp.dimM_block * jcp.dimM_reg_block)
+            * jcp.dimK_block * simd_w * simd_w;
+
+        L(Loop_j);
+        {
+            for (int i = 0; i < alpha; i++) {
+                // touch pages
+                vmovups(zmm_load(0), ptr[wreg_Fw_aux
+                    + (i * simd_w * simd_w) * typesize]);
+                mov(wreg_dst_idx, i * dim5 * typesize);
+                vmovntps(ptr[wreg_dst_aux + wreg_dst_idx], zmm_load(0));
+            }
+            for (int i = 0; i < alpha; i++) {
+                for (int v1 = 1; v1 < simd_w; v1++) {
+                    int offset_Fw = (i * simd_w * simd_w  + v1 * simd_w)
+                        * typesize;
+                    vmovups(zmm_load(v1), ptr[wreg_Fw_aux + offset_Fw]);
+                }
+                mov(wreg_dst_idx, i * dim5 * typesize);
+                for (int v1 = 1; v1 < simd_w; v1++) {
+                    int offset_dst = v1 * simd_w * typesize;
+                    vmovntps(ptr[wreg_dst_aux + wreg_dst_idx + offset_dst],
+                        zmm_load(v1));
+                }
+            }
+            add(wreg_Fw_aux, alpha * simd_w * simd_w * typesize);
+            add(wreg_dst_aux, alpha * dim5 * typesize);
+            add(wreg_cnt_j, 1);
+            cmp(wreg_cnt_j, alpha);
+            jl(Loop_j, T_NEAR);
+        }
+    };
+
+    auto trans_W_4x4_3x3 = [=]() {
+        auto fma4 = [=](Zmm dst, Zmm a, Zmm b, Zmm c) {
+            vmovups(dst, a);
+            vfmadd231ps(dst, b, c);
+        };
+        auto fms4 = [=](Zmm dst, Zmm a, Zmm b, Zmm c) {
+            vmulps(zmm_temp, b, c);
+            vsubps(dst, a, zmm_temp);
+        };
+        auto fnms4 = [=](Zmm dst, Zmm a, Zmm b, Zmm c) {
+            vsubps(dst, zmm_zero, a);
+            vfnmadd231ps(dst, b, c);
+        };
+
+        mov(wreg_Fw, ptr[param1 + GET_OFF(Mw)]);
+        mov(wreg_F, ptr[param1 + GET_OFF(M)]);
+        mov(wreg_T, ptr[param1 + GET_OFF(T)]);
+
+        Label Loop_j;
+        mov(wreg_cnt_j, 0);
+        L(Loop_j);
+            mov(wreg_F_aux, wreg_F);
+            mov(wreg_Fw_aux, wreg_Fw);
+            mov(wreg_temp, wreg_cnt_j);
+            shl(wreg_temp, 4 + 2);
+            lea(wreg_F_aux, ptr[wreg_F + wreg_temp]);
+            lea(wreg_Fw_aux, ptr[wreg_Fw + wreg_temp]);
+
+            for (int i = 0; i < 3; i++) {
+                for (int idx = 0; idx < 3; idx ++) {
+                    vmovups(zmm_F(idx), ptr[wreg_F_aux + (idx * 3 * simd_w
+                        * simd_w + i * simd_w * simd_w) * typesize]);
+                }
+                vmulps(zmm_t(0), zmm_G(0), zmm_F(2));
+                fnms4(zmm_t(1), zmm_t(0), zmm_G(1), zmm_F(0));
+                fma4(zmm_t(2), zmm_t(0), zmm_G(2), zmm_F(0));
+
+                vmulps(zmm_T(0), zmm_G(3), zmm_F(0));
+                fms4(zmm_T(1), zmm_t(1), zmm_G(4), zmm_F(1));
+                fma4(zmm_T(2), zmm_t(1), zmm_G(4), zmm_F(1));
+                fma4(zmm_T(3), zmm_t(2), zmm_G(5), zmm_F(1));
+                fms4(zmm_T(4), zmm_t(2), zmm_G(5), zmm_F(1));
+                vmovaps(zmm_T(5), zmm_F(2));
+
+                for (int idx = 0; idx < 6; idx ++) {
+                    vmovups(ptr[wreg_T + (idx * 3 * simd_w + i * simd_w)
+                        * typesize], zmm_T(idx));
+                }
+            }
+            for (int i = 0; i < 6; i++) {
+
+                for (int idx = 0; idx < 3; idx ++) {
+                    vmovups(zmm_T(idx), ptr[wreg_T
+                        + (i * 3 * simd_w + idx * simd_w) * typesize]);
+                }
+                vmulps(zmm_t(0), zmm_G(0), zmm_T(2));
+                fnms4(zmm_t(1), zmm_t(0), zmm_G(1), zmm_T(0));
+                fma4(zmm_t(2), zmm_t(0), zmm_G(2), zmm_T(0));
+
+                vmulps(zmm_F(0), zmm_G(3), zmm_T(0));
+                fms4(zmm_F(1), zmm_t(1), zmm_G(4), zmm_T(1));
+                fma4(zmm_F(2), zmm_t(1), zmm_G(4), zmm_T(1));
+                fma4(zmm_F(3), zmm_t(2), zmm_G(5), zmm_T(1));
+                fms4(zmm_F(4), zmm_t(2), zmm_G(5), zmm_T(1));
+                vmovaps(zmm_F(5), zmm_T(2));
+
+                for (int l = 0; l < 6; l++) {
+                    vmovups(ptr[wreg_Fw_aux + (i * 6 * simd_w * simd_w
+                        + l * simd_w * simd_w) * typesize], zmm_F(l));
+                }
+            }
+        add(wreg_cnt_j, 1);
+        cmp(wreg_cnt_j, 16);
+        jl(Loop_j, T_NEAR);
+    };
+
+    auto inner_loops = [=]() {
+        load_src();
+        init_G();
+        trans_W_4x4_3x3();
+        store_dst();
+    };
+
+    preamble();
+    inner_loops();
+    postamble();
+}
+
+void _jit_avx512_core_fp32_wino_conv_4x3_data_kernel
+    ::output_transform_data_ker_generate()
+{
+    bool is_fwd = one_of(jcp.prop_kind,
+        mkldnn_forward_training, mkldnn_forward_inference);
+    int outw = is_fwd ? jcp.ow : jcp.iw;
+    int outh = is_fwd ? jcp.oh : jcp.ih;
+    bool not_tiled = jcp.sched_policy == WSCHED_DATA_W_S_G_D;
+    bool with_bias = jcp.with_bias;
+    bool with_relu = jcp.with_eltwise;
+    bool with_relu_postsum = jcp.with_relu_postsum;
+    bool with_sum = jcp.with_sum;
+
+    auto zmm_zero = Xbyak::Zmm(0);
+    auto zmm_temp = Xbyak::Zmm(31);
+    auto zmm_G = [=](int i) {
+        return Xbyak::Zmm(1 + i);
+    };
+    auto zmm_O = [=](int i) {
+        return Xbyak::Zmm(1 + alpha + i);
+    };
+    auto zmm_T = [=](int i) {
+        return Xbyak::Zmm(1 + 2 * alpha + i);
+    };
+    auto zmm_t = [=](int i) {
+        return Xbyak::Zmm(1 + 3 * alpha + i);
+    };
+
+    auto init_G = [=]() {
+        mov(oreg_temp, ptr[param1 + GET_OFF(G)]);
+        for (int i = 0; i < 6; i++) {
+            vbroadcastss(zmm_G(i), ptr[oreg_temp + i * typesize]);
+        }
+    };
+
+    auto load_src = [=]() {
+        mov(oreg_Ow, ptr[param1 + GET_OFF(Mw)]);
+        mov(oreg_src, ptr[param1 + GET_OFF(src)]);
+
+        mov(oreg_nb_tile_block_ur, ptr[param1 + GET_OFF(nb_tile_block_ur)]);
+        imul(oreg_nb_tile_block_ur, oreg_nb_tile_block_ur,
+            (jcp.dimM_block * jcp.dimM_reg_block) * jcp.dimN_reg_block
+            * jcp.dimM_simd_block * typesize);
+        add(oreg_src, oreg_nb_tile_block_ur);
+
+        mov(oreg_tile_block_ur, ptr[param1 + GET_OFF(tile_block_ur)]);
+        imul(oreg_tile_block_ur, oreg_tile_block_ur,
+            jcp.dimM_simd_block * typesize);
+        add(oreg_src, oreg_tile_block_ur);
+
+        if (not_tiled) {
+            mov(oreg_tile_block, ptr[param1 + GET_OFF(tile_block)]);
+            imul(oreg_tile_block, oreg_tile_block,
+                jcp.dimM_nb_block * alpha * alpha * jcp.dimN_block
+                * (jcp.dimM_block * jcp.dimM_reg_block) * jcp.dimN_reg_block
+                * jcp.dimM_simd_block * typesize);
+            add(oreg_src, oreg_tile_block);
+        }
+
+        int last4dim = jcp.dimN_block * (jcp.dimM_block * jcp.dimM_reg_block)
+            * jcp.dimN_reg_block * jcp.dimM_simd_block * typesize;
+        for (int j = 0; j < alpha; j++) {
+            for (int i = 0; i < alpha; i++) {
+                int j_base_offset = j * alpha * last4dim;
+                int i_base_offset = i * last4dim;
+                vmovups(zmm_temp, ptr[oreg_src + j_base_offset + i_base_offset]);
+                vmovups(ptr[oreg_Ow + (j * alpha * simd_w + i * simd_w)
+                    * typesize], zmm_temp);
+            }
+        }
+    };
+
+    auto store_dst = [=]() {
+        vpxord(zmm_zero, zmm_zero, zmm_zero);
+        mov(oreg_dst, ptr[param1 + GET_OFF(dst)]);
+        mov(oreg_O, ptr[param1 + GET_OFF(M)]);
+        mov(oreg_ydim, ptr[param1 + GET_OFF(tj)]);
+        shl(oreg_ydim, 2); // tj * tile_size (==4)
+        mov(oreg_xdim, ptr[param1 + GET_OFF(ti)]);
+        shl(oreg_xdim, 2); // ti * tilesize (==4)
+
+        if (with_bias)
+            mov(oreg_bias, ptr[param1 + GET_OFF(bias)]);
+
+        auto store_one = [=](int j, int i, bool is_aligned) {
+            auto zmm_O = Xbyak::Zmm(31);
+            auto zmm_relu_ns = Xbyak::Zmm(30);
+            auto xmm_relu_ns = Xbyak::Xmm(30);
+            int offset = (j * tile_size * simd_w + i * simd_w) * typesize;
+
+            vmovups(zmm_O, ptr[oreg_O + offset]);
+            if (is_fwd) {
+                if (with_bias) {
+                    vaddps(zmm_O, zmm_O, ptr[oreg_bias]);
+                }
+                if (with_relu) {
+                    if (jcp.eltwise.alpha == 0) {
+                        vmaxps(zmm_O, zmm_O, zmm_zero);
+                    } else {
+                        Opmask kmask = Opmask(7);
+                        mov(imm_addr64, float2int(jcp.eltwise.alpha));
+                        vmovq(xmm_relu_ns, imm_addr64);
+                        vbroadcastss(zmm_relu_ns, xmm_relu_ns);
+                        vcmpps(kmask, zmm_O, zmm_zero, _cmp_lt_os);
+                        vmulps(zmm_O | kmask, zmm_O, zmm_relu_ns);
+                    }
+                }
+            }
+            if (with_sum) {
+                vaddps(zmm_O, zmm_O, ptr[oreg_out_j + oreg_temp]);
+                if (with_relu_postsum) // orig: with_relu_postsum
+                    vmaxps(zmm_O, zmm_O, zmm_zero);
+            }
+            if (is_aligned)
+                vmovntps(ptr[oreg_out_j + oreg_temp], zmm_O);
+            else
+                vmovups(ptr[oreg_out_j + oreg_temp], zmm_O);
+        };
+
+        auto i_loop = [=](int j, bool is_aligned) {
+            for (int i = 0; i < tile_size; i++) {
+                Label next;
+                mov(oreg_temp, oreg_xdim);
+                add(oreg_temp, i);
+                cmp(oreg_temp, outw);
+                jge(next, T_NEAR);
+                shl(oreg_temp, 4 + 2); // * 16 * 4
+
+                store_one(j, i, is_aligned);
+
+                L(next);
+            }
+        };
+
+
+        for (int j = 0; j < tile_size; j++) {
+            Label next, unaligned;
+            mov(oreg_temp, oreg_ydim);
+            add(oreg_temp, j);
+            cmp(oreg_temp, outh);
+            jge(next, T_NEAR);
+
+            mov(oreg_out_j, oreg_dst);
+            imul(oreg_temp, oreg_temp, outw * simd_w * typesize);
+            add(oreg_out_j, oreg_temp);
+
+            test(oreg_dst, 63);
+            jnz(unaligned, T_NEAR);
+
+            i_loop(j, true);
+            jmp(next, T_NEAR);
+
+            L(unaligned);
+            i_loop(j, false);
+
+            L(next);
+        }
+    };
+
+    auto trans_O_4x4_3x3 = [=]() {
+        auto fma2 = [=](Zmm dst, Zmm v1, Zmm u1, Zmm v2, Zmm u2){
+            vmulps(dst, v1, u1);
+            vfmadd231ps(dst, v2, u2);
+        };
+        mov(oreg_Ow, ptr[param1 + GET_OFF(Mw)]);
+        mov(oreg_T, ptr[param1 + GET_OFF(T)]);
+        mov(oreg_O, ptr[param1 + GET_OFF(M)]);
+
+        for (int i = 0; i < alpha; i++) {
+            for (int j = 0; j < alpha; j++) {
+                vmovups(zmm_O(j), ptr[oreg_Ow + (j * alpha * simd_w
+                    + i * simd_w) * typesize]);
+            }
+
+            vaddps(zmm_t(0), zmm_O(1), zmm_O(2));
+            vaddps(zmm_t(1), zmm_O(3), zmm_O(4));
+            vsubps(zmm_t(2), zmm_O(1), zmm_O(2));
+            vsubps(zmm_t(3), zmm_O(3), zmm_O(4));
+
+            vaddps(zmm_T(0), zmm_t(0), zmm_t(1));
+            vaddps(zmm_T(0), zmm_T(0), zmm_O(0));
+            fma2(zmm_T(1), zmm_t(2), zmm_G(0), zmm_t(3), zmm_G(1));
+            fma2(zmm_T(2), zmm_t(0), zmm_G(2), zmm_t(1), zmm_G(3));
+            fma2(zmm_T(3), zmm_t(2), zmm_G(4), zmm_t(3), zmm_G(5));
+            vaddps(zmm_T(3), zmm_T(3), zmm_O(5));
+
+            for (int j = 0; j < tile_size; j++) {
+                vmovups(ptr[oreg_T + (j * alpha * simd_w
+                    + i * simd_w) * typesize], zmm_T(j));
+            }
+        }
+        for (int j = 0; j < tile_size; j++) {
+            for (int i = 0; i < alpha; i++) {
+                vmovups(zmm_T(i), ptr[oreg_T + (j * alpha * simd_w
+                    + i * simd_w) * typesize]);
+            }
+            vaddps(zmm_t(0), zmm_T(1), zmm_T(2));
+            vaddps(zmm_t(1), zmm_T(3), zmm_T(4));
+            vsubps(zmm_t(2), zmm_T(1), zmm_T(2));
+            vsubps(zmm_t(3), zmm_T(3), zmm_T(4));
+
+            vaddps(zmm_O(0), zmm_t(0), zmm_t(1));
+            vaddps(zmm_O(0), zmm_O(0), zmm_T(0));
+            fma2(zmm_O(1), zmm_t(2), zmm_G(0), zmm_t(3), zmm_G(1));
+            fma2(zmm_O(2), zmm_t(0), zmm_G(2), zmm_t(1), zmm_G(3));
+            fma2(zmm_O(3), zmm_t(2), zmm_G(4), zmm_t(3), zmm_G(5));
+            vaddps(zmm_O(3), zmm_O(3), zmm_T(5));
+
+            for (int i = 0; i < tile_size; i++) {
+                vmovups(ptr[oreg_O + (j * tile_size * simd_w
+                    + i * simd_w) * typesize], zmm_O(i));
+            }
+        }
+    };
+
+    auto inner_loops = [=]() {
+        init_G();
+        load_src();
+        trans_O_4x4_3x3();
+        store_dst();
+    };
+
+    preamble();
+    inner_loops();
+    postamble();
+}
+
+void _jit_avx512_core_fp32_wino_conv_4x3_data_kernel
+    ::input_transform_data_ker_generate()
+{
+    bool is_fwd = one_of(jcp.prop_kind,
+        mkldnn_forward_training, mkldnn_forward_inference);
+    int inpw = is_fwd ? jcp.iw : jcp.ow;
+    int inph = is_fwd ? jcp.ih : jcp.oh;
+    int l_pad = is_fwd ? jcp.l_pad : jcp.iw + jcp.r_pad - jcp.ow;
+    int t_pad = is_fwd ? jcp.t_pad : jcp.ih + jcp.t_pad - jcp.oh;
+    int wp_max = inpw + l_pad;
+    int hp_max = inph + t_pad;
+    bool not_tiled = jcp.sched_policy == WSCHED_DATA_W_S_G_D;
+    int G_size = 9;
+
+    auto zmm_zero = Xbyak::Zmm(0);
+    auto zmm_temp = Xbyak::Zmm(31);
+    auto zmm_G = [=](int i) {
+        return Xbyak::Zmm(1 + i);
+    };
+    auto zmm_I = [=](int i) {
+        return Xbyak::Zmm(1 + G_size + i);
+    };
+    auto zmm_T = [=](int i) {
+        return Xbyak::Zmm(1 + G_size + alpha + i);
+    };
+    auto zmm_t = [=](int i) {
+        return Xbyak::Zmm(1 + G_size + 2 * alpha + i);
+    };
+
+    auto init_G = [=]() {
+        mov(ireg_temp, ptr[param1 + GET_OFF(G)]);
+        for (int i = 0; i < G_size; i++) {
+            vbroadcastss(zmm_G(i), ptr[ireg_temp + i * typesize]);
+        }
+    };
+
+    auto load_src = [=]() {
+        mov(ireg_src, ptr[param1 + GET_OFF(src)]); // base addr of inp
+        mov(ireg_I, ptr[param1 + GET_OFF(M)]);
+
+        xor_(ireg_zero,  ireg_zero);
+        vpxord(zmm_zero, zmm_zero, zmm_zero);
+
+        mov(ireg_ydim, ptr[param1 + GET_OFF(tj)]);
+        shl(ireg_ydim, 2); // tj * tile_size (==4)
+        mov(ireg_xdim, ptr[param1 + GET_OFF(ti)]);
+        shl(ireg_xdim, 2); // ti * tilesize (==4)
+
+        for (int j = 0; j < alpha; j++) {
+            mov(ireg_temp, ireg_ydim);
+            add(ireg_temp, j);
+
+            mov(ireg_mask_j, 0xffff);
+            cmp(ireg_temp, t_pad);
+            cmovl(ireg_mask_j, ireg_zero);
+            cmp(ireg_temp, hp_max);
+            cmovge(ireg_mask_j, ireg_zero);
+
+            sub(ireg_temp, t_pad);
+            imul(ireg_temp, ireg_temp, inpw * simd_w * typesize);
+            mov(ireg_inp_j, ireg_src);
+            add(ireg_inp_j, ireg_temp);
+
+            for (int i = 0; i < alpha; i++) {
+
+                mov(ireg_temp, ireg_xdim);
+                add(ireg_temp, i);
+
+                mov(ireg_mask, 0xffff);
+                cmp(ireg_temp, l_pad);
+                cmovl(ireg_mask, ireg_zero);
+                cmp(ireg_temp, wp_max);
+                cmovge(ireg_mask, ireg_zero);
+                and_(ireg_mask, ireg_mask_j);
+
+                sub(ireg_temp, l_pad);
+                shl(ireg_temp, 4 + 2);
+
+                vpxord(zmm_temp, zmm_temp, zmm_temp);
+                Opmask kmask = Opmask(7);
+                kmovw(kmask, ireg_mask_32);
+                vmovups(zmm_temp | kmask, ptr[ireg_inp_j + ireg_temp]);
+                vmovups(ptr[ireg_I + (j * alpha * simd_w + i * simd_w)
+                    * typesize], zmm_temp);
+            }
+        }
+    };
+
+    auto store_Iw = [=]() {
+
+        mov(ireg_Iw, ptr[param1 + GET_OFF(Mw)]);
+        mov(ireg_output, ptr[param1 + GET_OFF(dst)]);
+
+       bool streamout
+          = jcp.dimN * jcp.dimK * alpha * alpha * sizeof(float)
+            > 2 * LLC_data_size
+            ? true : false;
+
+        if (not_tiled) {
+            mov(ireg_tile_block, ptr[param1 + GET_OFF(tile_block)]);
+            imul(ireg_tile_block, ireg_tile_block,
+                alpha * alpha * jcp.dimN_block * jcp.dimK_nb_block
+                * jcp.dimK_block * jcp.dimN_reg_block * jcp.dimK_reg_block
+                * typesize);
+        }
+
+        mov(ireg_nb_tile_block_ur, ptr[param1 + GET_OFF(nb_tile_block_ur)]);
+        imul(ireg_nb_tile_block_ur, ireg_nb_tile_block_ur,
+            jcp.dimK_nb_block * jcp.dimK_block * jcp.dimN_reg_block
+            * jcp.dimK_reg_block * typesize);
+
+        mov(ireg_tile_block_ur, ptr[param1 + GET_OFF(tile_block_ur)]);
+        imul(ireg_tile_block_ur, ireg_tile_block_ur,
+            jcp.dimK_reg_block * typesize);
+
+        add(ireg_output, ireg_nb_tile_block_ur);
+        add(ireg_output, ireg_tile_block_ur);
+        if (not_tiled)
+            add(ireg_output, ireg_tile_block);
+
+        for (int j = 0; j < alpha; j++) {
+            for (int i = 0; i < alpha; i++) {
+                vmovups(zmm_temp,ptr[ireg_Iw + (j * alpha * simd_w
+                    + i * simd_w) * typesize]);
+
+                int j_base_offset =
+                    j * alpha * jcp.dimN_block * jcp.dimK_nb_block
+                    * jcp.dimK_block * jcp.dimN_reg_block * jcp.dimK_reg_block
+                    * typesize;
+                int i_base_offset =
+                    i * jcp.dimN_block * jcp.dimK_nb_block * jcp.dimK_block
+                    * jcp.dimN_reg_block * jcp.dimK_reg_block * typesize;
+
+                if (not_tiled && streamout)
+                    vmovntps(ptr[ireg_output + j_base_offset + i_base_offset],
+                        zmm_temp);
+                else
+                    vmovups(ptr[ireg_output + j_base_offset + i_base_offset],
+                        zmm_temp);
+            }
+        }
+    };
+
+    auto fma4 = [=](Zmm dst, Zmm a, Zmm b, Zmm c) {
+        vmulps(zmm_temp, a, b);
+        vaddps(dst, zmm_temp, c);
+    };
+
+    auto trans_I_4x4_3x3 = [=]() {
+        mov(ireg_Iw, ptr[param1 + GET_OFF(Mw)]);
+        mov(ireg_T, ptr[param1 + GET_OFF(T)]);
+        mov(ireg_I, ptr[param1 + GET_OFF(M)]);
+
+        mov(ireg_output, ptr[param1 + GET_OFF(dst)]); // for prefetch
+        for (int i = 0; i < alpha; i++) {
+            for (int idx = 0; idx < alpha; idx++) {
+                vmovups(zmm_I(idx), ptr[ireg_I + (idx * alpha * simd_w
+                    + i * simd_w) * typesize]);
+                int j_base_offset =
+                    i * alpha * jcp.dimN_block * jcp.dimK_nb_block
+                    * jcp.dimK_block * jcp.dimN_reg_block * jcp.dimK_reg_block
+                    * typesize;
+                int idx_base_offset =
+                    idx * jcp.dimN_block * jcp.dimK_nb_block * jcp.dimK_block
+                    * jcp.dimN_reg_block * jcp.dimK_reg_block * typesize;
+                prefetcht0(ptr[ireg_output + j_base_offset + idx_base_offset]);
+            }
+
+            fma4(zmm_t(0), zmm_I(2), zmm_G(0), zmm_I(4));
+            fma4(zmm_t(1), zmm_I(1), zmm_G(0), zmm_I(3));
+            fma4(zmm_t(2), zmm_I(2), zmm_G(1), zmm_I(4));
+            fma4(zmm_t(3), zmm_I(1), zmm_G(1), zmm_I(3));
+            fma4(zmm_t(4), zmm_I(0), zmm_G(2), zmm_I(4));
+            fma4(zmm_t(5), zmm_I(1), zmm_G(2), zmm_I(5));
+
+            fma4(zmm_T(0), zmm_I(2), zmm_G(3), zmm_t(4));
+            fma4(zmm_T(1), zmm_t(1), zmm_G(4), zmm_t(0));
+            fma4(zmm_T(2), zmm_t(1), zmm_G(5), zmm_t(0));
+            fma4(zmm_T(3), zmm_t(3), zmm_G(6), zmm_t(2));
+            fma4(zmm_T(4), zmm_t(3), zmm_G(7), zmm_t(2));
+            fma4(zmm_T(5), zmm_I(3), zmm_G(8), zmm_t(5));
+
+            for (int idx = 0; idx < alpha; idx++) {
+                vmovups(ptr[ireg_T + (idx * alpha * simd_w + i * simd_w)
+                    * typesize],zmm_T(idx));
+            }
+        }
+        for (int i = 0; i < alpha; i++) {
+            for (int idx = 0; idx < alpha; idx++) {
+                vmovups(zmm_T(idx), ptr[ireg_T + (i * alpha * simd_w + idx
+                    * simd_w) * typesize]);
+            }
+
+            fma4(zmm_t(0), zmm_T(2), zmm_G(0), zmm_T(4));
+            fma4(zmm_t(1), zmm_T(1), zmm_G(0), zmm_T(3));
+            fma4(zmm_t(2), zmm_T(2), zmm_G(1), zmm_T(4));
+            fma4(zmm_t(3), zmm_T(1), zmm_G(1), zmm_T(3));
+            fma4(zmm_t(4), zmm_T(0), zmm_G(2), zmm_T(4));
+            fma4(zmm_t(5), zmm_T(1), zmm_G(2), zmm_T(5));
+
+            fma4(zmm_I(0), zmm_T(2), zmm_G(3), zmm_t(4));
+            fma4(zmm_I(1), zmm_t(1), zmm_G(4), zmm_t(0));
+            fma4(zmm_I(2), zmm_t(1), zmm_G(5), zmm_t(0));
+            fma4(zmm_I(3), zmm_t(3), zmm_G(6), zmm_t(2));
+            fma4(zmm_I(4), zmm_t(3), zmm_G(7), zmm_t(2));
+            fma4(zmm_I(5), zmm_T(3), zmm_G(8), zmm_t(5));
+
+            for (int idx = 0; idx < alpha; idx++) {
+                vmovups(ptr[ireg_Iw + (i * alpha * simd_w + idx * simd_w)
+                    * typesize],zmm_I(idx));
+            }
+        }
+    };
+
+    auto inner_loops = [=]() {
+        init_G();
+        load_src();
+        trans_I_4x4_3x3();
+        store_Iw();
+    };
+
+    preamble();
+    inner_loops();
+    postamble();
+}
+
+status_t _jit_avx512_core_fp32_wino_conv_4x3_data_kernel::init_conf_common(
+        jit_conv_winograd_conf_t &jcp, const convolution_desc_t &cd,
+        const memory_desc_wrapper &src_d, const memory_desc_wrapper &weights_d,
+        const memory_desc_wrapper &dst_d)
+{
+    if (!mayiuse(avx512_core)) {
+        return status::unimplemented;
+    }
+
+    jcp.nthr = mkldnn_get_max_threads();
+
+    jcp.ver = ver_avx512_core;
+    jcp.prop_kind = cd.prop_kind;
+
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+    jcp.ih = src_d.dims()[2];
+    jcp.iw = src_d.dims()[3];
+    jcp.oh = dst_d.dims()[2];
+    jcp.ow = dst_d.dims()[3];
+    jcp.kh = weights_d.dims()[with_groups + 2];
+    jcp.kw = weights_d.dims()[with_groups + 3];
+    jcp.t_pad = cd.padding[0][0];
+    jcp.l_pad = cd.padding[0][1];
+    jcp.stride_h = cd.strides[0];
+    jcp.stride_w = cd.strides[1];
+    jcp.dilate_h = cd.dilates[0];
+    jcp.dilate_w = cd.dilates[1];
+    jcp.r_pad = nstl::max(
+            0, (jcp.ow - 1) * jcp.stride_w + jcp.kw - jcp.iw - jcp.l_pad);
+    jcp.b_pad = nstl::max(
+            0, (jcp.oh - 1) * jcp.stride_h + jcp.kh - jcp.ih - jcp.t_pad);
+    jcp.ihp = jcp.ih + jcp.t_pad + jcp.b_pad;
+    jcp.iwp = jcp.iw + jcp.l_pad + jcp.r_pad;
+    jcp.ohp = jcp.oh;
+    jcp.owp = jcp.ow;
+
+    bool ok_to_pad_channels = jcp.ngroups == 1;
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, simd_w);
+        jcp.ic = rnd_up(jcp.ic, simd_w);
+    }
+
+    // Checking conditions not supported by these kernels
+    if (!IMPLICATION(cd.alg_kind == alg_kind::convolution_auto,
+               is_winograd_faster_than_direct(jcp)))
+        return status::unimplemented;
+
+    if (jcp.ngroups != 1)
+        return status::unimplemented;
+    if ((jcp.kh != 3) || (jcp.kw != 3))
+        return status::unimplemented;
+    if ((jcp.dilate_h != 0) || (jcp.dilate_w != 0))
+        return status::unimplemented;
+    if ((jcp.stride_h != 1) || (jcp.stride_w != 1))
+        return status::unimplemented;
+    if ((jcp.ic % simd_w) != 0 || (jcp.oc % simd_w) != 0)
+        return status::unimplemented;
+
+    format_tag_t dat_tag = nChw16c;
+    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    jcp.dst_tag = dst_d.matches_one_of_tag(dat_tag);
+
+    if (jcp.src_tag != dat_tag) return status::unimplemented;
+    if (jcp.dst_tag != dat_tag) return status::unimplemented;
+
+    if (!one_of(weights_d.format_kind(), format_kind::any, format_kind::wino)) {
+        format_tag_t wei_tag = with_groups ? gOIhw16i16o : OIhw16i16o;
+        jcp.wei_tag = weights_d.matches_one_of_tag(wei_tag);
+        if (jcp.wei_tag != wei_tag)
+            return status::unimplemented;
+    }
+
+    bool layout_consistency = true
+            && jcp.ic <= src_d.padded_dims()[1]
+            && jcp.oc <= dst_d.padded_dims()[1]
+            && (one_of(weights_d.format_kind(),
+                        format_kind::any, format_kind::wino)
+                    || (jcp.ic <= weights_d.padded_dims()[with_groups + 1]
+                        && jcp.oc <= weights_d.padded_dims()[with_groups + 0]));
+    if (!layout_consistency)
+        return status::unimplemented;
+
+    return status::success;
+}
+
+void set_kernel_dims_reg_block(jit_conv_winograd_conf_t &jcp) {
+
+    /* ----------- dimM reg block ---------------------*/
+    auto test_cond_dimM_reg_block = [](jit_conv_winograd_conf_t &jcp,
+            int dimM_reg_block, int current_best) {
+        int max_dimM_reg_block = jcp.kernel_kind == embd_bcast ? 1 : 4;
+        return (dimM_reg_block >= 1)
+                && (dimM_reg_block <= max_dimM_reg_block )
+                && (dimM_reg_block > current_best);
+    };
+    jcp.dimM_reg_block = get_divisor_satisfying_cond(jcp,
+        jcp.dimM/jcp.dimM_simd_block, 1, test_cond_dimM_reg_block);
+
+    /* ----------- dimN reg block ---------------------*/
+
+    auto test_cond_dimN_reg_block = [](jit_conv_winograd_conf_t &jcp,
+            int dimN_reg_block, int current_best) {
+        return jcp.kernel_kind == embd_bcast
+            ? dimN_reg_block < jcp.nb_reg && dimN_reg_block > current_best
+            : dimN_reg_block >= 1
+              && (dimN_reg_block * jcp.dimM_reg_block + dimN_reg_block)
+                 < jcp.nb_reg
+              && dimN_reg_block > current_best;
+    };
+    jcp.dimN_reg_block = get_divisor_satisfying_cond(jcp,
+        jcp.dimN, 1, test_cond_dimN_reg_block);
+}
+
+status_t set_wsched_DATA_W_SGD_avx512_core(jit_conv_winograd_conf_t &jcp) {
+    if (jcp.ver != ver_avx512_core)
+        return status::unimplemented;
+
+    jcp.kernel_kind = embd_bcast;
+
+    set_kernel_dims_reg_block(jcp);
+
+    /*-------------- L2 blocking for dimN block ---------*/
+
+    auto test_cond_dimN_block = [](jit_conv_winograd_conf_t &jcp,
+        int dimN_block, int current_best) {
+        return check_L2_block_per_thread(jcp, dimN_block, 0.1, 2.0)
+            && (dimN_block > current_best)
+            && ((jcp.dimN / dimN_block / jcp.dimN_reg_block)
+            >= 1.5 * mkldnn_get_max_threads());
+    };
+
+    jcp.dimN_block = get_divisor_satisfying_cond(
+            jcp, jcp.dimN / jcp.dimN_reg_block, 1, test_cond_dimN_block);
+    jcp.dimN_nb_block = jcp.dimN / jcp.dimN_block / jcp.dimN_reg_block;
+
+    if (check_L2_block_per_thread(jcp, jcp.dimN_block, 0.1, 3.2)
+        && (jcp.dimN_nb_block >= 1.5 * mkldnn_get_max_threads())) {
+
+        /* ------------------- L1 blocking for GEMM --------------*/
+        /* -------------------- Choose dimK block ----------------*/
+
+        auto test_cond_dimK_block = [](jit_conv_winograd_conf_t &jcp,
+                int dimK_block, int current_best) {
+            return check_L1_block_gemm(jcp, dimK_block, 1, 0.1, 0.5)
+                && (dimK_block > current_best);
+        };
+
+        jcp.dimK_block = get_divisor_satisfying_cond(
+                jcp, jcp.dimK / jcp.dimK_reg_block, 1, test_cond_dimK_block);
+
+        if (check_L1_block_gemm(jcp, jcp.dimK_block, 1, 0.1, 1.0)) {
+            jcp.dimK_nb_block = jcp.dimK / jcp.dimK_block / jcp.dimK_reg_block;
+
+            /* -------------- Choose dimM block -------------------*/
+            auto test_cond_dimM_block = [](jit_conv_winograd_conf_t &jcp,
+                    int dimM_block, int current_best) {
+                return check_L1_block_gemm(jcp, jcp.dimK_block, dimM_block,
+                    0.2, 0.5) && (dimM_block > current_best);
+            };
+
+            jcp.dimM_block = get_divisor_satisfying_cond(jcp,
+                jcp.dimM / (jcp.dimM_simd_block * jcp.dimM_reg_block), 1,
+                test_cond_dimM_block);
+            jcp.dimM_nb_block = jcp.dimM / jcp.dimM_block / jcp.dimM_reg_block
+                / jcp.dimM_simd_block;
+
+            jcp.sched_policy = WSCHED_DATA_W_SGD;
+            return status::success;
+        }
+
+    }
+    return status::unimplemented;
+}
+
+void set_kernel_blocking_DATA_W_S_G_D(jit_conv_winograd_conf_t &jcp) {
+
+    set_kernel_dims_reg_block(jcp);
+
+    //********************* Choosing dimK_block **********************//
+    auto test_cond1_dimK_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimK_block, int current_best) {
+        return check_cond1(jcp.dimN_reg_block, dimK_block, jcp.dimK_reg_block,
+                       1, jcp.dimM_reg_block, jcp.dimM_simd_block, .75f)
+                && (dimK_block > current_best);
+    };
+
+    auto test_cond1_bis_dimK_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimK_block, int current_best) {
+        return check_cond1_bis(jcp.dimN_reg_block, dimK_block,
+                   jcp.dimK_reg_block, 1, jcp.dimM_reg_block,
+                   jcp.dimM_simd_block, .9f)
+                && (dimK_block > current_best);
+    };
+
+    jcp.dimK_block = get_divisor_satisfying_cond(
+            jcp, jcp.dimK / jcp.dimK_reg_block, 1, test_cond1_bis_dimK_block);
+    // If we are not able to use streams, we fall back to condition [1]
+    if (jcp.dimK_block < jcp.dimK / jcp.dimK_reg_block)
+        jcp.dimK_block = get_divisor_satisfying_cond(
+                jcp, jcp.dimK / jcp.dimK_reg_block, 1, test_cond1_dimK_block);
+    jcp.dimK_nb_block = (jcp.dimK / jcp.dimK_reg_block) / jcp.dimK_block;
+
+    //********************* Choosing dimM_block **********************//
+    auto test_cond1_dimM_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimM_block, int current_best) {
+        return check_cond1(jcp.dimN_reg_block, jcp.dimK_block,
+                   jcp.dimK_reg_block, dimM_block, jcp.dimM_reg_block,
+                   jcp.dimM_simd_block, .5f)
+                && (dimM_block > current_best);
+    };
+
+    auto test_cond1_bis_dimM_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimM_block, int current_best) {
+        return check_cond1_bis(jcp.dimN_reg_block, jcp.dimK_block,
+                   jcp.dimK_reg_block, dimM_block, jcp.dimM_reg_block,
+                   jcp.dimM_simd_block, .3f)
+                && (dimM_block > current_best);
+    };
+
+    if (jcp.dimK_block < jcp.dimK / jcp.dimK_reg_block)
+        jcp.dimM_block = get_divisor_satisfying_cond(
+                jcp, jcp.dimM / (jcp.dimM_simd_block*jcp.dimM_reg_block), 1,
+                test_cond1_dimM_block);
+    else
+        jcp.dimM_block = get_divisor_satisfying_cond(jcp,
+                jcp.dimM / (jcp.dimM_simd_block*jcp.dimM_reg_block), 1,
+                test_cond1_bis_dimM_block);
+    jcp.dimM_nb_block = jcp.dimM / (jcp.dimM_simd_block * jcp.dimM_block
+                        * jcp.dimM_reg_block);
+
+    //******************* Choosing dimN_block *******************//
+    auto test_cond2_dimN_block = [](
+            jit_conv_winograd_conf_t &jcp, int dimN_block, int current_best) {
+        return check_cond2(dimN_block, jcp.dimN_reg_block, jcp.dimK_nb_block,
+                       jcp.dimK_block, jcp.dimK_reg_block, jcp.dimM_block,
+                       jcp.dimM_reg_block, jcp.dimM_simd_block, .9f)
+                && (dimN_block > current_best);
+    };
+
+    jcp.dimN_block = get_divisor_satisfying_cond(
+            jcp, jcp.dimN / jcp.dimN_reg_block, 1, test_cond2_dimN_block);
+    jcp.dimN_nb_block = jcp.dimN / (jcp.dimN_reg_block * jcp.dimN_block);
+}
+
+status_t set_wsched_DATA_W_S_G_D_avx512_core(jit_conv_winograd_conf_t &jcp) {
+
+    jcp.kernel_kind = expl_bcast;
+    set_kernel_blocking_DATA_W_S_G_D(jcp);
+    if (!(check_kernel_cond(jcp.dimM_block, jcp.dimM_reg_block,
+        jcp.dimM_simd_block, jcp.dimN_block, jcp.dimN_reg_block, jcp.dimK,
+        .1f, .35f))) {
+        jcp.kernel_kind = embd_bcast;
+        set_kernel_blocking_DATA_W_S_G_D(jcp);
+    }
+    jcp.sched_policy = WSCHED_DATA_W_S_G_D;
+    return status::success;
+}
+
+status_t _jit_avx512_core_fp32_wino_conv_4x3_data_kernel::init_conf_kernel(
+        jit_conv_winograd_conf_t &jcp, int dimM, int dimN, int dimK)
+{
+    jcp.nb_reg = 32;
+    jcp.dimN = dimN;
+    jcp.dimK = dimK;
+    jcp.dimM = dimM;
+    jcp.sched_policy = WSCHED_INVALID;
+
+    jcp.dimK_reg_block = 16;
+    jcp.dimM_simd_block = 16;
+
+    if (jcp.kernel_kind == embd_bcast) {
+        jcp.dimM_reg_block = 1;
+    }
+
+    if (!(set_wsched_DATA_W_SGD_avx512_core(jcp) == status::success))
+        set_wsched_DATA_W_S_G_D_avx512_core(jcp);
+
+    assert(jcp.sched_policy != WSCHED_INVALID);
+    return status::success;
+}
+
+bool jit_avx512_core_fp32_wino_conv_4x3_fwd_kernel::post_ops_ok(
+        jit_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    const auto &p = attr.post_ops_;
+
+    auto is_relu = [&](int idx) { return p.entry_[idx].is_relu(); };
+    auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); };
+
+    switch (p.len_) {
+    case 0: return true; // no post_ops
+    case 1: return is_relu(0) || is_sum(0); // relu or sum
+    case 2: return (is_sum(0) && is_relu(1))
+                      || (is_relu(0) && is_sum(1)); // sum->relu or relu->sum
+    case 3: return is_relu(0) && is_sum(1) && is_relu(2); // relu->sum->relu
+    default: return false;
+    }
+
+    return false;
+}
+
+status_t jit_avx512_core_fp32_wino_conv_4x3_fwd_kernel::init_conf(
+        jit_conv_winograd_conf_t &jcp, const convolution_desc_t &cd,
+        const memory_desc_t &src_md, memory_desc_t &weights_md,
+        const memory_desc_t &dst_md, const primitive_attr_t &attr) {
+
+    status_t st = init_conf_common(jcp, cd, src_md, weights_md, dst_md);
+
+    if (st != status::success)
+        return st;
+
+    // Winograd specific initialization
+    jcp.itiles = (jcp.ow + tile_size - 1) / tile_size;
+    jcp.jtiles = (jcp.oh + tile_size - 1) / tile_size;
+    jcp.ntiles = jcp.mb * jcp.itiles * jcp.jtiles;
+
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    const int eltwise_ind = p.find(primitive_kind::eltwise, 0, 1);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise)
+        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+
+    jcp.with_sum = p.find(primitive_kind::sum, 0) != -1;
+    jcp.with_relu_postsum = p.find(primitive_kind::eltwise, 1) != -1;
+
+    status_t res = init_conf_kernel(jcp, jcp.oc, jcp.ntiles, jcp.ic);
+
+    jcp.ic_simd_block = jcp.dimK_reg_block;
+    jcp.ic_block = jcp.dimK_block;
+    jcp.nb_ic = jcp.dimK_nb_block;
+    jcp.oc_simd_block = jcp.dimM_simd_block;
+    jcp.oc_block = jcp.dimM_block;
+    jcp.oc_reg_block = jcp.dimM_reg_block;
+    jcp.ic_reg_block = 1;
+    jcp.nb_oc = jcp.dimM_nb_block;
+    jcp.tile_block_ur = jcp.dimN_reg_block;
+    jcp.nb_tile_block_ur = jcp.dimN_block;
+    jcp.tile_block = jcp.dimN_nb_block;
+
+    /* re-create weights primitive descriptor
+    and set weights wino_blocking */
+    if (cd.prop_kind == mkldnn_forward_inference) {
+        memory_desc_t expect_wei_md = weights_md;
+
+        expect_wei_md.format_kind = format_kind::wino;
+        expect_wei_md.data_type = data_type::f32;
+        mkldnn_wino_desc_t &wd = expect_wei_md.format_desc.wino_desc;
+        wd.wino_format = mkldnn_wino_wei_OBaaIBOIio;
+        wd.r = 3;
+        wd.alpha = 6;
+
+        wd.ic = jcp.ic;
+        wd.oc = jcp.oc;
+        wd.ic_block = jcp.dimK_reg_block;
+        wd.oc_block = jcp.dimM_simd_block;
+        wd.ic2_block = jcp.dimK_block;
+        wd.oc2_block = jcp.dimM_block * jcp.dimM_reg_block;
+        size_t max_size = sizeof(float) * wd.alpha * wd.alpha * jcp.ic * jcp.oc;
+        wd.size = max_size;
+        wd.adj_scale = 1.f;
+
+        if (weights_md.format_kind == format_kind::any)
+            weights_md = expect_wei_md;
+        if (weights_md != expect_wei_md)
+            return status::unimplemented;
+    }
+
+    return res;
+}
+
+status_t jit_avx512_core_fp32_wino_conv_4x3_bwd_data_kernel::init_conf(
+        jit_conv_winograd_conf_t &jcp, const convolution_desc_t &cd,
+        const memory_desc_wrapper &diff_src_d,
+        const memory_desc_wrapper &weights_d,
+        const memory_desc_wrapper &diff_dst_d)
+{
+    status_t st = init_conf_common(jcp, cd, diff_src_d, weights_d, diff_dst_d);
+
+    if (st != status::success)
+        return st;
+
+    jcp.itiles = (jcp.iw + tile_size - 1) / tile_size;
+    jcp.jtiles = (jcp.ih + tile_size - 1) / tile_size;
+    jcp.ntiles = jcp.mb * jcp.itiles * jcp.jtiles;
+
+    status_t res = init_conf_kernel(jcp, jcp.ic, jcp.ntiles, jcp.oc);
+
+    jcp.oc_simd_block = jcp.dimK_reg_block;
+    jcp.oc_block = jcp.dimK_block;
+    jcp.nb_oc = jcp.dimK_nb_block;
+    jcp.ic_simd_block = jcp.dimM_simd_block;
+    jcp.ic_block = jcp.dimM_block;
+    jcp.ic_reg_block = jcp.dimM_reg_block;
+    jcp.oc_reg_block = 1;
+    jcp.nb_ic = jcp.dimM_nb_block;
+    jcp.tile_block_ur = jcp.dimN_reg_block;
+    jcp.nb_tile_block_ur = jcp.dimN_block;
+    jcp.tile_block = jcp.dimN_nb_block;
+
+    return res;
+}
+
+void jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_kernel::
+src_transform_generate() {
+    constexpr int G_size = 9;
+    const size_t ifwp = jcp.iw + jcp.l_pad;
+    const size_t ifhp = jcp.ih + jcp.t_pad;
+
+    auto zmm_G = [=](int i) {
+        return Xbyak::Zmm(i);
+    };
+    auto zmm_I = [=](int i) {
+        return Xbyak::Zmm(G_size + i);
+    };
+    auto zmm_T = [=](int i) {
+        return Xbyak::Zmm(G_size + alpha + i);
+    };
+    auto zmm_t = [=](int i) {
+        return Xbyak::Zmm(G_size + 2 * alpha + i);
+    };
+
+    auto init_G = [=]() {
+        mov(reg_G, ptr[reg_transp + GET_OFF(G)]);
+        for (int i = 0; i < G_size; i++) {
+            vbroadcastss(zmm_G(i), ptr[reg_G + i * typesize]);
+        }
+    };
+
+    auto load_src = [=]() {
+        mov(reg_I, ptr[reg_transp + GET_OFF(M)]);
+        xor_(reg_zero, reg_zero);
+
+        mov(reg_ydim, reg_tj);
+        shl(reg_ydim, 2); //tj * tile_size(=4)
+
+        for (int j = 0; j < alpha; j++) {
+            /* check if tile index is within physical spatial boundaries*/
+            mov(reg_maskj, 0xffff);
+            cmp(reg_ydim, jcp.t_pad);
+            cmovl(reg_maskj, reg_zero);
+            cmp(reg_ydim, ifhp);
+            cmovge(reg_maskj, reg_zero);
+
+            /*address offset for tile in src*/
+            mov(reg_src_offset, reg_ydim);
+            sub(reg_src_offset, jcp.t_pad); // tj*tile_size - t_pad
+            imul(reg_src_offset, reg_src_offset, jcp.iw);
+
+            mov(reg_xdim, reg_ti);
+            shl(reg_xdim, 2); // xdim = ti * tile_size
+
+            add(reg_src_offset, reg_xdim);
+            sub(reg_src_offset, jcp.l_pad);
+            imul(reg_src_offset, reg_src_offset, simd_w * typesize);
+            for (int i = 0; i < alpha; i++) {
+                /* check if tile index is within physical spatial boundaries*/
+                mov(reg_maski, 0xffff);
+                cmp(reg_xdim, jcp.l_pad);
+                cmovl(reg_maski, reg_zero);
+                cmp(reg_xdim, ifwp);
+                cmovge(reg_maski, reg_zero);
+                and_(reg_maski, reg_maskj);
+
+                Opmask kmask_src = Xbyak::Opmask(7);
+                auto zmm_src = Xbyak::Zmm(31);
+                kmovw(kmask_src, reg_maski_32);
+                vpxord(zmm_src, zmm_src, zmm_src);
+                vmovups(zmm_src | kmask_src, ptr[reg_src + reg_src_offset]);
+                vmovups(ptr[reg_I], zmm_src);
+
+                add(reg_xdim, 1); //xdim = ti * tile_size + i
+                add(reg_src_offset, simd_w * typesize);
+                add(reg_I, simd_w * typesize);
+            }
+            add(reg_ydim, 1);
+        }
+    };
+
+    auto fma4 = [=](Xbyak::Zmm dst, Xbyak::Zmm a, Xbyak::Zmm b, Xbyak::Zmm c) {
+        vmovups(dst, c);
+        vfmadd231ps(dst, a, b);
+    };
+
+    auto trans_I_3x3_4x4 = [=]() {
+        //Use 24 registers
+        mov(reg_I, ptr[reg_transp + GET_OFF(M)]);
+        mov(reg_T, ptr[reg_transp + GET_OFF(T)]);
+        for (int i = 0; i < alpha; i++) {
+            for (int j = 0; j < alpha; j++) {
+                size_t I_off = (j * alpha + i) * simd_w * typesize;
+                vmovups(zmm_I(j), ptr[reg_I + I_off]);
+            }
+
+            fma4(zmm_t(0), zmm_I(2), zmm_G(0), zmm_I(4));
+            fma4(zmm_t(1), zmm_I(1), zmm_G(0), zmm_I(3));
+            fma4(zmm_t(2), zmm_I(2), zmm_G(1), zmm_I(4));
+            fma4(zmm_t(3), zmm_I(1), zmm_G(1), zmm_I(3));
+            fma4(zmm_t(4), zmm_I(0), zmm_G(2), zmm_I(4));
+            fma4(zmm_t(5), zmm_I(1), zmm_G(2), zmm_I(5));
+
+            fma4(zmm_T(0), zmm_I(2), zmm_G(3), zmm_t(4));
+            fma4(zmm_T(1), zmm_t(1), zmm_G(4), zmm_t(0));
+            fma4(zmm_T(2), zmm_t(1), zmm_G(5), zmm_t(0));
+            fma4(zmm_T(3), zmm_t(3), zmm_G(6), zmm_t(2));
+            fma4(zmm_T(4), zmm_t(3), zmm_G(7), zmm_t(2));
+            fma4(zmm_T(5), zmm_I(3), zmm_G(8), zmm_t(5));
+
+            for (int j = 0; j < alpha; j++) {
+                vmovups(ptr[reg_T + (j * alpha + i) * simd_w * typesize],
+                        zmm_T(j));
+            }
+
+        }
+
+        for (int j = 0; j < alpha; j++) {
+            for (int i = 0; i < alpha; i++) {
+                vmovups(zmm_T(i), ptr[reg_T + (j * alpha + i) * simd_w * typesize]);
+            }
+
+            fma4(zmm_t(0), zmm_T(2), zmm_G(0), zmm_T(4));
+            fma4(zmm_t(1), zmm_T(1), zmm_G(0), zmm_T(3));
+            fma4(zmm_t(2), zmm_T(2), zmm_G(1), zmm_T(4));
+            fma4(zmm_t(3), zmm_T(1), zmm_G(1), zmm_T(3));
+            fma4(zmm_t(4), zmm_T(0), zmm_G(2), zmm_T(4));
+            fma4(zmm_t(5), zmm_T(1), zmm_G(2), zmm_T(5));
+
+            fma4(zmm_I(0), zmm_T(2), zmm_G(3), zmm_t(4));
+            fma4(zmm_I(1), zmm_t(1), zmm_G(4), zmm_t(0));
+            fma4(zmm_I(2), zmm_t(1), zmm_G(5), zmm_t(0));
+            fma4(zmm_I(3), zmm_t(3), zmm_G(6), zmm_t(2));
+            fma4(zmm_I(4), zmm_t(3), zmm_G(7), zmm_t(2));
+            fma4(zmm_I(5), zmm_T(3), zmm_G(8), zmm_t(5));
+
+            for (int i = 0; i < alpha; i++) {
+                size_t dst_off = (j * alpha * jcp.ic_block
+                    * jcp.nb_tile_block_ur * jcp.tile_block_ur
+                    + i * jcp.ic_block * jcp.nb_tile_block_ur * jcp.tile_block_ur)
+                    * simd_w * typesize;
+                vmovups(ptr[reg_dst + dst_off], zmm_I(i));
+            }
+        }
+    };
+
+    auto compute_transform_SDGtWo = [=]() {
+        mov(reg_ti, ptr[reg_transp + GET_OFF(ti)]);
+        mov(reg_tj, ptr[reg_transp + GET_OFF(tj)]);
+        mov(reg_src, ptr[reg_transp + GET_OFF(src)]);
+        mov(reg_dst, ptr[reg_transp + GET_OFF(dst)]);
+        xor_(reg_tile_count, reg_tile_count);
+        Label loop_mb, loop_jtiles, loop_itiles, done;
+        L(loop_mb);
+        {
+            L(loop_jtiles);
+            {
+                L(loop_itiles);
+                {
+                    load_src();
+
+                    trans_I_3x3_4x4();
+
+                    add(reg_tile_count, 1);
+                    cmp(reg_tile_count, jcp.nb_tile_block_ur * jcp.tile_block_ur);
+                    jge(done);
+
+                    add(reg_dst, simd_w * typesize);
+                    add(reg_ti, 1);
+                    cmp(reg_ti, jcp.itiles);
+                    jl(loop_itiles);
+                }
+                xor_(reg_ti, reg_ti);
+                add(reg_tj, 1);
+                cmp(reg_tj, jcp.jtiles);
+                jl(loop_jtiles);
+            }
+            xor_(reg_tj, reg_tj);
+            add(reg_src, jcp.ic * jcp.iw * jcp.ih * typesize);
+            jmp(loop_mb);
+        }
+        L(done);
+    };
+
+    auto compute_transform = [=]() {
+        mov(reg_src, ptr[reg_transp + GET_OFF(src)]);
+        xor_(reg_ti, reg_ti);
+        xor_(reg_tj, reg_tj);
+
+        mov(reg_dst, ptr[reg_transp + GET_OFF(dst)]);
+        mov(reg_tile_count, ptr[reg_transp + GET_OFF(tile_count)]);
+        imul(reg_temp, reg_tile_count, simd_w * typesize);
+        add(reg_dst, reg_temp);
+
+        Label loop_jtiles, loop_itiles, next_tile_block, next_tile;
+        L(loop_jtiles);
+
+        {
+            L(loop_itiles);
+            {
+                load_src();
+
+                trans_I_3x3_4x4();
+
+                add(reg_tile_count, 1);
+                cmp(reg_tile_count, jcp.nb_tile_block_ur * jcp.tile_block_ur);
+                jge(next_tile_block);
+                add(reg_dst, simd_w * typesize);
+                jmp(next_tile);
+
+                L(next_tile_block);
+                sub(reg_dst, (jcp.nb_tile_block_ur * jcp.tile_block_ur - 1)
+                        * simd_w * typesize);
+                size_t tblk_off = alpha * alpha * jcp.ic_block
+                    * jcp.nb_tile_block_ur * jcp.tile_block_ur
+                    * simd_w * typesize;
+                add(reg_dst, tblk_off);
+                xor_(reg_tile_count, reg_tile_count);
+
+                L(next_tile);
+                add(reg_ti, 1);
+                cmp(reg_ti, jcp.itiles);
+                jl(loop_itiles);
+            }
+            xor_(reg_ti, reg_ti);
+            add(reg_tj, 1);
+            cmp(reg_tj, jcp.jtiles);
+            jl(loop_jtiles);
+        }
+    };
+
+    preamble();
+    init_G();
+    if (jcp.sched_policy == WSCHED_WEI_SDGtWo)
+        compute_transform_SDGtWo();
+    else
+        compute_transform();
+    postamble();
+}
+
+void jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_kernel::
+diff_dst_transform_generate(bool with_bias) {
+
+    constexpr int G_size = 8;
+    auto zmm_G = [](int i) {
+        return Xbyak::Zmm(31);
+    };
+
+    auto zmm_src = [=](int j, int i) {
+        return Xbyak::Zmm(G_size + j * 4 + i);
+    };
+
+    auto zmm_bias = Xbyak::Zmm(31);
+
+    auto load_src = [=]() {
+        if (with_bias) vmovups(zmm_bias, ptr[reg_bias]);
+        mov(reg_ydim, reg_tj);
+        shl(reg_ydim, 2); //tj * tile_size(=4)
+        for (int j = 0; j < tile_size; j++) {
+            /* check if tile index is within physical spatial boundaries*/
+            mov(reg_maskj, 0xffff);
+            cmp(reg_ydim, jcp.oh);
+            cmovge(reg_maskj, reg_zero);
+
+            /*address offset for tile in src*/
+            mov(reg_src_offset, reg_ydim);
+            imul(reg_src_offset, reg_src_offset, jcp.ow);
+
+            mov(reg_xdim, reg_ti);
+            shl(reg_xdim, 2); // xdim = ti * tile_size
+
+            add(reg_src_offset, reg_xdim);
+            imul(reg_src_offset, reg_src_offset, simd_w * typesize);
+            for (int i = 0; i < tile_size; i++) {
+                /* check if tile index is within physical spatial boundaries*/
+                mov(reg_maski, 0xffff);
+                cmp(reg_xdim, jcp.ow);
+                cmovge(reg_maski, reg_zero);
+                and_(reg_maski, reg_maskj);
+
+                Opmask kmask_src = Xbyak::Opmask(7);
+                kmovw(kmask_src, reg_maski_32);
+                vpxord(zmm_src(j, i), zmm_src(j, i), zmm_src(j, i));
+                vmovups(zmm_src(j, i) | kmask_src, ptr[reg_src + reg_src_offset]);
+                if (with_bias) vaddps(zmm_bias | kmask_src, zmm_bias,
+                        ptr[reg_src + reg_src_offset]);
+
+                add(reg_xdim, 1); //xdim = ti * tile_size + i
+                add(reg_src_offset, simd_w * typesize);
+            }
+            add(reg_ydim, 1);
+        }
+        if(with_bias) vmovups(ptr[reg_bias], zmm_bias);
+    };
+
+    auto zmm_t = [=](int i) {
+        return Xbyak::Zmm(G_size + 16 + i);
+    };
+
+    auto zmm_T = [=](int j, int i) {
+        return Xbyak::Zmm(j * 4 + i);
+    };
+
+    auto movps = [=](Xbyak::Reg64 reg_dst, size_t dst_off, Xbyak::Zmm a) {
+        if (jcp.sched_policy == WSCHED_WEI_SDGtWo)
+            vmovups(ptr[reg_dst + dst_off], a);
+        else
+            vmovntps(ptr[reg_dst + dst_off], a);
+    };
+
+    auto trans_W_3x3_4x4 = [=]() {
+        mov(reg_G, ptr[reg_transp + GET_OFF(G)]);
+        for (int i = 0; i < tile_size; i++) {
+            vbroadcastss(zmm_G(0), ptr[reg_G]);
+            vmulps(zmm_t(0), zmm_src(2, i), zmm_G(0));
+
+            vbroadcastss(zmm_G(1), ptr[reg_G + typesize]);
+            vmovups(zmm_t(1), zmm_t(0));
+            vfmsub231ps(zmm_t(1), zmm_src(0, i), zmm_G(1));
+
+            vbroadcastss(zmm_G(2), ptr[reg_G + 2 * typesize]);
+            vmovups(zmm_t(2), zmm_t(0));
+            vfmadd231ps(zmm_t(2), zmm_src(0, i), zmm_G(2));
+
+            vbroadcastss(zmm_G(3), ptr[reg_G + 3 * typesize]);
+            vmulps(zmm_t(3), zmm_src(1, i), zmm_G(3));
+
+            vbroadcastss(zmm_G(4), ptr[reg_G + 4 * typesize]);
+            vfmadd231ps(zmm_t(3), zmm_src(3, i), zmm_G(4));
+
+            vbroadcastss(zmm_G(5), ptr[reg_G + 5 * typesize]);
+            vmulps(zmm_t(4), zmm_src(1, i), zmm_G(5));
+
+            vbroadcastss(zmm_G(6), ptr[reg_G + 6 * typesize]);
+            vfmadd231ps(zmm_t(4), zmm_src(3, i), zmm_G(6));
+
+            vbroadcastss(zmm_G(7), ptr[reg_G + 7 * typesize]);
+            vmulps(zmm_T(0, i), zmm_src(0, i), zmm_G(7));
+            vsubps(zmm_T(1, i), zmm_t(1), zmm_t(3));
+            vaddps(zmm_T(2, i), zmm_t(1), zmm_t(3));
+            vaddps(zmm_T(3, i), zmm_t(2), zmm_t(4));
+            vsubps(zmm_T(4, i), zmm_t(2), zmm_t(4));
+            vmovups(zmm_T(5, i), zmm_src(3, i));
+        }
+
+        for (int j = 0; j < alpha; j++) {
+            vbroadcastss(zmm_G(0), ptr[reg_G]);
+            vmulps(zmm_t(0), zmm_T(j, 2), zmm_G(0));
+
+            vbroadcastss(zmm_G(1), ptr[reg_G + typesize]);
+            vmovups(zmm_t(1), zmm_t(0));
+            vfmsub231ps(zmm_t(1), zmm_T(j, 0), zmm_G(1));
+
+            vbroadcastss(zmm_G(2), ptr[reg_G + 2 * typesize]);
+            vmovups(zmm_t(2), zmm_t(0));
+            vfmadd231ps(zmm_t(2), zmm_T(j, 0), zmm_G(2));
+
+            vbroadcastss(zmm_G(3), ptr[reg_G + 3 * typesize]);
+            vmulps(zmm_t(3), zmm_T(j, 1), zmm_G(3));
+
+            vbroadcastss(zmm_G(4), ptr[reg_G + 4 * typesize]);
+            vfmadd231ps(zmm_t(3), zmm_T(j, 3), zmm_G(4));
+
+            vbroadcastss(zmm_G(5), ptr[reg_G + 5 * typesize]);
+            vmulps(zmm_t(4), zmm_T(j, 1), zmm_G(5));
+
+            vbroadcastss(zmm_G(6), ptr[reg_G + 6 * typesize]);
+            vfmadd231ps(zmm_t(4), zmm_T(j, 3), zmm_G(6));
+
+            vbroadcastss(zmm_G(7), ptr[reg_G + 7 * typesize]);
+            vmulps(zmm_t(0), zmm_T(j, 0), zmm_G(7));
+            vsubps(zmm_t(5), zmm_t(1), zmm_t(3));
+            vaddps(zmm_t(1), zmm_t(1), zmm_t(3));
+            vaddps(zmm_t(6), zmm_t(2), zmm_t(4));
+            vsubps(zmm_t(2), zmm_t(2), zmm_t(4));
+            vmovups(zmm_t(3), zmm_T(j, 3));
+
+            int alpha_offset = (jcp.oc / jcp.nb_oc)
+                * (jcp.ntiles / jcp.tile_block) * typesize;
+            int dst_off = j * alpha * alpha_offset;
+            movps(reg_dst, dst_off, zmm_t(0));
+            dst_off += alpha_offset;
+            movps(reg_dst, dst_off, zmm_t(5));
+            dst_off += alpha_offset;
+            movps(reg_dst, dst_off, zmm_t(1));
+            dst_off += alpha_offset;
+            movps(reg_dst, dst_off, zmm_t(6));
+            dst_off += alpha_offset;
+            movps(reg_dst, dst_off, zmm_t(2));
+            dst_off += alpha_offset;
+            movps(reg_dst, dst_off, zmm_t(3));
+        }
+
+    };
+    auto compute_transform_SDGtWo = [=]() {
+        mov(reg_src, ptr[reg_transp + GET_OFF(src)]);
+        mov(reg_dst, ptr[reg_transp + GET_OFF(dst)]);
+        if (with_bias) mov(reg_bias, ptr[reg_transp + GET_OFF(bias)]);
+
+        xor_(reg_zero, reg_zero);
+        xor_(reg_oc_ur, reg_oc_ur);
+        Label loop_mb, loop_jtiles, loop_itiles, loop_oc_ur, tiles_done;
+
+        L(loop_oc_ur);
+        {
+            mov(reg_ti, ptr[reg_transp + GET_OFF(ti)]);
+            mov(reg_tj, ptr[reg_transp + GET_OFF(tj)]);
+            xor_(reg_tile_count, reg_tile_count);
+            L(loop_mb);
+            {
+                L(loop_jtiles);
+                {
+                    L(loop_itiles);
+                    {
+                        load_src();
+
+                        trans_W_3x3_4x4();
+
+                        add(reg_tile_count, 1);
+                        cmp(reg_tile_count, jcp.nb_tile_block_ur * jcp.tile_block_ur);
+                        jge(tiles_done);
+
+                        add(reg_dst, jcp.oc_reg_block * simd_w * typesize);
+                        add(reg_ti, 1);
+                        cmp(reg_ti, jcp.itiles);
+                        jl(loop_itiles);
+                    }
+                    xor_(reg_ti, reg_ti);
+                    add(reg_tj, 1);
+                    cmp(reg_tj, jcp.jtiles);
+                    jl(loop_jtiles);
+                }
+                xor_(reg_tj, reg_tj);
+                add(reg_src, jcp.oc * jcp.ow * jcp.oh * typesize);
+                jmp(loop_mb);
+            }
+
+            L(tiles_done);
+            mov(reg_dst, ptr[reg_transp + GET_OFF(dst)]);
+            add(reg_dst, simd_w * typesize);
+            mov(reg_src, ptr[reg_transp + GET_OFF(src)]);
+            add(reg_src, jcp.oh * jcp.ow * simd_w * typesize);
+
+            if (with_bias) add(reg_bias, simd_w * typesize);
+            add(reg_oc_ur, 1);
+            cmp(reg_oc_ur, jcp.oc_reg_block);
+            jl(loop_oc_ur);
+        }
+    };
+
+    auto compute_transform = [=]() {
+        mov(reg_src, ptr[reg_transp + GET_OFF(src)]);
+        mov(reg_G, ptr[reg_transp + GET_OFF(G)]);
+        if (with_bias) mov(reg_bias, ptr[reg_transp + GET_OFF(bias)]);
+
+        mov(reg_dst, ptr[reg_transp + GET_OFF(dst)]);
+        mov(reg_tile_count, ptr[reg_transp + GET_OFF(tile_count)]);
+        imul(reg_temp, reg_tile_count, jcp.oc_reg_block * simd_w * typesize);
+        add(reg_dst, reg_temp);
+
+        xor_(reg_zero, reg_zero);
+        xor_(reg_oc_ur, reg_oc_ur);
+        Label loop_mb, loop_jtiles, loop_itiles, loop_oc_ur, next_tile_block, next_tile;
+
+        L(loop_oc_ur);
+        {
+            xor_(reg_ti, reg_ti);
+            xor_(reg_tj, reg_tj);
+
+            L(loop_jtiles);
+            {
+                L(loop_itiles);
+                {
+                    load_src();
+
+                    trans_W_3x3_4x4();
+
+                    add(reg_tile_count, 1);
+                    cmp(reg_tile_count, jcp.nb_tile_block_ur * jcp.tile_block_ur);
+                    jge(next_tile_block);
+                    add(reg_dst, jcp.oc_reg_block * simd_w * typesize);
+                    jmp(next_tile);
+
+                    L(next_tile_block);
+                    sub(reg_dst, (jcp.nb_tile_block_ur * jcp.tile_block_ur - 1)
+                            * jcp.oc_reg_block * simd_w * typesize);
+                    int tblk_off = alpha * alpha * (jcp.oc/jcp.nb_oc)
+                        * (jcp.ntiles/jcp.tile_block) * typesize;
+                    add(reg_dst, tblk_off);
+                    xor_(reg_tile_count, reg_tile_count);
+
+                    L(next_tile);
+                    add(reg_ti, 1);
+                    cmp(reg_ti, jcp.itiles);
+                    jl(loop_itiles);
+                }
+                xor_(reg_ti, reg_ti);
+                add(reg_tj, 1);
+                cmp(reg_tj, jcp.jtiles);
+                jl(loop_jtiles);
+            }
+
+            mov(reg_dst, ptr[reg_transp + GET_OFF(dst)]);
+            mov(reg_tile_count, ptr[reg_transp + GET_OFF(tile_count)]);
+            imul(reg_temp, reg_tile_count, jcp.oc_reg_block * simd_w * typesize);
+            add(reg_dst, reg_temp);
+            add(reg_dst, simd_w * typesize);
+            mov(reg_src, ptr[reg_transp + GET_OFF(src)]);
+            add(reg_src, jcp.oh * jcp.ow * simd_w * typesize);
+
+            if (with_bias) add(reg_bias, simd_w * typesize);
+            add(reg_oc_ur, 1);
+            cmp(reg_oc_ur, jcp.oc_reg_block);
+            jl(loop_oc_ur);
+        }
+    };
+
+    preamble();
+    if (jcp.sched_policy == WSCHED_WEI_SDGtWo) {
+        compute_transform_SDGtWo();
+    } else {
+        compute_transform();
+    }
+    postamble();
+}
+
+void jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_kernel::
+diff_weights_transform_generate(bool first_tile) {
+    int G_size = 4;
+
+    auto zmm_G = [](int i) {
+        return Xbyak::Zmm(i);
+    };
+
+    auto init_G = [=]() {
+        mov(reg_G, ptr[reg_transp + GET_OFF(G)]);
+        for (int i = 0; i  < G_size; i++)
+            vbroadcastss(zmm_G(i), ptr[reg_G + i * typesize]);
+    };
+
+    auto zmm_src = [=](int i) {
+        return Xbyak::Zmm(G_size + i);
+    };
+
+    auto load_src = [=](int i) {
+        for (int j = 0; j < alpha; j++) {
+            size_t alpha_offset = jcp.oc_block * jcp.oc_reg_block
+                * jcp.ic_block * simd_w * simd_w * typesize;
+            size_t src_off = (j * alpha + i) * alpha_offset;
+            vmovups(zmm_src(j), EVEX_compress_addr(reg_src, src_off));
+        }
+    };
+
+    auto zmm_t = [=](int i) {
+        return Xbyak::Zmm(G_size + 6 + i);
+    };
+
+    auto zmm_T = [=](int j, int i) {
+        return Xbyak::Zmm(G_size + 6 + 3 + j * 6 + i);
+    };
+
+    auto zmm_dst = [=](int i) {
+        return Xbyak::Zmm(G_size + i);
+    };
+
+    auto zmm_temp = Xbyak::Zmm(31);
+
+    auto store_dst = [=](int j) {
+        for (int i = 0; i < jcp.kw; i++) {
+            size_t dst_off = (j * jcp.kw + i) * simd_w * simd_w * typesize;
+
+            if (!first_tile) {
+                vmovups(zmm_temp, EVEX_compress_addr(reg_dst, dst_off));
+                vaddps(zmm_dst(i), zmm_dst(i), zmm_temp);
+            }
+            vmovntps(EVEX_compress_addr(reg_dst, dst_off), zmm_dst(i));
+        }
+    };
+
+    auto compute_transform = [=] () {
+        mov(reg_src, ptr[reg_transp + GET_OFF(src)]);
+        mov(reg_dst, ptr[reg_transp + GET_OFF(dst)]);
+
+        xor_(reg_ic_simd, reg_ic_simd);
+        Label loop_ic_simd;
+        L(loop_ic_simd);
+        {
+            for (int i = 0; i < alpha; i++) {
+                load_src(i);
+
+                vaddps(zmm_t(0), zmm_src(1), zmm_src(2));
+                vaddps(zmm_t(1), zmm_src(3), zmm_src(4));
+                vmovups(zmm_t(2), zmm_src(5));
+                vfmadd231ps(zmm_t(2), zmm_t(1), zmm_G(0));
+
+                vaddps(zmm_T(0, i), zmm_src(0), zmm_t(0));
+                vaddps(zmm_T(0, i), zmm_T(0, i), zmm_t(1));
+                vsubps(zmm_T(1, i), zmm_src(1), zmm_src(2));
+                vmulps(zmm_T(1, i), zmm_T(1, i), zmm_G(1));
+                vsubps(zmm_temp, zmm_src(3), zmm_src(4));
+                vfmadd231ps(zmm_T(1, i), zmm_temp, zmm_G(2));
+                vmovups(zmm_T(2, i), zmm_t(2));
+                vfmadd231ps(zmm_T(2, i), zmm_t(0), zmm_G(3));
+            }
+
+            for (int j = 0; j < jcp.kh; j++) {
+                vaddps(zmm_t(0), zmm_T(j, 1), zmm_T(j, 2));
+                vaddps(zmm_t(1), zmm_T(j, 3), zmm_T(j, 4));
+                vmovups(zmm_t(2), zmm_T(j, 5));
+                vfmadd231ps(zmm_t(2), zmm_t(1), zmm_G(0));
+
+                vaddps(zmm_dst(0), zmm_T(j, 0), zmm_t(0));
+                vaddps(zmm_dst(0), zmm_dst(0), zmm_t(1));
+                vsubps(zmm_dst(1), zmm_T(j, 1), zmm_T(j, 2));
+                vmulps(zmm_dst(1), zmm_dst(1), zmm_G(1));
+                vsubps(zmm_temp, zmm_T(j, 3), zmm_T(j, 4));
+                vfmadd231ps(zmm_dst(1), zmm_temp, zmm_G(2));
+                vmovups(zmm_dst(2), zmm_t(2));
+                vfmadd231ps(zmm_dst(2), zmm_t(0), zmm_G(3));
+
+                store_dst(j);
+            }
+
+            add(reg_src, jcp.oc_reg_block * simd_w * typesize);
+            add(reg_dst, simd_w * typesize);
+            add(reg_ic_simd, 1);
+            cmp(reg_ic_simd, simd_w);
+            jl(loop_ic_simd);
+        }
+    };
+    preamble();
+    push(reg_EVEX_max_8b_offt);
+    mov(reg_EVEX_max_8b_offt, 2 * EVEX_max_8b_offt);
+    init_G();
+    compute_transform();
+    pop(reg_EVEX_max_8b_offt);
+    postamble();
+}
+
+void jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_kernel::gemm_loop_generate(
+        bool is_first_tile)
+{
+    auto zmm_srcA = [=]() {
+        return Xbyak::Zmm(0);
+    };
+
+    auto zmm_srcB = [=] (size_t N_ur){
+        return Xbyak::Zmm(N_ur + 1);
+    };
+
+    auto broadcastB = [=](size_t K_ur) {
+        for (int N_bcast = 0; N_bcast < jcp.dimN_bcast_ur; N_bcast++) {
+            size_t srcB_off = (K_ur * jcp.dimN_reg_block + N_bcast)
+                * sizeof(float);
+            vbroadcastss(zmm_srcB(N_bcast), EVEX_compress_addr(reg_srcB, srcB_off));
+        }
+    };
+
+    auto load_srcA = [=] (size_t K_ur, int M_ur) {
+        size_t srcA_off = (K_ur * jcp.dimM_reg_block * jcp.dimM_simd_block
+                        + M_ur * jcp.dimM_simd_block) * sizeof(float);
+        vmovups(zmm_srcA(), EVEX_compress_addr(reg_srcA, srcA_off));
+    };
+
+    auto zmm_dstC = [=](size_t M_reg_ur, int N_bcast){
+        size_t idx = 1 // zmm_srcA
+            + jcp.dimN_bcast_ur // zmm_srcB
+            + M_reg_ur * jcp.dimN_bcast_ur + N_bcast;
+        assert(idx < 32);
+        return Xbyak::Zmm(idx);
+    };
+    auto prepare_accumm = [=](){
+        for (int M_reg_ur = 0; M_reg_ur < jcp.dimM_reg_block; M_reg_ur++) {
+            for (int N_bcast = 0; N_bcast < jcp.dimN_bcast_ur; N_bcast++) {
+                Zmm zmm = zmm_dstC(M_reg_ur, N_bcast);
+                vpxord(zmm, zmm, zmm);
+            }
+        }
+    };
+
+    auto store_dstC = [=](){
+        /******** Write C back to memory *******/
+        for (int M_reg = 0; M_reg < jcp.dimM_reg_block; M_reg++) {
+            for (int N_ur = 0; N_ur < jcp.dimN_bcast_ur; ++N_ur) {
+                Zmm zmm = zmm_dstC(M_reg, N_ur);
+                size_t C_off = (N_ur * jcp.dimM_reg_block * jcp.dimM_simd_block
+                             + M_reg * jcp.dimM_simd_block) * sizeof(float);
+                if (!is_first_tile) {
+                    vmovups(Xbyak::Zmm(0), EVEX_compress_addr(reg_dstC, C_off));
+                    vaddps(zmm, zmm, Xbyak::Zmm(0));
+                }
+                vmovups(EVEX_compress_addr(reg_dstC, C_off), zmm);
+            }
+        }
+    };
+
+    auto inner_loops = [=]() {
+        Label dimM_block_loop, dimK_block_loop, dimN_block_loop, dimN_bcast_ur;
+
+        mov(reg_dimM_block_loop_cnt, jcp.dimM_block);
+        L(dimM_block_loop);
+        { /************* OC_block (M) loop ***********/
+            mov(reg_dimN_block_loop_cnt, jcp.dimN_block);
+            L(dimN_block_loop);
+            { /*************** IC_block (N) loop *********/
+
+                mov(reg_nb_dimN_bcast_ur, jcp.dimN_reg_block/jcp.dimN_bcast_ur);
+                L(dimN_bcast_ur);
+                {
+                    prepare_accumm();
+
+                    mov(reg_dimK_block_loop_cnt, jcp.dimK_block);
+                    L(dimK_block_loop);
+                    {
+                     /************* nb_tile_ur(K) loop ********/
+                        for (int K_ur = 0; K_ur < jcp.dimK_reg_block; K_ur++) {
+
+                            broadcastB(K_ur);
+
+                            for (int M_reg_ur = 0; M_reg_ur < jcp.dimM_reg_block; M_reg_ur++) {
+                                load_srcA(K_ur, M_reg_ur);
+                                for (int N_bcast = 0; N_bcast < jcp.dimN_bcast_ur; ++N_bcast) {
+                                    vfmadd231ps(zmm_dstC(M_reg_ur, N_bcast), zmm_srcA(),
+                                            zmm_srcB(N_bcast));
+                                }
+                            }
+                        }
+                        add(reg_srcA, jcp.dimK_reg_block
+                                      * jcp.dimM_reg_block * jcp.dimM_simd_block
+                                      * sizeof(float));
+                        add(reg_srcB, jcp.dimK_reg_block
+                                      * jcp.dimN_reg_block
+                                      * sizeof(float));
+                        sub(reg_dimK_block_loop_cnt, 1);
+                        jnz(dimK_block_loop);
+                    }
+
+                    store_dstC();
+
+                    sub(reg_srcA, jcp.dimK_block * jcp.dimK_reg_block
+                                  * jcp.dimM_reg_block * jcp.dimM_simd_block
+                                  * sizeof(float));
+                    sub(reg_srcB, jcp.dimK_block * jcp.dimK_reg_block
+                                  * jcp.dimN_reg_block
+                                  * sizeof(float));
+                    add(reg_srcB, jcp.dimN_bcast_ur * sizeof(float));
+                    add(reg_dstC, jcp.dimN_bcast_ur
+                            * jcp.dimM_reg_block * jcp.dimM_simd_block
+                            * sizeof(float));
+                    sub(reg_nb_dimN_bcast_ur, 1);
+                    jnz(dimN_bcast_ur);
+                }
+
+                sub(reg_srcB, jcp.dimN_reg_block * sizeof(float));
+                add(reg_srcB, jcp.dimK_block
+                        * jcp.dimK_reg_block
+                        * jcp.dimN_reg_block * sizeof(float));
+                sub(reg_dimN_block_loop_cnt, 1);
+                jnz(dimN_block_loop);
+            }
+
+            sub(reg_srcB, jcp.dimN_block
+                          * jcp.dimK_block * jcp.dimK_reg_block
+                          * jcp.dimN_reg_block
+                          * sizeof(float));
+            add(reg_srcA, jcp.dimK_block * jcp.dimK_reg_block
+                          * jcp.dimM_reg_block * jcp.dimM_simd_block
+                          * sizeof(float));
+            sub(reg_dimM_block_loop_cnt, 1);
+            jnz(dimM_block_loop);
+        }
+    };
+
+    /* Preamble */
+    preamble();
+
+    inner_loops();
+
+    /* Postamble */
+    postamble();
+    ret();
+}
+
+namespace {
+
+void set_jcp_WEI_params(jit_conv_winograd_conf_t &jcp) {
+/*M params*/
+    jcp.dimM_nb_block = jcp.dimM / jcp.dimM_block / jcp.dimM_reg_block
+        / jcp.dimM_simd_block;
+    jcp.oc_reg_block = jcp.dimM_reg_block;
+    jcp.oc_block = jcp.dimM_block;
+    jcp.nb_oc = jcp.dimM_nb_block;
+    /*N params*/
+    jcp.dimN_nb_block = jcp.dimN / jcp.dimN_block / jcp.dimN_reg_block;
+    jcp.ic_block = jcp.dimN_block;
+    jcp.nb_ic = jcp.dimN_nb_block;
+
+    /*K params*/
+    jcp.dimK_nb_block = jcp.dimK / jcp.dimK_block / jcp.dimK_reg_block;
+    jcp.tile_block_ur = jcp.dimK_reg_block;
+    jcp.nb_tile_block_ur = jcp.dimK_block;
+    jcp.tile_block = jcp.dimK_nb_block;
+}
+
+status_t set_wsched_WEI_SDGtWo(jit_conv_winograd_conf_t &jcp) {
+
+    size_t K_blk_ur, N_blk, M_blk;
+    /* IS this strategy feasible? */
+    auto test_MV_large_enough = [](jit_conv_winograd_conf_t &jcp) {
+        size_t M_sz = alpha * alpha * jcp.dimM * jcp.dimK * sizeof(float);
+        size_t V_sz = alpha * alpha * jcp.dimN * jcp.dimK * sizeof(float);
+        size_t nthreads = mkldnn_get_max_threads();
+        return (((V_sz + M_sz) / nthreads) >= 2 * L2_cache_size)
+            && (jcp.dimK / nthreads >= 1.0);
+    };
+
+    auto test_min_dimK_L1 = [](jit_conv_winograd_conf_t &jcp, int dimK_block_ur,
+            int max_block=1) {
+        size_t L1_block_M  = jcp.dimM_reg_block * jcp.dimM_simd_block * dimK_block_ur * sizeof(float);
+        size_t L1_block_N = jcp.dimN_reg_block * dimK_block_ur * sizeof(float);
+        size_t M_L2_block = alpha * alpha * jcp.dimM * dimK_block_ur * sizeof(float);
+        size_t nthreads = mkldnn_get_max_threads();
+        bool load_balance=true;
+        if (!(jcp.dimK % nthreads)) {
+            load_balance = ((jcp.dimK / dimK_block_ur) % nthreads == 0);
+        }
+        return (L1_block_M + L1_block_N >= 0.1 * L1_cache_size)
+            && (L1_block_M + L1_block_N <= 0.5 * L1_cache_size)
+            && load_balance
+            && (M_L2_block < L2_cache_size);
+    };
+
+    auto test_dimK_ur = [](jit_conv_winograd_conf_t &jcp, int dimK_ur,
+            int useless_arg=0) {
+        return (dimK_ur >= 2) && (dimK_ur <= 8);
+    };
+
+    auto blocking_ok =  [&](){
+        size_t M_L2_block = alpha * alpha * M_blk * jcp.dimM_reg_block * jcp.dimM_simd_block
+                          * K_blk_ur * sizeof(float);
+        size_t V_L2_block = alpha * alpha * N_blk * jcp.dimN_reg_block
+                          * K_blk_ur * sizeof(float);
+        size_t U_L2_block = alpha * alpha * M_blk * jcp.dimM_reg_block * jcp.dimM_simd_block
+                          * N_blk * jcp.dimN_reg_block * sizeof(float);
+        size_t L2_block = M_L2_block + V_L2_block + U_L2_block;
+        /*Replace 2.375 with L2+L3 cache size*/
+        return (L2_block > 0.1 * L2_cache_size) && (L2_block <= 1.2 * L2_cache_size);
+    };
+
+    if (test_MV_large_enough(jcp)) {
+        if ((jcp.dimM/jcp.dimM_simd_block) % 2 == 0) {
+            jcp.dimM_reg_block = 2;
+        } else {
+            jcp.dimM_reg_block = 1;
+        }
+        jcp.dimM_simd_block = jcp.oc_simd_block;
+        jcp.dimN_reg_block = jcp.ic_simd_block;
+        jcp.dimN_bcast_ur = 8;
+        /*dimK_block and dimK_ur*/
+        size_t min_dimK_block_ur = get_divisor_satisfying_cond(jcp, jcp.dimK, 1, test_min_dimK_L1);
+
+        jcp.dimM_block = jcp.dimM/jcp.dimM_reg_block/jcp.dimM_simd_block;
+        jcp.dimN_block = jcp.dimN/jcp.dimN_reg_block;
+        for (K_blk_ur = min_dimK_block_ur; K_blk_ur >= 1; --K_blk_ur) {
+            if (test_min_dimK_L1(jcp, K_blk_ur) && !(jcp.dimK % K_blk_ur)) {
+                for (N_blk = jcp.dimN_block; N_blk >= 1; --N_blk) {
+                    if (!(jcp.dimN_block % N_blk)) {
+                        for (M_blk = jcp.dimM_block; M_blk >= 1; --M_blk) {
+                            if (!(jcp.dimM_block % M_blk) && blocking_ok()) {
+                                jcp.dimK_reg_block = get_divisor_satisfying_cond(jcp, K_blk_ur, 1, test_dimK_ur);
+                                if (!test_dimK_ur(jcp, jcp.dimK_reg_block)) return status::unimplemented;
+                                jcp.dimK_block = K_blk_ur / jcp.dimK_reg_block;
+                                jcp.dimN_block = N_blk;
+                                jcp.dimM_block = M_blk;
+                                jcp.sched_policy = WSCHED_WEI_SDGtWo;
+                                set_jcp_WEI_params(jcp);
+                                jcp.nthr = nstl::min(mkldnn_get_max_threads(),
+                                        jcp.tile_block);
+                                return status::success;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+    return status::unimplemented;
+}
+
+status_t set_wsched_WEI_S_D_Giot_W(jit_conv_winograd_conf_t &jcp) {
+    if ((jcp.dimM/jcp.dimM_simd_block) % 2 == 0) {
+        jcp.dimM_reg_block = 2;
+    } else {
+        jcp.dimM_reg_block = 1;
+    }
+    jcp.dimN_bcast_ur = 8;
+    jcp.dimN_reg_block = jcp.ic_simd_block;
+    jcp.dimM_simd_block = jcp.oc_simd_block;
+    jcp.dimN_block = jcp.dimN / jcp.dimN_reg_block;
+    jcp.dimM_block = jcp.dimM / jcp.dimM_reg_block / jcp.dimM_simd_block;
+    float C1 = 0.0, C2 = 0.0;
+    float C1_max = 0.5, C2_max = 1.4;
+    int N_blk, M_blk, K_blk_ur;
+
+    auto test_dimK_ur = [](jit_conv_winograd_conf_t &jcp, int dimK_ur,
+            int useless_arg=0) {
+        return (dimK_ur >= 2) && (dimK_ur <= 8);
+    };
+
+    auto blocking_ok = [&]() -> bool {
+        size_t L1_block_M  = jcp.dimM_reg_block * jcp.dimM_simd_block * K_blk_ur * sizeof(float);
+        size_t L1_block_N = jcp.dimN_reg_block * K_blk_ur * sizeof(float);
+        bool L1_cond = ((L1_block_N + L1_block_M) >= C1 * L1_cache_size)
+                     && ((L1_block_N + L1_block_M) <= C1_max * L1_cache_size);
+
+        size_t nb_N_blk = jcp.dimN/N_blk/jcp.dimN_reg_block;
+        size_t nb_M_blk = jcp.dimM/M_blk/jcp.dimM_reg_block/jcp.dimM_simd_block;
+        size_t nb_K_blk = jcp.dimK / K_blk_ur;
+        size_t nthreads = mkldnn_get_max_threads();
+        bool load_balance = (nb_K_blk * nb_N_blk * nb_M_blk) >= nthreads;
+        if (!(nb_K_blk % nthreads)) {
+            load_balance = load_balance && (nb_K_blk % nthreads == 0);
+        }
+
+        size_t V_L2_block = alpha * alpha * N_blk * jcp.dimN_reg_block * K_blk_ur * sizeof(float);
+
+        size_t L2_block = V_L2_block;
+        /*Replace 2.375 with L2+L3 cache size*/
+        bool L2_cond = (L2_block >= C2 * L2_cache_size) && (L2_block <= C2_max * L2_cache_size);
+        return L1_cond && load_balance && L2_cond;
+    };
+
+    for (K_blk_ur = jcp.dimK; K_blk_ur >= 1; --K_blk_ur) {
+        if (jcp.dimK % K_blk_ur == 0) {
+            for (N_blk = jcp.dimN_block; N_blk >= 1; --N_blk) {
+                if (jcp.dimN_block % N_blk == 0) {
+                    for (M_blk = jcp.dimM_block; M_blk >= 1; --M_blk) {
+                        if (jcp.dimM_block % M_blk == 0) {
+                            if (blocking_ok()) {
+                                jcp.dimN_block = N_blk;
+                                jcp.dimM_block = M_blk;
+                                jcp.dimK_reg_block = get_divisor_satisfying_cond(jcp, K_blk_ur, 1, test_dimK_ur);
+                                jcp.dimK_block = K_blk_ur / jcp.dimK_reg_block;
+                                jcp.sched_policy = WSCHED_WEI_S_D_Giot_W;
+                                set_jcp_WEI_params(jcp);
+                                return status::success;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+    jcp.dimK_reg_block = 1;
+    jcp.dimK_block = 1;
+    jcp.sched_policy = WSCHED_WEI_S_D_Giot_W;
+    set_jcp_WEI_params(jcp);
+    return status::success;
+}
+} // namespace
+status_t jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_kernel::init_conf(
+        jit_conv_winograd_conf_t &jcp, const convolution_desc_t &cd,
+        const memory_desc_wrapper &src_d, const memory_desc_wrapper &diff_dst_d,
+        const memory_desc_wrapper &diff_weights_d) {
+    if (!mayiuse(avx512_core))
+        return status::unimplemented;
+    else
+        jcp.ver = ver_avx512_core;
+
+    jcp.nthr = mkldnn_get_max_threads();
+
+    jcp.prop_kind = cd.prop_kind;
+    const bool with_groups = diff_weights_d.ndims() == src_d.ndims() + 1;
+    jcp.mb = src_d.dims()[0];
+    jcp.ngroups = with_groups ? diff_weights_d.dims()[0] : 1;
+    jcp.oc = diff_dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+    jcp.ih = src_d.dims()[2];
+    jcp.iw = src_d.dims()[3];
+    jcp.oh = diff_dst_d.dims()[2];
+    jcp.ow = diff_dst_d.dims()[3];
+    jcp.kh = diff_weights_d.dims()[with_groups + 2];
+    jcp.kw = diff_weights_d.dims()[with_groups + 3];
+    jcp.t_pad = cd.padding[0][0];
+    jcp.l_pad = cd.padding[0][1];
+    jcp.stride_h = cd.strides[0];
+    jcp.stride_w = cd.strides[1];
+    jcp.r_pad = nstl::max(
+            0, (jcp.ow - 1) * jcp.stride_w + jcp.kw - jcp.iw - jcp.l_pad);
+    jcp.b_pad = nstl::max(
+            0, (jcp.oh - 1) * jcp.stride_h + jcp.kh - jcp.ih - jcp.t_pad);
+    jcp.ihp = jcp.ih + jcp.t_pad + jcp.b_pad;
+    jcp.iwp = jcp.iw + jcp.l_pad + jcp.r_pad;
+    jcp.ohp = jcp.oh;
+    jcp.owp = jcp.ow;
+    jcp.with_bias = (cd.diff_bias_desc.format_kind != format_kind::undef);
+    jcp.dilate_h = cd.dilates[0];
+    jcp.dilate_w = cd.dilates[1];
+
+    bool ok_to_pad_channels = jcp.ngroups == 1;
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, simd_w);
+        jcp.ic = rnd_up(jcp.ic, simd_w);
+    }
+
+    // Winograd specific initialization
+    jcp.itiles = (jcp.ow + tile_size - 1) / tile_size;
+    jcp.jtiles = (jcp.oh + tile_size - 1) / tile_size;
+    jcp.ntiles = jcp.mb * jcp.itiles * jcp.jtiles;
+
+    // Winograd kernel works only for 3x3 convolution with stride 1
+    if (!IMPLICATION(cd.alg_kind == alg_kind::convolution_auto,
+               is_winograd_faster_than_direct(jcp)))
+        return status::unimplemented;
+
+    if (jcp.ngroups != 1)
+        return status::unimplemented;
+    if ((jcp.kh != 3) || (jcp.kw != 3))
+        return status::unimplemented;
+    if ((jcp.dilate_h != 0) || (jcp.dilate_w != 0))
+        return status::unimplemented;
+    if ((jcp.stride_h != 1) || (jcp.stride_w != 1))
+        return status::unimplemented;
+    if ((jcp.ic % simd_w) != 0 || (jcp.oc % simd_w) != 0)
+        return status::unimplemented;
+
+    format_tag_t dat_tag = nChw16c;
+    format_tag_t wei_tag = with_groups ? gOIhw16i16o : OIhw16i16o;
+    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    jcp.wei_tag = diff_weights_d.matches_one_of_tag(wei_tag);
+    jcp.dst_tag = diff_dst_d.matches_one_of_tag(dat_tag);
+
+    if (jcp.src_tag != dat_tag) return status::unimplemented;
+    if (jcp.wei_tag != wei_tag) return status::unimplemented;
+    if (jcp.dst_tag != dat_tag) return status::unimplemented;
+
+    bool layout_consistency = true
+        && jcp.ic <= src_d.padded_dims()[1]
+        && jcp.oc <= diff_dst_d.padded_dims()[1]
+        && jcp.ic <= diff_weights_d.padded_dims()[with_groups + 1]
+        && jcp.oc <= diff_weights_d.padded_dims()[with_groups + 0];
+    if (!layout_consistency) return status::unimplemented;
+
+    /******************Kernel blocking Parameters ***********/
+    jcp.ic_simd_block = simd_w;
+    jcp.oc_simd_block = simd_w;
+
+    jcp.dimK = jcp.ntiles;
+    jcp.dimN = jcp.ic;
+    jcp.dimM = jcp.oc;
+    jcp.dimM_simd_block = jcp.oc_simd_block;
+    jcp.dimN_reg_block = jcp.ic_simd_block;
+    jcp.sched_policy = WSCHED_INVALID;
+    status_t res = set_wsched_WEI_SDGtWo(jcp);
+    if (res == status::unimplemented) {
+        res = set_wsched_WEI_S_D_Giot_W(jcp);
+        assert(res == status::success);
+    }
+    return res;
+}
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3_kernel.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3_kernel.hpp
new file mode 100644
index 0000000000..025a554d92
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_fp32_wino_conv_4x3_kernel.hpp
@@ -0,0 +1,291 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_AVX512_CORE_FP32_WINO_CONV_4x3_KERNEL_HPP
+#define JIT_AVX512_CORE_FP32_WINO_CONV_4x3_KERNEL_HPP
+
+#include "c_types_map.hpp"
+
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+
+#include "jit_avx512_common_conv_winograd_kernel_f32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct _jit_avx512_core_fp32_wino_conv_4x3_data_kernel
+    : public jit_generator {
+    _jit_avx512_core_fp32_wino_conv_4x3_data_kernel(
+            jit_conv_winograd_conf_t ajcp)
+        : jcp(ajcp) {
+        {
+            this->weights_transform_data_ker_generate();
+            weights_transform_data_ker
+                    = (decltype(weights_transform_data_ker)) this->getCode();
+        }
+        {
+            align();
+            const Xbyak::uint8 *addr = getCurr();
+            this->input_transform_data_ker_generate();
+            input_transform_data_ker = (decltype(input_transform_data_ker))addr;
+        }
+        {
+            align();
+            const Xbyak::uint8 *addr = getCurr();
+            this->output_transform_data_ker_generate();
+            output_transform_data_ker
+                = (decltype(output_transform_data_ker))addr;
+        }
+        {
+            align();
+            const Xbyak::uint8 *addr = getCurr();
+            this->gemm_loop_generate();
+            gemm_loop_ker = (decltype(gemm_loop_ker))addr;
+        }
+    }
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(_jit_avx512_core_fp32_wino_conv_4x3_data_kernel)
+
+    static status_t init_conf_common(jit_conv_winograd_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &dst_d);
+
+    static status_t init_conf_kernel(
+            jit_conv_winograd_conf_t &jcp, int dimM, int dimN, int dimK);
+
+    jit_conv_winograd_conf_t jcp;
+    void (*gemm_loop_ker)(float *, const float *, const float *, const int);
+    void (*input_transform_data_ker)(jit_wino_transform_call_s *);
+    void (*output_transform_data_ker)(jit_wino_transform_call_s *);
+    void (*weights_transform_data_ker)(jit_wino_transform_call_s *);
+
+protected:
+    using reg64_t = const Xbyak::Reg64;
+    using reg32_t = const Xbyak::Reg32;
+    enum { typesize = sizeof(float) };
+
+    void gemm_loop_generate();
+    void input_transform_data_ker_generate();
+    void output_transform_data_ker_generate();
+    void weights_transform_data_ker_generate();
+
+    /* registers used for GEMM */
+    reg64_t reg_dstC = abi_param1;
+    reg64_t reg_srcA = abi_param2;
+    reg64_t reg_srcB = abi_param3;
+    reg64_t reg_is_beta_zero = abi_param4;
+
+    reg64_t reg_dimM_block_loop_cnt = r10;
+    reg64_t reg_dimK_block_loop_cnt = r11;
+
+    /* registers used for transforms*/
+    reg64_t param = abi_param1;
+
+    /* registers used for output_transform_data_ker */
+    reg64_t oreg_temp = abi_not_param1;
+    reg64_t oreg_Ow = r9;
+    reg64_t oreg_src = r11;
+    reg64_t oreg_tile_block = r12;
+    reg64_t oreg_tile_block_ur = r13;
+    reg64_t oreg_nb_tile_block_ur = r14;
+    reg64_t oreg_O = r8;
+    reg64_t oreg_T = r10;
+    reg64_t oreg_dst = r11;
+    reg64_t oreg_ydim = r14;
+    reg64_t oreg_xdim = r15;
+    reg64_t oreg_out_j = r12;
+    reg64_t oreg_bias = rbx;
+    reg64_t imm_addr64 = rax;
+
+    /* registers used for input_transform_data_ker */
+    reg64_t ireg_temp = abi_not_param1;
+    reg64_t ireg_jtiles = rax;
+    reg64_t ireg_itiles = rbx;
+    reg64_t ireg_I = r8;
+    reg64_t ireg_src = r13;
+    reg64_t ireg_ydim = r14;
+    reg64_t ireg_xdim = r15;
+    reg64_t ireg_inp_j = r12;
+    reg64_t ireg_inp_i = rdx;
+    reg64_t ireg_mask_j = r11;
+    reg64_t ireg_mask = rsi;
+    reg32_t ireg_mask_32 = esi;
+    reg64_t ireg_zero = r9;
+    reg64_t ireg_Iw = r9;
+    reg64_t ireg_T = r10;
+    reg64_t ireg_tile_block = r12;
+    reg64_t ireg_tile_block_ur = r13;
+    reg64_t ireg_nb_tile_block_ur = r14;
+    reg64_t ireg_output = r15;
+
+    /* registers used for wei transform */
+    reg64_t wreg_temp = abi_not_param1;
+    reg64_t wreg_F = r8;
+    reg64_t wreg_src = r9;
+    reg64_t wreg_MT = r15;
+    reg64_t wreg_M = r14;
+    reg64_t wreg_dst = r10;
+    reg64_t wreg_dst_aux = r9;
+    reg64_t wreg_dst_idx = r8;
+    reg64_t wreg_Fw = r11;
+    reg64_t wreg_T = r12;
+    reg64_t wreg_cnt_j = rdx;
+    reg64_t wreg_F_aux = r14;
+    reg64_t wreg_Fw_aux = r15;
+};
+
+struct jit_avx512_core_fp32_wino_conv_4x3_fwd_kernel
+        : _jit_avx512_core_fp32_wino_conv_4x3_data_kernel {
+    using _jit_avx512_core_fp32_wino_conv_4x3_data_kernel::
+            _jit_avx512_core_fp32_wino_conv_4x3_data_kernel;
+
+    static bool post_ops_ok(jit_conv_conf_t &jcp, const primitive_attr_t &attr);
+
+    static status_t init_conf(jit_conv_winograd_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_t &src_md,
+            memory_desc_t &weights_md, const memory_desc_t &dst_md,
+            const primitive_attr_t &attr);
+};
+
+struct jit_avx512_core_fp32_wino_conv_4x3_bwd_data_kernel
+        : public _jit_avx512_core_fp32_wino_conv_4x3_data_kernel {
+    using _jit_avx512_core_fp32_wino_conv_4x3_data_kernel::
+            _jit_avx512_core_fp32_wino_conv_4x3_data_kernel;
+
+    static status_t init_conf(jit_conv_winograd_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &diff_src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &diff_dst_d);
+};
+
+struct jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_kernel
+        : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(
+        _jit_avx512_core_conv_winograd_bwd_weights_kernel_f32)
+
+    jit_avx512_core_fp32_wino_conv_4x3_bwd_weights_kernel(
+            jit_conv_winograd_conf_t ajcp)
+        : jcp(ajcp)
+    {
+        //******************* First iter kernel ********************//
+        this->gemm_loop_generate(true);
+        gemm_loop_ker_first_iter = (decltype(gemm_loop_ker_first_iter))this->getCode();
+
+        align();
+        const Xbyak::uint8 *addr = getCurr();
+        this->src_transform_generate();
+        src_transform = (decltype(src_transform))addr;
+
+        if (jcp.with_bias) {
+            align();
+            addr = getCurr();
+            this->diff_dst_transform_generate(true);
+            diff_dst_transform_wbias = (decltype(diff_dst_transform_wbias))addr;
+        }
+
+        align();
+        addr = getCurr();
+        this->diff_dst_transform_generate(false);
+        diff_dst_transform = (decltype(diff_dst_transform))addr;
+
+        if (jcp.sched_policy != WSCHED_WEI_SDGtWo && jcp.tile_block > 1) {
+            align();
+            addr = getCurr();
+            this->gemm_loop_generate(false);
+            gemm_loop_ker = (decltype(gemm_loop_ker))addr;
+        }
+
+        align();
+        addr = getCurr();
+        this->diff_weights_transform_generate(true);
+        diff_weights_transform = (decltype(diff_weights_transform))addr;
+
+        if (jcp.sched_policy == WSCHED_WEI_SDGtWo) {
+            align();
+            addr = getCurr();
+            this->diff_weights_transform_generate(false);
+            diff_weights_transform_accum =
+                (decltype(diff_weights_transform_accum))addr;
+        };
+    }
+
+    static status_t init_conf(jit_conv_winograd_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &diff_dst_d,
+            const memory_desc_wrapper &diff_weights_d);
+
+    jit_conv_winograd_conf_t jcp;
+    void (*gemm_loop_ker)(float *, const float *, const float *);
+    void (*gemm_loop_ker_first_iter)(float *, const float *, const float *);
+    void (*src_transform)(jit_wino_transform_call_s *);
+    void (*diff_dst_transform)(jit_wino_transform_call_s *);
+    void (*diff_dst_transform_wbias)(jit_wino_transform_call_s *);
+    void (*diff_weights_transform)(jit_wino_transform_call_s *);
+    void (*diff_weights_transform_accum)(jit_wino_transform_call_s *);
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    using reg32_t = const Xbyak::Reg32;
+    enum { typesize = sizeof(float) };
+
+    void src_transform_generate();
+    void diff_dst_transform_generate(bool with_bias);
+    void diff_weights_transform_generate(bool first_tile);
+
+    /*registers common to transforms*/
+    reg64_t reg_transp = abi_param1;
+    reg64_t reg_ti = rbx;
+    reg64_t reg_tj = abi_not_param1;
+    reg64_t reg_src = r8;
+    reg64_t reg_dst = r9;
+    reg64_t reg_G = rsi; /*TODO: check if this is ok*/
+    reg64_t reg_temp = rsi;
+
+    /*registers common to src/diff_dst transform*/
+    reg64_t reg_I = r10;
+    reg64_t reg_ydim = r11;
+    reg64_t reg_xdim = r12;
+    reg64_t reg_src_offset = r13;
+    reg64_t reg_zero = r14;
+    reg64_t reg_tile_count = r15;
+    reg64_t reg_maski = rsi;
+    reg32_t reg_maski_32 = esi;
+    reg64_t reg_maskj = rdx;
+
+    reg64_t reg_T = rax;
+    reg64_t reg_oc_ur = rax;
+    reg64_t reg_ic_simd = r14;
+    reg64_t reg_bias = r10;
+
+    void gemm_loop_generate(bool is_first_tile);
+
+    reg64_t reg_dstC = abi_param1;
+    reg64_t reg_srcA = abi_param2;
+    reg64_t reg_srcB = abi_param3;
+
+    reg64_t reg_dimM_block_loop_cnt = r9;
+    reg64_t reg_dimN_block_loop_cnt = r10;
+    reg64_t reg_nb_dimN_bcast_ur = r11;
+    reg64_t reg_dimK_block_loop_cnt = r12;
+};
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_u8s8s32x_wino_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_u8s8s32x_wino_convolution.cpp
new file mode 100644
index 0000000000..002010ffa2
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_u8s8s32x_wino_convolution.cpp
@@ -0,0 +1,1284 @@
+/*******************************************************************************
+ * Copyright 2018 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *******************************************************************************/
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_avx512_core_u8s8s32x_wino_convolution.hpp"
+#include "jit_generator.hpp"
+
+#include <string.h>
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+using namespace Xbyak;
+
+namespace {
+    // Below scales are applied to source and weights data accordingly
+    // because this winograd implementation
+    // transforms source which may increase values up to 4x
+    // and transforms weights which may increase values up to 9/4x
+    const float adj_src_scale = 1.f / 4.f;
+    const float adj_wei_scale = 4.f / 9.f;
+    // Winograd transforms need ic and oc to be multiples of 16
+    const int load_block = 16;
+}
+
+/// SRC TRANSFORMS /////////////////////////////////////////////////////////////
+struct jit_avx512_core_u8s8s32x_wino_conv_src_trans_t: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(
+            jit_avx512_core_u8s8s32x_wino_conv_src_trans_t)
+
+    jit_conv_conf_2x3_wino_t jcp;
+    const primitive_attr_t &attr_;
+
+    struct call_params_t {
+        const void *src;
+        const void *wino_src;
+        const void *v_y_masks;
+        const void *v_x_masks;
+    };
+    void (*ker_)(const call_params_t *);
+
+    jit_avx512_core_u8s8s32x_wino_conv_src_trans_t(
+        jit_conv_conf_2x3_wino_t ajcp, const primitive_attr_t &attr)
+        : jcp(ajcp), attr_(attr), unsign_val_in_wino_domain(5) {
+        generate();
+        ker_ = reinterpret_cast<decltype(ker_)>(const_cast<uint8_t*>(getCode()));
+    }
+    void generate();
+
+    int reg_inp_ind(int i) {
+        assert(i < jcp.alpha * jcp.alpha);
+        return (31 - i);
+    }
+
+    Xmm vreg_inp(int i) {
+        return Xmm(reg_inp_ind(i));
+    }
+
+    Zmm zmm_inp(int i) {
+        return Zmm(reg_inp_ind(i));
+    }
+
+    Xmm vreg_tmp(int i) {
+        assert(i < jcp.alpha * jcp.alpha);
+        return Xmm(15 - i);
+    }
+    Xmm vreg_out(int i) {
+        assert(i < jcp.alpha * jcp.alpha);
+        return Xmm(31 - i);
+    }
+
+    Opmask y_mask = Opmask(1);
+    Opmask r_mask = Opmask(2);
+    Opmask x_mask(int id) {
+        assert(id < 4);
+        return Opmask(3 + id);
+    }
+
+    Reg64 reg_ptr_src = r14;
+    Reg64 reg_ptr_dst = r13;
+
+    Reg64 reg_ptr_v_y_masks = r12;
+    Reg64 reg_ptr_v_x_masks = r11;
+
+    Reg64 reg_aux_ptr_src = r10;
+    Reg64 reg_aux_ptr_dst = r9;
+
+    Reg64 reg_ic_block = r8;
+
+    int unsign_val_in_wino_domain;
+
+    Reg64 reg_scratch_src_alpha = rdx;
+    Xmm xmm_src_alpha = Xmm(0);
+    Zmm zmm_src_alpha = Zmm(0);
+
+    Reg64 reg_shift = rax;
+    Xmm xmm_shift = Xmm(1);
+    Xmm xmm_zero = Xmm(0);
+
+    Reg64 reg_maskx = rbx;
+    Reg64 reg_masky = rsi;
+    Reg64 reg_nomask = reg_maskx;
+};
+
+void jit_avx512_core_u8s8s32x_wino_conv_src_trans_t::generate() {
+    Label ic_block_label;
+    Label end_label;
+    Label mask_label;
+    Label nomask_label;
+
+    auto load_src = [=](bool mask) {
+        for (int y = 0; y < jcp.alpha; y++) {
+            if (mask)
+                kmovw(y_mask, ptr[reg_ptr_v_y_masks + sizeof(uint16_t) * y]);
+            for (int x = 0; x < jcp.alpha; x++) {
+                Zmm zmm_i = zmm_inp(y * jcp.alpha + x);
+                Xmm vreg_i = vreg_inp(y * jcp.alpha + x);
+                int inp_offset = sizeof(uint8_t)
+                        * ((-jcp.t_pad + y) * jcp.iw * jcp.ic
+                                + (-jcp.l_pad + x) * jcp.ic);
+                if (mask) {
+                    kandw(r_mask, y_mask, x_mask(x));
+                    vmovdqu8(vreg_i | r_mask | T_z,
+                            EVEX_compress_addr(reg_aux_ptr_src, inp_offset));
+                } else {
+                    vmovdqu8(vreg_i,
+                            EVEX_compress_addr(reg_aux_ptr_src, inp_offset));
+                }
+                vpmovzxbd(zmm_i, vreg_i); // to int32
+                vcvtdq2ps(zmm_i, zmm_i); // to fp32
+                vmulps(zmm_i, zmm_i, zmm_src_alpha); // *alpha
+                vcvtps2dq(zmm_i, zmm_i); // to int32
+                vpmovusdb(vreg_i, zmm_i); // to u8
+            }
+        }
+    };
+
+    preamble();
+
+#   define READ_PARAM(reg, field) \
+        mov(reg, ptr[abi_param1 + offsetof(call_params_t, field)])
+    READ_PARAM(reg_ptr_src, src);
+    READ_PARAM(reg_ptr_dst, wino_src);
+    READ_PARAM(reg_ptr_v_y_masks, v_y_masks);
+    READ_PARAM(reg_ptr_v_x_masks, v_x_masks);
+#   undef READ_PARAM
+
+    mov(reg_maskx, ptr[reg_ptr_v_x_masks]);
+    mov(reg_masky, ptr[reg_ptr_v_y_masks]);
+    test(reg_maskx, reg_maskx);
+    jz(end_label, T_NEAR); // skip kernel if x mask is all 0's
+    test(reg_masky, reg_masky);
+    jz(end_label, T_NEAR); // skip kernel if y mask is all 0's
+    and_(reg_maskx, reg_masky);
+    mov(reg_nomask, reg_maskx);
+    not_(reg_nomask); // zero if x and y masks are all 1's
+
+    xor_(reg_shift, reg_shift);
+    mov(reg_shift.cvt8(), (int8_t)-128);
+
+    mov(reg_aux_ptr_src, reg_ptr_src);
+    mov(reg_aux_ptr_dst, reg_ptr_dst);
+
+    for (int i = 0; i < jcp.alpha; i++) {
+        kmovw(x_mask(i), ptr[reg_ptr_v_x_masks + sizeof(uint16_t) * i]);
+    }
+
+    mov(reg_scratch_src_alpha, float2int(adj_src_scale));
+
+    mov(reg_ic_block, jcp.ic / load_block);
+    L(ic_block_label);
+    {
+        vmovq(xmm_src_alpha, reg_scratch_src_alpha);
+        vbroadcastss(zmm_src_alpha, xmm_src_alpha);
+
+        test(reg_nomask, reg_nomask);
+        jz(nomask_label, T_NEAR);
+        load_src(true);
+        jmp(mask_label, T_NEAR);
+        L(nomask_label);
+        load_src(false);
+        L(mask_label);
+
+        for(int y = 0; y < 4; y++) {
+            vpsubb(vreg_tmp(y*4+0), vreg_inp(y*4+0), vreg_inp(y*4+2));
+            vpaddb(vreg_tmp(y*4+1), vreg_inp(y*4+1), vreg_inp(y*4+2));
+            vpsubb(vreg_tmp(y*4+2), vreg_inp(y*4+2), vreg_inp(y*4+1));
+            vpsubb(vreg_tmp(y*4+3), vreg_inp(y*4+1), vreg_inp(y*4+3));
+        }
+        for(int x = 0;x < 4; x++) {
+            vpsubb(vreg_out(x+0*4), vreg_tmp(x+4*0), vreg_tmp(x+4*2));
+            vpaddb(vreg_out(x+1*4), vreg_tmp(x+4*1), vreg_tmp(x+4*2));
+            vpsubb(vreg_out(x+2*4), vreg_tmp(x+4*2), vreg_tmp(x+4*1));
+            vpsubb(vreg_out(x+3*4), vreg_tmp(x+4*1), vreg_tmp(x+4*3));
+        }
+
+        vmovd(xmm_shift, reg_shift.cvt32());
+        vpxor(xmm_zero, xmm_zero, xmm_zero);
+        vpshufb(xmm_shift, xmm_shift, xmm_zero);
+
+        for (int i = 0; i < 16; i++) {
+            int out_offset = sizeof(uint8_t) * (jcp.inp_stride * i);
+            if (i != unsign_val_in_wino_domain)
+                vpsubb(vreg_out(i), vreg_out(i), Xmm(1));
+            vmovups(EVEX_compress_addr(reg_aux_ptr_dst, out_offset), vreg_out(i));
+        }
+
+        add(reg_aux_ptr_src, sizeof(uint8_t) * load_block);
+        add(reg_aux_ptr_dst, sizeof(uint8_t) * load_block);
+    }
+    dec(reg_ic_block);
+    jnz(ic_block_label, T_NEAR);
+
+    L(end_label);
+    postamble();
+}
+
+/// DST TRANSFORMS /////////////////////////////////////////////////////////////
+struct jit_avx512_core_u8s8s32x_wino_conv_dst_trans_t: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(
+            jit_avx512_core_u8s8s32x_wino_conv_dst_trans_t)
+
+    jit_conv_conf_2x3_wino_t jcp;
+    const primitive_attr_t &attr_;
+
+    struct call_params_t {
+        const void *wino_dst;
+        const void *dst;
+        const void *v_y_masks;
+        const void *v_x_masks;
+
+        const void *bias;
+        const void *scales;
+    };
+    void (*ker_)(const call_params_t *);
+
+    jit_avx512_core_u8s8s32x_wino_conv_dst_trans_t(
+        jit_conv_conf_2x3_wino_t ajcp, const primitive_attr_t &attr)
+        : jcp(ajcp), attr_(attr) {
+        generate();
+        ker_ = reinterpret_cast<decltype(ker_)>(const_cast<uint8_t*>(getCode()));
+    }
+
+    void generate();
+    bool maybe_relu(int position);
+
+    Zmm vreg_inp(int i) { // 16
+        assert(i < jcp.alpha * jcp.alpha);
+        return Zmm(31 - i);
+    }
+    Zmm vreg_stg(int id) { // 8
+        const int id_reg_stg = jcp.alpha * jcp.alpha + id;
+        assert(id < 8);
+        return Zmm(31 - id_reg_stg);
+    }
+    Zmm vreg_out(int id) { // 4
+        const int id_reg_out = jcp.alpha * jcp.alpha + 8 + id;
+        assert(id < 4);
+        return Zmm(31 - id_reg_out);
+    }
+    Xmm xmm_out(int id) { // 4
+        const int id_reg_out = jcp.alpha * jcp.alpha + 8 + id;
+        assert(id < 4);
+        return Xmm(31 - id_reg_out);
+    }
+    Zmm vreg_tmp(int id) { // 2
+        const int id_reg_tmp = jcp.alpha * jcp.alpha + 12 + id;
+        assert(id < 2);
+        return Zmm(31 - id_reg_tmp);
+    }
+
+    Zmm vreg_zero = Zmm(0);
+    Zmm vreg_bias = Zmm(1);
+    Zmm vreg_prev_dst = Zmm(2);
+    Zmm zmm_bias_alpha = Zmm(2);
+    Xmm xmm_bias_alpha = Xmm(2);
+
+    Opmask y_mask = Opmask(1);
+    Opmask r_mask = Opmask(2);
+    Opmask x_mask(int id) {
+        assert(id < 4);
+        return Opmask(3 + id);
+    }
+
+    Reg64 reg_scratch_bias_alpha = r15;
+
+    Reg64 reg_ptr_src = r14;
+    Reg64 reg_ptr_dst = r13;
+
+    Reg64 reg_ptr_v_y_masks = r12;
+    Reg64 reg_ptr_v_x_masks = r11;
+
+    Reg64 reg_aux_ptr_src = r10;
+    Reg64 reg_aux_ptr_dst = r9;
+
+    Reg64 reg_oc_block = r8;
+
+    Reg64 reg_ptr_bias = rbx;
+    Reg64 reg_ptr_scales = abi_not_param1;
+    Reg64 reg_ptr_sum_scale = rdx;
+};
+
+bool jit_avx512_core_u8s8s32x_wino_conv_dst_trans_t::maybe_relu(int position) {
+    using namespace primitive_kind;
+    const auto &p = attr_.post_ops_;
+
+    if (position == 0) {
+        /* relu before sum */
+        return false
+            || p.contain(eltwise, 0)
+            || (jcp.dst_dt == data_type::u8 && !p.contain(sum, 0));
+    } else if (position == 1) {
+        /* relu after sum */
+        const int sum_idx = p.contain(sum, 0)
+            ? 0 : (p.contain(sum, 1) ? 1 : -1);
+        if (sum_idx == -1)
+            return false;
+
+        return false
+            || p.contain(eltwise, sum_idx + 1)
+            || jcp.dst_dt == data_type::u8;
+    }
+
+    return false;
+}
+
+void jit_avx512_core_u8s8s32x_wino_conv_dst_trans_t::generate() {
+    Label oc_block_label;
+
+    auto loop_body = [=]() {
+        const auto &p = attr_.post_ops_;
+        const int sum_idx = p.find(primitive_kind::sum);
+        const float *p_sum_scale = (sum_idx != -1)
+                ? &p.entry_[sum_idx].sum.scale
+                : nullptr;
+        if (p_sum_scale && *p_sum_scale != 1.f)
+            mov(reg_ptr_sum_scale, (size_t)p_sum_scale);
+
+        for(int i = 0; i < 16; i++) {
+            int internal_offset = sizeof(int32_t) * jcp.out_stride * i;
+            vmovups(vreg_inp(i),
+                EVEX_compress_addr(reg_aux_ptr_src, internal_offset));
+        }
+        for(int y = 0; y < jcp.alpha; y++) {
+            vpaddd(vreg_tmp(0), vreg_inp(y*4 + 0), vreg_inp(y*4 + 1));
+            vpaddd(vreg_stg(y*2), vreg_tmp(0), vreg_inp(y*4 + 2));
+
+            vpsubd(vreg_tmp(1), vreg_inp(y*4 + 1), vreg_inp(y*4 + 2));
+            vpsubd(vreg_stg(y*2+1), vreg_tmp(1), vreg_inp(y*4 + 3));
+        }
+        for(int x = 0; x < jcp.m; x++) {
+            vpaddd(vreg_tmp(0), vreg_stg(x), vreg_stg(x+2*1));
+            vpaddd(vreg_out(x), vreg_tmp(0), vreg_stg(x+2*2));
+
+            vpsubd(vreg_tmp(1), vreg_stg(x+2*1), vreg_stg(x+2*2));
+            vpsubd(vreg_out(x+2), vreg_tmp(1), vreg_stg(x+2*3));
+        }
+
+
+        if (jcp.with_bias) {
+            vmovq(xmm_bias_alpha, reg_scratch_bias_alpha);
+            vbroadcastss(zmm_bias_alpha, xmm_bias_alpha);
+
+            auto bias_addr = ptr [ reg_ptr_bias ];
+            switch (jcp.bia_dt) {
+            case data_type::f32:
+            case data_type::s32: vmovups(vreg_bias, bias_addr); break;
+            case data_type::s8: vpmovsxbd(vreg_bias, bias_addr); break;
+            case data_type::u8: vpmovzxbd(vreg_bias, bias_addr); break;
+            default: assert(!"unsupported dst data type");
+            }
+            if (jcp.bia_dt != data_type::f32)
+                vcvtdq2ps(vreg_bias, vreg_bias);
+            vmulps(vreg_bias, vreg_bias, zmm_bias_alpha); // *alpha
+        }
+        for(int y = 0; y < jcp.m; y++) {
+            kmovw(y_mask, ptr[ reg_ptr_v_y_masks + sizeof(uint16_t) * y ]);
+            for(int x = 0; x < jcp.m; x++) {
+                kandw(r_mask, y_mask, x_mask(x));
+
+                int i = y * jcp.m + x;
+                int offset = jcp.typesize_out *
+                    (y * jcp.ow * jcp.oc + x * jcp.oc);
+                Address addr = EVEX_compress_addr(reg_aux_ptr_dst, offset);
+
+                Zmm zmm = vreg_out(i);
+                Xmm xmm = xmm_out(i);
+                vcvtdq2ps(zmm, zmm);
+                if (jcp.with_bias)
+                    vaddps(zmm, zmm, vreg_bias);
+                vmulps(zmm, zmm, ptr [reg_ptr_scales]);
+                if (maybe_relu(0))
+                    vmaxps(zmm, vreg_zero, zmm);
+                if (p_sum_scale) { // post_op: sum
+                    vpxord(vreg_prev_dst, vreg_prev_dst, vreg_prev_dst);
+                    switch (jcp.dst_dt) {
+                    case data_type::f32:
+                    case data_type::s32:
+                        vmovups(vreg_prev_dst | r_mask, addr); break;
+                    case data_type::s8:
+                        vpmovsxbd(vreg_prev_dst | r_mask, addr); break;
+                    case data_type::u8:
+                        vpmovzxbd(vreg_prev_dst | r_mask, addr); break;
+                    default: assert(!"unknown dst_dt");
+                    }
+                    if (jcp.dst_dt != data_type::f32)
+                        vcvtdq2ps(vreg_prev_dst, vreg_prev_dst);
+                    if (*p_sum_scale == 1.f)
+                        vaddps(zmm, vreg_prev_dst);
+                    else
+                        vfmadd231ps(zmm, vreg_prev_dst,
+                            zword_b[reg_ptr_sum_scale]);
+                }
+                if (maybe_relu(1))
+                    vmaxps(zmm, vreg_zero, zmm);
+                if (jcp.dst_dt != data_type::f32)
+                    vcvtps2dq(zmm, zmm);
+                switch (jcp.dst_dt) {
+                case data_type::f32:
+                case data_type::s32:
+                    vmovups(addr,  zmm | r_mask); break;
+                case data_type::s8:
+                    vpmovsdb(xmm, zmm); vmovups(addr, xmm | r_mask); break;
+                case data_type::u8:
+                    vpmovusdb(xmm, zmm); vmovups(addr, xmm | r_mask); break;
+                default: assert(!"unknown dst_dt");
+                }
+            }
+        }
+    };
+
+    preamble();
+
+#   define READ_PARAM(reg, field) \
+        mov(reg, ptr[abi_param1 + offsetof(call_params_t, field)])
+    READ_PARAM(reg_ptr_src, wino_dst);
+    READ_PARAM(reg_ptr_dst, dst);
+    READ_PARAM(reg_ptr_v_y_masks, v_y_masks);
+    READ_PARAM(reg_ptr_v_x_masks, v_x_masks);
+    READ_PARAM(reg_ptr_bias, bias);
+    READ_PARAM(reg_ptr_scales, scales);
+#   undef READ_PARAM
+
+    if (jcp.with_bias)
+        mov(reg_scratch_bias_alpha, float2int(adj_src_scale * adj_wei_scale));
+
+    mov(reg_aux_ptr_src, reg_ptr_src);
+    mov(reg_aux_ptr_dst, reg_ptr_dst);
+
+    vpxord(vreg_zero, vreg_zero, vreg_zero);
+
+    for (int i = 0; i < jcp.m; i++)
+        kmovw(x_mask(i), ptr[reg_ptr_v_x_masks + sizeof(uint16_t) * i]);
+
+    int oc_blocks = jcp.oc / load_block;
+    mov(reg_oc_block, oc_blocks);
+    L(oc_block_label); {
+        loop_body();
+        add(reg_aux_ptr_src, sizeof(int32_t) * load_block);
+        add(reg_aux_ptr_dst, jcp.typesize_out * load_block);
+
+        add(reg_ptr_scales, jcp.is_oc_scale * sizeof(float) * load_block);
+        add(reg_ptr_bias, sizeof(jcp.typesize_bia) * load_block);
+    }
+    dec(reg_oc_block);
+    jnz(oc_block_label, T_NEAR);
+
+    postamble();
+
+}
+
+/// GEMM kernel ////////////////////////////////////////////////////////////////
+struct jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t)
+    jit_conv_conf_2x3_wino_t jcp;
+    const primitive_attr_t &attr_;
+
+    struct call_params_t {
+        const void *src;
+        const void *dst;
+        const void *wei;
+        const void *dst_b;
+    };
+    void (*ker_)(const call_params_t *);
+
+    void generate();
+    static bool post_ops_ok(jit_conv_conf_2x3_wino_t &jcp,
+                            const primitive_attr_t &attr);
+
+    jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t(
+        jit_conv_conf_2x3_wino_t ajcp, const primitive_attr_t &attr)
+        : jcp(ajcp), attr_(attr)
+    {
+        generate();
+        ker_ = reinterpret_cast<decltype(ker_)>(const_cast<uint8_t*>(getCode()));
+    }
+
+    static status_t init_conf(
+            jit_conv_conf_2x3_wino_t &jcp, const convolution_desc_t &cd,
+            memory_desc_t &src_md, memory_desc_t &weights_md,
+            memory_desc_t &dst_md, memory_desc_t &bias_md,
+            const primitive_attr_t &attr);
+
+    Zmm vreg_out(int n, int m) {
+        const int id_reg_out = n * jcp.m_block + m;
+        assert(id_reg_out < jcp.n2_block * jcp.m_block);
+        return Zmm(31 - id_reg_out);
+    }
+    Zmm vreg_wei(int i) {
+        assert(31 - jcp.n2_block * jcp.m_block - i
+                > (jcp.ver == ver_vnni ? 0 : 2));
+        return Zmm(31 - jcp.n2_block * jcp.m_block - i);
+    }
+
+    Zmm vreg_src = Zmm(0);
+    Zmm vreg_one = Zmm(1);
+    Zmm vreg_tmp = Zmm(2);
+
+    Reg64 reg_ptr_src = r15;
+
+    Reg64 reg_aux_dst_b = r13;
+    Reg64 reg_aux_dst = r12;
+    Reg64 reg_aux_dst2 = r11;
+    Reg64 reg_aux_wei = r10;
+    Reg64 reg_aux_wei2 = r9;
+    Reg64 reg_aux_src = r8;
+    Reg64 reg_aux_src2 = rax;
+    Reg64 reg_mb = rbx;
+    Reg64 reg_nnb = abi_not_param1;
+    Reg64 reg_scratch = rdx;
+    Reg64 reg_K = rsi;
+};
+
+bool jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t::post_ops_ok(
+        jit_conv_conf_2x3_wino_t &jcp, const primitive_attr_t &attr) {
+    using namespace primitive_kind;
+    const auto &p = attr.post_ops_;
+
+    auto is_relu = [&](int idx) { return p.entry_[idx].is_relu(); };
+
+    switch (p.len_) {
+    case 0: return true;
+    case 1: return is_relu(0) || p.contain(sum, 0);
+    case 2: return (p.contain(sum, 0) && is_relu(1)) ||
+                       (p.contain(sum, 1) && is_relu(0));
+    case 3: return is_relu(0) && p.contain(sum, 1) && is_relu(2);
+    default: return false;
+    }
+
+    return false;
+}
+
+void jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t::generate() {
+    Label nnb_loop_label, K_loop_label, mb_loop_label;
+
+    auto compute = [=](Zmm vreg_acc, Zmm vreg_wei, Zmm vreg_src) {
+        if (jcp.ver == ver_vnni) {
+            vpdpbusd(vreg_acc, vreg_src, vreg_wei);
+        } else {
+            vpmaddubsw(vreg_tmp, vreg_src, vreg_wei);
+            vpmaddwd(vreg_tmp, vreg_tmp, vreg_one);
+            vpaddd(vreg_acc, vreg_acc, vreg_tmp);
+        }
+    };
+
+    preamble();
+#   define READ_PARAM(reg, field) \
+        mov(reg, ptr[abi_param1 + offsetof(call_params_t, field)])
+    READ_PARAM(reg_ptr_src, src);
+    READ_PARAM(reg_aux_dst, dst);
+    READ_PARAM(reg_aux_wei, wei);
+    READ_PARAM(reg_aux_dst_b, dst_b);
+#   undef READ_PARAM
+
+    if (jcp.ver != ver_vnni) {
+        xor_(reg_scratch, reg_scratch);
+        Reg16 _t = reg_scratch.cvt16();
+        mov(_t, 0x1);
+        vpbroadcastw(vreg_one, _t);
+    }
+
+    if (!jcp.small_mb) {
+        mov(reg_nnb, jcp.n_chunks);
+        L(nnb_loop_label);
+    }
+    mov(reg_aux_dst2, reg_aux_dst);
+    mov(reg_aux_src, reg_ptr_src);
+    mov(reg_mb, jcp.M / jcp.m_block);
+    L(mb_loop_label);
+    {
+        for (int nb2 = 0; nb2 < jcp.n2_block; nb2++) {
+            for (int m = 0; m < jcp.m_block; m++) {
+                int offset = jcp.typesize_acc * nb2 * jcp.n_block;
+                vmovups(vreg_out(nb2, m),
+                        EVEX_compress_addr(reg_aux_dst_b, offset));
+            }
+        }
+        mov(reg_aux_src2, reg_aux_src);
+        mov(reg_aux_wei2, reg_aux_wei);
+        mov(reg_K, jcp.k_chunks);
+        L(K_loop_label);
+        {
+            for (int k = 0; k < jcp.k2_block; k += 4) {
+                for (int nb2 = 0; nb2 < jcp.n2_block; nb2++) {
+                    int wei_offset
+                            = jcp.typesize_in * (nb2 * jcp.n_block * jcp.K);
+                    vmovups(vreg_wei(nb2),
+                            EVEX_compress_addr(reg_aux_wei2, wei_offset));
+                }
+                for (int m = 0; m < jcp.m_block; m++) {
+                    int inp_offset = jcp.typesize_in * m * jcp.K;
+                    vpbroadcastd(vreg_src,
+                            EVEX_compress_addr(reg_aux_src2, inp_offset));
+                    for (int nb2 = 0; nb2 < jcp.n2_block; nb2++)
+                        compute(vreg_out(nb2, m), vreg_wei(nb2), vreg_src);
+                }
+                add(reg_aux_src2, jcp.typesize_in * 4);
+                add(reg_aux_wei2, jcp.typesize_in * 4 * jcp.n_block);
+            }
+        }
+        dec(reg_K);
+        jnz(K_loop_label, T_NEAR);
+
+        for (int m = 0; m < jcp.m_block; m++) {
+            for (int nb2 = 0; nb2 < jcp.n2_block; nb2++) {
+                int offset = jcp.typesize_acc * (m * jcp.N + nb2 * jcp.n_block);
+                vmovups(EVEX_compress_addr(reg_aux_dst2, offset),
+                        vreg_out(nb2, m));
+            }
+        }
+        add(reg_aux_src, jcp.typesize_in * jcp.m_block * jcp.K);
+        add(reg_aux_dst2, jcp.typesize_acc * jcp.m_block * jcp.N);
+    }
+    dec(reg_mb);
+    jnz(mb_loop_label, T_NEAR);
+
+    if (!jcp.small_mb) {
+        add(reg_aux_dst, jcp.typesize_acc * jcp.n2_block * jcp.n_block);
+        add(reg_aux_dst_b, jcp.typesize_acc * jcp.n2_block * jcp.n_block);
+        add(reg_aux_wei, jcp.typesize_in * jcp.n2_block * jcp.n_block * jcp.K);
+
+        dec(reg_nnb);
+        jnz(nnb_loop_label, T_NEAR);
+    }
+
+    postamble();
+}
+namespace {
+bool is_winograd_faster_than_direct(const jit_conv_conf_2x3_wino_t &jcp) {
+    if (jcp.ver == ver_vnni) {
+        return (jcp.mb <= mkldnn_get_max_threads()
+            && (jcp.mb > 4
+                && jcp.ic > 64
+                && !(jcp.oc > 128 && jcp.ih < 14)))
+            || jcp.mb > mkldnn_get_max_threads();
+    }
+    return true;
+}
+}
+
+status_t jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t
+::init_conf(jit_conv_conf_2x3_wino_t &jcp,
+            const convolution_desc_t &cd, memory_desc_t &src_md,
+            memory_desc_t &wei_md, memory_desc_t &dst_md,
+            memory_desc_t &bias_md, const primitive_attr_t &attr) {
+    const memory_desc_wrapper src_d(&src_md);
+    const memory_desc_wrapper wei_d(&wei_md);
+    const memory_desc_wrapper dst_d(&dst_md);
+    const memory_desc_wrapper bias_d(&bias_md);
+
+    const bool with_groups = wei_d.ndims() == src_d.ndims() + 1;
+
+    jcp.nthr = mkldnn_get_max_threads();
+
+    jcp.ngroups = with_groups ? wei_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+    jcp.ih = src_d.dims()[2];
+    jcp.iw = src_d.dims()[3];
+    jcp.oh = dst_d.dims()[2];
+    jcp.ow = dst_d.dims()[3];
+    jcp.kh = wei_d.dims()[with_groups + 2];
+    jcp.kw = wei_d.dims()[with_groups + 3];
+    jcp.t_pad = cd.padding[0][0];
+    jcp.b_pad = cd.padding[1][0];
+    jcp.l_pad = cd.padding[0][1];
+    jcp.r_pad = cd.padding[1][1];
+    jcp.stride_h = cd.strides[0];
+    jcp.stride_w = cd.strides[1];
+    jcp.dilate_h = cd.dilates[0];
+    jcp.dilate_w = cd.dilates[1];
+
+    jcp.ver = ver_avx512_core;
+    if (!(mayiuse(avx512_core) &&
+            src_d.data_type() == data_type::u8
+         && wei_d.data_type() == data_type::s8
+         && one_of(dst_d.data_type(), data_type::f32, data_type::s32,
+            data_type::s8, data_type::u8)))
+        return status::unimplemented;
+    if (mayiuse(avx512_core_vnni))
+        jcp.ver = ver_vnni;
+
+    if (!IMPLICATION(cd.alg_kind == alg_kind::convolution_auto,
+               is_winograd_faster_than_direct(jcp)))
+        return status::unimplemented;
+
+    // block sizes needed for GEMM kernel
+    jcp.ic_block = 4;
+    jcp.oc_block = 16;
+
+    bool ok = true
+        && jcp.ngroups == 1
+        && jcp.oc % load_block == 0 && jcp.ic % load_block == 0
+        && jcp.oc % jcp.oc_block == 0 && jcp.ic % jcp.ic_block == 0
+        && everyone_is(3, jcp.kh, jcp.kw)
+        && everyone_is(1, jcp.stride_h, jcp.stride_w)
+        && everyone_is(0, jcp.dilate_h, jcp.dilate_w)
+        && jcp.t_pad == jcp.b_pad && jcp.l_pad == jcp.r_pad
+        && one_of(jcp.t_pad, 0, 1)
+        && one_of(jcp.l_pad, 0, 1);
+    if (!ok) return status::unimplemented;
+
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    jcp.bia_dt = jcp.with_bias ? cd.bias_desc.data_type : data_type::undef;
+    jcp.dst_dt = cd.dst_desc.data_type;
+
+    jcp.typesize_in = types::data_type_size(src_d.data_type());
+    jcp.typesize_out = types::data_type_size(dst_d.data_type());
+    jcp.typesize_acc = sizeof(int32_t);
+    jcp.typesize_bia = jcp.with_bias
+        ? types::data_type_size(bias_d.data_type())
+        : 0;
+
+    jcp.nb_oc = jcp.oc / jcp.oc_block;
+    jcp.nb_ic = jcp.ic / jcp.ic_block;
+
+    jcp.m = 2;
+    jcp.r = 3;
+    jcp.alpha = jcp.m + jcp.r - 1;
+
+    int aa = jcp.alpha * jcp.alpha;
+    int L1_cap = get_cache_size(1, true);
+    int L2_cap = get_cache_size(2, true);
+    // need 1 extra reg for bcast, and 2 tmp regs for non-vnni
+    int free_regs = jcp.ver == ver_vnni ? 31 : 29;
+
+    auto get_thr_eff = [&](int small_mb, int ix, int iy, int n2_b) {
+        float thr_eff;
+        float Z = (float)jcp.ic + jcp.oc;
+        float Y = (float)jcp.ic * jcp.oc;
+        if (small_mb == 0) { // outer par
+            int nblocks = jcp.mb * div_up(jcp.oh, iy) * div_up(jcp.ow, ix);
+            thr_eff = (float)nblocks / rnd_up(nblocks, jcp.nthr);
+        } else { // inner par
+            int tranw = iy * ix / jcp.alpha;
+            int gemmw = aa * (jcp.nb_oc / n2_b);
+            int tranw_r = rnd_up(tranw, jcp.nthr);
+            int gemmw_r = rnd_up(gemmw, jcp.nthr);
+            thr_eff = (Z * tranw / tranw_r + Y * gemmw / gemmw_r) / (Z + Y);
+        }
+        return thr_eff;
+    };
+
+    auto get_mem_eff = [&](int small_mb, int ix, int iy, int n2_b) {
+        float mem_eff, req_mem;
+        int M = ix * iy / jcp.alpha;
+        if (small_mb == 0) { // outer parallelization strategy
+            // memory for wino transforms (other memory has poor reuse)
+            req_mem = (float)aa * M * (jcp.ic + jcp.typesize_acc * jcp.oc);
+            mem_eff = req_mem < L1_cap ? 1.f : req_mem < L2_cap ? 0.5f : 0.f;
+        } else { // inner parallelization strategy
+            // memory used during gemm
+            int N = jcp.oc_block * n2_b;
+            req_mem = (float)jcp.ic * (M + N) + jcp.typesize_acc * M * N;
+            mem_eff = nstl::min(1.f, L2_cap / req_mem);
+            // memory used during wino transforms
+            int M_per_thr = div_up(M, jcp.nthr);
+            req_mem = (float)aa * M_per_thr
+                    * (jcp.ic + jcp.typesize_acc * jcp.oc);
+            if (req_mem > L2_cap)
+                mem_eff = 0.1f;
+        }
+        return mem_eff;
+    };
+
+    auto get_tot_eff = [&](int small_mb, float thr_eff, float work_eff,
+            float mem_eff, float reg_eff) {
+        // these coefficients are chosen empirically
+        float mem_fac = 0.1f, reg_fac = 0.2f;
+        // normalized overhead relative to memory and register components
+        float tot_eff = 1.f + mem_fac * mem_eff + reg_fac * reg_eff;
+        // thread and work components affect all others
+        tot_eff *= thr_eff * work_eff;
+        return tot_eff;
+    };
+
+    auto find_m_n2_blocks = [&](bool small_mb, int ix, int iy, float work_eff,
+            int &m_block, int &n2_block, float &tot_eff) {
+        int M = (ix * iy) / jcp.alpha;
+        int max_m_block = nstl::min(M, free_regs);
+        int max_n2_block = nstl::min(jcp.nb_oc, free_regs);
+        tot_eff = 0.f;
+        for (int im = max_m_block; im > 0; im--) {
+            if (M % im)
+                continue;
+            for (int in2 = max_n2_block; in2 > 0; in2--) {
+                int used_regs = (im + 1) * in2;
+                float mem_eff = get_mem_eff(small_mb, ix, iy, in2);
+                float reg_eff = (float)(im * in2) / (im + in2);
+                float thr_eff = get_thr_eff(small_mb, ix, iy, in2);
+                float cur_tot_eff = get_tot_eff(
+                        small_mb, thr_eff, work_eff, mem_eff, reg_eff);
+                if (jcp.nb_oc % in2 || used_regs > free_regs
+                        || cur_tot_eff <= tot_eff)
+                    continue;
+                tot_eff = cur_tot_eff;
+                m_block = im;
+                n2_block = in2;
+            }
+        }
+    };
+
+    /* Selecting xb and yb blocking */
+    int min_yb = jcp.m;
+    int min_xb = jcp.m;
+    int max_yb = nstl::max(min_yb, rnd_up(jcp.oh, 2));
+    int max_xb = nstl::max(min_xb, rnd_up(jcp.ow, 2));
+    float best_eff = 0.f;
+    for (int ix = min_xb; ix <= max_xb; ix += 2) {
+        assert(rnd_up(jcp.ow, ix) >= jcp.iw - 2);
+        for (int iy = max_yb; iy >= min_yb; iy -= 2) {
+            assert(rnd_up(jcp.oh, iy) >= jcp.ih - 2);
+
+            int m_b[2];
+            int n2_b[2];
+            bool small_mb;
+            float inner_eff, outer_eff, work_eff;
+
+            int tiled_area = rnd_up(jcp.oh, iy) * rnd_up(jcp.ow, ix);
+            work_eff = (float)jcp.oh * jcp.ow / tiled_area;
+            if (best_eff > 0.f && work_eff < 4.f / 9.f)
+                continue; // no gain from Winograd transformation
+
+            /* outer parallelization */
+            find_m_n2_blocks(0, ix, iy, work_eff, m_b[0], n2_b[0], outer_eff);
+
+            /* inner parallelization */
+            find_m_n2_blocks(1, ix, iy, work_eff, m_b[1], n2_b[1], inner_eff);
+
+            small_mb = inner_eff > outer_eff;
+            float eff = small_mb ? inner_eff : outer_eff;
+            if (eff > best_eff) {
+                best_eff = eff;
+                jcp.yb = iy;
+                jcp.xb = ix;
+                jcp.m_block = m_b[small_mb];
+                jcp.n2_block = n2_b[small_mb];
+                jcp.small_mb = small_mb;
+            }
+        }
+    }
+
+    assert((jcp.m_block + 1) * jcp.n2_block <= free_regs);
+    assert(jcp.xb % 2 == 0 && jcp.yb % 2 == 0);
+
+    jcp.mb_block = 1;
+    if (jcp.small_mb) {
+        // For small mb harness, set mb_block as large as possible subject to
+        // the constraint that winograd activations fit into available L3 cache
+        int L3_cap = get_cache_size(3, true);
+        int M = jcp.xb * jcp.yb / 4;
+        int wino_src_size = 16 * M * jcp.ic * jcp.typesize_in;
+        int wino_dst_size = 16 * M * jcp.oc * jcp.typesize_acc;
+        int max_mb_block = nstl::min(
+                jcp.mb, jcp.nthr * L3_cap / (wino_src_size + wino_dst_size));
+        for (int i = max_mb_block; i > 1; i--) {
+            if (jcp.mb % i == 0) {
+                jcp.mb_block = i;
+                break;
+            }
+        }
+    }
+    jcp.nb_mb = jcp.mb / jcp.mb_block;
+
+    jcp.M = jcp.mb_block * jcp.xb * jcp.yb / 4;
+    jcp.N = jcp.oc;
+    jcp.K = jcp.ic;
+
+    jcp.inp_stride = jcp.M * jcp.ic;
+    jcp.out_stride = jcp.M * jcp.oc;
+    jcp.wei_stride = jcp.ic * jcp.oc;
+    jcp.bia_stride = jcp.oc;
+
+    jcp.n_block = jcp.oc_block;
+    jcp.k_block = jcp.ic_block;
+
+    jcp.n_chunks = (jcp.N / jcp.n_block) / jcp.n2_block;
+
+    // We need jcp.k2_block to be a multiple of jcp.k_block = jcp.ic_block = 4
+    // and jcp.K = jcp.ic to be a multiple of jcp.k2_block. Since jcp.ic is
+    // a multiple of load_block = 16, we just use that for now.
+    jcp.k2_block = load_block;
+    jcp.k_chunks = jcp.K / jcp.k2_block;
+
+    const auto &oscales = attr.output_scales_;
+    jcp.is_oc_scale = oscales.mask_ == 1 << 1;
+    assert(IMPLICATION(!jcp.is_oc_scale, oscales.mask_ == 0));
+
+    /* re-create weights primitive descriptor
+                                    and set weights wino_blocking */
+    memory_desc_t expect_wei_md = wei_md;
+
+    expect_wei_md.format_kind = format_kind::wino;
+    expect_wei_md.data_type = data_type::s8;
+    mkldnn_wino_desc_t &wd = expect_wei_md.format_desc.wino_desc;
+    wd.wino_format = mkldnn_wino_wei_aaOIoi;
+    wd.r = jcp.r;
+    wd.alpha = jcp.alpha;
+    wd.ic = jcp.ic;
+    wd.oc = jcp.oc;
+    wd.ic_block = jcp.ic_block;
+    wd.oc_block = jcp.oc_block;
+    wd.oc2_block = jcp.n2_block;
+    wd.ic2_block = 1;
+    wd.adj_scale = adj_wei_scale;
+
+    size_t max_size = types::data_type_size(data_type::s8) *
+                        jcp.alpha * jcp.alpha * jcp.ic * jcp.oc;
+    max_size += types::data_type_size(data_type::s32) *
+                                jcp.alpha * jcp.alpha * jcp.oc;
+    wd.size = max_size;
+
+    if (wei_md.format_kind == format_kind::any)
+        wei_md = expect_wei_md;
+    if (wei_md != expect_wei_md)
+        return status::unimplemented;
+
+    const int tilesize = jcp.alpha * jcp.alpha;
+    const int numtiles = jcp.M;
+    const int alltiles = numtiles * tilesize;
+
+    jcp.size_wino_src
+        = utils::rnd_up(jcp.typesize_in * alltiles * jcp.ic, PAGE_4K)
+        / jcp.typesize_in;
+    jcp.size_wino_wei = tilesize * jcp.oc * jcp.ic;
+    jcp.size_wino_dst = alltiles * jcp.oc;
+
+    return status::success;
+}
+////////////////////////////////////////////////////////////////////////////////
+
+template <data_type_t dst_data_type>
+status_t jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<dst_data_type>::
+        pd_t::jit_conf() {
+    return jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t::init_conf(
+            jcp_, *this->desc(), this->src_md_, this->weights_md_,
+            this->dst_md_,this->bias_md_, *this->attr());
+}
+
+template <data_type_t dst_data_type>
+void jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<dst_data_type>::pd_t::
+init_scratchpad() {
+    auto scratchpad = this->scratchpad_registry().registrar();
+
+    int nthr_multiplier = jcp_.small_mb ? 1 : jcp_.nthr;
+    scratchpad.book(key_wino_V,
+            sizeof(src_data_t) * jcp_.size_wino_src * nthr_multiplier, PAGE_4K);
+    scratchpad.book(key_wino_M,
+            sizeof(acc_data_t) * jcp_.size_wino_dst * nthr_multiplier, PAGE_4K);
+
+    dim_t scale_count = attr()->output_scales_.count_;
+    scratchpad.book(key_conv_adjusted_scales,
+            sizeof(float) * nstl::max<dim_t>(scale_count, 16));
+}
+
+template <data_type_t dst_data_type>
+jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<dst_data_type>::
+        jit_avx512_core_u8s8s32x_wino_convolution_fwd_t(const pd_t *apd)
+    : cpu_primitive_t(apd)
+{
+    kernel_ = new jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t(
+            pd()->jcp_, *pd()->attr());
+    src_trans_ = new jit_avx512_core_u8s8s32x_wino_conv_src_trans_t(
+            pd()->jcp_, *pd()->attr());
+    dst_trans_ = new jit_avx512_core_u8s8s32x_wino_conv_dst_trans_t(
+            pd()->jcp_, *pd()->attr());
+}
+
+template <data_type_t dst_data_type>
+jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<dst_data_type>::
+        ~jit_avx512_core_u8s8s32x_wino_convolution_fwd_t() {
+    delete kernel_;
+    delete src_trans_;
+    delete dst_trans_;
+}
+
+template <data_type_t dst_data_type>
+const float *jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<dst_data_type>::
+adjust_oscales(const memory_tracking::grantor_t &scratchpad) const {
+    const float *oscales = pd()->attr()->output_scales_.scales_;
+    auto loc_scales = scratchpad.template get<float>(key_conv_adjusted_scales);
+    size_t count = pd()->attr()->output_scales_.count_;
+    float factor = 1.f / (adj_src_scale * adj_wei_scale);
+    if (count == 1)
+        utils::array_set(loc_scales, oscales[0] * factor, 16);
+    else
+        for (size_t c = 0; c < count; c++) loc_scales[c] = oscales[c] * factor;
+    return loc_scales;
+}
+
+template <data_type_t dst_data_type>
+void jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<dst_data_type>::
+execute_forward(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    const auto &jcp = kernel_->jcp;
+    if (jcp.small_mb)
+        execute_forward_small_mb(src, weights, bias, dst, this->scratchpad(ctx));
+    else
+        execute_forward_mbN(src, weights, bias, dst, this->scratchpad(ctx));
+}
+
+template <data_type_t dst_data_type>
+void jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<dst_data_type>::
+execute_forward_mbN(const src_data_t *src, const wei_data_t *wei,
+        const char *bia, dst_data_t *dst,
+        const memory_tracking::grantor_t &scratchpad) const {
+    const auto &jcp = kernel_->jcp;
+    const float *oscales = adjust_oscales(scratchpad);
+
+    auto dst_bias = (const acc_data_t *)(wei + jcp.size_wino_wei);
+    auto wino_src_base = scratchpad.template get<src_data_t>(key_wino_V);
+    auto wino_dst_base = scratchpad.template get<acc_data_t>(key_wino_M);
+
+    parallel_nd(jcp.mb, div_up(jcp.oh, jcp.yb), div_up(jcp.ow, jcp.xb),
+            [&](int mb, int tile_y_b, int tile_x_b) {
+
+        int tile_y = tile_y_b * jcp.yb;
+        int tile_x = tile_x_b * jcp.xb;
+
+        int ithr = mkldnn_get_thread_num();
+        auto wino_src = wino_src_base + jcp.size_wino_src * ithr;
+        auto wino_dst = wino_dst_base + jcp.size_wino_dst * ithr;
+
+        auto src_trans_p =
+            jit_avx512_core_u8s8s32x_wino_conv_src_trans_t::call_params_t();
+        auto dst_trans_p =
+            jit_avx512_core_u8s8s32x_wino_conv_dst_trans_t::call_params_t();
+        auto gemm_p =
+            jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t::call_params_t();
+
+        /* transformation of input tensor to winograd domain */
+        for (int y_in_block = 0; y_in_block < jcp.yb; y_in_block += 2) {
+            for (int x_in_block = 0; x_in_block < jcp.xb; x_in_block += 2) {
+                uint16_t v_y_masks[4], v_x_masks[4];
+
+                int y = y_in_block + tile_y;
+                int x = x_in_block + tile_x;
+                int m = (y_in_block / 2) * (jcp.xb / 2) + (x_in_block / 2);
+
+                int v_ys = nstl::max(0, jcp.t_pad - y);
+                int v_ye = nstl::min(jcp.alpha,
+                        nstl::max(0, jcp.ih + jcp.t_pad - y));
+
+                int v_xs = nstl::max(0, jcp.l_pad - x);
+                int v_xe = nstl::min(jcp.alpha,
+                        nstl::max(0, jcp.iw + jcp.l_pad - x));
+
+#pragma unroll(4)
+                for (int i = 0; i < jcp.alpha; i++) {
+                    v_y_masks[i] = uint16_t(i < v_ys || i >= v_ye ? 0 : 0xffff);
+                    v_x_masks[i] = uint16_t(i < v_xs || i >= v_xe ? 0 : 0xffff);
+                }
+                auto local_s = src
+                        + mb * jcp.ih * jcp.iw * jcp.ic
+                        + y * jcp.iw * jcp.ic + x * jcp.ic;
+                auto local_w = wino_src + m * jcp.ic;
+
+                src_trans_p.src = local_s;
+                src_trans_p.wino_src = local_w;
+                src_trans_p.v_y_masks = v_y_masks;
+                src_trans_p.v_x_masks = v_x_masks;
+
+                src_trans_->ker_(&src_trans_p);
+            }
+        }
+        /* gemms */
+        for (int tile_ij = 0; tile_ij < 16; tile_ij++) {
+            // start threads at different GEMMs to help bring weights into LLC
+            int offset = (tile_ij + ithr) % 16;
+            gemm_p.src = wino_src + jcp.inp_stride * offset;
+            gemm_p.dst = wino_dst + jcp.out_stride * offset;
+            gemm_p.wei = wei + jcp.wei_stride * offset;
+            gemm_p.dst_b = dst_bias + jcp.bia_stride * offset;
+
+            kernel_->ker_(&gemm_p);
+        }
+
+        /* transformation from winograd domain to output tensor */
+        for (int y_in_block = 0; y_in_block < jcp.yb; y_in_block += 2) {
+            for (int x_in_block = 0; x_in_block < jcp.xb; x_in_block += 2) {
+                uint16_t v_y_masks[2], v_x_masks[2];
+
+                int y = y_in_block + tile_y;
+                int x = x_in_block + tile_x;
+                int m = (y_in_block / 2) * (jcp.xb / 2) + (x_in_block / 2);
+
+#pragma unroll(2)
+                for (int i = 0; i < jcp.m; i++) {
+                    v_x_masks[i] = uint16_t(x + i < jcp.ow ? 0xffff : 0);
+                    v_y_masks[i] = uint16_t(y + i < jcp.oh ? 0xffff : 0);
+                }
+                auto local_d = dst
+                        + mb * jcp.oh * jcp.ow * jcp.oc
+                        + y * jcp.ow * jcp.oc + x * jcp.oc;
+                auto local_w = wino_dst + m * jcp.oc;
+
+                auto scales = oscales;
+                dst_trans_p.dst = local_d;
+                dst_trans_p.wino_dst = local_w;
+                dst_trans_p.v_y_masks = v_y_masks;
+                dst_trans_p.v_x_masks = v_x_masks;
+
+                dst_trans_p.scales = scales;
+                dst_trans_p.bias = bia;
+
+                dst_trans_->ker_(&dst_trans_p);
+            }
+        }
+    });
+}
+
+template <data_type_t dst_data_type>
+void jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<dst_data_type>::
+execute_forward_small_mb(const src_data_t *src, const wei_data_t *wei,
+        const char *bia, dst_data_t *dst,
+        const memory_tracking::grantor_t &scratchpad) const {
+    const auto &jcp = kernel_->jcp;
+    const float *oscales = adjust_oscales(scratchpad);
+
+    auto dst_bias = (const acc_data_t *)(wei + jcp.size_wino_wei);
+    auto wino_src = scratchpad.template get<src_data_t>(key_wino_V);
+    auto wino_dst = scratchpad.template get<acc_data_t>(key_wino_M);
+
+    for (int mbb = 0; mbb < jcp.nb_mb; mbb++) {
+    for (int tile_y = 0; tile_y < jcp.oh; tile_y += jcp.yb) {
+    for (int tile_x = 0; tile_x < jcp.ow; tile_x += jcp.xb) {
+        /* transformation of input tensor to winograd domain */
+        parallel_nd(div_up(jcp.yb, 2), div_up(jcp.xb, 2), jcp.mb_block,
+            [&](int y_in_block_b, int x_in_block_b, int mb) {
+            int y_in_block = y_in_block_b * 2;
+            int x_in_block = x_in_block_b * 2;
+
+            auto src_trans_p =
+                jit_avx512_core_u8s8s32x_wino_conv_src_trans_t::call_params_t();
+
+            uint16_t v_y_masks[4], v_x_masks[4];
+
+            int y = y_in_block + tile_y;
+            int x = x_in_block + tile_x;
+            int m = (mb * (jcp.yb / 2) + (y_in_block / 2)) * (jcp.xb / 2)
+                    + (x_in_block / 2);
+
+            int v_ys = nstl::max(0, jcp.t_pad - y);
+            int v_ye = nstl::min(
+                    jcp.alpha, nstl::max(0, jcp.ih + jcp.t_pad - y));
+
+            int v_xs = nstl::max(0, jcp.l_pad - x);
+            int v_xe = nstl::min(
+                    jcp.alpha, nstl::max(0, jcp.iw + jcp.l_pad - x));
+
+#pragma unroll(4)
+            for (int i = 0; i < jcp.alpha; i++) {
+                v_y_masks[i] = uint16_t(i < v_ys || i >= v_ye ? 0 : 0xffff);
+                v_x_masks[i] = uint16_t(i < v_xs || i >= v_xe ? 0 : 0xffff);
+            }
+            auto local_s = src
+                    + (mbb * jcp.mb_block + mb) * jcp.ih * jcp.iw * jcp.ic
+                    + y * jcp.iw * jcp.ic + x * jcp.ic;
+            auto local_w = wino_src + m * jcp.ic;
+
+            src_trans_p.src = local_s;
+            src_trans_p.wino_src = local_w;
+            src_trans_p.v_y_masks = v_y_masks;
+            src_trans_p.v_x_masks = v_x_masks;
+
+            src_trans_->ker_(&src_trans_p);
+        });
+
+        /* gemms */
+        parallel_nd(16, jcp.n_chunks, [&](int tile_ij, int nnb) {
+            auto gemm_p = jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t::
+                    call_params_t();
+
+            gemm_p.src = wino_src + jcp.inp_stride * tile_ij;
+            gemm_p.dst = wino_dst + jcp.out_stride * tile_ij
+                    + nnb * jcp.n2_block * jcp.n_block;
+            gemm_p.wei = wei + jcp.wei_stride * tile_ij
+                    + nnb * jcp.n2_block * jcp.n_block * jcp.K;
+            gemm_p.dst_b = dst_bias + jcp.bia_stride * tile_ij
+                    + nnb * jcp.n2_block * jcp.n_block;
+
+            kernel_->ker_(&gemm_p);
+        });
+
+        /* transformation from winograd domain to output tensor */
+        parallel_nd(div_up(jcp.yb, 2), div_up(jcp.xb, 2), jcp.mb_block,
+            [&](int y_in_block_b, int x_in_block_b, int mb) {
+            int y_in_block = y_in_block_b * 2;
+            int x_in_block = x_in_block_b * 2;
+
+            auto dst_trans_p =
+                jit_avx512_core_u8s8s32x_wino_conv_dst_trans_t::call_params_t();
+
+            uint16_t v_y_masks[2], v_x_masks[2];
+
+            int y = y_in_block + tile_y;
+            int x = x_in_block + tile_x;
+            int m = (mb * (jcp.yb / 2) + (y_in_block / 2)) * (jcp.xb / 2)
+                    + (x_in_block / 2);
+
+#pragma unroll(2)
+            for (int i = 0; i < jcp.m; i++) {
+                v_x_masks[i] = uint16_t(x + i < jcp.ow ? 0xffff : 0);
+                v_y_masks[i] = uint16_t(y + i < jcp.oh ? 0xffff : 0);
+            }
+            auto local_d = dst
+                    + (mbb * jcp.mb_block + mb) * jcp.oh * jcp.ow * jcp.oc
+                    + y * jcp.ow * jcp.oc + x * jcp.oc;
+            auto local_w = wino_dst + m * jcp.oc;
+
+            auto scales = oscales;
+            dst_trans_p.dst = local_d;
+            dst_trans_p.wino_dst = local_w;
+            dst_trans_p.v_y_masks = v_y_masks;
+            dst_trans_p.v_x_masks = v_x_masks;
+
+            dst_trans_p.scales = scales;
+            dst_trans_p.bias = bia;
+
+            dst_trans_->ker_(&dst_trans_p);
+        });
+    }}}
+}
+
+template struct jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<data_type::s8>;
+template struct jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<data_type::u8>;
+template struct jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<data_type::s32>;
+template struct jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<data_type::f32>;
+
+} // namespace cpu
+} // namespace impl
+} // namespace mkldnn
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_u8s8s32x_wino_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_u8s8s32x_wino_convolution.hpp
new file mode 100644
index 0000000000..9e6e57b051
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_u8s8s32x_wino_convolution.hpp
@@ -0,0 +1,128 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_CORE_U8S8S32X_WINO_CONVOLUTION_HPP
+#define CPU_JIT_AVX512_CORE_U8S8S32X_WINO_CONVOLUTION_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "jit_primitive_conf.hpp"
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t;
+struct jit_avx512_core_u8s8s32x_wino_conv_src_trans_t;
+struct jit_avx512_core_u8s8s32x_wino_conv_dst_trans_t;
+
+template <data_type_t dst_data_type>
+struct jit_avx512_core_u8s8s32x_wino_convolution_fwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            :  cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_()
+        {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_int8_wino:", avx512_core, ""),
+                jit_avx512_core_u8s8s32x_wino_convolution_fwd_t<dst_data_type>);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && utils::one_of(desc()->alg_kind,
+                           alg_kind::convolution_auto,
+                           alg_kind::convolution_winograd)
+                && expect_data_types(data_type::u8, data_type::s8,
+                        data_type::undef, dst_data_type, data_type::s32)
+                && IMPLICATION(with_bias(), utils::one_of(
+                            desc()->bias_desc.data_type, data_type::f32,
+                            data_type::s32, data_type::s8, data_type::u8))
+                && !has_zero_dim_memory()
+                && set_default_formats();
+
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_conf();
+            if (status != status::success) return status;
+            set_default_alg_kind(alg_kind::convolution_winograd);
+
+            init_scratchpad();
+
+            return status;
+        }
+
+        jit_conv_conf_2x3_wino_t jcp_;
+
+    protected:
+        status_t jit_conf();
+        void init_scratchpad();
+
+        bool set_default_formats() {
+            using namespace format_tag;
+            return set_default_formats_common(nhwc, any, nhwc);
+        }
+    };
+
+    typedef typename prec_traits<data_type::u8>::type src_data_t;
+    typedef typename prec_traits<data_type::s8>::type wei_data_t;
+    typedef typename prec_traits<data_type::s32>::type acc_data_t;
+    typedef typename prec_traits<dst_data_type>::type dst_data_t;
+
+    jit_avx512_core_u8s8s32x_wino_convolution_fwd_t(const pd_t *apd);
+    ~jit_avx512_core_u8s8s32x_wino_convolution_fwd_t();
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    const float *adjust_oscales(const memory_tracking::grantor_t &scratchpad)
+        const;
+    void execute_forward(const exec_ctx_t &ctx) const;
+    void execute_forward_small_mb(const src_data_t *src, const wei_data_t *wei,
+            const char *bia, dst_data_t *dst,
+            const memory_tracking::grantor_t &scratchpad) const;
+    void execute_forward_mbN(const src_data_t *src, const wei_data_t *wei,
+            const char *bia, dst_data_t *dst,
+            const memory_tracking::grantor_t &scratchpad) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx512_core_u8s8s32x_wino_conv_fwd_ker_t *kernel_;
+    jit_avx512_core_u8s8s32x_wino_conv_src_trans_t *src_trans_;
+    jit_avx512_core_u8s8s32x_wino_conv_dst_trans_t *dst_trans_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_conv_kernel.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_conv_kernel.cpp
new file mode 100644
index 0000000000..f4ec29ab00
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_conv_kernel.cpp
@@ -0,0 +1,820 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_memory.hpp"
+
+#include "jit_uni_1x1_conv_utils.hpp"
+#include "jit_avx512_core_x8s8s32x_1x1_conv_kernel.hpp"
+
+#define GET_OFF(field) offsetof(jit_1x1_conv_call_s, field)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::utils;
+
+using namespace Xbyak;
+
+bool jit_avx512_core_x8s8s32x_1x1_conv_kernel::maybe_eltwise(int position)
+{
+    using namespace primitive_kind;
+    const auto &p = attr_.post_ops_;
+
+    if (position == 0) {
+        /* eltwise before sum */
+        return p.contain(eltwise, 0);
+    } else if (position == 1) {
+        /* eltwise after sum */
+        return p.contain(sum, 0) && p.contain(eltwise, 1);
+    }
+
+    return false;
+}
+
+void jit_avx512_core_x8s8s32x_1x1_conv_kernel::bcast_loop(int load_loop_blk)
+{
+    mov(aux1_reg_bcast_data, reg_bcast_data);
+    mov(aux_reg_bcast_data, reg_bcast_data);
+
+    mov(aux_reg_output_data, reg_output_data);
+    mov(bcast_loop_iter, EVEX_compress_addr(rsp, bcast_loop_work_off));
+
+    Label bcast_loop;
+    Label bcast_loop_tail;
+
+    cmp(bcast_loop_iter, jcp.ur);
+    jl(bcast_loop_tail, T_NEAR);
+
+    L(bcast_loop); {
+        assert(jcp.bcast_block % jcp.ur == 0);
+        int num_substeps = jcp.bcast_block / jcp.ur;
+        assert(num_substeps > 0 && num_substeps < 10);
+        for (int i = 0; i < num_substeps; i++) {
+            reduce_loop(load_loop_blk, jcp.ur, i, false);
+            if (i < num_substeps - 1) {
+                add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_substep);
+                add(aux_reg_output_data, jcp.bcast_loop_output_substep);
+            }
+            else {
+                add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_step
+                    - (num_substeps - 1) * jcp.bcast_loop_bcast_substep);
+                int output_offset = jcp.bcast_loop_output_step
+                    - (num_substeps - 1) * jcp.bcast_loop_output_substep;
+
+                add(aux_reg_output_data, output_offset);
+            }
+        }
+        sub(bcast_loop_iter, jcp.bcast_block);
+        cmp(bcast_loop_iter, jcp.bcast_block);
+        jge(bcast_loop, T_NEAR);
+    }
+
+    L(bcast_loop_tail);
+    if (jcp.ur_tail) {
+        Label bcast_loop_tail_out;
+        cmp(bcast_loop_iter, 0);
+        jz(bcast_loop_tail_out, T_NEAR);
+        reduce_loop(load_loop_blk, jcp.ur_tail, 0, true);
+        L(bcast_loop_tail_out);
+    }
+}
+
+void jit_avx512_core_x8s8s32x_1x1_conv_kernel::cvt2ps(data_type_t type_in,
+        zmm_t zmm_in, const Xbyak::Operand &op, bool mask_flag) {
+    zmm_t zmm = mask_flag ? zmm_in | ktail_mask | T_z : zmm_in;
+    switch (type_in) {
+    case data_type::f32:
+    case data_type::s32: vmovups(zmm, op); break;
+    case data_type::s8: vpmovsxbd(zmm, op); break;
+    case data_type::u8: vpmovzxbd(zmm, op); break;
+    default: assert(!"unsupported data type");
+    }
+    if (type_in != data_type::f32)
+        vcvtdq2ps(zmm_in, zmm_in);
+}
+
+void jit_avx512_core_x8s8s32x_1x1_conv_kernel::reduce_loop(int load_loop_blk,
+         int ur, int substep, bool wraparound)
+{
+    auto vreg_load = [=](int i_load) {
+        return Zmm(ur * load_loop_blk + i_load);
+    };
+
+    auto vreg_accum = [=](int i_load, int i_ur) {
+        return Zmm(i_ur * load_loop_blk + i_load);
+    };
+
+    auto zmm_bias_alpha = [=]() {
+        return Zmm(ur * load_loop_blk);
+    };
+
+    auto xmm_bias_alpha = [=]() {
+        return Xmm(ur * load_loop_blk);
+    };
+    auto bias_ptr = [=](int i_load) {
+        return EVEX_compress_addr(reg_bias_data,
+                                  jcp.typesize_bia * jcp.oc_block * i_load);
+    };
+
+    auto comp_ptr = [=](int i_load) {
+        return EVEX_compress_addr(reg_comp_data,
+                                  sizeof(int32_t) * jcp.oc_block * i_load);
+    };
+
+    auto scale_ptr = [=](int i_load) {
+        return EVEX_compress_addr(reg_ptr_scales,
+                    jcp.is_oc_scale * (sizeof(float) * jcp.oc_block * i_load));
+    };
+
+    auto bcast_ptr = [=](int i_reduce, int i_ur, bool bcast) {
+        assert(i_ur < jcp.ur);
+        assert(i_reduce <= jcp.reduce_loop_unroll);
+        assert(jcp.reduce_loop_unroll == jcp.reduce_block);
+
+        int offt = (jcp.ic_without_padding * i_ur + i_reduce);
+
+        return EVEX_compress_addr(aux_reg_bcast_data, jcp.typesize_in * offt,
+                                bcast);
+    };
+
+    auto load_ptr = [=](int i_reduce, int i_load) {
+        int u0 = i_reduce % jcp.reduce_loop_unroll;
+        int u1 = i_reduce / jcp.reduce_loop_unroll;
+
+        int offt = (i_load * jcp.reduce_dim + u0) * jcp.load_block;
+
+        return EVEX_compress_addr(aux_reg_load_data,
+                                  u1 * jcp.reduce_loop_load_step
+                                  + jcp.typesize_in * offt);
+    };
+
+    auto output_ptr = [=](int i_load, int i_ur) {
+        return EVEX_compress_addr(aux_reg_output_data,
+            jcp.typesize_out * (jcp.oc_without_padding * i_ur
+                                + i_load * jcp.load_block));
+    };
+
+    auto init = [=]() {
+        for (int i_load = 0; i_load < load_loop_blk; ++i_load)
+            for (int i_ur = 0; i_ur < ur; ++i_ur) {
+                auto r = vreg_accum(i_load, i_ur);
+                vpxord(r, r, r);
+            }
+        if (jcp.signed_input) {
+            xor_(reg_scratch, reg_scratch);
+            Reg8 _t8 = reg_scratch.cvt8();
+            mov(_t8, (int8_t)-128);
+            vpbroadcastb(zmm_shift, _t8);
+        }
+    };
+
+    auto store = [=](const bool mask_flag_in) {
+        const auto &p = attr_.post_ops_;
+        const int sum_idx = p.find(primitive_kind::sum);
+        const float *p_sum_scale = (sum_idx != -1)
+            ? &p.entry_[sum_idx].sum.scale
+            : nullptr;
+        mov(EVEX_compress_addr(rsp, reg_bcast_data_off), reg_bcast_data);
+        mov(reg_ptr_scales, EVEX_compress_addr(rsp, reg_ptr_sum_scale_off));
+        if (p_sum_scale && *p_sum_scale != 1.f) {
+            mov(EVEX_compress_addr(rsp, reg_load_data_off), reg_load_data);
+            mov(reg_ptr_sum_scale, (size_t)p_sum_scale);
+        }
+        if (jcp.signed_input && jcp.ver != ver_vnni) {
+            mov(reg_scratch, float2int(jcp.wei_adj_scale));
+            vmovq(xmm_bias_alpha(), reg_scratch);
+            vbroadcastss(zmm_bias_alpha(), xmm_bias_alpha());
+        }
+        for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
+            const bool mask_flag = mask_flag_in && i_load == load_loop_blk - 1;
+            auto zmm_bias = zmm_tmp;
+            auto zmm_comp = zmm_bcast;
+            if (jcp.with_bias) {
+                if (jcp.signed_input)
+                    mov(reg_bias_data,
+                        EVEX_compress_addr(rsp,reg_bias_data_off));
+                cvt2ps(jcp.bia_dt, zmm_bias, bias_ptr(i_load), mask_flag);
+                if (jcp.signed_input && jcp.ver != ver_vnni)
+                    vmulps(zmm_bias, zmm_bias, zmm_bias_alpha());
+            }
+            if (jcp.signed_input) {
+                mov(reg_comp_data, EVEX_compress_addr(rsp, reg_comp_data_off));
+                cvt2ps(data_type::s32, zmm_comp, comp_ptr(i_load), mask_flag);
+            }
+
+            for (int i_ur = 0; i_ur < ur; ++i_ur) {
+                auto r = vreg_accum(i_load, i_ur);
+                vcvtdq2ps(r, r);
+                if (jcp.signed_input)
+                    vaddps(r, r, zmm_comp);
+                if (jcp.with_bias)
+                    vaddps(r, r, zmm_bias);
+
+                zmm_t mask_zmm = mask_flag ? r | ktail_mask | T_z : r;
+                vmulps(mask_zmm, r, scale_ptr(i_load));
+            }
+        }
+
+        if (maybe_eltwise(0))
+            eltwise_injector_->compute_vector_range(0, ur * load_loop_blk);
+
+        if (p_sum_scale) { // post_op: sum
+            for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
+                const bool mask_flag = mask_flag_in &&
+                                           i_load == load_loop_blk - 1;
+                for (int i_ur = 0; i_ur < ur; ++i_ur) {
+                    vpxord(zmm_zero, zmm_zero, zmm_zero);
+                    auto zmm_prev_dst = zmm_zero;
+
+                    auto r = vreg_accum(i_load, i_ur);
+                    cvt2ps(jcp.dst_dt, zmm_prev_dst, output_ptr(i_load, i_ur),
+                        mask_flag);
+
+                    if (*p_sum_scale == 1.f)
+                        vaddps(r, zmm_prev_dst);
+                    else
+                        vfmadd231ps(r, zmm_prev_dst, zword_b[reg_ptr_sum_scale]);
+                }
+            }
+        }
+
+        if (maybe_eltwise(1))
+            eltwise_injector_->compute_vector_range(0, ur * load_loop_blk);
+
+        for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
+            const bool mask_flag = mask_flag_in &&
+                                       i_load == load_loop_blk - 1;
+            for (int i_ur = 0; i_ur < ur; ++i_ur) {
+                auto r = vreg_accum(i_load, i_ur);
+                if (jcp.dst_dt == data_type::u8) {
+                    vpxord(zmm_zero, zmm_zero, zmm_zero);
+                    vmaxps(r, zmm_zero, r);
+                }
+                if (jcp.dst_dt != data_type::f32)
+                    vcvtps2dq(r, r);
+            }
+            for (int i_ur = 0; i_ur < ur; ++i_ur) {
+                auto r = vreg_accum(i_load, i_ur);
+                zmm_t r_zmm = mask_flag ? r | ktail_mask : r;
+
+                switch (jcp.dst_dt) {
+                case data_type::f32:
+                case data_type::s32:
+                    vmovups(output_ptr(i_load, i_ur), r_zmm); break;
+                case data_type::s8:
+                    vpmovsdb(output_ptr(i_load, i_ur), r_zmm); break;
+                case data_type::u8:
+                    vpmovusdb(output_ptr(i_load, i_ur), r_zmm); break;
+                default: assert(!"unknown dst_dt");
+                }
+            }
+        }
+        mov(reg_bcast_data, EVEX_compress_addr(rsp, reg_bcast_data_off));
+        if (p_sum_scale && *p_sum_scale != 1.f)
+            mov(reg_load_data, EVEX_compress_addr(rsp, reg_load_data_off));
+    };
+
+    auto compute = [=](Zmm vreg_acc, Zmm vreg_wei, Zmm vreg_src) {
+        if (jcp.ver == ver_vnni) {
+            vpdpbusd(vreg_acc, vreg_src, vreg_wei);
+        } else {
+            vpmaddubsw(zmm_tmp, vreg_src, vreg_wei);
+            vpmaddwd(zmm_tmp, zmm_tmp, zmm_one);
+            vpaddd(vreg_acc, vreg_acc, zmm_tmp);
+        }
+    };
+
+    auto fma_block = [=](bool last_block) {
+        int reduce_step = 4;
+        int tail_size = jcp.ic_without_padding % reduce_step;
+        int loop_unroll = last_block && jcp.ic != jcp.ic_without_padding
+            ? rnd_up(jcp.ic_without_padding % jcp.ic_block, reduce_step)
+            : jcp.reduce_loop_unroll;
+        for (int i_reduce = 0; i_reduce < loop_unroll;
+                i_reduce += reduce_step) {
+            for (int i_load = 0; i_load < load_loop_blk; ++i_load)
+                vmovups(vreg_load(i_load), load_ptr(i_reduce, i_load));
+            for (int i_ur = 0; i_ur < ur; ++i_ur) {
+                if (last_block && tail_size != 0
+                    && i_reduce == loop_unroll - reduce_step) {
+                    Xmm xmm_bcast = Xmm(zmm_bcast.getIdx());
+                    for (int r = 0; r < tail_size; ++r)
+                        vpinsrb(xmm_bcast, xmm_bcast, ptr[aux_reg_bcast_data
+                        + jcp.ic_without_padding * i_ur + i_reduce + r], r);
+                    vpbroadcastd(zmm_bcast, xmm_bcast);
+                } else {
+                    vpbroadcastd(zmm_bcast, bcast_ptr(i_reduce, i_ur, false));
+                }
+                if (jcp.signed_input)
+                    vpsubb(zmm_bcast, zmm_bcast, zmm_shift);
+                for (int i_load = 0; i_load < load_loop_blk; ++i_load) {
+                    compute(vreg_accum(i_load, i_ur),
+                                vreg_load(i_load), zmm_bcast);
+                }
+            }
+        }
+    };
+
+    Label reduce_loop;
+    Label reduce_loop_tail;
+
+    mov(aux_reg_load_data, reg_load_data);
+
+    mov(aux_reg_bcast_data, aux1_reg_bcast_data);
+    init();
+
+    mov(reduce_loop_iter, reg_reduce_loop_work);
+    sub(reduce_loop_iter, jcp.reduce_loop_unroll);
+    jle(reduce_loop_tail, T_NEAR);
+
+    L(reduce_loop); {
+        fma_block(false);
+        add(aux_reg_bcast_data, jcp.reduce_loop_bcast_step);
+        add(aux_reg_load_data, jcp.reduce_loop_load_step);
+        sub(reduce_loop_iter, jcp.reduce_loop_unroll);
+        jg(reduce_loop, T_NEAR);
+    }
+
+    L(reduce_loop_tail);
+    if (jcp.ic != jcp.ic_without_padding) {
+        fma_block(true);
+    } else {
+        fma_block(false);
+    }
+
+    if (jcp.oc_without_padding != jcp.oc) {
+        Label end_store, common_store;
+        mov(EVEX_compress_addr(rsp, reg_bcast_data_off), reg_bcast_data);
+
+        /*Check if it is the last load_loop_blk*/
+        sub(reg_load_loop_work, load_loop_blk * jcp.load_loop_iter_step);
+        cmp(reg_load_loop_work, 0);
+        jg(common_store, T_NEAR);
+
+        /*Check if it is the last ocb*/
+        test(reg_reduce_pos_flag, FLAG_OC_LAST);
+        jz(common_store, T_NEAR);
+
+        store(true);
+        jmp(end_store, T_NEAR);
+
+        L(common_store);
+        store(false);
+
+        L(end_store);
+
+        add(reg_load_loop_work, load_loop_blk * jcp.load_loop_iter_step);
+    } else {
+        store(false);
+    }
+}
+
+void jit_avx512_core_x8s8s32x_1x1_conv_kernel::generate()
+{
+    preamble();
+
+    xor_(reg_scratch, reg_scratch);
+    Reg16 _t = reg_scratch.cvt16();
+    mov(_t, 0x1);
+    vpbroadcastw(zmm_one, _t);
+
+    sub(rsp, stack_space_needed);
+
+    if (jcp.oc_without_padding != jcp.oc) {
+        int tail_size = jcp.oc_without_padding % jcp.oc_block;
+        int mask = (1 << tail_size) - 1;
+        Reg32 regw_tmp = reg_last_load.cvt32();
+        mov(regw_tmp, mask);
+        kmovw(ktail_mask, regw_tmp);
+    }
+
+    if (jcp.with_bias)
+        mov(reg_bias_data, ptr[param1 + GET_OFF(bias_data)]);
+    if (jcp.signed_input) {
+        mov(EVEX_compress_addr(rsp, reg_bias_data_off), reg_bias_data);
+        mov(reg_comp_data, ptr[param1 + GET_OFF(compensation)]);
+        mov(EVEX_compress_addr(rsp, reg_comp_data_off), reg_comp_data);
+    }
+    mov(reg_ptr_scales, ptr[param1 + GET_OFF(scales)]);
+    mov(EVEX_compress_addr(rsp, reg_ptr_sum_scale_off), reg_ptr_scales);
+    mov(reg_bcast_data, ptr[param1 + GET_OFF(bcast_data)]);
+    mov(reg_load_data, ptr[param1 + GET_OFF(load_data)]);
+    mov(reg_output_data, ptr[param1 + GET_OFF(output_data)]);
+
+    mov(reg_load_loop_work, ptr[param1 + GET_OFF(load_dim)]);
+    mov(reg_bcast_loop_work, ptr[param1 + GET_OFF(bcast_dim)]);
+    mov(EVEX_compress_addr(rsp, bcast_loop_work_off), reg_bcast_loop_work);
+    mov(reg_reduce_loop_work, ptr[param1 + GET_OFF(reduce_dim)]);
+    mov(reg_reduce_pos_flag, ptr[param1 + GET_OFF(first_last_flag)]);
+
+
+    auto load_loop_body = [=](int load_loop_blk) {
+        bcast_loop(load_loop_blk);
+        add(reg_load_data, load_loop_blk * jcp.load_loop_load_step);
+        if (jcp.with_bias) {
+            if (jcp.signed_input)
+                mov(reg_bias_data, EVEX_compress_addr(rsp, reg_bias_data_off));
+            add(reg_bias_data,
+                load_loop_blk * jcp.load_block * jcp.typesize_bia);
+            if (jcp.signed_input)
+                mov(EVEX_compress_addr(rsp, reg_bias_data_off), reg_bias_data);
+        }
+        if (jcp.signed_input) {
+            mov(reg_comp_data, EVEX_compress_addr(rsp, reg_comp_data_off));
+            add(reg_comp_data,
+                load_loop_blk * jcp.load_block * sizeof(int32_t));
+            mov(EVEX_compress_addr(rsp, reg_comp_data_off), reg_comp_data);
+        }
+        mov(EVEX_compress_addr(rsp, reg_bcast_data_off), reg_bcast_data);
+        mov(reg_ptr_scales, EVEX_compress_addr(rsp, reg_ptr_sum_scale_off));
+        add(reg_ptr_scales,
+            jcp.is_oc_scale * load_loop_blk * jcp.load_block * sizeof(float));
+        mov(EVEX_compress_addr(rsp, reg_ptr_sum_scale_off), reg_ptr_scales);
+        mov(reg_bcast_data, EVEX_compress_addr(rsp, reg_bcast_data_off));
+        add(reg_output_data,
+            load_loop_blk * jcp.load_block * jcp.typesize_out);
+        sub(reg_load_loop_work, load_loop_blk * jcp.load_loop_iter_step);
+    };
+
+    const int simd_w = 16;
+
+    Label load_loop_blk[7];
+
+    static const int ur_cases_fma_expl_bcast[] = { 2, 5, 6, 9, 14, 32 };
+    const int size_ur_cases_fma = sizeof(ur_cases_fma_expl_bcast);
+    const int *ur_cases_fma = ur_cases_fma_expl_bcast;
+    const int *ur_cases = ur_cases_fma;
+    const int num_ur_cases = (size_ur_cases_fma) / sizeof(*ur_cases);
+
+    for (int ur_idx = num_ur_cases - 1; ur_idx > 0; ur_idx--) {
+        int label_idx = num_ur_cases - ur_idx - 1;
+        if (jcp.ur <= ur_cases[ur_idx]) {
+            cmp(reg_load_loop_work, simd_w * (label_idx + 1));
+            jle(load_loop_blk[label_idx], T_NEAR);
+        }
+    }
+
+    for (int ur_idx = 0; ur_idx < num_ur_cases; ur_idx++) {
+        if (jcp.ur <= ur_cases[ur_idx]) {
+            int label_idx = num_ur_cases - ur_idx - 1;
+            L(load_loop_blk[label_idx]);
+            {
+                if (label_idx == 0) {
+                    cmp(reg_load_loop_work, 0);
+                    je(load_loop_blk[num_ur_cases], T_NEAR);
+                }
+
+                for (int _i = 1; _i <= label_idx + 1; _i++) {
+                    prefetcht0(ptr [ reg_load_data + _i * jcp.ic * jcp.oc_block ]);
+                    prefetcht1(ptr [ reg_output_data + _i * jcp.oc_block ]);
+                }
+
+                load_loop_body(label_idx + 1);
+                if (label_idx - 1 > 0) {
+                    cmp(reg_load_loop_work, 2 * label_idx * simd_w);
+                    je(load_loop_blk[label_idx - 1], T_NEAR);
+                }
+                cmp(reg_load_loop_work, (label_idx + 1) * simd_w);
+                jge(load_loop_blk[label_idx]);
+            }
+            for (int idx = label_idx - 1; idx > 0; --idx) {
+                cmp(reg_load_loop_work, simd_w * (idx + 1));
+                je(load_loop_blk[idx], T_NEAR);
+            }
+            if (ur_idx < num_ur_cases - 2) {
+                cmp(reg_load_loop_work, simd_w);
+                jle(load_loop_blk[0], T_NEAR);
+            }
+        }
+    }
+    L(load_loop_blk[num_ur_cases]);
+
+    add(rsp, stack_space_needed);
+
+    postamble();
+
+    if (jcp.with_eltwise)
+        eltwise_injector_->prepare_table();
+}
+
+bool jit_avx512_core_x8s8s32x_1x1_conv_kernel::post_ops_ok(
+        jit_1x1_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    using namespace primitive_kind;
+    const auto &p = attr.post_ops_;
+
+    auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); };
+
+    switch (p.len_) {
+    case 0: return true;
+    case 1: return is_eltwise(0) || p.contain(sum, 0);
+    case 2: return (p.contain(sum, 0) && is_eltwise(1))
+                       || (p.contain(sum, 1) && is_eltwise(0));
+    default: return false;
+    }
+
+    return false;
+}
+
+status_t jit_avx512_core_x8s8s32x_1x1_conv_kernel::init_conf(
+        jit_1x1_conv_conf_t &jcp, const convolution_desc_t &cd,
+        const memory_desc_wrapper &src_d, const memory_desc_wrapper &weights_d,
+        const memory_desc_wrapper &dst_d, const memory_desc_wrapper &bias_d,
+        const primitive_attr_t &attr, int nthreads, bool reduce_src) {
+    if (!mayiuse(avx512_core)) return status::unimplemented;
+
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+    if (!one_of(src_d.data_type(), data_type::u8, data_type::s8)
+        || weights_d.data_type() != data_type::s8
+        || !one_of(dst_d.data_type(),
+            data_type::f32, data_type::s32, data_type::s8, data_type::u8))
+        return status::unimplemented;
+    jcp.ver = ver_avx512_core;
+    if (mayiuse(avx512_core_vnni))
+        jcp.ver = ver_vnni;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+    jcp.ic_without_padding = jcp.ic;
+    jcp.ih = src_d.dims()[2];
+    jcp.iw = src_d.dims()[3];
+    jcp.oh = dst_d.dims()[2];
+    jcp.ow = dst_d.dims()[3];
+    jcp.kh = weights_d.dims()[with_groups + 2];
+    jcp.kw = weights_d.dims()[with_groups + 3];
+    jcp.t_pad = cd.padding[0][0];
+    jcp.l_pad = cd.padding[0][1];
+    jcp.stride_h = cd.strides[0];
+    jcp.stride_w = cd.strides[1];
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+
+    jcp.signed_input = (src_d.data_type() == data_type::s8) ? true : false;
+
+    jcp.os = jcp.oh * jcp.ow;
+    jcp.is = jcp.ih * jcp.iw;
+    jcp.tr_is = rnd_up(jcp.is, 4);
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    const int eltwise_ind = p.find(primitive_kind::eltwise);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise)
+        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+
+    format_tag_t dat_tag = format_tag::nhwc;
+    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    jcp.dst_tag = dst_d.matches_one_of_tag(dat_tag);
+
+    bool args_ok = true
+        && jcp.ngroups == 1
+        && jcp.src_tag == dat_tag
+        && jcp.dst_tag == dat_tag;
+    if (!args_ok) return status::unimplemented;
+
+    const int simd_w = 16;
+
+    jcp.oc = rnd_up(jcp.oc, simd_w);
+    jcp.ic = rnd_up(jcp.ic, simd_w);
+
+    args_ok = true
+        && jcp.oc % simd_w == 0 && jcp.ic % simd_w == 0
+        && jcp.t_pad == 0 && jcp.l_pad == 0
+        && jcp.stride_w == 1 && jcp.stride_h == 1 // TODO: support some strides
+        && jcp.kh == 1 && jcp.kw == 1;
+    if (!args_ok) return status::unimplemented;
+
+    jcp.bia_dt = jcp.with_bias ? cd.bias_desc.data_type : data_type::undef;
+    jcp.dst_dt = cd.dst_desc.data_type;
+
+    jcp.ic_block = jcp.oc_block = simd_w;
+
+    jcp.typesize_in = types::data_type_size(src_d.data_type());
+    jcp.typesize_out = types::data_type_size(dst_d.data_type());
+    jcp.typesize_bia = jcp.with_bias
+        ? types::data_type_size(bias_d.data_type())
+        : 0;
+
+    const int SMALL_SPATIAL = 7 * 7;
+    const int BIG_REDUCE_DIM = 1024;
+
+    int load_blocking = 0;
+    int load_blocking_max = 0;
+    int bcast_blocking = 0;
+    int bcast_blocking_max = 0;
+    int reduce_blocking = 0;
+    int reduce_blocking_max = 0;
+    jcp.load_grp_count = 1;
+    jcp.use_vmovntps = false;
+
+    const int L2_size = get_cache_size(2, true) / sizeof(jcp.typesize_in);
+    const int L2_capacity = (L2_size * 3) / 4;
+
+    int size_treshold = 28;
+    int max_regs = 0;
+    int min_regs = 6;
+    if (jcp.ver == ver_vnni)
+        max_regs = ((jcp.oh > size_treshold && jcp.ow > size_treshold)
+                    && (jcp.oc < 128 || jcp.ic < 128)) ?  min_regs : 9;
+    else
+        max_regs = 8;
+    jcp.expl_bcast = true;
+
+    if (jcp.mb == 1 && jcp.ic > 128
+        && (jcp.oh <= size_treshold && jcp.ow <= size_treshold)) {
+        if (jcp.os <= SMALL_SPATIAL && jcp.oc * jcp.ic < L2_size)
+            max_regs = min_regs; // mobilenet_v2 performance improvement
+        jcp.ur = nstl::min(max_regs, jcp.os);
+    } else {
+        const int spatial = jcp.oh;
+        jcp.ur = 1;
+        for (int ur_w = max_regs; ur_w >= min_regs; ur_w--) {
+            if ((spatial >= size_treshold && spatial % ur_w == 0)
+                    || (spatial < size_treshold && jcp.os % ur_w == 0)) {
+                jcp.ur = ur_w;
+                break;
+            }
+        }
+        if (jcp.ur == 1) {
+            jcp.ur = nstl::min(max_regs, jcp.os);
+            int os_tail = jcp.os % max_regs;
+            for (int i = max_regs; i >= min_regs; i--) {
+                int i_tail = jcp.os % i;
+                if (i_tail > os_tail || i_tail == 0) {
+                    jcp.ur = i;
+                    os_tail = i_tail;
+                    if (i_tail == 0)
+                        break;
+                }
+            }
+        }
+    }
+
+    jcp.reduce_dim = jcp.ic;
+    jcp.reduce_block = jcp.ic_block;
+
+    jcp.load_dim = jcp.oc;
+    jcp.load_block = jcp.oc_block;
+
+    jcp.bcast_dim = jcp.is;
+
+    jcp.bcast_block = jcp.ur;
+
+    jcp.reduce_loop_unroll = jcp.reduce_block;
+    jcp.reduce_loop_bcast_step
+            = jcp.reduce_loop_unroll * jcp.typesize_in;
+
+    jcp.reduce_loop_load_step
+            = jcp.reduce_loop_unroll * jcp.load_block * jcp.typesize_in;
+
+    jcp.bcast_loop_output_step = jcp.ur * jcp.oc_without_padding * jcp.typesize_out;
+    jcp.bcast_loop_output_substep = -1; // unused
+    jcp.bcast_loop_bcast_step = jcp.ur * jcp.ic_without_padding * jcp.typesize_in;
+    jcp.bcast_loop_bcast_substep = -1; // unused
+
+    jcp.load_loop_load_step
+            = jcp.reduce_dim * jcp.load_block * jcp.typesize_in;
+
+    jcp.load_loop_iter_step = jcp.load_block;
+
+    jcp.loop_order = reduce_src ? loop_blr : loop_lbr;
+
+    int nb_bcast = div_up(jcp.bcast_dim, jcp.bcast_block);
+    int nb_reduce = div_up(jcp.reduce_dim, jcp.reduce_block);
+
+    reduce_blocking = nb_reduce;
+    if (jcp.bcast_dim <= SMALL_SPATIAL && jcp.reduce_dim >= BIG_REDUCE_DIM)
+        reduce_blocking = 64;
+    else if (jcp.bcast_dim > SMALL_SPATIAL && jcp.reduce_dim >= BIG_REDUCE_DIM)
+        reduce_blocking = 16;
+    reduce_blocking = best_divider(nb_reduce, 1, reduce_blocking, true);
+    reduce_blocking *= jcp.reduce_block;
+
+    bool cmp_reduce = reduce_blocking <= jcp.reduce_dim;
+    if (cmp_reduce)
+        jcp.loop_order = reduce_src ? loop_rbl : loop_rlb;
+    load_blocking = jcp.load_dim;
+
+    jcp.load_grp_count = div_up(nthreads, jcp.mb * jcp.ngroups * nb_bcast);
+    jcp.load_grp_count = best_divider(
+            nthreads, jcp.load_grp_count, 2 * jcp.load_grp_count, false);
+
+    if (jcp.bcast_dim <= SMALL_SPATIAL && jcp.load_dim * jcp.reduce_dim >= L2_size) {
+        jcp.load_grp_count = nstl::max(jcp.load_grp_count, 4);
+    } else if (jcp.bcast_dim <= SMALL_SPATIAL && jcp.mb <= nthreads
+            && jcp.load_dim > 512 && jcp.load_dim / jcp.reduce_dim >= 4) {
+        jcp.load_grp_count = nstl::max(jcp.load_grp_count, 2); //
+        load_blocking = jcp.load_block;
+    }
+
+    bcast_blocking = div_up(jcp.mb * jcp.ngroups * nb_bcast,
+                             div_up(nthreads, jcp.load_grp_count)) * jcp.bcast_block;
+    bcast_blocking = nstl::min(jcp.bcast_dim, bcast_blocking);
+    bcast_blocking = rnd_up(bcast_blocking, jcp.bcast_block);
+
+    int space_for_bcast
+            = (L2_capacity - /* kernel_size - */
+                2 * jcp.load_block * reduce_blocking
+                    - jcp.ur * reduce_blocking - 3 * 1024);
+    if (jcp.reduce_dim * jcp.bcast_dim > L2_capacity)
+        space_for_bcast /= 2;
+
+    int bcast_in_cache
+            = nstl::max(jcp.bcast_block, space_for_bcast / reduce_blocking);
+    bcast_blocking = nstl::min(
+            bcast_blocking, rnd_dn(bcast_in_cache, jcp.bcast_block));
+
+    load_blocking_max = load_blocking;
+    bcast_blocking_max = bcast_blocking * 3 / 2;
+    reduce_blocking_max = reduce_blocking;
+
+    assert(load_blocking);
+    assert(load_blocking_max);
+    assert(bcast_blocking);
+    assert(bcast_blocking_max);
+    assert(reduce_blocking);
+    assert(reduce_blocking_max);
+    assert(load_blocking % jcp.load_block == 0);
+    assert(reduce_blocking % jcp.reduce_block == 0);
+    assert(load_blocking_max % jcp.load_block == 0);
+    assert(reduce_blocking_max % jcp.reduce_block == 0);
+
+    assert(jcp.reduce_loop_unroll % 4 == 0);
+    assert(jcp.reduce_dim % jcp.reduce_loop_unroll == 0);
+
+    assert(jcp.bcast_block % jcp.ur == 0);
+    assert(jcp.reduce_dim % jcp.reduce_block == 0);
+
+    jcp.ur_tail = jcp.bcast_dim % jcp.ur;
+
+    jcp.nb_bcast_blocking = bcast_blocking / jcp.bcast_block;
+    jcp.nb_bcast_blocking_max = bcast_blocking_max / jcp.bcast_block;
+    jcp.nb_load_blocking = load_blocking / jcp.load_block;
+    jcp.nb_load_blocking_max = load_blocking_max / jcp.load_block;
+    jcp.nb_reduce_blocking = reduce_blocking / jcp.reduce_block;
+    jcp.nb_reduce_blocking_max = reduce_blocking_max / jcp.reduce_block;
+
+    jcp.nb_bcast = div_up(jcp.bcast_dim, jcp.bcast_block);
+    jcp.nb_load = div_up(jcp.load_dim, jcp.load_block);
+    jcp.nb_reduce = div_up(jcp.reduce_dim, jcp.reduce_block);
+
+    // miniumum size of load dim chunk for work distribution within threads
+    jcp.nb_load_chunk = 1;
+    // peformance improvements for googlenet_v3, mb=1;
+    // TODO: generalize this condition and rewrite it in appropriate manner
+    if (jcp.mb == 1 && jcp.nb_load % 4 == 0 && jcp.ic / jcp.oc >= 4
+            && jcp.ic * jcp.oc <= L2_size) {
+        jcp.nb_load_chunk = 4;
+        jcp.load_grp_count = nstl::max(jcp.nb_load / 4, jcp.load_grp_count);
+    }
+
+    const auto &oscales = attr.output_scales_;
+    jcp.is_oc_scale = oscales.mask_ == 1 << 1;
+    assert(IMPLICATION(!jcp.is_oc_scale, oscales.mask_ == 0));
+
+    jcp.wei_adj_scale =
+        (weights_d.extra().flags | memory_extra_flags::scale_adjust)
+        ? weights_d.extra().scale_adjust : 1.f;
+
+    return status::success;
+}
+
+void jit_avx512_core_x8s8s32x_1x1_conv_kernel::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad,
+        const jit_1x1_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    using namespace mkldnn::impl::memory_tracking::names;
+
+    if (jcp.signed_input && jcp.ver != ver_vnni) {
+        dim_t count = nstl::max<dim_t>(attr.output_scales_.count_, 16);
+        scratchpad.book(key_conv_adjusted_scales, sizeof(float) * count);
+    }
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_conv_kernel.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_conv_kernel.hpp
new file mode 100644
index 0000000000..22e9732a1f
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_conv_kernel.hpp
@@ -0,0 +1,131 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_AVX512_CORE_X8S8S32X_1X1_CONV_KERNEL_HPP
+#define JIT_AVX512_CORE_X8S8S32X_1X1_CONV_KERNEL_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+#include "jit_uni_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_avx512_core_x8s8s32x_1x1_conv_kernel: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_x8s8s32x_1x1_conv_fwd_ker_t)
+    jit_avx512_core_x8s8s32x_1x1_conv_kernel(jit_1x1_conv_conf_t ajcp,
+            const primitive_attr_t &attr) : jcp(ajcp), attr_(attr),
+            eltwise_injector_(nullptr)
+    {
+        if (jcp.with_eltwise)
+            eltwise_injector_ = new jit_uni_eltwise_injector_f32<avx512_common>(
+                    this, jcp.eltwise);
+
+        this->generate();
+        jit_ker = (void (*)(jit_1x1_conv_call_s *)) this->getCode();
+    }
+
+    ~jit_avx512_core_x8s8s32x_1x1_conv_kernel() {
+        delete eltwise_injector_;
+    }
+
+    static bool post_ops_ok(jit_1x1_conv_conf_t &jcp,
+                                const primitive_attr_t &attr);
+
+    static status_t init_conf(jit_1x1_conv_conf_t &jcp,
+            const convolution_desc_t &cd,
+            const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &dst_d,
+            const memory_desc_wrapper &bias_d,
+            const primitive_attr_t &attr,
+            int nthreads, bool reduce_src);
+
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_1x1_conv_conf_t &jcp, const primitive_attr_t &attr);
+
+    bool maybe_eltwise(int position);
+
+    jit_1x1_conv_conf_t jcp;
+    const primitive_attr_t &attr_;
+    void (*jit_ker)(jit_1x1_conv_call_s *);
+
+  private:
+    jit_uni_eltwise_injector_f32<avx512_common> *eltwise_injector_;
+
+    using reg64_t = const Xbyak::Reg64;
+    using zmm_t = const Xbyak::Zmm;
+    using mask_t = const Xbyak::Opmask;
+
+    reg64_t reg_bcast_data = r8;
+    reg64_t reg_ptr_scales = r8;
+    reg64_t reg_output_data = r9;
+    reg64_t reg_load_data = r10;
+    reg64_t reg_ptr_sum_scale = r10;
+    reg64_t reg_reduce_loop_work = r11;
+    reg64_t reg_bias_data = r12;
+    reg64_t reg_comp_data = r12;
+    reg64_t reg_scratch = r13;
+    reg64_t aux_reg_bcast_data = r14;
+    reg64_t aux_reg_load_data = r15;
+    reg64_t imm_addr64 = r15;
+    reg64_t reg_reduce_pos_flag = rax;
+    reg64_t aux1_reg_bcast_data = rbx;
+    reg64_t reg_bcast_loop_work = rbx;
+    reg64_t bcast_loop_iter = rdx; // Note: Fix me
+    reg64_t reg_load_loop_work = rsi;
+    reg64_t aux_reg_output_data = abi_not_param1;
+    reg64_t reduce_loop_iter = abi_param1;
+
+    reg64_t reg_last_load = r8;
+    mask_t ktail_mask = k6;
+
+    mask_t vmask = k7;
+
+    Xbyak::Zmm zmm_tmp = Xbyak::Zmm(28);
+    Xbyak::Zmm zmm_one = Xbyak::Zmm(29);
+    Xbyak::Zmm zmm_zero = Xbyak::Zmm(30);
+    Xbyak::Zmm zmm_bcast = Xbyak::Zmm(31);
+    Xbyak::Zmm zmm_shift = Xbyak::Zmm(30);
+
+    Xbyak::Zmm zmm_bias_alpha = Xbyak::Zmm(31);
+    Xbyak::Xmm xmm_bias_alpha = Xbyak::Xmm(31);
+
+    int bcast_loop_work_off = 0;
+    int reg_bias_data_off = 8;
+    int reg_bcast_data_off = 16;
+    int reg_load_data_off = 24;
+    int reg_ptr_sum_scale_off = 32;
+    int reg_comp_data_off = 40;
+    int stack_space_needed = 48;
+
+    void bcast_loop(int load_loop_blk);
+    void reduce_loop(int load_loop_blk, int ur, int substep, bool wraparound);
+
+    void generate();
+    void cvt2ps(data_type_t type_in, zmm_t zmm_in, const Xbyak::Operand &op,
+        bool mask_flag);
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_convolution.cpp
new file mode 100644
index 0000000000..0bf09fc677
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_convolution.cpp
@@ -0,0 +1,292 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_generator.hpp"
+
+#include "jit_avx512_core_x8s8s32x_1x1_convolution.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+
+namespace {
+template <typename T, typename U>
+void balance2D(U nthr, U ithr, T ny, T &ny_start, T &ny_end,
+    T nx, T &nx_start, T &nx_end, T nx_divider)
+{
+    const T grp_size = utils::div_up(nthr, nx_divider);
+    const T grp_count = utils::div_up(nthr, grp_size);
+
+    T grp = ithr / grp_size;
+    T grp_ithr = ithr % grp_size;
+    T grp_nthr = grp_size;
+    T first_grps = nthr % grp_count;
+    if (first_grps > 0 && grp >= first_grps) {
+        ithr -= first_grps * grp_size;
+        grp_nthr--;
+        grp = ithr / grp_nthr + first_grps;
+        grp_ithr = ithr % grp_nthr;
+    }
+    balance211(nx, grp_count, grp, nx_start, nx_end);
+    balance211(ny, grp_nthr, grp_ithr, ny_start, ny_end);
+}
+}
+
+/* convolution forward */
+template <data_type_t src_type, data_type_t dst_type>
+void jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<src_type, dst_type>::
+execute_forward(const exec_ctx_t &ctx) const
+{
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    auto scratchpad = this->scratchpad(ctx);
+
+    if (pd()->jcp_.signed_input && pd()->jcp_.ver != ver_vnni) {
+        auto local_scales = scratchpad.template get<float>(
+                key_conv_adjusted_scales);
+        auto scales = pd()->attr()->output_scales_.scales_;
+        size_t count = pd()->attr()->output_scales_.count_;
+        float factor = 1.f / pd()->jcp_.wei_adj_scale;
+        if (count == 1) {
+            utils::array_set(local_scales, scales[0] * factor, 16);
+        } else {
+            for (size_t c = 0; c < count; c++)
+                local_scales[c] = scales[c] * factor;
+        }
+    }
+
+    parallel(kernel_->jcp.nthr, [&](const int ithr, const int nthr) {
+        execute_forward_thr(ithr, nthr, src, weights, bias, dst, scratchpad);
+    });
+}
+
+template <data_type_t src_type, data_type_t dst_type>
+void jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<src_type, dst_type>
+::execute_forward_thr(const int ithr, const int nthr, const src_data_t *src,
+        const wei_data_t *weights, const char *bias, dst_data_t *dst,
+        const memory_tracking::grantor_t &scratchpad) const {
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+
+    const size_t bia_dt_size = pd()->with_bias()
+        ? types::data_type_size(pd()->desc()->bias_desc.data_type) : 0;
+
+    const auto &jcp = kernel_->jcp;
+    auto rtus_space = scratchpad.get<src_data_t>(key_conv_rtus_space);
+    auto local_scales = scratchpad.get<float>(key_conv_adjusted_scales);
+
+    const int work_amount = jcp.mb * jcp.ngroups * jcp.nb_bcast;
+
+    const int stride_h = pd()->desc()->strides[0];
+    const int stride_w = pd()->desc()->strides[1];
+    const int pad_t = pd()->desc()->padding[0][0];
+    const int pad_l = pd()->desc()->padding[0][1];
+
+    const auto &oscales = pd()->attr()->output_scales_;
+
+    int offset = jcp.ngroups * (jcp.oc / jcp.oc_block) * (jcp.ic / jcp.ic_block)
+        * jcp.oc_block * jcp.ic_block;
+    wei_data_t *w = const_cast<wei_data_t *>(weights);
+    int32_t* compensation = (jcp.signed_input)
+        ? reinterpret_cast<int32_t *>(w + offset) : 0;
+
+    auto step = [](int default_step, int remaining, int tail_step) {
+        assert(default_step <= tail_step);
+        return remaining < tail_step ? remaining : default_step;
+    };
+
+    auto p = jit_1x1_conv_call_s();
+
+    auto rp = rtus_driver_t<avx512_common>::call_params_t();
+    const int nb_oc = jcp.nb_load;
+    const int os_block = jcp.bcast_block;
+
+    int bcast_start{0}, bcast_end{0}, ocb_start{0}, ocb_end{0};
+    balance2D(nthr, ithr, work_amount, bcast_start, bcast_end,
+        jcp.nb_load / jcp.nb_load_chunk, ocb_start, ocb_end,
+        jcp.load_grp_count);
+    if (jcp.nb_load_chunk > 1) {
+        ocb_start *= jcp.nb_load_chunk;
+        ocb_end *= jcp.nb_load_chunk;
+    }
+
+    auto init_bcast = [&](int iwork, int &n, int &g, int &bcast_step,
+            int &oh, int &ow, int &ih, int &iw)
+    {
+        int osb{0};
+        nd_iterator_init(iwork, n, jcp.mb, g, jcp.ngroups, osb,
+            jcp.nb_bcast);
+        bcast_step = step(jcp.nb_bcast_blocking, jcp.nb_bcast - osb,
+                jcp.nb_bcast_blocking_max);
+        bcast_step = nstl::min(bcast_step, bcast_end - iwork);
+
+        const int os = osb * os_block;
+        oh = os / jcp.ow;
+        ow = os % jcp.ow;
+
+        ih = nstl::max(oh * stride_h - pad_t, 0);
+        iw = nstl::max(ow * stride_w - pad_l, 0);
+        rp.iw_start = iw;
+
+        p.bcast_dim = this_block_size(os, jcp.os,
+            bcast_step * os_block);
+        rp.os = p.bcast_dim;
+    };
+
+    auto init_load = [&](int ocb, int &load_step)
+    {
+        load_step = step(jcp.nb_load_blocking, ocb_end - ocb,
+            jcp.nb_load_blocking_max);
+        p.load_dim = this_block_size(ocb * jcp.oc_block,
+            ocb_end * jcp.oc_block, load_step * jcp.oc_block);
+
+        if (ocb + load_step >= nb_oc)
+            p.first_last_flag |= FLAG_OC_LAST;
+        else
+            p.first_last_flag &= ~FLAG_OC_LAST;
+
+    };
+
+    auto init_reduce = [&]()
+    {
+        p.reduce_dim = this_block_size(0, jcp.ic, jcp.ic);
+        rp.icb = p.reduce_dim / jcp.reduce_block;
+    };
+
+    auto inner_ker = [&](int ocb, int n, int g, int oh, int ow,
+            int ih, int iw)
+    {
+        const int icb = 0; // Start from the first IC block
+        const int _ocb = g * nb_oc + ocb;
+        const int _icb = g;
+
+        const size_t dst_off = dst_d.blk_off(n, _ocb * jcp.oc_block, oh, ow);
+
+        p.output_data = &dst[dst_off];
+        p.load_data = &weights[pd()->with_groups()
+            ? weights_d.blk_off(g, ocb, icb)
+            : weights_d.blk_off(ocb, icb)];
+        p.bias_data = &bias[_ocb * jcp.oc_block * bia_dt_size];
+        p.compensation = (jcp.signed_input)
+            ? &compensation[_ocb * jcp.oc_block] : 0;
+        p.scales = (jcp.signed_input && jcp.ver != ver_vnni)
+            ? &local_scales[jcp.is_oc_scale * _ocb * jcp.oc_block]
+            : &oscales.scales_[jcp.is_oc_scale * _ocb * jcp.oc_block];
+        if (pd()->rtus_.reduce_src_) {
+            rp.ws = rtus_space + ithr * pd()->rtus_.space_per_thread_
+                + _icb * jcp.is * jcp.ic_block;
+            if (ocb == ocb_start) {
+                rp.src = src + src_d.blk_off(n, _icb * jcp.ic_block, ih, iw);
+                rtus_driver_->ker_(&rp);
+            }
+            p.bcast_data = rp.ws;
+        } else
+            p.bcast_data = src + src_d.blk_off(n, _icb * jcp.ic_block, ih, iw);
+
+        kernel_->jit_ker(&p);
+    };
+
+    if (jcp.loop_order == loop_rlb) {
+        init_reduce();
+        int ocb = ocb_start;
+        while (ocb < ocb_end) {
+            int load_step;
+            init_load(ocb, load_step);
+            int iwork = bcast_start;
+            while (iwork < bcast_end) {
+                int n, g, bcast_step, oh, ow, ih, iw;
+                init_bcast(iwork, n, g, bcast_step, oh, ow, ih, iw);
+                inner_ker(ocb, n, g, oh, ow, ih, iw);
+                iwork += bcast_step;
+            }
+            ocb += load_step;
+        }
+    } else if (jcp.loop_order == loop_lbr) {
+        int ocb = ocb_start;
+        while (ocb < ocb_end) {
+            int load_step;
+            init_load(ocb, load_step);
+            int iwork = bcast_start;
+            while (iwork < bcast_end) {
+                int n, g, bcast_step, oh, ow, ih, iw;
+                init_bcast(iwork, n, g, bcast_step, oh, ow, ih, iw);
+                init_reduce();
+                inner_ker(ocb, n, g, oh, ow, ih, iw);
+                iwork += bcast_step;
+            }
+            ocb += load_step;
+        }
+    } else if (jcp.loop_order == loop_rbl) {
+        init_reduce();
+        int iwork = bcast_start;
+        while (iwork < bcast_end) {
+            int n, g, bcast_step, oh, ow, ih, iw;
+            init_bcast(iwork, n, g, bcast_step, oh, ow, ih, iw);
+            int ocb = ocb_start;
+            while (ocb < ocb_end) {
+                int load_step;
+                init_load(ocb, load_step);
+                inner_ker(ocb, n, g, oh, ow, ih, iw);
+                ocb += load_step;
+            }
+            iwork += bcast_step;
+        }
+    } else if (jcp.loop_order == loop_blr) {
+        int iwork = bcast_start;
+        while (iwork < bcast_end) {
+            int n, g, bcast_step, oh, ow, ih, iw;
+            init_bcast(iwork, n, g, bcast_step, oh, ow, ih, iw);
+            int ocb = ocb_start;
+            while (ocb < ocb_end) {
+                int load_step;
+                init_load(ocb, load_step);
+                init_reduce();
+                inner_ker(ocb, n, g, oh, ow, ih, iw);
+                ocb += load_step;
+            }
+            iwork += bcast_step;
+        }
+    } else {
+        assert(!"unsupported loop order");
+    }
+}
+
+using namespace data_type;
+template struct jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<u8, u8>;
+template struct jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<s8, u8>;
+template struct jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<u8, s8>;
+template struct jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<s8, s8>;
+template struct jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<u8, s32>;
+template struct jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<s8, s32>;
+template struct jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<u8, f32>;
+template struct jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<s8, f32>;
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_convolution.hpp
new file mode 100644
index 0000000000..ad9027ac17
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_convolution.hpp
@@ -0,0 +1,159 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_CORE_X8S8S32X_1X1_CONVOLUTION_HPP
+#define CPU_JIT_AVX512_CORE_X8S8S32X_1X1_CONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "jit_avx512_core_x8s8s32x_1x1_conv_kernel.hpp"
+#include "jit_uni_1x1_conv_utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template<impl::data_type_t src_type, impl::data_type_t dst_type>
+struct jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t : public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_(), rtus_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_int8_1x1:", avx512_core, ""),
+                jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t<
+                src_type, dst_type>);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(src_type, data_type::s8, data_type::undef,
+                        dst_type, data_type::s32)
+                && IMPLICATION(with_bias(), utils::one_of(
+                            desc()->bias_desc.data_type, data_type::f32,
+                            data_type::s32, data_type::s8, data_type::u8))
+                && !has_zero_dim_memory()
+                && set_default_formats_common(dat_tag(), format_tag::any,
+                        dat_tag())
+                && set_or_check_wei_format();
+            if (!ok) return status::unimplemented;
+
+            const convolution_desc_t *conv_d = desc();
+            const memory_desc_t *src_d = src_md();
+            rtus_prepare(this, conv_d, src_d, dst_md());
+
+            status_t status = jit_avx512_core_x8s8s32x_1x1_conv_kernel::
+                init_conf(jcp_, *conv_d, *src_d, *weights_md(), *dst_md(),
+                        *weights_md(1), *attr(), mkldnn_get_max_threads(),
+                        rtus_.reduce_src_);
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx512_core_x8s8s32x_1x1_conv_kernel::init_scratchpad(
+                    scratchpad, jcp_, *attr());
+
+            rtus_prepare_space_info(this, scratchpad);
+
+            return status::success;
+        }
+
+        jit_1x1_conv_conf_t jcp_;
+        reduce_to_unit_stride_t rtus_;
+
+    protected:
+        format_tag_t dat_tag() const { return format_tag::nhwc; }
+
+        bool set_or_check_wei_format() {
+            using namespace format_tag;
+
+            const bool is_src_s8 = src_md_.data_type == data_type::s8;
+            format_tag_t wei_tag = with_groups() ? gOIhw4i16o4i : OIhw4i16o4i;
+
+            memory_desc_t want_wei_md = weights_md_;
+            memory_desc_init_by_tag(want_wei_md, wei_tag);
+            if (is_src_s8) {
+                want_wei_md.extra.flags = 0
+                    | memory_extra_flags::compensation_conv_s8s8
+                    | memory_extra_flags::scale_adjust;
+                want_wei_md.extra.compensation_mask = (1 << 0)
+                    + (with_groups() ? (1 << 1) : 0);
+                want_wei_md.extra.scale_adjust =
+                    mayiuse(avx512_core_vnni) ? 1.f : 0.5f;
+            }
+
+            if (weights_md_.format_kind == format_kind::any) {
+                weights_md_ = want_wei_md;
+                return true;
+            }
+
+            return weights_md_ == want_wei_md;
+        }
+    };
+
+    template <cpu_isa_t isa, typename conv_t>
+    friend void init_rtus_driver(conv_t *self);
+
+    jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t(const pd_t *apd)
+        : cpu_primitive_t(apd)
+        , kernel_(nullptr), rtus_driver_(nullptr)
+    {
+        kernel_ = new jit_avx512_core_x8s8s32x_1x1_conv_kernel(pd()->jcp_,
+                    *pd()->attr());
+        init_rtus_driver<avx512_common>(this);
+    }
+
+    ~jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t() {
+        delete kernel_;
+        delete rtus_driver_;
+    }
+
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<data_type::s8>::type wei_data_t;
+    typedef typename prec_traits<dst_type>::type dst_data_t;
+    typedef typename prec_traits<data_type::s32>::type acc_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+  private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    void execute_forward_thr(const int ithr, const int nthr,
+            const src_data_t *src, const wei_data_t *weights,
+            const char *bias, dst_data_t *dst,
+            const memory_tracking::grantor_t &scratchpad) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx512_core_x8s8s32x_1x1_conv_kernel *kernel_;
+    rtus_driver_t<avx512_common> *rtus_driver_;
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_deconvolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_deconvolution.hpp
new file mode 100644
index 0000000000..e89d068302
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_1x1_deconvolution.hpp
@@ -0,0 +1,140 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_CORE_X8S8S32X_1X1_DECONVOLUTION_HPP
+#define CPU_JIT_AVX512_CORE_X8S8S32X_1X1_DECONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+#include "type_helpers.hpp"
+#include "primitive_iterator.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_deconvolution_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "jit_uni_1x1_conv_utils.hpp"
+#include "jit_avx512_core_x8s8s32x_1x1_convolution.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t src_type, impl::data_type_t dst_type>
+struct jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t
+        : public cpu_primitive_t {
+    struct pd_t : public cpu_deconvolution_fwd_pd_t {
+        pd_t(engine_t *engine, const deconvolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const deconvolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_deconvolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , conv_pd_(nullptr) {}
+
+        pd_t(const pd_t &other)
+            : cpu_deconvolution_fwd_pd_t(other)
+            , conv_pd_(other.conv_pd_->clone())
+        {}
+
+        ~pd_t() { delete conv_pd_; }
+
+        DECLARE_COMMON_PD_T(conv_pd_->name(),
+                jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t<src_type, dst_type>);
+
+        status_t init_convolution() {
+            convolution_desc_t cd;
+            status_t status;
+
+            auto dd = desc();
+            status = conv_desc_init(&cd, prop_kind::forward_training,
+                    alg_kind::convolution_direct, &(dd->src_desc),
+                    &(dd->weights_desc), &(dd->bias_desc), &(dd->dst_desc),
+                    dd->strides, dd->dilates, dd->padding[0], dd->padding[1],
+                    dd->padding_kind);
+
+            if (status == status::success) {
+                status = mkldnn_primitive_desc::create<conv_pd_t>(
+                        &conv_pd_, (op_desc_t *)&cd, &attr_, engine_, nullptr);
+            }
+
+            if (status == status::success)
+                status = set_default_params();
+
+            return status;
+        };
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && desc()->alg_kind == alg_kind::deconvolution_direct
+                && !has_zero_dim_memory()
+                && desc()->src_desc.data_type == src_type
+                && desc()->dst_desc.data_type == dst_type
+                && desc()->weights_desc.data_type == data_type::s8
+                && IMPLICATION(with_bias(), utils::one_of(
+                            desc()->bias_desc.data_type, data_type::f32,
+                            data_type::s32, data_type::s8, data_type::u8))
+                && desc()->accum_data_type == data_type::s32;
+            if (!ok) return status::unimplemented;
+
+            CHECK(init_convolution());
+
+            return status::success;
+        }
+
+        virtual void init_scratchpad_md() override {
+            const auto conv_1x1_pd = static_cast<conv_pd_t *>(conv_pd_);
+            scratchpad_md_ = *conv_1x1_pd->scratchpad_md();
+        }
+
+    protected:
+        status_t set_default_params() {
+            auto conv_1x1_pd_ = static_cast<conv_pd_t *>(conv_pd_);
+            src_md_ = *conv_1x1_pd_->src_md();
+            dst_md_ = *conv_1x1_pd_->dst_md();
+            weights_md_ = *conv_1x1_pd_->weights_md();
+            if (with_bias())
+                bias_md_ = *conv_1x1_pd_->weights_md(1);
+            return status::success;
+        }
+
+        using conv_pd_t = typename jit_avx512_core_x8s8s32x_1x1_convolution_fwd_t
+            <src_type, dst_type>::pd_t;
+        friend jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t;
+        primitive_desc_t *conv_pd_;
+    };
+
+    jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t(const pd_t *apd)
+        : cpu_primitive_t(apd)
+    { pd()->conv_pd_->create_primitive((primitive_t **)&conv_p_); }
+
+    ~jit_avx512_core_x8s8s32x_1x1_deconvolution_fwd_t()
+    { delete conv_p_; }
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        return conv_p_->execute(ctx);
+    }
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    primitive_t *conv_p_;
+};
+
+}
+}
+}
+
+#endif /* CPU_JIT_AVX512_CORE_X8S8S32X_1X1_DECONVOLUTION_HPP */
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_conv_kernel.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_conv_kernel.cpp
new file mode 100644
index 0000000000..10e98a00c4
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_conv_kernel.cpp
@@ -0,0 +1,1182 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_memory.hpp"
+
+#include "jit_avx512_core_x8s8s32x_conv_kernel.hpp"
+
+#define GET_OFF(field) offsetof(jit_conv_call_s, field)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+using namespace Xbyak;
+
+namespace {
+void pick_loop_order(jit_conv_conf_t &jcp, int nthr)
+{
+    jcp.loop_order = loop_cwgn;
+    if (jcp.ngroups > 1) {
+        jcp.loop_order = loop_ngcw;
+        if (jcp.mb < nthr)
+            jcp.loop_order = jcp.ndims == 3 ? loop_nwcg : loop_nhwcg;
+    }
+}
+}
+
+template<typename Vmm>
+bool _jit_avx512_core_x8s8s32x_fwd_kernel<Vmm>::maybe_eltwise(int position)
+{
+    using namespace primitive_kind;
+    const auto &p = attr_.post_ops_;
+
+    if (position == 0) {
+        /* eltwise before sum */
+        return p.contain(eltwise, 0);
+    } else if (position == 1) {
+        /* eltwise after sum */
+        return p.contain(sum, 0) && p.contain(eltwise, 1);
+    }
+
+    return false;
+}
+
+template<typename Vmm>
+void _jit_avx512_core_x8s8s32x_fwd_kernel<Vmm>::prepare_output(int ur_w)
+{
+    int nb_oc_block
+            = jcp.is_depthwise ? jcp.nb_ch_blocking : jcp.nb_oc_blocking;
+    for (int k = 0; k < nb_oc_block; k++)
+        for (int j = 0; j < ur_w; j++) {
+            Vmm vmm = vmm_out(j, k);
+            vpxord(vmm, vmm, vmm);
+        }
+    if (jcp.signed_input) {
+        xor_(reg_scratch, reg_scratch);
+        if (jcp.is_depthwise && !jcp.is_fast_depthwise) {
+            Reg32 _t32 = reg_scratch.cvt32();
+            mov(_t32, (uint32_t)128);
+            vpbroadcastd(vmm_shift, _t32);
+        } else {
+            Reg8 _t8 = reg_scratch.cvt8();
+            mov(_t8, (int8_t)128);
+            vpbroadcastb(vmm_shift, _t8);
+        }
+    }
+}
+
+template<typename Vmm>
+const Vmm _jit_avx512_core_x8s8s32x_fwd_kernel<Vmm>::
+    vmm_mask(const Vmm vmm_in, bool mask_flag, bool store) {
+    return vmm_in;
+}
+
+template<>
+const Zmm _jit_avx512_core_x8s8s32x_fwd_kernel<Zmm>::
+    vmm_mask(const Zmm zmm_in, bool mask_flag, bool store) {
+    return mask_flag ? (store ? zmm_in | ktail_mask : zmm_in | ktail_mask | T_z)
+                     : zmm_in;
+}
+
+
+template<typename Vmm>
+void _jit_avx512_core_x8s8s32x_fwd_kernel<Vmm>::cvt2ps(data_type_t type_in,
+        const Vmm vmm_in, const Operand &op, bool mask_flag) {
+    //const Vmm vmm = mask_flag ? vmm_in | ktail_mask | T_z : vmm_in;
+    const Vmm vmm = vmm_mask(vmm_in, mask_flag);
+    switch (type_in) {
+    case data_type::f32:
+    case data_type::s32: vmovups(vmm, op); break;
+    case data_type::s8: vpmovsxbd(vmm, op); break;
+    case data_type::u8: vpmovzxbd(vmm, op); break;
+    default: assert(!"unsupported data type");
+    }
+    if (type_in != data_type::f32)
+        vcvtdq2ps(vmm_in, vmm_in);
+}
+
+template<typename Vmm>
+void _jit_avx512_core_x8s8s32x_fwd_kernel<Vmm>::compute_eltwise(int ur_w) {
+    int nb_oc_block
+            = jcp.is_depthwise ? jcp.nb_ch_blocking : jcp.nb_oc_blocking;
+    if (ur_w == jcp.ur_w)
+        eltwise_injector_->compute_vector_range(0, nb_oc_block * jcp.ur_w);
+    else
+        for (int k = 0; k < nb_oc_block; k++)
+            eltwise_injector_->compute_vector_range(k * jcp.ur_w,
+                k * jcp.ur_w + ur_w);
+}
+
+template<typename Vmm>
+void _jit_avx512_core_x8s8s32x_fwd_kernel<Vmm>::store_output(
+        int ur_w, bool last_oc_block_flag) {
+    int nb_oc_block
+            = jcp.is_depthwise ? jcp.nb_ch_blocking : jcp.nb_oc_blocking;
+    int oc_block = jcp.is_depthwise ? jcp.ch_block : jcp.oc_block;
+
+    mov(reg_bias, ptr[param1 + GET_OFF(bias)]);
+    mov(reg_ptr_scales, ptr[param1 + GET_OFF(scales)]);
+    if (jcp.signed_input)
+        mov(reg_compensation, ptr[param1 + GET_OFF(compensation)]);
+
+    const auto &p = attr_.post_ops_;
+    const int sum_idx = p.find(primitive_kind::sum);
+    const float *p_sum_scale = nullptr;
+    if (sum_idx != -1) {
+        const auto &p_entry = p.entry_[sum_idx];
+        p_sum_scale = &p_entry.sum.scale;
+    }
+
+    if (p_sum_scale && *p_sum_scale != 1.f)
+        mov(reg_ptr_sum_scale, (size_t)p_sum_scale);
+
+    if (jcp.signed_input && jcp.ver != ver_vnni) {
+        /* put 'wei_adj_scale = 0.5' for bias calculation */
+        mov(reg_bias_alpha, float2int(jcp.wei_adj_scale));
+        vmovq(xmm_bias_alpha(), reg_bias_alpha);
+        vbroadcastss(vmm_bias_alpha(), xmm_bias_alpha());
+    }
+
+    for (int k = 0; k < nb_oc_block; k++) {
+        const bool mask_flag = last_oc_block_flag && k == nb_oc_block - 1;
+        int scale_offset = jcp.is_oc_scale * (sizeof(float) * k * oc_block);
+        if (jcp.with_bias) {
+            int bias_offset = jcp.typesize_bia * k * oc_block;
+            auto bias_addr = EVEX_compress_addr(reg_bias, bias_offset);
+
+            cvt2ps(jcp.bia_dt, vmm_bias, bias_addr, mask_flag);
+            if (jcp.signed_input && jcp.ver != ver_vnni)
+                /* bias *= 0.5 */
+                vmulps(vmm_bias, vmm_bias, vmm_bias_alpha());
+        }
+        if (jcp.signed_input) {
+            int comp_offset = sizeof(int32_t) * k * oc_block;
+            auto comp_addr = EVEX_compress_addr(reg_compensation, comp_offset);
+
+            cvt2ps(data_type::s32, vmm_comp, comp_addr, mask_flag);
+        }
+        /* add to zmm_accum: compensation, bias and permute */
+        for (int j = 0; j < ur_w; j++) {
+            Vmm vmm = vmm_out(j, k);
+            if (jcp.is_fast_depthwise)
+                vpermd(zmm_out(j, k), zmm_permute, zmm_out(j, k));
+            vcvtdq2ps(vmm, vmm);
+            if (jcp.signed_input)
+                vaddps(vmm, vmm, vmm_comp);
+            if (jcp.with_bias)
+                vaddps(vmm, vmm, vmm_bias);
+
+            const Vmm vmm_k = vmm_mask(vmm, mask_flag);
+            vmulps(vmm_k, vmm,
+                    EVEX_compress_addr(reg_ptr_scales, scale_offset));
+        }
+    }
+
+    /* Do post-ops */
+    if (maybe_eltwise(0)) compute_eltwise(ur_w);
+    if (p_sum_scale) { // post_op: sum
+        for (int k = 0; k < nb_oc_block; k++) {
+            const bool mask_flag = last_oc_block_flag && k == nb_oc_block - 1;
+            for (int j = 0; j < ur_w; j++) {
+                int aux_output_offset
+                        = jcp.typesize_out
+                        * (k * oc_block
+                                  + j * jcp.oc_without_padding * jcp.ngroups);
+                auto addr = EVEX_compress_addr(reg_out, aux_output_offset);
+                Vmm vmm = vmm_out(j, k);
+                cvt2ps(jcp.dst_dt, vmm_prev_dst, addr, mask_flag);
+                if (*p_sum_scale == 1.f)
+                    vaddps(vmm, vmm_prev_dst);
+                else
+                    vfmadd231ps(vmm, vmm_prev_dst, zword_b[reg_ptr_sum_scale]);
+            }
+        }
+    }
+    if (maybe_eltwise(1)) compute_eltwise(ur_w);
+
+    /* write out register to output_addr */
+    for (int k = 0; k < nb_oc_block; k++) {
+        const bool mask_flag = last_oc_block_flag && k == nb_oc_block - 1;
+        for (int j = 0; j < ur_w; j++) {
+            Vmm vmm = vmm_out(j, k);
+            if (jcp.dst_dt == data_type::u8) {
+                vpxord(vmm_zero, vmm_zero, vmm_zero);
+                vmaxps(vmm, vmm_zero, vmm);
+            }
+
+            if (jcp.dst_dt != data_type::f32) {
+                /* Note: using Zmm for rounding in Xmm/Ymm kernel
+                   because there is no instruction to do rounding
+                   from Xmm/Ymm -> Xmm/Ymm.
+                   Embedded rounding is not supported for Xmm.
+                   TODO: maybe avoid Zmm if it helps performance.*/
+                Zmm zmm = zmm_out(j, k);
+                vcvtps2dq(zmm, zmm);
+            }
+        }
+
+        for (int j = 0; j < ur_w; j++) {
+            int aux_output_offset = jcp.typesize_out
+                    * (k * oc_block + j * jcp.oc_without_padding * jcp.ngroups);
+            auto addr = EVEX_compress_addr(reg_out, aux_output_offset);
+
+            Vmm vmm = vmm_out(j, k);
+            const Vmm r_vmm = vmm_mask(vmm, mask_flag, true);
+
+            switch (jcp.dst_dt) {
+            case data_type::f32:
+            case data_type::s32: vmovups(addr, r_vmm); break;
+            case data_type::s8: vpmovsdb(addr, r_vmm); break;
+            case data_type::u8: vpmovusdb(addr, r_vmm); break;
+            default: assert(!"unknown dst_dt");
+            }
+        }
+    }
+
+}
+
+template <typename Vmm>
+void _jit_avx512_core_x8s8s32x_fwd_kernel<Vmm>::compute_ker_dw(
+        int ur_w, int pad_l, int pad_r, ic_block_t last_ic_block_flag, bool h_padded) {
+    assert(!"invalid group blocking for depthwise convolution");
+}
+
+template <>
+void _jit_avx512_core_x8s8s32x_fwd_kernel<Zmm>::compute_ker_dw(
+        int ur_w, int pad_l, int pad_r, ic_block_t last_ic_block_flag, bool h_padded) {
+
+    auto input_spatial_index = [=](int oi, int ki) {
+        return (ki * (jcp.dilate_w + 1) + oi * jcp.stride_w - pad_l);
+    };
+
+    auto input_offset2 = [=](int ii, int ci) {
+        return jcp.typesize_in * (ii * jcp.ngroups + ci * jcp.ch_block);
+    };
+
+    auto input_offset3 = [=](int oi, int ci, int ki) {
+        return jcp.typesize_in * input_offset2(input_spatial_index(oi, ki), ci);
+    };
+
+    auto kernel_offset = [=](int ci, int ki) {
+        return jcp.typesize_in * ((ci * jcp.kh * jcp.kw + ki) * jcp.ch_block);
+    };
+
+    auto compute = [=](Zmm vreg_acc, Zmm vreg_wei, Zmm vreg_src) {
+        // okay for depthwise since src is zero-extended
+        if (jcp.ver == ver_vnni) {
+            vpdpbusd(vreg_acc, vreg_src, vreg_wei);
+        } else {
+            vpmaddwd(zmm_tmp, vreg_src, vreg_wei);
+            vpaddd(vreg_acc, vreg_acc, zmm_tmp);
+        }
+    };
+
+    int ii_start = 0;
+    int ii_end = -1;
+    if (jcp.is_resrc_depthwise && !h_padded) {
+        // find bounds of input spatial indices
+        bool first = true;
+        for (int ki = 0; ki < jcp.kw; ki++) {
+            int oi_start = get_ow_start(ki, pad_l);
+            int oi_end = get_ow_end(ur_w, ki, pad_r);
+            for (int oi = oi_start; oi < oi_end; oi++) {
+                int ii = input_spatial_index(oi, ki);
+                if (first || ii < ii_start)
+                    ii_start = ii;
+                if (first || ii > ii_end)
+                    ii_end = ii;
+                first = false;
+            }
+        }
+    }
+
+    if (jcp.signed_input) {
+        vpxord(zmm_shifted_zero, zmm_shifted_zero, zmm_shifted_zero);
+        vpaddb(zmm_shifted_zero, zmm_shifted_zero, vmm_shift);
+    }
+    for (int ci = 0; ci < jcp.nb_ch_blocking; ci++) {
+        const bool mask_flag = last_ic_block_flag != no_last_block
+                && ci == jcp.nb_ch_blocking - 1;
+        if (jcp.is_resrc_depthwise && !h_padded) {
+            // now we can load input once and reuse up to jcp.kw times
+            for (int ii = ii_start; ii <= ii_end; ii++) {
+                int aux_input_offset = input_offset2(ii, ci);
+                const Zmm zmm_inp_tmp = zmm_inp(ii, jcp.nb_ch_blocking);
+                const Zmm zmm_inp_msk = mask_flag
+                        ? zmm_inp_tmp | ktail_mask | T_z
+                        : zmm_inp_tmp;
+                if (jcp.is_fast_depthwise) {
+                    assert(!mask_flag);
+                    vbroadcasti32x4(zmm_inp_msk,
+                            EVEX_compress_addr(aux_reg_inp, aux_input_offset));
+                } else {
+                    vpmovzxbd(zmm_inp_msk,
+                            EVEX_compress_addr(aux_reg_inp, aux_input_offset));
+                }
+                if (jcp.signed_input)
+                    vpaddb(zmm_inp_tmp, zmm_inp_tmp, vmm_shift);
+            }
+        }
+        for (int ki = 0; ki < jcp.kw; ki++) {
+            int aux_kernel_offset = kernel_offset(ci, ki);
+            if (jcp.is_fast_depthwise) {
+                vbroadcasti32x4(zmm_wei,
+                        EVEX_compress_addr(aux_reg_ker, aux_kernel_offset));
+                vmovdqu8(zmm_wei | kblend_mask | T_z, zmm_wei);
+            } else {
+                vpmovsxbd(zmm_wei,
+                        EVEX_compress_addr(aux_reg_ker, aux_kernel_offset));
+            }
+            if (h_padded) {
+                assert(jcp.signed_input);
+                for (int oi = 0; oi < ur_w; oi++)
+                    compute(zmm_out(oi, ci), zmm_wei, zmm_shifted_zero);
+            } else {
+                const Zmm r_zmm_src = mask_flag ? zmm_src | ktail_mask : zmm_src;
+                int oi_start = get_ow_start(ki, pad_l);
+                int oi_end = get_ow_end(ur_w, ki, pad_r);
+                int start_ = jcp.signed_input ? 0 : oi_start;
+                int end_ = jcp.signed_input ? ur_w : oi_end;
+                for (int oi = start_; oi < end_; oi++) {
+                    if (oi >= oi_start && oi < oi_end) {
+                        if (jcp.is_resrc_depthwise) {
+                            int ii = input_spatial_index(oi, ki);
+                            zmm_src = zmm_inp(ii, jcp.nb_ch_blocking);
+                        } else {
+                            int aux_input_offset = input_offset3(oi, ci, ki);
+                            if (jcp.is_fast_depthwise) {
+                                assert(!mask_flag);
+                                vbroadcasti32x4(r_zmm_src,
+                                        EVEX_compress_addr(aux_reg_inp,
+                                                        aux_input_offset));
+                            } else {
+                                vpmovzxbd(r_zmm_src,
+                                        EVEX_compress_addr(aux_reg_inp,
+                                                  aux_input_offset));
+                            }
+                            if (jcp.signed_input)
+                                vpaddb(zmm_src, zmm_src, vmm_shift);
+                        }
+                    } else if (jcp.signed_input) {
+                        zmm_src = zmm_shifted_zero;
+                    }
+                    compute(zmm_out(oi, ci), zmm_wei, zmm_src);
+                }
+            }
+        }
+    }
+}
+
+template<typename Vmm>
+void _jit_avx512_core_x8s8s32x_fwd_kernel<Vmm>::compute_ker(int ur_w, int pad_l,
+        int pad_r, ic_block_t last_ic_block_flag, bool h_padded) {
+    if (jcp.is_depthwise)
+        return compute_ker_dw(ur_w, pad_l, pad_r, last_ic_block_flag, h_padded);
+
+    int kw = jcp.kw;
+    int stride_w = jcp.stride_w;
+    int ic_block = jcp.ic_block;
+    int oc_block = jcp.oc_block;
+    int ch_block_all = jcp.ch_block * ic_block * oc_block;
+
+    int nb_oc_block = jcp.nb_oc_blocking;
+
+    auto input_offset = [=](int oi, int ic, int ki) {
+        return jcp.typesize_in
+                * ((ki * (jcp.dilate_w + 1) + oi * stride_w - pad_l)
+                          * jcp.ic_without_padding * jcp.ngroups + 4 * ic);
+    };
+    auto kernel_offset = [=](int ii, int ic, int ki) {
+        return jcp.typesize_in
+                * ((ii * jcp.nb_ic * jcp.kh * jcp.kw + ki) * ch_block_all
+                    + 4 * ic * oc_block);
+    };
+    auto compute = [=](Vmm vreg_acc, Vmm vreg_wei, Vmm vreg_src) {
+        if (jcp.ver == ver_vnni) {
+            vpdpbusd(vreg_acc, vreg_src, vreg_wei);
+        } else {
+            vpmaddubsw(vmm_tmp, vreg_src, vreg_wei);
+            vpmaddwd(vmm_tmp, vmm_tmp, vmm_one);
+            vpaddd(vreg_acc, vreg_acc, vmm_tmp);
+        }
+    };
+
+    for (int ki = 0; ki < kw; ki++) {
+        int jj_start = get_ow_start(ki, pad_l);
+        int jj_end = get_ow_end(ur_w, ki, pad_r);
+        int tail_size = jcp.ic_without_padding % 4;
+        int _start = (jcp.signed_input) ? 0 : jj_start;
+        int _end = (jcp.signed_input) ? ur_w : jj_end;
+        /* Skip the last loads of input if (ic%16)/4 < ic_block/4 */
+        int icb = (last_ic_block_flag != no_last_block)
+            ? div_up((jcp.ic_without_padding % ic_block), 4)
+            : ic_block / 4;
+        for (int ic = 0; ic < icb; ic++) {
+            if (h_padded == true) {
+                /* fill padded area with shifted values */
+                Vmm inp = vmm_inp(0,nb_oc_block);
+                vpxord(inp, inp, inp);
+                vpaddb(inp, inp, vmm_shift);
+            } else {
+                for (int jj = _start; jj < _end; jj++) {
+                    int aux_input_offset = input_offset(jj, ic, ki);
+                    if (jj >= jj_start && jj < jj_end) {
+                        if (last_ic_block_flag == last_sp_block
+                                && tail_size != 0 && ic == icb - 1) {
+                            Xmm xmm_tmp = Xmm(vmm_inp(jj, nb_oc_block).getIdx());
+                            for (int r = 0; r < tail_size; ++r)
+                                vpinsrb(xmm_tmp, xmm_tmp,
+                                    ptr[aux_reg_inp + aux_input_offset + r], r);
+                            vpbroadcastd(vmm_inp(jj, nb_oc_block), xmm_tmp);
+                        } else {
+                            vpbroadcastd(vmm_inp(jj, nb_oc_block),
+                                    EVEX_compress_addr(
+                                                 aux_reg_inp, aux_input_offset));
+                        }
+                        if (jcp.signed_input)
+                            vpaddb(vmm_inp(jj, nb_oc_block),
+                                   vmm_inp(jj, nb_oc_block), vmm_shift);
+                    } else {
+                        /* fill padded area with shifted values */
+                        if (jcp.signed_input) {
+                            Vmm inp = vmm_inp(jj, nb_oc_block);
+                            vpxord(inp, inp, inp);
+                            vpaddb(inp, inp, vmm_shift);
+                        }
+                    }
+                }
+            }
+            for (int ii = 0; ii < nb_oc_block; ii++) {
+                int aux_kernel_offset = kernel_offset(ii, ic, ki);
+                vmovups(vmm_wei,
+                        EVEX_compress_addr(aux_reg_ker, aux_kernel_offset));
+                for (int jj = _start; jj < _end; jj++)  {
+                    Vmm inp = (h_padded == true)
+                        ? vmm_inp(0,nb_oc_block) : vmm_inp(jj, nb_oc_block);
+                    compute(vmm_out(jj, ii), vmm_wei, inp);
+                }
+            }
+        }
+    }
+}
+
+template<typename Vmm>
+void _jit_avx512_core_x8s8s32x_fwd_kernel<Vmm>::kh_loop(
+        int ur_w, int pad_l, int pad_r, ic_block_t last_ic_block_flag) {
+    Label kh_label, skip_kh_loop;
+    Label t_overflow_label, no_t_overflow_label,
+          b_overflow_label, no_b_overflow_label;
+
+    int ch_block_all = jcp.ch_block * jcp.ic_block * jcp.oc_block;
+    int shift_kernel_ptr = jcp.typesize_in * jcp.kw * ch_block_all;
+    int shift_input_ptr = jcp.typesize_in * (jcp.dilate_h + 1) * jcp.iw
+        * jcp.ic_without_padding * jcp.ngroups;
+
+    mov(aux_reg_inp, reg_inp);
+    mov(aux_reg_ker, reg_ker);
+
+    if (jcp.signed_input && jcp.ndims > 3) {
+        mov(reg_overflow, ptr[param1 + GET_OFF(t_overflow)]);
+        cmp(reg_overflow, 0);
+        je(no_t_overflow_label, T_NEAR);
+        L(t_overflow_label); {
+            compute_ker(ur_w, pad_l, pad_r, last_ic_block_flag, true);
+
+            add(aux_reg_ker, shift_kernel_ptr);
+            dec(reg_overflow);
+            cmp(reg_overflow, 0);
+            jg(t_overflow_label, T_NEAR);
+        }
+        L(no_t_overflow_label);
+    }
+    mov(reg_kj, ptr[param1 + GET_OFF(kh_padding)]);
+    if ((jcp.signed_input) || (!jcp.signed_input &&
+       (jcp.kh - 1) * (jcp.dilate_h + 1) < nstl::max(jcp.t_pad, jcp.b_pad))) {
+        cmp(reg_kj, 0);
+        je(skip_kh_loop, T_NEAR);
+    }
+    L(kh_label); {
+        compute_ker(ur_w, pad_l, pad_r, last_ic_block_flag, false);
+
+        add(aux_reg_ker, shift_kernel_ptr);
+        add(aux_reg_inp, shift_input_ptr);
+        dec(reg_kj);
+        cmp(reg_kj, 0);
+        jg(kh_label, T_NEAR);
+    }
+    L(skip_kh_loop);
+    if (jcp.signed_input && jcp.ndims > 3) {
+        mov(reg_overflow, ptr[param1 + GET_OFF(b_overflow)]);
+        cmp(reg_overflow, 0);
+        je(no_b_overflow_label, T_NEAR);
+        L(b_overflow_label); {
+            compute_ker(ur_w, pad_l, pad_r, last_ic_block_flag, true);
+
+            add(aux_reg_ker, shift_kernel_ptr);
+            dec(reg_overflow);
+            cmp(reg_overflow, 0);
+            jg(b_overflow_label, T_NEAR);
+        }
+        L(no_b_overflow_label);
+    }
+}
+
+template<typename Vmm>
+void _jit_avx512_core_x8s8s32x_fwd_kernel<Vmm>::icb_loop(
+        int ur_w, int pad_l, int pad_r, bool is_last_sp_block)
+{
+    prepare_output(ur_w);
+
+    // IC loop
+    Label icb_label;
+    mov(reg_icb, jcp.nb_ic);
+    L(icb_label);
+    if (jcp.ngroups % jcp.ch_block != 0 || jcp.ic_without_padding != jcp.ic) {
+        Label common_ker, end_ker;
+
+        cmp(reg_icb, 1); // The last IC block
+        jne(common_ker, T_NEAR);
+
+        kh_loop(ur_w, pad_l, pad_r,
+                is_last_sp_block ? last_sp_block : last_ic_block);
+        jmp(end_ker, T_NEAR);
+
+        L(common_ker);
+        kh_loop(ur_w, pad_l, pad_r, no_last_block);
+
+        L(end_ker);
+    } else {
+        kh_loop(ur_w, pad_l, pad_r, no_last_block);
+    }
+    // End of IC Loop
+    int inp_step = jcp.ic_block;
+    int ker_step = jcp.kh * jcp.kw * jcp.oc_block * jcp.ic_block;
+    add(reg_inp, jcp.typesize_in * inp_step);
+    add(reg_ker, jcp.typesize_in * ker_step);
+
+    dec(reg_icb);
+    cmp(reg_icb, 0);
+    jg(icb_label, T_NEAR);
+
+    sub(reg_inp, jcp.typesize_in * inp_step * jcp.nb_ic);
+    sub(reg_ker, jcp.typesize_in * ker_step * jcp.nb_ic);
+
+    if (jcp.ngroups % jcp.ch_block != 0 || jcp.oc_without_padding != jcp.oc) {
+        Label common_store, end_store;
+
+        if (jcp.is_depthwise)
+            cmp(reg_oc_blocks, jcp.nb_ch - jcp.nb_ch_blocking);
+        else
+            cmp(reg_oc_blocks, jcp.nb_oc - jcp.nb_oc_blocking);
+
+        jne(common_store, T_NEAR);
+
+        store_output(ur_w, true); // last oc block
+        jmp(end_store, T_NEAR);
+
+        L(common_store);
+        store_output(ur_w, false);
+
+        L(end_store);
+    } else {
+        store_output(ur_w, false);
+    }
+}
+
+template<typename Vmm>
+void _jit_avx512_core_x8s8s32x_fwd_kernel<Vmm>::generate()
+{
+    Label permute_index_table;
+    int inp_shift_pad = jcp.typesize_in * (jcp.ur_w * jcp.stride_w - jcp.l_pad)
+        * jcp.ic_without_padding * jcp.ngroups;
+    int inp_shift_pad_second_block = -1 * jcp.typesize_in * jcp.l_pad
+        * jcp.ic_without_padding * jcp.ngroups;
+    int inp_shift = jcp.typesize_in *
+                        (jcp.ur_w * jcp.stride_w * jcp.ic_without_padding
+                         * jcp.ngroups);
+    int out_shift = jcp.typesize_out *
+                        (jcp.ur_w * jcp.oc_without_padding * jcp.ngroups);
+    preamble();
+
+    if (jcp.is_depthwise) {
+        int idx = jcp.max_regs_ur - 1;
+        if (!jcp.is_resrc_depthwise)
+            zmm_src = Zmm(++idx);
+        if (jcp.ver != ver_vnni)
+            zmm_tmp = Zmm(++idx);
+        if (jcp.is_fast_depthwise)
+            zmm_permute = Zmm(++idx);
+        if (jcp.signed_input) {
+            zmm_shifted_zero = Zmm(++idx);
+            ++idx; // due to extra register used for shifts and compensations
+        }
+        assert(idx == ker_dw_reg_base_idx);
+    }
+
+    if (!jcp.is_depthwise && jcp.ver != ver_vnni) {
+        xor_(reg_scratch, reg_scratch);
+        Reg16 _t16 = reg_scratch.cvt16();
+        mov(_t16, 0x1);
+        vpbroadcastw(vmm_one, _t16);
+    }
+
+    mov(reg_inp, ptr[param1 + GET_OFF(src)]);
+    mov(reg_out, ptr[param1 + GET_OFF(dst)]);
+    mov(reg_ker, ptr[param1 + GET_OFF(filt)]);
+
+    if (jcp.ngroups % jcp.ch_block != 0 || jcp.oc_without_padding != jcp.oc) {
+        int tail_size = jcp.is_depthwise
+            ? jcp.ngroups % jcp.ch_block
+            : jcp.oc_without_padding % jcp.oc_block;
+        int mask = (1 << tail_size) - 1;
+        mov(reg_oc_blocks, ptr[param1 + GET_OFF(oc_blocks)]);
+        Reg32 regw_tmp = reg_oi.cvt32();
+        mov(regw_tmp, mask);
+        kmovw(ktail_mask, regw_tmp);
+    }
+    if (jcp.is_fast_depthwise) {
+        // prepare mask register for blending weights
+        mov(reg_scratch, 0x8888444422221111);
+        kmovq(kblend_mask, reg_scratch);
+        // load permute indices from data section
+        mov(reg_scratch, permute_index_table);
+        vmovdqu32(zmm_permute, ptr[reg_scratch]);
+    }
+
+    int r_pad = nstl::max(0, (jcp.ow - 1) * jcp.stride_w
+                    + (jcp.kw - 1) * (jcp.dilate_w + 1)
+                    - (jcp.iw + jcp.l_pad - 1));
+    int n_oi = jcp.ow / jcp.ur_w;
+    int r_pad1 = (jcp.ur_w * n_oi - 1) * jcp.stride_w
+        + (jcp.kw - 1) * (jcp.dilate_w + 1) - (jcp.iw + jcp.l_pad - 1);
+
+    if (jcp.nb_ow == 1) {
+        if (r_pad1 > 0 || jcp.ur_w_tail == 0)
+            n_oi--;
+
+        xor_(reg_oi, reg_oi);
+        if (jcp.ow == jcp.ur_w) {
+            icb_loop(jcp.ur_w, jcp.l_pad, r_pad, true);
+        } else {
+            if (n_oi == 0) {
+                icb_loop(jcp.ur_w, jcp.l_pad, r_pad1, jcp.ur_w_tail == 0);
+                add(reg_inp, inp_shift_pad);
+                add(reg_out, out_shift);
+                if (jcp.ur_w_tail != 0) {
+                    icb_loop(jcp.ur_w_tail, 0, r_pad, true);
+                }
+            } else {
+                if (jcp.l_pad > 0) {
+                    icb_loop(jcp.ur_w, jcp.l_pad, 0, false);
+                    add(reg_inp, inp_shift_pad);
+                    add(reg_out, out_shift);
+
+                    inc(reg_oi);
+                }
+                if ((jcp.l_pad <= 0 && n_oi > 0) || (jcp.l_pad > 0 && n_oi > 1))
+                {
+                    Label ow_loop_label;
+                    L(ow_loop_label); {
+                        icb_loop(jcp.ur_w, 0, 0, false);
+                        add(reg_inp, inp_shift);
+                        add(reg_out, out_shift);
+
+                        inc(reg_oi);
+                        cmp(reg_oi, n_oi);
+                        jl(ow_loop_label, T_NEAR);
+                    }
+                }
+                if (r_pad1 > 0 || jcp.ur_w_tail == 0) {
+                    icb_loop(jcp.ur_w, 0, r_pad1, jcp.ur_w_tail == 0);
+                    add(reg_inp, inp_shift);
+                    add(reg_out, out_shift);
+                }
+                if (jcp.ur_w_tail != 0) {
+                    icb_loop(jcp.ur_w_tail, 0, r_pad, true);
+                }
+            }
+        }
+    } else {
+        // ow block is only processed.
+        // Number of block is passed as parameter owb,
+        // and padding processing depends on this number.
+        Label end_label, last_oi_label, middle_ow_blocks_label, tail_label,
+            oi_loop_label, oi_loop_end_label;
+
+        assert(jcp.ow_block % jcp.ur_w == 0);
+        int n_oi_not_last_ow_block = jcp.ow_block / jcp.ur_w;
+        // to simplify code (and general regs usage),
+        // size of ow block must be >= 2 * ur_w
+        assert(n_oi_not_last_ow_block > 1);
+        int n_oi_next_last_ow_block = n_oi_not_last_ow_block;
+        int n_oi_first_ow_block = n_oi_not_last_ow_block;
+        int n_oi_last_ow_block
+            = (jcp.ow - jcp.ow_block * (jcp.nb_ow - 1)) / jcp.ur_w;
+        // prepare right padding
+        bool next_last_ow_block_padded = r_pad1 > 0 && n_oi_last_ow_block == 0;
+        bool first_ow_block_padded
+                = next_last_ow_block_padded && jcp.nb_ow == 2;
+        bool last_ow_block_padded
+                = (r_pad1 > 0 || jcp.ur_w_tail == 0) && n_oi_last_ow_block > 0;
+
+        if (last_ow_block_padded) n_oi_last_ow_block--;
+        else if (first_ow_block_padded) n_oi_first_ow_block--;
+        else if (next_last_ow_block_padded) n_oi_next_last_ow_block--;
+
+        mov(reg_owb, ptr[param1 + GET_OFF(owb)]);
+        cmp(reg_owb, 0); // is that the first ow-block ?
+        jg(middle_ow_blocks_label, T_NEAR);
+
+        // the first ow block, compute left padding
+        mov(reg_oi, n_oi_first_ow_block);
+        if (jcp.l_pad > 0) {
+            icb_loop(jcp.ur_w, jcp.l_pad, 0, false);
+            add(reg_inp, inp_shift_pad);
+            add(reg_out, out_shift);
+
+            dec(reg_oi);
+        }
+        jmp(oi_loop_label, T_NEAR);
+
+        // middle or last ow block entry
+        L(middle_ow_blocks_label);
+
+        if (jcp.l_pad > 0) {
+            // just to consider left padding, not compute
+            add(reg_inp, inp_shift_pad_second_block);
+        }
+
+        // set number of iteration for oi-loop
+        if (n_oi_last_ow_block != n_oi_not_last_ow_block) {
+            cmp(reg_owb, jcp.nb_ow - 1); // last ow-block ?
+            mov(reg_oi, n_oi_last_ow_block);
+            je(oi_loop_label, T_NEAR);
+        }
+
+        if (n_oi_next_last_ow_block != n_oi_not_last_ow_block) {
+            cmp(reg_owb, jcp.nb_ow - 2); // next to last ow-block ?
+
+            mov(reg_oi, n_oi_next_last_ow_block);
+            je(oi_loop_label, T_NEAR);
+        }
+        mov(reg_oi, n_oi_not_last_ow_block); // other middle ow-blocks
+
+        // oi loop w/o padding
+        L(oi_loop_label); {
+            cmp(reg_oi, 0);
+            jle(oi_loop_end_label, T_NEAR);
+
+            icb_loop(jcp.ur_w, 0, 0, false);
+
+            add(reg_inp, inp_shift);
+            add(reg_out, out_shift);
+            dec(reg_oi);
+
+            jmp(oi_loop_label, T_NEAR);
+        }
+        L(oi_loop_end_label);
+
+        mov(reg_owb, ptr[param1 + GET_OFF(owb)]);
+        cmp(reg_owb, 0); // first ow-block ?
+        if (first_ow_block_padded)
+            je(last_oi_label, T_NEAR);
+        else
+            je(end_label, T_NEAR);
+
+        cmp(reg_owb, jcp.nb_ow - 2); // next to last ow-block ?
+        jl(end_label, T_NEAR);
+        if (next_last_ow_block_padded)
+            je(last_oi_label, T_NEAR);
+        else
+            je(end_label, T_NEAR);
+
+        // that is last block
+        if (!last_ow_block_padded)
+            jmp(tail_label, T_NEAR);
+
+        // last oi block with right padding
+        L(last_oi_label);
+        icb_loop(jcp.ur_w, 0, r_pad1, jcp.ur_w_tail == 0);
+        add(reg_inp, inp_shift);
+        add(reg_out, out_shift);
+
+        mov(reg_owb, ptr[param1 + GET_OFF(owb)]);
+        cmp(reg_owb, jcp.nb_ow - 1); // last ow_block?
+        jl(end_label, T_NEAR);
+
+        // ur_w tail
+        L(tail_label);
+        if (jcp.ur_w_tail != 0) {
+            icb_loop(jcp.ur_w_tail, 0, r_pad, true);
+        }
+        L(end_label);
+    }
+    postamble();
+
+    if (jcp.with_eltwise)
+        eltwise_injector_->prepare_table();
+
+    if (jcp.is_fast_depthwise) {
+        align(64);
+        L(permute_index_table);
+        const uint32_t _idx[]
+                = { 0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15 };
+        for (size_t i = 0; i < sizeof(_idx) / sizeof(_idx[0]); ++i)
+            dd(_idx[i]);
+    }
+}
+
+bool jit_avx512_core_x8s8s32x_fwd_kernel::post_ops_ok(
+        jit_conv_conf_t &jcp, const primitive_attr_t &attr)
+{
+    using namespace primitive_kind;
+    const auto &p = attr.post_ops_;
+
+    auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); };
+
+    switch (p.len_) {
+    case 0: return true;
+    case 1: return is_eltwise(0) || p.contain(sum, 0);
+    case 2: return (p.contain(sum, 0) && is_eltwise(1)) ||
+                        (p.contain(sum, 1) && is_eltwise(0));
+    default: return false;
+    }
+
+    return false;
+}
+
+status_t jit_avx512_core_x8s8s32x_fwd_kernel::init_conf(jit_conv_conf_t &jcp,
+            const convolution_desc_t &cd, memory_desc_t &src_md,
+            memory_desc_t &weights_md, memory_desc_t &dst_md,
+            memory_desc_t &bias_md, const primitive_attr_t &attr,
+            int nthreads)
+{
+    using namespace prop_kind;
+
+    const memory_desc_wrapper src_d(&src_md);
+    const memory_desc_wrapper weights_d(&weights_md);
+    const memory_desc_wrapper dst_d(&dst_md);
+    const memory_desc_wrapper bias_d(&bias_md);
+
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+    int ndims = src_d.ndims();
+    bool is_1d = ndims == 3;
+
+    if (!(mayiuse(avx512_core)
+         && one_of(src_d.data_type(), data_type::u8, data_type::s8)
+         && weights_d.data_type() == data_type::s8
+         && one_of(dst_d.data_type(), data_type::f32, data_type::s32,
+            data_type::s8, data_type::u8)))
+        return status::unimplemented;
+
+    jcp = zero<decltype(jcp)>();
+    jcp.ndims = ndims;
+    jcp.prop_kind = cd.prop_kind;
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+    jcp.ic_without_padding = jcp.ic;
+    jcp.ih = is_1d ? 1 : src_d.dims()[ndims - 2];
+    jcp.iw = src_d.dims()[ndims - 1];
+    jcp.oh = is_1d ? 1 : dst_d.dims()[ndims - 2];
+    jcp.ow = dst_d.dims()[ndims - 1];
+    jcp.kh = is_1d ? 1 : weights_d.dims()[with_groups + ndims - 2];
+    jcp.kw = weights_d.dims()[with_groups + ndims - 1];
+    jcp.t_pad = is_1d ? 0 : cd.padding[0][ndims - 4];
+    jcp.l_pad = cd.padding[0][ndims - 3];
+    jcp.stride_h = is_1d ? 1 : cd.strides[ndims - 4];
+    jcp.stride_w = cd.strides[ndims - 3];
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+
+    jcp.ur_h = 1; /* no code-unrolling by h so far */
+
+    jcp.dilate_h = is_1d ? 0 : cd.dilates[ndims - 4];
+    jcp.dilate_w = cd.dilates[ndims - 3];
+
+    jcp.signed_input = (src_d.data_type() == data_type::s8) ? true : false;
+    jcp.is_depthwise = true && with_groups && everyone_is(1, jcp.ic, jcp.oc);
+
+    if (jcp.is_depthwise) {
+        jcp.ch_block = 16;
+        jcp.ic_block = 1;
+        jcp.oc_block = 1;
+    } else {
+        jcp.ch_block = 1;
+        jcp.ic_block = 16;
+        jcp.oc_block = 16;
+
+        if (jcp.ngroups == 1) {
+            /* For non grouped convolutions, pad channels by 16 if needed */
+            jcp.oc = rnd_up(jcp.oc, jcp.oc_block);
+            jcp.ic = rnd_up(jcp.ic, jcp.ic_block);
+        } else if (!is_1d && jcp.ngroups != 1 && jcp.ic % jcp.ic_block != 0) {
+            /* For grouped convolutions, MKL-DNN doesn't support padding.
+               Use Ymm when channels per group is multiple of 8,
+               Xmm when channels per group is multiple of 4 */
+            jcp.ic_block = jcp.ic % 8 == 0 ? 8 : 4;
+            jcp.oc_block = jcp.ic_block;
+        }
+        if (jcp.ic % jcp.ic_block !=0 || jcp.oc % jcp.oc_block != 0)
+            return status::unimplemented;
+    }
+
+    jcp.b_pad = (jcp.oh - 1) * jcp.stride_h + (jcp.kh - 1) * (jcp.dilate_h + 1)
+            - (jcp.ih + jcp.t_pad - 1);
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    const int eltwise_ind = p.find(primitive_kind::eltwise);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise)
+        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+
+    jcp.ver = mayiuse(avx512_core_vnni) ? ver_vnni : ver_avx512_core;
+    jcp.is_fast_depthwise = true && jcp.is_depthwise && jcp.ver == ver_vnni
+            && jcp.ngroups % jcp.ch_block == 0; // for groups not multiple of 16
+                                                // would require byte masking
+                                                // for load from src
+    jcp.is_resrc_depthwise = jcp.is_depthwise && jcp.stride_w < jcp.kw
+            && jcp.kw < 4 && jcp.dilate_w == 0;
+    if (jcp.is_depthwise) {
+        jcp.max_regs_ur = 31 - jcp.is_fast_depthwise - !jcp.is_resrc_depthwise
+                - 2 * jcp.signed_input - (jcp.ver != ver_vnni);
+    } else {
+        jcp.max_regs_ur = jcp.ver == ver_vnni ? 31 : 28;
+    }
+
+    auto set_or_check_wei_format = [&]() {
+        using namespace format_tag;
+        format_tag_t wei_tag;
+        if (jcp.ic_block == 16 || jcp.ch_block == 16) {
+            if (is_1d) {
+                wei_tag = with_groups
+                    ? jcp.is_depthwise ? Goiw16g : gOIw4i16o4i
+                    : OIw4i16o4i;
+            } else {
+                wei_tag = with_groups
+                    ? jcp.is_depthwise ? Goihw16g : gOIhw4i16o4i
+                    : OIhw4i16o4i;
+            }
+        } else if (with_groups && jcp.ic_block == 8) {
+            wei_tag = gOIhw2i8o4i;
+        } else
+            wei_tag = gOIhw4o4i;
+
+        memory_desc_t want_wei_md = weights_md;
+        memory_desc_init_by_tag(want_wei_md, wei_tag);
+        if (jcp.signed_input) {
+            want_wei_md.extra.flags = 0
+                | memory_extra_flags::compensation_conv_s8s8
+                | memory_extra_flags::scale_adjust;
+            want_wei_md.extra.compensation_mask = (1 << 0)
+                + (with_groups && !jcp.is_depthwise ? (1 << 1) : 0);
+            want_wei_md.extra.scale_adjust =
+                mayiuse(avx512_core_vnni) ? 1.f : 0.5f;
+        }
+
+        if (weights_md.format_kind == format_kind::any) {
+            weights_md = want_wei_md;
+            return true;
+        }
+
+        return weights_md == want_wei_md;
+    };
+
+    if (!set_or_check_wei_format())
+        return status::unimplemented;
+
+    format_tag_t dat_tag = utils::pick(ndims - 3,
+            format_tag::nwc, format_tag::nhwc);
+
+    if (src_d.format_kind() == format_kind::any) {
+        CHECK(memory_desc_init_by_tag(src_md, dat_tag));
+        jcp.src_tag = dat_tag;
+    } else {
+        jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    }
+    if (jcp.src_tag != dat_tag)
+        return status::unimplemented;
+
+    if (dst_d.format_kind() == format_kind::any) {
+        CHECK(memory_desc_init_by_tag(dst_md, dat_tag));
+        jcp.dst_tag = dat_tag;
+    } else {
+        jcp.dst_tag = dst_d.matches_one_of_tag(dat_tag);
+    }
+    if (jcp.dst_tag != dat_tag)
+        return status::unimplemented;
+
+    if (jcp.with_bias) {
+        if (bias_d.format_kind() == format_kind::any)
+            CHECK(memory_desc_init_by_tag(bias_md, format_tag::x));
+    }
+
+    jcp.bia_dt = jcp.with_bias ? cd.bias_desc.data_type : data_type::undef;
+    jcp.dst_dt = cd.dst_desc.data_type;
+
+    jcp.typesize_in = types::data_type_size(src_d.data_type());
+    jcp.typesize_out = types::data_type_size(dst_d.data_type());
+    jcp.typesize_bia = jcp.with_bias
+        ? types::data_type_size(bias_d.data_type())
+        : 0;
+
+    jcp.nb_ch = div_up(jcp.ngroups, jcp.ch_block);
+    jcp.nb_ic = jcp.ic / jcp.ic_block;
+    jcp.nb_oc = jcp.oc / jcp.oc_block;
+
+    // Try to use 4 channel-groups at a time to avoid false sharing (depthwise)
+    int nb_ch_blocking = 4;
+    for ( /* init above */ ; nb_ch_blocking > 1; nb_ch_blocking--)
+        if (jcp.nb_ch % nb_ch_blocking == 0)
+            break;
+    jcp.nb_ch_blocking = jcp.is_depthwise ? nb_ch_blocking : 1;
+
+    // If OC blocking is incommensurate with the number of OC blocks (general
+    // requirement for all convolutions), or if it results in an unrolling
+    // factor smaller than the left padding (special requirement for SSD:fc6),
+    // then search for a smaller OC blocking that satisfies both constraints.
+    auto is_oc_blocking_ok = [&](int block) {
+        int ur_w = nstl::min(jcp.ow, jcp.max_regs_ur / (block + 1));
+        return jcp.nb_oc % block == 0
+                && jcp.l_pad <= ur_w && jcp.ow % ur_w != 1;
+    };
+
+    // choose nb_oc work chunk size for distribution within threads
+    int max_threading_nb_oc_chunk = 4;
+    // Performance improvements for googlenet_v3 and resnet_50 with mb = 1;
+    // TODO: generalize this condition and rewrite it in appropriate manner
+    if (jcp.ver == ver_vnni && jcp.mb == 1 && jcp.kh == 3 && jcp.kw == 3
+            && jcp.stride_w == 1 && jcp.ic % 64 == 0)
+        max_threading_nb_oc_chunk = 2;
+    jcp.nb_oc_blocking_thr_chunk =
+        nstl::min(max_threading_nb_oc_chunk, jcp.nb_oc);
+    for (; jcp.nb_oc_blocking_thr_chunk > 1; jcp.nb_oc_blocking_thr_chunk--) {
+        if (is_oc_blocking_ok(jcp.nb_oc_blocking_thr_chunk))
+            break;
+    }
+
+    // choose oc blocking for computational kernel
+    jcp.nb_oc_blocking = jcp.nb_oc_blocking_thr_chunk;
+    // Performance improvements for googlenet_v3 with mb = 1;
+    // TODO: generalize this condition and rewrite it in appropriate manner
+    const int size_treshold_for_nb_oc_blocking_reduction = 17;
+    if (jcp.mb == 1 && jcp.ow <= size_treshold_for_nb_oc_blocking_reduction
+            && jcp.stride_w == 1
+            && !(jcp.kh == 1 && jcp.kw == 3)
+            && !(jcp.kh >= 7 && jcp.oc % 64 == 0)) {
+        const int max_nb_oc_blocking = 2;
+        jcp.nb_oc_blocking = nstl::min(max_nb_oc_blocking, jcp.nb_oc);
+        for (; jcp.nb_oc_blocking > 1; jcp.nb_oc_blocking--)
+            if (jcp.nb_oc_blocking_thr_chunk % jcp.nb_oc_blocking == 0
+                && is_oc_blocking_ok(jcp.nb_oc_blocking))
+                break;
+    }
+
+    if (jcp.is_resrc_depthwise)
+        jcp.ur_w = (jcp.max_regs_ur - jcp.kw + jcp.stride_w)
+                / (jcp.nb_ch_blocking + jcp.stride_w);
+    else
+        jcp.ur_w
+                = jcp.max_regs_ur / (jcp.is_depthwise ? jcp.nb_ch_blocking
+                                                      : jcp.nb_oc_blocking + 1);
+    if (jcp.ow < jcp.ur_w)
+        jcp.ur_w = jcp.ow;
+    jcp.ur_w_tail = jcp.ow % jcp.ur_w;
+
+    jcp.ow_block = jcp.ow;
+    int base_work_amount = jcp.mb * jcp.nb_ch * jcp.oh
+                         * (jcp.nb_oc / jcp.nb_oc_blocking_thr_chunk);
+    float best_thr_eff
+            = (float)base_work_amount / rnd_up(base_work_amount, nthreads);
+    int max_nb_ow = div_up(jcp.ow, 2 * jcp.ur_w);
+    for (int nb_ow = 1; nb_ow <= max_nb_ow; nb_ow++) {
+        int ow_block
+                = nstl::min(rnd_up(div_up(jcp.ow, nb_ow), jcp.ur_w), jcp.ow);
+        if (ow_block < jcp.nb_oc_blocking_thr_chunk * jcp.oc_block
+             && best_thr_eff > 0.8f)
+            break;
+        if (div_up(jcp.ow, ow_block) != nb_ow)
+            continue;
+        auto work_amount = base_work_amount * nb_ow;
+        float thr_eff = (float)work_amount / rnd_up(work_amount, nthreads);
+        if (ow_block >= 2 * jcp.ur_w && thr_eff > 1.1f * best_thr_eff) {
+            jcp.ow_block = ow_block;
+            best_thr_eff = thr_eff;
+        }
+        if (best_thr_eff > 0.9f)
+            break;
+    }
+    jcp.nb_ow = div_up(jcp.ow, jcp.ow_block);
+
+    bool args_ok = true
+        && jcp.oc % jcp.oc_block == 0
+        && jcp.l_pad <= jcp.ur_w
+        && IMPLICATION(!jcp.is_1stconv, jcp.ic % jcp.ic_block == 0);
+    if (!args_ok)
+        return status::unimplemented;
+
+    int r_pad_no_tail = nstl::max(0, (jcp.ow - jcp.ur_w_tail - 1) * jcp.stride_w
+                    + (jcp.kw - 1) * (jcp.dilate_w + 1)
+                    - (jcp.iw + jcp.l_pad - 1));
+    if (r_pad_no_tail > jcp.ur_w)
+        return status::unimplemented;
+
+    pick_loop_order(jcp, nthreads);
+
+    jcp.nb_ic_L2 = jcp.nb_ic;
+
+    const auto &oscales = attr.output_scales_;
+    jcp.is_oc_scale = oscales.mask_ == 1 << 1;
+
+    assert(IMPLICATION(!jcp.is_oc_scale, oscales.mask_ == 0));
+
+    jcp.wei_adj_scale =
+        (weights_d.extra().flags | memory_extra_flags::scale_adjust)
+        ? weights_d.extra().scale_adjust : 1.f;
+
+    return status::success;
+}
+
+void jit_avx512_core_x8s8s32x_fwd_kernel::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad, const jit_conv_conf_t &jcp,
+        const primitive_attr_t &attr) {
+    if (jcp.signed_input && jcp.ver != ver_vnni) {
+        dim_t count = nstl::max(attr.output_scales_.count_, (dim_t)jcp.ic_block);
+        scratchpad.book(key_conv_adjusted_scales, sizeof(float) * count);
+    }
+}
+
+template struct  _jit_avx512_core_x8s8s32x_fwd_kernel<Zmm>;
+template struct  _jit_avx512_core_x8s8s32x_fwd_kernel<Ymm>;
+template struct  _jit_avx512_core_x8s8s32x_fwd_kernel<Xmm>;
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_conv_kernel.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_conv_kernel.hpp
new file mode 100644
index 0000000000..d8a05ad53e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_conv_kernel.hpp
@@ -0,0 +1,239 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_CORE_X8S8S32X_CONV_KERNEL_HPP
+#define CPU_JIT_AVX512_CORE_X8S8S32X_CONV_KERNEL_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+#include "jit_uni_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template<typename Vmm>
+struct _jit_avx512_core_x8s8s32x_fwd_kernel : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(_jit_avx512_core_x8s8s32x_conv_fwd_ker_t)
+
+    enum { STATE_FIRST_DST_LOAD = 0x1U };
+
+    _jit_avx512_core_x8s8s32x_fwd_kernel(jit_conv_conf_t ajcp,
+            const primitive_attr_t &attr) : jcp(ajcp), attr_(attr),
+            eltwise_injector_(nullptr)
+    {
+        if (jcp.with_eltwise)
+            eltwise_injector_ = new jit_uni_eltwise_injector_f32<avx512_common>(
+                this, jcp.eltwise);
+
+        generate();
+        jit_ker_ = (void (*)(jit_conv_call_s *))getCode();
+    }
+
+    ~_jit_avx512_core_x8s8s32x_fwd_kernel() {
+        delete eltwise_injector_;
+    }
+
+    jit_conv_conf_t jcp;
+    const primitive_attr_t &attr_;
+    void (*jit_ker_)(jit_conv_call_s *);
+
+private:
+    jit_uni_eltwise_injector_f32<avx512_common> *eltwise_injector_;
+
+    enum {
+        typesize = sizeof(float),
+        ker_reg_base_idx = 28,
+        ker_dw_reg_base_idx = 30,
+    };
+    typedef enum {
+        no_last_block,
+        last_ic_block,
+        last_sp_block,
+    } ic_block_t;
+
+    /* data regs */
+    const Xbyak::Reg64 reg_ptr_scales = rax;
+    const Xbyak::Reg64 reg_inp = r8;
+    const Xbyak::Reg64 reg_ker = r9;
+    const Xbyak::Reg64 reg_out = r10;
+    const Xbyak::Reg64 aux_reg_inp = r11;
+    const Xbyak::Reg64 reg_ptr_sum_scale = r11;
+    const Xbyak::Reg64 aux_reg_ker = r12;
+    const Xbyak::Reg64 reg_compensation = r14;
+    /* counter regs */
+    const Xbyak::Reg64 reg_bias_alpha = abi_not_param1;
+    const Xbyak::Reg64 reg_oi = rbx;
+    const Xbyak::Reg64 reg_bias = rdx;
+    const Xbyak::Reg64 reg_oc_blocks = rsi;
+    const Xbyak::Reg64 reg_owb = aux_reg_ker;
+    const Xbyak::Reg64 reg_scratch = reg_compensation;
+    const Xbyak::Reg64 reg_kj = reg_ptr_scales;
+    const Xbyak::Reg64 reg_overflow = reg_ptr_scales;
+    const Xbyak::Reg64 reg_icb = reg_bias;
+
+    const Xbyak::Opmask ktail_mask = Xbyak::Opmask(2);
+    const Xbyak::Opmask kblend_mask = Xbyak::Opmask(3);
+
+    const Vmm vmm_wei = Vmm(31);
+    /* used during bias section of store_output */
+    const Vmm vmm_comp = Vmm(30); // only for signed input
+    const Vmm vmm_bias = Vmm(31);
+    /* used during post_op sum section of store_output */
+    const Vmm vmm_prev_dst = Vmm(31);
+    /* used during write-out section of store_output */
+    const Vmm vmm_zero = Vmm(31);
+
+    /* used in compute_ker (but set during prepare_output) */
+    const Vmm vmm_shift = vmm_comp; // only for signed input
+    /* used in compute_ker (but only for pre-VNNI machines) */
+    const Vmm vmm_tmp = Vmm(28); // not used for depthwise
+    const Vmm vmm_one = Vmm(29); // set at start of kernel, not used for depthwise.
+
+    /* registers use only for depthwise
+       groups are always blocked by 16(padded if needed),
+       hence use only Zmm registers */
+    const Xbyak::Zmm zmm_wei = Xbyak::Zmm(31);
+    Xbyak::Zmm zmm_tmp;
+    Xbyak::Zmm zmm_src;
+    Xbyak::Zmm zmm_shifted_zero;
+    Xbyak::Zmm zmm_permute;
+
+    Vmm vmm_out(int i_ur, int i_oc) {
+        int idx = i_ur + i_oc * jcp.ur_w;
+        assert(idx < (jcp.is_depthwise
+                    ? ker_dw_reg_base_idx : ker_reg_base_idx));
+        return Vmm(idx);
+    }
+    Xbyak::Zmm zmm_out(int i_ur, int i_oc) {
+        int idx = i_ur + i_oc * jcp.ur_w;
+        assert(idx < (jcp.is_depthwise
+                    ? ker_dw_reg_base_idx : ker_reg_base_idx));
+        return Xbyak::Zmm(idx);
+    }
+    Vmm vmm_inp(int i_ic, int nb_x_blocking) {
+        int idx = i_ic + nb_x_blocking * jcp.ur_w;
+        assert(idx < 31);
+        return Vmm(idx);
+    }
+    Xbyak::Zmm zmm_inp(int i_ic, int nb_x_blocking) {
+        int idx = i_ic + nb_x_blocking * jcp.ur_w;
+        assert(idx < 31);
+        return Xbyak::Zmm(idx);
+    }
+    Vmm vmm_bias_alpha() {
+        int nb_c_block = jcp.is_depthwise ? jcp.nb_ch_blocking : jcp.nb_oc_blocking;
+        return Vmm(nb_c_block * jcp.ur_w);
+    }
+    Xbyak::Xmm xmm_bias_alpha() {
+        int nb_c_block = jcp.is_depthwise ? jcp.nb_ch_blocking : jcp.nb_oc_blocking;
+        return Xbyak::Xmm(nb_c_block * jcp.ur_w);
+    }
+    int get_ow_start(int ki, int pad_l) {
+        return nstl::max(0,
+                utils::div_up(pad_l - ki * (jcp.dilate_w + 1), jcp.stride_w));
+    }
+    int get_ow_end(int ur_w, int ki, int pad_r) {
+        return ur_w - nstl::max(0, utils::div_up(pad_r
+                                                   - (jcp.kw - 1 - ki)
+                                                           * (jcp.dilate_w + 1),
+                                           jcp.stride_w));
+    }
+
+    bool maybe_eltwise(int position);
+    void prepare_output(int ur_w);
+    void store_output(int ur_w, bool last_oc_block_flag);
+    void compute_ker_dw(
+            int ur_w, int pad_l, int pad_r, ic_block_t last_ic_block_flag, bool h_padded);
+    void compute_ker(int ur_w, int pad_l, int pad_r,
+            ic_block_t last_ic_block_flag, bool h_padded = false);
+    void compute_eltwise(int ur_w);
+    void kh_loop(int ur_w, int pad_l, int pad_r, ic_block_t last_ic_block_flag);
+    void icb_loop(
+            int ur_w, int pad_l, int pad_r, bool is_last_spatial_block);
+    void generate();
+    void cvt2ps(data_type_t type_in, Vmm ymm_in, const Xbyak::Operand &op,
+        bool mask_flag);
+    const Vmm vmm_mask(const Vmm vmm_in, bool mask_flag, bool store = false);
+};
+
+struct jit_avx512_core_x8s8s32x_fwd_kernel {
+
+    jit_avx512_core_x8s8s32x_fwd_kernel(jit_conv_conf_t ajcp,
+            const primitive_attr_t &attr) :
+        jit_ker(nullptr),
+        zmm_kernel_(nullptr),
+        ymm_kernel_(nullptr),
+        xmm_kernel_(nullptr) {
+            int ch_block = ajcp.is_depthwise ? ajcp.ch_block : ajcp.ic_block;
+            switch (ch_block) {
+                case 16:
+                    zmm_kernel_ =
+                        new _jit_avx512_core_x8s8s32x_fwd_kernel<Xbyak::Zmm>(
+                                ajcp, attr);
+                    jit_ker = zmm_kernel_->jit_ker_;
+                    return;
+                case 8:
+                    ymm_kernel_ =
+                        new _jit_avx512_core_x8s8s32x_fwd_kernel<Xbyak::Ymm>(
+                                ajcp, attr);
+                    jit_ker = ymm_kernel_->jit_ker_;
+                    return;
+                case 4:
+                    xmm_kernel_ =
+                        new _jit_avx512_core_x8s8s32x_fwd_kernel<Xbyak::Xmm>(
+                                ajcp, attr);
+                    jit_ker = xmm_kernel_->jit_ker_;
+                    return;
+                default:
+                    assert(!"invalid channel blocking");
+            }
+    }
+
+    ~jit_avx512_core_x8s8s32x_fwd_kernel() {
+        delete xmm_kernel_;
+        delete ymm_kernel_;
+        delete zmm_kernel_;
+    }
+
+    static bool post_ops_ok(jit_conv_conf_t &jcp,
+            const primitive_attr_t &attr);
+
+    static status_t init_conf(jit_conv_conf_t &jcp,
+            const convolution_desc_t &cd,
+            memory_desc_t &src_pd,
+            memory_desc_t &weights_pd,
+            memory_desc_t &dst_pd,
+            memory_desc_t &bias_pd,
+            const primitive_attr_t &attr,
+            int nthreads);
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_conv_conf_t &jcp, const primitive_attr_t &attr);
+
+    void (*jit_ker)(jit_conv_call_s *);
+    _jit_avx512_core_x8s8s32x_fwd_kernel<Xbyak::Zmm> *zmm_kernel_;
+    _jit_avx512_core_x8s8s32x_fwd_kernel<Xbyak::Ymm> *ymm_kernel_;
+    _jit_avx512_core_x8s8s32x_fwd_kernel<Xbyak::Xmm> *xmm_kernel_;
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_convolution.cpp
new file mode 100644
index 0000000000..cdbf333d5e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_convolution.cpp
@@ -0,0 +1,423 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_avx512_core_x8s8s32x_convolution.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+
+using namespace nstl;
+
+using jit_conv_ker_t = void (*)(jit_conv_call_s *);
+
+#define wht_blk_off(d, g, ...) \
+        (pd()->with_groups() \
+         ? (d).blk_off((g), __VA_ARGS__) \
+         : (d).blk_off(__VA_ARGS__))
+
+template <data_type_t src_type, data_type_t dst_type>
+void jit_avx512_core_x8s8s32x_convolution_fwd_t<src_type,
+        dst_type>::execute_forward_1d(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    const size_t bia_dt_size = pd()->with_bias()
+        ? types::data_type_size(pd()->desc()->bias_desc.data_type) : 0;
+
+    const auto &jcp = pd()->jcp_;
+    assert(jcp.nb_oc % jcp.nb_oc_blocking == 0);
+    assert(jcp.nb_ch % jcp.nb_ch_blocking == 0);
+
+    const float *oscales = pd()->attr()->output_scales_.scales_;
+    if (jcp.signed_input && jcp.ver != ver_vnni) {
+        auto local_scales = scratchpad(ctx).template get<float>(
+                key_conv_adjusted_scales);
+        size_t count = pd()->attr()->output_scales_.count_;
+        float factor = 1.f / pd()->jcp_.wei_adj_scale;
+        if (count == 1) {
+            utils::array_set(local_scales, oscales[0] * factor, 16);
+        } else {
+            for (size_t c = 0; c < count; c++)
+                local_scales[c] = oscales[c] * factor;
+        }
+        oscales = local_scales;
+    }
+
+    size_t offset = weights_d.size() - weights_d.additional_buffer_size();
+    auto w = const_cast<wei_data_t *>(weights);
+    int32_t* compensation = (jcp.signed_input)
+                                ? reinterpret_cast<int32_t *>(&w[offset]) : 0;
+    int oc_chunks = jcp.nb_oc / jcp.nb_oc_blocking;
+    int nb_groups = jcp.nb_ch / jcp.nb_ch_blocking;
+    int group_block = jcp.ch_block;
+    int work_amount = jcp.mb * nb_groups * oc_chunks * jcp.nb_ow;
+
+    parallel(0, [&](const int ithr, const int nthr) {
+
+        int start{ 0 }, end{ 0 };
+        balance211(work_amount, nthr, ithr, start, end);
+
+        auto p = jit_conv_call_s();
+
+        int n{ 0 }, gg{ 0 }, occ{ 0 }, owb{ 0 };
+        switch (jcp.loop_order) {
+        case loop_cwgn:
+            nd_iterator_init(start, occ, oc_chunks, owb, jcp.nb_ow, gg,
+                    nb_groups, n, jcp.mb);
+            break;
+        case loop_gncw:
+            nd_iterator_init(start, gg, nb_groups, n, jcp.mb, occ, oc_chunks,
+                    owb, jcp.nb_ow);
+            break;
+        case loop_ngcw:
+            nd_iterator_init(start, n, jcp.mb, gg, nb_groups, occ, oc_chunks,
+                    owb, jcp.nb_ow);
+            break;
+        case loop_nwcg:
+            nd_iterator_init(start, n, jcp.mb, owb, jcp.nb_ow, occ, oc_chunks,
+                    gg, nb_groups);
+            break;
+        default: assert(!"unsupported loop order");
+        }
+        while (start < end) {
+            int ocb = occ * jcp.nb_oc_blocking;
+            int gb = gg * jcp.nb_ch_blocking;
+            int g = gb * group_block;
+            int g_oc = (g * jcp.nb_oc + ocb) * jcp.oc_block;
+            int g_ic = g * jcp.nb_ic * jcp.ic_block;
+            int ow_s = owb * jcp.ow_block;
+            int iw_s = ow_s * jcp.stride_w;
+
+            p.bias = bias ? bias + (bias_d.blk_off(g_oc) * bia_dt_size) : 0;
+            p.compensation = (jcp.signed_input) ? compensation + g_oc : 0;
+            p.dst = dst + dst_d.blk_off(n, g_oc, ow_s);
+            p.src = src + src_d.blk_off(n, g_ic, iw_s);
+            p.filt = weights + wht_blk_off(weights_d, gb, ocb, 0);
+            p.scales = &oscales[jcp.is_oc_scale * g_oc];
+            p.oc_blocks = jcp.is_depthwise ? gb : ocb;
+            p.kh_padding = jcp.kh;
+            p.t_overflow = 0;
+            p.b_overflow = 0;
+            p.owb = owb;
+
+            kernel_->jit_ker(&p);
+
+            ++start;
+            switch (jcp.loop_order) {
+            case loop_cwgn:
+                nd_iterator_step(occ, oc_chunks, owb, jcp.nb_ow, gg, nb_groups,
+                        n, jcp.mb);
+                break;
+            case loop_gncw:
+                nd_iterator_step(gg, nb_groups, n, jcp.mb, occ, oc_chunks, owb,
+                        jcp.nb_ow);
+                break;
+            case loop_ngcw:
+                nd_iterator_step(n, jcp.mb, gg, nb_groups, occ, oc_chunks, owb,
+                        jcp.nb_ow);
+                break;
+            case loop_nwcg:
+                nd_iterator_step(n, jcp.mb, owb, jcp.nb_ow, occ, oc_chunks, gg,
+                        nb_groups);
+                break;
+            default: assert(!"unsupported loop order");
+            }
+        }
+    });
+}
+
+template <data_type_t src_type, data_type_t dst_type>
+void jit_avx512_core_x8s8s32x_convolution_fwd_t<src_type,
+        dst_type>::execute_forward_2d(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    const size_t bia_dt_size = pd()->with_bias()
+        ? types::data_type_size(pd()->desc()->bias_desc.data_type) : 0;
+
+    const auto &jcp = pd()->jcp_;
+    assert(jcp.ch_block == 1);
+    assert(jcp.nb_ch_blocking == 1);
+    assert(jcp.nb_oc % jcp.nb_oc_blocking == 0);
+    assert(jcp.nb_ch % jcp.nb_ch_blocking == 0);
+
+    const float *oscales = pd()->attr()->output_scales_.scales_;
+    if (jcp.signed_input && jcp.ver != ver_vnni) {
+        auto local_scales = scratchpad(ctx).template get<float>(
+                key_conv_adjusted_scales);
+        size_t count = pd()->attr()->output_scales_.count_;
+        float factor = 1.f / pd()->jcp_.wei_adj_scale;
+        if (count == 1) {
+            utils::array_set(local_scales, oscales[0] * factor, 16);
+        } else {
+            for (size_t c = 0; c < count; c++)
+                local_scales[c] = oscales[c] * factor;
+        }
+        oscales = local_scales;
+    }
+
+    size_t offset = weights_d.size() - weights_d.additional_buffer_size();
+    auto w = const_cast<wei_data_t *>(weights);
+    int32_t* compensation = (jcp.signed_input)
+                                ? reinterpret_cast<int32_t *>(&w[offset]) : 0;
+    int oc_chunks = jcp.nb_oc / jcp.nb_oc_blocking_thr_chunk;
+    int nb_groups = jcp.nb_ch;
+    int work_amount = jcp.mb * nb_groups * oc_chunks * jcp.oh * jcp.nb_ow;
+
+    parallel(0, [&](const int ithr, const int nthr) {
+
+        int start{0}, end{0};
+        balance211(work_amount, nthr, ithr, start, end);
+
+        auto p = jit_conv_call_s();
+
+        size_t src_h_stride = src_d.blk_off(0, 0, 1);
+        size_t dst_h_stride = dst_d.blk_off(0, 0, 1);
+        size_t wht_h_stride = wht_blk_off(weights_d, 0, 0, 0, 1);
+
+        int n{ 0 }, g{ 0 }, occ{ 0 }, oh_s{ 0 }, owb{ 0 };
+        switch (jcp.loop_order) {
+        case loop_cwgn:
+            nd_iterator_init(start, occ, oc_chunks, owb, jcp.nb_ow, g,
+                    nb_groups, n, jcp.mb, oh_s, jcp.oh);
+            break;
+        case loop_ngcw:
+            nd_iterator_init(start, n, jcp.mb, g, nb_groups, occ, oc_chunks,
+                    owb, jcp.nb_ow, oh_s, jcp.oh);
+            break;
+        case loop_nhwcg:
+            nd_iterator_init(start, n, jcp.mb, oh_s, jcp.oh, owb, jcp.nb_ow,
+                    occ, oc_chunks, g, nb_groups);
+            break;
+        default: assert(!"unsupported loop order");
+        }
+        while (start < end) {
+            for (int occ1 = 0; occ1 < jcp.nb_oc_blocking_thr_chunk;
+                occ1 += jcp.nb_oc_blocking) {
+                int ocb = occ * jcp.nb_oc_blocking_thr_chunk + occ1;
+                int g_oc = (g * jcp.nb_oc + ocb) * jcp.oc_block;
+
+                int g_ic = g * jcp.nb_ic * jcp.ic_block;
+
+                int work_rem = end - start;
+                int ih_s = -jcp.t_pad + oh_s * jcp.stride_h;
+                int oh_e = oh_s + work_rem > jcp.oh ? jcp.oh : oh_s + work_rem;
+                if (jcp.loop_order == loop_nhwcg)
+                    oh_e = oh_s + 1; // step instead
+                int ow_s = owb * jcp.ow_block;
+                int iw_s = ow_s * jcp.stride_w;
+
+                auto bias_w = bias
+                    ? bias + (bias_d.blk_off(g_oc) * bia_dt_size)
+                    : 0;
+                int32_t *compensation_w = (jcp.signed_input)
+                                          ? compensation + g_oc : 0;
+
+                auto dst_w = dst + dst_d.blk_off(n, g_oc, oh_s, ow_s);
+                auto src_w = src + src_d.blk_off(n, g_ic, ih_s, iw_s);
+                auto wht_w = weights + wht_blk_off(weights_d, g, ocb, 0);
+
+                auto scales = &oscales[jcp.is_oc_scale * g_oc];
+
+                for (int oj = oh_s, ij = ih_s; oj < oh_e;
+                    ++oj, ij += jcp.stride_h) {
+                    int dilate_h = jcp.dilate_h + 1;
+                    int i_t_overflow = nstl::min(jcp.kh,
+                                                div_up(max(0, -ij), dilate_h));
+                    int i_b_overflow = nstl::min(jcp.kh, div_up(
+                            max(0, ij - jcp.ih + (jcp.kh - 1) * dilate_h + 1),
+                            dilate_h));
+                    int kh_padding = nstl::max(0,
+                        jcp.kh - i_t_overflow - i_b_overflow);
+
+                    size_t wei_stride = (!jcp.signed_input)
+                                            ? i_t_overflow * wht_h_stride : 0;
+                    p.src = src_w + i_t_overflow * dilate_h * src_h_stride;
+                    p.dst = dst_w;
+                    p.filt = wht_w + wei_stride;
+                    p.bias = bias_w;
+                    p.compensation = compensation_w;
+                    p.oc_blocks = ocb;
+                    p.kh_padding = kh_padding;
+                    p.scales = scales;
+                    p.t_overflow = i_t_overflow;
+                    p.b_overflow = i_b_overflow;
+                    p.owb = owb;
+
+                    kernel_->jit_ker(&p);
+                    src_w += src_h_stride * jcp.stride_h;
+                    dst_w += dst_h_stride;
+                }
+            }
+            switch (jcp.loop_order) {
+            case loop_cwgn:
+                nd_iterator_jump(start, end, occ, oc_chunks, owb, jcp.nb_ow, g,
+                        nb_groups, n, jcp.mb, oh_s, jcp.oh);
+                break;
+            case loop_ngcw:
+                nd_iterator_jump(start, end, n, jcp.mb, g, nb_groups, occ,
+                        oc_chunks, owb, jcp.nb_ow, oh_s, jcp.oh);
+                break;
+            case loop_nhwcg:
+                ++start;
+                nd_iterator_step(n, jcp.mb, oh_s, jcp.oh, owb, jcp.nb_ow, occ,
+                        oc_chunks, g, nb_groups);
+                break;
+            default: assert(!"unsupported loop order");
+            }
+        }
+    });
+}
+
+template <data_type_t src_type, data_type_t dst_type>
+void jit_avx512_core_x8s8s32x_convolution_fwd_t<src_type,
+        dst_type>::execute_forward_2d_dw(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    const size_t bia_dt_size = pd()->with_bias()
+            ? types::data_type_size(pd()->desc()->bias_desc.data_type) : 0;
+
+    const auto &jcp = pd()->jcp_;
+    assert(jcp.ic_block == 1);
+    assert(jcp.oc_block == 1);
+    assert(jcp.nb_ic == 1);
+    assert(jcp.nb_oc == 1);
+    assert(jcp.nb_oc_blocking == 1);
+    assert(jcp.nb_ch % jcp.nb_ch_blocking == 0);
+
+    const float *oscales = pd()->attr()->output_scales_.scales_;
+    if (jcp.signed_input && jcp.ver != ver_vnni) {
+        auto local_scales = scratchpad(ctx).template get<float>(
+                key_conv_adjusted_scales);
+        size_t count = pd()->attr()->output_scales_.count_;
+        float factor = 1.f / pd()->jcp_.wei_adj_scale;
+        if (count == 1) {
+            utils::array_set(local_scales, oscales[0] * factor, 16);
+        } else {
+            for (size_t c = 0; c < count; c++)
+                local_scales[c] = oscales[c] * factor;
+        }
+        oscales = local_scales;
+    }
+
+    size_t offset = weights_d.size() - weights_d.additional_buffer_size();
+    auto w = const_cast<wei_data_t *>(weights);
+    int32_t* compensation = (jcp.signed_input)
+                                ? reinterpret_cast<int32_t *>(&w[offset]) : 0;
+    int nb_groups = jcp.nb_ch / jcp.nb_ch_blocking;
+    int group_block = jcp.ch_block;
+
+    parallel_nd(jcp.mb, jcp.oh, jcp.nb_ow, nb_groups,
+            [&](int n, int oh_s, int owb, int gg) {
+
+        auto p = jit_conv_call_s();
+
+        size_t src_h_stride = src_d.blk_off(0, 0, 1);
+        size_t wht_h_stride = wht_blk_off(weights_d, 0, 0, 0, 1);
+
+        int gb = gg * jcp.nb_ch_blocking;
+        int g = gb * group_block;
+
+        int ih_s = -jcp.t_pad + oh_s * jcp.stride_h;
+        int ow_s = owb * jcp.ow_block;
+        int iw_s = ow_s * jcp.stride_w;
+
+        auto bias_w = bias ? bias + (bias_d.blk_off(g) * bia_dt_size) : 0;
+        int32_t *compensation_w = jcp.signed_input ? compensation + g : 0;
+
+        auto dst_w = dst + dst_d.blk_off(n, g, oh_s, ow_s);
+        auto src_w = src + src_d.blk_off(n, g, ih_s, iw_s);
+        auto wht_w = weights + wht_blk_off(weights_d, gb, 0);
+
+        auto scales = &oscales[jcp.is_oc_scale * g];
+
+        int dilate_h = jcp.dilate_h + 1;
+        int i_t_overflow = nstl::min(jcp.kh, div_up(max(0, -ih_s), dilate_h));
+        int i_b_overflow = nstl::min(jcp.kh,
+                div_up(max(0, ih_s - jcp.ih + (jcp.kh - 1) * dilate_h + 1),
+                                             dilate_h));
+        int kh_padding = nstl::max(0, jcp.kh - i_t_overflow - i_b_overflow);
+
+        size_t wei_stride = jcp.signed_input ? 0 : i_t_overflow * wht_h_stride;
+        p.src = src_w + i_t_overflow * dilate_h * src_h_stride;
+        p.dst = dst_w;
+        p.filt = wht_w + wei_stride;
+        p.bias = bias_w;
+        p.compensation = compensation_w;
+        p.oc_blocks = gb;
+        p.kh_padding = kh_padding;
+        p.scales = scales;
+        p.t_overflow = i_t_overflow;
+        p.b_overflow = i_b_overflow;
+        p.owb = owb;
+
+        kernel_->jit_ker(&p);
+    });
+}
+
+template struct jit_avx512_core_x8s8s32x_convolution_fwd_t<
+                                                data_type::s8, data_type::u8>;
+template struct jit_avx512_core_x8s8s32x_convolution_fwd_t<
+                                                data_type::u8, data_type::u8>;
+template struct jit_avx512_core_x8s8s32x_convolution_fwd_t<
+                                                data_type::s8, data_type::s8>;
+template struct jit_avx512_core_x8s8s32x_convolution_fwd_t<
+                                                data_type::u8, data_type::s8>;
+template struct jit_avx512_core_x8s8s32x_convolution_fwd_t<
+                                                data_type::s8, data_type::s32>;
+template struct jit_avx512_core_x8s8s32x_convolution_fwd_t<
+                                                data_type::u8, data_type::s32>;
+template struct jit_avx512_core_x8s8s32x_convolution_fwd_t<
+                                                data_type::s8, data_type::f32>;
+template struct jit_avx512_core_x8s8s32x_convolution_fwd_t<
+                                                data_type::u8, data_type::f32>;
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_convolution.hpp
new file mode 100644
index 0000000000..203ebdf942
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_convolution.hpp
@@ -0,0 +1,115 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_CORE_X8S8S32X_CONVOLUTION_HPP
+#define CPU_JIT_AVX512_CORE_X8S8S32X_CONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "jit_avx512_core_x8s8s32x_conv_kernel.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t src_type, impl::data_type_t dst_type>
+struct jit_avx512_core_x8s8s32x_convolution_fwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_()
+        {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_int8:", avx512_core, ""),
+                jit_avx512_core_x8s8s32x_convolution_fwd_t<src_type, dst_type>);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(src_type, data_type::s8, data_type::undef,
+                        dst_type, data_type::s32)
+                && IMPLICATION(with_bias(), utils::one_of(bias_md_.data_type,
+                            data_type::f32, data_type::s32, data_type::s8,
+                            data_type::u8))
+                && !has_zero_dim_memory();
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_avx512_core_x8s8s32x_fwd_kernel::init_conf(
+                    jcp_, *desc(), src_md_, weights_md_, dst_md_, bias_md_,
+                    *attr(), mkldnn_get_max_threads());
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx512_core_x8s8s32x_fwd_kernel::init_scratchpad(scratchpad,
+                    jcp_, *attr());
+
+            return status;
+        }
+
+        jit_conv_conf_t jcp_;
+    };
+
+    jit_avx512_core_x8s8s32x_convolution_fwd_t(const pd_t *apd)
+        : cpu_primitive_t(apd)
+    {
+        kernel_ = new jit_avx512_core_x8s8s32x_fwd_kernel(pd()->jcp_,
+                    *pd()->attr());
+    }
+
+    ~jit_avx512_core_x8s8s32x_convolution_fwd_t() { delete kernel_; }
+
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<data_type::s8>::type wei_data_t;
+    typedef typename prec_traits<dst_type>::type dst_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override
+    {
+        const auto &_pd = pd();
+        if (_pd->ndims() == 3)
+            execute_forward_1d(ctx);
+        else if (_pd->jcp_.is_depthwise)
+            execute_forward_2d_dw(ctx);
+        else
+            execute_forward_2d(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward_1d(const exec_ctx_t &ctx) const;
+    void execute_forward_2d(const exec_ctx_t &ctx) const;
+    void execute_forward_2d_dw(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_avx512_core_x8s8s32x_fwd_kernel *kernel_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_deconvolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_deconvolution.cpp
new file mode 100644
index 0000000000..142af1f541
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_deconvolution.cpp
@@ -0,0 +1,1034 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "jit_avx512_core_x8s8s32x_deconvolution.hpp"
+
+#define GET_OFF(field) offsetof(jit_deconv_call_s, field)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+using namespace Xbyak;
+
+using namespace nstl;
+
+#define wht_blk_off(d, g, ...)                             \
+    (pd()->with_groups() ? (d).blk_off((g), __VA_ARGS__) : \
+                           (d).blk_off(__VA_ARGS__))
+
+status_t jit_avx512_core_x8s8s32x_deconv_fwd_kernel::init_conf(
+        jit_conv_conf_t &jcp, const deconvolution_desc_t &cd,
+        memory_desc_t &src_md, memory_desc_t &weights_md,
+        memory_desc_t &dst_md, const bool with_bias,
+        memory_desc_t &bias_md, const primitive_attr_t &attr) {
+    const memory_desc_wrapper src_d(&src_md);
+    const memory_desc_wrapper dst_d(&dst_md);
+    const memory_desc_wrapper weights_d(&weights_md);
+    const memory_desc_wrapper bias_d(&bias_md);
+
+    if (!(mayiuse(avx512_core)
+                && one_of(src_d.data_type(), data_type::u8, data_type::s8)
+                && weights_d.data_type() == data_type::s8
+                && one_of(dst_d.data_type(), data_type::f32, data_type::s32,
+                           data_type::s8, data_type::u8)))
+        return status::unimplemented;
+
+    jcp = zero<decltype(jcp)>();
+
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+    jcp.signed_input = src_d.data_type() == data_type::s8;
+    const int ndims = jcp.ndims = dst_d.ndims();
+    const bool is_1d = ndims == 3;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+    jcp.oc_without_padding = dst_d.dims()[1] / jcp.ngroups;
+    jcp.ic_without_padding = src_d.dims()[1] / jcp.ngroups;
+    jcp.is_depthwise = true && with_groups
+            && utils::everyone_is(1, jcp.ic_without_padding,
+                               jcp.oc_without_padding);
+
+    /* TODO: future work, on hold until depthwise specialized kernel is
+     * implemented. */
+    if (jcp.is_depthwise && jcp.signed_input)
+        return status::unimplemented;
+
+    format_tag_t dat_tag = utils::pick(ndims - 3,
+            format_tag::nwc, format_tag::nhwc);
+
+    if (src_d.format_kind() == format_kind::any) {
+        CHECK(memory_desc_init_by_tag(src_md, dat_tag));
+        jcp.src_tag = dat_tag;
+    } else {
+        jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    }
+    if (jcp.src_tag != dat_tag)
+        return status::unimplemented;
+
+    if (dst_d.format_kind() == format_kind::any) {
+        CHECK(memory_desc_init_by_tag(dst_md, dat_tag));
+        jcp.dst_tag = dat_tag;
+    } else {
+        jcp.dst_tag = dst_d.matches_one_of_tag(dat_tag);
+    }
+    if (jcp.dst_tag != dat_tag)
+        return status::unimplemented;
+
+    auto set_or_check_wei_format = [&]() {
+        using namespace format_tag;
+
+        format_tag_t wei_tag = is_1d
+            ? (jcp.is_depthwise
+                    ? Goiw16g : (with_groups ? gOIw4i16o4i : OIw4i16o4i))
+            : (jcp.is_depthwise
+                    ? Goihw16g : (with_groups ? gOIhw4i16o4i : OIhw4i16o4i));
+
+        memory_desc_t want_wei_md = weights_md;
+        memory_desc_init_by_tag(want_wei_md, wei_tag);
+        if (jcp.signed_input && !jcp.is_depthwise) {
+            want_wei_md.extra.flags = 0
+                | memory_extra_flags::compensation_conv_s8s8
+                | memory_extra_flags::scale_adjust;
+            want_wei_md.extra.compensation_mask = (1 << 0)
+                + (with_groups && !jcp.is_depthwise ? (1 << 1) : 0);
+            want_wei_md.extra.scale_adjust =
+                mayiuse(avx512_core_vnni) ? 1.f : 0.5f;
+        }
+
+        if (weights_md.format_kind == format_kind::any) {
+            weights_md = want_wei_md;
+            return true;
+        }
+
+        return weights_md == want_wei_md;
+    };
+
+    if (!set_or_check_wei_format())
+        return status::unimplemented;
+
+    jcp.with_bias = with_bias;
+    if (jcp.with_bias) {
+        if (bias_d.format_kind() == format_kind::any)
+            CHECK(memory_desc_init_by_tag(bias_md, format_tag::x));
+    }
+
+    jcp.prop_kind = cd.prop_kind;
+    jcp.mb = src_d.dims()[0];
+    jcp.ih = is_1d ? 1 : src_d.dims()[ndims - 2];
+    jcp.iw = src_d.dims()[ndims - 1];
+    jcp.oh = is_1d ? 1 : dst_d.dims()[ndims - 2];
+    jcp.ow = dst_d.dims()[ndims - 1];
+    jcp.kh = is_1d ? 1 : weights_d.dims()[with_groups + ndims - 2];
+    jcp.kw = weights_d.dims()[with_groups + ndims - 1];
+    jcp.t_pad = is_1d ? 0 : cd.padding[0][ndims - 4];
+    jcp.l_pad = cd.padding[0][ndims - 3];
+    jcp.stride_h = is_1d ? 1 : cd.strides[ndims - 4];
+    jcp.stride_w = cd.strides[ndims - 3];
+
+    if (jcp.is_depthwise) {
+        jcp.ch_block = 16;
+        jcp.oc_block = 1;
+        jcp.ic_block = 1;
+    } else {
+        jcp.ch_block = 1;
+        jcp.oc_block = 16;
+        jcp.ic_block = 16;
+
+        if (jcp.ngroups == 1) {
+            jcp.oc = utils::rnd_up(jcp.oc_without_padding, jcp.oc_block);
+            jcp.ic = utils::rnd_up(jcp.ic_without_padding, jcp.ic_block);
+        }
+        if (jcp.ic % jcp.ic_block != 0)
+            return status::unimplemented;
+    }
+
+    jcp.dilate_h = is_1d ? 0 : cd.dilates[ndims - 4];
+    jcp.dilate_w = cd.dilates[ndims - 3];
+
+    if (!IMPLICATION(jcp.dilate_h, jcp.stride_h == 1)
+            || !IMPLICATION(jcp.dilate_w, jcp.stride_w == 1))
+        return status::unimplemented;
+
+    /* padding: bottom and right */
+    jcp.b_pad = (jcp.ih - 1) * jcp.stride_h + (jcp.kh - 1) * (jcp.dilate_h + 1)
+            - (jcp.oh + jcp.t_pad - 1);
+    jcp.r_pad = (jcp.iw - 1) * jcp.stride_w + (jcp.kw - 1) * (jcp.dilate_w + 1)
+            - (jcp.ow + jcp.l_pad - 1);
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    const int eltwise_ind = p.find(primitive_kind::eltwise);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise)
+        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+
+    jcp.ver = ver_avx512_core;
+    if (mayiuse(avx512_core_vnni))
+        jcp.ver = ver_vnni;
+    const auto &oscales = attr.output_scales_;
+    jcp.is_oc_scale = oscales.mask_ == 1 << 1;
+
+    assert(IMPLICATION(!jcp.is_oc_scale, oscales.mask_ == 0));
+
+    jcp.dst_dt = dst_d.data_type();
+    jcp.bia_dt = jcp.with_bias ? bias_d.data_type() : data_type::undef;
+    jcp.typesize_bia
+            = jcp.with_bias ? types::data_type_size(bias_d.data_type()) : 0;
+    jcp.typesize_in = types::data_type_size(src_d.data_type());
+    jcp.typesize_out = types::data_type_size(dst_d.data_type());
+
+    jcp.nb_ch = div_up(jcp.ngroups, jcp.ch_block);
+    jcp.nb_oc = jcp.oc / jcp.oc_block;
+    jcp.nb_ic = jcp.ic / jcp.ic_block;
+
+    /* kernel blocking params */
+    const int regs = jcp.ver == ver_vnni ? 30 : 28;
+    jcp.nb_oc_blocking = nstl::min(4, jcp.nb_oc);
+    for (; jcp.nb_oc_blocking > 1; jcp.nb_oc_blocking--)
+        if (jcp.nb_oc % jcp.nb_oc_blocking == 0
+                && jcp.l_pad <= regs / (jcp.nb_oc_blocking + 1))
+            break;
+
+    jcp.ur_w = regs / (jcp.nb_oc_blocking + 1);
+    int l_overflow = max(
+            0, ((jcp.kw - 1) * (jcp.dilate_w + 1) - jcp.l_pad) / jcp.stride_w);
+
+    if (jcp.ow < jcp.ur_w) {
+        jcp.ur_w = jcp.ow;
+        jcp.ur_w_tail = 0;
+    } else {
+        for (; jcp.ur_w >= 1; jcp.ur_w--) {
+            /* ur_w should be multiple of stride_w in order
+               to simplify logic for get_ow_start and get_ow_end */
+            bool is_multiple_of_stride = jcp.ur_w % jcp.stride_w == 0;
+
+            /* boundary conditions:
+               These conditions ensure all elements close to boundary
+               are computed in a single call of compute loop */
+            bool left_boundary_covered = jcp.ur_w >= l_overflow * jcp.stride_w;
+            jcp.ur_w_tail = jcp.ow % jcp.ur_w;
+            int r_overflow_no_tail
+                    = max(0, ((jcp.kw - 1) * (jcp.dilate_w + 1)
+                                     - max(0, jcp.r_pad) - jcp.ur_w_tail)
+                                    / jcp.stride_w);
+            bool right_boundary_covered
+                    = jcp.ur_w >= r_overflow_no_tail * jcp.stride_w;
+
+            if (is_multiple_of_stride && left_boundary_covered
+                    && right_boundary_covered)
+                break;
+            else if (jcp.ur_w == 1)
+                /* The boundary conditions above are also important
+                   to maintain simplicity of calls to icb_loop,
+                   if those conditions are not satisfied,
+                   then special cases will need to be added
+                   to use correct l_overflow/r_overflow values
+                   when different iterations of compute loop
+                   work on the locations close to boundary.
+                   So to keep code simple, return unimplemented
+                   for extreme case when a good ur_w cannot be found.
+                 */
+                return status::unimplemented;
+        }
+    }
+
+    jcp.wei_adj_scale =
+        (weights_d.extra().flags | memory_extra_flags::scale_adjust)
+        ? weights_d.extra().scale_adjust : 1.f;
+
+    jcp.loop_order = jcp.ngroups > 1 ? loop_ngc : loop_cgn;
+    return status::success;
+}
+
+bool jit_avx512_core_x8s8s32x_deconv_fwd_kernel::maybe_eltwise(int position) {
+    using namespace primitive_kind;
+    const auto &p = attr_.post_ops_;
+
+    if (position == 0) {
+        /* eltwise before sum */
+        return p.contain(eltwise, 0);
+    } else if (position == 1) {
+        /* eltwise after sum */
+        return p.contain(sum, 0) && p.contain(eltwise, 1);
+    }
+    return false;
+}
+
+void jit_avx512_core_x8s8s32x_deconv_fwd_kernel::compute_eltwise(int ur_w) {
+    int nb_oc_block
+            = jcp.is_depthwise ? jcp.nb_ch_blocking : jcp.nb_oc_blocking;
+    eltwise_injector_->compute_vector_range(0, nb_oc_block * ur_w);
+}
+
+bool jit_avx512_core_x8s8s32x_deconv_fwd_kernel::post_ops_ok(
+        jit_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    using namespace primitive_kind;
+    const auto &p = attr.post_ops_;
+
+    auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); };
+
+    switch (p.len_) {
+    case 0: return true;
+    case 1: return is_eltwise(0) || p.contain(sum, 0);
+    case 2:
+        return (p.contain(sum, 0) && is_eltwise(1))
+                || (p.contain(sum, 1) && is_eltwise(0));
+    default: return false;
+    }
+
+    return false;
+}
+
+void jit_avx512_core_x8s8s32x_deconv_fwd_kernel::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad, const jit_conv_conf_t &jcp,
+        const primitive_attr_t &attr) {
+    if (jcp.signed_input && jcp.ver != ver_vnni) {
+        dim_t count = nstl::max<dim_t>(attr.output_scales_.count_, 16);
+        scratchpad.book(key_conv_adjusted_scales, sizeof(float) * count);
+    }
+}
+
+void jit_avx512_core_x8s8s32x_deconv_fwd_kernel::compute_ker(int ur_w,
+        int l_overflow, int r_overflow, ker_block_t last_ic_block_flag,
+        bool h_padded) {
+
+    const int ch_block_all = jcp.ch_block * jcp.ic_block * jcp.oc_block;
+    const int ur_w_stride = jcp.signed_input ? 1 : jcp.stride_w;
+
+    auto src_offset = [=](int oj, int icb, int ki) {
+        return jcp.typesize_in
+                * (((oj + jcp.l_pad - ki * (jcp.dilate_w + 1)) / jcp.stride_w)
+                                  * jcp.ngroups * jcp.ic_without_padding
+                          + icb * 4);
+    };
+
+    auto kernel_offset = [=](int ocb, int icb, int ki) {
+        return jcp.typesize_in
+                * (ocb * jcp.nb_ic * jcp.kh * jcp.kw * ch_block_all
+                          + icb * jcp.oc_block * jcp.ic_block / 4
+                          + ki * ch_block_all);
+    };
+
+    auto compute = [=](zmm_t vreg_acc, zmm_t vreg_wei, zmm_t vreg_src) {
+        if (jcp.ver == ver_vnni) {
+            vpdpbusd(vreg_acc, vreg_src, vreg_wei);
+        } else if (jcp.is_depthwise) {
+            vpmulld(zmm_tmp, vreg_src, vreg_wei);
+            vpaddd(vreg_acc, vreg_acc, zmm_tmp);
+        } else {
+            vpmaddubsw(zmm_tmp, vreg_src, vreg_wei);
+            vpmaddwd(zmm_tmp, zmm_tmp, zmm_one);
+            vpaddd(vreg_acc, vreg_acc, zmm_tmp);
+        }
+    };
+
+    for (int ki = 0; ki < jcp.kw; ki++) {
+
+        int jj_start = get_ow_start(ki, l_overflow);
+        int jj_end = get_ow_end(ur_w, ki, r_overflow);
+
+        int _start = (jcp.signed_input) ? 0 : jj_start;
+        int _end = (jcp.signed_input) ? ur_w : jj_end;
+
+        int tail_size = jcp.ic_without_padding % 4;
+        int n_ic_blocks = jcp.is_depthwise ?
+                1 :
+                (last_ic_block_flag & ~no_last_block ?
+                                div_up(jcp.ic_without_padding % jcp.ic_block,
+                                        4) :
+                                jcp.ic_block / 4);
+
+        for (int icb1 = 0; icb1 < n_ic_blocks; icb1++) {
+            if (h_padded == true) {
+                /* fill padded area with shifted values */
+                Zmm inp = zmm_inp(0, jcp.nb_oc_blocking);
+                vpxord(inp, inp, inp);
+                vpsubb(inp, inp, zmm_shift);
+            } else {
+
+                for (int jj = _start; jj < _end; jj += ur_w_stride) {
+
+                    int aux_src_off = src_offset(jj, icb1, ki);
+
+                    if (jj >= jj_start && jj < jj_end
+                            && ((jj + jcp.l_pad - ki) % jcp.stride_w == 0)) {
+                        if (jcp.is_depthwise) {
+                            vpmovzxbd(zmm_inp(jj, jcp.nb_oc_blocking),
+                                    EVEX_compress_addr(
+                                              aux_reg_src, aux_src_off));
+                        } else if ((last_ic_block_flag & last_sp_block)
+                                && tail_size != 0 && icb1 == n_ic_blocks - 1) {
+                            xmm_t xmm_tmp = xmm_t(
+                                    zmm_inp(jj, jcp.nb_oc_blocking).getIdx());
+                            for (int r = 0; r < tail_size; ++r)
+                                vpinsrb(xmm_tmp, xmm_tmp,
+                                        ptr[aux_reg_src + aux_src_off + r], r);
+                            vpbroadcastd(
+                                    zmm_inp(jj, jcp.nb_oc_blocking), xmm_tmp);
+                        } else {
+                            vpbroadcastd(zmm_inp(jj, jcp.nb_oc_blocking),
+                                    EVEX_compress_addr(
+                                                 aux_reg_src, aux_src_off));
+                        }
+                        if (jcp.signed_input)
+                            vpsubb(zmm_inp(jj, jcp.nb_oc_blocking),
+                                    zmm_inp(jj, jcp.nb_oc_blocking), zmm_shift);
+                    } else {
+                        /* fill padded area with shifted values */
+                        if (jcp.signed_input) {
+                            Zmm inp = zmm_inp(jj, jcp.nb_oc_blocking);
+                            vpxord(inp, inp, inp);
+                            vpsubb(inp, inp, zmm_shift);
+                        }
+                    }
+                }
+            }
+            for (int ocb = 0; ocb < jcp.nb_oc_blocking; ocb++) {
+                int aux_filt_off = kernel_offset(ocb, icb1, ki);
+
+                if (_end - _start > 0) {
+                    if (jcp.is_depthwise)
+                        vpmovsxbd(zmm_wei,
+                                EVEX_compress_addr(aux_reg_filt, aux_filt_off));
+                    else
+                        vmovups(zmm_wei,
+                                EVEX_compress_addr(aux_reg_filt, aux_filt_off));
+                }
+                for (int jj = _start; jj < _end; jj += ur_w_stride) {
+                    Zmm inp = (h_padded == true) ?
+                            zmm_inp(0, jcp.nb_oc_blocking) :
+                            zmm_inp(jj, jcp.nb_oc_blocking);
+                    compute(zmm_out(jj, ocb), zmm_wei, inp);
+                }
+            }
+        }
+    }
+}
+
+void jit_avx512_core_x8s8s32x_deconv_fwd_kernel::kh_loop(int ur_w,
+        int l_overflow, int r_overflow, ker_block_t last_ic_block_flag) {
+
+    int ch_block_all = jcp.ch_block * jcp.ic_block * jcp.oc_block;
+    int shift_src_ih = jcp.typesize_in * (jcp.dilate_h + 1) * jcp.iw
+            * jcp.ngroups * jcp.ic_without_padding;
+    const int stride_h = jcp.signed_input ? 1 : jcp.stride_h;
+    int shift_filt_kh = jcp.typesize_in * jcp.kw * ch_block_all * stride_h;
+
+    Label kh_loop_label, skip_kh_loop;
+    Label t_overflow_label, no_t_overflow_label, b_overflow_label,
+            no_b_overflow_label;
+
+    mov(aux_reg_src, reg_src);
+    mov(aux_reg_filt, reg_filt);
+
+    if (jcp.signed_input && jcp.ndims > 3) {
+        /* Weights are transposed, so first compute 'bottom' padding. */
+        mov(reg_overflow, ptr[param1 + GET_OFF(b_overflow)]);
+        cmp(reg_overflow, 0);
+        je(no_b_overflow_label, T_NEAR);
+        L(b_overflow_label); {
+            compute_ker(ur_w, 0, 0, last_ic_block_flag, true);
+
+            add(aux_reg_filt, shift_filt_kh);
+            dec(reg_overflow);
+            cmp(reg_overflow, 0);
+            jg(b_overflow_label, T_NEAR);
+        }
+        L(no_b_overflow_label);
+    }
+
+    mov(reg_kh, ptr[param1 + GET_OFF(kh_padding)]);
+
+    if (jcp.signed_input || ((!jcp.signed_input)
+        && ((min(jcp.t_pad, jcp.b_pad) < 0)
+            || ((jcp.kh - 1) * (jcp.dilate_h + 1)
+                < nstl::max(jcp.t_pad, jcp.b_pad))))) {
+        cmp(reg_kh, 0);
+        je(skip_kh_loop, T_NEAR);
+    }
+
+    L(kh_loop_label); {
+        compute_ker(ur_w, l_overflow, r_overflow, last_ic_block_flag, false);
+        sub(aux_reg_src, shift_src_ih);
+        add(aux_reg_filt, shift_filt_kh);
+        dec(reg_kh);
+
+        /* Insert weight compensation in stride 'holes' */
+        if (jcp.signed_input && jcp.stride_h > 1) {
+            Label kh_comp_loop;
+
+            cmp(reg_kh, 0);
+            je(skip_kh_loop, T_NEAR);
+            mov(reg_comp_strides, jcp.stride_h - 1);
+            L(kh_comp_loop);
+            {
+                compute_ker(
+                        ur_w, 0, 0, last_ic_block_flag, true);
+                add(aux_reg_filt, shift_filt_kh);
+                dec(reg_comp_strides);
+                cmp(reg_comp_strides, 0);
+                jg(kh_comp_loop, T_NEAR);
+            }
+        }
+        cmp(reg_kh, 0);
+        jg(kh_loop_label, T_NEAR);
+    }
+    L(skip_kh_loop);
+    if (jcp.signed_input && jcp.ndims > 3) {
+        mov(reg_overflow, ptr[param1 + GET_OFF(t_overflow)]);
+        cmp(reg_overflow, 0);
+        je(no_t_overflow_label, T_NEAR);
+        L(t_overflow_label); {
+            compute_ker(ur_w, 0, 0, last_ic_block_flag, true);
+
+            add(aux_reg_filt, shift_filt_kh);
+            dec(reg_overflow);
+            cmp(reg_overflow, 0);
+            jg(t_overflow_label, T_NEAR);
+        }
+        L(no_t_overflow_label);
+    }
+}
+
+void jit_avx512_core_x8s8s32x_deconv_fwd_kernel::prepare_output(int ur_w) {
+    for (int ocb = 0; ocb < jcp.nb_oc_blocking; ocb++) {
+        for (int ur = 0; ur < ur_w; ur++) {
+            zmm_t zmm = zmm_out(ur, ocb);
+            vpxord(zmm, zmm, zmm);
+        }
+    }
+    if (jcp.signed_input) {
+        xor_(reg_scratch, reg_scratch);
+        Reg8 _t8 = reg_scratch.cvt8();
+        mov(_t8, (int8_t)-128);
+        vpbroadcastb(zmm_shift, _t8);
+    }
+}
+
+void jit_avx512_core_x8s8s32x_deconv_fwd_kernel::cvt2ps(
+        data_type_t type_in, zmm_t zmm_in, const Operand &op, bool mask_flag) {
+    zmm_t zmm = mask_flag ? zmm_in | ktail_mask | T_z : zmm_in;
+    switch (type_in) {
+    case data_type::f32:
+    case data_type::s32: vmovups(zmm, op); break;
+    case data_type::s8: vpmovsxbd(zmm, op); break;
+    case data_type::u8: vpmovzxbd(zmm, op); break;
+    default: assert(!"unsupported data type");
+    }
+    if (type_in != data_type::f32)
+        vcvtdq2ps(zmm_in, zmm_in);
+}
+
+void jit_avx512_core_x8s8s32x_deconv_fwd_kernel::store_output(
+        int ur_w, bool last_oc_block) {
+    mov(reg_bias, ptr[param1 + GET_OFF(bias)]);
+    mov(reg_ptr_scales, ptr[param1 + GET_OFF(scales)]);
+
+    if (jcp.signed_input)
+        mov(reg_compensation, ptr[param1 + GET_OFF(compensation)]);
+
+    const auto &p = attr_.post_ops_;
+    const int sum_idx = p.find(primitive_kind::sum);
+    const float *p_sum_scale
+            = (sum_idx != -1) ? &p.entry_[sum_idx].sum.scale : nullptr;
+    if (p_sum_scale && *p_sum_scale != 1.f)
+        mov(reg_ptr_sum_scale, (size_t)p_sum_scale);
+
+    if (jcp.with_bias && jcp.signed_input && jcp.ver != ver_vnni) {
+        mov(reg_bias_alpha, float2int(jcp.wei_adj_scale));
+        vmovq(xmm_bias_alpha(), reg_bias_alpha);
+        vbroadcastss(zmm_bias_alpha(), xmm_bias_alpha());
+    }
+
+    for (int ocb = 0; ocb < jcp.nb_oc_blocking; ocb++) {
+        const bool mask_flag = last_oc_block && ocb == jcp.nb_oc_blocking - 1;
+        int scale_offset
+                = jcp.is_oc_scale * (sizeof(float) * ocb * jcp.oc_block);
+
+        auto zmm_bias = zmm_tmp;
+        if (jcp.with_bias) {
+            int bias_offset = jcp.typesize_bia * ocb * jcp.oc_block;
+            auto bias_addr = EVEX_compress_addr(reg_bias, bias_offset);
+            cvt2ps(jcp.bia_dt, zmm_bias, bias_addr, mask_flag);
+            if (jcp.signed_input && jcp.ver != ver_vnni)
+                vmulps(zmm_bias, zmm_bias, zmm_bias_alpha());
+        }
+        if (jcp.signed_input) {
+            int comp_offset = sizeof(int32_t) * ocb * jcp.oc_block;
+            auto comp_addr = EVEX_compress_addr(reg_compensation, comp_offset);
+            cvt2ps(data_type::s32, zmm_comp, comp_addr, mask_flag);
+        }
+
+        for (int ur = 0; ur < ur_w; ur++) {
+            zmm_t zmm = zmm_out(ur, ocb);
+            vcvtdq2ps(zmm, zmm);
+            if (jcp.signed_input)
+                vaddps(zmm, zmm, zmm_comp);
+            if (jcp.with_bias)
+                vaddps(zmm, zmm, zmm_bias);
+            zmm_t mask_zmm = mask_flag ? zmm | ktail_mask | T_z : zmm;
+            vmulps(mask_zmm, zmm,
+                    EVEX_compress_addr(reg_ptr_scales, scale_offset));
+        }
+    }
+    if (maybe_eltwise(0))
+        compute_eltwise(ur_w);
+    if (p_sum_scale) { // post_op: sum
+        for (int k = 0; k < jcp.nb_oc_blocking; k++) {
+            const bool mask_flag
+                    = last_oc_block == 1 && k == jcp.nb_oc_blocking - 1;
+            for (int j = 0; j < ur_w; j++) {
+                int aux_output_offset
+                        = jcp.typesize_out
+                        * (k * jcp.oc_block
+                                  + j * jcp.oc_without_padding * jcp.ngroups);
+                auto addr = EVEX_compress_addr(reg_dst, aux_output_offset);
+                Zmm zmm = zmm_out(j, k);
+                cvt2ps(jcp.dst_dt, zmm_prev_dst, addr, mask_flag);
+                if (*p_sum_scale == 1.f)
+                    vaddps(zmm, zmm_prev_dst);
+                else
+                    vfmadd231ps(zmm, zmm_prev_dst, zword_b[reg_ptr_sum_scale]);
+            }
+        }
+    }
+    if (maybe_eltwise(1))
+        compute_eltwise(ur_w);
+
+    for (int ocb = 0; ocb < jcp.nb_oc_blocking; ocb++) {
+        const bool mask_flag = last_oc_block && ocb == jcp.nb_oc_blocking - 1;
+        for (int ur = 0; ur < ur_w; ur++) {
+            zmm_t zmm = zmm_out(ur, ocb);
+            if (jcp.dst_dt == data_type::u8) {
+                vpxord(zmm_zero, zmm_zero, zmm_zero);
+                vmaxps(zmm, zmm_zero, zmm);
+            }
+            if (jcp.dst_dt != data_type::f32)
+                vcvtps2dq(zmm, zmm);
+        }
+        for (int ur = 0; ur < ur_w; ur++) {
+            int aux_dst_off = jcp.typesize_out
+                    * (ur * jcp.ngroups * jcp.oc_without_padding
+                                      + ocb * jcp.oc_block);
+            auto addr = EVEX_compress_addr(reg_dst, aux_dst_off);
+
+            zmm_t zmm = zmm_out(ur, ocb);
+            zmm_t r_zmm = mask_flag ? zmm | ktail_mask : zmm;
+            switch (jcp.dst_dt) {
+            case data_type::f32:
+            case data_type::s32: vmovups(addr, r_zmm); break;
+            case data_type::s8: vpmovsdb(addr, r_zmm); break;
+            case data_type::u8: vpmovusdb(addr, r_zmm); break;
+            default: assert(!"unknown dst_dt");
+            }
+        }
+    }
+}
+
+void jit_avx512_core_x8s8s32x_deconv_fwd_kernel::icb_loop(
+        int ur_w, int l_overflow, int r_overflow, bool is_last_sp_block) {
+
+    int shift_src_icb = jcp.typesize_in * jcp.ic_block;
+    int shift_filt_icb
+            = jcp.typesize_in * jcp.kh * jcp.kw * jcp.ic_block * jcp.oc_block;
+
+    prepare_output(ur_w);
+
+    Label skip_icb_loop, icb_loop_label;
+
+    mov(reg_icb, jcp.nb_ic);
+    L(icb_loop_label); {
+
+        if (jcp.ic_without_padding != jcp.ic) {
+            Label common_ker, end_ker;
+            cmp(reg_icb, 1);
+            jg(common_ker, T_NEAR);
+
+            kh_loop(ur_w, l_overflow, r_overflow,
+                    is_last_sp_block ? last_sp_block : last_ic_block);
+            jmp(end_ker, T_NEAR);
+
+            L(common_ker);
+            kh_loop(ur_w, l_overflow, r_overflow, no_last_block);
+
+            L(end_ker);
+        } else {
+            kh_loop(ur_w, l_overflow, r_overflow, no_last_block);
+        }
+
+        add(reg_src, shift_src_icb);
+        add(reg_filt, shift_filt_icb);
+        dec(reg_icb);
+        cmp(reg_icb, 0);
+        jg(icb_loop_label, T_NEAR);
+    }
+
+    /* come-back pointers */
+    sub(reg_src, jcp.nb_ic * shift_src_icb);
+    sub(reg_filt, jcp.nb_ic * shift_filt_icb);
+    L(skip_icb_loop);
+
+    if (jcp.ngroups % jcp.ch_block != 0 || jcp.oc_without_padding != jcp.oc) {
+        Label common_store, end_store;
+        mov(reg_oc_blocks, ptr[param1 + GET_OFF(oc_blocks)]);
+        if (jcp.is_depthwise)
+            cmp(reg_oc_blocks, jcp.nb_ch - 1);
+        else
+            cmp(reg_oc_blocks, jcp.nb_oc - jcp.nb_oc_blocking);
+        jne(common_store, T_NEAR);
+
+        store_output(ur_w, true);
+        jmp(end_store, T_NEAR);
+
+        L(common_store);
+        store_output(ur_w, false);
+
+        L(end_store);
+
+    } else {
+        store_output(ur_w, false);
+    }
+}
+
+void jit_avx512_core_x8s8s32x_deconv_fwd_kernel::generate() {
+    preamble();
+
+    xor_(reg_scratch, reg_scratch);
+    Reg16 _t = reg_scratch.cvt16();
+    mov(_t, 0x1);
+    vpbroadcastw(zmm_one, _t);
+
+    if (jcp.ngroups % jcp.ch_block != 0 || jcp.oc_without_padding != jcp.oc) {
+        int tail_size = jcp.is_depthwise ?
+                jcp.ngroups % jcp.ch_block :
+                jcp.oc_without_padding % jcp.oc_block;
+        int mask = (1 << tail_size) - 1;
+        Reg32 regw_tmp = reg_nur_w.cvt32();
+        mov(regw_tmp, mask);
+        kmovw(ktail_mask, regw_tmp);
+    }
+
+    mov(reg_src, ptr[param1 + GET_OFF(src)]);
+    mov(reg_filt, ptr[param1 + GET_OFF(filt)]);
+    mov(reg_dst, ptr[param1 + GET_OFF(dst)]);
+
+    int dst_shift = jcp.typesize_out * jcp.ur_w * jcp.ngroups
+            * jcp.oc_without_padding;
+    int src_shift = jcp.typesize_in * (jcp.ur_w / jcp.stride_w) * jcp.ngroups
+            * jcp.ic_without_padding;
+
+    int l_overflow = max(
+            0, ((jcp.kw - 1) * (jcp.dilate_w + 1) - jcp.l_pad) / jcp.stride_w);
+    int r_overflow
+            = max(0, ((jcp.kw - 1) * (jcp.dilate_w + 1) - max(0, jcp.r_pad))
+                            / jcp.stride_w);
+
+    int r_overflow1
+            = nstl::max(0, ((jcp.kw - 1) * (jcp.dilate_w + 1)
+                                   - nstl::max(0, jcp.r_pad) - jcp.ur_w_tail)
+                            / jcp.stride_w);
+    int nur_w = jcp.ow / jcp.ur_w;
+    if (r_overflow1 > 0)
+        nur_w--;
+
+    if (jcp.ur_w == jcp.ow) {
+        icb_loop(jcp.ur_w, l_overflow, r_overflow, true);
+    } else if (nur_w == 0) {
+        icb_loop(jcp.ur_w, l_overflow, r_overflow1, jcp.ur_w_tail == 0);
+        add(reg_src, src_shift);
+        add(reg_dst, dst_shift);
+        if (jcp.ur_w_tail != 0)
+            icb_loop(jcp.ur_w_tail, 0, r_overflow, true);
+    } else {
+        xor_(reg_nur_w, reg_nur_w);
+        if (l_overflow > 0) {
+            icb_loop(jcp.ur_w, l_overflow, 0, false);
+            add(reg_src, src_shift);
+            add(reg_dst, dst_shift);
+            inc(reg_nur_w);
+        }
+        if ((l_overflow <= 0 && nur_w > 0) || (l_overflow > 0 && nur_w > 1)) {
+            Label ow_loop_label;
+            L(ow_loop_label);
+            {
+                icb_loop(jcp.ur_w, 0, 0, false);
+                add(reg_src, src_shift);
+                add(reg_dst, dst_shift);
+                inc(reg_nur_w);
+                cmp(reg_nur_w, nur_w);
+                jl(ow_loop_label, T_NEAR);
+            }
+        }
+        if (r_overflow1 > 0) {
+            icb_loop(jcp.ur_w, 0, r_overflow1, jcp.ur_w_tail == 0);
+            add(reg_src, src_shift);
+            add(reg_dst, dst_shift);
+        }
+        if (jcp.ur_w_tail != 0) {
+            icb_loop(jcp.ur_w_tail, 0, r_overflow, true);
+        }
+    }
+    postamble();
+
+    if (jcp.with_eltwise)
+        eltwise_injector_->prepare_table();
+}
+
+template <data_type_t src_type, data_type_t dst_type>
+void _jit_avx512_core_x8s8s32x_deconvolution_fwd_t<src_type,
+        dst_type>::execute_forward_1d(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    auto &jcp = kernel_->jcp;
+
+    int oc_chunks = jcp.nb_oc / jcp.nb_oc_blocking;
+    int nb_groups = jcp.nb_ch;
+
+    const float *oscales = pd()->attr()->output_scales_.scales_;
+    if (jcp.signed_input && jcp.ver != ver_vnni) {
+        auto local_scales
+                = scratchpad(ctx).template get<float>(key_conv_adjusted_scales);
+        size_t count = pd()->attr()->output_scales_.count_;
+        float factor = 1.f / pd()->jcp_.wei_adj_scale;
+        if (count == 1) {
+            utils::array_set(local_scales, oscales[0] * factor, 16);
+        } else {
+            for (size_t c = 0; c < count; c++)
+                local_scales[c] = oscales[c] * factor;
+        }
+        oscales = local_scales;
+    }
+    size_t offset = (size_t)jcp.ngroups * jcp.oc * jcp.ic * jcp.kh * jcp.kw;
+    auto w = const_cast<wei_data_t *>(weights);
+    int32_t *compensation
+            = (jcp.signed_input) ? reinterpret_cast<int32_t *>(&w[offset]) : 0;
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        int start{ 0 }, end{ 0 };
+        int work_amount = jcp.mb * nb_groups * oc_chunks;
+        balance211(work_amount, nthr, ithr, start, end);
+
+        auto p = jit_deconv_call_s();
+
+        int n{ 0 }, g{ 0 }, occ{ 0 };
+        if (jcp.loop_order == loop_ngc)
+            nd_iterator_init(start, n, jcp.mb, g, nb_groups, occ, oc_chunks);
+        else if (jcp.loop_order == loop_cgn)
+            nd_iterator_init(start, occ, oc_chunks, g, nb_groups, n, jcp.mb);
+        else
+            assert(!"unsupported loop order");
+        while (start < end) {
+
+            int ocb = occ * jcp.nb_oc_blocking;
+            int g_oc = (g * jcp.ch_block * jcp.nb_oc + ocb) * jcp.oc_block;
+            int g_ic = g * jcp.ch_block * jcp.ic;
+
+            p.dst = dst + dst_d.blk_off(n, g_oc);
+            p.src = src + src_d.blk_off(n, g_ic);
+            p.filt = weights + wht_blk_off(weights_d, g, ocb, 0);
+            p.bias = jcp.with_bias ?
+                    bias + (bias_d.blk_off(g_oc) * jcp.typesize_bia) :
+                    0;
+            p.compensation = (jcp.signed_input) ? compensation + g_oc : 0;
+            p.scales = &oscales[jcp.is_oc_scale * g_oc];
+            p.t_overflow = 0;
+            p.b_overflow = 0;
+            p.kh_padding = jcp.kh;
+            p.oc_blocks = jcp.is_depthwise ? g : ocb;
+
+            kernel_->jit_ker(&p);
+
+            ++start;
+            if (jcp.loop_order == loop_ngc)
+                nd_iterator_step(n, jcp.mb, g, nb_groups, occ, oc_chunks);
+            else if (jcp.loop_order == loop_cgn)
+                nd_iterator_step(occ, oc_chunks, g, nb_groups, n, jcp.mb);
+            else
+                assert(!"unsupported loop order");
+        }
+    });
+}
+
+template <data_type_t src_type, data_type_t dst_type>
+void _jit_avx512_core_x8s8s32x_deconvolution_fwd_t<src_type,
+        dst_type>::execute_forward_2d(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    auto &jcp = kernel_->jcp;
+
+    int oc_chunks = jcp.nb_oc / jcp.nb_oc_blocking;
+    int nb_groups = jcp.nb_ch;
+
+    size_t src_h_stride = src_d.blk_off(0, 0, 1);
+    size_t dst_h_stride = dst_d.blk_off(0, 0, 1);
+    size_t wht_kh_stride = wht_blk_off(weights_d, 0, 0, 0, 1);
+
+    const float *oscales = pd()->attr()->output_scales_.scales_;
+    if (jcp.signed_input && jcp.ver != ver_vnni) {
+        auto local_scales
+                = scratchpad(ctx).template get<float>(key_conv_adjusted_scales);
+        size_t count = pd()->attr()->output_scales_.count_;
+        float factor = 1.f / pd()->jcp_.wei_adj_scale;
+        if (count == 1) {
+            utils::array_set(local_scales, oscales[0] * factor, 16);
+        } else {
+            for (size_t c = 0; c < count; c++)
+                local_scales[c] = oscales[c] * factor;
+        }
+        oscales = local_scales;
+    }
+    size_t offset = (size_t)jcp.ngroups * jcp.oc * jcp.ic * jcp.kh * jcp.kw;
+    auto w = const_cast<wei_data_t *>(weights);
+    int32_t *compensation
+            = (jcp.signed_input) ? reinterpret_cast<int32_t *>(&w[offset]) : 0;
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        int start{ 0 }, end{ 0 };
+        int work_amount = jcp.mb * nb_groups * oc_chunks * jcp.oh;
+        balance211(work_amount, nthr, ithr, start, end);
+
+        auto p = jit_deconv_call_s();
+
+        /*loop order = cgn*/
+        int n{ 0 }, g{ 0 }, occ{ 0 }, oh_s{ 0 };
+        if (jcp.loop_order == loop_ngc)
+            nd_iterator_init(start, n, jcp.mb, g, nb_groups, occ, oc_chunks,
+                    oh_s, jcp.oh);
+        else if (jcp.loop_order == loop_cgn)
+            nd_iterator_init(start, occ, oc_chunks, g, nb_groups, n, jcp.mb,
+                    oh_s, jcp.oh);
+        else
+            assert(!"unsupported loop order");
+        while (start < end) {
+
+            int ocb = occ * jcp.nb_oc_blocking;
+            int g_oc = (g * jcp.ch_block * jcp.nb_oc + ocb) * jcp.oc_block;
+            int g_ic = g * jcp.ch_block * jcp.ic;
+            int work_rem = end - start;
+            int oh_e = oh_s + work_rem > jcp.oh ? jcp.oh : oh_s + work_rem;
+
+            auto dst_w = dst + dst_d.blk_off(n, g_oc);
+            auto src_w = src + src_d.blk_off(n, g_ic);
+            auto wht_w = weights + wht_blk_off(weights_d, g, ocb, 0);
+            auto bias_w = jcp.with_bias ?
+                    bias + (bias_d.blk_off(g_oc) * jcp.typesize_bia) :
+                    0;
+            int32_t *compensation_w
+                    = (jcp.signed_input) ? compensation + g_oc : 0;
+
+            auto scales = &oscales[jcp.is_oc_scale * g_oc];
+            for (int oj = oh_s; oj < oh_e; oj++) {
+                int ih_max = 0, kh_lo = 0, kh_len = 0;
+                if (jcp.dilate_h != 0 && jcp.stride_h == 1) {
+                    /* dilation */
+                    int dilate_h = jcp.dilate_h + 1;
+                    // Note: use div_up to account for "holes" in filter
+                    int o_t_overflow = div_up(
+                            max(0, (jcp.kh - 1) * dilate_h - oj - jcp.t_pad),
+                            dilate_h);
+                    int o_b_overflow
+                            = div_up(max(0, (jcp.kh - 1) * dilate_h + 1 - jcp.oh
+                                                     + oj - jcp.b_pad),
+                                    dilate_h);
+                    kh_len = jcp.kh - o_t_overflow - o_b_overflow;
+                    kh_lo = o_b_overflow;
+                    ih_max = oj + jcp.t_pad - o_b_overflow * dilate_h;
+                } else {
+                    int o_t_overflow = max(
+                            0, (jcp.kh - (oj + 1 + jcp.t_pad)) / jcp.stride_h);
+                    int o_b_overflow
+                            = max(0, ((oj + jcp.kh) - (jcp.oh + jcp.b_pad))
+                                            / jcp.stride_h);
+                    int overflow_kh_hi = jcp.kh - 1
+                            - abs(jcp.oh + jcp.b_pad - (oj + 1)) % jcp.stride_h;
+                    int overflow_kh_lo = (oj + jcp.t_pad) % jcp.stride_h;
+
+                    kh_len = (overflow_kh_hi - overflow_kh_lo) / jcp.stride_h
+                            + 1 - o_t_overflow - o_b_overflow;
+                    kh_lo = overflow_kh_lo + o_b_overflow * jcp.stride_h;
+                    ih_max = (oj + jcp.t_pad - kh_lo) / jcp.stride_h;
+                }
+
+                int wei_stride
+                        = (!jcp.signed_input) ? kh_lo * wht_kh_stride : 0;
+                p.src = src_w + ih_max * src_h_stride;
+                p.dst = dst_w + oj * dst_h_stride;
+                p.filt = wht_w + wei_stride;
+                p.bias = bias_w;
+                p.compensation = compensation_w;
+                p.t_overflow = max(
+                        0, jcp.kh - (kh_lo + max(0, kh_len - 1) * jcp.stride_h
+                                            + 1));
+                p.b_overflow = kh_lo;
+                p.kh_padding = kh_len;
+                p.scales = scales;
+                p.oc_blocks = jcp.is_depthwise ? g : ocb;
+                kernel_->jit_ker(&p);
+            }
+            if (jcp.loop_order == loop_ngc)
+                nd_iterator_jump(start, end, n, jcp.mb, g, nb_groups, occ,
+                        oc_chunks, oh_s, jcp.oh);
+            else if (jcp.loop_order == loop_cgn)
+                nd_iterator_jump(start, end, occ, oc_chunks, g, nb_groups, n,
+                        jcp.mb, oh_s, jcp.oh);
+            else
+                assert(!"unsupported loop order");
+        }
+    });
+}
+
+template struct _jit_avx512_core_x8s8s32x_deconvolution_fwd_t<data_type::u8,
+        data_type::u8>;
+template struct _jit_avx512_core_x8s8s32x_deconvolution_fwd_t<data_type::u8,
+        data_type::s8>;
+template struct _jit_avx512_core_x8s8s32x_deconvolution_fwd_t<data_type::u8,
+        data_type::f32>;
+template struct _jit_avx512_core_x8s8s32x_deconvolution_fwd_t<data_type::u8,
+        data_type::s32>;
+template struct _jit_avx512_core_x8s8s32x_deconvolution_fwd_t<data_type::s8,
+        data_type::u8>;
+template struct _jit_avx512_core_x8s8s32x_deconvolution_fwd_t<data_type::s8,
+        data_type::s8>;
+template struct _jit_avx512_core_x8s8s32x_deconvolution_fwd_t<data_type::s8,
+        data_type::f32>;
+template struct _jit_avx512_core_x8s8s32x_deconvolution_fwd_t<data_type::s8,
+        data_type::s32>;
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_deconvolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_deconvolution.hpp
new file mode 100644
index 0000000000..901038fa48
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_avx512_core_x8s8s32x_deconvolution.hpp
@@ -0,0 +1,237 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX512_CORE_U8S8S32X_DECONVOLUTION_HPP
+#define CPU_JIT_AVX512_CORE_U8S8S32X_DECONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "cpu_primitive.hpp"
+#include "cpu_memory.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+#include "nstl.hpp"
+
+#include "cpu_deconvolution_pd.hpp"
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+#include "jit_uni_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+typedef enum {
+    no_last_block = 0x1U,
+    last_ic_block = 0x2U,
+    last_sp_block = 0x4U,
+} ker_block_t;
+
+struct jit_avx512_core_x8s8s32x_deconv_fwd_kernel : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_avx512_core_x8s8s32x_deconv_fwd_ker_t);
+
+    jit_avx512_core_x8s8s32x_deconv_fwd_kernel(
+            const jit_conv_conf_t &ajcp, const primitive_attr_t &attr)
+        : jcp(ajcp), attr_(attr), eltwise_injector_(nullptr) {
+        if (jcp.with_eltwise)
+            eltwise_injector_ = new jit_uni_eltwise_injector_f32<avx512_common>(
+                    this, jcp.eltwise);
+        generate();
+        jit_ker = (void (*)(jit_deconv_call_s *))getCode();
+    }
+
+    ~jit_avx512_core_x8s8s32x_deconv_fwd_kernel() {
+            delete eltwise_injector_;
+    }
+
+    static bool post_ops_ok(jit_conv_conf_t &jcp,
+            const primitive_attr_t &attr);
+
+    static status_t init_conf(jit_conv_conf_t &jcp,
+            const deconvolution_desc_t &cd,
+            memory_desc_t &src_md,
+            memory_desc_t &weights_md,
+            memory_desc_t &dst_md,
+            const bool with_bias,
+            memory_desc_t &bias_md,
+            const primitive_attr_t &attr);
+
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_conv_conf_t &jcp, const primitive_attr_t &attr);
+
+    const jit_conv_conf_t &jcp;
+    const primitive_attr_t &attr_;
+    void (*jit_ker)(jit_deconv_call_s *);
+private:
+    jit_uni_eltwise_injector_f32<avx512_common> *eltwise_injector_;
+    using reg64_t = const Xbyak::Reg64;
+    using zmm_t = const Xbyak::Zmm;
+    using xmm_t = const Xbyak::Xmm;
+
+    reg64_t reg_src = r8;
+    reg64_t reg_filt = r9;
+    reg64_t reg_dst = r10;
+    reg64_t param1 = abi_param1;
+    reg64_t reg_kh = abi_not_param1;
+    reg64_t reg_nur_w = rbx;
+    reg64_t reg_bias = rdx;
+    reg64_t reg_icb = reg_bias;
+    reg64_t reg_ptr_scales = rax;
+    reg64_t reg_oc_blocks = rsi;
+
+    reg64_t aux_reg_src = r11;
+    reg64_t aux_reg_filt = r12;
+
+    reg64_t reg_compensation = r14;
+    reg64_t reg_scratch = r14;
+    reg64_t reg_ptr_sum_scale = r11;
+    reg64_t reg_bias_alpha = abi_not_param1;
+    reg64_t reg_overflow = rax;
+    reg64_t reg_comp_strides = reg_overflow;
+
+    Xbyak::Opmask ktail_mask = Xbyak::Opmask(2);
+    zmm_t zmm_tmp = zmm_t(28);
+    zmm_t zmm_one = zmm_t(29);
+    /* used during write-out section of store_output */
+    zmm_t zmm_zero = zmm_t(31);
+    zmm_t zmm_wei = zmm_t(31);
+
+    /* signed input */
+    zmm_t zmm_shift = zmm_t(30);
+    zmm_t zmm_comp = zmm_t(30);
+    zmm_t zmm_bias = zmm_t(31);
+    zmm_t zmm_prev_dst = zmm_t(31);
+
+    zmm_t zmm_out(int i_ur, int i_oc) {
+        int idx = i_ur * jcp.nb_oc_blocking + i_oc;
+        assert(idx < 31);
+        return zmm_t(idx);
+    }
+    zmm_t zmm_inp(int i_ic, int nb_x_blocking) {
+        int idx = i_ic + nb_x_blocking * jcp.ur_w;
+        assert(idx < 31);
+        return zmm_t(idx);
+    }
+    zmm_t zmm_bias_alpha() {
+        return zmm_t(jcp.nb_oc_blocking * jcp.ur_w);
+    }
+    xmm_t xmm_bias_alpha() {
+        return xmm_t(jcp.nb_oc_blocking * jcp.ur_w);
+    }
+
+    int get_ow_start(int ki, int l_overflow) {
+        int res = (jcp.ow - 1 + jcp.r_pad) % jcp.stride_w
+                + l_overflow * jcp.stride_w
+                - (jcp.kw - 1 - ki) * (jcp.dilate_w + 1);
+        while (res < 0)
+            res += jcp.stride_w;
+        return res;
+    }
+
+    int get_ow_end(int ur_w, int ki, int r_overflow) {
+        if (utils::one_of(ur_w, jcp.ow, jcp.ur_w_tail))
+                ur_w += nstl::min(0, jcp.r_pad); // remove negative padding
+        int res = (ur_w - 1 + jcp.l_pad) % jcp.stride_w
+            + r_overflow * jcp.stride_w - ki * (jcp.dilate_w + 1);
+        while (res < 0)
+            res += jcp.stride_w;
+        return ur_w - res;
+    }
+    bool maybe_eltwise(int position);
+    void compute_eltwise(int ur_w);
+    void prepare_output(int ur_w);
+    void store_output(int ur_w, bool last_oc_block);
+    void compute_ker(int ur_w, int l_overflow, int r_overflow,
+             ker_block_t last_ic_block_flag, bool h_padded = false);
+    void kh_loop(int ur_w, int pad_l, int pad_r, ker_block_t last_ker_block);
+    void icb_loop(int ur_w, int pad_l, int pad_r, bool last_block);
+    void generate();
+    void cvt2ps(data_type_t type_in, zmm_t zmm_in, const Xbyak::Operand &op,
+        bool mask_flag);
+};
+
+template <impl::data_type_t src_type, impl::data_type_t dst_type>
+struct _jit_avx512_core_x8s8s32x_deconvolution_fwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_deconvolution_fwd_pd_t {
+        using cpu_deconvolution_fwd_pd_t::cpu_deconvolution_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_deconvolution:", avx512_core, ""),
+                _jit_avx512_core_x8s8s32x_deconvolution_fwd_t<src_type, dst_type>);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && (desc()->alg_kind & alg_kind::deconvolution_direct)
+                && desc()->src_desc.data_type == src_type
+                && desc()->dst_desc.data_type == dst_type
+                && IMPLICATION(with_bias(), utils::one_of(
+                            desc()->bias_desc.data_type, data_type::f32,
+                            data_type::s32, data_type::s8, data_type::u8))
+                && desc()->accum_data_type == data_type::s32;
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_avx512_core_x8s8s32x_deconv_fwd_kernel::
+                init_conf(jcp_, *desc(), src_md_, weights_md_, dst_md_,
+                        with_bias(), bias_md_, *attr());
+
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_avx512_core_x8s8s32x_deconv_fwd_kernel::init_scratchpad(scratchpad,
+                    jcp_, *attr());
+
+            return status::success;
+        }
+
+        jit_conv_conf_t jcp_;
+    };
+
+    _jit_avx512_core_x8s8s32x_deconvolution_fwd_t(const pd_t *apd)
+        : cpu_primitive_t(apd)
+    {
+        kernel_ = new jit_avx512_core_x8s8s32x_deconv_fwd_kernel(pd()->jcp_,
+                *pd()->attr());
+    }
+
+    ~_jit_avx512_core_x8s8s32x_deconvolution_fwd_t() { delete kernel_; }
+
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<data_type::s8>::type wei_data_t;
+    typedef typename prec_traits<dst_type>::type dst_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        if(pd()->ndims() == 3)
+            execute_forward_1d(ctx);
+        else
+            execute_forward_2d(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward_1d(const exec_ctx_t &ctx) const;
+    void execute_forward_2d(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    jit_avx512_core_x8s8s32x_deconv_fwd_kernel *kernel_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_generator.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_generator.hpp
new file mode 100644
index 0000000000..c09592d5c9
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_generator.hpp
@@ -0,0 +1,773 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_AVX2_GENERATOR_HPP
+#define CPU_JIT_AVX2_GENERATOR_HPP
+
+#include <limits.h>
+
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "cpu_isa_traits.hpp"
+#include "jit_utils/jit_utils.hpp"
+
+#if defined(_WIN32) && !defined(__GNUC__)
+#   define STRUCT_ALIGN(al, ...) __declspec(align(al)) __VA_ARGS__
+#else
+#   define STRUCT_ALIGN(al, ...) __VA_ARGS__ __attribute__((__aligned__(al)))
+#endif
+
+#if defined(_WIN32)
+#   define OFFSET_SHADOWSPACE 0x28
+#endif
+
+#define DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_name) \
+    const char *name() const override { return STRINGIFY(jit_name); } \
+    const char *source_file() const override { return __FILE__; }
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+// TODO: move this to jit_generator class?
+namespace {
+
+typedef enum {
+    PAGE_4K = 4096,
+    PAGE_2M = 2097152,
+} cpu_page_size_t;
+
+// TODO: move this somewhere else? Although this is only used by jit kernels
+// (Roma)
+static inline int float2int(float x) {
+    union {
+        float vfloat;
+        int vint;
+    } cvt;
+    cvt.vfloat = x;
+    return cvt.vint;
+}
+
+// TODO: A GPR class that hides ABI details from the JIT kernels and allows
+// numbering registers from 0 to 14 (x86_64) / 6 (x32) (gpr0, gpr1, ...) and
+// stack register (sr).
+//
+// This will allow using syntax like this:
+//
+// param = gpr0;
+// reg_input = gpr0;
+// reg_output =  gpr1;
+// ...
+//
+// #ifndef XBYAK64
+// mov(param, ptr[sr])
+// #endif
+//
+// (Roma)
+
+#ifdef XBYAK64
+constexpr Xbyak::Operand::Code abi_save_gpr_regs[] = {
+    Xbyak::Operand::RBX, Xbyak::Operand::RBP, Xbyak::Operand::R12,
+    Xbyak::Operand::R13, Xbyak::Operand::R14, Xbyak::Operand::R15,
+#ifdef _WIN32
+    Xbyak::Operand::RDI, Xbyak::Operand::RSI,
+#endif
+};
+
+#ifdef _WIN32
+static const Xbyak::Reg64 abi_param1(Xbyak::Operand::RCX),
+             abi_param2(Xbyak::Operand::RDX),
+             abi_param3(Xbyak::Operand::R8),
+             abi_param4(Xbyak::Operand::R9),
+             abi_not_param1(Xbyak::Operand::RDI);
+#else
+static const Xbyak::Reg64 abi_param1(Xbyak::Operand::RDI),
+             abi_param2(Xbyak::Operand::RSI),
+             abi_param3(Xbyak::Operand::RDX),
+             abi_param4(Xbyak::Operand::RCX),
+             abi_param5(Xbyak::Operand::R8),
+             abi_param6(Xbyak::Operand::R9),
+             abi_not_param1(Xbyak::Operand::RCX);
+#endif
+#endif
+
+inline unsigned int get_cache_size(int level, bool per_core = true){
+    unsigned int l = level - 1;
+    // Currently, if XByak is not able to fetch the cache topology
+    // we default to 32KB of L1, 512KB of L2 and 1MB of L3 per core.
+    if (cpu.getDataCacheLevels() == 0){
+        const int L1_cache_per_core = 32000;
+        const int L2_cache_per_core = 512000;
+        const int L3_cache_per_core = 1024000;
+        int num_cores = per_core ? 1 : mkldnn_get_max_threads();
+        switch(l){
+        case(0): return L1_cache_per_core * num_cores;
+        case(1): return L2_cache_per_core * num_cores;
+        case(2): return L3_cache_per_core * num_cores;
+        default: return 0;
+        }
+    }
+    if (l < cpu.getDataCacheLevels()) {
+        return cpu.getDataCacheSize(l)
+            / (per_core ? cpu.getCoresSharingDataCache(l) : 1);
+    } else
+        return 0;
+}
+
+}
+
+class jit_generator : public Xbyak::CodeGenerator
+{
+private:
+    const size_t xmm_len = 16;
+#ifdef _WIN32
+    const size_t xmm_to_preserve_start = 6;
+    const size_t xmm_to_preserve = 10;
+#else
+    const size_t xmm_to_preserve_start = 0;
+    const size_t xmm_to_preserve = 0;
+#endif
+
+    const size_t num_abi_save_gpr_regs
+        = sizeof(abi_save_gpr_regs) / sizeof(abi_save_gpr_regs[0]);
+
+    const size_t size_of_abi_save_regs
+        = num_abi_save_gpr_regs * rax.getBit() / 8
+        + xmm_to_preserve * xmm_len;
+
+public:
+    enum {
+        _cmp_eq_oq = 0u,
+        _cmp_lt_os = 1u,
+        _cmp_le_os = 2u,
+        _cmp_neq_uq = 4u,
+        _cmp_nlt_us = 5u,
+        _cmp_nle_us = 6u,
+
+        _op_floor = 1u,
+        _op_mxcsr = 4u,
+    };
+
+    Xbyak::Reg64 param1 = abi_param1;
+    const int EVEX_max_8b_offt = 0x200;
+    const Xbyak::Reg64 reg_EVEX_max_8b_offt = rbp;
+
+    inline size_t get_size_of_abi_save_regs() {
+        return size_of_abi_save_regs;
+    }
+
+    void preamble() {
+        if (xmm_to_preserve) {
+            sub(rsp, xmm_to_preserve * xmm_len);
+            for (size_t i = 0; i < xmm_to_preserve; ++i)
+                movdqu(ptr[rsp + i * xmm_len], Xbyak::Xmm(xmm_to_preserve_start + i));
+        }
+        for (size_t i = 0; i < num_abi_save_gpr_regs; ++i)
+            push(Xbyak::Reg64(abi_save_gpr_regs[i]));
+        if (mayiuse(avx512_common)) {
+            mov(reg_EVEX_max_8b_offt, 2 * EVEX_max_8b_offt);
+        }
+    }
+
+    void mic_prefetcht0(Xbyak::Address a) {
+        if (mayiuse(avx512_mic))
+            prefetcht0(a);
+    }
+
+    void mic_prefetcht1(Xbyak::Address a) {
+        if (mayiuse(avx512_mic))
+            prefetcht1(a);
+    }
+
+    void mic_prefetcht2(Xbyak::Address a) {
+        if (mayiuse(avx512_mic))
+            prefetcht2(a);
+    }
+
+    void uni_vzeroupper() {
+        if (mayiuse(avx) && !mayiuse(avx512_mic))
+            vzeroupper();
+    }
+
+    void postamble() {
+        for (size_t i = 0; i < num_abi_save_gpr_regs; ++i)
+            pop(Xbyak::Reg64(abi_save_gpr_regs[num_abi_save_gpr_regs - 1 - i]));
+        if (xmm_to_preserve) {
+            for (size_t i = 0; i < xmm_to_preserve; ++i)
+                movdqu(Xbyak::Xmm(xmm_to_preserve_start + i), ptr[rsp + i * xmm_len]);
+            add(rsp, xmm_to_preserve * xmm_len);
+        }
+        uni_vzeroupper();
+        ret();
+    }
+
+    template<typename T>
+    Xbyak::Address EVEX_compress_addr(Xbyak::Reg64 base,
+            T raw_offt, bool bcast = false)
+    {
+        using Xbyak::Zmm;
+        using Xbyak::Reg64;
+        using Xbyak::Address;
+        using Xbyak::RegExp;
+
+        assert(raw_offt <= INT_MAX);
+        auto offt = static_cast<int>(raw_offt);
+
+        int scale = 0;
+
+        if (EVEX_max_8b_offt <= offt && offt < 3 * EVEX_max_8b_offt) {
+            offt = offt - 2 * EVEX_max_8b_offt;
+            scale = 1;
+        } else if (3 * EVEX_max_8b_offt <= offt && offt < 5 * EVEX_max_8b_offt) {
+            offt = offt - 4 * EVEX_max_8b_offt;
+            scale = 2;
+        }
+
+        auto re = RegExp() + base + offt;
+        if (scale)
+            re = re + reg_EVEX_max_8b_offt * scale;
+
+        if (bcast)
+            return zword_b [re];
+        else
+            return zword [re];
+    }
+
+    Xbyak::Address make_safe_addr(const Xbyak::Reg64 &reg_out, size_t offt,
+        const Xbyak::Reg64 &tmp_reg, bool bcast = false) {
+        if (offt > INT_MAX) {
+            mov(tmp_reg, offt);
+            return bcast ? ptr_b[reg_out + tmp_reg] : ptr[reg_out + tmp_reg];
+        } else {
+            return bcast ? ptr_b[reg_out + offt] : ptr[reg_out + offt];
+        }
+    }
+
+    Xbyak::Address EVEX_compress_addr_safe(const Xbyak::Reg64 &base,
+        size_t raw_offt, const Xbyak::Reg64 &reg_offt, bool bcast = false) {
+        if (raw_offt > INT_MAX) {
+            return make_safe_addr(base, raw_offt, reg_offt, bcast);
+        } else {
+            return EVEX_compress_addr(base, raw_offt, bcast);
+        }
+    }
+
+    void safe_add(const Xbyak::Reg64 &base, size_t raw_offt,
+        const Xbyak::Reg64 &reg_offt) {
+        if (raw_offt > INT_MAX) {
+            mov(reg_offt, raw_offt);
+            add(base, reg_offt);
+        } else {
+            add(base, raw_offt);
+        }
+    }
+
+    void safe_sub(const Xbyak::Reg64 &base, size_t raw_offt,
+        const Xbyak::Reg64 &reg_offt) {
+        if (raw_offt > INT_MAX) {
+            mov(reg_offt, raw_offt);
+            sub(base, reg_offt);
+        } else {
+            sub(base, raw_offt);
+        }
+    }
+
+    // Disallow char-based labels completely
+    void L(const char *label) = delete;
+    void L(Xbyak::Label& label) { Xbyak::CodeGenerator::L(label); }
+
+    void uni_vpxor(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2,
+                   const Xbyak::Operand &op) {
+        assert(x1.getIdx() == x2.getIdx());
+        pxor(x2, op);
+    }
+    void uni_vpxor(const Xbyak::Ymm &x1, const Xbyak::Ymm &x2,
+                   const Xbyak::Operand &op) {
+        if (mayiuse(avx2)) {
+            vpxor(x1, x2, op);
+        } else {
+            vxorps(x1, x2, op);
+        }
+    }
+    void uni_vpxor(const Xbyak::Zmm &x1, const Xbyak::Zmm &x2,
+                   const Xbyak::Operand &op) {
+        vpxord(x1, x2, op);
+    }
+
+    void uni_vmovss(const Xbyak::Address& addr, const Xbyak::Xmm &x) {
+        movss(addr, x);
+    }
+    void uni_vmovss(const Xbyak::Address& addr, const Xbyak::Ymm &x) {
+        vmovss(addr, x);
+    }
+    void uni_vmovss(const Xbyak::Xmm &x, const Xbyak::Address& addr) {
+        movss(x, addr);
+    }
+    void uni_vmovss(const Xbyak::Ymm &x, const Xbyak::Address& addr) {
+        vmovss(x, addr);
+    }
+
+    void uni_vmovsd(const Xbyak::Address& addr, const Xbyak::Xmm &x) {
+        movsd(addr, x);
+    }
+    void uni_vmovsd(const Xbyak::Address& addr, const Xbyak::Ymm &x) {
+        vmovsd(addr, x);
+    }
+    void uni_vmovsd(const Xbyak::Xmm &x, const Xbyak::Address& addr) {
+        movsd(x, addr);
+    }
+    void uni_vmovsd(const Xbyak::Ymm &x, const Xbyak::Address& addr) {
+        vmovsd(x, addr);
+    }
+
+    void uni_vmovdqu(const Xbyak::Address &addr, const Xbyak::Xmm &x) {
+        movdqu(addr, x);
+    }
+    void uni_vmovdqu(const Xbyak::Address &addr, const Xbyak::Ymm &x) {
+        vmovdqu(addr, x);
+    }
+    void uni_vmovdqu(const Xbyak::Address &addr, const Xbyak::Zmm &x) {
+        vmovdqu32(addr, x);
+    }
+
+    void uni_vmovdqu(const Xbyak::Xmm &x, const Xbyak::Address &addr) {
+        movdqu(x, addr);
+    }
+    void uni_vmovdqu(const Xbyak::Ymm &x, const Xbyak::Address &addr) {
+        vmovdqu(x, addr);
+    }
+    void uni_vmovdqu(const Xbyak::Zmm &x, const Xbyak::Address &addr) {
+        vmovdqu32(x, addr);
+    }
+
+    void uni_vmovups(const Xbyak::Address &addr, const Xbyak::Xmm &x) {
+        movups(addr, x);
+    }
+    void uni_vmovups(const Xbyak::Address &addr, const Xbyak::Ymm &x) {
+        vmovups(addr, x);
+    }
+
+    void uni_vmovups(const Xbyak::Xmm &x, const Xbyak::Operand &op) {
+        movups(x, op);
+    }
+    void uni_vmovups(const Xbyak::Ymm &x, const Xbyak::Operand &op) {
+        vmovups(x, op);
+    }
+
+    void uni_vmovntps(const Xbyak::Address &addr, const Xbyak::Xmm &x) {
+        movntps(addr, x);
+    }
+    void uni_vmovntps(const Xbyak::Address &addr, const Xbyak::Ymm &x) {
+        vmovntps(addr, x);
+    }
+
+    void uni_vbroadcastss(const Xbyak::Xmm &x, const Xbyak::Operand &op) {
+        movss(x, op);
+        shufps(x, x, 0x0);
+    }
+    void uni_vbroadcastss(const Xbyak::Ymm &x, const Xbyak::Operand &op) {
+        if (op.isMEM() || mayiuse(avx2)) {
+            vbroadcastss(x, op);
+        } else {
+            Xbyak::Xmm t(x.getIdx());
+            if (t.getIdx() != op.getIdx()) movss(t, op);
+            vinsertf128(x, x, t, 1);
+            vshufps(x, x, x, 0);
+        }
+    }
+
+    void uni_vpbroadcastd(const Xbyak::Xmm &x, const Xbyak::Operand &op) {
+        movsd(x, op);
+        pshufd(x, x, 0x0);
+    }
+    void uni_vpbroadcastd(const Xbyak::Ymm &x, const Xbyak::Operand &op) {
+        if (mayiuse(avx2)) {
+            vpbroadcastd(x, op);
+        } else {
+            Xbyak::Xmm t(x.getIdx());
+            if (t.getIdx() != op.getIdx()) movsd(t, op);
+            vinsertf128(x, x, t, 1);
+            vshufps(x, x, x, 0);
+        }
+    }
+
+    void uni_vrcpss(const Xbyak::Xmm &x, const Xbyak::Operand &op) {
+        rcpss(x, op);
+    }
+    void uni_vrcpss(const Xbyak::Ymm &x1, const Xbyak::Xmm &x2) {
+        Xbyak::Xmm x1_(x1.getIdx());
+        Xbyak::Xmm x2_(x2.getIdx());
+        vrcpss(x1_, x1_, x2_);
+    }
+    void uni_vrcpss(const Xbyak::Ymm &x, const Xbyak::Address &op) {
+        Xbyak::Xmm x_(x.getIdx());
+        vrcpss(x_, x_, op);
+    }
+
+    void uni_vrcpps(const Xbyak::Xmm &x, const Xbyak::Operand &op) {
+        rcpps(x, op);
+    }
+    void uni_vrcpps(const Xbyak::Ymm &x, const Xbyak::Operand &op) {
+        vrcpps(x, op);
+    }
+    void uni_vrcpps(const Xbyak::Zmm &x, const Xbyak::Operand &op) {
+        vrcp14ps(x, op);
+    }
+
+    void uni_vdivps(const Xbyak::Xmm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        assert(x.getIdx() == op1.getIdx());
+        divps(x, op2);
+    }
+    void uni_vdivps(const Xbyak::Ymm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        vdivps(x, op1, op2);
+    }
+
+    void uni_vdivps(const Xbyak::Xmm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2, const Xbyak::Xmm &buf) {
+        movups(buf, op1);
+        divps(buf, op2);
+        if (x.getIdx() != buf.getIdx()) {
+            movups(x, buf);
+        }
+    }
+
+    void uni_vdivps(const Xbyak::Ymm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2, const Xbyak::Ymm &buf) {
+        vdivps(x, op1, op2);
+    }
+
+    void uni_vaddps(const Xbyak::Xmm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        assert(x.getIdx() == op1.getIdx());
+        addps(x, op2);
+    }
+    void uni_vaddps(const Xbyak::Ymm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        vaddps(x, op1, op2);
+    }
+
+    void uni_vpsignd(const Xbyak::Xmm& x1, const Xbyak::Xmm& x2,
+                     const Xbyak::Operand& op) {
+        assert(x1.getIdx() == x2.getIdx());
+        psignd(x1, op);
+    }
+    void uni_vpsignd(const Xbyak::Ymm& x1, const Xbyak::Ymm& x2,
+                     const Xbyak::Operand& op) {
+        vpsignd(x1, x2, op);
+    }
+
+    void uni_vsubps(const Xbyak::Xmm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        assert(x.getIdx() == op1.getIdx());
+        subps(x, op2);
+    }
+    void uni_vsubps(const Xbyak::Ymm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        vsubps(x, op1, op2);
+    }
+
+    void uni_vsubps(const Xbyak::Xmm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2, const Xbyak::Xmm &buf) {
+        movups(buf, op1);
+        subps(buf, op2);
+        if (x.getIdx() != buf.getIdx()) {
+            movups(x, buf);
+        }
+    }
+
+    void uni_vsubps(const Xbyak::Ymm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2, const Xbyak::Ymm &buf) {
+        vsubps(x, op1, op2);
+    }
+
+    void uni_vmulps(const Xbyak::Xmm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        assert(x.getIdx() == op1.getIdx());
+        mulps(x, op2);
+    }
+    void uni_vmulps(const Xbyak::Ymm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        vmulps(x, op1, op2);
+    }
+
+    void uni_vfmadd213ps(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2,
+                         const Xbyak::Operand &op) {
+        mulps(x1, x2);
+        addps(x1, op);
+    }
+    void uni_vfmadd213ps(const Xbyak::Ymm &x1, const Xbyak::Ymm &x2,
+                         const Xbyak::Operand &op) {
+        vfmadd213ps(x1, x2, op);
+    }
+
+    void uni_vfmadd231ps(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2,
+                         const Xbyak::Operand &op) {
+        mulps(x2, op);
+        addps(x1, x2);
+    }
+    void uni_vfmadd231ps(const Xbyak::Ymm &x1, const Xbyak::Ymm &x2,
+                         const Xbyak::Operand &op) {
+        vfmadd231ps(x1, x2, op);
+    }
+
+    void uni_vfnmadd231ps(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2,
+                           const Xbyak::Operand &op) {
+        mulps(x2, op);
+        subps(x1, x2);
+    }
+
+    void uni_vfnmadd231ps(const Xbyak::Ymm &x1, const Xbyak::Ymm &x2,
+                           const Xbyak::Operand &op) {
+        vfnmadd231ps(x1, x2, op);
+    }
+
+    void uni_vsqrtps(const Xbyak::Xmm &x, const Xbyak::Operand &op) {
+        sqrtps(x, op);
+    }
+    void uni_vsqrtps(const Xbyak::Ymm &x, const Xbyak::Operand &op) {
+        vsqrtps(x, op);
+    }
+
+    void uni_vpaddd(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2,
+                    const Xbyak::Operand &op) {
+        assert(x1.getIdx() == x2.getIdx());
+        paddd(x2, op);
+    }
+    void uni_vpaddd(const Xbyak::Ymm &x1, const Xbyak::Xmm &x2,
+                    const Xbyak::Operand &op) {
+        vpaddd(x1, x2, op);
+    }
+
+    void uni_vandps(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2,
+                    const Xbyak::Operand &op = Xbyak::Operand()) {
+        assert(x1.getIdx() == x2.getIdx());
+        andps(x1, op);
+    }
+    void uni_vandps(const Xbyak::Ymm &x1, const Xbyak::Ymm &x2,
+                    const Xbyak::Operand &op = Xbyak::Operand()) {
+        if (!mayiuse(avx512_common) || x1.getBit() < 512)
+            vandps(x1, x2, op);
+        else
+            vpandd(x1, x2, op);
+    }
+
+    void uni_vorps(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2,
+                    const Xbyak::Operand &op = Xbyak::Operand()) {
+        assert(x1.getIdx() == x2.getIdx());
+        orps(x1, op);
+    }
+    void uni_vorps(const Xbyak::Ymm &x1, const Xbyak::Ymm &x2,
+                    const Xbyak::Operand &op = Xbyak::Operand()) {
+        if (!mayiuse(avx512_common) || x1.getBit() < 512)
+            vorps(x1, x2, op);
+        else
+            vpord(x1, x2, op);
+    }
+
+    void uni_vpslld(const Xbyak::Xmm &x, const Xbyak::Operand &op,
+                    const int imm) {
+        assert(x.getIdx() == op.getIdx());
+        pslld(x, imm);
+    }
+    void uni_vpslld(const Xbyak::Ymm &x, const Xbyak::Operand &op,
+                    const int imm) {
+        vpslld(x, op, imm);
+    }
+
+    void uni_vpsrld(const Xbyak::Xmm &x, const Xbyak::Operand &op,
+                    const int imm) {
+        assert(x.getIdx() == op.getIdx());
+        psrld(x, imm);
+    }
+    void uni_vpsrld(const Xbyak::Ymm &x, const Xbyak::Operand &op,
+                    const int imm) {
+        vpsrld(x, op, imm);
+    }
+
+    void uni_vmaxps(const Xbyak::Xmm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        assert(x.getIdx() == op1.getIdx());
+        maxps(x, op2);
+    }
+    void uni_vmaxps(const Xbyak::Ymm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        vmaxps(x, op1, op2);
+    }
+
+    void uni_vminps(const Xbyak::Xmm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        assert(x.getIdx() == op1.getIdx());
+        minps(x, op2);
+    }
+    void uni_vminps(const Xbyak::Ymm &x, const Xbyak::Operand &op1,
+                    const Xbyak::Operand &op2 = Xbyak::Operand()) {
+        vminps(x, op1, op2);
+    }
+
+    void uni_vcmpgtps(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2,
+                      const Xbyak::Operand &op) {
+        assert(x1.getIdx() == x2.getIdx());
+        cmpps(x1, op, _cmp_nle_us);
+    }
+
+    void uni_vcmpgtps(const Xbyak::Ymm &x1, const Xbyak::Ymm &x2,
+                      const Xbyak::Operand &op) {
+        vcmpgtps(x1, x2, op);
+    }
+
+    void uni_vcmpgeps(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2,
+                      const Xbyak::Operand &op) {
+        assert(x1.getIdx() == x2.getIdx());
+        cmpps(x1, op, _cmp_nlt_us);
+    }
+
+    void uni_vcmpgeps(const Xbyak::Ymm &x1, const Xbyak::Ymm &x2,
+                      const Xbyak::Operand &op) {
+        vcmpps(x1, x2, op, _cmp_nlt_us);
+    }
+
+    void uni_vtestps(const Xbyak::Xmm &x1, const Xbyak::Operand &op) {
+        ptest(x1, op);
+    }
+
+    void uni_vtestps(const Xbyak::Ymm &x1, const Xbyak::Operand &op) {
+        assert(!(x1.isZMM() || op.isZMM()));
+        vtestps(x1, op);
+    }
+
+    void uni_vblendvps(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2,
+                       const Xbyak::Operand &op, const Xbyak::Xmm &msk) {
+        assert(x1.getIdx() == x2.getIdx());
+        assert(msk.getIdx() == 0);
+        blendvps(x1, op);
+    }
+    void uni_vblendvps(const Xbyak::Ymm &x1, const Xbyak::Ymm &x2,
+                       const Xbyak::Operand &op, const Xbyak::Ymm &msk) {
+        vblendvps(x1, x2, op, msk);
+    }
+
+    void uni_vroundps(const Xbyak::Xmm &x, const Xbyak::Operand &op,
+                      const int imm) {
+        roundps(x, op, imm);
+    }
+    void uni_vroundps(const Xbyak::Ymm &x, const Xbyak::Operand &op,
+                      const int imm) {
+        vroundps(x, op, imm);
+    }
+
+    void uni_vcvtps2dq(const Xbyak::Xmm &x, const Xbyak::Operand &op) {
+        cvtps2dq(x, op);
+    }
+    void uni_vcvtps2dq(const Xbyak::Ymm &x, const Xbyak::Operand &op) {
+        vcvtps2dq(x, op);
+    }
+
+    void uni_vcvtdq2ps(const Xbyak::Xmm &x, const Xbyak::Operand &op) {
+        cvtdq2ps(x, op);
+    }
+    void uni_vcvtdq2ps(const Xbyak::Ymm &x, const Xbyak::Operand &op) {
+        vcvtdq2ps(x, op);
+    }
+
+    void uni_vmovmskps(const Xbyak::Reg &x1, const Xbyak::Xmm &x2) {
+        movmskps(x1.cvt64(), x2);
+    }
+    void uni_vmovmskps(const Xbyak::Reg &x1, const Xbyak::Ymm &x2) {
+        vmovmskps(x1, x2);
+    }
+
+    void uni_vpackssdw(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2, const Xbyak::Operand &op){
+        assert(x1.getIdx() == x1.getIdx());
+        packssdw(x1, op);
+    }
+    void uni_vpackssdw(const Xbyak::Ymm &x1, const Xbyak::Ymm &x2, const Xbyak::Operand &op){
+        vpackssdw(x1, x2, op);
+    }
+
+    void uni_vpackuswb(const Xbyak::Xmm &x1, const Xbyak::Xmm &x2, const Xbyak::Operand &op){
+        assert(x1.getIdx() == x1.getIdx());
+        packuswb(x1, op);
+    }
+    void uni_vpackuswb(const Xbyak::Ymm &x1, const Xbyak::Ymm &x2, const Xbyak::Operand &op){
+        vpackuswb(x1, x2, op);
+    }
+
+
+    void mul_by_const(const Xbyak::Reg &out,
+            const Xbyak::Reg64 &tmp, int value) {
+        // Generates a shift + add sequence for multiplicating contents of the
+        // out register by a known JIT-time value. Clobbers the tmp register.
+        //
+        // Pros compared to mul/imul:
+        // - does not require using known registers
+        // - not microcoded on Intel(R) Xeon Phi(TM) processors
+        // Still, there are probably a lot of cases when mul/imul is faster on
+        // Intel(R) Core(TM) processors. Not intended for critical path.
+
+        // TODO: detect when overflow is emminent (Roma)
+        // TODO: detect when using mul/imul is a better option (Roma)
+
+        int p = 0; // the current power of 2
+        int old_p = 0; // the last seen power of 2 such that value[old_p] != 0
+
+        xor_(tmp, tmp);
+        while (value) {
+            if (value & 1) {
+                int shift = p - old_p;
+                if (shift) {
+                    shl(out, shift);
+                    old_p = p;
+                }
+                add(tmp, out);
+            }
+            value >>= 1;
+            p++;
+        }
+        mov(out, tmp);
+    }
+
+public:
+    jit_generator(
+        void *code_ptr = nullptr,
+        size_t code_size = 256 * 1024
+        ) : Xbyak::CodeGenerator(code_size, code_ptr)
+    {
+    }
+    virtual ~jit_generator() {}
+
+    virtual const char *name() const = 0;
+    virtual const char *source_file() const = 0;
+
+    const Xbyak::uint8 *getCode() {
+        const Xbyak::uint8 *code = CodeGenerator::getCode();
+        size_t code_size = getSize();
+        jit_utils::register_jit_code(code, code_size, name(), source_file());
+        return code;
+    }
+
+    template<typename F> const F getCode() {
+        return (const F)getCode();
+    }
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_primitive_conf.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_primitive_conf.hpp
new file mode 100644
index 0000000000..56d7f592e2
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_primitive_conf.hpp
@@ -0,0 +1,481 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_PRIMITIVE_CONF_HPP
+#define JIT_PRIMITIVE_CONF_HPP
+
+#include <stdint.h>
+
+#include "common/primitive_attr.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+/* convolution */
+enum conv_version_t {ver_unused, ver_fma, ver_avx512_core, ver_4fma, ver_vnni};
+enum conv_loop_order_t {loop_cgn, loop_gnc, loop_ngc, loop_gncw, loop_cwgn,
+                            loop_ngcw, loop_nhwcg, loop_nwcg};
+enum conv_1x1_loop_order_t {loop_rbl, loop_rlb, loop_lbr, loop_lrb, loop_blr,
+                            loop_brl};
+enum conv_kernel_kind_t {embd_bcast, expl_bcast};
+
+enum {
+    FLAG_MB_FIRST = 1 << 0, FLAG_MB_LAST = 1 << 1,
+    FLAG_OC_FIRST = 1 << 2, FLAG_OC_LAST = 1 << 3,
+    FLAG_IC_FIRST = 1 << 4, FLAG_IC_LAST = 1 << 5,
+    FLAG_SP_FIRST = 1 << 6, FLAG_SP_LAST = 1 << 7,
+    FLAG_REDUCE_FIRST = 1<<8, FLAG_REDUCE_LAST = 1<<9,
+    FLAG_ZERO_FILTER = 1 << 0, /* Controls whether the inner kernel skips
+                                   loading weights-data from memory; this
+                                   needs to happen on the first Group/16
+                                   iteration. */
+    FLAG_ZERO_BIAS = 1 << 1, /* Controls whether the inner kernel skip
+                               loading bias data from memory */
+    FLAG_COMPUTE_BIAS = 1 << 2, /* Controls bias computation during execution
+                                    pass */
+};
+
+struct jit_conv_conf_t {
+    prop_kind_t prop_kind;
+    conv_version_t ver;
+    conv_loop_order_t loop_order;
+
+    int simd_w;
+    int ndims;
+    int mb;
+    int ngroups, ic, oc, oc_without_padding, ic_without_padding;
+    int id, ih, iw, od, oh, ow;
+    int f_pad, l_pad, t_pad;
+    int back_pad, r_pad, b_pad;
+    int kd, kh, kw;
+    int stride_d, stride_h, stride_w;
+    int dilate_d, dilate_h, dilate_w;
+    format_tag_t src_tag, wei_tag, dst_tag; // temporary workaround
+    bool with_bias;
+    bool with_sum;
+    bool with_eltwise;
+
+    post_ops_t::entry_t::eltwise_t eltwise;
+
+    int nthr, nthr_mb, nthr_g, nthr_oc_b, nthr_ic_b;
+
+    int idp, ihp, iwp, ohp, owp;
+    int nb_ic, ic_block;
+    int nb_oc, oc_block;
+    int nb_ow, ow_block;
+    int nb_oc_blocking; /* used in jit kernels for nb_oc work bloking taking
+                           into account vector registers distribution */
+    int nb_oc_blocking_thr_chunk; /* used for distibution of nb_oc work
+                                      within threads */
+    int nb_ic_blocking, nb_ic_blocking_max; // blocking of nb_ic work
+    int nb_ic_L2;
+    int h_blocking;
+    int nb_oc_L2;
+    int ur_h, ur_w;
+    int ur_w_tail;
+    bool is_1stconv;
+    int nonblk_group_off;
+    /* fma avx512_core */
+    conv_kernel_kind_t kernel_kind;
+    /* 4fma */
+    int tr_iw;
+    int tr_src_num_guard_elems;
+    /* 1st conv: 4fma */
+    int tr_ld;
+    int kh_step;
+    /* 4vnni */
+    int typesize_in;
+    int typesize_out;
+    int typesize_bia;
+    int typesize_acc;
+    /* avx512_u8s8u8 */
+    int ic_nb1, ic_nb2;
+    int oc_nb1;
+    int ur_ow_max, ur_ow, ur_ow_tail;
+    int ur_ow_nsteps;
+    data_type_t bia_dt;
+    data_type_t dst_dt;
+    /* avx512: max possible value is nregs(32) - aux_regs(4) */
+    int src_offsets[28];
+    int src_count;
+    bool expl_bcast;
+    bool large_spatial;
+    int is_oc_scale;
+    int max_regs_ur; // maximum accumulation registers
+    // dw conv
+    int nb_ch, ch_block, nb_ch_blocking;
+    bool is_depthwise, is_fast_depthwise, is_resrc_depthwise;
+    int aligned_threads;
+    // large spatial
+    int oh_blk_size;
+    // s8s8 convolution
+    bool signed_input;
+    float wei_adj_scale;
+};
+
+struct jit_conv_conf_2x3_wino_t {
+    conv_version_t ver;
+
+    int m;
+    int r;
+    int alpha;
+    int tile_h, tile_w;
+
+    int mb;
+    int ngroups, ic, oc, oc_without_padding;
+    int ih, iw, oh, ow;
+    int l_pad, t_pad;
+    int r_pad, b_pad;
+    int kh, kw;
+    int stride_h, stride_w;
+    int dilate_h, dilate_w;
+
+    int nb_ic, ic_block;
+    int nb_oc, oc_block;
+
+    int w_block_size, h_block_size;
+
+    data_type_t bia_dt;
+    data_type_t dst_dt;
+
+    int is_oc_scale;
+    int typesize_in;
+    int typesize_out;
+    int typesize_bia;
+    int typesize_acc;
+
+    format_tag_t src_tag, dst_tag; // temporary workaround
+    bool with_bias;
+    bool small_mb;
+
+    int xb, yb;
+    int inp_stride;
+    int out_stride;
+    int wei_stride;
+    int bia_stride;
+
+    int M, N, K;
+    int m_block, n_block, k_block;
+    int n2_block, n_chunks;
+    int k2_block, k_chunks;
+
+    int mb_block, nb_mb;
+
+    size_t size_wino_src, size_wino_wei, size_wino_dst;
+
+    int nthr;
+};
+
+/*
+   Winograd sched policy:
+
+   Computation Unit:
+   W: weights transform
+   S: src transform
+   D: dst transform
+   G: gemm
+
+   Thread grouping by:
+   i: nb_ic
+   o: nb_oc
+   t: tile_block
+   e: element in tile
+
+   Note: 'i' and 'o' are omited if
+   i. not comblined with t or
+   ii. with discrete transforms
+
+   Current policies supported:
+*/
+enum winograd_sched_t {
+    WSCHED_INVALID = 0,
+
+    /* Forward & backward-data */
+    /* W_S_G_D implements discrete transforms */
+    WSCHED_DATA_W_S_G_D,
+    /* W_SGD implements tiled transforms s.t. GEMM could reuse data in L2*/
+    WSCHED_DATA_W_SGD,
+
+    /* Backward-weights */
+    WSCHED_WEI_S_D_G_W,
+    WSCHED_WEI_SDGtWo,
+    WSCHED_WEI_S_D_Giot_W,
+    WSCHED_WEI_SDGt_W,
+};
+
+struct jit_conv_winograd_conf_t : public jit_conv_conf_t {
+    int itiles;
+    int jtiles;
+    int ntiles;
+    int ic_simd_block=16;
+    int tile_4fma_padding;
+    int tile_4fma;
+    int oc_simd_block=16;
+    int oc_reg_block;
+    int ic_reg_block;
+    int tile_block;
+    int tile_block_ur;
+    int nb_tile_block_ur;
+
+    bool double_buffering;
+    bool with_relu_postsum;
+    int zmm_start;
+    int nb_reg;
+
+    int dimK;
+    int dimK_4fma;
+    int dimK_reg_block;
+    int dimK_block;
+    int dimK_nb_block;
+
+    int dimM;
+    int dimM_reg_block;
+    int dimM_simd_block;
+    int dimM_block;
+    int dimM_nb_block;
+
+    int dimN;
+    int dimN_reg_block;
+    int dimN_bcast_ur;
+    int dimN_block;
+    int dimN_nb_block;
+
+    winograd_sched_t sched_policy;
+};
+
+struct jit_conv_call_s {
+    const void *src; /* hack, non-const for backward_data */
+    const void *dst; /* hack, non-const for forward */
+    const void *filt; /* hack, non-const for backward_weights */
+    const void *bias; /* hack, non-const for backward_bias */
+    const void *src_prf;
+    const void *dst_prf;
+    const void *filt_prf;
+    const void *bias_prf;
+    const void *scales;
+    const void *acc_s32;
+    const void *compensation;
+    size_t kd_offset;
+    size_t kd_offset_prf;
+    size_t d_index;
+    size_t d_index_prf;
+    size_t d_worksize;
+    size_t d_worksize_prf;
+    size_t kd_padding;
+    size_t kd_padding_prf;
+    size_t kh_padding;
+    size_t kh_padding_prf;
+    size_t owb;
+    size_t owb_prf;
+    size_t kw_padding;
+    size_t channel;
+    size_t channel_prf;
+    size_t oc_blocks;
+    size_t ur_w;
+    size_t ur_str_w;
+    size_t ch_blocks;
+    size_t t_overflow;
+    size_t b_overflow;
+    int flags;
+};
+
+struct jit_deconv_call_s {
+    const void *src; /* hack, non-const for backward_data */
+    const void *dst; /* hack, non-const for forward */
+    const void *filt; /* hack, non-const for backward_weights */
+    const void *bias; /* hack, non-const for backward_bias */
+    const void *scales;
+    const void *compensation;
+    size_t t_overflow;
+    size_t b_overflow;
+    size_t kh_padding;
+    size_t oc_blocks;
+};
+
+struct jit_dw_conv_call_s {
+    const void *input;
+    const void *output;
+    const void *filter;
+    const void *bias;
+    size_t kh_count;
+    size_t oh_count;
+    size_t oh_index;
+    size_t filter_pad_off;
+    unsigned char
+            exec_flags; /* Flags passed by driver execution to inner kernel */
+};
+
+struct jit_wino_transform_call_s {
+    size_t tile_block;
+    size_t tile_block_ur;
+    size_t nb_tile_block_ur;
+    size_t tile_count;
+    size_t tj;
+    size_t ti;
+    void *src;
+    void *dst;
+    void *Mw;
+    void *M;
+    void *T;
+    void *G;
+    void *bias;
+};
+
+struct jit_1x1_conv_conf_t {
+    prop_kind_t prop_kind;
+    conv_version_t ver;
+
+    int mb;
+    int ngroups, ic, oc, oc_without_padding, ic_without_padding;
+    int iw, ih, ow, oh;
+    int l_pad, t_pad;
+    int kh, kw;
+    int stride_h, stride_w;
+    format_tag_t src_tag, wei_tag, dst_tag; // temporary workaround
+    bool with_bias;
+    bool with_sum;
+    bool with_eltwise;
+
+    post_ops_t::entry_t::eltwise_t eltwise;
+
+    int is, os;
+    int ic_block, oc_block;
+
+    int ur, ur_tail;
+
+    int reduce_dim, reduce_block, nb_reduce,
+        nb_reduce_blocking, nb_reduce_blocking_max;
+    int load_dim, load_block, nb_load,
+        nb_load_blocking, nb_load_blocking_max, nb_load_chunk;
+    int bcast_dim, bcast_block, nb_bcast,
+        nb_bcast_blocking, nb_bcast_blocking_max;
+
+    int reduce_loop_unroll, reduce_loop_bcast_step, reduce_loop_load_step;
+    int load_loop_load_step, load_loop_iter_step;
+    int bcast_loop_output_step, bcast_loop_output_substep;
+    int bcast_loop_bcast_step, bcast_loop_bcast_substep;
+    int fma_step;
+    int load_grp_count;
+    conv_1x1_loop_order_t loop_order;
+    bool use_vmovntps;
+    /* avx512 core */
+    bool expl_bcast;
+    /* 4vnni */
+    int typesize_in;
+    int typesize_out;
+    int typesize_bia;
+    int typesize_acc;
+    /* 4fma */
+    bool transpose_src;
+    int tr_is;
+    int nthr, nthr_mb, nthr_g, nthr_oc_b, nthr_ic_b;
+    int is_oc_scale;
+    data_type_t bia_dt;
+    data_type_t dst_dt;
+    bool signed_input;
+    float wei_adj_scale;
+};
+
+struct jit_gemm_conv_conf_t {
+    prop_kind_t prop_kind;
+
+    int mb;
+    int ngroups, ic, oc;
+    int iw, ih, id, ow, oh, od;
+    int l_pad, t_pad, f_pad;
+    int kh, kw, kd;
+    int stride_h, stride_w, stride_d;
+    int dilate_h, dilate_w, dilate_d;
+    bool with_bias;
+
+    int is, os, ks;
+    int ic_block, oc_block;
+
+    int nthr;
+    ptrdiff_t im2col_sz;
+    bool need_wei_reduction;
+    bool signed_input;
+    int oh_block;
+    int ow_block;
+    bool outer_threading;
+};
+
+struct jit_1x1_conv_call_s {
+    const void *bcast_data;
+    const void *load_data;
+    const void *output_data;
+    const void *bias_data; // used in forward and backward_weights only
+    const void *acc_s32;
+    const void *scales;
+    const void *compensation;
+
+    size_t load_dim;
+    size_t bcast_dim;
+    size_t reduce_dim;
+
+    size_t output_stride; // used in backward_weights only
+
+    size_t first_last_flag;
+};
+
+/* pooling */
+struct jit_pool_conf_t {
+    int ndims;
+    int mb, c;
+    int id, ih, iw, od, oh, ow;
+    int stride_d, stride_h, stride_w;
+    int kd, kh, kw;
+    int f_pad, t_pad, l_pad;
+    alg_kind_t alg;
+    bool is_training;
+    bool pad_w_is_null;
+    bool is_backward;
+    bool simple_alg;
+    data_type_t ind_dt;
+
+    int c_block, c_tail, nb_c;
+    int ur_c, ur_c_tail;
+    int ur_w;
+    int ur_w_tail;
+    size_t tail[4];
+    data_type_t src_dt;
+    data_type_t dst_dt;
+};
+
+struct jit_pool_call_s {
+    const float *src;
+    const float *dst;
+    const void *indices;
+    const float *src_prf;
+    const float *dst_prf;
+    const void *indices_prf;
+    size_t oh;
+    size_t kd_padding;
+    size_t kh_padding;
+    size_t kh_padding_shift;
+    size_t kd_padding_shift;
+    size_t kw_padding;
+    const float* init_value;
+    float ker_area_h;
+};
+
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_conv_kernel_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_conv_kernel_f32.cpp
new file mode 100644
index 0000000000..94d2101d6e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_conv_kernel_f32.cpp
@@ -0,0 +1,677 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+#include "cpu_memory.hpp"
+
+#include "jit_sse42_1x1_conv_kernel_f32.hpp"
+
+#define GET_OFF(field) offsetof(jit_1x1_conv_call_s, field)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::utils;
+
+using namespace Xbyak;
+
+void jit_sse42_1x1_conv_kernel_f32::generate_bcast_loop(int load_loop_blk)
+{
+    mov(aux1_reg_bcast_data, reg_bcast_data);
+    mov(aux_reg_output_data, reg_output_data);
+    mov(bcast_loop_iter, reg_bcast_loop_work);
+
+    Label bcast_loop;
+    Label bcast_loop_tail;
+
+    cmp(bcast_loop_iter, jcp.ur);
+    jl(bcast_loop_tail, T_NEAR);
+
+    L(bcast_loop); {
+        assert(jcp.bcast_block % jcp.ur == 0);
+        int num_substeps = jcp.bcast_block / jcp.ur;
+        assert(num_substeps > 0 && num_substeps < 10);
+        for (int i = 0; i < num_substeps; i++) {
+            generate_reduce_loop(load_loop_blk, jcp.ur);
+            if (i < num_substeps - 1) {
+                add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_substep);
+                add(aux_reg_output_data, jcp.bcast_loop_output_substep);
+            } else {
+                add(aux1_reg_bcast_data, jcp.bcast_loop_bcast_step
+                        - (num_substeps - 1) * jcp.bcast_loop_bcast_substep);
+                add(aux_reg_output_data, jcp.bcast_loop_output_step
+                        - (num_substeps - 1) * jcp.bcast_loop_output_substep);
+            }
+        }
+        sub(bcast_loop_iter, jcp.bcast_block);
+        cmp(bcast_loop_iter, jcp.bcast_block);
+        jge(bcast_loop, T_NEAR);
+    }
+
+    L(bcast_loop_tail);
+    if (jcp.ur_tail) {
+        Label bcast_loop_tail_out;
+        cmp(bcast_loop_iter, 0);
+        jz(bcast_loop_tail_out, T_NEAR);
+        generate_reduce_loop(load_loop_blk, jcp.ur_tail);
+        L(bcast_loop_tail_out);
+    }
+}
+
+void jit_sse42_1x1_conv_kernel_f32::generate_reduce_loop(
+        int load_loop_blk, int ur)
+{
+    auto reg_load = [=](int i, int n) {
+        return Xmm(2*ur * load_loop_blk + 2*i + n + 1);
+    };
+
+    auto reg_accum = [=](int i, int j, int n) {
+        return Xmm(2*j * load_loop_blk + 2*i + n + 1);
+    };
+
+    auto bias_ptr = [=](int i, int n) {
+        return ptr[reg_bias_data + sizeof(float) * jcp.oc_block * i + n*4*sizeof(float)];
+    };
+
+    auto bcast_ptr = [=](int u, int j) {
+        assert(j < jcp.ur);
+        assert(u <= jcp.reduce_loop_unroll);
+        size_t offt;
+        if (one_of(jcp.prop_kind,
+                    forward_training, forward_inference, backward_data)) {
+            assert(jcp.reduce_loop_unroll == (jcp.prop_kind == backward_data)
+                    ? jcp.oc_block : jcp.ic_block);
+            auto height = (jcp.prop_kind == backward_data) ? jcp.os : jcp.is;
+            offt = (u == jcp.reduce_loop_unroll)
+                ? (height + j) * jcp.reduce_loop_unroll
+                : j * jcp.reduce_loop_unroll + u;
+        } else
+            offt = u * jcp.ic_block + j;
+        return ptr[aux_reg_bcast_data + sizeof(float) * offt];
+    };
+
+    auto load_ptr = [=](int u, int i, int n) {
+        size_t offt;
+        size_t u0 = u % jcp.reduce_loop_unroll;
+        size_t u1 = u / jcp.reduce_loop_unroll;
+        switch (jcp.prop_kind) {
+        case backward_data:
+            offt = (i * jcp.oc_block + u0) * jcp.ic_block;
+            break;
+        case backward_weights:
+            offt = (i * jcp.os + u0) * jcp.oc_block;
+            break;
+        default:
+            offt = (i * jcp.ic + u0) * jcp.oc_block;
+        }
+        return ptr[aux_reg_load_data
+            + u1 * jcp.reduce_loop_load_step + sizeof(float) * offt + n * 4 * sizeof(float)];
+    };
+
+    auto output_ptr = [=](int i, int j, int n) {
+        switch (jcp.prop_kind) {
+        case backward_data:
+            return ptr[aux_reg_output_data +
+                (i * jcp.is + j) * jcp.ic_block * sizeof(float) + n * 4 * sizeof(float)];
+        case backward_weights:
+            return ptr[aux_reg_output_data
+                + (i ? reg_output_stride * i : 0) // TODO: Xbyak should allow 0 scale
+                + sizeof(float) * jcp.oc_block * j + n * 4 * sizeof(float)];
+        default:
+            return ptr[aux_reg_output_data +
+                (i * jcp.os + j) * jcp.oc_block * sizeof(float) + n*4*sizeof(float)];
+        }
+    };
+
+    auto init = [=]() {
+        Label init_done;
+        Label init_zero;
+
+        if (jcp.with_bias && one_of(jcp.prop_kind, forward_training,
+                    forward_inference)) {
+            test(reg_reduce_pos_flag, FLAG_REDUCE_FIRST);
+            jz(init_zero);
+
+            for (int i = 0; i < load_loop_blk; i++)
+                for (int j = 0; j < ur; ++j) {
+                    movups(reg_accum(i, j, 0), bias_ptr(i, 0));
+                    movups(reg_accum(i, j, 1), bias_ptr(i, 1));
+                }
+            jmp(init_done);
+        }
+
+        L(init_zero);
+        for (int i = 0; i < load_loop_blk; ++i)
+            for (int j = 0; j < ur; ++j) {
+                auto r0 = reg_accum(i, j, 0);
+                auto r1 = reg_accum(i, j, 1);
+                xorps(r0, r0);
+                xorps(r1, r1);
+            }
+
+        L(init_done);
+
+        // load weights
+        for (int i = 0; i < load_loop_blk; ++i) {
+            movups(reg_load(i, 0), load_ptr(0, i, 0));
+            movups(reg_load(i, 1), load_ptr(0, i, 1));
+        }
+
+        movss(reg_bcast, bcast_ptr(0, 0));
+        shufps(reg_bcast, reg_bcast, 0);
+    }; // init()
+
+    auto store = [=]() {
+        Label store_noadd;
+
+        if (!jcp.with_sum) {
+            test(reg_reduce_pos_flag, FLAG_REDUCE_FIRST);
+            jnz(store_noadd, T_NEAR);
+        }
+
+        for (int j = 0; j < ur; ++j)
+            for (int i = 0; i < load_loop_blk; ++i) {
+                auto r0 = reg_accum(i, j, 0);
+                auto r1 = reg_accum(i, j, 1);
+                addps(r0, output_ptr(i, j, 0));
+                addps(r1, output_ptr(i, j, 1));
+            }
+
+        L(store_noadd);
+
+        if (jcp.with_eltwise) {
+            assert(ur * load_loop_blk < 14);
+
+            Label store_norelu;
+            test(reg_reduce_pos_flag, FLAG_REDUCE_LAST);
+            jz(store_norelu, T_NEAR);
+
+            eltwise_injector_->compute_vector_range(1,
+                    2 * ur * load_loop_blk + 1);
+
+            L(store_norelu);
+        }
+
+        for (int j = 0; j < ur; ++j)
+            for (int i = 0; i < load_loop_blk; ++i) {
+                movups(output_ptr(i, j, 0), reg_accum(i, j, 0));
+                movups(output_ptr(i, j, 1), reg_accum(i, j, 1));
+            }
+    };
+
+    auto fma_block = [=](bool last_block) {
+        for (int u = 0; u < jcp.reduce_loop_unroll; ++u) {
+            for (int j = 0; j < ur; ++j) {
+                for (int i = 0; i < load_loop_blk; ++i) {
+                    mulps(reg_load(i, 0), reg_bcast);
+                    mulps(reg_load(i, 1), reg_bcast);
+                    addps(reg_accum(i, j, 0), reg_load(i, 0));
+                    addps(reg_accum(i, j, 1), reg_load(i, 1));
+
+                    if (j == ur - 1 && !(last_block && u == jcp.reduce_loop_unroll - 1)) {
+                        movups(reg_load(i, 0), load_ptr(u + 1, i, 0));
+                        movups(reg_load(i, 1), load_ptr(u + 1, i, 1));
+                    }
+                }
+                if (j < ur - 1) {
+                    movss(reg_bcast, bcast_ptr(u, j + 1));
+                    shufps(reg_bcast, reg_bcast, 0);
+                }
+            } // for ur
+            if (!last_block || u < jcp.reduce_loop_unroll - 1) {
+                movss(reg_bcast, bcast_ptr(u + 1, 0));
+                shufps(reg_bcast, reg_bcast, 0);
+            }
+        } // for reduce_loop_unroll
+    };
+
+    Label reduce_loop;
+    Label reduce_loop_tail;
+
+    mov(aux_reg_load_data, reg_load_data);
+    mov(aux_reg_bcast_data, aux1_reg_bcast_data);
+
+    init();
+
+    mov(reduce_loop_iter, reg_reduce_loop_work);
+    sub(reduce_loop_iter, jcp.reduce_loop_unroll);
+    jle(reduce_loop_tail, T_NEAR);
+
+    L(reduce_loop); {
+        fma_block(false);
+        add(aux_reg_bcast_data, jcp.reduce_loop_bcast_step);
+        add(aux_reg_load_data, jcp.reduce_loop_load_step);
+        sub(reduce_loop_iter, jcp.reduce_loop_unroll);
+        jg(reduce_loop, T_NEAR);
+    }
+
+    L(reduce_loop_tail);
+    fma_block(true);
+
+    store();
+} // reduce_loop()
+
+void jit_sse42_1x1_conv_kernel_f32::generate_diff_bias_loop(int load_loop_blk)
+{
+    if (!jcp.with_bias || jcp.prop_kind != backward_weights)
+        return;
+
+    Label diff_bias_loop, diff_bias_loop_out, diff_bias_init_out;
+    Label diff_bias_load;
+
+    auto diff_bias_ptr = [=](int i, int n) {
+        return ptr[reg_diff_bias_data + i * jcp.oc_block * sizeof(float)+ 4*n*sizeof(float)];
+    };
+
+    auto load_ptr = [=](int u, int i, int n) {
+        return ptr[aux_reg_load_data
+            + (i * jcp.os + u) * jcp.oc_block * sizeof(float) + 4*n*sizeof(float)];
+    };
+
+    auto diff_bias_reg = [=](int i, int n) { return Xmm(2*i + n + 1); };
+
+    mov(reg_diff_bias_data, ptr[rsp + reg_diff_bias_data_stack_offt]);
+    cmp(reg_diff_bias_data, 0);
+    je(diff_bias_loop_out, T_NEAR);
+
+    test(reg_reduce_pos_flag, FLAG_REDUCE_FIRST);
+    jz(diff_bias_load, T_NEAR);
+
+    for (int i = 0; i < load_loop_blk; ++i) {
+        auto r0 = diff_bias_reg(i, 0);
+        auto r1 = diff_bias_reg(i, 1);
+        xorps(r0, r0);
+        xorps(r1, r1);
+    }
+    jmp(diff_bias_init_out, T_NEAR);
+
+    L(diff_bias_load);
+    for (int i = 0; i < load_loop_blk; ++i) {
+        movups(diff_bias_reg(i, 0), diff_bias_ptr(i, 0));
+        movups(diff_bias_reg(i, 1), diff_bias_ptr(i, 1));
+    }
+
+    L(diff_bias_init_out);
+    mov(aux_reg_load_data, reg_load_data);
+    mov(reduce_loop_iter, reg_reduce_loop_work);
+    L(diff_bias_loop); {
+        for(int u = 0; u < jcp.reduce_loop_unroll; ++u)
+            for (int i = 0; i < load_loop_blk; ++i) {
+                addps(diff_bias_reg(i, 0), load_ptr(u, i, 0));
+                addps(diff_bias_reg(i, 1), load_ptr(u, i, 1));
+            }
+        assert(jcp.reduce_dim % jcp.reduce_loop_unroll == 0);
+        add(aux_reg_load_data, jcp.reduce_loop_load_step);
+        sub(reduce_loop_iter, jcp.reduce_loop_unroll);
+        jnz(diff_bias_loop, T_NEAR);
+    }
+
+    for (int i = 0; i < load_loop_blk; i++) {
+        movups(diff_bias_ptr(i, 0), diff_bias_reg(i, 0));
+        movups(diff_bias_ptr(i, 1), diff_bias_reg(i, 1));
+    }
+
+    add(reg_diff_bias_data, load_loop_blk * jcp.oc_block * sizeof(float));
+    mov(ptr[rsp + reg_diff_bias_data_stack_offt], reg_diff_bias_data);
+
+    L(diff_bias_loop_out);
+}
+
+void jit_sse42_1x1_conv_kernel_f32::generate()
+{
+    preamble();
+
+    mov(reg_bcast_data, ptr[param1 + GET_OFF(bcast_data)]);
+    mov(reg_load_data, ptr[param1 + GET_OFF(load_data)]);
+    mov(reg_output_data, ptr[param1 + GET_OFF(output_data)]);
+    if (jcp.with_bias) {
+        if (jcp.prop_kind == backward_weights) {
+            sub(rsp, stack_space_needed);
+            mov(reg_diff_bias_data, ptr[param1 + GET_OFF(bias_data)]);
+            mov(ptr[rsp + reg_diff_bias_data_stack_offt], reg_diff_bias_data);
+        } else
+            mov(reg_bias_data, ptr[param1 + GET_OFF(bias_data)]);
+    }
+
+    mov(reg_load_loop_work, ptr[param1 + GET_OFF(load_dim)]);
+    mov(reg_bcast_loop_work, ptr[param1 + GET_OFF(bcast_dim)]);
+    mov(reg_reduce_loop_work, ptr[param1 + GET_OFF(reduce_dim)]);
+    mov(reg_reduce_pos_flag, ptr[param1 + GET_OFF(first_last_flag)]);
+    if (jcp.prop_kind == backward_weights)
+        mov(reg_output_stride, ptr[param1 + GET_OFF(output_stride)]);
+
+    auto generate_load_loop_body = [=] (int load_loop_blk) {
+        generate_bcast_loop(load_loop_blk);
+        add(reg_load_data, load_loop_blk * jcp.load_loop_load_step);
+        switch (jcp.prop_kind) {
+        case forward_training:
+        case forward_inference:
+            add(reg_bias_data, load_loop_blk * jcp.oc_block * sizeof(float));
+            add(reg_output_data,
+                    load_loop_blk * jcp.os * jcp.oc_block * sizeof(float));
+            break;
+        case backward_data:
+            add(reg_output_data,
+                    load_loop_blk * jcp.is * jcp.ic_block * sizeof(float));
+            break;
+        case backward_weights:
+            for (int i = 0; i < load_loop_blk; i++)
+                add(reg_output_data, reg_output_stride);
+            break;
+        default:
+            assert(!"invalid prop_kind");
+        }
+        sub(reg_load_loop_work, load_loop_blk * jcp.load_loop_iter_step);
+    };
+
+    Label load_loop_blk_8;
+    Label load_loop_blk_16;
+    Label load_loop_blk_24;
+    Label load_loop_blk_end;
+
+    cmp(reg_load_loop_work, 8);
+    jle(load_loop_blk_8, T_NEAR);
+
+    cmp(reg_load_loop_work, 32);
+    je(load_loop_blk_16, T_NEAR);
+
+    cmp(reg_load_loop_work, 16);
+    jle(load_loop_blk_16, T_NEAR);
+
+    L(load_loop_blk_24); {
+        generate_diff_bias_loop(3);
+        generate_load_loop_body(3);
+        cmp(reg_load_loop_work, 32);
+        je(load_loop_blk_16);
+        cmp(reg_load_loop_work, 24);
+        jge(load_loop_blk_24);
+    }
+
+    cmp(reg_load_loop_work, 8);
+    jle(load_loop_blk_8, T_NEAR);
+
+    L(load_loop_blk_16); {
+        generate_diff_bias_loop(2);
+        generate_load_loop_body(2);
+        cmp(reg_load_loop_work, 16);
+        jge(load_loop_blk_16);
+    }
+
+    L(load_loop_blk_8); {
+        cmp(reg_load_loop_work, 0);
+        je(load_loop_blk_end, T_NEAR);
+        generate_diff_bias_loop(1);
+        generate_load_loop_body(1);
+    }
+
+    L(load_loop_blk_end);
+
+    if (jcp.with_bias && jcp.prop_kind == backward_weights)
+        add(rsp, stack_space_needed);
+
+    postamble();
+
+    if (jcp.with_eltwise)
+        eltwise_injector_->prepare_table();
+}
+
+bool jit_sse42_1x1_conv_kernel_f32::post_ops_ok(
+        jit_1x1_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    const auto &p = attr.post_ops_;
+
+    auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); };
+    auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); };
+
+    switch (p.len_) {
+    case 0: return true; // no post_ops
+    case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise
+    case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise
+    default: return false;
+    }
+
+    return false;
+}
+
+status_t jit_sse42_1x1_conv_kernel_f32::init_conf(jit_1x1_conv_conf_t &jcp,
+        const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+        const memory_desc_wrapper &weights_d, const memory_desc_wrapper &dst_d,
+        const primitive_attr_t &attr)
+{
+    if (!mayiuse(sse42))
+        return status::unimplemented;
+
+    // TODO (Roma): this code is duplicated from the generic kernel; maybe the
+    // configuration struct could do some stuff below
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+    const int ndims = src_d.ndims();
+
+    jcp.prop_kind = cd.prop_kind;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+
+    jcp.ih = (ndims == 3) ? 1 : src_d.dims()[2];
+    jcp.iw = src_d.dims()[ndims - 1];
+    jcp.oh = (ndims == 3) ? 1 : dst_d.dims()[2];
+    jcp.ow = dst_d.dims()[ndims - 1];
+
+    jcp.kh = (ndims == 3) ? 1 : weights_d.dims()[with_groups + 2];
+    jcp.kw = weights_d.dims()[with_groups + ndims - 1];
+
+    jcp.t_pad = (ndims == 3) ? 0 : cd.padding[0][0];
+    jcp.l_pad = cd.padding[0][ndims - 3];
+
+    jcp.stride_h = (ndims == 3) ? 1 : cd.strides[0];
+    jcp.stride_w = cd.strides[ndims - 3];
+
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+
+    jcp.os = jcp.oh * jcp.ow;
+    jcp.is = jcp.ih * jcp.iw;
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    jcp.with_sum = p.find(primitive_kind::sum) != -1;
+    const int eltwise_ind = p.find(primitive_kind::eltwise);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise)
+        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+
+    const int is_bwd_d = jcp.prop_kind == backward_data;
+
+    format_tag_t dat_tag = ndims == 3 ? nCw8c : nChw8c;
+    format_tag_t wei_tag = with_groups
+        ? utils::pick(2 * ndims - 6 + is_bwd_d, gOIw8i8o, gOIw8o8i, gOIhw8i8o,
+            gOIhw8o8i)
+        : utils::pick(2 * ndims - 6 + is_bwd_d, OIw8i8o, OIw8o8i, OIhw8i8o,
+            OIhw8o8i);
+
+    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    jcp.wei_tag = weights_d.matches_one_of_tag(wei_tag);
+    jcp.dst_tag = dst_d.matches_one_of_tag(dat_tag);
+
+    bool args_ok = true
+        && jcp.ngroups == 1
+        && jcp.src_tag == dat_tag
+        && jcp.wei_tag == wei_tag
+        && jcp.dst_tag == dat_tag;
+    if (!args_ok) return status::unimplemented;
+
+    const int simd_w = 4;
+    jcp.ic_block = jcp.oc_block = simd_w*2;
+
+    args_ok = true
+        && jcp.oc % jcp.oc_block == 0
+        && jcp.ic % jcp.ic_block == 0
+        && jcp.t_pad == 0 && jcp.l_pad == 0
+        && jcp.stride_w == 1 && jcp.stride_h == 1 // TODO: support some strides
+        && jcp.kh == 1 && jcp.kw == 1;
+    if (!args_ok) return status::unimplemented;
+
+    jcp.ur = 1;
+
+    int load_blocking{ 0 };
+    int load_blocking_max{ 0 };
+    int bcast_blocking{ 0 };
+    int bcast_blocking_max{ 0 };
+    int reduce_blocking{ 0 };
+
+    if (one_of(jcp.prop_kind, forward_training, forward_inference)) {
+        jcp.reduce_dim = jcp.ic;
+        jcp.reduce_block = jcp.ic_block;
+
+        jcp.load_dim = jcp.oc;
+        jcp.load_block = jcp.oc_block;
+
+        jcp.bcast_dim = jcp.is;
+        jcp.bcast_block = jcp.ur;
+
+        jcp.reduce_loop_unroll = jcp.reduce_block;
+        jcp.reduce_loop_bcast_step
+            = jcp.reduce_loop_unroll * jcp.is * sizeof(float);
+        jcp.reduce_loop_load_step
+            = jcp.reduce_loop_unroll * jcp.oc_block * sizeof(float);
+
+        jcp.bcast_loop_output_step = jcp.ur * jcp.oc_block * sizeof(float);
+        jcp.bcast_loop_output_substep = -1; // unused
+        jcp.bcast_loop_bcast_step = jcp.ur * jcp.ic_block * sizeof(float);
+        jcp.bcast_loop_bcast_substep = -1; // unused
+
+        jcp.load_loop_load_step = jcp.ic * jcp.oc_block * sizeof(float);
+        jcp.load_loop_iter_step = jcp.oc_block;
+
+        load_blocking = 120; // assumes the kernel is jcp.ur x 3
+        load_blocking_max = 144;
+        bcast_blocking = 128; // affects load balancing across threads
+        bcast_blocking_max = 192;
+        reduce_blocking = 128; // affects L1$ utilization
+    } else if (jcp.prop_kind == backward_data) {
+        jcp.reduce_dim = jcp.oc;
+        jcp.reduce_block = jcp.oc_block;
+
+        jcp.load_dim = jcp.ic;
+        jcp.load_block = jcp.oc_block;
+
+        jcp.bcast_dim = jcp.os;
+        jcp.bcast_block = jcp.ur;
+
+        jcp.reduce_loop_unroll = jcp.reduce_block;
+        jcp.reduce_loop_bcast_step
+            = jcp.reduce_loop_unroll * jcp.os * sizeof(float);
+        jcp.reduce_loop_load_step
+            = jcp.reduce_loop_unroll * jcp.ic * sizeof(float);
+
+        jcp.bcast_loop_output_step = jcp.ur * jcp.ic_block * sizeof(float);
+        jcp.bcast_loop_output_substep = -1; // unused
+        jcp.bcast_loop_bcast_step = jcp.ur * jcp.oc_block * sizeof(float);
+        jcp.bcast_loop_bcast_substep = -1; // unused
+
+        jcp.load_loop_load_step = jcp.oc_block * jcp.ic_block * sizeof(float);
+        jcp.load_loop_iter_step = jcp.ic_block;
+
+        load_blocking = 96; // assumes the kernel is jcp.ur x 3
+        load_blocking_max = 144;
+        bcast_blocking = 128; // affects load balancing across threads
+        bcast_blocking_max = 196;
+        reduce_blocking = 64; // affects L1$ utilization
+    } else if (jcp.prop_kind == backward_weights) {
+        jcp.reduce_dim = jcp.os;
+        jcp.reduce_block = 1;
+
+        jcp.load_dim = jcp.oc;
+        jcp.load_block = jcp.oc_block;
+
+        jcp.bcast_dim = jcp.ic;
+        jcp.bcast_block = jcp.ic_block;
+
+        jcp.reduce_loop_unroll = jcp.reduce_block;
+        jcp.reduce_loop_bcast_step
+            = jcp.reduce_loop_unroll * jcp.ic_block * sizeof(float);
+        jcp.reduce_loop_load_step
+            = jcp.reduce_loop_unroll * jcp.oc_block * sizeof(float);
+
+        jcp.bcast_loop_output_step = jcp.oc_block * jcp.ic_block * sizeof(float);
+        jcp.bcast_loop_output_substep = jcp.oc_block * jcp.ur * sizeof(float);
+        jcp.bcast_loop_bcast_step = jcp.ic_block * jcp.is * sizeof(float);
+        jcp.bcast_loop_bcast_substep = jcp.ur * sizeof(float);
+
+        jcp.load_loop_load_step = jcp.oc_block * jcp.os * sizeof(float);
+        jcp.load_loop_iter_step = jcp.oc_block;
+
+        /* --- */
+
+        load_blocking = div_up(jcp.load_dim, jcp.load_block);
+        while (true) {
+            if (load_blocking <= 32) break;
+            else if (load_blocking % 2 == 0) load_blocking /= 2;
+            else if (load_blocking % 3 == 0) load_blocking /= 3;
+            else break;
+        }
+        load_blocking *= jcp.load_block;
+        load_blocking_max = load_blocking;
+        assert(jcp.load_dim % load_blocking == 0);
+
+        bcast_blocking = div_up(jcp.bcast_dim, jcp.bcast_block);
+        while (true) {
+            if (bcast_blocking <= 9) break;
+            else if (bcast_blocking % 2 == 0) bcast_blocking /= 2;
+            else if (bcast_blocking % 3 == 0) bcast_blocking /= 3;
+            else break;
+        }
+        bcast_blocking *= jcp.bcast_block;
+        bcast_blocking_max = bcast_blocking;
+        assert(jcp.bcast_dim % bcast_blocking == 0);
+
+        reduce_blocking = 128; // affects L1$ utilization
+    } else
+        return status::unimplemented;
+
+    assert(load_blocking);
+    assert(load_blocking_max);
+    assert(bcast_blocking);
+    assert(bcast_blocking_max);
+    assert(reduce_blocking);
+
+    assert(jcp.bcast_block % jcp.ur == 0);
+    jcp.ur_tail = jcp.bcast_dim % jcp.ur;
+
+    jcp.nb_bcast_blocking = bcast_blocking / jcp.bcast_block;
+    jcp.nb_bcast_blocking_max = bcast_blocking_max / jcp.bcast_block;
+    jcp.nb_load_blocking = load_blocking / jcp.load_block;
+    jcp.nb_load_blocking_max = load_blocking_max / jcp.load_block;
+    jcp.nb_reduce_blocking = reduce_blocking / jcp.reduce_block;
+
+    jcp.nb_bcast = div_up(jcp.bcast_dim, jcp.bcast_block);
+    jcp.nb_load = div_up(jcp.load_dim, jcp.load_block);
+    jcp.nb_reduce = div_up(jcp.reduce_dim, jcp.reduce_block);
+
+    return status::success;
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_conv_kernel_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_conv_kernel_f32.hpp
new file mode 100644
index 0000000000..b314a5098c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_conv_kernel_f32.hpp
@@ -0,0 +1,104 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_SSE42_1x1_CONV_KERNEL_F32_HPP
+#define JIT_SSE42_1x1_CONV_KERNEL_F32_HPP
+
+#include "c_types_map.hpp"
+#include "cpu_memory.hpp"
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+#include "jit_uni_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_sse42_1x1_conv_kernel_f32: public jit_generator {
+    jit_sse42_1x1_conv_kernel_f32(jit_1x1_conv_conf_t ajcp,
+            const primitive_attr_t &attr)
+        : jcp(ajcp), attr_(attr), eltwise_injector_(nullptr)
+    {
+        if (jcp.with_eltwise)
+            eltwise_injector_ = new jit_uni_eltwise_injector_f32<sse42>(this,
+                    jcp.eltwise);
+
+        this->generate();
+        jit_ker = (void (*)(jit_1x1_conv_call_s *))this->getCode();
+    }
+
+    ~jit_sse42_1x1_conv_kernel_f32() {
+        delete eltwise_injector_;
+    }
+
+    static bool post_ops_ok(jit_1x1_conv_conf_t &jcp,
+            const primitive_attr_t &attr);
+
+    static status_t init_conf(jit_1x1_conv_conf_t &jcp,
+            const convolution_desc_t &cd,
+            const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &dst_d,
+            const primitive_attr_t &attr);
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_sse42_1x1_conv_kernel_f32)
+
+    jit_1x1_conv_conf_t jcp;
+    const primitive_attr_t &attr_;
+    void (*jit_ker)(jit_1x1_conv_call_s *);
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    using xmm_t = const Xbyak::Xmm;
+
+    reg64_t reg_bcast_data = rax;
+    reg64_t reg_load_data = rsi;
+    reg64_t reg_output_data = rbx;
+    reg64_t aux_reg_bcast_data = rdx;
+    reg64_t aux1_reg_bcast_data = abi_not_param1;
+    reg64_t aux_reg_load_data = abi_param1;
+    reg64_t aux_reg_output_data = rbp;
+    reg64_t reg_load_loop_work = r9;
+    reg64_t reg_bcast_loop_work = r10;
+    reg64_t reg_reduce_loop_work = r11;
+    reg64_t load_loop_iter = r13;
+    reg64_t imm_addr64 = load_loop_iter;
+    reg64_t bcast_loop_iter = r14;
+    reg64_t reduce_loop_iter = r15;
+    reg64_t reg_reduce_pos_flag = r8;
+    reg64_t reg_output_stride = r12;
+    reg64_t reg_bias_data = r12;
+    reg64_t reg_diff_bias_data = bcast_loop_iter;
+
+    int reg_diff_bias_data_stack_offt = 0;
+    int stack_space_needed = 8;
+
+    xmm_t reg_bcast = xmm_t(15);
+
+    jit_uni_eltwise_injector_f32<sse42> *eltwise_injector_;
+
+    void generate_bcast_loop(int load_loop_blk);
+    void generate_reduce_loop(int load_loop_blk, int ur);
+    void generate_diff_bias_loop(int load_loop_blk);
+
+    void generate();
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_convolution.cpp
new file mode 100644
index 0000000000..30c137641e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_convolution.cpp
@@ -0,0 +1,134 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn_types.h"
+
+#include "c_types_map.hpp"
+#include "jit_sse42_1x1_convolution.hpp"
+#include "utils.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+#define data_blk_off(f, n, c, h, w) \
+    ((ndims == 3) \
+    ? (f).blk_off(n, c, w) \
+    : (f).blk_off(n, c, h, w))
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::utils;
+
+void jit_sse42_1x1_convolution_fwd_t::execute_forward(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const data_t *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+    const int ndims = src_d.ndims();
+
+    const int work_amount = jcp.mb * jcp.ngroups * jcp.nb_bcast;
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        // TODO (Roma): remove this restriction
+        assert(jcp.stride_w == 1 && jcp.stride_h == 1);
+
+        auto par_conv = jit_1x1_conv_call_s();
+
+        const int nb_oc = jcp.nb_load;
+        const int nb_ic = jcp.nb_reduce;
+        const int nb_ic_blocking = jcp.nb_reduce_blocking;
+        const int os_block = jcp.bcast_block;
+
+        int start{0}, end{0};
+        balance211(work_amount, nthr, ithr, start, end);
+
+        int iwork = start;
+        while (iwork < end) {
+            int n{0}, g{0}, osb{0};
+            nd_iterator_init(iwork, n, jcp.mb, g, jcp.ngroups, osb,
+                    jcp.nb_bcast);
+
+            const int bcast_step_rem = jcp.nb_bcast - osb;
+            int bcast_step = bcast_step_rem <= jcp.nb_bcast_blocking_max
+                ? bcast_step_rem : jcp.nb_bcast_blocking;
+            bcast_step = nstl::min<int>(bcast_step, end - iwork);
+
+            const int os = osb * os_block;
+            const int ow = os % jcp.ow;
+            const int oh = os / jcp.ow;
+            const int iw = nstl::max<int>(ow * jcp.stride_w - jcp.l_pad, 0);
+            const int ih = nstl::max<int>(oh * jcp.stride_h - jcp.t_pad, 0);
+
+            par_conv.bcast_dim = this_block_size(os, jcp.os,
+                    bcast_step * os_block);
+
+            int ocb = 0;
+            while (ocb < jcp.nb_load) {
+                const int load_step_rem = jcp.nb_load - ocb;
+                const int load_step = load_step_rem < jcp.nb_load_blocking_max
+                    ? load_step_rem : jcp.nb_load_blocking;
+
+                const size_t _ocb = g * nb_oc + ocb;
+                par_conv.load_dim = this_block_size(ocb * jcp.oc_block, jcp.oc,
+                        load_step * jcp.oc_block);
+
+                const size_t dst_off = data_blk_off(dst_d, n, _ocb, oh, ow);
+                par_conv.output_data = &dst[dst_off];
+
+                par_conv.bias_data = &bias[_ocb * jcp.oc_block];
+
+                for (int icb = 0; icb < nb_ic; icb += nb_ic_blocking) {
+                    par_conv.first_last_flag = 0
+                        | (icb == 0) * FLAG_REDUCE_FIRST
+                        | (icb + nb_ic_blocking >= nb_ic) * FLAG_REDUCE_LAST;
+
+                    par_conv.reduce_dim = this_block_size(icb * jcp.ic_block,
+                            jcp.ic, nb_ic_blocking * jcp.ic_block);
+
+                    const size_t _icb = g * nb_ic + icb;
+                    const size_t src_off = data_blk_off(src_d, n, _icb, ih, iw);
+                    par_conv.bcast_data = &src[src_off];
+
+                    par_conv.load_data = &weights[pd()->with_groups()
+                        ? weights_d.blk_off(g, ocb, icb)
+                        : weights_d.blk_off(ocb, icb)];
+
+                    kernel_->jit_ker(&par_conv);
+                }
+
+                ocb += load_step;
+            }
+
+            iwork += bcast_step;
+        }
+    });
+
+    if (pd()->wants_zero_pad_dst())
+        ctx.memory(MKLDNN_ARG_DST)->zero_pad();
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_convolution.hpp
new file mode 100644
index 0000000000..b32b1e4784
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_1x1_convolution.hpp
@@ -0,0 +1,96 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_SSE42_1x1_CONVOLUTION_HPP
+#define CPU_JIT_SSE42_1x1_CONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+#include "jit_sse42_1x1_conv_kernel_f32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_sse42_1x1_convolution_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_1x1:", sse42, ""),
+                jit_sse42_1x1_convolution_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            return jit_sse42_1x1_conv_kernel_f32::init_conf(jcp_, *desc(),
+                    *src_md(), *weights_md(), *dst_md(), *attr());
+        }
+
+        jit_1x1_conv_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
+            auto wei_tag = with_groups()
+                ? utils::pick(ndims() - 3, gOIw8i8o, gOIhw8i8o)
+                : utils::pick(ndims() - 3, OIw8i8o, OIhw8i8o);
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    jit_sse42_1x1_convolution_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {
+        kernel_ = new jit_sse42_1x1_conv_kernel_f32(pd()->jcp_, *pd()->attr());
+    }
+    ~jit_sse42_1x1_convolution_fwd_t() { delete kernel_; };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    jit_sse42_1x1_conv_kernel_f32 *kernel_;
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_conv_kernel_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_conv_kernel_f32.cpp
new file mode 100644
index 0000000000..17cabc1186
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_conv_kernel_f32.cpp
@@ -0,0 +1,497 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "cpu_memory.hpp"
+
+#include "jit_sse42_conv_kernel_f32.hpp"
+
+#define GET_OFF(field) offsetof(jit_conv_call_s, field)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::utils;
+
+using namespace Xbyak;
+
+void jit_sse42_conv_fwd_kernel_f32::oh_step_unroll_kw(int ur_w,
+        int pad_l, int pad_r, int oc_blocks)
+{
+    int iw = jcp.iw;
+    int ih = jcp.ih;
+    int kw = jcp.kw;
+    int kh = jcp.kh;
+    int nb_ic = jcp.nb_ic;
+    int stride_w = jcp.stride_w;
+    int dilate_w = jcp.dilate_w + 1;
+    int ic_blk = jcp.ic_block;
+    int oc_blk = jcp.oc_block;
+
+    for (int ki = 0; ki < kw; ki++) {
+        int jj_start = nstl::max(0, div_up(pad_l - ki * dilate_w, stride_w));
+        int jj_end = ur_w
+        - nstl::max(0, div_up(ki*dilate_w + pad_r - (kw-1)*dilate_w, stride_w));
+        for (int ifm2 = 0; ifm2 < ic_blk; ifm2++) {
+            for (int jj = jj_start; jj < jj_end; jj++) {
+                int inp_off;
+                if (one_of(jcp.src_tag, ncw, nchw))
+                    inp_off = ifm2*ih*iw + (ki*dilate_w + jj*stride_w - pad_l);
+                else
+                    inp_off = (ki*dilate_w + jj*stride_w - pad_l)*ic_blk + ifm2;
+
+                movss(Xmm(oc_blocks * ur_w + jj + 1),
+                      ptr[aux_reg_input + sizeof(float) * inp_off]);
+                shufps(Xmm(oc_blocks * ur_w + jj + 1),
+                       Xmm(oc_blocks * ur_w + jj + 1), 0x0);
+            }
+
+            for (int ii = 0; ii < oc_blocks; ii++) {
+                int ker_off = ii * nb_ic * kh * kw * ic_blk * oc_blk
+                              + ki * ic_blk * oc_blk + ifm2 * oc_blk;
+
+                for (int jj = jj_start; jj < jj_end; jj++)
+                {
+                    movups(xmm0,
+                      ptr[aux_reg_kernel + sizeof(float) * ker_off]);
+                    mulps(xmm0, Xmm(oc_blocks * ur_w + jj + 1));
+                    addps(Xmm(ur_w * ii + jj + 1), xmm0);
+                }
+            }
+        }
+    }
+}
+
+void jit_sse42_conv_fwd_kernel_f32::oh_step_nopad(int ur_w,
+        int pad_l, int pad_r, int oc_blocks)
+{
+    Label kw_loop;
+
+    int iw = jcp.iw;
+    int ih = jcp.ih;
+    int kw = jcp.kw;
+    int kh = jcp.kh;
+    int nb_ic = jcp.nb_ic;
+    int stride_w = jcp.stride_w;
+    int dilate_w = jcp.dilate_w + 1;
+    int ic_blk = jcp.ic_block;
+    int oc_blk = jcp.oc_block;
+
+    xor_(ki_iter, ki_iter);
+    L(kw_loop);
+    {
+        int jj_start = 0;
+        int jj_end = ur_w;
+        for (int ifm2 = 0; ifm2 < ic_blk; ifm2++) {
+            for (int jj = jj_start; jj < jj_end; jj++) {
+                int inp_off;
+                if (one_of(jcp.src_tag, ncw, nchw))
+                    inp_off = ifm2 * ih * iw + (jj * stride_w - pad_l);
+                else
+                    inp_off = (jj * stride_w - pad_l) * ic_blk + ifm2;
+
+                movss(Xmm(oc_blocks * ur_w + jj + 1),
+                      ptr[aux_reg_input + sizeof(float) * inp_off]);
+                shufps(Xmm(oc_blocks * ur_w + jj + 1),
+                       Xmm(oc_blocks * ur_w + jj + 1), 0x0);
+            }
+            for (int ii = 0; ii < oc_blocks; ii++) {
+                int aux_kernel_offset = ii * nb_ic * kh * kw * ic_blk * oc_blk
+                                        + ifm2 * oc_blk;
+                for (int jj = jj_start; jj < jj_end; jj++) {
+                    movups(xmm0,
+                      ptr[aux_reg_kernel + sizeof(float) * aux_kernel_offset]);
+                    mulps(xmm0, Xmm(oc_blocks * ur_w + jj + 1));
+                    addps(Xmm(ur_w * ii + jj + 1), xmm0);
+                }
+            }
+        }
+        add(aux_reg_kernel, sizeof(float) * oc_blk * ic_blk);
+        add(aux_reg_input, sizeof(float) * (one_of(jcp.src_tag, ncw, nchw) ?
+            dilate_w : ic_blk * dilate_w));
+
+        inc(ki_iter);
+        cmp(ki_iter, kw);
+        jl(kw_loop, T_NEAR);
+    }
+}
+
+void jit_sse42_conv_fwd_kernel_f32::width_blk_step(int ur_w,
+        int pad_l, int pad_r, int oc_blocks)
+{
+    int iw = jcp.iw;
+    int kw = jcp.kw;
+    int ow = jcp.ow;
+    int oh = jcp.oh;
+    int dilate_h = jcp.dilate_h + 1;
+    int dilate_w = jcp.dilate_w + 1;
+    int ic_blk = jcp.ic_block;
+    int oc_blk = jcp.oc_block;
+    const int inp_mult = one_of(jcp.src_tag, ncw, nchw)
+        ? dilate_h : ic_blk * dilate_h;
+    const int inp_off = one_of(jcp.src_tag, ncw, nchw)
+        ? dilate_w : ic_blk * dilate_w;
+
+    xor_(simd_iter, simd_iter);
+
+    mov(aux_reg_input, reg_input);
+    mov(aux_reg_kernel, reg_kernel);
+
+    Label init_simd_iter_loop;
+    Label init_done;
+    Label init_first;
+
+    L(init_simd_iter_loop);
+
+    if (!jcp.with_sum) {
+        test(reg_ci_flag, FLAG_IC_FIRST);
+        jne(init_first, T_NEAR);
+    }
+
+    for (int ii = 0; ii < oc_blocks; ii++)
+        for (int jj = 0; jj < ur_w; jj++)
+            movups(Xmm(ur_w * ii + jj + 1), xword[reg_output
+                   + sizeof(float) * (ii * oh * ow + jj) * oc_blk]);
+
+    if (jcp.with_sum && jcp.with_bias) {
+        test(reg_ci_flag, FLAG_IC_FIRST);
+        je(init_done, T_NEAR);
+
+        for (int ii = 0; ii < oc_blocks; ii++)
+            for (int jj = 0; jj < ur_w; jj++)
+                addps(Xmm(ur_w * ii + jj + 1),
+                    xword[reg_bias + sizeof(float) * ii * oc_blk]);
+    }
+
+    jmp(init_done);
+
+    L(init_first);
+    if (this->jcp.with_bias) {
+        for (int ii = 0; ii < oc_blocks; ii++)
+            for (int jj = 0; jj < ur_w; jj++)
+                movups(Xmm(ur_w * ii + jj + 1),
+                       xword[reg_bias + sizeof(float) * ii * oc_blk]);
+    } else {
+        for (int ii = 0; ii < oc_blocks; ii++)
+            for (int jj = 0; jj < ur_w; jj++)
+                pxor(Xmm(ur_w * ii + jj + 1), Xmm(ur_w * ii + jj + 1));
+    }
+
+    L(init_done);
+
+    Label skip_kh_loop;
+    mov(kj, reg_kh);
+    if ((jcp.dilate_h >= jcp.ih)
+            || (jcp.kh - 1) * (jcp.dilate_h + 1) < nstl::max(jcp.t_pad, jcp.b_pad)) {
+        cmp(kj, 0);
+        je(skip_kh_loop, T_NEAR);
+    }
+    Label kh_loop;
+    L(kh_loop);
+    {
+        if (jcp.kw >= 5 && pad_l == 0 && pad_r == 0) {
+            oh_step_nopad(ur_w, pad_l, pad_r, oc_blocks);
+            sub(aux_reg_input, sizeof(float) * kw * inp_off);
+            add(aux_reg_input, sizeof(float) * iw * inp_mult);
+        } else {
+            oh_step_unroll_kw(ur_w, pad_l, pad_r, oc_blocks);
+            add(aux_reg_kernel, sizeof(float) * kw * oc_blk * ic_blk);
+            add(aux_reg_input, sizeof(float) * iw * inp_mult);
+        }
+
+        dec(kj);
+        cmp(kj, 0);
+        jg(kh_loop, T_NEAR);
+    }
+
+    L(skip_kh_loop);
+
+    if (jcp.with_eltwise) {
+        Label regular_store;
+        test(reg_ci_flag, FLAG_IC_LAST);
+        je(regular_store, T_NEAR);
+
+        eltwise_injector_->compute_vector_range(1, oc_blocks * ur_w + 1);
+
+        L(regular_store);
+    }
+
+    for (int ii = 0; ii < oc_blocks; ii++) {
+        for (int jj = 0; jj < ur_w; jj++) {
+            const size_t o_off = (ii * oh * ow + jj) * oc_blk;
+
+            Xmm reg_out = Xmm(ur_w * ii + jj + 1);
+            movups(xword[reg_output + sizeof(float) * o_off], reg_out);
+        }
+    }
+
+    mov(aux_reg_kernel, reg_kernel);
+    mov(aux_reg_input, reg_input);
+    add(aux_reg_kernel, sizeof(float) * 4);
+    add(reg_output, sizeof(float) * 4);
+    add(reg_bias,   sizeof(float) * 4);
+
+    inc(simd_iter);
+    cmp(simd_iter, 2);
+    jl(init_simd_iter_loop, T_NEAR);
+
+    sub(reg_output, sizeof(float) * 8);
+    sub(reg_bias,   sizeof(float) * 8);
+}
+
+inline void jit_sse42_conv_fwd_kernel_f32::solve_common(int oc_blocks)
+{
+    int ur_w = jcp.ur_w;
+    int ur_w_tail = jcp.ur_w_tail;
+    int n_oi = jcp.ow / ur_w;
+    int iw = jcp.iw;
+    int kw = jcp.kw;
+    int ic_blk = jcp.ic_block;
+    int oc_blk = jcp.oc_block;
+    int dilate_w = jcp.dilate_w + 1;
+    int str_w = jcp.stride_w;
+    const int inp_mult = one_of(jcp.src_tag, ncw, nchw) ? 1 : ic_blk;
+
+    int l_pad = jcp.l_pad;
+    int r_pad = nstl::max(0, (int(jcp.ow) - 1) * str_w + (kw - 1) * dilate_w
+        - (iw + l_pad - 1));
+    int r_pad1 = (ur_w * n_oi - 1) * str_w + (kw - 1) * dilate_w
+        - (iw + l_pad - 1);
+    if (r_pad1 > 0) n_oi--;
+
+    if (l_pad > 0) {
+        n_oi--;
+        if (n_oi < 0 && r_pad1 > 0)
+            width_blk_step(ur_w, l_pad, r_pad1, oc_blocks); // "lrpad"
+        else
+            width_blk_step(ur_w, l_pad, 0, oc_blocks); // "lpad"
+        add(reg_input, sizeof(float) * (ur_w * str_w - l_pad) * inp_mult);
+        add(reg_output, sizeof(float) * ur_w * oc_blk);
+    }
+
+    Label ow_loop;
+    xor_(oi_iter, oi_iter);
+
+    if (n_oi > 0) {
+        L(ow_loop);
+
+        width_blk_step(ur_w, 0, 0, oc_blocks); // "middle"
+        add(reg_input, sizeof(float) * ur_w * str_w * inp_mult);
+        add(reg_output, sizeof(float) * ur_w * oc_blk);
+
+        inc(oi_iter);
+        cmp(oi_iter, n_oi);
+        jl(ow_loop, T_NEAR);
+    }
+
+    if (r_pad1 > 0 && n_oi >=0) {
+        width_blk_step(ur_w, 0, r_pad1, oc_blocks); // "rpad"
+        add(reg_input, sizeof(float) * ur_w * str_w * inp_mult);
+        add(reg_output, sizeof(float) * ur_w * oc_blk);
+    }
+
+    if (ur_w_tail != 0)
+        width_blk_step(ur_w_tail, 0, r_pad, oc_blocks); // "tail"
+}
+
+void jit_sse42_conv_fwd_kernel_f32::generate()
+{
+    this->preamble();
+
+    mov(reg_input, ptr[this->param1 + GET_OFF(src)]);
+    mov(reg_output, ptr[this->param1 + GET_OFF(dst)]);
+    mov(reg_kernel, ptr[this->param1 + GET_OFF(filt)]);
+    if (jcp.with_bias)
+        mov(reg_bias, ptr[this->param1 + GET_OFF(bias)]);
+    mov(reg_kh, ptr[this->param1 + GET_OFF(kh_padding)]);
+    mov(reg_ci_flag, ptr[this->param1 + GET_OFF(flags)]);
+    mov(reg_oc_blocks, ptr[this->param1 + GET_OFF(oc_blocks)]);
+
+    int nb_oc_tail = jcp.nb_oc % jcp.nb_oc_blocking;
+    Label tail, exit;
+
+    cmp(reg_oc_blocks, jcp.nb_oc_blocking);
+    jne(nb_oc_tail ? tail : exit, T_NEAR);
+
+    solve_common(jcp.nb_oc_blocking);
+    jmp(exit, T_NEAR);
+
+    if (nb_oc_tail) {
+        L(tail);
+        cmp(reg_oc_blocks, nb_oc_tail);
+        jne(exit, T_NEAR);
+        solve_common(nb_oc_tail);
+    }
+
+    L(exit);
+
+    this->postamble();
+
+    if (jcp.with_eltwise)
+        eltwise_injector_->prepare_table();
+}
+
+bool jit_sse42_conv_fwd_kernel_f32::post_ops_ok(
+        jit_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    const auto &p = attr.post_ops_;
+
+    auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); };
+    auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); };
+
+    switch (p.len_) {
+    case 0: return true; // no post_ops
+    case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise
+    case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise
+    default: return false;
+    }
+
+    return false;
+}
+
+status_t jit_sse42_conv_fwd_kernel_f32::init_conf(jit_conv_conf_t &jcp,
+        const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+        const memory_desc_wrapper &weights_d, const memory_desc_wrapper &dst_d,
+        const primitive_attr_t &attr)
+{
+    if (!mayiuse(sse42)) return status::unimplemented;
+
+    jcp.prop_kind = cd.prop_kind;
+
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+    const int ndims = src_d.ndims();
+    jcp.ndims = ndims;
+
+    jcp.ngroups = with_groups ? weights_d.dims()[0] : 1;
+    jcp.mb = src_d.dims()[0];
+
+    jcp.oc = dst_d.dims()[1] / jcp.ngroups;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+
+    jcp.ih = (ndims == 3) ? 1 : src_d.dims()[2];
+    jcp.iw = src_d.dims()[ndims - 1];
+    jcp.oh = (ndims == 3) ? 1 : dst_d.dims()[2];
+    jcp.ow = dst_d.dims()[ndims - 1];
+
+    jcp.kh = (ndims == 3) ? 1 : weights_d.dims()[with_groups + 2];
+    jcp.kw = weights_d.dims()[with_groups + ndims - 1];
+
+    jcp.t_pad = (ndims == 3) ? 0 : cd.padding[0][0];
+    jcp.l_pad = cd.padding[0][ndims - 3];
+
+    jcp.stride_h = (ndims == 3) ? 1 : cd.strides[0];
+    jcp.stride_w = cd.strides[ndims - 3];
+
+    jcp.dilate_h = (ndims == 3) ? 0 : cd.dilates[0];
+    jcp.dilate_w = cd.dilates[ndims - 3];
+    jcp.b_pad = (jcp.oh - 1) * jcp.stride_h + (jcp.kh - 1) * (jcp.dilate_h + 1)
+            - (jcp.ih + jcp.t_pad - 1);
+
+    if (ndims == 3) {
+        jcp.src_tag = src_d.matches_one_of_tag(ncw, nwc, nCw8c);
+        jcp.wei_tag = weights_d.matches_one_of_tag(
+                Owi8o, gOwi8o, OIw8i8o, gOIw8i8o);
+        jcp.dst_tag = dst_d.matches_one_of_tag(nCw8c);
+    } else if (ndims == 4) {
+        jcp.src_tag = src_d.matches_one_of_tag(nchw, nhwc, nChw8c);
+        jcp.wei_tag = weights_d.matches_one_of_tag(
+                Ohwi8o, gOhwi8o, OIhw8i8o, gOIhw8i8o);
+        jcp.dst_tag = dst_d.matches_one_of_tag(nChw8c);
+    }
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    jcp.with_sum = p.find(primitive_kind::sum) != -1;
+    const int eltwise_ind = p.find(primitive_kind::eltwise);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise)
+        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+
+    const bool flat = jcp.ic == 3;
+    const bool mimo = !flat;
+
+    bool args_ok = true
+        && IMPLICATION(flat, one_of(jcp.src_tag, ncw, nwc, nchw, nhwc)
+                && one_of(jcp.wei_tag, Owi8o, gOwi8o, Ohwi8o, gOhwi8o))
+        && IMPLICATION(mimo, one_of(jcp.src_tag, nCw8c, nChw8c)
+                && one_of(jcp.wei_tag, OIw8i8o, gOIw8i8o, OIhw8i8o, gOIhw8i8o))
+        && one_of(jcp.dst_tag, nCw8c, nChw8c);
+    if (!args_ok) return status::unimplemented;
+
+    const int simd_w = 8; // 2 SSE vectors processing at once
+
+    jcp.ur_h = 1; /* no code-unrolling by h so far */
+    jcp.ur_w = 3;
+    if (jcp.ow < jcp.ur_w) jcp.ur_w = jcp.ow;
+    jcp.ur_w_tail = jcp.ow % jcp.ur_w;
+
+    jcp.nb_oc_blocking = 4; /* the optimal value for the kernel */
+
+    args_ok = true
+        && jcp.oc % simd_w == 0
+        && jcp.l_pad <= jcp.ur_w
+        && IMPLICATION(jcp.kw > 7, (jcp.t_pad == 0 && jcp.l_pad == 0)
+                || (jcp.stride_w == 1 && jcp.stride_h == 1))
+        && IMPLICATION(mimo, jcp.ic % simd_w == 0);
+    if (!args_ok) return status::unimplemented;
+
+    int r_pad_no_tail = nstl::max(0, (jcp.ow - jcp.ur_w_tail - 1) * jcp.stride_w
+        + (jcp.kw - 1) * (jcp.dilate_w + 1) - (jcp.iw + jcp.l_pad - 1));
+
+    // kernel needs 1 temporary YMM register
+    const int num_avail_regs = 15;
+    if (r_pad_no_tail > jcp.ur_w * jcp.stride_w && jcp.ow / jcp.ur_w > 1) {
+        /* recalculate ur_w, nb_oc_blocking and ur_w_tail */
+        jcp.ur_w = nstl::min(r_pad_no_tail / jcp.stride_w + jcp.ur_w_tail,
+                nstl::min(jcp.ow, num_avail_regs / 2));
+        jcp.nb_oc_blocking = (num_avail_regs - jcp.ur_w) / jcp.ur_w;
+        jcp.ur_w_tail = jcp.ow % jcp.ur_w;
+        /* check again ... */
+        r_pad_no_tail = nstl::max(0, (jcp.ow - jcp.ur_w_tail - 1) * jcp.stride_w
+            + (jcp.kw - 1) * (jcp.dilate_w + 1) - (jcp.iw + jcp.l_pad - 1));
+        if (jcp.ur_w < nstl::max(jcp.l_pad, r_pad_no_tail))
+            return status::unimplemented;
+    }
+    assert(jcp.nb_oc_blocking > 0);
+    assert(jcp.ur_w * (jcp.nb_oc_blocking + 1) <= num_avail_regs);
+
+    jcp.ic_block = (jcp.ic % simd_w != 0) ? jcp.ic : simd_w;
+    jcp.nb_ic = jcp.ic / jcp.ic_block;
+
+    jcp.oc_block = simd_w;
+    jcp.nb_oc = jcp.oc / jcp.oc_block;
+
+    if (one_of(jcp.prop_kind, forward_training, forward_inference)) {
+        jcp.nb_ic_blocking = 12;
+        jcp.nb_ic_blocking_max = 16;
+    } else {
+        jcp.nb_ic_blocking = 1;
+        jcp.nb_ic_blocking_max = jcp.nb_ic_blocking;
+    }
+
+    return status::success;
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_conv_kernel_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_conv_kernel_f32.hpp
new file mode 100644
index 0000000000..33c26ef081
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_conv_kernel_f32.hpp
@@ -0,0 +1,93 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_SSE42_CONV_KERNEL_F32_HPP
+#define JIT_SSE42_CONV_KERNEL_F32_HPP
+
+#include "c_types_map.hpp"
+#include "cpu_memory.hpp"
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+#include "jit_uni_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_sse42_conv_fwd_kernel_f32: public jit_generator {
+    jit_sse42_conv_fwd_kernel_f32(jit_conv_conf_t ajcp,
+            const primitive_attr_t &attr)
+        : jcp(ajcp), attr_(attr), eltwise_injector_(nullptr)
+    {
+        if (jcp.with_eltwise)
+            eltwise_injector_ = new jit_uni_eltwise_injector_f32<sse42>(this,
+                    jcp.eltwise);
+
+        this->generate();
+        jit_ker = (void (*)(jit_conv_call_s *))this->getCode();
+    }
+
+    ~jit_sse42_conv_fwd_kernel_f32() {
+        delete eltwise_injector_;
+    }
+
+    static bool post_ops_ok(jit_conv_conf_t &jcp,
+            const primitive_attr_t &attr);
+
+    static status_t init_conf(jit_conv_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &dst_d, const primitive_attr_t &attr);
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_sse42_conv_fwd_kernel_f32)
+    jit_conv_conf_t jcp;
+    const primitive_attr_t &attr_;
+    void (*jit_ker)(jit_conv_call_s *);
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    reg64_t reg_input = rax;
+    reg64_t aux_reg_input = r8;
+    reg64_t reg_kernel = rdx;
+    reg64_t aux_reg_kernel = r9;
+    reg64_t reg_output = rsi;
+    reg64_t reg_bias = rbx;
+
+    reg64_t kj = r10;
+    reg64_t oi_iter = r11;
+    reg64_t ki_iter = r12;
+    reg64_t reg_kh = abi_not_param1;
+    reg64_t simd_iter = r15;
+    reg64_t reg_oc_blocks = r14;
+    reg64_t imm_addr64 = reg_oc_blocks;
+    Xbyak::Reg32 reg_ci_flag = r13d;
+
+    jit_uni_eltwise_injector_f32<sse42> *eltwise_injector_;
+
+    inline void oh_step_unroll_kw(int ur_w, int pad_l, int pad_r,
+            int oc_blocks);
+    inline void oh_step_nopad(int ur_w, int pad_l, int pad_r, int oc_blocks);
+    inline void width_blk_step(int ur_w, int pad_l, int pad_r, int oc_blocks);
+    inline void solve_common(int oc_blocks);
+
+    void generate();
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_convolution.cpp
new file mode 100644
index 0000000000..5f77d692f5
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_convolution.cpp
@@ -0,0 +1,136 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn_types.h"
+
+#include "c_types_map.hpp"
+#include "jit_sse42_convolution.hpp"
+#include "mkldnn_thread.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::utils;
+
+#define src_blk_off(f, n, c, h, w) \
+    (pd()->ndims() == 3) \
+    ? (f).blk_off(n, c, w) \
+    : (f).blk_off(n, c, h, w)
+
+#define wht_blk_off_(f, g, ...) \
+    pd()->with_groups() \
+    ? (f).blk_off(g, __VA_ARGS__) \
+    : (f).blk_off(__VA_ARGS__)
+#define wht_blk_off(f, g, oc, ic, kh, kw) \
+        pd()->ndims() == 3 \
+        ? wht_blk_off_(f, g, oc, ic, kw) \
+        : wht_blk_off_(f, g, oc, ic, kh, kw)
+
+void jit_sse42_convolution_fwd_t::execute_forward(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const data_t *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    const auto &jcp = kernel_->jcp;
+
+    int ocb_work = div_up(jcp.nb_oc, jcp.nb_oc_blocking);
+    const size_t work_amount = jcp.mb * jcp.ngroups * ocb_work * jcp.oh;
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        size_t start{ 0 }, end{ 0 };
+        balance211(work_amount, nthr, ithr, start, end);
+
+        int icbb = 0;
+        while (icbb < jcp.nb_ic) {
+            int icb_step = jcp.nb_ic_blocking;
+            int icb_step_rem = jcp.nb_ic - icbb;
+            if (icb_step_rem < jcp.nb_ic_blocking_max)
+                icb_step = icb_step_rem;
+
+            size_t n{0}, g{0}, ocbb{0}, oh{0};
+            nd_iterator_init(start, n, jcp.mb, g, jcp.ngroups, ocbb, ocb_work,
+                             oh, jcp.oh);
+            for (size_t iwork = start; iwork < end; ++iwork) {
+                int ocb = ocbb * jcp.nb_oc_blocking;
+                int ocb_num = jcp.nb_oc_blocking;
+
+                for (int icb = icbb; icb < icbb + icb_step; ++icb) {
+                    auto par_conv = jit_conv_call_s();
+
+                    const int ij = oh * jcp.stride_h;
+                    const int i_t_overflow = nstl::max(0, jcp.t_pad - ij);
+                    const int i_b_overflow = nstl::max(jcp.ih, ij
+                        + (jcp.kh-1) * (jcp.dilate_h+1) - jcp.t_pad+1) - jcp.ih;
+
+                    const size_t _oc = g * jcp.nb_oc + ocb;
+                    const size_t _ic = g * jcp.nb_ic + icb;
+
+                    const int ih = nstl::max(ij - jcp.t_pad
+                        + div_up(i_t_overflow,
+                                 (jcp.dilate_h+1)) * (jcp.dilate_h + 1), 0);
+                    par_conv.src = &src[src_blk_off(src_d, n,
+                        jcp.ic == 3 ? 0 : _ic, ih, 0)];
+
+                    par_conv.dst = &dst[src_blk_off(dst_d, n, _oc, oh, 0)];
+
+                    const int wh = div_up(i_t_overflow, (jcp.dilate_h + 1));
+                    par_conv.filt = &weights[wht_blk_off(weights_d, g, ocb,
+                        jcp.ic == 3 ? 0 : icb, wh, 0)];
+
+                    if (icb == 0) {
+                        if (bias)
+                            par_conv.bias =
+                                    &bias[bias_d.blk_off(_oc * jcp.oc_block)];
+                        par_conv.flags |= FLAG_IC_FIRST;
+                    }
+
+                    if (jcp.with_eltwise && icb + 1 == jcp.nb_ic) {
+                        par_conv.flags |= FLAG_IC_LAST;
+                    }
+
+                    par_conv.oc_blocks =
+                            nstl::min(ocb + ocb_num, jcp.nb_oc) - ocb;
+
+                    par_conv.kw_padding = 0;
+                    const int kh_padding = jcp.kh
+                        - div_up(i_t_overflow, (jcp.dilate_h + 1))
+                        - div_up(i_b_overflow, (jcp.dilate_h + 1));
+                    par_conv.kh_padding = nstl::max(0, kh_padding);
+                    kernel_->jit_ker(&par_conv);
+                }
+                nd_iterator_step(n, jcp.mb, g, jcp.ngroups, ocbb, ocb_work,
+                                 oh, jcp.oh);
+            }
+            icbb += icb_step;
+        }
+    });
+
+    if (pd()->wants_zero_pad_dst())
+        ctx.memory(MKLDNN_ARG_DST)->zero_pad();
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_convolution.hpp
new file mode 100644
index 0000000000..d2f0a38c5c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_sse42_convolution.hpp
@@ -0,0 +1,103 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_SSE42_CONVOLUTION_HPP
+#define CPU_JIT_SSE42_CONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "utils.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "jit_primitive_conf.hpp"
+#include "jit_sse42_conv_kernel_f32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_sse42_convolution_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", sse42, ""),
+                jit_sse42_convolution_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            return jit_sse42_conv_fwd_kernel_f32::init_conf(jcp_, *desc(),
+                    *src_md(), *weights_md(), *dst_md(), *attr());
+        }
+
+        jit_conv_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            const bool flat = IC() == 3;
+            auto src_tag = flat
+                ? utils::pick(ndims() - 3, ncw, nchw, ncdhw)
+                : utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
+            auto dst_tag =
+                utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c);
+            auto wei_tag = with_groups()
+                ? utils::pick(2 * ndims() - 6 + flat, gOIw8i8o, gOwi8o,
+                        gOIhw8i8o, gOhwi8o, gOIdhw8i8o, gOdhwi8o)
+                : utils::pick(2 * ndims() - 6 + flat, OIw8i8o, Owi8o,
+                        OIhw8i8o, Ohwi8o, OIdhw8i8o, Odhwi8o);
+
+            return set_default_formats_common(src_tag, wei_tag, dst_tag);
+        }
+    };
+
+    jit_sse42_convolution_fwd_t(const pd_t *apd): cpu_primitive_t(apd)
+    { kernel_ = new jit_sse42_conv_fwd_kernel_f32(pd()->jcp_, *pd()->attr()); }
+    ~jit_sse42_convolution_fwd_t() { delete kernel_; };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    jit_sse42_conv_fwd_kernel_f32 *kernel_;
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_transpose_src_utils.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_transpose_src_utils.cpp
new file mode 100644
index 0000000000..0e734f7265
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_transpose_src_utils.cpp
@@ -0,0 +1,1192 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "nstl.hpp"
+#include "utils.hpp"
+#include "jit_generator.hpp"
+#include "cpu_barrier.hpp"
+
+#include "jit_transpose_src_utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace Xbyak;
+
+#define GET_OFF(x) offsetof(ctx_t, x)
+
+struct jit_trans_iw_ic_t: public jit_trans_src_t, public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_trans_iw_ic_t)
+
+    jit_trans_iw_ic_t(const jit_conv_conf_t *conf): jit_trans_src_t(conf) {
+        generate();
+        ker_ = (decltype(ker_))this->getCode();
+    }
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    using reg32_t = const Xbyak::Reg32;
+    using opmask_t = const Xbyak::Opmask;
+
+    enum { typesize = sizeof(float), transpose_size = 16, small_spatial = 14 };
+    int src_stride, tr_src_stride;
+    int tail;
+    bool enable_prefetch;
+
+    opmask_t k3333 = k1;
+    opmask_t k5555 = k2;
+    opmask_t kAAAA = k3;
+    opmask_t kCCCC = k4;
+    opmask_t k0F0F = k5;
+    opmask_t kF0F0 = k6;
+    opmask_t kTail = k7;
+
+    reg64_t reg_src = r8;
+    reg64_t reg_tr_src = r9;
+    reg64_t reg_src_prf = r10;
+    reg64_t reg_tr_src_prf = r11;
+    reg64_t reg_loop = r12;
+    reg64_t reg_tr_src_tmp = r13;
+    reg32_t regw_tmp = r14d;
+
+    void transpose(int nrows, int l_pad, int r_pad, bool nontemporal_stores);
+    void generate();
+};
+
+void jit_trans_iw_ic_t::transpose(int nrows, int l_pad, int r_pad,
+    bool nontemporal_stores) {
+    assert(nrows >= 0 && nrows <= transpose_size);
+    static_assert(transpose_size == 16, "Unsupported transpose size");
+    if (!nrows)
+        return;
+
+    auto pf_src_t0 = [=](int i) {
+        if(enable_prefetch) prefetcht0(EVEX_compress_addr(reg_src,
+            (transpose_size + i) * src_stride));
+    };
+
+    auto pf_tr_src_t0 = [=](int i) {
+        int offset = (transpose_size) * typesize + i * tr_src_stride;
+        if(enable_prefetch) prefetcht0(EVEX_compress_addr(reg_tr_src, offset));
+        if(enable_prefetch) prefetcht0(EVEX_compress_addr(reg_tr_src,
+            offset + 64));
+    };
+
+    auto pf_src_t1 = [=](int i) {
+        if(enable_prefetch) prefetcht1(EVEX_compress_addr(reg_src_prf,
+            i * src_stride));
+    };
+
+    auto pf_tr_src_t1 = [=](int i) {
+        if(enable_prefetch) prefetchwt1(EVEX_compress_addr(reg_tr_src_prf,
+            i * tr_src_stride));
+    };
+
+    auto src_zmm = [=](int i) {
+        assert(i >= 0 && i < 16);
+        return Zmm(i);
+    };
+
+    auto tmp_zmm = [=](int i) {
+        assert(i >= 0 && i < 16);
+        return Zmm(16 + i);
+    };
+
+    auto load = [=](int i) {
+        vmovups(src_zmm(i), EVEX_compress_addr(reg_src, i * src_stride));
+    };
+
+    auto store = [=](Zmm r, int i) {
+        auto kmovw = [=](Opmask k, unsigned w) {
+            mov(regw_tmp, w);
+            jit_generator::kmovw(k, regw_tmp);
+        };
+
+        auto padding = [=] (Reg64 reg, int pad) {
+            kmovw(kTail, (1 << pad) - 1);
+            auto k = kTail;
+            auto base = reg;
+            base.setOpmaskIdx(k.getIdx(), true);
+
+            auto zmm_zero = r;
+            vpxord(zmm_zero, zmm_zero, zmm_zero);
+            auto addr = EVEX_compress_addr(base, i * tr_src_stride);
+            vmovups(addr, zmm_zero);
+        };
+
+        mov(reg_tr_src_tmp, reg_tr_src);
+        if (l_pad > 0)
+            add(reg_tr_src_tmp, l_pad * typesize);
+
+        if (tail != transpose_size)
+            kmovw(kTail, (1 << tail) - 1);
+
+        // Xbyak does not allow k0 to be specified explicitly via the '|'
+        // operator, so we have to do this via a method call (implicitly
+        // EVEX encoding uses k0 to mean 'no mask')
+        bool partial_store = nrows < 16;
+        auto k = partial_store ? kTail : k0;
+        auto base = reg_tr_src_tmp;
+        base.setOpmaskIdx(k.getIdx(), true);
+
+        auto addr = EVEX_compress_addr(base, i * tr_src_stride);
+        if (nontemporal_stores && !partial_store)
+            vmovntps(addr, r);
+        else
+            vmovups(addr, r);
+
+        if (r_pad > 0) {
+            add(reg_tr_src_tmp, tail * typesize);
+            padding(reg_tr_src_tmp, r_pad);
+        }
+
+        if (l_pad > 0) {
+            padding(reg_tr_src, l_pad);
+        }
+    };
+
+    auto transpose16x8 = [=](int base_idx) {
+        assert(base_idx == 0 || base_idx == 8);
+
+        // swap 1
+        for (int i = 0; i < 4; i++) {
+            int src_idx0 = base_idx + i * 2;
+            int src_idx1 = src_idx0 + 1;
+
+            int next_src_idx0 = src_idx0 + 2;
+            int next_src_idx1 = src_idx1 + 2;
+            bool load_next = base_idx == 0 || i < 3;
+
+            if (base_idx == 0 && i == 0) {
+                load(src_idx0);
+                load(src_idx1);
+            }
+
+            auto tmp0 = tmp_zmm(src_idx0);
+            auto tmp1 = tmp_zmm(src_idx1);
+            auto src0 = src_zmm(src_idx0);
+            auto src1 = src_zmm(src_idx1);
+
+            if (next_src_idx0 < nrows && load_next)
+                load(next_src_idx0);
+            valignd(tmp0, src0, src0, 0x1);
+            pf_src_t1(base_idx + i);
+
+            if (next_src_idx1 < nrows && load_next)
+                load(next_src_idx1);
+            valignd(tmp1, src1, src1, 0xf);
+            pf_src_t0(base_idx + i);
+
+            vmovaps(src0 | kAAAA, tmp1);
+            vmovaps(src1 | k5555, tmp0);
+        }
+        // swap 2
+        for (int i = 0; i < 4; i++) {
+            int select_half = (i < 2) ? 0 : 2;
+            int src_idx0 = base_idx + i + select_half + 0;
+            int src_idx2 = src_idx0 + 2;
+
+            auto tmp0 = tmp_zmm(src_idx0);
+            auto tmp1 = tmp_zmm(src_idx2);
+            auto src0 = src_zmm(src_idx0);
+            auto src2 = src_zmm(src_idx2);
+
+            valignd(tmp0, src0, src0, 0x2);
+            pf_src_t1(base_idx + 4 + i);
+            valignd(tmp1, src2, src2, 0xe);
+            pf_src_t0(base_idx + 4 + i);
+            vmovaps(src2 | k3333, tmp0);
+            vmovaps(src0 | kCCCC, tmp1);
+        }
+
+        // swap 4
+        for (int i = 0; i < 4; i++) {
+            int src_idx0 = base_idx + i;
+            int src_idx4 = src_idx0 + 4;
+
+            auto tmp0 = tmp_zmm(src_idx0);
+            auto src0 = src_zmm(src_idx0);
+            auto src4 = src_zmm(src_idx4);
+
+            vmovaps(tmp0, src0);
+            vshuff32x4(src0 | kF0F0, src4, src4, 0xb1);
+            pf_tr_src_t1(base_idx / 2 + i);
+            vshuff32x4(src4 | k0F0F, tmp0, tmp0, 0xb1);
+            pf_tr_src_t0(base_idx / 2 + i);
+        }
+    };
+
+    auto fixup16x16 = [=]() {
+        // swap 8
+        for (int i = 0; i < 8; i++) {
+            auto tmp = tmp_zmm(i);
+            auto src0 = src_zmm(i);
+            auto src8 = src_zmm(8 + i);
+            vshuff64x2(tmp, src0, src8, 0x44);
+            store(tmp, i);
+            if (i % 2 == 0) {
+                pf_tr_src_t1(8 + i / 2);
+                pf_tr_src_t0(8 + i / 2);
+            }
+        }
+
+        for (int i = 0; i < 8; i++) {
+            auto tmp = tmp_zmm(8 + i);
+            auto src0 = src_zmm(i);
+            auto src8 = src_zmm(8 + i);
+            vshuff64x2(tmp, src0, src8, 0xee);
+            store(tmp, 8 + i);
+            if (i % 2 == 0) {
+                pf_tr_src_t1(12 + i / 2);
+                pf_tr_src_t0(12 + i / 2);
+            }
+        }
+    };
+
+    transpose16x8(0);
+    transpose16x8(8);
+    fixup16x16();
+}
+
+void jit_trans_iw_ic_t::generate() {
+    preamble();
+
+    const int ic_block = conf_->ic_block;
+    const int iw = conf_->iw;
+    const int tr_iw = conf_->tr_iw;
+    const int transposes = utils::div_up(iw, transpose_size);
+    int loop_iters = nstl::max(0, transposes - 1);
+    tail = iw - loop_iters * transpose_size;
+
+    src_stride = ic_block * typesize;
+    assert(src_stride == 64);
+    tr_src_stride = tr_iw * typesize;
+
+    bool nontemporal_stores = false;
+    enable_prefetch = iw > small_spatial ? 1 : 0;
+
+    assert(transpose_size == ic_block);
+    const int src_step = ic_block * transpose_size * typesize;
+    const int tr_src_step = ic_block * typesize;
+
+    const int left_pad = conf_->l_pad;
+    const int right_pad = tr_iw - iw - left_pad;
+
+    mov(reg_src, ptr [param1 + GET_OFF(src)]);
+    mov(reg_tr_src, ptr [param1 + GET_OFF(tr_src)]);
+    mov(reg_src_prf, ptr [param1 + GET_OFF(src_prf)]);
+    mov(reg_tr_src_prf, ptr [param1 + GET_OFF(tr_src_prf)]);
+
+    auto kmovw = [=](Opmask k, unsigned w) {
+        mov(regw_tmp, w);
+        jit_generator::kmovw(k, regw_tmp);
+    };
+
+    kmovw(k3333, 0x3333); // 0011001100110011
+    kmovw(k5555, 0x5555); // 0101010101010101
+    kmovw(kAAAA, 0xaaaa); // 1010101010101010
+    kmovw(kCCCC, 0xcccc); // 1100110011001100
+    kmovw(k0F0F, 0x0f0f); // 0000111100001111
+    kmovw(kF0F0, 0xf0f0); // 1111000011110000
+
+    if (left_pad > 0 && loop_iters > 0) {
+        loop_iters--;
+        transpose(transpose_size, left_pad, 0, nontemporal_stores);
+        add(reg_src, src_step);
+        add(reg_tr_src, tr_src_step + left_pad * typesize);
+        add(reg_src_prf, src_step);
+        add(reg_tr_src_prf, tr_src_step + left_pad * typesize);
+    }
+
+    if (loop_iters) {
+        mov(reg_loop, loop_iters);
+        Label loop;
+        L(loop); {
+            transpose(transpose_size, 0, 0, nontemporal_stores);
+            add(reg_src, src_step);
+            add(reg_tr_src, tr_src_step);
+            add(reg_src_prf, src_step);
+            add(reg_tr_src_prf, tr_src_step);
+            sub(reg_loop, 1);
+            jnz(loop);
+        }
+    }
+    if (transposes > 1)
+        transpose(tail, 0, right_pad, nontemporal_stores);
+    else
+        transpose(tail, left_pad, right_pad, nontemporal_stores);
+
+    postamble();
+}
+
+struct jit_trans_iw_ic_int16_t: public jit_trans_src_t, public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_trans_iw_ic_int16_t)
+    jit_trans_iw_ic_int16_t(const jit_conv_conf_t *conf):
+        jit_trans_src_t(conf) {
+        generate();
+        ker_ = (decltype(ker_))this->getCode();
+    }
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    using reg32_t = const Xbyak::Reg32;
+    using opmask_t = const Xbyak::Opmask;
+
+    enum { typesize = sizeof(int16_t), transpose_size = 16, small_spatial = 14 };
+    int src_stride, tr_src_stride;
+    int tail;
+    bool enable_prefetch;
+
+    opmask_t kFFFF = k1;
+    opmask_t k5555 = k2;
+    opmask_t kAAAA = k3;
+    opmask_t kAA = k4;
+    opmask_t k55 = k5;
+    opmask_t kCC = k6;
+    opmask_t k33 = k7;
+    opmask_t kTail = k1;
+
+    reg64_t reg_src = r8;
+    reg64_t reg_tr_src = r9;
+    reg64_t reg_src_prf = r10;
+    reg64_t reg_tr_src_prf = r11;
+    reg64_t reg_loop = r12;
+    reg64_t reg_tr_src_tmp = r13;
+    reg32_t regw_tmp = r14d;
+    reg64_t imm_addr64 = rbx;
+
+    Xbyak::Zmm vidx1 = zmm31;
+    Xbyak::Zmm vidx2 = zmm30;
+    Xbyak::Zmm vidx3 = zmm29;
+    Xbyak::Zmm vidx4 = zmm28;
+    Xbyak::Zmm vidx5 = zmm27;
+    Xbyak::Zmm zmm_tmp  = zmm26;
+
+
+    void transpose(int nrows, int l_pad, int r_pad, bool nontemporal_stores);
+    void generate();
+};
+
+void jit_trans_iw_ic_int16_t::transpose(int nrows, int l_pad, int r_pad,
+    bool nontemporal_stores) {
+    assert(nrows >= 0 && nrows <= transpose_size);
+    static_assert(transpose_size == 16, "Unsupported transpose size");
+    if (!nrows)
+        return;
+
+    auto src_zmm = [=](int i) {
+        return Zmm(i);
+    };
+
+    auto src_ymm = [=](int i) {
+        assert(i >= 0 && i < 16);
+        return Ymm(i);
+    };
+
+    auto load_ymm = [=](int i) {
+        vmovups(src_ymm(i), EVEX_compress_addr(reg_src, i * src_stride));
+    };
+
+    auto kmovw = [=](Opmask k, unsigned w) {
+        mov(regw_tmp, w);
+        jit_generator::kmovw(k, regw_tmp);
+    };
+
+    auto store = [=](Zmm r, int i) {
+
+        auto padding = [=] (Reg64 reg, int pad) {
+            kmovw(kTail, (1 << pad) - 1);
+            auto k = kTail;
+            auto base = reg;
+            base.setOpmaskIdx(k.getIdx(), true);
+
+            auto zmm_zero = zmm_tmp;
+            vpxord(zmm_zero, zmm_zero, zmm_zero);
+            auto addr = EVEX_compress_addr(base, i * tr_src_stride);
+            vmovups(addr, zmm_zero);
+        };
+
+        int store_tail = (nrows%2) ? nrows+1 : nrows;
+
+        int store_pad = (l_pad%2) ? l_pad/2 + 1 : l_pad/2;
+        mov(reg_tr_src_tmp, reg_tr_src);
+        if (l_pad > 0) {
+            padding(reg_tr_src, store_pad);
+            add(reg_tr_src_tmp, l_pad * typesize);
+        }
+        if (r_pad > 0) {
+            store_pad = (r_pad%2) ? r_pad/2 + 1 : r_pad/2;
+            int addr_shift = (r_pad%2) ? 1 : 0;
+            add(reg_tr_src_tmp, (nrows - addr_shift) * typesize);
+            padding(reg_tr_src_tmp, store_pad);
+        }
+
+        mov(reg_tr_src_tmp, reg_tr_src);
+        add(reg_tr_src_tmp, l_pad * typesize);
+
+        kmovw(kTail, (1 << store_tail/2) - 1);
+        auto k = kTail;
+        auto base = reg_tr_src_tmp;
+        base.setOpmaskIdx(k.getIdx(), true);
+
+        auto addr = EVEX_compress_addr(base, i * tr_src_stride);
+        vmovups(addr, r);
+
+    };
+
+    kmovw(kFFFF, 0xffff);
+    //all loads
+    for (int i=0; i<16; i++){
+        vpxord(src_zmm(i), src_zmm(i), src_zmm(i));
+    }
+
+    for (int i = 0; i < nrows/2; i++) {
+        auto src0 = src_ymm(2*i);
+        auto src1 = src_ymm(2*i+1);
+        auto zmm_src0 = src_zmm(2*i);
+        load_ymm(2*i);
+
+        vpunpcklwd(src1, src0,
+            EVEX_compress_addr(reg_src, (2*i+1) * src_stride));
+        vpunpckhwd(src0, src0,
+            EVEX_compress_addr(reg_src, (2*i+1) * src_stride));
+        vinserti64x4(zmm_src0, zmm_src0, src1, 1);
+        vpermps(zmm_src0 | kFFFF, vidx4, zmm_src0);
+    }
+
+    // for odd numbers we need to mix row with zeroes
+    if (nrows%2) {
+        int i = nrows-1;
+        auto src0 = src_ymm(i);
+        auto src1 = src_ymm(i+1); //zero
+
+        auto zmm_src0 = src_zmm(i);
+        vpxor(src1, src1, src1);
+
+        load_ymm(i);
+        vpunpckhwd(src0, src0, src1);
+        vinserti64x4(zmm_tmp, zmm_tmp, src0, 0);
+        vpxor(src0, src0, src0);
+        load_ymm(i);
+        vpunpcklwd(src1, src0, src1);
+        vinserti64x4(zmm_tmp, zmm_tmp, src1, 1);
+        vpxord(zmm_src0, zmm_src0, zmm_src0);
+        vmovups(zmm_src0, zmm_tmp);
+        vpermps(zmm_src0 | kFFFF, vidx4, zmm_src0);
+    }
+
+    // swap 1
+    for (int i=0; i<4; i++) {
+        auto zmm0 = src_zmm(4*i);
+        auto zmm1 = src_zmm(4*i+2);
+        auto tmp0 = src_zmm(4*i+1);
+        auto tmp1 = src_zmm(4*i+3);
+
+        vmovups(tmp0, zmm0);
+        vmovups(tmp1, zmm1);
+
+        vpermps(tmp0 | kAAAA, vidx3, zmm1);
+        vpermps(tmp1 | k5555, vidx3, zmm0);
+    }
+    // swap 2
+    int base_idx;
+    base_idx=0;
+    for (int i=0; i<2; i++) {
+        auto zmm0 = src_zmm(base_idx+2*i+1);
+        auto zmm1 = src_zmm(base_idx+2*i+5);
+
+        auto tmp0 = src_zmm(base_idx+2*i);
+        auto tmp1 = src_zmm(base_idx+2*i+4);
+
+        vmovupd(tmp0, zmm0);
+        vmovupd(tmp1, zmm1);
+
+        vpermpd(tmp0 | kAA, vidx2, zmm1);
+        vpermpd(tmp1 | k55, vidx2, zmm0);
+    }
+    base_idx=8;
+    for (int i=0; i<2; i++) {
+        auto zmm0 = src_zmm(base_idx+2*i+1);
+        auto zmm1 = src_zmm(base_idx+2*i+5);
+
+        auto tmp0 = src_zmm(base_idx+2*i);
+        auto tmp1 = src_zmm(base_idx+2*i+4);
+
+        vmovupd(tmp0, zmm0);
+        vmovupd(tmp1, zmm1);
+
+        vpermpd(tmp0 | kAA, vidx2, zmm1);
+        vpermpd(tmp1 | k55, vidx2, zmm0);
+    }
+
+    // swap 3
+    for (int i=0; i<4; i++) {
+        auto zmm0 = src_zmm(2*i);
+        auto zmm1 = src_zmm(2*i+8);
+
+        auto tmp0 = src_zmm(2*i+1);
+        auto tmp1 = src_zmm(2*i+9);
+
+        vmovupd(tmp0, zmm0);
+        vmovupd(tmp1, zmm1);
+
+        vpermpd(tmp0 | kCC, vidx1, zmm1);
+        vpermpd(tmp1 | k33, vidx1, zmm0);
+    }
+
+    // all stores
+    for (int i=0; i<8; i++)
+        vextracti64x4(src_ymm(2*i), src_zmm(2*i+1), 1);
+
+    store(src_zmm(1), 0);
+    store(src_zmm(0), 1);
+    store(src_zmm(3), 2);
+    store(src_zmm(2), 3);
+    store(src_zmm(9), 4);
+    store(src_zmm(8), 5);
+    store(src_zmm(11), 6);
+    store(src_zmm(10), 7);
+    store(src_zmm(5), 8);
+    store(src_zmm(4), 9);
+    store(src_zmm(7), 10);
+    store(src_zmm(6), 11);
+    store(src_zmm(13), 12);
+    store(src_zmm(12), 13);
+    store(src_zmm(15), 14);
+    store(src_zmm(14), 15);
+
+}
+
+void jit_trans_iw_ic_int16_t::generate() {
+    preamble();
+
+    alignas(64) static constexpr const int64_t idx1[8]
+        = { 2, 3, 0, 1, 6, 7, 4, 5 };
+    alignas(64) static constexpr const int64_t idx2[8]
+        = { 1, 0, 3, 2, 5, 4, 7, 6 };
+    alignas(64) static constexpr const int32_t idx3[16]
+        = { 1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10, 13, 12, 15, 14 };
+    alignas(64) static constexpr const int32_t idx4[16]
+        = { 8, 10, 12, 14, 0, 2, 4, 6, 9, 11, 13, 15, 1, 3, 5, 7 };
+    alignas(64) static constexpr const int32_t idx5[16]
+        = { 8, 10, 12, 14, 0, 2, 4, 6, 9, 11, 13, 15, 1, 3, 5, 7 };
+
+    const int ic_block = conf_->ic_block;
+    const int iw = conf_->iw;
+    const int tr_iw = conf_->tr_iw;
+    const int transposes = utils::div_up(iw, transpose_size);
+    int loop_iters = nstl::max(0, transposes - 1);
+    tail = iw - loop_iters * transpose_size;
+
+    src_stride = ic_block * typesize;
+    tr_src_stride = tr_iw * typesize;
+
+    bool nontemporal_stores = false;
+    enable_prefetch = iw > small_spatial ? 1 : 0;
+
+    assert(transpose_size == ic_block);
+    const int src_step = ic_block * transpose_size * typesize;
+    const int tr_src_step = ic_block * typesize;
+
+    const int left_pad = conf_->l_pad;
+    const int right_pad = tr_iw - iw - left_pad;
+
+    mov(reg_src, ptr [param1 + GET_OFF(src)]);
+    mov(reg_tr_src, ptr [param1 + GET_OFF(tr_src)]);
+    mov(reg_src_prf, ptr [param1 + GET_OFF(src_prf)]);
+    mov(reg_tr_src_prf, ptr [param1 + GET_OFF(tr_src_prf)]);
+
+    auto kmovw = [=](Opmask k, unsigned w) {
+        mov(regw_tmp, w);
+        jit_generator::kmovw(k, regw_tmp);
+    };
+
+    kmovw(kFFFF, 0xffff);
+    kmovw(k5555, 0x5555);
+    kmovw(kAAAA, 0xaaaa);
+    kmovw(kAA, 0xaa);
+    kmovw(k55, 0x55);
+    kmovw(kCC, 0xcc);
+    kmovw(k33, 0x33);
+
+    auto vmovdqa64 = [=](Zmm z, const int64_t *addr) {
+        mov(imm_addr64, reinterpret_cast<size_t>(addr));
+        jit_generator::vmovdqa64(z, ptr[imm_addr64]);
+    };
+
+    auto vmovdqa32 = [=](Zmm z, const int32_t *addr) {
+        mov(imm_addr64, reinterpret_cast<size_t>(addr));
+        jit_generator::vmovdqa32(z, ptr[imm_addr64]);
+    };
+
+    vmovdqa64(vidx1, idx1);
+    vmovdqa64(vidx2, idx2);
+    vmovdqa32(vidx3, idx3);
+    vmovdqa32(vidx4, idx4);
+    vmovdqa32(vidx5, idx5);
+
+    if (left_pad > 0 && loop_iters > 0) {
+        loop_iters--;
+        transpose(transpose_size, left_pad, 0, nontemporal_stores);
+        add(reg_src, src_step);
+        add(reg_tr_src, tr_src_step + left_pad * typesize);
+        add(reg_src_prf, src_step);
+        add(reg_tr_src_prf, tr_src_step + left_pad * typesize);
+    }
+
+    if (loop_iters) {
+        mov(reg_loop, loop_iters);
+        Label loop;
+        L(loop); {
+            transpose(transpose_size, 0, 0, nontemporal_stores);
+            add(reg_src, src_step);
+            add(reg_tr_src, tr_src_step);
+            add(reg_src_prf, src_step);
+            add(reg_tr_src_prf, tr_src_step);
+            sub(reg_loop, 1);
+            jnz(loop);
+        }
+    }
+    if (transposes > 1)
+        transpose(tail, 0, right_pad, nontemporal_stores);
+    else
+        transpose(tail, left_pad, right_pad, nontemporal_stores);
+
+    postamble();
+
+}
+
+struct jit_trans_ow_oc_t: public jit_trans_dst_t, public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_trans_ow_oc_t)
+    jit_trans_ow_oc_t(const jit_conv_conf_t *conf): jit_trans_dst_t(conf) {
+        generate();
+        ker_ = (decltype(ker_))this->getCode();
+    }
+
+private:
+    using reg64_t = const Xbyak::Reg64;
+    using reg32_t = const Xbyak::Reg32;
+    using opmask_t = const Xbyak::Opmask;
+    using zmm = const Xbyak::Zmm;
+
+    enum { typesize = sizeof(int16_t), transpose_size = 16, small_spatial = 14 };
+    int src_stride, tr_src_stride;
+    int tail;
+    bool enable_prefetch;
+
+    opmask_t kFF = k1;
+
+    zmm vidx1 = zmm31;
+
+    reg64_t reg_src = r8;
+    reg64_t reg_tr_src = r9;
+    reg64_t reg_src_prf = r10;
+    reg64_t reg_tr_src_prf = r11;
+    reg64_t reg_loop = r12;
+    reg64_t reg_tr_src_tmp = r13;
+    reg32_t regw_tmp = r14d;
+    reg64_t imm_addr64 = rbx;
+
+    void transpose(int nrows, int l_pad, int r_pad, bool nontemporal_stores);
+    void generate();
+};
+
+void jit_trans_ow_oc_t::transpose(int nrows, int l_pad, int r_pad,
+    bool nontemporal_stores) {
+    assert(nrows >= 0 && nrows <= transpose_size);
+    static_assert(transpose_size == 16, "Unsupported transpose size");
+    if (!nrows)
+        return;
+
+    auto src_zmm = [=](int i) {
+        return Zmm(i);
+    };
+
+    auto src_ymm = [=](int i) {
+        assert(i >= 0 && i < 16);
+        return Ymm(i);
+    };
+
+    auto load_ymm = [=](int i) {
+        vmovups(src_ymm(i), EVEX_compress_addr(reg_src, i * src_stride));
+    };
+
+
+    auto store = [=](Zmm r, int i) {
+        auto addr = EVEX_compress_addr(reg_tr_src, i * tr_src_stride);
+        if (nontemporal_stores)
+            vmovntps(addr, r);
+        else
+            vmovups(addr, r);
+    };
+
+    for (int i = 0; i < nrows/2; i++) {
+        auto src0 = src_ymm(2*i);
+        auto src1 = src_ymm(2*i+1);
+        auto zmm_src0 = src_zmm(2*i);
+        load_ymm(2*i);
+        vpunpcklwd(src1, src0,
+            EVEX_compress_addr(reg_src, (2*i+1) * src_stride));
+        vpunpckhwd(src0, src0,
+            EVEX_compress_addr(reg_src, (2*i+1) * src_stride));
+        vinserti64x4(zmm_src0, zmm_src0, src1, 1);
+        vpermpd(zmm_src0 | kFF, vidx1, zmm_src0);
+        store(zmm_src0, 2*i);
+    }
+    if (r_pad > 0) {
+        auto src0 = src_ymm(nrows-1);
+        auto src1 = src_ymm(nrows);
+        auto zmm_src0 = src_zmm(30);
+        load_ymm(nrows-1);
+
+        vpxor(src1, src1, src1);
+        vpunpckhwd(src1, src0, src1);
+        vinserti64x4(zmm_src0, zmm_src0, src1, 0);
+        vpxor(src1, src1, src1);
+        vpunpcklwd(src0, src0, src1);
+        vinserti64x4(zmm_src0, zmm_src0, src0, 1);
+        vpermpd(zmm_src0 | kFF, vidx1, zmm_src0);
+        store(zmm_src0, nrows-1);
+    }
+}
+
+void jit_trans_ow_oc_t::generate() {
+    preamble();
+
+    alignas(64) static constexpr const int64_t idx1[8]
+          = { 4, 5, 0, 1, 6, 7, 2, 3 };
+
+    const int oc_block = conf_->oc_block;
+    const int ow = conf_->ow;
+    const int transposes = utils::div_up(ow, transpose_size);
+    int loop_iters = nstl::max(0, transposes - 1);
+    tail = ow - loop_iters * transpose_size;
+
+    src_stride = oc_block * typesize;
+    tr_src_stride = oc_block * typesize;
+
+    bool nontemporal_stores = false;
+    enable_prefetch = ow > small_spatial ? 1 : 0;
+
+    const int src_step = oc_block * transpose_size * typesize;
+    const int tr_src_step = oc_block * transpose_size * typesize;
+    const int right_pad = ow % 2;
+
+    mov(reg_src, ptr [param1 + GET_OFF(src)]);
+    mov(reg_tr_src, ptr [param1 + GET_OFF(tr_src)]);
+    mov(reg_src_prf, ptr [param1 + GET_OFF(src_prf)]);
+    mov(reg_tr_src_prf, ptr [param1 + GET_OFF(tr_src_prf)]);
+
+    auto kmovw = [=](Opmask k, unsigned w) {
+        mov(regw_tmp, w);
+        jit_generator::kmovw(k, regw_tmp);
+    };
+
+    kmovw(kFF, 0xFF);
+
+    auto vmovdqa64 = [=](Zmm z, const int64_t *addr) {
+        mov(imm_addr64, reinterpret_cast<size_t>(addr));
+        jit_generator::vmovdqa64(z, ptr[imm_addr64]);
+    };
+
+    vmovdqa64(vidx1, idx1);
+    if (loop_iters) {
+        mov(reg_loop, loop_iters);
+        Label loop;
+        L(loop); {
+            transpose(transpose_size, 0, 0, nontemporal_stores);
+            add(reg_src, src_step);
+            add(reg_tr_src, tr_src_step);
+            add(reg_src_prf, src_step);
+            add(reg_tr_src_prf, tr_src_step);
+            sub(reg_loop, 1);
+            jnz(loop);
+        }
+    }
+    transpose(tail, 0, right_pad, nontemporal_stores);
+
+    postamble();
+}
+
+struct jit_trans_iw_x4_4x_t: public jit_trans_src_t, public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_trans_iw_x4_4x_t)
+
+    jit_trans_iw_x4_4x_t(const jit_conv_conf_t *conf): jit_trans_src_t(conf) {
+        generate();
+        ker_ = (decltype(ker_))this->getCode();
+    }
+
+    void generate();
+    enum { typesize = (int)sizeof(float) };
+};
+
+/** @brief transposition of the form [:][iw/4][4] -> [:][4][iw/4]
+ * required for 1st 4fma backward by weights convolution */
+void jit_trans_iw_x4_4x_t::generate() {
+    using namespace utils;
+
+    /* TODO: put into code */
+    static int mask[16] = {
+        0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15, };
+
+    const auto &c = *conf_;
+    const int simd_w = cpu_isa_traits<avx512_common>::vlen / typesize;
+    const int niters = c.tr_ld / simd_w;
+
+    assert(niters <= 4); /* [bwd_w:tr_src:r1] */
+
+    Reg64 reg_ptr_src = r8;
+    Reg64 reg_ptr_tr_src = r9;
+
+    Reg64 reg_ih = rax;
+    Reg64 reg_ih_end = rbx;
+
+    Reg64 reg_nthr_oc_b = rsi;
+    Reg64 reg_ptr_tr_src_bctx = abi_not_param1;
+
+    Reg64 reg_tmp = rdx;
+
+    Zmm vmsk = Zmm(31);
+    Opmask kmsk = k7;
+
+    auto emit_tr_sync = [&]() {
+        simple_barrier::generate(*this, reg_ptr_tr_src_bctx, reg_nthr_oc_b);
+    };
+
+    auto emit_tr_iw = [&]() {
+        auto vreg = [](int iter, int i) {
+            assert(4 * iter + i < 24);
+            return Zmm(4 * iter + i);
+        };
+        auto vtmp = [](int i) { return Zmm(24 + i); };
+
+        auto emit_load = [&](int iter) {
+            for (int i = 0; i < 4; ++i) {
+                auto v = vreg(iter, i);
+                const int off = (iter * 4 + i) * simd_w;
+
+                if (off + simd_w <= c.iw)
+                    vmovups(v, ptr[reg_ptr_src + off * typesize]);
+                else if (off < c.iw)
+                    vmovups(v | kmsk | T_z, ptr[reg_ptr_src + off * typesize]);
+                else
+                    vpxord(v, v, v);
+            }
+        };
+
+        auto emit_tr = [&](int iter) {
+            for (int i = 0; i < 4; ++i)
+                vpermps(vreg(iter, i), vmsk, vreg(iter, i));
+
+            vshuff32x4(vtmp(0), vreg(iter, 0), vreg(iter, 1), 0x88);
+            vshuff32x4(vtmp(1), vreg(iter, 0), vreg(iter, 1), 0xdd);
+            vshuff32x4(vtmp(2), vreg(iter, 2), vreg(iter, 3), 0x88);
+            vshuff32x4(vtmp(3), vreg(iter, 2), vreg(iter, 3), 0xdd);
+
+            vshuff32x4(vreg(iter, 0), vtmp(0), vtmp(2), 0x88);
+            vshuff32x4(vreg(iter, 2), vtmp(0), vtmp(2), 0xdd);
+            vshuff32x4(vreg(iter, 1), vtmp(1), vtmp(3), 0x88);
+            vshuff32x4(vreg(iter, 3), vtmp(1), vtmp(3), 0xdd);
+        };
+
+        auto emit_store = [&]() {
+            for (int i = 0; i < 4; ++i) {
+                for (int iter = 0; iter < niters; ++iter) {
+                    const size_t off = i * c.tr_ld + iter * simd_w;
+                    vmovups(ptr[reg_ptr_tr_src + off * typesize], vreg(iter, i));
+                }
+            }
+        };
+
+        for (int iter = 0; iter < niters; ++iter)
+            emit_load(iter);
+
+        for (int iter = 0; iter < niters; ++iter)
+            emit_tr(iter);
+
+        emit_store();
+    };
+
+    preamble();
+
+    mov(reg_ptr_src, ptr[abi_param1 + GET_OFF(src)]);
+    mov(reg_ptr_tr_src, ptr[abi_param1 + GET_OFF(tr_src)]);
+
+    mov(reg_nthr_oc_b.cvt32(), ptr[abi_param1 + GET_OFF(nthr_oc_b)]);
+    mov(reg_ih.cvt32(), ptr[abi_param1 + GET_OFF(tr_src_ih_start)]);
+    mov(reg_ih_end.cvt32(), ptr[abi_param1 + GET_OFF(tr_src_ih_end)]);
+    mov(reg_ptr_tr_src_bctx, ptr[abi_param1 + GET_OFF(tr_src_bctx)]);
+
+    emit_tr_sync();
+
+    Label l_ih_loop, l_tr_done;
+    cmp(reg_ih, reg_ih_end);
+    je(l_tr_done, T_NEAR);
+
+    mov(reg_tmp, (size_t)&mask[0]);
+    vmovups(vmsk, ptr[reg_tmp]);
+
+    if (c.iw % simd_w) {
+        const char load_mask = (1 << (c.iw % simd_w)) - 1;
+        mov(reg_tmp, load_mask);
+        kmovw(kmsk, reg_tmp.cvt32());
+    }
+
+    /* src += ih_start * c.iw; */
+    imul(reg_tmp, reg_ih, c.iw * typesize);
+    add(reg_ptr_src, reg_tmp);
+    /* tr_src += ih_start * c.stride_w * c.tr_ld; */
+    imul(reg_tmp, reg_ih, c.stride_w * c.tr_ld * typesize);
+    add(reg_ptr_tr_src, reg_tmp);
+
+    L(l_ih_loop); {
+        emit_tr_iw();
+
+        add(reg_ptr_src, c.iw * typesize);
+        add(reg_ptr_tr_src, c.stride_w * c.tr_ld * typesize);
+
+        inc(reg_ih);
+        cmp(reg_ih, reg_ih_end);
+        jl(l_ih_loop, T_NEAR);
+    }
+
+    L(l_tr_done);
+
+    emit_tr_sync();
+
+    postamble();
+}
+
+/*
+// -------------------------------------------------
+// jit_transpose4x16_src
+// -------------------------------------------------
+*/
+
+void jit_transpose4x16_src::transpose(int nrows)
+{
+    assert(nrows >= 0 && nrows <= transpose_size);
+    static_assert(transpose_size == 4, "Unsupported transpose size");
+    if (!nrows)
+        return;
+
+    auto pf_src_t0 = [=](int i) {
+        if (tparams->src_pf0_distance)
+            prefetcht0(EVEX_compress_addr(
+                    reg_src, (tparams->src_pf0_distance + i) * src_stride));
+    };
+
+    auto pf_tr_src_t0 = [=](int i) {
+        if (tparams->tr_src_pf0_distance)
+            prefetcht0(EVEX_compress_addr(reg_tr_src,
+                    (tparams->tr_src_pf0_distance + i) * src_stride));
+    };
+
+    auto pf_src_t1 = [=](int i) {
+        if (tparams->src_pf1)
+            prefetcht1(EVEX_compress_addr(reg_src_prf, i * src_stride));
+    };
+
+    auto pf_tr_src_t1 = [=](int i) {
+        if (tparams->tr_src_pf1)
+            prefetchwt1(EVEX_compress_addr(reg_tr_src_prf, i * tr_src_stride));
+    };
+
+    auto src_zmm = [=](int i) {
+        assert(i >= 0 && i < 4);
+        return Zmm(i);
+    };
+
+    auto tmp_zmm = [=](int i) {
+        assert(i >= 0 && i < 4);
+        return Zmm(4 + i);
+    };
+
+    auto load = [=](int i) {
+        vmovups(src_zmm(i), EVEX_compress_addr(reg_src, i * src_stride));
+    };
+
+    auto store = [=](Zmm r, int i) {
+        vmovups(EVEX_compress_addr(reg_tr_src, i * tr_src_stride), r);
+    };
+
+    auto tmp0 = tmp_zmm(0);
+    auto tmp1 = tmp_zmm(1);
+    auto tmp2 = tmp_zmm(2);
+    auto tmp3 = tmp_zmm(3);
+
+    auto src0 = src_zmm(0);
+    auto src1 = src_zmm(1);
+    auto src2 = src_zmm(2);
+    auto src3 = src_zmm(3);
+    for (int i = 0; i < nrows; i++) {
+        load(i);
+    }
+
+    for (size_t i = nrows; i < 4; i++) {
+        vpxord(src_zmm(i), src_zmm(i), src_zmm(i));
+    }
+
+    vmovupd(tmp0, src0);
+    vmovupd(tmp1, src1);
+    pf_src_t0(0);
+    vpermpd(tmp0 | kF0, vidx01, src2);
+    vpermpd(tmp1 | kF0, vidx01, src3);
+
+    valignd(src0, src0, src0, 8);
+    valignd(src1, src1, src1, 8);
+    pf_src_t0(1);
+    vmovupd(tmp2, src0);
+    vmovupd(tmp3, src1);
+    pf_src_t0(2);
+    vpermpd(tmp2 | kF0, vidx10, src2);
+    vpermpd(tmp3 | kF0, vidx10, src3);
+    pf_src_t0(3);
+
+    vmovupd(src0, tmp0);
+    pf_src_t1(0);
+    vmovupd(src1, tmp2);
+    pf_src_t1(1);
+    vmovupd(src2, tmp1);
+    pf_src_t1(2);
+    vmovupd(src3, tmp3);
+    pf_src_t1(3);
+    vpermpd(src0 | kCC, vidx1, tmp1);
+    vpermpd(src1 | kCC, vidx1, tmp3);
+    pf_tr_src_t0(0);
+    vpermpd(src2 | k33, vidx1, tmp0);
+    vpermpd(src3 | k33, vidx1, tmp2);
+    pf_tr_src_t0(1);
+
+    vmovupd(tmp0, src0);
+    vmovupd(tmp1, src2);
+    pf_tr_src_t0(2);
+    vmovupd(tmp2, src1);
+    vmovupd(tmp3, src3);
+    pf_tr_src_t0(3);
+    vpermps(tmp0 | kFFFF, vidxP, src0);
+    pf_tr_src_t1(0);
+    vpermps(tmp1 | kFFFF, vidxP, src2);
+    pf_tr_src_t1(1);
+    vpermps(tmp2 | kFFFF, vidxP, src1);
+    pf_tr_src_t1(3);
+    vpermps(tmp3 | kFFFF, vidxP, src3);
+    pf_tr_src_t1(4);
+
+    store(tmp0, 0);
+    store(tmp1, 1);
+    store(tmp2, 2);
+    store(tmp3, 3);
+}
+
+alignas(64) static constexpr const int64_t idx01[8]
+        = { 0, 0, 0, 0, 0, 1, 2, 3 };
+alignas(64) static constexpr const int64_t idx10[8]
+        = { 0, 0, 0, 0, 4, 5, 6, 7 };
+alignas(64) static constexpr const int64_t idx1[8] = { 2, 3, 0, 1, 6, 7, 4, 5 };
+alignas(64) static constexpr const int32_t idxP[16]
+        = { 0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15 };
+
+void jit_transpose4x16_src::generate()
+{
+    preamble();
+
+    const int ic_block = params->ic_block;
+    const int is = params->is;
+    int tail = is % transpose_size;
+
+    src_stride = ic_block * typesize;
+    assert(src_stride == 64);
+    tr_src_stride = ic_block * typesize;
+
+    const int src_step = ic_block * transpose_size * typesize;
+    const int tr_src_step = ic_block * transpose_size * typesize;
+
+#define GET_TR_OFF(x) offsetof(jit_src_transpose_s, x)
+    mov(reg_loop, ptr[param1 + GET_TR_OFF(size)]);
+    mov(reg_src, ptr[param1 + GET_TR_OFF(src)]);
+    mov(reg_tr_src, ptr[param1 + GET_TR_OFF(tr_src)]);
+    mov(reg_src_prf, ptr[param1 + GET_TR_OFF(src_prf)]);
+    mov(reg_tr_src_prf, ptr[param1 + GET_TR_OFF(tr_src_prf)]);
+#undef GET_TR_OFF
+
+    auto kmovw = [=](Opmask k, unsigned w) {
+        mov(regw_tmp, w);
+        jit_generator::kmovw(k, regw_tmp);
+    };
+
+    auto vmovdqa64 = [=](Zmm z, const int64_t *addr) {
+        mov(imm_addr64, reinterpret_cast<size_t>(addr));
+        jit_generator::vmovdqa64(z, ptr[imm_addr64]);
+    };
+
+    auto vmovdqa32 = [=](Zmm z, const int32_t *addr) {
+        mov(imm_addr64, reinterpret_cast<size_t>(addr));
+        jit_generator::vmovdqa32(z, ptr[imm_addr64]);
+    };
+
+    kmovw(kF0, 0xf0); // 11110000
+    kmovw(kCC, 0xcc); // 11001100
+    kmovw(k33, 0x33); // 00110011
+    kmovw(kFFFF, 0xffff); // 1111111111111111
+
+    vmovdqa64(vidx01, idx01);
+    vmovdqa64(vidx10, idx10);
+    vmovdqa64(vidx1, idx1);
+    vmovdqa32(vidxP, idxP);
+
+    Label loop_label;
+    Label tail_label;
+
+    cmp(reg_loop, transpose_size);
+    jl(tail_label, T_NEAR);
+
+    L(loop_label);
+    {
+        transpose(transpose_size);
+        add(reg_src, src_step);
+        add(reg_tr_src, tr_src_step);
+        add(reg_src_prf, src_step);
+        add(reg_tr_src_prf, tr_src_step);
+        sub(reg_loop, transpose_size);
+        cmp(reg_loop, transpose_size);
+        jge(loop_label, T_NEAR);
+    }
+    L(tail_label);
+    transpose(tail);
+
+    postamble();
+}
+
+jit_trans_src_t *create_trans_src(const jit_conv_conf_t *conf) {
+    if (conf->ver == ver_4fma && !conf->is_1stconv)
+        return new jit_trans_iw_ic_t(conf);
+    if (conf->ver == ver_4fma && conf->is_1stconv)
+        return new jit_trans_iw_x4_4x_t(conf);
+    assert(!"unsupported configuration");
+    return nullptr;
+}
+
+jit_trans_dst_t *create_trans_dst(const jit_conv_conf_t *conf) {
+    assert(!"unsupported configuration");
+    return nullptr;
+}
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_transpose_src_utils.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_transpose_src_utils.hpp
new file mode 100644
index 0000000000..565e97e4fc
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_transpose_src_utils.hpp
@@ -0,0 +1,145 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_TRANSPOSE_SRC_HPP
+#define CPU_JIT_TRANSPOSE_SRC_HPP
+
+#include "cpu_barrier.hpp"
+#include "jit_primitive_conf.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_trans_src_t {
+    struct ctx_t {
+        const void *src;
+        const void *tr_src;
+        const void *src_prf;
+        const void *tr_src_prf;
+
+        /* 1st conv 4fma: backward by weights */
+        int nthr_oc_b; /* number of threads process given src image */
+        int tr_src_ih_start, tr_src_ih_end; /* thread's transposition bounds */
+        simple_barrier::ctx_t *tr_src_bctx; /* transposition synchronization */
+    };
+
+    jit_trans_src_t(const jit_conv_conf_t *conf)
+        : conf_(conf), ker_(nullptr) {}
+    virtual ~jit_trans_src_t() {}
+
+    void operator()(const ctx_t *ctx)
+    { assert(ker_); ker_(ctx); }
+
+    const jit_conv_conf_t *conf_;
+    void (*ker_)(const ctx_t *);
+};
+
+struct jit_src_transpose_s {
+    size_t size;
+    const void *src;
+    const void *tr_src;
+    const void *src_prf;
+    const void *tr_src_prf;
+};
+
+struct jit_trans_dst_t {
+    struct ctx_t {
+        const void *src;
+        const void *tr_src;
+        const void *src_prf;
+        const void *tr_src_prf;
+
+        /* 1st conv 4fma: backward by weights */
+        int nthr_oc_b; /* number of threads process given src image */
+        int tr_src_ih_start, tr_src_ih_end; /* thread's transposition bounds */
+        simple_barrier::ctx_t *tr_src_bctx; /* transposition synchronization */
+    };
+
+    jit_trans_dst_t(const jit_conv_conf_t *conf)
+        : conf_(conf), ker_(nullptr) {}
+    virtual ~jit_trans_dst_t() {}
+
+    void operator()(const ctx_t *ctx)
+    { assert(ker_); ker_(ctx); }
+
+    const jit_conv_conf_t *conf_;
+    void (*ker_)(const ctx_t *);
+};
+
+struct jit_transpose4x16_src_t {
+    int src_pf0_distance;
+    int tr_src_pf0_distance;
+    bool src_pf1;
+    bool tr_src_pf1;
+};
+
+struct jit_transpose4x16_src : public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_transpose4x16_src)
+
+    jit_transpose4x16_src(const jit_1x1_conv_conf_t *aparams,
+            jit_transpose4x16_src_t *tparams_)
+        : params(aparams), tparams(tparams_)
+    {
+        this->generate();
+        jit_ker = (decltype(jit_ker))this->getCode();
+    }
+
+    const jit_1x1_conv_conf_t *params;
+    const jit_transpose4x16_src_t *tparams;
+    void (*jit_ker)(jit_src_transpose_s *);
+
+    void operator()(jit_src_transpose_s *arg) { jit_ker(arg); }
+
+    static const int transpose_size = 4;
+private:
+    static const int typesize = sizeof(float);
+
+    int src_stride, tr_src_stride;
+
+    Xbyak::Reg64 imm_addr64 = rbx;
+
+    Xbyak::Opmask kF0 = k1;
+    Xbyak::Opmask kCC = k2;
+    Xbyak::Opmask k33 = k3;
+    Xbyak::Opmask kFFFF = k4;
+
+    Xbyak::Zmm vidx01 = zmm31;
+    Xbyak::Zmm vidx10 = zmm30;
+    Xbyak::Zmm vidx1 = zmm29;
+    Xbyak::Zmm vidxP = zmm28;
+
+    Xbyak::Reg64 reg_src = r8;
+    Xbyak::Reg64 reg_tr_src = r9;
+    Xbyak::Reg64 reg_src_prf = r10;
+    Xbyak::Reg64 reg_tr_src_prf = r11;
+    Xbyak::Reg64 reg_loop = r12;
+    Xbyak::Reg64 reg_tr_src_tmp = r13;
+    Xbyak::Reg32 regw_tmp = r14d;
+
+    void transpose_block(int ur, int nrows);
+    void transpose(int nrows);
+    void generate();
+};
+
+jit_trans_src_t *create_trans_src(const jit_conv_conf_t *conf);
+jit_trans_dst_t *create_trans_dst(const jit_conv_conf_t *conf);
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_1x1_conv_utils.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_1x1_conv_utils.hpp
new file mode 100644
index 0000000000..53313f9f01
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_1x1_conv_utils.hpp
@@ -0,0 +1,327 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_UNI_1x1_CONV_UTILS_HPP
+#define JIT_UNI_1x1_CONV_UTILS_HPP
+
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::utils;
+
+struct reduce_to_unit_stride_t {
+    convolution_desc_t conv_d_;
+    bool reduce_src_;
+    size_t space_per_thread_;
+};
+
+/* 1x1-kernel does not support non-unit strides so far, so the idea is:
+ *  - for fwd or bwd_weights: to copy src to a scratch memory (with strides
+ *    equal to 1) and then call the kernel
+ *  - for bwd_data: reduce the problem to the one with unit stride by
+ *    performing computations in a scratch memory (with strides equal to 1)
+ *    and then copy the result to diff_src */
+template <typename conv_pd_t>
+inline void rtus_prepare(conv_pd_t *self, const convolution_desc_t *&conv_d,
+        const memory_desc_t *&src_d, const memory_desc_t *dst_d) {
+    const bool is_bwd_data = self->desc()->prop_kind
+        == prop_kind::backward_data;
+
+    const int ndims = src_d->ndims;
+    const auto dat_tag = ndims == 3
+        ? memory_desc_wrapper(dst_d).matches_one_of_tag(
+                format_tag::nCw8c, format_tag::nCw16c)
+        : memory_desc_wrapper(dst_d).matches_one_of_tag(
+                format_tag::nChw8c, format_tag::nChw16c);
+
+    bool rtus_applicable = true
+        && utils::pick(ndims - 3,
+            (conv_d->strides[0] != 1 && !one_of(conv_d->src_desc.data_type,
+                data_type::s32)),
+            (conv_d->strides[0] != 1 || conv_d->strides[1] != 1))
+        && dat_tag != format_tag::undef;
+    for (int d = 2; d < ndims; ++d) {
+        /* TODO: relax these conditions (by improving reducer) */
+        rtus_applicable = rtus_applicable
+            && conv_d->padding[0][d - 2] == 0
+            && dst_d->dims[d] * conv_d->strides[d - 2] == src_d->dims[d];
+    }
+
+    if (rtus_applicable) {
+        self->rtus_.reduce_src_ = true;
+        conv_d = &(self->rtus_.conv_d_ = *conv_d);
+        self->rtus_.conv_d_.strides[0] = 1;
+        if (ndims == 4)
+            self->rtus_.conv_d_.strides[1] = 1;
+        utils::array_set(self->rtus_.conv_d_.padding[0], 0, 2);
+        if (ndims == 4)
+            utils::array_set(self->rtus_.conv_d_.padding[1], 0, 2);
+        const int ic = src_d->dims[1];
+        if (is_bwd_data) {
+            src_d = &(self->rtus_.conv_d_.diff_src_desc = *dst_d);
+            self->rtus_.conv_d_.diff_src_desc.dims[1] = ic;
+            memory_desc_wrapper::compute_blocking(
+                    self->rtus_.conv_d_.diff_src_desc, dat_tag);
+        } else {
+            data_type_t data_type = self->rtus_.conv_d_.src_desc.data_type;
+            src_d = &(self->rtus_.conv_d_.src_desc = *dst_d);
+            self->rtus_.conv_d_.src_desc.dims[1] = ic;
+            self->rtus_.conv_d_.src_desc.data_type = data_type;
+            memory_desc_wrapper::compute_blocking(
+                    self->rtus_.conv_d_.src_desc, dat_tag);
+        }
+    }
+}
+
+template <typename conv_pd_t>
+inline void rtus_prepare_space_info(conv_pd_t *self,
+        memory_tracking::registrar_t &scratchpad) {
+    const auto &jcp = self->jcp_;
+
+    const int max_threads = mkldnn_get_max_threads();
+    const size_t factor = utils::pick_by_prop_kind(self->desc()->prop_kind,
+            jcp.nb_reduce, jcp.nb_load_blocking_max, jcp.nb_bcast_blocking);
+    size_t typesize = types::data_type_size(
+            conv_prop_invariant_src_d(self->desc())->data_type);
+
+    self->rtus_.space_per_thread_ = factor * jcp.is * jcp.ic_block;
+    scratchpad.book(memory_tracking::names::key_conv_rtus_space,
+            typesize * max_threads * self->rtus_.space_per_thread_);
+}
+
+template <cpu_isa_t isa>
+struct rtus_driver_t: public jit_generator {
+
+    struct call_params_t {
+        const void *ws; /* reduced image (w/ strides = 1) */
+        const void *src; /* source image (w/ non-unit strides) */
+        size_t icb;
+        size_t os;
+        size_t iw_start;
+    };
+
+    void (*ker_)(const call_params_t *p);
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(rtus_driver_t)
+
+    /* cpu specific part */
+    using Vmm = typename utils::conditional<isa == avx2, Xbyak::Ymm,
+          Xbyak::Zmm>::type;
+
+    Xbyak::Reg64 reg_ws = abi_param1;
+    Xbyak::Reg64 reg_src = abi_not_param1;
+    Xbyak::Reg64 reg_icb = rdx;
+    Xbyak::Reg64 reg_os = r11;
+    Xbyak::Reg64 reg_iw_start = r8;
+
+    Xbyak::Reg64 reg_cur_os = rax;
+    Xbyak::Reg64 reg_cur_iw = r9;
+    Xbyak::Reg64 reg_cur_src = r10;
+
+    int iw_, stride_w_;
+    int src_step_h_, src_step_icb_, ws_step_icb_, vlen_, vlen_shift_;
+    bool src_to_ws_;
+    size_t typesize_;
+    Vmm reg_zero;
+    Vmm reg_v;
+
+    rtus_driver_t(int iw, int stride_w, int src_step_h,
+            int src_step_icb, int ws_step_icb, bool src_to_ws, size_t typesize)
+        : iw_(iw), stride_w_(stride_w), src_step_h_(src_step_h)
+        , src_step_icb_(src_step_icb), ws_step_icb_(ws_step_icb)
+        , src_to_ws_(src_to_ws), typesize_(typesize)
+    {
+        using namespace Xbyak;
+        vlen_ = cpu_isa_traits<isa>::vlen;
+        vlen_shift_ = cpu_isa_traits<isa>::vlen_shift;
+        if (typesize_ == 2) {
+            vlen_ /= 2;
+            vlen_shift_--;
+        }
+
+        reg_zero = Vmm(0);
+        reg_v = Vmm(1);
+
+        generate();
+    }
+
+    void loop_is() {
+        using namespace Xbyak;
+
+        mov(reg_cur_src, reg_src);
+        mov(reg_cur_iw, reg_iw_start);
+        mov(reg_cur_os, reg_os);
+
+        Label is_loop, skip_h_step;
+        L(is_loop);
+
+        if (src_to_ws_) {
+            vmovups(reg_v, ptr[reg_cur_src]);
+            vmovups(ptr[reg_ws], reg_v);
+        } else {
+            vmovups(reg_v, ptr[reg_ws]);
+            vmovups(ptr[reg_cur_src], reg_v);
+            for (int w = 1; w < stride_w_; ++w)
+                vmovups(ptr[reg_cur_src + w * vlen_], reg_zero);
+        }
+
+        add(reg_ws, vlen_);
+
+        add(reg_cur_iw, stride_w_);
+        add(reg_cur_src, stride_w_ * vlen_);
+
+        cmp(reg_cur_iw, iw_);
+        jl(skip_h_step);
+        /* for 1d convolution the loop over h should be skipped */
+        if (src_step_icb_ == iw_) jmp(skip_h_step);
+
+        if (src_to_ws_) {
+            add(reg_cur_src, (src_step_h_ - iw_) * vlen_);
+        } else {
+            Xbyak::Reg64 reg_cur_src_fin = reg_cur_iw; /* just reuse */
+            mov(reg_cur_src_fin, reg_cur_src);
+            add(reg_cur_src_fin, (src_step_h_ - iw_) * vlen_);
+            Label ih_loop;
+            L(ih_loop);
+
+            for (int w = 0; w < stride_w_; ++w)
+                vmovups(ptr[reg_cur_src + w * vlen_], reg_zero);
+
+            add(reg_cur_src, stride_w_ * vlen_);
+            cmp(reg_cur_src, reg_cur_src_fin);
+            jl(ih_loop);
+        }
+        xor_(reg_cur_iw, reg_cur_iw);
+
+        L(skip_h_step);
+
+        sub(reg_cur_os, vlen_);
+        jnz(is_loop);
+
+        /* restore dst */
+        sub(reg_ws, reg_os);
+    }
+
+    void generate() {
+        using namespace Xbyak;
+        assert(isa == avx2 || isa == avx512_common
+                || isa == avx512_core || isa == avx512_mic);
+
+#if defined(_WIN32)
+        assert(reg_src == abi_not_param1 && abi_not_param1 == rdi);
+        push(rdi);
+#endif
+
+#define READ_PARAM(what) \
+        mov(reg_ ## what, ptr[abi_param1 + offsetof(call_params_t, what)])
+        READ_PARAM(src);
+        READ_PARAM(icb);
+        READ_PARAM(os);
+        READ_PARAM(iw_start);
+
+        assert(reg_ws == abi_param1);
+        READ_PARAM(ws); /* reg_ws should always be read the last */
+#undef  READ_PARAM
+
+        shl(reg_os, vlen_shift_);
+
+        if (!src_to_ws_)
+            uni_vpxor(reg_zero, reg_zero, reg_zero);
+
+        Label icb_loop;
+        L(icb_loop);
+
+        loop_is();
+
+        add(reg_ws, ws_step_icb_ * vlen_);
+        add(reg_src, src_step_icb_ * vlen_);
+
+        dec(reg_icb);
+        jnz(icb_loop, T_NEAR);
+
+#if defined(_WIN32)
+        pop(rdi);
+#endif
+
+        uni_vzeroupper();
+        ret();
+        this->ker_ = reinterpret_cast<decltype(ker_)>(const_cast<uint8_t*>(
+                    this->getCode()));
+    }
+};
+
+template <cpu_isa_t isa, typename conv_t>
+inline void init_rtus_driver(conv_t *self) {
+    const auto &conf = *self->pd();
+    if (!conf.rtus_.reduce_src_) return;
+
+    const auto &cd = *conf.desc();
+    const int ndims = conf.ndims();
+    const int stride_h = (conf.ndims() == 3) ? 1 : cd.strides[0];
+    const int stride_w = cd.strides[ndims - 3];
+
+    const bool is_bwd_data = cd.prop_kind == prop_kind::backward_data;
+    const auto &src_d = is_bwd_data ? *conf.diff_src_md() : *conf.src_md();
+
+    const int ih = ndims == 3 ? 1 : src_d.dims[2];
+    const int iw = src_d.dims[ndims - 1];
+
+    const int src_step_h = stride_h * iw;
+    const int src_step_icb = ih * iw;
+    const int ws_step_icb = conf.jcp_.is;
+    const bool src_to_ws = !is_bwd_data;
+    const size_t typesize = types::data_type_size(
+            conv_prop_invariant_src_d(self->pd()->desc())->data_type);
+
+    self->rtus_driver_ = new rtus_driver_t<isa>(iw, stride_w, src_step_h,
+            src_step_icb, ws_step_icb, src_to_ws, typesize);
+}
+
+inline int best_divider(int value, int min_divider, int max_divider,
+        bool find_max, int step = 1)
+{
+    max_divider = nstl::max(1, nstl::min(max_divider, value));
+    min_divider = nstl::max(1, nstl::min(min_divider, max_divider));
+
+    auto loss_ratio = [](int total, int chunk)
+    { return float(rnd_up(total, chunk) - total) / rnd_up(total, chunk); };
+
+    float min_loss = FLT_MAX;
+    int x_divider = max_divider;
+    for (int divider = max_divider; divider >= min_divider; divider -= step) {
+        const float loss = loss_ratio(value, divider);
+        if ((find_max && loss < min_loss) || (!find_max && loss <= min_loss)) {
+            min_loss = loss;
+            x_divider = divider;
+        }
+    }
+    return x_divider;
+}
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_batch_normalization.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_batch_normalization.cpp
new file mode 100644
index 0000000000..72fe3a8109
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_batch_normalization.cpp
@@ -0,0 +1,1407 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "math_utils.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_barrier.hpp"
+#include "cpu_batch_normalization_utils.hpp"
+#include "jit_generator.hpp"
+
+#include "jit_uni_batch_normalization.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace {
+
+using namespace memory_tracking::names;
+
+using namespace Xbyak;
+namespace barrier = simple_barrier;
+
+typedef float data_t;
+
+template <cpu_isa_t isa>
+struct jit_bnorm_t: public jit_generator {
+    struct call_params_t {
+        // keep all sizes at 8 bytes -- jit code expects this
+        size_t N_ithr, N_nthr;
+        size_t coff_max, soff_max;
+        size_t mb_stride_Bc, spat_size, spat_size_loc;
+        size_t S_s, S_tail;
+        size_t is_cblk_tail;
+        data_t chan_size, eps, one;
+        const data_t *scale_shift;
+        const data_t *mean, *var;
+        const data_t *diff_scale_shift;
+        const data_t *src, *dst;
+        const data_t *diff_src, *diff_dst;
+        const data_t *rbuf1, *rbuf2;
+        const uint8_t *ws;
+        barrier::ctx_t *barrier;
+    };
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_bnorm_t)
+
+    /* cpu specific part */
+    using Vmm = typename utils::conditional3<isa == sse42, Xmm,
+                                             isa == avx2, Ymm, Zmm>::type;
+    const AddressFrame &vmmword = (isa == sse42) ? xword :
+                                  (isa == avx2) ? yword : zword;
+
+    const int vlen = isa == sse42 ? 32 : cpu_isa_traits<isa>::vlen;
+
+    const batch_normalization_pd_t *bdesc_;
+    bool is_spatial_thr_;
+
+    void (*ker)(const call_params_t *);
+    void operator()(const call_params_t *p) { (*ker)(p); }
+
+    Reg64 reg_param = abi_param1;
+
+    Reg64 reg_scale_shift = rbx;
+    Reg64 reg_rbuf1 = abi_not_param1;
+    Reg64 reg_rbuf2 = rdx;
+
+    Reg64 reg_mean = rbp;
+    Reg64 reg_var = reg_param;
+    Reg64 reg_diff_scale_shift = rax;
+
+    Reg64 reg_coff = r8;
+    Reg64 reg_coff_max = r9;
+    Reg64 reg_soff = r10;
+    Reg64 reg_soff_max = r11;
+    Reg64 reg_ctr = r12;
+    Reg64 reg_roff = r13;
+
+    Reg64 reg_mb_stride_Bc = r14;
+
+    Reg64 reg_src = r15;
+    Reg64 reg_diff_src = reg_rbuf1;
+    Reg64 reg_dst = rsi;
+    Reg64 reg_diff_dst = reg_dst;
+
+    Reg64 reg_tmp_off = reg_roff;
+
+    // Reuse loop counters
+    Reg64 reg_bar = reg_coff;
+    Reg64 reg_nnthr = reg_soff; // must be usable w/ loops over coff
+    Reg64 reg_tmp = reg_ctr;
+
+    // Relu section
+    bool with_relu, with_relu_inf_only;
+    Vmm vzero; // is_fwd() ? vdiff_beta : vbeta
+    Reg64 reg_ws = reg_roff;
+    Label l_relu_mask_avx2;
+    Opmask kstore_mask = Opmask(1);
+
+    // channel tail processing
+    Opmask ktail_mask = Opmask(2);
+
+    size_t unroll_blocks;
+    size_t unroll_regs;
+    Vmm vbuf = Vmm(isa == avx512_common ? 20 : 5);
+    Vmm vdiff_beta = Vmm(isa == avx512_common ? 21 : 6);
+    Vmm vdiff_gamma = Vmm(isa == avx512_common ? 22 : 7);
+    Vmm vsqrtvar = Vmm(isa == avx512_common ? 23 : 8);
+    Vmm vone = Vmm(isa == avx512_common ? 24 : 9);
+    Vmm vmean = Vmm(isa == avx512_common ? 25 : 10);
+    Vmm vgamma = Vmm(isa == avx512_common ? 26 : 11);
+    Vmm vbeta = Vmm(isa == avx512_common ? 27 : 12);
+    Vmm veps = Vmm(isa == avx512_common ? 28 : 13);
+    Vmm vchan_size = Vmm(isa == avx512_common ? 29 : 14);
+    Vmm vtail_mask = Vmm(isa == avx512_common ? 30 : 15);
+
+    size_t t0_pf_offt;
+    size_t t1_pf_offt;
+    size_t spat_size;
+    size_t chan_data_offt;
+
+    enum {
+        stack_off_N_nthr = 0,
+        stack_off_N_ithr = 8,
+        stack_off_src = 16,
+        stack_off_dst = 24,
+        stack_off_diff_src = 32,
+        stack_off_diff_dst = 40,
+        stack_off_diff_scale_shift = 48,
+        stack_off_ws = 56,
+        stack_off_barrier = 64,
+        stack_off_spat_size_loc = 72,
+        stack_off_s_s = 80,
+        stack_off_s_tail = 88,
+        stack_off_is_cblk_tail = 96,
+        stack_size_required = 104,
+    };
+
+    bool is_c_padded() const {
+        const memory_desc_wrapper data_d(bdesc_->src_md());
+        return bdesc_->C() != data_d.padded_dims()[1];
+    }
+
+    void compute_static_strides() {
+        spat_size = bdesc_->D() * bdesc_->W() * bdesc_->H();
+        chan_data_offt = bdesc_->C() * sizeof(data_t);
+
+        if (isa == avx512_mic) {
+            t0_pf_offt = 4096;
+            t1_pf_offt = 0;
+        } else {
+            t0_pf_offt = 0;
+            t1_pf_offt = 0;
+        }
+    }
+
+    void load_common_params() {
+#       define PARAM_OFF(x) offsetof(call_params_t, x)
+        mov(reg_rbuf1, ptr[reg_param + PARAM_OFF(rbuf1)]);
+        if (bdesc_->is_bwd())
+            mov(reg_rbuf2, ptr[reg_param + PARAM_OFF(rbuf2)]);
+        mov(reg_coff_max, ptr[reg_param + PARAM_OFF(coff_max)]);
+        mov(reg_soff_max, ptr[reg_param + PARAM_OFF(soff_max)]);
+        mov(reg_mb_stride_Bc, ptr[reg_param + PARAM_OFF(mb_stride_Bc)]);
+        shl(reg_coff_max, 2);
+        shl(reg_soff_max, 2);
+        shl(reg_mb_stride_Bc, 2);
+
+        mov(reg_mean, ptr[reg_param + PARAM_OFF(mean)]);
+        mov(reg_scale_shift, ptr[reg_param + PARAM_OFF(scale_shift)]);
+
+        uni_vbroadcastss(vchan_size, vmmword[reg_param + PARAM_OFF(chan_size)]);
+        uni_vbroadcastss(vone, vmmword[reg_param + PARAM_OFF(one)]);
+        uni_vbroadcastss(veps, vmmword[reg_param + PARAM_OFF(eps)]);
+
+        mov(reg_tmp, ptr[reg_param + PARAM_OFF(N_nthr)]);
+        mov(ptr[rsp + stack_off_N_nthr], reg_tmp);
+        mov(reg_tmp, ptr[reg_param + PARAM_OFF(N_ithr)]);
+        mov(ptr[rsp + stack_off_N_ithr], reg_tmp);
+        mov(reg_tmp, ptr[reg_param + PARAM_OFF(src)]);
+        mov(ptr[rsp + stack_off_src], reg_tmp);
+        mov(reg_tmp, ptr[reg_param + PARAM_OFF(dst)]);
+        mov(ptr[rsp + stack_off_dst], reg_tmp);
+        mov(reg_tmp, ptr[reg_param + PARAM_OFF(diff_src)]);
+        mov(ptr[rsp + stack_off_diff_src], reg_tmp);
+        mov(reg_tmp, ptr[reg_param + PARAM_OFF(diff_dst)]);
+        mov(ptr[rsp + stack_off_diff_dst], reg_tmp);
+        mov(reg_tmp, ptr[reg_param + PARAM_OFF(ws)]);
+        mov(ptr[rsp + stack_off_ws], reg_tmp);
+        mov(reg_tmp, ptr[reg_param + PARAM_OFF(barrier)]);
+        mov(ptr[rsp + stack_off_barrier], reg_tmp);
+        if (is_spatial_thr_) {
+            mov(reg_tmp, ptr[reg_param + PARAM_OFF(spat_size_loc)]);
+            mov(ptr[rsp + stack_off_spat_size_loc], reg_tmp);
+            mov(reg_tmp, ptr[reg_param + PARAM_OFF(S_s)]);
+            mov(ptr[rsp + stack_off_s_s], reg_tmp);
+            mov(reg_tmp, ptr[reg_param + PARAM_OFF(S_tail)]);
+            mov(ptr[rsp + stack_off_s_tail], reg_tmp);
+        }
+        if (is_c_padded()) {
+            mov(reg_tmp, ptr[reg_param + PARAM_OFF(is_cblk_tail)]);
+            mov(ptr[rsp + stack_off_is_cblk_tail], reg_tmp);
+        }
+
+        if (bdesc_->is_fwd()) {
+            mov(reg_tmp, ptr[reg_param + PARAM_OFF(var)]);
+            mov(reg_var, reg_tmp);
+        } else {
+            mov(reg_tmp, ptr[reg_param + PARAM_OFF(diff_scale_shift)]);
+            mov(ptr[rsp + stack_off_diff_scale_shift], reg_tmp);
+            mov(reg_tmp, ptr[reg_param + PARAM_OFF(var)]);
+            mov(reg_var, reg_tmp);
+        }
+#       undef PARAM_OFF
+    }
+
+    void prepare_tail_mask_avx512_common() {
+        if (!is_c_padded()) return;
+
+        const int tail = bdesc_->C() % (int)(vlen / sizeof(float));
+        const int mask = (1 << tail) - 1;
+
+        Reg32 regw_tmp = reg_tmp.cvt32();
+        mov(regw_tmp, mask);
+        kmovw(ktail_mask, regw_tmp);
+    }
+
+    void prepare_tail_mask_avx2_common() {
+        if (!is_c_padded()) return;
+
+        const int tail = bdesc_->C() % (int)(vlen / sizeof(float));
+        static const uint32_t mask[16] = {0xffffffff, 0xffffffff, 0xffffffff,
+                0xffffffff, 0xffffffff, 0xffffffff, 0xffffffff, 0xffffffff,
+                0, 0, 0, 0, 0, 0, 0, 0};
+
+        mov(reg_tmp, reinterpret_cast<size_t>(&mask[8 - tail]));
+        vmovups(vtail_mask, ptr[reg_tmp]);
+    }
+
+    void prepare_relu() {
+        with_relu = bdesc_->is_fwd()
+            ? bdesc_->with_relu_post_op() || bdesc_->fuse_bn_relu()
+            : bdesc_->fuse_bn_relu();
+        with_relu_inf_only = with_relu && bdesc_->is_fwd()
+            && !(bdesc_->fuse_bn_relu() && bdesc_->is_training());
+
+        vzero = bdesc_->is_fwd() ? vdiff_beta : vbeta;
+        if (with_relu) {
+            uni_vpxor(vzero, vzero, vzero);
+            if (!bdesc_->is_fwd() && isa == avx2)
+                prepare_l_relu_mask_avx2();
+        }
+    }
+
+    void prepare_l_relu_mask_avx2() {
+        Label l_mask_after;
+        jmp(l_mask_after);
+        align(32);
+        L(l_relu_mask_avx2); /* [0x80 0x40 0x20 0x10 0x08 0x04 0x02 0x01] */
+        for (int i = 0; i < 8; ++i) dd(1<<i);
+        L(l_mask_after);
+    }
+
+    void fwd_process_relu_avx2(Vmm vdst, int offt, Vmm vstore_mask) {
+        Reg64 reg_store_mask = reg_diff_scale_shift;
+        shr(reg_soff, 5);
+        vcmpps(vstore_mask, vzero, vdst, _cmp_lt_os);
+        vmovmskps(reg_store_mask, vstore_mask);
+        mov(ptr[reg_ws + reg_soff + offt / (1 << 5)], reg_store_mask.cvt8());
+        vblendvps(vdst, vzero, vdst, vstore_mask);
+        shl(reg_soff, 5);
+    }
+
+    void fwd_process_relu_avx512_common(Vmm vdst, int offt) {
+        shr(reg_soff, 5);
+        vcmpps(kstore_mask, vzero, vdst, _cmp_lt_os);
+        kmovw(ptr[reg_ws + reg_soff + offt / (1 << 5)], kstore_mask);
+        vblendmps(vdst | kstore_mask, vzero, vdst);
+        shl(reg_soff, 5);
+    }
+
+    void bwd_process_relu_avx2(Vmm vdiff_dst, int offt, Vmm vstore_mask) {
+        shr(reg_soff, 5);
+        vpbroadcastb(vstore_mask, ptr[reg_ws + reg_soff + offt / (1 << 5)]);
+        vpand(vstore_mask, vstore_mask, ptr[rip + l_relu_mask_avx2]);
+        vpcmpeqd(vstore_mask, vstore_mask, ptr[rip + l_relu_mask_avx2]);
+        vblendvps(vdiff_dst, vzero, vdiff_dst, vstore_mask);
+        shl(reg_soff, 5);
+    }
+
+    void bwd_process_relu_avx512_common(Vmm vdiff_dst, int offt) {
+        shr(reg_soff, 5);
+        kmovw(kstore_mask, ptr[reg_ws + reg_soff + offt / (1 << 5)]);
+        vmovups(vdiff_dst | kstore_mask | T_z, vdiff_dst);
+        shl(reg_soff, 5);
+    }
+
+    void uni_vmovups_tail_avx2_common(const Operand &dst,
+            const Operand &src, Label &l_ret) {
+        if (dst.isMEM()) {
+            vmaskmovps(dst.getAddress(), vtail_mask, Vmm(src.getIdx()));
+        } else {
+            vmaskmovps(Vmm(dst.getIdx()), vtail_mask, src.getAddress());
+        }
+        jmp(l_ret);
+    }
+
+    void uni_vmovups_tail_avx512_common(const Operand &dst,
+            const Operand &src, Label &l_ret) {
+        if (dst.isMEM())
+            uni_vmovups(dst.getAddress() | ktail_mask | T_z, Vmm(src.getIdx()));
+        else
+            uni_vmovups(Vmm(dst.getIdx()) | ktail_mask | T_z, src.getAddress());
+
+        jmp(l_ret);
+    }
+
+    void uni_vmovups_maybe_tail(const Operand &dst, const Operand &src) {
+        Label l_no_mask, l_ret;
+
+        if (is_c_padded()) {
+            mov(reg_tmp, ptr[rsp + stack_off_is_cblk_tail]);
+            cmp(reg_tmp, 0);
+            jz(l_no_mask);
+
+            lea(reg_tmp, ptr[reg_coff + vlen]);
+            cmp(reg_tmp, reg_coff_max);
+            jl(l_no_mask);
+            assert(isa == avx512_common || isa == avx2);
+            if (isa == avx512_common)
+                uni_vmovups_tail_avx512_common(dst, src, l_ret);
+            else if (isa == avx2)
+                uni_vmovups_tail_avx2_common(dst, src, l_ret);
+        }
+        L(l_no_mask);
+        if (dst.isMEM())
+            uni_vmovups(dst.getAddress(), Vmm(src.getIdx()));
+        else
+            uni_vmovups(Vmm(dst.getIdx()), src.getAddress());
+
+        L(l_ret);
+    }
+
+    void barrier() {
+        mov(reg_nnthr, ptr[rsp + stack_off_N_nthr]);
+        mov(reg_bar, ptr[rsp + stack_off_barrier]);
+        simple_barrier::generate(*this, reg_bar, reg_nnthr);
+    }
+
+    Address mean_ptr(size_t offt = 0) {
+        return vmmword[reg_mean + reg_coff + offt + 0 * chan_data_offt];
+    }
+
+    Address var_ptr(size_t offt = 0) {
+        return vmmword[reg_var + reg_coff + offt + 0 * chan_data_offt];
+    }
+
+    Address diff_gamma_ptr(size_t offt = 0) {
+        return vmmword[reg_diff_scale_shift + reg_coff + offt
+            + 0 * chan_data_offt];
+    }
+
+    Address diff_beta_ptr(size_t offt = 0) {
+        return vmmword[reg_diff_scale_shift + reg_coff + offt
+            + 1 * chan_data_offt];
+     }
+
+    Address gamma_ptr(size_t offt = 0) {
+        return vmmword[reg_scale_shift + reg_coff + offt + 0 * chan_data_offt];
+    }
+
+    Address beta_ptr(size_t offt = 0) {
+        return vmmword[reg_scale_shift + reg_coff + offt + 1 * chan_data_offt];
+    }
+
+    template <typename init_t, typename body_t, typename fini_t>
+    void spat_loop(size_t len, size_t blocks, size_t regs,
+            init_t init, body_t body, fini_t fini) {
+        size_t factor = regs * blocks;
+        size_t loop_unroll = len / factor * factor;
+        size_t loop_tail = len - loop_unroll;
+        size_t num_active_regs = (len < regs) ? len : regs;
+        for (size_t i = 0; i < num_active_regs; i++)
+            init(i);
+        if (loop_unroll) {
+            if (is_spatial_thr_) {
+                mov(reg_ctr, ptr[rsp + stack_off_spat_size_loc]);
+                add(reg_soff, ptr[rsp + stack_off_s_s]);
+            } else {
+                mov(reg_ctr, loop_unroll);
+            }
+            Label label;
+            L(label); {
+                for (size_t i = 0; i < factor; i++) {
+                    size_t base_reg = i % regs;
+                    body(base_reg, i);
+                }
+                add(reg_soff, factor * vlen);
+                sub(reg_ctr, factor);
+                jnz(label);
+            }
+            if (is_spatial_thr_) {
+                add(reg_soff, ptr[rsp + stack_off_s_tail]);
+            }
+        }
+
+        for (size_t i = 0; i < loop_tail; i++) {
+            size_t base_reg = i % regs;
+            body(base_reg, i);
+        }
+        if (loop_tail)
+            add(reg_soff, loop_tail * vlen);
+
+        for (size_t i = 0; i < num_active_regs; i++)
+            fini(i);
+    }
+
+    void mean_channels() {
+        Label ch_label;
+        L(ch_label); {
+            uni_vmovups(Vmm(0), vmmword[reg_rbuf1 + reg_coff]);
+            spat_loop(spat_size, unroll_blocks,
+                unroll_regs,
+                    [=](size_t base_reg) {
+                        Vmm v = Vmm(base_reg * 2);
+                        if (base_reg)
+                            uni_vpxor(v, v, v);
+                    },
+                    [=](size_t base_reg, size_t i) {
+                        Vmm v0 = Vmm(base_reg * 2 + 0);
+                        Vmm v1 = Vmm(base_reg * 2 + 1);
+                        size_t offt = i * vlen;
+                        uni_vmovups(v1,
+                            vmmword[reg_src + reg_soff + offt]);
+                        uni_vaddps(v0, v0, v1);
+                        mic_prefetcht0(ptr[reg_src + reg_soff + offt
+                                + t0_pf_offt]);
+                        mic_prefetcht1(ptr[reg_src + reg_soff + offt
+                                + t1_pf_offt]);
+                    },
+                    [=](size_t base_reg) {
+                        Vmm b = Vmm(0);
+                        Vmm v = Vmm(base_reg * 2);
+                        if (base_reg)
+                            uni_vaddps(b, b, v);
+                    });
+            uni_vmovups(vmmword[reg_rbuf1 + reg_coff], Vmm(0));
+
+            add(reg_coff, vlen);
+            cmp(reg_coff, reg_coff_max);
+            jl(ch_label);
+        }
+    }
+
+    void var_channels() {
+        Label ch_label;
+        L(ch_label); {
+            uni_vmovups_maybe_tail(vmean, mean_ptr());
+            uni_vmovups(Vmm(0), vmmword[reg_rbuf1 + reg_coff]);
+            spat_loop(spat_size, unroll_blocks, unroll_regs,
+                    [=](size_t base_reg) {
+                        Vmm v = Vmm(base_reg * 3);
+                        if (base_reg > 0)
+                            uni_vpxor(v, v, v);
+                    },
+                    [=](size_t base_reg, size_t i) {
+                        Vmm v = Vmm(3 * base_reg);
+                        Vmm vtmp0 = Vmm(3 * base_reg + 1);
+                        Vmm vtmp1 = Vmm(3 * base_reg + 2);
+                        size_t offt = i * vlen;
+                        uni_vmovups(vtmp0,
+                            vmmword[reg_src + reg_soff + offt]);
+                        if (isa == sse42) {
+                            movups(vtmp1, vmean);
+                            subps(vtmp1, vtmp0);
+                        } else {
+                            vsubps(vtmp1, vmean, vtmp0);
+                        }
+                        uni_vfmadd231ps(v, vtmp1, vtmp1);
+
+                        mic_prefetcht0(ptr[reg_src + reg_soff + offt
+                                + t0_pf_offt]);
+                        mic_prefetcht1(ptr[reg_src + reg_soff + offt
+                                + t1_pf_offt]);
+                    },
+                    [=](size_t base_reg) {
+                        Vmm b = Vmm(0);
+                        Vmm v = Vmm(base_reg * 3);
+                        if (base_reg)
+                            uni_vaddps(b, b, v);
+                    });
+            uni_vmovups(vmmword[reg_rbuf1 + reg_coff], Vmm(0));
+            add(reg_coff, vlen);
+            cmp(reg_coff, reg_coff_max);
+            jl(ch_label);
+        }
+    }
+
+    void compute_mean_variance() {
+        uni_vpxor(Vmm(0), Vmm(0), Vmm(0));
+        xor_(reg_coff, reg_coff);
+        Label zero_rbuf;
+        L(zero_rbuf); {
+            uni_vmovups(vmmword[reg_rbuf1 + reg_coff], Vmm(0));
+            add(reg_coff, isa == sse42 ? vlen / 2 : vlen);
+            cmp(reg_coff, reg_coff_max);
+            jne(zero_rbuf);
+        }
+
+        mov(reg_src, ptr[rsp + stack_off_src]);
+
+        xor_(reg_soff, reg_soff);
+        Label mean_spatial;
+        L(mean_spatial); {
+            xor_(reg_coff, reg_coff);
+
+            if (isa == sse42)
+                mov(reg_tmp_off, reg_soff);
+
+            mean_channels();
+
+            if (isa == sse42) {
+                mov(reg_soff, reg_tmp_off);
+                add(reg_src, vlen / 2);
+                mov(reg_coff, vlen / 2);
+
+                mean_channels();
+
+                sub(reg_src, vlen / 2);
+            }
+
+            add(reg_soff, reg_mb_stride_Bc);
+            cmp(reg_soff, reg_soff_max);
+            jne(mean_spatial);
+        }
+
+        Label no_mean_reduction;
+        barrier(); {
+            mov(reg_tmp, ptr[rsp + stack_off_N_ithr]);
+            cmp(reg_tmp, 0);
+            jne(no_mean_reduction);
+            mov(reg_nnthr, ptr[rsp + stack_off_N_nthr]);
+            xor_(reg_coff, reg_coff);
+            Label mean_reduction_channels;
+            L(mean_reduction_channels); {
+                mov(reg_roff, reg_coff);
+                uni_vpxor(Vmm(0), Vmm(0), Vmm(0));
+                uni_vpxor(Vmm(1), Vmm(1), Vmm(1));
+                mov(reg_ctr, reg_nnthr);
+                Label mean_reduction_thrs;
+                L(mean_reduction_thrs); {
+                    uni_vaddps(Vmm(1), Vmm(1), vmmword[reg_rbuf1 + reg_roff]);
+                    uni_vmovups(vmmword[reg_rbuf1 + reg_roff], Vmm(0));
+                    add(reg_roff, reg_coff_max);
+                    sub(reg_ctr, 1);
+                    jnz(mean_reduction_thrs);
+                }
+                uni_vdivps(Vmm(1), Vmm(1), vchan_size);
+                uni_vmovups_maybe_tail(mean_ptr(), Vmm(1));
+
+                add(reg_coff, isa == sse42 ? vlen / 2 : vlen);
+
+                cmp(reg_coff, reg_coff_max);
+                jne(mean_reduction_channels);
+            }
+        }
+        L(no_mean_reduction);
+        barrier();
+
+        xor_(reg_soff, reg_soff);
+        Label var_spatial;
+        L(var_spatial); {
+            xor_(reg_coff, reg_coff);
+
+            if (isa == sse42)
+                mov(reg_tmp_off, reg_soff);
+
+            var_channels();
+
+            if (isa == sse42) {
+                mov(reg_soff, reg_tmp_off);
+                add(reg_src, vlen / 2);
+                mov(reg_coff, vlen / 2);
+
+                var_channels();
+
+                sub(reg_src, vlen / 2);
+            }
+
+            add(reg_soff, reg_mb_stride_Bc);
+            cmp(reg_soff, reg_soff_max);
+            jne(var_spatial);
+        }
+
+        Label no_var_reduction;
+        barrier(); {
+            mov(reg_tmp, ptr[rsp + stack_off_N_ithr]);
+            cmp(reg_tmp, 0);
+            jne(no_var_reduction);
+
+            mov(reg_nnthr, ptr[rsp + stack_off_N_nthr]);
+            xor_(reg_coff, reg_coff);
+            Label var_reduction_channels;
+            L(var_reduction_channels); {
+                mov(reg_roff, reg_coff);
+                uni_vpxor(Vmm(1), Vmm(1), Vmm(1));
+                mov(reg_ctr, reg_nnthr);
+                Label var_reduction_thrs;
+                L(var_reduction_thrs); { // TODO: unroll (?)
+                    uni_vaddps(Vmm(1), Vmm(1), vmmword[reg_rbuf1 + reg_roff]);
+                    add(reg_roff, reg_coff_max);
+                    sub(reg_ctr, 1);
+                    jnz(var_reduction_thrs);
+                }
+                uni_vdivps(Vmm(1), Vmm(1), vchan_size);
+                uni_vmovups_maybe_tail(var_ptr(), Vmm(1));
+                add(reg_coff, isa == sse42 ? vlen / 2 : vlen);
+
+                cmp(reg_coff, reg_coff_max);
+                jne(var_reduction_channels);
+            }
+        }
+        L(no_var_reduction);
+        barrier();
+    }
+
+    void forward_channels() {
+        Label ch_label;
+        L(ch_label); {
+            uni_vmovups_maybe_tail(vmean, mean_ptr());
+            uni_vmovups_maybe_tail(vsqrtvar, var_ptr());
+            uni_vaddps(vsqrtvar, vsqrtvar, veps);
+            uni_vsqrtps(vsqrtvar, vsqrtvar);
+
+            if (bdesc_->use_scaleshift()) {
+                uni_vmovups_maybe_tail(vgamma, gamma_ptr());
+                uni_vmovups_maybe_tail(vbeta, beta_ptr());
+            }
+
+            Vmm vscale = bdesc_->use_scaleshift() ? vgamma : vone;
+            Vmm vdiv = bdesc_->use_scaleshift() ? vgamma : vsqrtvar;
+
+            if (isa == sse42) {
+                movups(vbuf, vscale);
+                divps(vbuf, vsqrtvar);
+                movups(vdiv, vbuf);
+            } else {
+                vdivps(vdiv, vscale, vsqrtvar);
+            }
+
+            auto compute = [=](bool output_is_aligned) {
+                spat_loop(spat_size, unroll_blocks, unroll_regs,
+                        [](size_t base_reg) {UNUSED(base_reg);},
+                        [=](size_t base_reg, size_t i) {
+                             Vmm v = Vmm(base_reg);
+                             size_t offt = i * vlen;
+                             uni_vmovups(v,
+                                 vmmword[reg_src + reg_soff + offt]);
+                             mic_prefetcht0(ptr[reg_src + reg_soff + offt
+                                     + t0_pf_offt]);
+                             mic_prefetcht1(ptr[reg_src + reg_soff + offt
+                                     + t1_pf_offt]);
+                             uni_vsubps(v, v, vmean);
+                             if (bdesc_->use_scaleshift()) {
+                                 uni_vfmadd213ps(v, vgamma, vbeta);
+                             } else {
+                                uni_vmulps(v, v, vsqrtvar);
+                             }
+                             if (with_relu_inf_only) {
+                                 uni_vmaxps(v, v, vzero);
+                             } else if (with_relu) {
+                                 if (isa == avx512_common)
+                                     fwd_process_relu_avx512_common(v, offt);
+                                 else
+                                     fwd_process_relu_avx2(v, offt, Vmm(3));
+                             }
+                             if (output_is_aligned) {
+                                 uni_vmovntps(
+                                     vmmword[reg_dst + reg_soff + offt], v);
+                             } else {
+                                 uni_vmovups(
+                                     vmmword[reg_dst + reg_soff + offt], v);
+                             }
+                        },
+                        [](size_t base_reg) {UNUSED(base_reg);});
+            };
+
+            Label unaligned_store, end_store;
+            test(reg_dst, vlen - 1);
+            jnz(unaligned_store, T_NEAR);
+            compute(true);
+            jmp(end_store, T_NEAR);
+            L(unaligned_store); {
+                compute(false);
+            }
+            L(end_store);
+
+            add(reg_coff, vlen);
+            cmp(reg_coff, reg_coff_max);
+            jl(ch_label);
+        }
+    }
+
+    void forward() {
+        mov(reg_src, ptr[rsp + stack_off_src]);
+        mov(reg_dst, ptr[rsp + stack_off_dst]);
+        mov(reg_ws, ptr[rsp + stack_off_ws]);
+
+        xor_(reg_soff, reg_soff);
+        Label dst_spatial;
+        L(dst_spatial); {
+            xor_(reg_coff, reg_coff);
+            if (isa == sse42)
+                mov(reg_tmp_off, reg_soff);
+
+            forward_channels();
+
+            if (isa == sse42) {
+                mov(reg_soff, reg_tmp_off);
+                add(reg_src, vlen / 2);
+                add(reg_dst, vlen / 2);
+                mov(reg_coff, vlen / 2);
+
+                forward_channels();
+
+                sub(reg_src, vlen / 2);
+                sub(reg_dst, vlen / 2);
+            }
+
+            add(reg_soff, reg_mb_stride_Bc);
+            cmp(reg_soff, reg_soff_max);
+            jnz(dst_spatial);
+        }
+    }
+
+    void backward_sh_channels() {
+        Label sh_channels;
+        L(sh_channels); {
+            uni_vmovups_maybe_tail(vmean, mean_ptr());
+            uni_vmovups(Vmm(0), vmmword[reg_rbuf1 + reg_coff]);
+            uni_vmovups(Vmm(1), vmmword[reg_rbuf2 + reg_coff]);
+            spat_loop(spat_size, 1, 1,
+                    [=](size_t base_reg) {
+                        if (base_reg > 0) {
+                            for (int i = 0; i < 2; i++) {
+                                Vmm v(base_reg * 5 + i);
+                                uni_vpxor(v, v, v);
+                            }
+                        }
+                    },
+                    [=](size_t base_reg, size_t i) {
+                        Vmm o0 = Vmm(base_reg * 5 + 0);
+                        Vmm o1 = Vmm(base_reg * 5 + 1);
+                        Vmm t1 = Vmm(base_reg * 5 + 2);
+                        Vmm t2 = Vmm(base_reg * 5 + 3);
+                        Vmm t3 = Vmm(base_reg * 5 + 4);
+                        size_t offt = i * vlen;
+                        uni_vmovups(t1, vmmword[reg_src + reg_soff + offt]);
+                        uni_vmovups(t2, vmmword[reg_diff_dst + reg_soff
+                                + offt]);
+                        if (with_relu) {
+                            if (isa == avx512_common)
+                                bwd_process_relu_avx512_common(t2, offt);
+                            else if (isa == avx2)
+                                bwd_process_relu_avx2(t2, offt, t3);
+                            else
+                                assert(false);
+                        }
+                        uni_vsubps(t3, vmean, t1, t3);
+                        if (isa == sse42) {
+                            mulps(t3, t2);
+                            subps(o0, t3);
+                        } else {
+                            vfnmadd231ps(o0, t3, t2);
+                        }
+                        uni_vaddps(o1, o1, t2);
+                        mic_prefetcht0(ptr[reg_diff_dst + reg_soff + offt
+                                + t0_pf_offt]);
+                        mic_prefetcht0(ptr[reg_src + reg_soff + offt
+                                + t0_pf_offt]);
+                        mic_prefetcht1(ptr[reg_diff_dst + reg_soff + offt
+                                + t1_pf_offt]);
+                        mic_prefetcht1(ptr[reg_src + reg_soff + offt
+                                + t1_pf_offt]);
+                    },
+                    [=](size_t base_reg) {
+                        Vmm b0 = Vmm(0);
+                        Vmm b1 = Vmm(1);
+                        if (base_reg) {
+                            uni_vaddps(b0, b0, Vmm(base_reg * 5 + 0));
+                            uni_vaddps(b1, b1, Vmm(base_reg * 5 + 1));
+                        }
+                    });
+            uni_vmovups(vmmword[reg_rbuf1 + reg_coff], Vmm(0));
+            uni_vmovups(vmmword[reg_rbuf2 + reg_coff], Vmm(1));
+            add(reg_coff, vlen);
+            cmp(reg_coff, reg_coff_max);
+            jl(sh_channels);
+        }
+    }
+
+    void backward_diff_channels() {
+        Label diff_channels;
+        L(diff_channels); {
+            uni_vmovups_maybe_tail(vmean, mean_ptr());
+            uni_vmovups_maybe_tail(vsqrtvar, var_ptr());
+            uni_vaddps(vsqrtvar, vsqrtvar, veps);
+            uni_vsqrtps(vsqrtvar, vsqrtvar);
+            uni_vdivps(vsqrtvar, vone, vsqrtvar, vbuf);
+            if (bdesc_->use_scaleshift())
+                uni_vmovups_maybe_tail(vgamma, gamma_ptr());
+            uni_vmovups_maybe_tail(vdiff_gamma, diff_gamma_ptr());
+            uni_vmovups_maybe_tail(vdiff_beta, diff_beta_ptr());
+            uni_vmulps(vdiff_gamma, vdiff_gamma, vsqrtvar);
+            uni_vdivps(vdiff_beta, vdiff_beta, vchan_size);
+            uni_vdivps(vdiff_gamma, vdiff_gamma, vchan_size);
+
+            auto compute = [=](bool output_is_aligned) {
+                spat_loop(spat_size, unroll_blocks, unroll_regs,
+                        [=](size_t base_reg) {UNUSED(base_reg);},
+                        [=](size_t base_reg, size_t i) {
+                            Vmm v(base_reg * 2 + 0);
+                            Vmm t(base_reg * 2 + 1);
+                            Vmm t1(base_reg * 2 + 2);
+                            size_t offt = i * vlen;
+                            uni_vmovups(v, vmmword[reg_diff_dst + reg_soff
+                                    + offt]);
+                            if (with_relu) {
+                                if (isa == avx512_common)
+                                    bwd_process_relu_avx512_common(v, offt);
+                                else if (isa == avx2)
+                                    bwd_process_relu_avx2(v, offt, t);
+                                else
+                                    assert(false);
+                            }
+                            if (!bdesc_->use_global_stats()) {
+                                uni_vsubps(v, v, vdiff_beta);
+                                uni_vmovups(t, vmmword[reg_src + reg_soff
+                                        + offt]);
+                                uni_vsubps(t, vmean, t, t1);
+                                uni_vmulps(t, t, vdiff_gamma);
+                                uni_vaddps(v, v, t);
+                            }
+                            uni_vmulps(v, v, vsqrtvar);
+                            if (bdesc_->use_scaleshift()) {
+                                uni_vmulps(v, v, vgamma);
+                            }
+                            if (output_is_aligned) {
+                                uni_vmovntps(
+                                    vmmword[reg_diff_src + reg_soff + offt],
+                                    v);
+                            } else {
+                                uni_vmovups(
+                                    vmmword[reg_diff_src + reg_soff + offt],
+                                    v);
+                            }
+                            mic_prefetcht0(ptr[reg_diff_dst + reg_soff + offt
+                                    + t0_pf_offt]);
+                            mic_prefetcht0(ptr[reg_src + reg_soff + offt
+                                    + t0_pf_offt]);
+                            mic_prefetcht1(ptr[reg_diff_dst + reg_soff
+                                    + offt + t1_pf_offt]);
+                            mic_prefetcht1(ptr[reg_src + reg_soff + offt
+                                    + t1_pf_offt]);
+                        },
+                        [=](size_t base_reg) {UNUSED(base_reg);});
+            };
+
+            Label unaligned_store, end_store;
+            test(reg_diff_src, vlen - 1);
+            jnz(unaligned_store, T_NEAR);
+            compute(true);
+            jmp(end_store, T_NEAR);
+            L(unaligned_store); {
+                compute(false);
+            }
+            L(end_store);
+
+            add(reg_coff, vlen);
+            cmp(reg_coff, reg_coff_max);
+            jl(diff_channels);
+        }
+    }
+
+    void backward() {
+        uni_vpxor(Vmm(0), Vmm(0), Vmm(0));
+        xor_(reg_coff, reg_coff);
+        Label zero_rbuf, sh_spatial;
+
+        L(zero_rbuf); {
+            uni_vmovups(vmmword[reg_rbuf1 + reg_coff], Vmm(0));
+            uni_vmovups(vmmword[reg_rbuf2 + reg_coff], Vmm(0));
+            add(reg_coff, isa == sse42 ? vlen / 2 : vlen);
+            cmp(reg_coff, reg_coff_max);
+            jne(zero_rbuf);
+        }
+
+        mov(reg_src, ptr[rsp + stack_off_src]);
+        mov(reg_diff_dst, ptr[rsp + stack_off_diff_dst]);
+        if (with_relu) {
+            assert(isa == avx2 || isa == avx512_common);
+            mov(reg_ws, ptr[rsp + stack_off_ws]);
+        }
+
+        xor_(reg_soff, reg_soff);
+        L(sh_spatial); {
+            xor_(reg_coff, reg_coff);
+            if (isa == sse42) {
+                mov(reg_tmp_off, reg_soff);
+            }
+            backward_sh_channels();
+            if (isa == sse42) {
+                mov(reg_soff, reg_tmp_off);
+                add(reg_diff_dst, vlen / 2);
+                add(reg_src, vlen / 2);
+                mov(reg_coff, vlen / 2);
+                backward_sh_channels();
+                sub(reg_diff_dst, vlen / 2);
+                sub(reg_src, vlen / 2);
+            }
+            add(reg_soff, reg_mb_stride_Bc);
+            cmp(reg_soff, reg_soff_max);
+            jne(sh_spatial);
+        }
+
+        mov(reg_diff_scale_shift, ptr[rsp + stack_off_diff_scale_shift]);
+
+        Label no_sh_reduction;
+        barrier(); {
+            mov(reg_tmp, ptr[rsp + stack_off_N_ithr]);
+            cmp(reg_tmp, 0);
+            Label sh_reduction_channels;
+            jne(no_sh_reduction, T_NEAR);
+
+            mov(reg_nnthr, ptr[rsp + stack_off_N_nthr]);
+            xor_(reg_coff, reg_coff);
+            L(sh_reduction_channels); {
+                mov(reg_roff, reg_coff);
+                uni_vpxor(Vmm(0), Vmm(0), Vmm(0));
+                uni_vpxor(Vmm(1), Vmm(1), Vmm(1));
+                uni_vmovups_maybe_tail(vsqrtvar, var_ptr());
+                uni_vaddps(vsqrtvar, vsqrtvar, veps);
+                uni_vsqrtps(vsqrtvar, vsqrtvar);
+                uni_vdivps(vsqrtvar, vone, vsqrtvar, vbuf);
+                mov(reg_ctr, reg_nnthr);
+                Label sh_reduction_thrs;
+                L(sh_reduction_thrs); { // TODO: unroll (?)
+                    uni_vaddps(Vmm(0), Vmm(0), vmmword[reg_rbuf1 + reg_roff]);
+                    uni_vaddps(Vmm(1), Vmm(1), vmmword[reg_rbuf2 + reg_roff]);
+                    add(reg_roff, reg_coff_max);
+                    sub(reg_ctr, 1);
+                    jnz(sh_reduction_thrs);
+                }
+                uni_vmulps(Vmm(0), Vmm(0), vsqrtvar);
+                uni_vmovups_maybe_tail(diff_gamma_ptr(), Vmm(0));
+                uni_vmovups_maybe_tail(diff_beta_ptr(), Vmm(1));
+                add(reg_coff, isa == sse42 ? vlen / 2 : vlen);
+                cmp(reg_coff, reg_coff_max);
+                jne(sh_reduction_channels);
+            }
+        }
+        L(no_sh_reduction);
+        barrier();
+
+        mov(reg_diff_src, ptr[rsp + stack_off_diff_src]);
+        if (with_relu) {
+            assert(isa == avx2 || isa == avx512_common);
+            mov(reg_ws, ptr[rsp + stack_off_ws]);
+        }
+
+        xor_(reg_soff, reg_soff);
+        Label diff_spatial;
+        L(diff_spatial); {
+            xor_(reg_coff, reg_coff);
+            if (isa == sse42) {
+                mov(reg_tmp_off, reg_soff);
+            }
+            backward_diff_channels();
+            if (isa == sse42) {
+                mov(reg_soff, reg_tmp_off);
+                add(reg_diff_dst, vlen / 2);
+                add(reg_diff_src, vlen / 2);
+                add(reg_src, vlen / 2);
+                mov(reg_coff, vlen / 2);
+                backward_diff_channels();
+                sub(reg_diff_dst, vlen / 2);
+                sub(reg_diff_src, vlen / 2);
+                sub(reg_src, vlen / 2);
+            }
+            add(reg_soff, reg_mb_stride_Bc);
+            cmp(reg_soff, reg_soff_max);
+            jne(diff_spatial);
+        }
+    }
+
+    jit_bnorm_t(const batch_normalization_pd_t *bdesc): bdesc_(bdesc) {
+        static_assert(isa == sse42 || isa == avx2 || isa == avx512_common
+                || isa == avx512_mic, "unsupported isa");
+
+        const int simd_w = isa == sse42 ? 8 :
+            cpu_isa_traits<isa>::vlen / sizeof(data_t);
+        is_spatial_thr_ =
+            bnorm_utils::is_spatial_thr(bdesc_, simd_w, sizeof(data_t));
+
+        unroll_blocks = isa == avx512_common && !is_spatial_thr_ ? 4 : 1;
+        unroll_regs = isa == avx512_common && !is_spatial_thr_ ? 4 : 1;
+
+        preamble();
+
+        if (isa == avx512_common)
+            prepare_tail_mask_avx512_common();
+        else if (isa == avx2)
+            prepare_tail_mask_avx2_common();
+
+        compute_static_strides();
+        sub(rsp, stack_size_required);
+        load_common_params();
+        prepare_relu();
+
+        if (bdesc_->is_fwd()) {
+            if (!bdesc_->stats_is_src()) {
+                compute_mean_variance();
+            }
+            forward();
+        } else {
+            backward();
+        }
+        add(rsp, stack_size_required);
+        postamble();
+
+        ker = reinterpret_cast<decltype(ker)>(const_cast<uint8_t*>(
+                    this->getCode()));
+    }
+};
+
+template <cpu_isa_t isa>
+struct uni_bnorm_driver_t: public c_compatible {
+    uni_bnorm_driver_t(const batch_normalization_pd_t *bdesc)
+        : bdesc_(bdesc), ker_(bdesc_)
+    {
+        const int nthrs = mkldnn_get_max_threads();
+        const dim_t C_PADDED = get_c_padded(bdesc_);
+
+        size_t data_size = sizeof(data_t) * bdesc_->MB() * C_PADDED
+            * bdesc_->D() * bdesc_->H() * bdesc_->W();
+        l3_size_ = get_cache_size(3, true) * nthrs / 2;
+        do_blocking_ = (data_size >= l3_size_ / 2 && l3_size_ > 0);
+    }
+
+    ~uni_bnorm_driver_t() {}
+
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const batch_normalization_pd_t *bdesc) {
+        int nthrs = mkldnn_get_max_threads();
+        dim_t C_PADDED = get_c_padded(bdesc);
+
+        int sbuf_sz = use_tmp_stats(bdesc) * 2 * C_PADDED;
+        int pbuf_sz = use_tmp_diff_scale_shift(bdesc) * 2 * C_PADDED;
+        int rbuf_sz = (bdesc->is_fwd() ? 1 : 2) * C_PADDED * nthrs;
+
+        scratchpad.book(key_bnorm_tmp_stats, sizeof(data_t) * sbuf_sz);
+        scratchpad.book(key_bnorm_tmp_diff_ss, sizeof(data_t) * pbuf_sz);
+        scratchpad.book(key_bnorm_reduction, sizeof(data_t) * rbuf_sz);
+
+        if (mkldnn_thr_syncable()) {
+            int n_barriers = C_PADDED / simd_w;
+            scratchpad.book(key_barrier, sizeof(barrier::ctx_t) * n_barriers);
+        }
+    }
+
+    void exec(int ithr, int nthr, const data_t *src, data_t *diff_src,
+            data_t *dst, const data_t *diff_dst, const data_t *scale_shift,
+            data_t *diff_scale_shift, const data_t *mean, const data_t *var,
+            const uint8_t *ws, const memory_tracking::grantor_t &scratchpad) {
+        auto sbuf = scratchpad.get<data_t>(key_bnorm_tmp_stats);
+        auto pbuf = scratchpad.get<data_t>(key_bnorm_tmp_diff_ss);
+        auto rbuf = scratchpad.get<data_t>(key_bnorm_reduction);
+        auto barriers = scratchpad.get<barrier::ctx_t>(key_barrier);
+
+        dim_t N = bdesc_->MB();
+        dim_t C = bdesc_->C();
+        dim_t C_PADDED = get_c_padded(bdesc_);
+        dim_t D = bdesc_->D();
+        dim_t H = bdesc_->H();
+        dim_t W = bdesc_->W();
+        dim_t SP = D * H * W;
+        dim_t img_size = C_PADDED * D * H * W;
+        const int vlen = isa == sse42 ? 32 : cpu_isa_traits<isa>::vlen;
+
+        typename jit_bnorm_t<isa>::call_params_t p;
+
+        p.eps = bdesc_->desc()->batch_norm_epsilon;
+        p.one = 1.0f;
+        p.spat_size = D * H * W;
+        p.chan_size = 1.0f * N * p.spat_size;
+
+        dim_t C_blks = C_PADDED / simd_w;
+
+        int C_ithr{0}, C_nthr{0}, N_ithr{0}, N_nthr{0}, S_ithr{0}, S_nthr{0};
+        dim_t C_blk_s{0}, C_blk_e{0}, N_s{0}, N_e{0}, S_s{0}, S_e{0};
+
+        dim_t C_blks_per_iter{ 1 };
+        int64_t iters{ 1 };
+        if (do_blocking_) {
+            int num_tensors = bdesc_->is_fwd() ? 1 : 2;
+            size_t working_set_size
+                = (N * D * H * W * simd_w * sizeof(data_t)) * num_tensors;
+            bnorm_utils::cache_balance(working_set_size, C_blks,
+                C_blks_per_iter, iters);
+        }
+
+        bool spatial_thr_allowed = bnorm_utils::thread_balance(do_blocking_,
+                true, ithr, nthr, N, do_blocking_ ? C_blks_per_iter : C_blks,
+                SP, C_ithr, C_nthr, C_blk_s, C_blk_e, N_ithr, N_nthr, N_s, N_e,
+                S_ithr, S_nthr, S_s, S_e);
+
+        int SP_N_ithr = N_ithr * S_nthr + S_ithr;
+        int SP_N_nthr = N_nthr * S_nthr;
+        assert(IMPLICATION(!mkldnn_thr_syncable(), SP_N_nthr == 1));
+
+        p.N_ithr = SP_N_ithr;
+        p.N_nthr = SP_N_nthr;
+
+        int last_iter_blks = C_blks - (iters - 1) * C_blks_per_iter;
+        int global_C_blk_s;
+        int global_barriers_per_iter = C_nthr;
+
+        for (int64_t it = 0; it < iters; it++) {
+            if (it == iters - 1 && iters > 1) {
+                C_blk_s = C_blk_e = N_s = N_e = 0;
+                spatial_thr_allowed = bnorm_utils::thread_balance(do_blocking_,
+                        spatial_thr_allowed, ithr, nthr, N, last_iter_blks, SP,
+                        C_ithr, C_nthr, C_blk_s, C_blk_e, N_ithr, N_nthr, N_s,
+                        N_e, S_ithr, S_nthr, S_s, S_e);
+
+                // Update call parameters for JIT, last iteration
+                p.N_ithr = N_ithr * S_nthr + S_ithr;
+                p.N_nthr = N_nthr * S_nthr;
+            }
+
+            global_C_blk_s = do_blocking_ ?
+                    (C_blk_s == -1) ? -1 : it * C_blks_per_iter + C_blk_s :
+                    C_blk_s;
+
+            int C_blks_thr = C_blk_e - C_blk_s;
+            int N_thr = N_e - N_s;
+
+            size_t coff_base = global_C_blk_s * simd_w;
+            size_t soff_base
+                    = global_C_blk_s * p.spat_size * simd_w + N_s * img_size;
+
+            p.spat_size_loc = S_e - S_s;
+            p.S_s = S_s * vlen;
+            p.S_tail = (p.spat_size - S_e) * vlen;
+            p.coff_max = C_blks_thr * simd_w;
+            p.mean = (use_tmp_stats(bdesc_) ? sbuf : mean) + coff_base;
+            p.var = (use_tmp_stats(bdesc_) ? sbuf + C_PADDED : var) + coff_base;
+            p.scale_shift = scale_shift + coff_base;
+            p.diff_scale_shift = (use_tmp_diff_scale_shift(bdesc_)
+                    ? pbuf : diff_scale_shift) + coff_base;
+
+            p.soff_max = N_thr * img_size;
+            p.src = src + soff_base;
+            p.dst = dst + soff_base;
+            p.diff_src = diff_src + soff_base;
+            p.diff_dst = diff_dst + soff_base;
+            p.ws = ws + soff_base / 8;
+
+            p.mb_stride_Bc = img_size - p.coff_max * p.spat_size;
+
+            // use SP_N_nthr which is the same as p.N_nthr except maybe for
+            // the last iteration.
+            p.rbuf1 = rbuf + ((it * C_blks_per_iter) * SP_N_nthr
+                    + C_blk_s * p.N_nthr + p.N_ithr * C_blks_thr) * simd_w;
+            // rbuf1 and rbuf2 have to be disjoint
+            p.rbuf2 = p.rbuf1 + C_PADDED * nthr;
+            p.is_cblk_tail = (it * C_blks_per_iter + C_blk_e) * simd_w > C;
+
+            size_t iter_bariers
+                    = do_blocking_ ? it * global_barriers_per_iter : 0;
+            p.barrier = barriers + C_ithr + iter_bariers;
+            if (p.soff_max != 0 && p.coff_max != 0)
+                ker_(&p);
+        }
+    }
+
+    void init_barriers(const memory_tracking::grantor_t &scratchpad) {
+        auto barriers = scratchpad.get<barrier::ctx_t>(key_barrier);
+        if (barriers) {
+            const int n_barriers = get_c_padded(bdesc_) / simd_w;
+            for (int i = 0; i < n_barriers; ++i)
+                barrier::ctx_init(&barriers[i]);
+        }
+    }
+
+private:
+    enum {
+        simd_w = isa == sse42 ? 8 : cpu_isa_traits<isa>::vlen / sizeof(data_t)
+    };
+
+    static bool use_tmp_stats(const batch_normalization_pd_t *bdesc) {
+        return true
+            && !bdesc->stats_is_src()
+            && bdesc->desc()->prop_kind == prop_kind::forward_inference;
+    }
+
+    static bool use_tmp_diff_scale_shift(const batch_normalization_pd_t *bdesc)
+    {
+        return false
+            || (bdesc->is_bwd() && !bdesc->use_scaleshift())
+            || bdesc->desc()->prop_kind == prop_kind::backward_data;
+    }
+
+    static dim_t get_c_padded(const batch_normalization_pd_t *bdesc)
+    { return bdesc->src_md()->padded_dims[1]; }
+
+    const batch_normalization_pd_t *bdesc_;
+    bool do_blocking_;
+    size_t l3_size_;
+
+    jit_bnorm_t<isa> ker_;
+};
+
+}
+
+using namespace data_type;
+using namespace format_tag;
+using namespace utils;
+
+/* fwd */
+
+template <cpu_isa_t isa>
+status_t jit_uni_batch_normalization_fwd_t<isa>::pd_t::init() {
+    auto desired_fmt_tag = (ndims() == 4)
+        ? isa == avx512_common ? nChw16c : nChw8c
+        : isa == avx512_common ? nCdhw16c : nCdhw8c;
+
+    bool ok = true
+        && mayiuse(isa)
+        && is_fwd()
+        && !has_zero_dim_memory()
+        && one_of(ndims(), 4, 5)
+        && src_md()->data_type == f32
+        && IMPLICATION(use_scaleshift(), weights_md()->data_type == f32)
+        && memory_desc_matches_tag(*src_md(), desired_fmt_tag)
+        && (attr()->has_default_values() || this->with_relu_post_op());
+    if (!ok) return status::unimplemented;
+
+    if (is_training() && fuse_bn_relu()) {
+        if (isa < avx2) return status::unimplemented;
+        init_default_ws(1);
+    }
+
+    if (memory_desc_wrapper(src_md()).padded_dims()[1] != C()
+            && isa < avx2)
+        return status::unimplemented;
+
+    auto scratchpad = scratchpad_registry().registrar();
+    uni_bnorm_driver_t<isa>::init_scratchpad(scratchpad, this);
+
+    return status::success;
+}
+
+template <cpu_isa_t isa>
+jit_uni_batch_normalization_fwd_t<isa>::jit_uni_batch_normalization_fwd_t(
+        const pd_t *apd): cpu_primitive_t(apd)
+{ bnorm_driver_ = new uni_bnorm_driver_t<isa>(pd()); }
+
+template <cpu_isa_t isa>
+status_t jit_uni_batch_normalization_fwd_t<isa>::execute(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto scale_shift = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SCALE_SHIFT);
+
+    auto mean = pd()->stats_is_src()
+        ? const_cast<data_t *>(CTX_IN_MEM(const data_t *, MKLDNN_ARG_MEAN))
+        : CTX_OUT_MEM(data_t *, MKLDNN_ARG_MEAN);
+    auto var = pd()->stats_is_src()
+        ? const_cast<data_t *>(CTX_IN_MEM(const data_t *, MKLDNN_ARG_VARIANCE))
+        : CTX_OUT_MEM(data_t *, MKLDNN_ARG_VARIANCE);
+
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+    auto ws = CTX_OUT_MEM(uint8_t *, MKLDNN_ARG_WORKSPACE);
+
+    auto scratchpad = this->scratchpad(ctx);
+
+    bnorm_driver_->init_barriers(scratchpad);
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        bnorm_driver_->exec(ithr, nthr, src, nullptr, dst, nullptr,
+                scale_shift, nullptr, mean, var, ws, scratchpad);
+    });
+
+    return status::success;
+}
+
+template <cpu_isa_t isa>
+jit_uni_batch_normalization_fwd_t<isa>::~jit_uni_batch_normalization_fwd_t()
+{ delete bnorm_driver_; }
+
+/* bwd */
+
+template <cpu_isa_t isa>
+status_t jit_uni_batch_normalization_bwd_t<isa>::pd_t::init() {
+    auto desired_fmt_tag = (ndims() == 4)
+        ? one_of(isa, sse42, avx2) ? nChw8c : nChw16c
+        : one_of(isa, sse42, avx2) ? nCdhw8c : nCdhw16c;
+
+    bool ok = true
+        && mayiuse(isa)
+        && is_bwd()
+        && !has_zero_dim_memory()
+        && one_of(ndims(), 4, 5)
+        && everyone_is(f32, src_md()->data_type, diff_src_md()->data_type)
+        && IMPLICATION(use_scaleshift(),
+                utils::everyone_is(f32,
+                    weights_md()->data_type,
+                    diff_weights_md()->data_type))
+        && memory_desc_matches_tag(*src_md(), desired_fmt_tag)
+        && memory_desc_matches_tag(*diff_src_md(), desired_fmt_tag)
+        && attr()->has_default_values();
+    if (!ok) return status::unimplemented;
+
+    if (memory_desc_wrapper(src_md()).padded_dims()[1] != C()
+            && isa < avx2)
+        return status::unimplemented;
+
+    if (fuse_bn_relu()) {
+        if (isa < avx2) return status::unimplemented;
+        init_default_ws(1);
+        if (!compare_ws(hint_fwd_pd_))
+            return status::unimplemented;
+    }
+
+    /* TODO: extra checks required */
+
+    auto scratchpad = scratchpad_registry().registrar();
+    uni_bnorm_driver_t<isa>::init_scratchpad(scratchpad, this);
+
+    return status::success;
+}
+
+template <cpu_isa_t isa>
+jit_uni_batch_normalization_bwd_t<isa>::jit_uni_batch_normalization_bwd_t(
+        const pd_t *apd): cpu_primitive_t(apd)
+{ bnorm_driver_ = new uni_bnorm_driver_t<isa>(pd()); }
+
+template <cpu_isa_t isa>
+status_t jit_uni_batch_normalization_bwd_t<isa>::execute(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto mean = CTX_IN_MEM(const data_t *, MKLDNN_ARG_MEAN);
+    auto var = CTX_IN_MEM(const data_t *, MKLDNN_ARG_VARIANCE);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto scale_shift = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SCALE_SHIFT);
+    auto ws = CTX_IN_MEM(const uint8_t *, MKLDNN_ARG_WORKSPACE);
+
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+    auto diff_scale_shift = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SCALE_SHIFT);
+
+    auto scratchpad = this->scratchpad(ctx);
+
+    bnorm_driver_->init_barriers(scratchpad);
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        bnorm_driver_->exec(ithr, nthr, src, diff_src, nullptr, diff_dst,
+                scale_shift, diff_scale_shift, mean, var, ws, scratchpad);
+    });
+
+    return status::success;
+}
+
+template <cpu_isa_t isa>
+jit_uni_batch_normalization_bwd_t<isa>::~jit_uni_batch_normalization_bwd_t()
+{ delete bnorm_driver_; }
+
+/* struct instantiation */
+template struct jit_uni_batch_normalization_fwd_t<sse42>;
+template struct jit_uni_batch_normalization_bwd_t<sse42>;
+template struct jit_uni_batch_normalization_fwd_t<avx2>;
+template struct jit_uni_batch_normalization_bwd_t<avx2>;
+template struct jit_uni_batch_normalization_fwd_t<avx512_common>;
+template struct jit_uni_batch_normalization_bwd_t<avx512_common>;
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_batch_normalization.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_batch_normalization.hpp
new file mode 100644
index 0000000000..96410ec84e
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_batch_normalization.hpp
@@ -0,0 +1,100 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_UNI_BATCH_NORMALIZATION_HPP
+#define JIT_UNI_BATCH_NORMALIZATION_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_batch_normalization_pd.hpp"
+#include "cpu_isa_traits.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace { template <cpu_isa_t isa> struct uni_bnorm_driver_t; }
+
+template <cpu_isa_t isa>
+struct jit_uni_batch_normalization_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_batch_normalization_fwd_pd_t {
+        pd_t(engine_t *engine, const batch_normalization_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const batch_normalization_fwd_pd_t *hint_fwd_pd)
+            : cpu_batch_normalization_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+        {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", isa, ""),
+                jit_uni_batch_normalization_fwd_t<isa>);
+
+        status_t init();
+    };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    jit_uni_batch_normalization_fwd_t(const pd_t *apd);
+    ~jit_uni_batch_normalization_fwd_t();
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override;
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    uni_bnorm_driver_t<isa> *bnorm_driver_;
+};
+
+template <cpu_isa_t isa>
+struct jit_uni_batch_normalization_bwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_batch_normalization_bwd_pd_t {
+        pd_t(engine_t *engine, const batch_normalization_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const batch_normalization_fwd_pd_t *hint_fwd_pd)
+            : cpu_batch_normalization_bwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+        {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", isa, ""),
+                jit_uni_batch_normalization_bwd_t<isa>);
+
+        status_t init();
+    };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    jit_uni_batch_normalization_bwd_t(const pd_t *apd);
+    ~jit_uni_batch_normalization_bwd_t();
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override;
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    uni_bnorm_driver_t<isa> *bnorm_driver_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_f32.cpp
new file mode 100644
index 0000000000..b7dc5f85c5
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_f32.cpp
@@ -0,0 +1,1302 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+#include "cpu_memory.hpp"
+
+#include "jit_uni_dw_conv_kernel_f32.hpp"
+
+#define GET_OFF(field) offsetof(jit_conv_call_s, field)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::prop_kind;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+
+using namespace Xbyak;
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_fwd_kernel_f32<isa>::load_src(int ur_ch_blocks, int ur_w) {
+    int repeats = isa == sse42 ? 2 : 1;
+    for (int i = 0; i < repeats; i++) {
+        for (int ch = 0; ch < ur_ch_blocks; ch++) {
+            for (int ow = 0; ow < ur_w; ow++) {
+                Vmm vmm_acc = get_acc_reg(i*ur_ch_blocks*ur_w + ch*ur_w + ow);
+
+                int b_off = ch*jcp.ch_block + i*4;
+                if (this->jcp.with_bias)
+                    uni_vmovups(vmm_acc,
+                        vmmword[reg_bias + b_off*sizeof(float)]);
+                else
+                    uni_vpxor(vmm_acc, vmm_acc, vmm_acc);
+
+                int o_off = ch*jcp.oh*jcp.ow*jcp.ch_block
+                    + ow*jcp.ch_block + i*4;
+                if (this->jcp.with_sum)
+                    uni_vaddps(vmm_acc, vmm_acc,
+                        vmmword[reg_output + o_off*sizeof(float)]);
+            }
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_fwd_kernel_f32<isa>::apply_filter(
+        int ur_ch_blocks, int ur_w) {
+    int ch_blk = jcp.ch_block;
+    int dilate_h = jcp.dilate_h + 1;
+    int dilate_w = jcp.dilate_w + 1;
+    int stride_w = jcp.stride_w;
+
+    Label iter_exit_label;
+
+    cmp(reg_kh, 0);
+    je(iter_exit_label, T_NEAR);
+    cmp(reg_kw, 0);
+    je(iter_exit_label, T_NEAR);
+
+    mov(iter_kh, reg_kh);
+    Label kh_label;
+    L(kh_label); {
+        mov(iter_kw, reg_kw);
+        mov(aux1_reg_input, aux_reg_input);
+        mov(aux1_reg_kernel, aux_reg_kernel);
+
+        Label kw_label;
+        L(kw_label); {
+            int repeats = isa == sse42 ? 2 : 1;
+            for (int i = 0; i < repeats; i++) {
+                for (int ch = 0; ch < ur_ch_blocks; ch++) {
+                    int ker_off = ch*jcp.kh*jcp.kw*ch_blk + i*4;
+                    Vmm vmm_ker = get_ker_reg(0);
+                    uni_vmovups(vmm_ker, ptr[aux1_reg_kernel
+                        + ker_off*sizeof(float)]);
+
+                    for (int ow = 0; ow < ur_w; ow++) {
+                        int inp_off = ch*jcp.ih*jcp.iw*ch_blk
+                            + ow*stride_w*ch_blk + i*4;
+                        Vmm vmm_src = get_src_reg(0);
+                        uni_vmovups(vmm_src, ptr[aux1_reg_input
+                            + inp_off*sizeof(float)]);
+
+                        Vmm vmm_acc = get_acc_reg(i*ur_ch_blocks*ur_w
+                            + ch*ur_w + ow);
+                        uni_vfmadd231ps(vmm_acc, vmm_src, vmm_ker);
+                    }
+                }
+            }
+            add(aux1_reg_kernel, ch_blk*sizeof(float));
+            add(aux1_reg_input, ch_blk*dilate_w*sizeof(float));
+
+            dec(iter_kw);
+            cmp(iter_kw, 0);
+            jg(kw_label, T_NEAR);
+        }
+        add(aux_reg_kernel, jcp.kw*ch_blk*sizeof(float));
+        add(aux_reg_input, jcp.iw*ch_blk*dilate_h*sizeof(float));
+
+        dec(iter_kh);
+        cmp(iter_kh, 0);
+        jg(kh_label, T_NEAR);
+    }
+
+    L(iter_exit_label);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_fwd_kernel_f32<isa>::apply_filter_unrolled(
+        int ur_ch_blocks, int ur_w) {
+    int ch_blk = jcp.ch_block;
+    int dilate_h = jcp.dilate_h + 1;
+    int dilate_w = jcp.dilate_w + 1;
+    int stride_w = jcp.stride_w;
+
+    Label iter_exit_label;
+
+    cmp(reg_kh, 0);
+    je(iter_exit_label, T_NEAR);
+
+    mov(iter_kh, reg_kh);
+    Label kh_label;
+    L(kh_label); {
+        int repeats = isa == sse42 ? 2 : 1;
+        for (int i = 0; i < repeats; i++) {
+            for (int ch = 0; ch < ur_ch_blocks; ch++) {
+                for (int kw = 0; kw < jcp.kw; kw++) {
+                    int ker_off = ch*jcp.kh*jcp.kw*ch_blk + kw*ch_blk + i*4;
+
+                    Vmm vmm_ker = get_ker_reg(0);
+                    uni_vmovups(vmm_ker, ptr[aux_reg_kernel
+                        + ker_off*sizeof(float)]);
+
+                    for (int ow = 0; ow < ur_w; ow++) {
+                        int inp_off = ch*jcp.ih*jcp.iw*ch_blk
+                            + ow*stride_w*ch_blk + kw*ch_blk*dilate_w + i*4;
+
+                        Vmm vmm_src = get_src_reg(0);
+                        uni_vmovups(vmm_src, ptr[aux_reg_input
+                            + inp_off*sizeof(float)]);
+
+                        Vmm vmm_acc = get_acc_reg(i*ur_ch_blocks*ur_w
+                            + ch*ur_w + ow);
+                        uni_vfmadd231ps(vmm_acc, vmm_src, vmm_ker);
+                    }
+                }
+            }
+        }
+
+        add(aux_reg_kernel, jcp.kw*ch_blk*sizeof(float));
+        add(aux_reg_input, jcp.iw*ch_blk*dilate_h*sizeof(float));
+
+        dec(iter_kh);
+        cmp(iter_kh, 0);
+        jg(kh_label, T_NEAR);
+    }
+
+    L(iter_exit_label);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_fwd_kernel_f32<isa>::apply_activation(
+        int ur_ch_blocks, int ur_w) {
+    if (this->jcp.with_eltwise) {
+        int repeats = isa == sse42 ? 2 : 1;
+        eltwise_injector_->compute_vector_range(4, repeats * ur_w * ur_ch_blocks + 4);
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_fwd_kernel_f32<isa>::store_dst(
+        int ur_ch_blocks, int ur_w) {
+    int ch_blk = jcp.ch_block;
+
+    int repeats = isa == sse42 ? 2 : 1;
+    for (int i = 0; i < repeats; i++) {
+        for (int ch = 0; ch < ur_ch_blocks; ch++) {
+            for (int ow = 0; ow < ur_w; ow++) {
+                int o_off = ch*jcp.oh*jcp.ow*ch_blk + ow*ch_blk + i*4;
+                Vmm vmm_dst = get_acc_reg(i*ur_ch_blocks*ur_w + ch*ur_w + ow);
+
+                uni_vmovups(vmmword[reg_output + o_off*sizeof(float)], vmm_dst);
+            }
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_fwd_kernel_f32<isa>::loop_body(int ur_ch_blocks) {
+    Label unrolled_w_label;
+    Label tail_w_label;
+    Label exit_label;
+
+    L(unrolled_w_label); {
+        int ur_w = jcp.ur_w;
+
+        cmp(reg_ur_w, ur_w);
+        jl(tail_w_label, T_NEAR);
+
+        mov(aux_reg_input, reg_input);
+        mov(aux_reg_kernel, reg_kernel);
+
+        load_src(ur_ch_blocks, ur_w);
+        apply_filter_unrolled(ur_ch_blocks, ur_w);
+        apply_activation(ur_ch_blocks, ur_w);
+        store_dst(ur_ch_blocks, ur_w);
+
+        add(reg_input, sizeof(float) * ur_w * jcp.ch_block * jcp.stride_w);
+        add(reg_output, sizeof(float) * ur_w * jcp.ch_block);
+
+        sub(reg_ur_w, ur_w);
+        jmp(unrolled_w_label);
+    }
+
+    L(tail_w_label); {
+        int ur_w = 1;
+
+        cmp(reg_ur_w, ur_w);
+        jl(exit_label, T_NEAR);
+
+        mov(aux_reg_input, reg_input);
+        mov(aux_reg_kernel, reg_kernel);
+
+        load_src(ur_ch_blocks, ur_w);
+        apply_filter(ur_ch_blocks, ur_w);
+        apply_activation(ur_ch_blocks, ur_w);
+        store_dst(ur_ch_blocks, ur_w);
+
+        add(reg_input, sizeof(float) * ur_w * jcp.ch_block * jcp.stride_w);
+        add(reg_output, sizeof(float) * ur_w * jcp.ch_block);
+
+        sub(reg_ur_w, ur_w);
+        jmp(tail_w_label);
+    }
+
+    L(exit_label);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_fwd_kernel_f32<isa>::generate() {
+    this->preamble();
+
+    mov(reg_input, ptr[this->param1 + GET_OFF(src)]);
+    mov(reg_output, ptr[this->param1 + GET_OFF(dst)]);
+    mov(reg_kernel, ptr[this->param1 + GET_OFF(filt)]);
+    if (jcp.with_bias)
+        mov(reg_bias, ptr[this->param1 + GET_OFF(bias)]);
+    mov(reg_kh, ptr[this->param1 + GET_OFF(kh_padding)]);
+    mov(reg_kw, ptr[this->param1 + GET_OFF(kw_padding)]);
+    mov(reg_ch_blocks, ptr[this->param1 + GET_OFF(ch_blocks)]);
+    mov(reg_ur_w, ptr[this->param1 + GET_OFF(ur_w)]);
+
+    Label ch_blocks_tail_label;
+    Label exit_label;
+
+    int ch_blocks_tail = jcp.nb_ch % jcp.nb_ch_blocking;
+
+    cmp(reg_ch_blocks, jcp.nb_ch_blocking);
+    jne(ch_blocks_tail ? ch_blocks_tail_label : exit_label, T_NEAR);
+
+    loop_body(jcp.nb_ch_blocking); // channel main loop
+
+    if (ch_blocks_tail) {
+        L(ch_blocks_tail_label);
+
+        cmp(reg_ch_blocks, ch_blocks_tail);
+        jne(exit_label, T_NEAR);
+
+        loop_body(ch_blocks_tail); // channel tail loop
+    }
+
+    L(exit_label);
+
+    this->postamble();
+
+    if (jcp.with_eltwise)
+        eltwise_injector_->prepare_table();
+}
+
+template <cpu_isa_t isa>
+bool jit_uni_dw_conv_fwd_kernel_f32<isa>::post_ops_ok(
+        jit_conv_conf_t &jcp, const primitive_attr_t &attr) {
+    const auto &p = attr.post_ops_;
+
+    auto is_eltwise = [&](int idx) { return p.entry_[idx].is_eltwise(); };
+    auto is_sum = [&](int idx) { return p.entry_[idx].is_sum(); };
+
+    switch (p.len_) {
+    case 0: return true; // no post_ops
+    case 1: return is_eltwise(0) || is_sum(0); // sum OR eltwise
+    case 2: return is_sum(0) && is_eltwise(1); // sum -> eltwise
+    default: return false;
+    }
+
+    return false;
+}
+
+template <cpu_isa_t isa>
+status_t jit_uni_dw_conv_fwd_kernel_f32<isa>::init_conf(jit_conv_conf_t &jcp,
+        const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+        const memory_desc_wrapper &weights_d, const memory_desc_wrapper &dst_d,
+        const primitive_attr_t &attr)
+{
+    if (!mayiuse(isa)) return status::unimplemented;
+
+    const int simd_w = isa == avx512_common ? 16 : 8;
+
+    jcp.prop_kind = cd.prop_kind;
+
+    const bool with_groups = weights_d.ndims() == src_d.ndims() + 1;
+    if (!with_groups) return status::unimplemented;
+
+    jcp.ngroups = weights_d.dims()[0];
+    jcp.mb = src_d.dims()[0];
+
+    jcp.oc = dst_d.dims()[1];
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = src_d.dims()[1];
+
+    jcp.ih = src_d.dims()[2];
+    jcp.iw = src_d.dims()[3];
+    jcp.oh = dst_d.dims()[2];
+    jcp.ow = dst_d.dims()[3];
+
+    jcp.kh = weights_d.dims()[3];
+    jcp.kw = weights_d.dims()[4];
+
+    jcp.t_pad = cd.padding[0][0];
+    jcp.l_pad = cd.padding[0][1];
+    jcp.b_pad = cd.padding[1][0];
+    jcp.r_pad = cd.padding[1][1];
+
+    jcp.stride_h = cd.strides[0];
+    jcp.stride_w = cd.strides[1];
+
+    jcp.dilate_h = cd.dilates[0];
+    jcp.dilate_w = cd.dilates[1];
+
+    jcp.with_bias = cd.bias_desc.format_kind != format_kind::undef;
+
+    if (!post_ops_ok(jcp, attr))
+        return status::unimplemented;
+
+    const auto &p = attr.post_ops_;
+    jcp.with_sum = p.find(primitive_kind::sum) != -1;
+    const int eltwise_ind = p.find(primitive_kind::eltwise);
+    jcp.with_eltwise = eltwise_ind != -1;
+    if (jcp.with_eltwise)
+        jcp.eltwise = p.entry_[eltwise_ind].eltwise;
+
+    bool ok_to_pad_channels = true
+        && jcp.oc == jcp.ngroups
+        && jcp.ic == jcp.ngroups
+        && one_of(isa, avx512_common, avx2);
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, simd_w);
+        jcp.ic = rnd_up(jcp.oc, simd_w);
+        jcp.ngroups = rnd_up(jcp.ngroups, simd_w);
+    }
+
+    auto dat_tag = isa == avx512_common ? nChw16c : nChw8c;
+    auto wei_tag = isa == avx512_common ? Goihw16g : Goihw8g;
+
+    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    jcp.wei_tag = weights_d.matches_one_of_tag(wei_tag);
+    jcp.dst_tag = dst_d.matches_one_of_tag(dat_tag);
+
+    bool args_ok = true
+        && jcp.oc == jcp.ngroups
+        && jcp.ic == jcp.ngroups
+        && jcp.ngroups % simd_w == 0
+        && jcp.src_tag == dat_tag
+        && jcp.wei_tag == wei_tag
+        && jcp.dst_tag == dat_tag
+        && jcp.ic <= src_d.padded_dims()[1]
+        && jcp.oc <= dst_d.padded_dims()[1]
+        && jcp.ngroups <= weights_d.padded_dims()[0];
+    if (!args_ok) return status::unimplemented;
+
+    jcp.ur_w = isa == avx512_common ? 6 : isa == avx2 ? 4 : 3;
+
+    jcp.ch_block = simd_w;
+    jcp.nb_ch = jcp.oc / jcp.ch_block;
+    jcp.nb_ch_blocking = isa == avx512_common ? 4 : isa == avx2 ? 3 : 2;
+    if (jcp.nb_ch < jcp.nb_ch_blocking)
+        jcp.nb_ch_blocking = jcp.nb_ch;
+
+    return status::success;
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_fwd_kernel_f32<isa>::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad, const jit_conv_conf_t &jcp) {
+    if (jcp.with_bias && jcp.oc_without_padding != jcp.oc)
+        scratchpad.book(key_conv_padded_bias, sizeof(float) * jcp.oc);
+}
+
+template struct jit_uni_dw_conv_fwd_kernel_f32<avx512_common>;
+template struct jit_uni_dw_conv_fwd_kernel_f32<avx2>;
+template struct jit_uni_dw_conv_fwd_kernel_f32<sse42>;
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_data_kernel_f32<isa>::load_ddst(
+        int ur_ch_blocks, int ur_str_w) {
+    int repeats = isa == sse42 ? 2 : 1;
+    for (int i = 0; i < repeats; i++) {
+        for (int ch = 0; ch < ur_ch_blocks; ch++) {
+            for (int w = 0; w < ur_str_w; w++) {
+                Vmm vmm_acc = get_acc_reg(i*ur_ch_blocks*ur_str_w
+                    + ch*ur_str_w + w);
+                uni_vpxor(vmm_acc, vmm_acc, vmm_acc);
+            }
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_data_kernel_f32<isa>::apply_filter(
+        int ur_ch_blocks, int ur_str_w) {
+    int kw = jcp.kw;
+    int kh = jcp.kh;
+    int ow = jcp.ow;
+    int oh = jcp.oh;
+
+    int ch_blk = jcp.ch_block;
+    int stride_h = jcp.stride_h;
+    int stride_w = jcp.stride_w;
+
+    Label iter_exit_label;
+
+    cmp(reg_kh, 0);
+    je(iter_exit_label, T_NEAR);
+
+    cmp(reg_kw, 0);
+    je(iter_exit_label, T_NEAR);
+
+    mov(iter_kh, reg_kh);
+    Label kh_label;
+    L(kh_label); {
+        mov(aux1_reg_ddst, aux_reg_ddst);
+        mov(aux1_reg_kernel, aux_reg_kernel);
+
+        mov(iter_kw, reg_kw);
+        Label kw_label;
+        L(kw_label); {
+            int repeats = isa == sse42 ? 2 : 1;
+            for (int i = 0; i < repeats; i++) {
+                for (int ch = 0; ch < ur_ch_blocks; ch++) {
+                    int ker_off = ch*kh*kw*ch_blk + i*4;
+                    Vmm vmm_ker = get_ker_reg(0);
+                    uni_vmovups(vmm_ker, ptr[aux1_reg_kernel
+                        + ker_off*sizeof(float)]);
+
+                    for (int w = 0; w < ur_str_w; w++) {
+                        int ddst_off = (ch*oh*ow + w)*ch_blk + i*4;
+
+                        Vmm vmm_src = get_src_reg(0);
+                        uni_vmovups(vmm_src, ptr[aux1_reg_ddst
+                            + ddst_off*sizeof(float)]);
+
+                        Vmm vmm_acc = get_acc_reg(i*ur_ch_blocks*ur_str_w
+                            + ch*ur_str_w + w);
+                        uni_vfmadd231ps(vmm_acc, vmm_src, vmm_ker);
+                    }
+                }
+            }
+
+            add(aux1_reg_kernel, ch_blk*stride_w*sizeof(float));
+            sub(aux1_reg_ddst, ch_blk*sizeof(float));
+
+            sub(iter_kw, stride_w);
+            cmp(iter_kw, 0);
+            jg(kw_label, T_NEAR);
+        }
+
+        add(aux_reg_kernel, kw*ch_blk*stride_h*sizeof(float));
+        sub(aux_reg_ddst, ow*ch_blk*sizeof(float));
+
+        sub(iter_kh, stride_h);
+        cmp(iter_kh, 0);
+        jg(kh_label, T_NEAR);
+    }
+
+    L(iter_exit_label);
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_data_kernel_f32<isa>::store_dsrc(
+        int ur_ch_blocks, int ur_str_w) {
+    int ch_blk = jcp.ch_block;
+    int iw = jcp.iw;
+    int ih = jcp.ih;
+    int stride_w = jcp.stride_w;
+
+    int repeats = isa == sse42 ? 2 : 1;
+    for (int i = 0; i < repeats; i++) {
+        for (int ch = 0; ch < ur_ch_blocks; ch++) {
+            for (int w = 0; w < ur_str_w; w++) {
+                int dsrc_off = (ch*ih*iw + w*stride_w)*ch_blk + i*4;
+                Vmm vmm_acc = get_acc_reg(i*ur_ch_blocks*ur_str_w
+                    + ch*ur_str_w + w);
+
+                uni_vmovups(ptr[reg_dsrc + dsrc_off*sizeof(float)], vmm_acc);
+            }
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_data_kernel_f32<isa>::loop_body(
+        int ur_ch_blocks) {
+    Label unrolled_w_label;
+    Label tail_w_label;
+    Label exit_label;
+
+    L(unrolled_w_label); {
+        int ur_w = jcp.ur_w;
+
+        cmp(reg_ur_str_w, ur_w);
+        jl(tail_w_label, T_NEAR);
+
+        mov(aux_reg_ddst, reg_ddst);
+        mov(aux_reg_kernel, reg_kernel);
+
+        load_ddst(ur_ch_blocks, ur_w);
+        apply_filter(ur_ch_blocks, ur_w);
+        store_dsrc(ur_ch_blocks, ur_w);
+
+        add(reg_dsrc, sizeof(float) * ur_w * jcp.ch_block * jcp.stride_w);
+        add(reg_ddst, sizeof(float) * ur_w * jcp.ch_block);
+
+        sub(reg_ur_str_w, ur_w);
+        jmp(unrolled_w_label);
+    }
+
+    L(tail_w_label); {
+        int ur_w = 1;
+
+        cmp(reg_ur_str_w, ur_w);
+        jl(exit_label, T_NEAR);
+
+        mov(aux_reg_ddst, reg_ddst);
+        mov(aux_reg_kernel, reg_kernel);
+
+        load_ddst(ur_ch_blocks, ur_w);
+        apply_filter(ur_ch_blocks, ur_w);
+        store_dsrc(ur_ch_blocks, ur_w);
+
+        add(reg_dsrc, sizeof(float) * ur_w * jcp.ch_block * jcp.stride_w);
+        add(reg_ddst, sizeof(float) * ur_w * jcp.ch_block);
+
+        sub(reg_ur_str_w, ur_w);
+        jmp(tail_w_label);
+    }
+
+    L(exit_label);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_bwd_data_kernel_f32<isa>::generate() {
+    preamble();
+
+    mov(reg_dsrc, ptr[this->param1 + GET_OFF(src)]);
+    mov(reg_ddst, ptr[this->param1 + GET_OFF(dst)]);
+    mov(reg_kernel, ptr[this->param1 + GET_OFF(filt)]);
+    mov(reg_kh, ptr[this->param1 + GET_OFF(kh_padding)]);
+    mov(reg_kw, ptr[this->param1 + GET_OFF(kw_padding)]);
+    mov(reg_ch_blocks, ptr[this->param1 + GET_OFF(ch_blocks)]);
+    mov(reg_ur_str_w, ptr[this->param1 + GET_OFF(ur_str_w)]);
+
+    Label ch_blocks_tail_label;
+    Label exit_label;
+
+    int ch_blocks_tail = jcp.nb_ch % jcp.nb_ch_blocking;
+
+    cmp(reg_ch_blocks, jcp.nb_ch_blocking);
+    jne(ch_blocks_tail ? ch_blocks_tail_label : exit_label, T_NEAR);
+
+    loop_body(jcp.nb_ch_blocking); // channel main loop
+
+    if (ch_blocks_tail) {
+        L(ch_blocks_tail_label);
+
+        cmp(reg_ch_blocks, ch_blocks_tail);
+        jne(exit_label, T_NEAR);
+
+        loop_body(ch_blocks_tail); // channel tail loop
+    }
+
+    L(exit_label);
+
+    this->postamble();
+}
+
+template <cpu_isa_t isa>
+status_t jit_uni_dw_conv_bwd_data_kernel_f32<isa>::init_conf(
+        jit_conv_conf_t &jcp, const convolution_desc_t &cd,
+        const memory_desc_wrapper &diff_src_d,
+        const memory_desc_wrapper &weights_d,
+        const memory_desc_wrapper &diff_dst_d) {
+    if (!mayiuse(isa)) return status::unimplemented;
+
+    const int simd_w = isa == avx512_common ? 16 : 8;
+
+    const bool with_groups = weights_d.ndims() == diff_src_d.ndims() + 1;
+    if (!with_groups) return status::unimplemented;
+
+    jcp.ngroups = weights_d.dims()[0];
+    jcp.mb = diff_src_d.dims()[0];
+
+    jcp.oc = diff_dst_d.dims()[1];
+    jcp.oc_without_padding = jcp.oc;
+    jcp.ic = diff_src_d.dims()[1];
+
+    jcp.ih = diff_src_d.dims()[2];
+    jcp.iw = diff_src_d.dims()[3];
+    jcp.oh = diff_dst_d.dims()[2];
+    jcp.ow = diff_dst_d.dims()[3];
+
+    jcp.kh = weights_d.dims()[3];
+    jcp.kw = weights_d.dims()[4];
+
+    jcp.t_pad = cd.padding[0][0];
+    jcp.l_pad = cd.padding[0][1];
+    jcp.b_pad = cd.padding[1][0];
+    jcp.r_pad = cd.padding[1][1];
+
+    jcp.stride_h = cd.strides[0];
+    jcp.stride_w = cd.strides[1];
+
+    jcp.dilate_h = cd.dilates[0];
+    jcp.dilate_w = cd.dilates[1];
+
+    jcp.ihp = jcp.ih + jcp.t_pad + jcp.b_pad;
+    jcp.iwp = jcp.iw + jcp.l_pad + jcp.r_pad;
+
+    bool ok_to_pad_channels = true
+        && jcp.oc == jcp.ngroups
+        && jcp.ic == jcp.ngroups
+        && one_of(isa, avx512_common, avx2);
+    if (ok_to_pad_channels) {
+        jcp.oc = rnd_up(jcp.oc, simd_w);
+        jcp.ic = rnd_up(jcp.oc, simd_w);
+        jcp.ngroups = rnd_up(jcp.ngroups, simd_w);
+    }
+
+    auto dat_tag = isa == avx512_common ? nChw16c : nChw8c;
+    auto wei_tag = isa == avx512_common ? Goihw16g : Goihw8g;
+
+    jcp.src_tag = diff_src_d.matches_one_of_tag(dat_tag);
+    jcp.wei_tag = weights_d.matches_one_of_tag(wei_tag);
+    jcp.dst_tag = diff_dst_d.matches_one_of_tag(dat_tag);
+
+    bool args_ok = true
+        && jcp.oc == jcp.ngroups
+        && jcp.ic == jcp.ngroups
+        && jcp.ngroups % simd_w == 0
+        && jcp.dilate_h == 0
+        && jcp.dilate_w == 0
+        && jcp.src_tag == dat_tag
+        && jcp.wei_tag == wei_tag
+        && jcp.dst_tag == dat_tag
+        && jcp.oh == (jcp.ihp - jcp.kh) / jcp.stride_h + 1
+        && jcp.ow == (jcp.iwp - jcp.kw) / jcp.stride_w + 1
+        && jcp.ic <= diff_src_d.padded_dims()[1]
+        && jcp.oc <= diff_dst_d.padded_dims()[1]
+        && jcp.ngroups <= weights_d.padded_dims()[0];
+    if (!args_ok) return status::unimplemented;
+
+    jcp.ur_w = isa == avx512_common ? 6 : isa == avx2 ? 4 : 3;
+
+    jcp.ch_block = simd_w;
+    jcp.nb_ch = jcp.ic / jcp.ch_block;
+    jcp.nb_ch_blocking = isa == avx512_common ? 4 : isa == avx2 ? 3 : 2;
+    if (jcp.nb_ch < jcp.nb_ch_blocking)
+        jcp.nb_ch_blocking = jcp.nb_ch;
+
+    return status::success;
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_bwd_data_kernel_f32<isa>::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad, const jit_conv_conf_t &jcp) {
+    UNUSED(scratchpad);
+    UNUSED(jcp);
+}
+
+template struct jit_uni_dw_conv_bwd_data_kernel_f32<avx512_common>;
+template struct jit_uni_dw_conv_bwd_data_kernel_f32<avx2>;
+template struct jit_uni_dw_conv_bwd_data_kernel_f32<sse42>;
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::zero_filter() {
+    for (int r = 0; r < reg_repeats; ++r) {
+        for (int i = 0; i < jcp.kw; ++i) {
+            Vmm vmm_acc = get_acc_reg(r * jcp.kw + i);
+            uni_vpxor(vmm_acc, vmm_acc, vmm_acc);
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::load_filter() {
+    for (int r = 0; r < reg_repeats; ++r) {
+        const int reg_set = r * jcp.kw;
+        for (int i = 0; i < jcp.kw; ++i) {
+            int off_filter = (reg_set + i) * simd_w;
+            Vmm vmm_acc = get_acc_reg(reg_set + i);
+            uni_vmovups(vmm_acc,
+                    vmmword[reg_tmp_filter + off_filter * sizeof(float)]);
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::zero_bias() {
+    for (int r = 0; r < reg_repeats; ++r) {
+        Vmm vmm_bias = get_bias_reg(r);
+        uni_vpxor(vmm_bias, vmm_bias, vmm_bias);
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::load_bias() {
+    for (int r = 0; r < reg_repeats; ++r) {
+        Vmm vmm_bias = get_bias_reg(r);
+        uni_vmovups(
+                vmm_bias, vmmword[reg_bias_baddr + r * simd_w * sizeof(float)]);
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::compute_ow_step_unroll(
+        int unroll_w, int l_pad, int pad_offset, int ow_block) {
+
+    const int iw_block = ow_block * jcp.stride_w;
+    const int right_border = jcp.iw - iw_block;
+
+    const int cascade_input = nstl::min(jcp.stride_w, jcp.kw);
+
+    /* preamble count for number of cascaded LOAD + FMA operation */
+    const int input_overlap = nstl::max(jcp.kw - l_pad, 0);
+
+    /* LOAD initial input registers, then cascade LOADs and FMAs*/
+    for (int r = 0; r < reg_repeats; ++r) {
+        for (int i_ur = 0; i_ur < unroll_w; ++i_ur) {
+            int off_output = (i_ur * reg_repeats + r) * simd_w;
+            Vmm vmm_output = get_output_reg(r);
+            uni_vmovups(vmm_output,
+                    ptr[reg_tmp_output + off_output * sizeof(float)]);
+            if (i_ur == 0) {
+                for (int c = 0; c < input_overlap; ++c) {
+                    int off_input
+                            = ((c - pad_offset) * reg_repeats + r) * simd_w;
+                    Vmm vmm_input
+                            = get_input_reg((c % jcp.kw) * reg_repeats + r);
+                    uni_vmovups(vmm_input,
+                            ptr[reg_tmp_input + off_input * sizeof(float)]);
+                }
+            } else {
+                for (int c = 0; c < cascade_input; ++c) {
+                    int overlap = (i_ur - 1) * jcp.stride_w + input_overlap;
+                    int off_input
+                            = ((overlap + c - pad_offset) * reg_repeats + r)
+                            * simd_w;
+                    Vmm vmm_input = get_input_reg(
+                            ((overlap + c) % jcp.kw) * reg_repeats + r);
+                    uni_vmovups(vmm_input,
+                            ptr[reg_tmp_input + off_input * sizeof(float)]);
+                }
+            }
+
+            for (int i_kw = 0; i_kw < jcp.kw; ++i_kw) {
+                int io_overlap = i_kw + (i_ur * jcp.stride_w);
+
+                /* Don't apply FMAs that fall into the padded region */
+                if (io_overlap - l_pad < 0
+                        || io_overlap - jcp.l_pad >= right_border)
+                    continue;
+
+                Vmm vmm_input = get_input_reg(
+                        ((io_overlap - l_pad) % jcp.kw) * reg_repeats + r);
+                Vmm vmm_acc = get_acc_reg(i_kw * reg_repeats + r);
+                Vmm vmm_aux = isa == sse42 ? get_aux_reg() : vmm_input;
+                if (isa == sse42)
+                    uni_vmovups(vmm_aux, vmm_input);
+                uni_vfmadd231ps(vmm_acc, vmm_aux, vmm_output);
+            }
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+inline void
+jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::compute_bias_step_unroll(
+        const int unroll_w) {
+    for (int r = 0; r < reg_repeats; ++r) {
+        for (int i = 0; i < unroll_w; ++i) {
+            Vmm vmm_bias = get_bias_reg(r);
+            int off_output = (i * reg_repeats + r) * simd_w;
+            if (isa == sse42) {
+                /* Need to support unaligned address loads for SSE42*/
+                Vmm vmm_output = get_output_reg(1 + r);
+                uni_vmovups(vmm_output,
+                        ptr[reg_tmp_output + off_output * sizeof(float)]);
+                uni_vaddps(vmm_bias, vmm_bias, vmm_output);
+            } else {
+                uni_vaddps(vmm_bias, vmm_bias,
+                        vmmword[reg_tmp_output + off_output * sizeof(float)]);
+            }
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::store_filter() {
+    for (int r = 0; r < reg_repeats; ++r) {
+        const int reg_set = r * jcp.kw;
+        for (int i = 0; i < jcp.kw; ++i) {
+            int off_filter = (i + reg_set) * simd_w;
+            Vmm vmm_acc = get_acc_reg(i + reg_set);
+            uni_vmovups(vmmword[reg_tmp_filter + off_filter * sizeof(float)],
+                    vmm_acc);
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::store_bias() {
+    for (int r = 0; r < reg_repeats; ++r) {
+        Vmm vmm_bias = get_bias_reg(r);
+        uni_vmovups(
+                vmmword[reg_bias_baddr + r * simd_w * sizeof(float)], vmm_bias);
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::compute_bias_loop(
+        const int block_size) {
+    Label oh_label;
+    Label ow_blk_label;
+
+    const int unroll_w = nstl::min(block_size, jcp.ow);
+    const int unroll_w_trips = jcp.ow / unroll_w;
+    const int tail_w = jcp.ow > block_size ? jcp.ow % block_size : 0;
+
+    const int ch_offset = jcp.ch_block;
+
+    mov(reg_oh, ptr[this->param1 + offsetof(jit_dw_conv_call_s, oh_index)]);
+    mov(reg_oh_worksize,
+            ptr[this->param1 + offsetof(jit_dw_conv_call_s, oh_count)]);
+
+    mov(reg_tmp_output, reg_output_baddr);
+    L(oh_label);
+    {
+
+        mov(iter_ow_blk, unroll_w_trips);
+        L(ow_blk_label);
+        {
+
+            compute_bias_step_unroll(unroll_w);
+            add(reg_tmp_output, unroll_w * ch_offset * sizeof(float));
+
+            dec(iter_ow_blk);
+            cmp(iter_ow_blk, 0);
+            jg(ow_blk_label, T_NEAR);
+        }
+
+        if (tail_w > 0) {
+            compute_bias_step_unroll(tail_w);
+            add(reg_tmp_output, tail_w * ch_offset * sizeof(float));
+        }
+
+        inc(reg_oh);
+        cmp(reg_oh, reg_oh_worksize);
+        jl(oh_label, T_NEAR);
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::compute_zero_filter() {
+
+    const int ch_offset = jcp.ch_block;
+
+    Label kh_loop_label, skip_zeroing_label;
+
+    mov(reg_exec_flags,
+            ptr[this->param1 + offsetof(jit_dw_conv_call_s, exec_flags)]);
+    and_(reg_exec_flags, FLAG_ZERO_FILTER);
+    test(reg_exec_flags, reg_exec_flags);
+    je(skip_zeroing_label);
+
+    zero_filter();
+
+    mov(reg_tmp_filter, reg_filter_baddr);
+    mov(reg_kh, jcp.kh);
+    L(kh_loop_label);
+    {
+        store_filter();
+
+        add(reg_tmp_filter, jcp.kw * ch_offset * sizeof(float));
+        dec(reg_kh);
+        cmp(reg_kh, 0);
+        jg(kh_loop_label);
+    }
+
+    /* Comeback pointers */
+    sub(reg_tmp_filter, jcp.kh * jcp.kw * ch_offset * sizeof(float));
+
+    L(skip_zeroing_label);
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::compute_h_step(
+        int unroll_w, int l_pad, int pad_offset, int ow_block) {
+
+    const int ch_offset = jcp.ch_block;
+
+    Label kh_loop_label, skip_loop_label;
+
+    cmp(reg_kh_count, 0);
+    je(skip_loop_label, T_NEAR);
+
+    mov(reg_kh, reg_kh_count);
+    L(kh_loop_label);
+    {
+        load_filter();
+        compute_ow_step_unroll(unroll_w, l_pad, pad_offset, ow_block);
+        store_filter();
+
+        add(reg_tmp_filter, jcp.kw * ch_offset * sizeof(float));
+        add(reg_tmp_input, jcp.iw * ch_offset * sizeof(float));
+        dec(reg_kh);
+        cmp(reg_kh, 0);
+        jg(kh_loop_label);
+    }
+
+    /* Comeback pointers */
+    Label kh_comeback_label;
+    mov(reg_kh, reg_kh_count);
+    L(kh_comeback_label);
+    {
+        sub(reg_tmp_input, jcp.iw * ch_offset * sizeof(float));
+        sub(reg_tmp_filter, jcp.kw * ch_offset * sizeof(float));
+        dec(reg_kh);
+        cmp(reg_kh, 0);
+        jg(kh_comeback_label, T_NEAR);
+    }
+
+    L(skip_loop_label);
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::compute_h_loop(
+        int unroll_w, int l_pad, int pad_offset, int ow_block) {
+
+    const size_t io_overlap = jcp.ih / jcp.stride_h < jcp.oh ?
+            jcp.ih / jcp.stride_h - 1 :
+            jcp.oh - jcp.b_pad - 1;
+    const int ch_offset = jcp.ch_block;
+    const int t_overlap_off = jcp.t_pad % jcp.stride_h == 0 ? jcp.stride_h : 1;
+    const int b_overlap_off = jcp.b_pad % jcp.stride_h == 0 ? jcp.stride_h : 1;
+
+    Label tpad_loop_label, h_loop_label, skip_tpad_label, skip_bpad_label,
+            end_h_loop_label;
+
+    mov(reg_oh, ptr[this->param1 + offsetof(jit_dw_conv_call_s, oh_index)]);
+    mov(reg_oh_worksize,
+            ptr[this->param1 + offsetof(jit_dw_conv_call_s, oh_count)]);
+    mov(reg_kh_count,
+            ptr[this->param1 + offsetof(jit_dw_conv_call_s, kh_count)]);
+
+    mov(reg_tmp_output, reg_output_baddr);
+    mov(reg_tmp_input, reg_input_baddr);
+    mov(reg_tmp_filter, reg_filter_baddr);
+
+    L(h_loop_label);
+    {
+
+        compute_h_step(unroll_w, l_pad, pad_offset, ow_block);
+
+        add(reg_tmp_output, jcp.ow * ch_offset * sizeof(float));
+
+        /* If within the top_pad region */
+        if (jcp.t_pad > 0) {
+            /* Skip t_pad area if no longer in initial h_block */
+            cmp(reg_oh, jcp.t_pad);
+            jg(skip_tpad_label, T_NEAR);
+
+            cmp(reg_kh_count, jcp.kh);
+            jge(skip_tpad_label, T_NEAR);
+
+            add(reg_kh_count, t_overlap_off);
+            sub(reg_tmp_filter,
+                    t_overlap_off * jcp.kw * ch_offset * sizeof(float));
+
+            /* kernel has moved beyond padding (adjust for stride effects) */
+            if (jcp.t_pad % jcp.stride_h != 0) {
+                int inp_corr = jcp.stride_h - jcp.t_pad % jcp.stride_h;
+                add(reg_tmp_input,
+                        inp_corr * jcp.iw * ch_offset * sizeof(float));
+            }
+            jmp(tpad_loop_label, T_NEAR);
+        }
+
+        L(skip_tpad_label);
+
+        cmp(reg_oh, io_overlap);
+        jl(skip_bpad_label, T_NEAR);
+        sub(reg_kh_count, b_overlap_off);
+
+        L(skip_bpad_label);
+        add(reg_tmp_input, jcp.stride_h * jcp.iw * ch_offset * sizeof(float));
+
+        L(tpad_loop_label);
+
+        cmp(reg_oh, jcp.ih / jcp.stride_h);
+        jge(end_h_loop_label, T_NEAR);
+
+        inc(reg_oh);
+
+        cmp(reg_oh, reg_oh_worksize);
+        jl(h_loop_label, T_NEAR);
+    }
+    L(end_h_loop_label);
+}
+
+template <cpu_isa_t isa>
+inline void
+jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::compute_ow_block_unroll() {
+
+    const int ch_offset = jcp.ch_block;
+    int ow = jcp.ow;
+    int pad_offset = 0;
+    int l_pad = jcp.l_pad;
+
+    /* Calculate effective padding */
+    int r_pad = nstl::max(0, (ow - 1) * jcp.stride_w
+                    + (jcp.kw - 1) * (jcp.dilate_w + 1)
+                    - (jcp.iw + jcp.l_pad - 1));
+
+    /* Is this strictly defined by:
+     * -code-size (?)
+     * -address size (?) */
+    const int max_unroll_w = 30;
+    const int block_size = 15;
+
+    int unroll_w_tail = 0;
+    int unroll_w = 0;
+    int unroll_w_trips = 0;
+
+    if (jcp.ow > max_unroll_w) {
+        unroll_w = nstl::min(block_size, jcp.ow);
+        unroll_w_trips = ow / unroll_w;
+        /* calculate tail */
+        unroll_w_tail = ow % unroll_w;
+        /* Perform some rebalancing if tail too small*/
+        if ((unroll_w_tail == 0 && r_pad != 0)
+                || (r_pad > 0 && r_pad >= unroll_w_tail)) {
+            if (unroll_w_trips > 1) {
+                unroll_w_tail += unroll_w;
+                unroll_w_trips--;
+            } else {
+                /* Idealy, this case shouldn't happen */
+                unroll_w_tail += (unroll_w - unroll_w / 2);
+                unroll_w = unroll_w / 2;
+            }
+        }
+    } else {
+        unroll_w = jcp.ow;
+        unroll_w_trips = nstl::max(1, ow / unroll_w);
+    }
+    if (jcp.with_bias) {
+        Label skip_load_bias;
+        mov(reg_bias_baddr,
+                ptr[this->param1 + offsetof(jit_dw_conv_call_s, bias)]);
+
+        zero_bias();
+
+        mov(reg_exec_flags,
+                ptr[this->param1 + offsetof(jit_dw_conv_call_s, exec_flags)]);
+        and_(reg_exec_flags, FLAG_ZERO_BIAS);
+        test(reg_exec_flags, reg_exec_flags);
+        jne(skip_load_bias);
+
+        load_bias();
+
+        L(skip_load_bias);
+        compute_bias_loop(block_size);
+
+        store_bias();
+    }
+
+    /* Pass filter address, then offset for h_padding. */
+    compute_zero_filter();
+    mov(reg_kh_offset,
+            ptr[this->param1 + offsetof(jit_dw_conv_call_s, filter_pad_off)]);
+    add(reg_filter_baddr, reg_kh_offset);
+
+    /* compute left padded block */
+    if (l_pad) {
+        compute_h_loop(unroll_w, l_pad, 0, 0);
+        add(reg_output_baddr, unroll_w * ch_offset * sizeof(float));
+        add(reg_input_baddr,
+                unroll_w * jcp.stride_w * ch_offset * sizeof(float));
+        unroll_w_trips--;
+        pad_offset = l_pad;
+        l_pad = 0;
+    }
+
+    /* compute middle block */
+    Label ow_blk_label;
+
+    /* Insert loop for 'ow' block when middle block needs to execute more
+     * than once */
+    bool do_ow_blk_loop = unroll_w_trips > 1;
+    if (do_ow_blk_loop) {
+        mov(iter_ow_blk, unroll_w_trips);
+        L(ow_blk_label);
+    }
+    if (unroll_w_trips > 0) {
+        compute_h_loop(unroll_w, l_pad, pad_offset, 0);
+        add(reg_output_baddr, unroll_w * ch_offset * sizeof(float));
+        add(reg_input_baddr,
+                unroll_w * jcp.stride_w * ch_offset * sizeof(float));
+    }
+    if (do_ow_blk_loop) {
+        dec(iter_ow_blk);
+        cmp(iter_ow_blk, 0);
+        jg(ow_blk_label, T_NEAR);
+    }
+
+    /* compute right padded block */
+    if (unroll_w_tail) {
+        compute_h_loop(unroll_w_tail, 0, pad_offset, jcp.ow - unroll_w_tail);
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::generate() {
+    preamble();
+
+    mov(reg_input_baddr,
+            ptr[this->param1 + offsetof(jit_dw_conv_call_s, input)]);
+    mov(reg_output_baddr,
+            ptr[this->param1 + offsetof(jit_dw_conv_call_s, output)]);
+    mov(reg_filter_baddr,
+            ptr[this->param1 + offsetof(jit_dw_conv_call_s, filter)]);
+
+    compute_ow_block_unroll();
+
+    this->postamble();
+}
+
+template <cpu_isa_t isa>
+status_t jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::init_conf(
+        jit_conv_conf_t &jcp, const convolution_desc_t &cd,
+        const memory_desc_wrapper &src_d,
+        const memory_desc_wrapper &diff_weights_d,
+        const memory_desc_wrapper &diff_dst_d, int nthreads) {
+    if (!mayiuse(isa))
+        return status::unimplemented;
+
+    jcp.ngroups = diff_weights_d.dims()[0];
+    jcp.oc = diff_dst_d.dims()[1] / jcp.ngroups;
+    jcp.ic = src_d.dims()[1] / jcp.ngroups;
+
+    const bool with_groups = diff_weights_d.ndims() == src_d.ndims() + 1;
+
+    jcp.is_depthwise = true && with_groups && everyone_is(1, jcp.oc, jcp.ic);
+
+    if (!jcp.is_depthwise)
+        return status::unimplemented;
+
+    jcp.ch_block = isa == avx512_common ? 16 : 8;
+
+    jcp.mb = src_d.dims()[0];
+
+    jcp.ih = src_d.dims()[2];
+    jcp.iw = src_d.dims()[3];
+    jcp.oh = diff_dst_d.dims()[2];
+    jcp.ow = diff_dst_d.dims()[3];
+
+    jcp.kh = diff_weights_d.dims()[3];
+    jcp.kw = diff_weights_d.dims()[4];
+
+    jcp.stride_h = cd.strides[0];
+    jcp.stride_w = cd.strides[1];
+
+    jcp.t_pad = cd.padding[0][0];
+    jcp.b_pad = cd.padding[1][0];
+
+    jcp.l_pad = cd.padding[0][1];
+    jcp.r_pad = cd.padding[1][1];
+
+    jcp.dilate_h = cd.dilates[0];
+    jcp.dilate_w = cd.dilates[1];
+
+    jcp.ihp = jcp.ih + jcp.t_pad + jcp.b_pad;
+    jcp.iwp = jcp.iw + jcp.l_pad + jcp.r_pad;
+
+    jcp.with_bias = cd.diff_bias_desc.format_kind != format_kind::undef;
+
+    auto dat_tag = isa == avx512_common ? nChw16c : nChw8c;
+    auto wei_tag = isa == avx512_common ? Goihw16g : Goihw8g;
+
+    jcp.src_tag = src_d.matches_one_of_tag(dat_tag);
+    jcp.wei_tag = diff_weights_d.matches_one_of_tag(wei_tag);
+    jcp.dst_tag = diff_dst_d.matches_one_of_tag(dat_tag);
+
+    bool args_ok = true
+        && jcp.src_tag == dat_tag
+        && jcp.wei_tag == wei_tag
+        && jcp.dst_tag == dat_tag
+        && jcp.ngroups % jcp.ch_block == 0 && jcp.dilate_h == 0
+        && jcp.dilate_w == 0 && jcp.kw <= 3
+        && jcp.oh == (jcp.ihp - jcp.kh) / jcp.stride_h + 1
+        && jcp.ow == (jcp.iwp - jcp.kw) / jcp.stride_w + 1;
+    if (!args_ok)
+        return status::unimplemented;
+
+    jcp.nb_ch = jcp.ngroups / jcp.ch_block;
+
+    /* kernel applicability check wrt boundaries
+     * the conditions are quite general across the kernels we have,
+     * but ideally the check should belong to a specific kernel... */
+    const int max_hpad = (jcp.kh - 1 + 1) / 2;
+    const int max_wpad = (jcp.kw - 1 + 1) / 2;
+    const bool boundaries_ok = true && jcp.t_pad <= max_hpad
+            && jcp.b_pad <= max_hpad && jcp.l_pad <= max_wpad
+            && jcp.r_pad <= max_wpad;
+    if (!boundaries_ok)
+        return status::unimplemented;
+
+    balance(jcp, nthreads);
+
+    return status::success;
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::init_scratchpad(
+        memory_tracking::registrar_t &scratchpad, const jit_conv_conf_t &jcp) {
+    /* Notes: if splitting thread work on 'mb', then a reduction has to take
+     * place. Hence, book a per-thread, local weights-buffer for the
+     * reduction */
+    if (jcp.nthr_mb > 1) {
+        const size_t wei_size = jcp.ngroups * jcp.kh * jcp.kw;
+        scratchpad.book(key_conv_wei_reduction,
+                sizeof(float) * wei_size * (jcp.nthr_mb - 1));
+
+        if (jcp.with_bias)
+            scratchpad.book(key_conv_bia_reduction,
+                    sizeof(float) * jcp.ngroups * (jcp.nthr_mb - 1));
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::balance(jit_conv_conf_t &jcp,
+        int nthreads) {
+    jcp.nthr = nthreads;
+    jcp.nthr_g = jcp.nthr_mb = 1;
+
+    /* Basic-Heuristics for parallel strategy:
+     * 1) Tries to parallel on the number of Groups (g) where tasks are
+     * independent. Otherwise,
+     * 2) Tries to split the work across g and MiniBatch (mb).
+     * Parallelizing on mb requires computing a reduction for weights.
+     *
+     * NOTE: because of 'task partitioning' scheme, there will be unbalanced
+     * per-thread load when the number of threads is high (e.g. > 16).
+     */
+    jcp.nthr_g = nstl::min(jcp.nb_ch, jcp.nthr);
+    jcp.nthr_mb = nstl::min(nstl::max(1, jcp.nthr / jcp.nthr_g), jcp.mb);
+
+    jcp.nthr = jcp.nthr_g * jcp.nthr_mb;
+}
+
+template struct jit_uni_dw_conv_bwd_weights_kernel_f32<avx512_common>;
+template struct jit_uni_dw_conv_bwd_weights_kernel_f32<avx2>;
+template struct jit_uni_dw_conv_bwd_weights_kernel_f32<sse42>;
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_f32.hpp
new file mode 100644
index 0000000000..9c08fc4a09
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_conv_kernel_f32.hpp
@@ -0,0 +1,253 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_UNI_DW_CONV_KERNEL_F32_HPP
+#define JIT_UNI_DW_CONV_KERNEL_F32_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+#include "jit_uni_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <cpu_isa_t isa>
+struct jit_uni_dw_conv_fwd_kernel_f32: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_dw_conv_fwd_kernel_f32)
+
+    jit_uni_dw_conv_fwd_kernel_f32(jit_conv_conf_t ajcp)
+        : jcp(ajcp), eltwise_injector_(nullptr)
+    {
+        if (jcp.with_eltwise)
+            eltwise_injector_ = new jit_uni_eltwise_injector_f32<isa>(this,
+                    jcp.eltwise);
+
+        this->generate();
+        jit_ker = (void (*)(jit_conv_call_s *))this->getCode();
+    }
+
+    ~jit_uni_dw_conv_fwd_kernel_f32() {
+        delete eltwise_injector_;
+    }
+
+    static bool post_ops_ok(jit_conv_conf_t &jcp,
+            const primitive_attr_t &attr);
+    static status_t init_conf(jit_conv_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &dst_d, const primitive_attr_t &attr);
+
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_conv_conf_t &jcp);
+
+    jit_conv_conf_t jcp;
+    void (*jit_ker)(jit_conv_call_s *);
+
+private:
+    using Vmm = typename utils::conditional3<isa == sse42, Xbyak::Xmm,
+        isa == avx2, Xbyak::Ymm, Xbyak::Zmm>::type;
+    using reg64_t = const Xbyak::Reg64;
+    const Xbyak::AddressFrame &vmmword = (isa == sse42)
+        ? xword : (isa == avx2) ? yword : zword;
+    const int vlen = cpu_isa_traits<isa>::vlen;
+
+    // dw convolution
+    reg64_t reg_input = r8;
+    reg64_t aux_reg_input = r9;
+    reg64_t aux1_reg_input = r10;
+    reg64_t reg_kernel = r11;
+    reg64_t aux_reg_kernel = r12;
+    reg64_t aux1_reg_kernel = r13;
+    reg64_t reg_output = r14;
+    reg64_t reg_bias = r15;
+    reg64_t reg_kh = rax;
+    reg64_t reg_kw = rbx;
+    reg64_t iter_kh = rdx;
+    reg64_t iter_kw = rsi;
+    reg64_t reg_ur_w = rbp;
+    reg64_t reg_ch_blocks = aux1_reg_input;
+    reg64_t imm_addr64 = aux1_reg_input;
+
+    inline Vmm get_ker_reg(int idx) { return Vmm(idx + 0); }
+    inline Vmm get_src_reg(int idx) { return Vmm(idx + 1); }
+    inline Vmm get_acc_reg(int idx) { return Vmm(idx + 4); }
+
+    inline void load_src(int ur_ch_blocks, int ur_w);
+    inline void apply_filter(int ur_ch_blocks, int ur_w);
+    inline void apply_filter_unrolled(int ur_ch_blocks, int ur_w);
+    inline void apply_activation(int ur_ch_blocks, int ur_w);
+    inline void store_dst(int ur_ch_blocks, int ur_w);
+    inline void loop_body(int ur_ch_blocks);
+
+    jit_uni_eltwise_injector_f32<isa> *eltwise_injector_;
+
+    void generate();
+};
+
+template <cpu_isa_t isa>
+struct jit_uni_dw_conv_bwd_data_kernel_f32: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_dw_conv_bwd_data_kernel_f32)
+
+    jit_uni_dw_conv_bwd_data_kernel_f32(jit_conv_conf_t ajcp): jcp(ajcp) {
+        this->generate();
+        jit_ker = (void (*)(jit_conv_call_s *))this->getCode();
+    }
+
+    static status_t init_conf(jit_conv_conf_t &jcp,
+            const convolution_desc_t &cd,
+            const memory_desc_wrapper &diff_src_d,
+            const memory_desc_wrapper &weights_d,
+            const memory_desc_wrapper &diff_dst_d);
+
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_conv_conf_t &jcp);
+
+    jit_conv_conf_t jcp;
+    void (*jit_ker)(jit_conv_call_s *);
+
+private:
+    using Vmm = typename utils::conditional3<isa == sse42, Xbyak::Xmm,
+        isa == avx2, Xbyak::Ymm, Xbyak::Zmm>::type;
+    using reg64_t = const Xbyak::Reg64;
+
+    inline Vmm get_ker_reg(int idx) { return Vmm(idx + 0); }
+    inline Vmm get_src_reg(int idx) { return Vmm(idx + 1); }
+    inline Vmm get_acc_reg(int idx) { return Vmm(idx + 4); }
+
+    reg64_t reg_ddst       = rax;
+    reg64_t aux_reg_ddst   = r8;
+    reg64_t aux1_reg_ddst = abi_not_param1;
+    reg64_t reg_kernel     = rdx;
+    reg64_t aux_reg_kernel = r10;
+    reg64_t aux1_reg_kernel = rbp;
+    reg64_t reg_dsrc       = rsi;
+
+    reg64_t reg_ur_str_w = r9;
+    reg64_t reg_ch_blocks = rbx;
+
+    reg64_t iter_kh = r11;
+    reg64_t iter_kw = r12;
+    reg64_t reg_kh  = r13;
+    reg64_t reg_kw  = r14;
+
+    inline void loop_body(int ur_ch_blocks);
+    inline void load_ddst(int ur_ch_blocks, int ur_str_w);
+    inline void apply_filter(int ur_ch_blocks, int ur_str_w);
+    inline void store_dsrc(int ur_ch_blocks, int ur_str_w);
+
+    void generate();
+};
+
+template <cpu_isa_t isa>
+struct jit_uni_dw_conv_bwd_weights_kernel_f32 : public jit_generator {
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_dw_conv_bwd_weights_kernel_f32)
+
+    jit_uni_dw_conv_bwd_weights_kernel_f32(jit_conv_conf_t ajcp) : jcp(ajcp) {
+        this->generate();
+        jit_ker = (void (*)(jit_dw_conv_call_s *)) this->getCode();
+    }
+
+    static status_t init_conf(jit_conv_conf_t &jcp,
+            const convolution_desc_t &cd, const memory_desc_wrapper &src_d,
+            const memory_desc_wrapper &diff_weights_d,
+            const memory_desc_wrapper &diff_dst_d, int nthreads);
+
+    static void init_scratchpad(memory_tracking::registrar_t &scratchpad,
+            const jit_conv_conf_t &jcp);
+
+    static void balance(jit_conv_conf_t &jcp, int nthreads);
+
+    jit_conv_conf_t jcp;
+    void (*jit_ker)(jit_dw_conv_call_s *);
+
+private:
+    using Vmm = typename utils::conditional3<isa == sse42, Xbyak::Xmm,
+            isa == avx2, Xbyak::Ymm, Xbyak::Zmm>::type;
+    using reg64_t = const Xbyak::Reg64;
+    const int simd_w = cpu_isa_traits<isa>::vlen / sizeof(float);
+    const int reg_repeats = (isa == sse42) ? 2 : 1;
+
+    const Xbyak::AddressFrame &vmmword
+            = (isa == sse42) ? xword : (isa == avx2) ? yword : zword;
+
+    /* XXX: offset between input and accummulators is 3, therefore, assume 'kw'
+     * is no larger than 3*/
+    inline Vmm get_bias_reg(int idx = 0) { return Vmm(idx); }
+    inline Vmm get_output_reg(int idx) { return Vmm(idx + 1); }
+    inline Vmm get_input_reg(int idx) { return Vmm(idx + 4 * reg_repeats + 1); }
+    inline Vmm get_acc_reg(int idx) { return Vmm(idx + 1 * reg_repeats + 1); }
+    inline Vmm get_aux_reg() { return Vmm(0); }
+
+    reg64_t reg_tmp_input = r9;
+    reg64_t reg_tmp_output = r10;
+    reg64_t reg_tmp_filter = r13;
+    reg64_t reg_kh_offset = rax;
+
+    /* parameter passed by driver into kernel */
+    Xbyak::Reg8 reg_exec_flags = bl;
+
+    reg64_t reg_oh_worksize = r14;
+    reg64_t reg_oh = rax;
+
+    reg64_t iter_ow_blk = r11;
+
+    reg64_t reg_kh = rsi;
+    reg64_t reg_kh_count = rdx;
+
+    /* Base addresses for convolution parameters. */
+    reg64_t reg_input_baddr = r15;
+    reg64_t reg_output_baddr = r12;
+    reg64_t reg_filter_baddr = abi_not_param1;
+    reg64_t reg_bias_baddr = r13;
+
+    /* Micro-kernel JIT'ing, fusing 'kw' and 'ow_block' loops into unrolled FMAs
+     */
+    inline void compute_ow_step_unroll(
+            int unroll_w, int l_pad, int pad_offset, int ow_block);
+
+    /* JIT'ing the outer loops for the micro-kernel -> {kh, oh_block} */
+    inline void compute_h_step(
+            int unroll_w, int l_pad, int pad_offset, int ow_block);
+    inline void compute_h_loop(
+            int unroll_w, int l_pad, int pad_offset, int ow_block);
+
+    /* Write 'width' micro-kernel JITs; depending on the padding and convolution
+     * size, write a micro-kernel for the left ow-block, middle ow-block(s), and
+     * right ow-block.*/
+    inline void compute_ow_block_unroll();
+
+    inline void compute_zero_filter();
+    inline void load_filter();
+    inline void zero_filter();
+    inline void load_bias();
+    inline void zero_bias();
+    inline void compute_bias_step_unroll(const int unroll_w);
+    inline void compute_bias_loop(const int block_size);
+    inline void store_filter();
+    inline void store_bias();
+
+    void generate();
+};
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_convolution.cpp
new file mode 100644
index 0000000000..58449601a3
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_convolution.cpp
@@ -0,0 +1,427 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "mkldnn_thread.hpp"
+
+#include "jit_uni_dw_convolution.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace mkldnn::impl::utils;
+
+template <cpu_isa_t isa>
+void _jit_uni_dw_convolution_fwd_t<isa>::execute_forward(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const data_t *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    const auto &jcp = kernel_->jcp;
+
+    if (pd()->wants_padded_bias()) {
+        auto padded_bias = this->scratchpad(ctx).template get<data_t>(
+                key_conv_padded_bias);
+        utils::array_copy(padded_bias, bias, jcp.oc_without_padding);
+        utils::array_set(padded_bias + jcp.oc_without_padding, 0.f,
+                jcp.oc - jcp.oc_without_padding);
+        bias = padded_bias;
+    }
+
+    int dil_h = jcp.dilate_h + 1;
+    int dil_w = jcp.dilate_w + 1;
+    int str_h = jcp.stride_h;
+    int str_w = jcp.stride_w;
+
+    auto kernel_params = [&](int ur_w_step, int ow, int oh, int ih, int kh,
+            int kh_padding, int ch, int ch_num, int n) {
+        auto par_conv = jit_conv_call_s();
+
+        const int i_l_overflow = nstl::max(0, (jcp.l_pad - ow * str_w));
+        const int i_r_overflow = nstl::max(jcp.iw, (ow * str_w
+            + (jcp.kw - 1)*dil_w - jcp.l_pad + 1)) - jcp.iw;
+
+        const int iw = nstl::max((ow*str_w - jcp.l_pad
+            + div_up(i_l_overflow, dil_w)*dil_w), 0);
+        const int kw = div_up(i_l_overflow, dil_w);
+
+        const int kw_padding = jcp.kw - div_up(i_l_overflow, dil_w)
+            - div_up(i_r_overflow, dil_w);
+
+        par_conv.src = &src[src_d.blk_off(n, ch, ih, iw)];
+        par_conv.dst = &dst[dst_d.blk_off(n, ch, oh, ow)];
+
+        par_conv.filt = &weights[weights_d.blk_off(ch, 0, 0, kh, kw)];
+        if (bias) par_conv.bias = &bias[bias_d.blk_off(ch*jcp.ch_block)];
+
+        par_conv.kh_padding = (size_t)nstl::max(0, kh_padding);
+        par_conv.kw_padding = (size_t)nstl::max(0, kw_padding);
+
+        par_conv.ur_w = (size_t)ur_w_step;
+
+        par_conv.ch_blocks = nstl::min(ch + ch_num, jcp.nb_ch) - ch;
+
+        return par_conv;
+    };
+
+    const int chb_work = utils::div_up(jcp.nb_ch, jcp.nb_ch_blocking);
+    parallel_nd(jcp.mb, chb_work, jcp.oh,
+            [&](int n, int chb, int oh) {
+        int ch = chb * jcp.nb_ch_blocking;
+        int ch_num = jcp.nb_ch_blocking;
+
+        const int i_t_overflow = nstl::max(0, (int)(jcp.t_pad - oh*str_h));
+        const int i_b_overflow = nstl::max(jcp.ih,
+            (int)(oh*str_h + (jcp.kh - 1)*dil_h - jcp.t_pad + 1)) - jcp.ih;
+
+        const int ih = nstl::max((int)(oh*str_h - jcp.t_pad
+            + div_up(i_t_overflow, dil_h)*dil_h), 0);
+        const int kh = div_up(i_t_overflow, dil_h);
+        const int kh_padding = jcp.kh - div_up(i_t_overflow, dil_h)
+            - div_up(i_b_overflow, dil_h);
+
+        // left border
+        int ow = 0;
+        int l_border = nstl::min(div_up(jcp.l_pad, str_w), jcp.ow);
+        int ur_w_step = 1;
+        for (; ow < l_border; ow++) {
+            jit_conv_call_s par_conv = kernel_params(ur_w_step, ow, oh, ih,
+                                        kh, kh_padding, ch, ch_num, n);
+
+            kernel_->jit_ker(&par_conv);
+        }
+
+        // main loop
+        ur_w_step = (jcp.iw - (jcp.kw - 1)*dil_w + jcp.l_pad - 1)
+            / jcp.stride_w - ow + 1;
+        if (ur_w_step > 0) {
+            jit_conv_call_s par_conv = kernel_params(ur_w_step, ow, oh, ih,
+                                        kh, kh_padding, ch, ch_num, n);
+
+            kernel_->jit_ker(&par_conv);
+
+            ow += ur_w_step;
+        }
+
+        // right border
+        ur_w_step = 1;
+        for (; ow < jcp.ow; ow++) {
+            jit_conv_call_s par_conv = kernel_params(ur_w_step, ow, oh, ih,
+                                        kh, kh_padding, ch, ch_num, n);
+
+            kernel_->jit_ker(&par_conv);
+        }
+    });
+
+    if (pd()->wants_zero_pad_dst())
+        ctx.memory(MKLDNN_ARG_DST)->zero_pad();
+}
+
+template struct _jit_uni_dw_convolution_fwd_t<avx512_common>;
+template struct _jit_uni_dw_convolution_fwd_t<avx2>;
+template struct _jit_uni_dw_convolution_fwd_t<sse42>;
+
+template <cpu_isa_t isa>
+void _jit_uni_dw_convolution_bwd_data_t<isa>::execute_backward_data(
+        const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto weights = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WEIGHTS);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+
+    const auto &jcp = kernel_->jcp;
+
+    auto kernel_params = [&](int ur_str_w, int iw, int oh, int ih,
+            int i_t_overflow, int i_b_overflow, int stride_off_h,
+            int ch, int ch_num, int n) {
+        auto par_conv = jit_conv_call_s();
+
+        const int i_l_overflow = nstl::max(0, (jcp.kw - 1 - iw - jcp.l_pad));
+        const int i_r_overflow = nstl::max(0, (jcp.kw - 1 - (jcp.iw - 1 - iw)
+            - jcp.r_pad));
+
+        int ow = iw + jcp.l_pad - i_r_overflow;
+        int stride_off_w = ow % jcp.stride_w;
+        ow /= jcp.stride_w;
+
+        par_conv.src = &diff_src[diff_src_d.blk_off(n, ch, ih, iw)];
+        par_conv.dst = &diff_dst[diff_dst_d.blk_off(n, ch, oh, ow)];
+        par_conv.filt = &weights[weights_d.blk_off(ch, 0, 0, i_b_overflow
+            + stride_off_h, i_r_overflow + stride_off_w)];
+
+        par_conv.kh_padding = nstl::max(0, jcp.kh - i_t_overflow - i_b_overflow
+            - stride_off_h);
+        par_conv.kw_padding = nstl::max(0, jcp.kw - i_l_overflow - i_r_overflow
+            - stride_off_w);
+
+        par_conv.ur_str_w = ur_str_w;
+
+        par_conv.ch_blocks = nstl::min(ch + ch_num, jcp.nb_ch) - ch;
+
+        return par_conv;
+    };
+
+    const int chb_work = utils::div_up(jcp.nb_ch, jcp.nb_ch_blocking);
+    parallel_nd(jcp.mb, chb_work, jcp.ih,
+        [&](int n, int chb, int ih) {
+        int ch = chb * jcp.nb_ch_blocking;
+        int ch_num = jcp.nb_ch_blocking;
+
+        const int i_t_overflow = nstl::max(0, (int)(jcp.kh - 1 - ih
+            - jcp.t_pad));
+        const int i_b_overflow = nstl::max(0, (int)(jcp.kh - 1
+            - (jcp.ih - 1 - ih) - jcp.b_pad));
+
+        int oh = ih + jcp.t_pad - i_b_overflow;
+        int stride_off_h = oh % jcp.stride_h;
+        oh /= jcp.stride_h;
+
+        for (int i_str_w = 0; i_str_w < jcp.stride_w; i_str_w++) {
+            // left border
+            int iw = i_str_w;
+            int l_border = nstl::min(jcp.kw - 1 - jcp.l_pad, jcp.iw);
+            int ur_str_w = 1;
+            for (; iw < l_border; iw += jcp.stride_w) {
+                jit_conv_call_s par_conv = kernel_params(ur_str_w, iw, oh,
+                                             ih, i_t_overflow, i_b_overflow,
+                                             stride_off_h, ch, ch_num, n);
+
+                kernel_->jit_ker(&par_conv);
+            }
+
+            // main loop
+            ur_str_w = nstl::min((jcp.iw - jcp.kw + jcp.r_pad - iw)
+                 / jcp.stride_w, jcp.iw);
+            if (ur_str_w > 0) {
+                jit_conv_call_s par_conv = kernel_params(ur_str_w, iw, oh,
+                                             ih, i_t_overflow, i_b_overflow,
+                                             stride_off_h, ch, ch_num, n);
+
+                kernel_->jit_ker(&par_conv);
+
+                iw += ur_str_w * jcp.stride_w;
+            }
+
+            // right border
+            ur_str_w = 1;
+            for (; iw < jcp.iw; iw += jcp.stride_w) {
+                jit_conv_call_s par_conv = kernel_params(ur_str_w, iw, oh,
+                                             ih, i_t_overflow, i_b_overflow,
+                                             stride_off_h, ch, ch_num, n);
+
+                kernel_->jit_ker(&par_conv);
+            }
+        }
+    });
+}
+
+template struct _jit_uni_dw_convolution_bwd_data_t<avx512_common>;
+template struct _jit_uni_dw_convolution_bwd_data_t<avx2>;
+template struct _jit_uni_dw_convolution_bwd_data_t<sse42>;
+
+template <cpu_isa_t isa>
+_jit_uni_dw_convolution_bwd_weights_t<isa>::
+_jit_uni_dw_convolution_bwd_weights_t(const pd_t *apd)
+    : cpu_primitive_t(apd)
+    , kernel_(nullptr), acc_ker_(nullptr)
+{
+    kernel_ = new jit_uni_dw_conv_bwd_weights_kernel_f32<isa>(pd()->jcp_);
+    if (pd()->jcp_.nthr_mb > 1 && do_parallel_reduction())
+        acc_ker_ = new cpu_accumulator_1d_t<data_type::f32>();
+}
+
+template <cpu_isa_t isa>
+void _jit_uni_dw_convolution_bwd_weights_t<isa>::execute_backward_weights(
+        const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_weights = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
+    auto diff_bias = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_BIAS);
+
+    auto diff_wei_reduction_buf =
+        scratchpad(ctx).template get<data_t>(key_conv_wei_reduction);
+    auto diff_bia_reduction_buf =
+        scratchpad(ctx).template get<data_t>(key_conv_bia_reduction);
+
+    const auto &jcp = kernel_->jcp;
+
+    /* Used when executing a parallel reduction */
+    simple_barrier::ctx_t reduction_bctx;
+    simple_barrier::ctx_init(&reduction_bctx);
+
+    const size_t wei_size = jcp.ngroups * jcp.kh * jcp.kw;
+    const size_t bias_size = jcp.with_bias ? jcp.ngroups : 0;
+
+    const int ch_block = jcp.ch_block;
+
+    auto set_kernel_params = [&](jit_dw_conv_call_s *conv_params,
+            const int batch, const int group, const int oh_start,
+            const int work_size, const unsigned char exec_flag,
+            const size_t kh_padding, const size_t filter_off) {
+        const int tpad_underflow_off = jcp.t_pad - filter_off;
+
+        conv_params->exec_flags = exec_flag;
+        conv_params->kh_count = jcp.kh - kh_padding;
+
+        const int oh_s = oh_start;
+        const int oh_e = oh_start + work_size;
+        const int ih_s = oh_s * jcp.stride_h;
+
+        conv_params->filter_pad_off
+                = filter_off * jcp.kw * ch_block * sizeof(float);
+        conv_params->oh_index = oh_s;
+        conv_params->oh_count = oh_e;
+
+        size_t diff_dst_off
+                = ((batch * (jcp.ngroups / ch_block) + group) * jcp.oh
+                          + oh_start)
+                * jcp.ow;
+
+        size_t src_off = ((batch * (jcp.ngroups / ch_block) + group) * jcp.ih
+                + ih_s - tpad_underflow_off) * jcp.iw;
+
+        conv_params->output = &diff_dst[diff_dst_off * ch_block];
+        conv_params->input = &src[src_off * ch_block];
+    };
+
+    parallel(jcp.nthr, [&](const int ithr, const int nthr) {
+        assert(nthr == jcp.nthr);
+
+        auto conv_params = jit_dw_conv_call_s();
+        const int h_block_size = 15;
+
+        /* assign iteration space to thread */
+        const int ithr_g = ithr % jcp.nthr_g;
+        const int ithr_mb = (ithr / jcp.nthr_g) % jcp.nthr_mb;
+
+        /* split dimensions */
+        int g_start{ 0 }, g_end{ 0 };
+        balance211(jcp.nb_ch, jcp.nthr_g, ithr_g, g_start, g_end);
+
+        int mb_start{ 0 }, mb_end{ 0 };
+        balance211(jcp.mb, jcp.nthr_mb, ithr_mb, mb_start, mb_end);
+
+        auto diff_wei = ithr_mb == 0
+            ? diff_weights : diff_wei_reduction_buf + (ithr_mb - 1) * wei_size;
+        auto diff_bia = ithr_mb == 0
+            ? diff_bias : diff_bia_reduction_buf + (ithr_mb - 1) * bias_size;
+
+        for (int g = g_start; g < g_end; ++g) {
+            unsigned char zero_filter_flag = FLAG_ZERO_FILTER;
+            unsigned char zero_bias_flag = jcp.with_bias ? FLAG_ZERO_BIAS : 0;
+
+            size_t diff_wei_off = g * jcp.kh * jcp.kw;
+            conv_params.filter = &diff_wei[diff_wei_off * ch_block];
+
+            if (jcp.with_bias)
+                conv_params.bias = &diff_bia[g * ch_block];
+
+            for (int mb = mb_start; mb < mb_end; ++mb) {
+                int oh = 0;
+                while (oh < jcp.oh) {
+                    const int h_work = nstl::min(h_block_size, jcp.oh - oh);
+                    auto kh_t_padding = nstl::max(0, jcp.t_pad - oh);
+                    auto kh_b_padding
+                            = (oh * jcp.stride_h + jcp.kh - 1 > jcp.ih) ?
+                            jcp.b_pad - (h_work - 1) :
+                            0;
+
+                    set_kernel_params(&conv_params, mb, g, oh, h_work,
+                            zero_filter_flag | zero_bias_flag,
+                            kh_t_padding + kh_b_padding, kh_t_padding);
+                    kernel_->jit_ker(&conv_params);
+
+                    zero_bias_flag &= ~FLAG_ZERO_BIAS;
+                    zero_filter_flag &= ~FLAG_ZERO_FILTER;
+                    oh += h_work;
+                }
+            }
+        }
+
+        if (do_parallel_reduction() && jcp.nthr_mb > 1) {
+            size_t reduct_start{ 0 }, reduct_end{ 0 };
+            balance211(wei_size, nthr, ithr, reduct_start, reduct_end);
+
+            const int acc_size = reduct_end - reduct_start;
+            const size_t reduct_off = reduct_start;
+            auto *acc_data = diff_weights + reduct_off;
+
+            simple_barrier::barrier(&reduction_bctx, nthr);
+
+            for (int thr_mb = 1; thr_mb < jcp.nthr_mb; ++thr_mb) {
+                auto *src_data = diff_wei_reduction_buf
+                        + (thr_mb - 1) * wei_size + reduct_off;
+                acc_ker_->accumulate(acc_data, src_data, acc_size);
+            }
+        }
+    });
+
+    if (jcp.nthr_mb <= 1) return;
+
+    /* Apply single-threaded 'mb' reduction */
+    for (int thr_mb = 1; thr_mb < jcp.nthr_mb; ++thr_mb) {
+        size_t mb_accum_offset = (thr_mb - 1) * wei_size;
+        size_t b_accum_offset = (thr_mb - 1) * bias_size;
+
+        for (int g = 0; g < jcp.nb_ch; ++g) {
+            /* Reduction on Bias */
+            if (jcp.with_bias) {
+                PRAGMA_OMP_SIMD()
+                for (int g_block = 0; g_block < ch_block; ++g_block) {
+                    size_t bias_offset = g * ch_block + g_block;
+                    diff_bias[bias_offset] += diff_bia_reduction_buf[
+                        b_accum_offset + bias_offset];
+                }
+            }
+
+            if (do_parallel_reduction()) continue;
+
+            for (int kh = 0; kh < jcp.kh; ++kh)
+            for (int kw = 0; kw < jcp.kw; ++kw)
+            {
+                size_t wei_offset = (g * jcp.kh + kh) * jcp.kw + kw;
+                PRAGMA_OMP_SIMD()
+                for (int g_block = 0; g_block < ch_block; ++g_block) {
+                    const size_t off = wei_offset * ch_block + g_block;
+                    diff_weights[off] +=
+                        diff_wei_reduction_buf[mb_accum_offset + off];
+                }
+            }
+        }
+    }
+}
+
+template struct _jit_uni_dw_convolution_bwd_weights_t<avx512_common>;
+template struct _jit_uni_dw_convolution_bwd_weights_t<avx2>;
+template struct _jit_uni_dw_convolution_bwd_weights_t<sse42>;
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_convolution.hpp
new file mode 100644
index 0000000000..ca53749ec2
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_dw_convolution.hpp
@@ -0,0 +1,266 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_UNI_DW_CONVOLUTION_HPP
+#define CPU_JIT_UNI_DW_CONVOLUTION_HPP
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+
+#include "cpu_barrier.hpp"
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+#include "cpu_reducer.hpp"
+
+#include "jit_uni_dw_conv_kernel_f32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <cpu_isa_t isa>
+struct _jit_uni_dw_convolution_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_fwd_pd_t {
+        pd_t(engine_t *engine, const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const typename pd_t::base_class *hint_fwd_pd)
+            : cpu_convolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_dw:", isa, ""),
+                _jit_uni_dw_convolution_fwd_t<isa>);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_uni_dw_conv_fwd_kernel_f32<isa>::init_conf(
+                    jcp_, *desc(), src_md(), *weights_md(), *dst_md(), *attr());
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_uni_dw_conv_fwd_kernel_f32<isa>::init_scratchpad(scratchpad,
+                    jcp_);
+
+            return status::success;
+        }
+
+        jit_conv_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = isa == avx512_common ? nChw16c : nChw8c;
+            auto wei_tag = isa == avx512_common ? Goihw16g : Goihw8g;
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    _jit_uni_dw_convolution_fwd_t(const pd_t *apd): cpu_primitive_t(apd)
+    { kernel_ = new jit_uni_dw_conv_fwd_kernel_f32<isa>(pd()->jcp_); }
+
+    ~_jit_uni_dw_convolution_fwd_t() { delete kernel_; }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_uni_dw_conv_fwd_kernel_f32<isa> *kernel_;
+};
+
+using jit_avx512_common_dw_convolution_fwd_t =
+    _jit_uni_dw_convolution_fwd_t<avx512_common>;
+using jit_avx2_dw_convolution_fwd_t = _jit_uni_dw_convolution_fwd_t<avx2>;
+using jit_sse42_dw_convolution_fwd_t = _jit_uni_dw_convolution_fwd_t<sse42>;
+
+template <cpu_isa_t isa>
+struct _jit_uni_dw_convolution_bwd_data_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_bwd_data_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_()
+        {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_dw:", isa, ""),
+                _jit_uni_dw_convolution_bwd_data_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_data
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::undef, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+
+            if (!ok) return status::unimplemented;
+
+            status_t status = jit_uni_dw_conv_bwd_data_kernel_f32<isa>::
+                init_conf(jcp_, *desc(), *diff_src_md(), *weights_md(),
+                        *diff_dst_md());
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_uni_dw_conv_bwd_data_kernel_f32<isa>::init_scratchpad(
+                    scratchpad, jcp_);
+
+            return status::success;
+        }
+
+        jit_conv_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = isa == avx512_common ? nChw16c : nChw8c;
+            auto wei_tag = isa == avx512_common ? Goihw16g : Goihw8g;
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    _jit_uni_dw_convolution_bwd_data_t(const pd_t *apd): cpu_primitive_t(apd)
+    { kernel_ = new jit_uni_dw_conv_bwd_data_kernel_f32<isa>(pd()->jcp_); }
+    ~_jit_uni_dw_convolution_bwd_data_t() { delete kernel_; };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_data(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_data(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_uni_dw_conv_bwd_data_kernel_f32<isa> *kernel_;
+};
+
+using jit_avx512_common_dw_convolution_bwd_data_t =
+    _jit_uni_dw_convolution_bwd_data_t<avx512_common>;
+using jit_avx2_dw_convolution_bwd_data_t =
+    _jit_uni_dw_convolution_bwd_data_t<avx2>;
+using jit_sse42_dw_convolution_bwd_data_t =
+    _jit_uni_dw_convolution_bwd_data_t<sse42>;
+
+template <cpu_isa_t isa>
+struct _jit_uni_dw_convolution_bwd_weights_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_bwd_weights_pd_t {
+        pd_t(engine_t *engine,
+                const convolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const convolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_convolution_bwd_weights_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , jcp_() {}
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit_dw:", isa, ""),
+                _jit_uni_dw_convolution_bwd_weights_t<isa>);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_weights
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(data_type::f32, data_type::f32,
+                        data_type::f32, data_type::f32, data_type::f32)
+                && !has_zero_dim_memory()
+                && set_default_formats();
+            if (!ok) return status::unimplemented;
+
+            const int max_threads = mkldnn_in_parallel()
+                ? 1 : mkldnn_get_max_threads();
+
+            status_t status = jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::
+                init_conf(jcp_, *desc(), *src_md(), *diff_weights_md(),
+                        *diff_dst_md(), max_threads);
+            if (status != status::success) return status;
+
+            auto scratchpad = scratchpad_registry().registrar();
+            jit_uni_dw_conv_bwd_weights_kernel_f32<isa>::init_scratchpad(
+                    scratchpad, jcp_);
+
+            return status::success;
+        }
+
+        jit_conv_conf_t jcp_;
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+
+            auto dat_tag = isa == avx512_common ? nChw16c : nChw8c;
+            auto wei_tag = isa == avx512_common ? Goihw16g : Goihw8g;
+
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    _jit_uni_dw_convolution_bwd_weights_t(const pd_t *apd);
+    ~_jit_uni_dw_convolution_bwd_weights_t() {
+        delete kernel_;
+        delete acc_ker_;
+    };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_weights(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_weights(const exec_ctx_t &ctx) const;
+    bool do_parallel_reduction() const { return false; }
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_uni_dw_conv_bwd_weights_kernel_f32<isa> *kernel_;
+    cpu_accumulator_1d_t<data_type::f32> *acc_ker_;
+};
+
+using jit_avx512_common_dw_convolution_bwd_weights_t =
+    _jit_uni_dw_convolution_bwd_weights_t<avx512_common>;
+using jit_avx2_dw_convolution_bwd_weights_t =
+    _jit_uni_dw_convolution_bwd_weights_t<avx2>;
+using jit_sse42_dw_convolution_bwd_weights_t =
+    _jit_uni_dw_convolution_bwd_weights_t<sse42>;
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_eltwise.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_eltwise.cpp
new file mode 100644
index 0000000000..2af6435871
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_eltwise.cpp
@@ -0,0 +1,1142 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "nstl.hpp"
+#include "utils.hpp"
+
+#include "jit_uni_eltwise.hpp"
+
+#define GET_OFF(field) offsetof(jit_args, field)
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace Xbyak;
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::injector_preamble(size_t start_idx,
+        size_t end_idx) {
+    preserved_vecs_count = 0;
+    vecs_to_preserve = (size_t)aux_vecs_count(alg_);
+    start_idx_tail = start_idx;
+
+    // For sse42 mask register has to be Xmm(0)
+    if (isa == sse42 && vecs_to_preserve > 0) {
+        size_t idx = 0;
+        assert(idx < start_idx);
+        preserved_vec_idxs[preserved_vecs_count++] = idx;
+    }
+
+    for (size_t idx = preserved_vecs_count; idx < vecs_count; idx++) {
+        if (preserved_vecs_count >= vecs_to_preserve) break;
+        if (start_idx <= idx && idx < end_idx) continue;
+
+        preserved_vec_idxs[preserved_vecs_count++] = idx;
+    }
+
+    size_t preserved_vecs_count_tail = vecs_to_preserve - preserved_vecs_count;
+    for (size_t i = 0; i < preserved_vecs_count_tail; i++) {
+        preserved_vec_idxs[preserved_vecs_count++] = start_idx_tail++;
+    }
+
+    assert(preserved_vecs_count == vecs_to_preserve);
+
+    if (save_state_) {
+        h->push(p_table);
+
+        if (preserved_vecs_count)
+            h->sub(h->rsp, preserved_vecs_count * vlen);
+
+        for (size_t i = 0; i < preserved_vecs_count; ++i)
+            h->uni_vmovups(h->ptr[h->rsp + i * vlen],
+                    Vmm(preserved_vec_idxs[i]));
+
+        load_table_addr();
+    }
+
+    assign_regs();
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::injector_preamble_tail(size_t start_idx)
+{
+    size_t tail_vecs_to_preserve = start_idx_tail - start_idx;
+    if (tail_vecs_to_preserve == 0) return;
+
+    const int idx_off = vecs_to_preserve - tail_vecs_to_preserve;
+
+    if (save_state_) {
+        if (idx_off)
+            h->add(h->rsp, idx_off * vlen);
+
+        for (size_t i = 0; i < tail_vecs_to_preserve; ++i)
+            h->uni_vmovups(Vmm(preserved_vec_idxs[idx_off + i]),
+                    h->ptr[h->rsp + i * vlen]);
+    }
+
+    for (size_t i = 0; i < tail_vecs_to_preserve; ++i)
+        preserved_vec_idxs[idx_off + i] += tail_vecs_to_preserve;
+
+    if (save_state_) {
+        for (size_t i = 0; i < tail_vecs_to_preserve; ++i)
+            h->uni_vmovups(h->ptr[h->rsp + i * vlen],
+                    Vmm(preserved_vec_idxs[idx_off + i]));
+
+        if (idx_off)
+            h->sub(h->rsp, idx_off * vlen);
+    }
+
+    assign_regs();
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::injector_postamble() {
+    if (!save_state_) return;
+
+    for (size_t i = 0; i < preserved_vecs_count; ++i)
+        h->uni_vmovups(Vmm(preserved_vec_idxs[i]),
+                h->ptr[h->rsp + i * vlen]);
+
+    if (preserved_vecs_count)
+        h->add(h->rsp, preserved_vecs_count * vlen);
+
+    h->pop(p_table);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::assign_regs() {
+    vmm_mask = Vmm(preserved_vec_idxs[0]);
+    vmm_aux0 = Vmm(preserved_vec_idxs[0]);
+    vmm_aux1 = Vmm(preserved_vec_idxs[1]);
+    vmm_aux2 = Vmm(preserved_vec_idxs[2]);
+    vmm_aux3 = Vmm(preserved_vec_idxs[3]);
+    vmm_aux4 = Vmm(preserved_vec_idxs[4]);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::exp_compute_vector(const Vmm &vmm_src) {
+    h->uni_vminps(vmm_src, vmm_src, table_val(10));
+    h->uni_vmaxps(vmm_src, vmm_src, table_val(11));
+    h->uni_vmovups(vmm_aux0, vmm_src);
+    //calculate exp(x)
+    // fx = x * log2ef + 0.5
+    h->uni_vmulps(vmm_src, vmm_src, table_val(2));
+    h->uni_vaddps(vmm_src, vmm_src, table_val(1));
+
+    // tmp = floorf(fx)
+    if (isa == avx512_common) {
+        h->vcvtps2dq(vmm_aux1 | h->T_rd_sae, vmm_src);
+        h->vcvtdq2ps(vmm_aux1, vmm_aux1);
+
+        h->vcmpps(k_mask, vmm_aux1, vmm_src, _cmp_nle_us);
+        h->vmovups(vmm_aux3 | k_mask | h->T_z, table_val(0));
+
+        h->uni_vsubps(vmm_aux1, vmm_aux1, vmm_aux3);
+    } else {
+        h->uni_vroundps(vmm_aux1, vmm_src, _op_floor);
+    }
+
+    //keep fx for further computations
+    h->uni_vmovups(vmm_src, vmm_aux1); //vmm_src = fx
+
+    //x = x - fx * ln2
+    h->uni_vfnmadd231ps(vmm_aux0, vmm_aux1, table_val(3));
+
+    // compute 2^n
+    h->uni_vcvtps2dq(vmm_aux1, vmm_src);
+    h->uni_vpaddd(vmm_aux1, vmm_aux1, table_val(4));
+    h->uni_vpslld(vmm_aux1, vmm_aux1, 23); //Vmm(6) = 2^-fx
+
+    // y = p5
+    h->uni_vmovups(vmm_src, table_val(9));
+    // y = y * x + p4
+    h->uni_vfmadd213ps(vmm_src, vmm_aux0, table_val(8));
+    // y = y * x + p3
+    h->uni_vfmadd213ps(vmm_src, vmm_aux0, table_val(7));
+    // y = y * x + p2
+    h->uni_vfmadd213ps(vmm_src, vmm_aux0, table_val(6));
+    // y = y * x + p1
+    h->uni_vfmadd213ps(vmm_src, vmm_aux0, table_val(0));
+    // y = y * x + p0
+    h->uni_vfmadd213ps(vmm_src, vmm_aux0, table_val(5));  //exp(q)
+    // y = y * 2^n
+    h->uni_vmulps(vmm_src, vmm_src, vmm_aux1);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::relu_compute_vector(const Vmm &vmm_src)
+{
+    const int alpha_off = 0, zero_off = 1;
+
+    h->uni_vmovups(vmm_aux1, vmm_src);
+    if (isa == sse42) {
+        h->movups(vmm_mask, vmm_src);
+        h->mulps(vmm_src, table_val(alpha_off));
+        h->cmpps(vmm_mask, table_val(zero_off), _cmp_nle_us);
+        h->blendvps(vmm_src, vmm_aux1);
+    } else if (isa == avx2) {
+        h->vmulps(vmm_src, vmm_src, table_val(alpha_off));
+        h->vcmpgtps(vmm_mask, vmm_aux1, table_val(zero_off));
+        h->vblendvps(vmm_src, vmm_src, vmm_aux1, vmm_mask);
+    } else if (isa == avx512_common) {
+        h->vmulps(vmm_src, vmm_src, table_val(alpha_off));
+        h->vcmpps(k_mask, vmm_aux1, table_val(zero_off), _cmp_nle_us);
+        h->vblendmps(vmm_src | k_mask, vmm_src, vmm_aux1);
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::relu_zero_ns_compute_vector(
+        const Vmm &vmm_src) {
+    const int zero_off = 1;
+    h->uni_vmaxps(vmm_src, vmm_src, table_val(zero_off));
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::elu_compute_vector(const Vmm &vmm_src) {
+    const int alpha_off = 23, zero_off = 24;
+
+    // compute exponent
+    h->uni_vmovups(vmm_aux2, vmm_src);
+    exp_compute_vector(vmm_src);
+
+    // alpha * (exp(x) - 1)
+    h->uni_vsubps(vmm_src, vmm_src, table_val(0));
+    h->uni_vmulps(vmm_src, vmm_src, table_val(alpha_off));
+
+    // combine with mask
+    if (isa == sse42) {
+        h->pxor(vmm_mask, vmm_mask);
+        h->cmpps(vmm_mask,  vmm_aux2, _cmp_le_os);
+        h->blendvps(vmm_src, vmm_aux2);
+    } else if (isa == avx2) {
+        h->uni_vcmpgtps(vmm_mask, vmm_aux2, table_val(zero_off));
+        h->uni_vblendvps(vmm_src, vmm_src, vmm_aux2, vmm_mask);
+    } else if (isa == avx512_common) {
+        h->vcmpps(k_mask, vmm_aux2, table_val(zero_off), _cmp_nle_us);
+        h->vblendmps(vmm_src | k_mask, vmm_src, vmm_aux2);
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::tanh_compute_vector(const Vmm &vmm_src)
+{
+    // # comes from Taylor expansion error bound
+    //  > linear_sat_point = single(sqrt(3) * 1b-12);
+    // # comes from the exp formula cancellation
+    //  > exp_bound_point = (single(log(3)/2));
+    // # comes from rounding accuracy in float
+    //  > one_sat_point = round(atanh(1 - 1b-25), single, RU);
+    //  > P = fpminimax(f, [|1, 3, 5, 7, 9|], [|24... |],
+    //            [linear_sat_point, exp_bound_point], relative, floating);
+    //  > err_bound = D(sup(supnorm(P, tanh(x),
+    //          [linear_sat_point, exp_bound_point], relative, theta)));
+    //    0x1.fffd6f00b9539p-25
+    //  > P;
+    //    x * (0x1.fffffep-1 + x^0x1p1 * (-0x1.55539ep-2 + x^0x1p1 *
+    //        (0x1.10be3ep-3 + x^0x1p1 * (-0x1.ae57b4p-5
+    //        + x^0x1p1 * 0x1.09fa1p-6))))
+
+    // register mapping
+    // vmm_src contains input
+    // vmm_aux0 contains mask of currently valid results.
+    //     1 is need computation, 0 is already computed
+    // vmm_aux1 contains current output
+    // vmm_aux2, vmm_aux3 contains auxiliary values
+    // vmm_aux4 contains the original sign of inputs
+
+    Label end_tanh_label;
+
+    auto test_exit =[&](Xbyak::Address threshold){
+        // is not necessary for >AVX, but should not matter on perf
+        h->uni_vmovups(vmm_aux0, vmm_src);
+        if (isa == avx512_common){
+            h->vcmpps(k_mask, vmm_aux0, threshold, 0x5);
+            h->kortestw(k_mask, k_mask);
+        } else {
+            h->uni_vcmpgeps(vmm_aux0, vmm_aux0, threshold);
+            h->uni_vtestps(vmm_aux0, vmm_aux0);
+        }
+        h->jz(end_tanh_label, Xbyak::CodeGenerator::T_NEAR);
+    };
+
+    auto blend_results=[&](Vmm vmm_partial_res){
+        if (isa == avx512_common)
+            h->vblendmps(vmm_aux1 | k_mask, vmm_aux1, vmm_partial_res);
+        else
+            h->uni_vblendvps(vmm_aux1, vmm_aux1, vmm_partial_res, vmm_aux0);
+    };
+
+    // because tanh(x) = -tanh(-x), we extract sign to make x postive
+    // and reapply sign at the end
+    // mov is not necessary for >AVX, but should not matter for performance
+    h->uni_vmovups(vmm_aux4, vmm_src);
+    h->uni_vandps(vmm_aux4, vmm_aux4, table_val(12));
+    h->uni_vandps(vmm_src, vmm_src, table_val(17));
+
+    // if x < linear_sat_point for all inputs, we just return the input
+    h->uni_vmovups(vmm_aux1, vmm_src);
+    test_exit(table_val(13));
+
+    // if one of the mask is one, we have to compute an better approx
+    h->uni_vmovups(vmm_aux2, vmm_src);
+    h->uni_vmulps(vmm_aux2, vmm_aux2, vmm_aux2);
+    h->uni_vmovups(vmm_aux3, table_val(22));
+    h->uni_vfmadd213ps(vmm_aux3, vmm_aux2, table_val(21));
+    h->uni_vfmadd213ps(vmm_aux3, vmm_aux2, table_val(20));
+    h->uni_vfmadd213ps(vmm_aux3, vmm_aux2, table_val(19));
+    h->uni_vfmadd213ps(vmm_aux3, vmm_aux2, table_val(18));
+    h->uni_vmulps(vmm_aux3, vmm_aux3, vmm_src);
+
+    // we blend only the result that need update
+    blend_results(vmm_aux3);
+
+    // if x < exp_bound_point, we go to return point
+    test_exit(table_val(14));
+
+    // if not we use a better approx 1 - 2 / (1 + exp(2x))
+    // compute 2x
+    h->uni_vmovups(vmm_aux3, vmm_src);
+    h->uni_vaddps(vmm_aux3, vmm_aux3, vmm_aux3);
+
+    // Compute exp(2x)
+    // We need to save kmask, vmm_aux0, vmm_aux1 and vmm_src as exp can use them
+    // vmm_src is not more read afterwards, so we do not have to save it
+    auto stack_size = 3 * vlen + (isa == avx512_common) * 4;
+    h->sub(h->rsp, stack_size);
+    h->uni_vmovups(h->ptr[h->rsp + 0 * vlen], vmm_aux0);
+    h->uni_vmovups(h->ptr[h->rsp + 1 * vlen], vmm_aux1);
+    h->uni_vmovups(h->ptr[h->rsp + 2 * vlen], vmm_src);
+    if (isa == avx512_common)
+        h->kmovw(h->ptr[h->rsp + 3 * vlen], k_mask);
+
+    exp_compute_vector(vmm_aux3);
+
+    h->uni_vmovups(vmm_aux0, h->ptr[h->rsp + 0 * vlen]);
+    h->uni_vmovups(vmm_aux1, h->ptr[h->rsp + 1 * vlen]);
+    h->uni_vmovups(vmm_src, h->ptr[h->rsp + 2 * vlen]);
+    if (isa == avx512_common)
+        h->kmovw(k_mask, h->ptr[h->rsp + 3 * vlen]);
+    h->add(h->rsp, stack_size);
+
+    // 1 + exp(2x)
+    h->uni_vaddps(vmm_aux3, vmm_aux3, table_val(0));
+
+    // 1 - 2 / (1 + exp(2x))
+    h->uni_vmovups(vmm_aux2, table_val(16));
+    h->uni_vdivps(vmm_aux2, vmm_aux2, vmm_aux3);
+    h->uni_vaddps(vmm_aux2, vmm_aux2, table_val(0));
+
+    // we blend only the result that need update
+    blend_results(vmm_aux2);
+
+    // finally, we saturate to 1 if needed
+    // TODO: maybe move that up if most inputs saturate in practice
+    if (isa == avx512_common)
+        h->vcmpps(k_mask, vmm_aux0, table_val(15), 0x5);
+    else {
+        h->uni_vmovups(vmm_aux0, vmm_src);
+        h->uni_vcmpgeps(vmm_aux0, vmm_aux0, table_val(15));
+    }
+    h->uni_vmovups(vmm_aux2, table_val(0));
+    blend_results(vmm_aux2);
+
+    h->L(end_tanh_label);
+    {
+        // we apply the sign of x to the result and we are done
+        h->uni_vmovups(vmm_src, vmm_aux1);
+        h->uni_vpxor(vmm_src, vmm_src, vmm_aux4);
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::square_compute_vector(
+        const Vmm &vmm_src) {
+    h->uni_vmulps(vmm_src, vmm_src, vmm_src);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::abs_compute_vector(const Vmm &vmm_src) {
+    // compute abs(x) = _mm_and_ps(x, 01111..111));
+    h->uni_vandps(vmm_src, vmm_src, table_val(0));
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::sqrt_compute_vector(const Vmm &vmm_src)
+{
+    if (isa == avx512_common) {
+        h->vcmpps(k_mask, vmm_src, table_val(0), _cmp_nle_us);
+        h->uni_vsqrtps(vmm_aux1, vmm_src);
+        h->uni_vmovups(vmm_src, table_val(0));
+        h->vblendmps(vmm_src | k_mask, vmm_src, vmm_aux1);
+    } else {
+        h->uni_vmovups(vmm_mask, vmm_src);
+        h->uni_vcmpgtps(vmm_mask, vmm_mask, table_val(0));
+        h->uni_vsqrtps(vmm_aux1, vmm_src);
+        h->uni_vmovups(vmm_src, table_val(0));
+        h->uni_vblendvps(vmm_src, vmm_src, vmm_aux1, vmm_mask);
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::linear_compute_vector(
+        const Vmm &vmm_src) {
+    // compute x = alpha * x + beta;
+    h->uni_vmovups(vmm_aux0, table_val(0));
+    h->uni_vfmadd213ps(vmm_src, vmm_aux0, table_val(1));
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::bounded_relu_compute_vector(
+        const Vmm &vmm_src) {
+    // compute bounded relu */
+    h->uni_vmaxps(vmm_src, vmm_src, table_val(1));
+    h->uni_vminps(vmm_src, vmm_src, table_val(0));
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::soft_relu_compute_vector(
+        const Vmm &vmm_src) {
+    // duplicate src
+    h->uni_vmovups(vmm_aux2, vmm_src);
+
+    h->uni_vminps(vmm_src, vmm_src, table_val(24));
+    h->uni_vmaxps(vmm_src, vmm_src, table_val(25));
+    h->uni_vmovups(vmm_aux1, vmm_src);
+    // calculate exp(x)
+    // fx = x * log2ef + 0.5
+    h->uni_vmulps(vmm_src, vmm_src, table_val(2));
+    h->uni_vaddps(vmm_src, vmm_src, table_val(1));
+
+    // tmp = floorf(fx)
+    if (isa == avx512_common) {
+        h->vcvtps2dq(vmm_aux0 | h->T_rd_sae, vmm_src);
+        h->vcvtdq2ps(vmm_aux0, vmm_aux0);
+
+        h->vcmpps(k_mask, vmm_aux0, vmm_src, _cmp_nle_us);
+        h->vmovups(vmm_aux3 | k_mask | h->T_z, table_val(0));
+
+        h->vsubps(vmm_aux0, vmm_aux0, vmm_aux3);
+    } else {
+        h->uni_vroundps(vmm_aux0, vmm_src, _op_floor);
+    }
+
+    // keep fx for further computations
+    h->uni_vmovups(vmm_src, vmm_aux0); //vmm_src = fx
+    // calculation fx * ln2
+    h->uni_vmulps(vmm_aux0, vmm_aux0, table_val(3));
+    // x = x - fx * ln2
+    h->uni_vsubps(vmm_aux1, vmm_aux1, vmm_aux0);
+    // y = p5
+    h->uni_vmovups(vmm_aux3, table_val(22));
+    // y = y * x + p4
+    h->uni_vfmadd213ps(vmm_aux3, vmm_aux1, table_val(21));
+    // y = y * x + p3
+    h->uni_vfmadd213ps(vmm_aux3, vmm_aux1, table_val(20));
+    // y = y * x + p2
+    h->uni_vfmadd213ps(vmm_aux3, vmm_aux1, table_val(19));
+    // y = y * x + p1
+    h->uni_vfmadd213ps(vmm_aux3, vmm_aux1, table_val(0));
+    // y = y * x + p0
+    h->uni_vfmadd213ps(vmm_aux3, vmm_aux1, table_val(17));
+
+    // compute 2^(-n)
+    if (isa == avx512_common) {
+        h->vmulps(vmm_aux1, vmm_src, table_val(23));
+        h->vcvtps2dq(vmm_aux1, vmm_aux1);
+    } else {
+        h->uni_vcvtps2dq(vmm_aux1, vmm_src);
+        h->uni_vpsignd(vmm_aux1, vmm_aux1, table_val(23));
+    }
+
+    h->uni_vpaddd(vmm_aux1, vmm_aux1, table_val(4));
+    h->uni_vpslld(vmm_aux1, vmm_aux1, 23); //vmm_aux1 = 2^-fx
+    // calculate ln(1 + y)
+    h->uni_vaddps(vmm_aux3, vmm_aux3, vmm_aux1);
+    // x = y; y is free; keep x for further computations
+    h->uni_vmovups(vmm_src, vmm_aux3);
+    // frexp()
+    h->uni_vpsrld(vmm_src, vmm_src, 23);
+    h->uni_vcvtdq2ps(vmm_src, vmm_src);
+    // got n. where n is x = 2^n * y. y = 0.5 .. 1
+    h->uni_vsubps(vmm_src, vmm_src, table_val(5));
+
+    h->uni_vandps(vmm_aux3, vmm_aux3, table_val(6));
+    // got y. (mantisa)  0.5 < y < 1
+    h->uni_vorps(vmm_aux3, vmm_aux3, table_val(7));
+    // y  = y - 1
+    h->uni_vsubps(vmm_aux3, vmm_aux3, table_val(0));
+    // y = p8
+    h->uni_vmovups(vmm_aux1, table_val(16));
+    // y = y * x + p7
+    h->uni_vfmadd213ps(vmm_aux1, vmm_aux3, table_val(15));
+    // y = y * x + p6
+    h->uni_vfmadd213ps(vmm_aux1, vmm_aux3, table_val(14));
+    // y = y * x + p5
+    h->uni_vfmadd213ps(vmm_aux1, vmm_aux3, table_val(13));
+    // y = y * x + p4
+    h->uni_vfmadd213ps(vmm_aux1, vmm_aux3, table_val(12));
+    // y = y * x + p3
+    h->uni_vfmadd213ps(vmm_aux1, vmm_aux3, table_val(11));
+    // y = y * x + p2
+    h->uni_vfmadd213ps(vmm_aux1, vmm_aux3, table_val(10));
+    // y = y * x + p1
+    h->uni_vfmadd213ps(vmm_aux1, vmm_aux3, table_val(9));
+    // y = y * x + p0 ; p0 = 0
+    h->uni_vfmadd213ps(vmm_aux1, vmm_aux3, table_val(8));
+    //calculate ln(2) * n
+    h->uni_vmulps(vmm_src, vmm_src, table_val(3));
+    h->uni_vaddps(vmm_aux1, vmm_aux1, vmm_src);
+    h->uni_vaddps(vmm_aux1, vmm_aux1, vmm_aux0);
+
+    // get vmm_mask = src > max logf
+    h->uni_vmovups(vmm_mask, vmm_aux2);
+    if (isa == avx512_common) {
+        // y = (x < max log f) ? soft_relu(x) : x
+        h->vcmpps(k_mask, vmm_mask, table_val(24), _cmp_nle_us);
+        h->vblendmps(vmm_aux1 | k_mask, vmm_aux1, vmm_aux2);
+    } else {
+        // y = (x < max log f) ? soft_relu(x) : x
+        h->uni_vcmpgtps(vmm_mask, vmm_mask, table_val(24));
+        h->uni_vblendvps(vmm_aux1, vmm_aux1, vmm_aux2, vmm_mask);
+    }
+
+    h->uni_vmovups(vmm_src, vmm_aux1);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::logistic_compute_vector(
+        const Vmm &vmm_src) {
+    // we store the original sign and make x negative
+    // IMPORTANT: we assume vmm_aux0 to be xmm0, as for sse4.2 path it is required
+    // IMPORTANT: we use vmm_aux2 for the mask as exp_compute does not use it.
+    h->uni_vmovups(vmm_aux2, vmm_src);
+    h->uni_vandps(vmm_aux2, vmm_aux2, table_val(12));
+    h->uni_vorps(vmm_src, vmm_src, table_val(12));
+
+    exp_compute_vector(vmm_src);
+    // dup exp(x)
+    h->uni_vmovups(vmm_aux1, vmm_src);
+    // (exp(x) + 1)
+    h->uni_vaddps(vmm_aux1, vmm_aux1, table_val(0));
+    // y = exp(x) / (exp(x) + 1)
+    h->uni_vdivps(vmm_src, vmm_src, vmm_aux1);
+
+    // Now we have to apply the "symmetry" based on original sign
+    h->uni_vmovups(vmm_aux3, table_val(0));
+    h->uni_vsubps(vmm_aux3, vmm_aux3, vmm_src);
+    if (isa == avx512_common) {
+        h->vptestmd(k_mask, vmm_aux2, vmm_aux2);
+        h->vblendmps(vmm_aux3 | k_mask, vmm_aux3, vmm_src);
+    } else {
+        h->uni_vmovups(vmm_aux0, vmm_aux2);// The mask should be xmm0 for sse4.2
+        h->uni_vblendvps(vmm_aux3, vmm_aux3, vmm_src, vmm_aux0);
+    }
+    h->uni_vmovups(vmm_src, vmm_aux3);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::relu_prepare_table() {
+    for (size_t d = 0; d < vlen / sizeof(float); ++d) h->dd(float2int(alpha_));
+    for (size_t d = 0; d < vlen / sizeof(float); ++d) h->dd(0);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::elu_prepare_table() {
+    const unsigned int cvals[] = {
+            0x3f800000, // [0] 1.0f
+            0x3f000000, // [1] 0.5f
+            0x3fb8aa3b, // [2] log2ef = 1.44269502f
+            0x3f317218, // [3] ln2f =   0.69314718f
+            0x0000007f, // [4] 0x7f
+            // exp(x) polynom
+            0x3f800001, // [5] p0 = 1.0000001f
+            0x3efffe85, // [6] p2 = 0.4999887f
+            0x3e2aaa3e, // [7] p3 = 0.16666505f
+            0x3d2bb1b1, // [8] p4 = 0.041917507f
+            0x3c091ec1, // [9] p5 = 0.008369149f
+            0x42b0c0a5, //[10] max logf = 88.3762589f
+            0xc1766666, //[11] min logf = -14.5f
+            // tanh(x) constants,
+            0x80000000, //[12] mask to extract sign
+            0x39ddb3d7, //[13] arg below which tanh(x) = x
+            0x3f0c9f54, //[14] arg below which pol approx is valid
+            0x41102cb4, //[15] arg after which tanh(x) = 1
+            0xc0000000, //[16] -2.0f
+            0x7fffffff, //[17] mask to make positive
+            // tanh pol approx
+            0x3f7fffff, //[18] p0
+            0xbeaaa9cf, //[19] p1
+            0x3e085f1f, //[20] p2
+            0xbd572bda, //[21] p3
+            0x3c84fd08, //[22] p4
+    };
+
+    for (size_t i = 0; i < sizeof(cvals) / sizeof(cvals[0]); ++i) {
+        for (size_t d = 0; d < vlen / sizeof(float); ++d) h->dd(cvals[i]);
+    }
+
+    for (size_t d = 0; d < vlen / sizeof(float); ++d) h->dd(float2int(alpha_));
+    for (size_t d = 0; d < vlen / sizeof(float); ++d) h->dd(0);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::soft_relu_prepare_table() {
+    const unsigned int cvals[] = {
+            0x3f800000, // [0] 1.0f
+            0x3f000000, // [1] 0.5f
+            0x3fb8aa3b, // [2] log2ef = 1.44269502f
+            0x3f317218, // [3] ln2f =   0.69314718f
+            0x0000007f, // [4] 0x7f
+            0x42fc0000, // [5] 126
+            0x807fffff, // [6] and with (to get 0.5 * mantissa)
+            0x3f000000, // [7] or with (to get 0.5 * mantissa)
+            // ln(1 + x) polynomial
+            0xb2b4637d, // [8]  p0 = 0.0000000244f
+            0x3f7fff8e, // [9]  p1 = 0.9999976971f
+            0xbf001759, //[10]  p2 = -0.5002478215f
+            0x3ea70608, //[11]  p3 = 0.3272714505f
+            0xbea3d7bf, //[12]  p4 = -0.3153830071f
+            0xbe361d04, //[13]  p5 = -0.1701777461f
+            0xbfa8f1e6, //[14]  p6 = -1.3254635147f
+            0xbfe1e812, //[15]  p7 = -1.7971917960f
+            0xbfc4d30e, //[16]  p8 = -1.5652673123f
+            // exp(x) polynomial
+            0x3f800001, //[17]  p0 = 1.0000001f
+            0x3f800000, //[18]  p1 = 1.0f
+            0x3efffe85, //[19]  p2 = 0.4999887f
+            0x3e2aaa3e, //[20]  p3 = 0.16666505f
+            0x3d2bb1b1, //[21]  p4 = 0.041917507f
+            0x3c091ec1, //[22]  p5 = 0.008369149f
+            0xbf800000, //[23] is required for sign changing
+            0x42b0c0a5, //[24] max logf = 88.3762589f
+            0xc1766666  //[25] min logf = -14.5f
+    };
+
+    for (size_t i = 0; i < sizeof(cvals) / sizeof(cvals[0]); ++i) {
+        for (size_t d = 0; d < vlen / sizeof(float); ++d) {
+            h->dd(cvals[i]);
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::abs_prepare_table() {
+    for (size_t d = 0; d < vlen / sizeof(float); ++d) h->dd(0x7fffffff);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::sqrt_prepare_table() {
+    for (size_t d = 0; d < vlen / sizeof(float); ++d) h->dd(0);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::linear_prepare_table() {
+    for (size_t d = 0; d < vlen / sizeof(float); ++d) h->dd(float2int(alpha_));
+    for (size_t d = 0; d < vlen / sizeof(float); ++d) h->dd(float2int(beta_));
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::bounded_relu_prepare_table() {
+    for (size_t d = 0; d < vlen / sizeof(float); ++d) h->dd(float2int(alpha_));
+    for (size_t d = 0; d < vlen / sizeof(float); ++d) h->dd(0);
+}
+
+template <cpu_isa_t isa>
+int jit_uni_eltwise_injector_f32<isa>::aux_vecs_count(alg_kind_t alg_) {
+    switch (alg_) {
+    case alg_kind::eltwise_relu: return (alpha_ == 0.f) ? 0 : 2;
+    case alg_kind::eltwise_elu: return 4;
+    case alg_kind::eltwise_tanh: return 5;
+    case alg_kind::eltwise_square: return 0;
+    case alg_kind::eltwise_abs: return 0;
+    case alg_kind::eltwise_sqrt: return 2;
+    case alg_kind::eltwise_linear: return 1;
+    case alg_kind::eltwise_bounded_relu: return 0;
+    case alg_kind::eltwise_soft_relu: return 4;
+    case alg_kind::eltwise_logistic: return 4;
+    default: assert(!"unsupported eltwise algorithm");
+    }
+
+    return 0;
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::compute_body(size_t start_idx,
+        size_t end_idx) {
+    using namespace alg_kind;
+    for (size_t idx = start_idx; idx < end_idx; idx++) {
+        switch (alg_) {
+        case eltwise_relu:
+            if (alpha_ == 0.f) relu_zero_ns_compute_vector(Vmm(idx));
+            else relu_compute_vector(Vmm(idx));
+            break;
+        case eltwise_elu: elu_compute_vector(Vmm(idx)); break;
+        case eltwise_tanh: tanh_compute_vector(Vmm(idx)); break;
+        case eltwise_square: square_compute_vector(Vmm(idx)); break;
+        case eltwise_abs: abs_compute_vector(Vmm(idx)); break;
+        case eltwise_sqrt: sqrt_compute_vector(Vmm(idx)); break;
+        case eltwise_linear: linear_compute_vector(Vmm(idx)); break;
+        case eltwise_bounded_relu: bounded_relu_compute_vector(Vmm(idx)); break;
+        case eltwise_soft_relu: soft_relu_compute_vector(Vmm(idx)); break;
+        case eltwise_logistic: logistic_compute_vector(Vmm(idx)); break;
+        default: assert(!"unsupported eltwise algorithm");
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::compute_vector_range(size_t start_idx,
+        size_t end_idx) {
+    assert(start_idx < end_idx && end_idx <= vecs_count);
+
+    injector_preamble(start_idx, end_idx);
+    compute_body(start_idx_tail, end_idx);
+    injector_preamble_tail(start_idx);
+    compute_body(start_idx, start_idx_tail);
+    injector_postamble();
+}
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_injector_f32<isa>::prepare_table(bool gen_table) {
+    using namespace alg_kind;
+
+    h->align(64);
+    h->L(l_table);
+
+    if (gen_table) {
+        switch (alg_) {
+        case eltwise_relu: relu_prepare_table(); break;
+        case eltwise_elu:
+        case eltwise_tanh:
+        case eltwise_logistic:
+            elu_prepare_table(); break;
+        case eltwise_soft_relu: soft_relu_prepare_table(); break;
+        case eltwise_abs: abs_prepare_table(); break;
+        case eltwise_sqrt: sqrt_prepare_table(); break;
+        case eltwise_linear: linear_prepare_table(); break;
+        case eltwise_bounded_relu: bounded_relu_prepare_table(); break;
+        case eltwise_square: break;
+        default: assert(!"unsupported eltwise algorithm");
+    }
+    }
+}
+
+template struct jit_uni_eltwise_injector_f32<avx512_common>;
+template struct jit_uni_eltwise_injector_f32<avx2>;
+template struct jit_uni_eltwise_injector_f32<sse42>;
+
+
+struct jit_args {
+    const float *from;
+    const float *for_comparison;
+    const float *to;
+    size_t work_amount;
+};
+
+struct jit_uni_eltwise_kernel_f32 : public c_compatible {
+    const eltwise_desc_t &desc_;
+
+    void (*ker_)(const jit_args *);
+    void operator()(const jit_args *args) { assert(ker_); ker_(args); }
+
+    jit_uni_eltwise_kernel_f32(const eltwise_desc_t &desc)
+        : desc_(desc), ker_(nullptr) {}
+    virtual ~jit_uni_eltwise_kernel_f32() {}
+
+protected:
+    bool is_bwd() const { return desc_.prop_kind == prop_kind::backward_data; }
+};
+
+/* jit kernels */
+namespace {
+
+template <cpu_isa_t isa>
+struct jit_uni_relu_kernel_f32 : public jit_uni_eltwise_kernel_f32,
+    public jit_generator
+{
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_relu_kernel_f32)
+
+    void compute_step(bool vectorize, const int uf, const int shift) {
+        for (int i = 0; i < uf; i++) {
+            if (vectorize) {
+                uni_vmovups(Vmm(i + 1), ptr[reg_from + i * shift]);
+                if (is_bwd())
+                    uni_vmovups(Vmm(uf + i + 1),
+                                ptr[reg_for_comparison + i * shift]);
+            } else {
+                movss(Xmm(i + 1), ptr[reg_from + i * shift]);
+                if (is_bwd())
+                    movss(Xmm(uf + i + 1),
+                          ptr[reg_for_comparison + i * shift]);
+            }
+        }
+
+        if (isa == sse42) {
+            for (int i = 0; i < uf; i++) {
+                movups(Vmm(2 * uf + i + 1), Vmm(i + 1));
+                mulps(Vmm(2 * uf + i + 1), vmm_ns);
+
+                Vmm mask = Vmm(0);
+                if (is_bwd()) {
+                    movups(mask, Vmm(uf + i + 1));
+                    cmpps(mask, vmm_zero, _cmp_nle_us);
+                } else {
+                    movups(mask, Vmm(i + 1));
+                    cmpps(mask, vmm_zero, _cmp_nle_us);
+                }
+                blendvps(Vmm(2 * uf + i + 1), Vmm(i + 1));
+            }
+        } else {
+            for (int i = 0; i < uf; i++) {
+                vmulps(Vmm(2 * uf + i + 1), Vmm(i + 1), vmm_ns);
+                if (isa == avx2) {
+                    if (is_bwd())
+                        vcmpgtps(vmm_mask, Vmm(uf + i + 1), vmm_zero);
+                    else
+                        vcmpgtps(vmm_mask, Vmm(i + 1), vmm_zero);
+
+                    vblendvps(Vmm(2 * uf + i + 1), Vmm(2 * uf + i + 1),
+                              Vmm(i + 1), vmm_mask);
+
+                } else {
+                    if (is_bwd())
+                        vcmpps(k_mask, Vmm(uf + i + 1), vmm_zero, _cmp_nle_us);
+                    else
+                        vcmpps(k_mask, Vmm(i + 1), vmm_zero, _cmp_nle_us);
+                    vblendmps(Vmm(2 * uf + i + 1) | k_mask, Vmm(2 * uf + i + 1),
+                              Vmm(i + 1));
+                }
+            }
+        }
+
+        for (int i = 0; i < uf; i++) {
+            if (vectorize) {
+                uni_vmovups(ptr[reg_to + i * shift], Vmm(2 * uf + i + 1));
+            } else {
+                movss(ptr[reg_to + i * shift], Xmm(2 * uf + i + 1));
+            }
+        }
+    }
+
+    jit_uni_relu_kernel_f32(const eltwise_desc_t &desc)
+        : jit_uni_eltwise_kernel_f32(desc), jit_generator() {
+        assert(desc.alg_kind == alg_kind::eltwise_relu);
+        assert(isa == sse42 || isa == avx2 || isa == avx512_common);
+
+        Reg64 param = abi_param1;
+
+        const int simd_w = cpu_isa_traits<isa>::vlen / sizeof(float);
+        const int loop_dec[] = {simd_w, 1};
+        const int uf[] = {1, 1};
+        const int shift[] = {cpu_isa_traits<isa>::vlen, sizeof(float)};
+        const bool loop_vectorize[] = {true, false};
+
+        this->preamble();
+
+        mov(reg_from, ptr[param + GET_OFF(from)]);
+        if (is_bwd())
+            mov(reg_for_comparison, ptr[param + GET_OFF(for_comparison)]);
+        mov(reg_to, ptr[param + GET_OFF(to)]);
+        mov(reg_work_amount, ptr[param + GET_OFF(work_amount)]);
+
+        mov(imm_addr64, float2int(desc.alpha));
+        movq(xmm_ns, imm_addr64);
+        uni_vbroadcastss(vmm_ns, xmm_ns);
+
+        uni_vpxor(vmm_zero, vmm_zero, vmm_zero);
+
+        Label loop_label[3];
+
+        for (int id = 0; id < 2; id++) {
+            L(loop_label[id]);
+            cmp(reg_work_amount, uf[id] * loop_dec[id] - 1);
+            jle(loop_label[id + 1], T_NEAR);
+
+            compute_step(loop_vectorize[id], uf[id], shift[id]);
+
+            add(reg_from, uf[id] * shift[id]);
+            add(reg_to, uf[id] * shift[id]);
+            if (is_bwd())
+                add(reg_for_comparison, uf[id] * shift[id]);
+
+            sub(reg_work_amount, uf[id] * loop_dec[id]);
+            jmp(loop_label[id]);
+        }
+
+        L(loop_label[2]);
+        this->postamble();
+
+        ker_ = (decltype(ker_))this->getCode();
+    }
+
+private:
+    using Vmm = typename utils::conditional3<isa == sse42, Xmm,
+                                             isa == avx2, Ymm, Zmm>::type;
+
+    Reg64 reg_from = rax;
+    Reg64 reg_for_comparison = is_bwd() ? rdx : reg_from;
+    Reg64 reg_to = r8;
+    Reg64 reg_work_amount = rsi;
+    Reg64 imm_addr64 = rbx;
+
+    Xmm xmm_ns = Xmm(14);
+
+    Vmm vmm_ns = Vmm(isa == avx512_common ? 30 : 14);
+    Vmm vmm_zero = Vmm(isa == avx512_common ? 31 : 15);
+
+    Vmm vmm_mask = Vmm(isa == avx512_common ? 28 : 12);
+    Opmask k_mask = Opmask(1);
+};
+
+template <cpu_isa_t isa>
+struct jit_uni_kernel_fwd_f32: public jit_uni_eltwise_kernel_f32,
+    public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_kernel_fwd_f32)
+
+    jit_uni_kernel_fwd_f32(const eltwise_desc_t &desc)
+        : jit_uni_eltwise_kernel_f32(desc), jit_generator() {
+
+        eltwise_injector_ = new jit_uni_eltwise_injector_f32<isa>(this,
+                desc.alg_kind, desc.alpha, desc.beta, false, r9, Opmask(1));
+
+        using namespace alg_kind;
+
+        assert(is_bwd() == false);
+        assert(utils::one_of(desc.alg_kind, eltwise_tanh, eltwise_elu,
+                    eltwise_square, eltwise_abs, eltwise_sqrt, eltwise_linear,
+                    eltwise_bounded_relu, eltwise_soft_relu, eltwise_logistic));
+
+        preamble();
+
+        Reg64 param = abi_param1;
+        mov(reg_from, ptr[param + GET_OFF(from)]);
+        mov(reg_to, ptr[param + GET_OFF(to)]);
+        mov(reg_work_amount, ptr[param + GET_OFF(work_amount)]);
+        eltwise_injector_->load_table_addr();
+
+        Label reminder_loop_start, reminder_loop_end;
+        Label vectorized_loop_start, vectorized_loop_end;
+
+        cmp(reg_work_amount, simd_w);
+        jl(reminder_loop_start, T_NEAR);
+
+        L(vectorized_loop_start);
+
+        uni_vmovups(vmm_src, ptr[reg_from]);
+        eltwise_injector_->compute_vector(vmm_src.getIdx());
+        uni_vmovups(ptr[reg_to], vmm_src);
+
+        add(reg_from, vlen);
+        add(reg_to, vlen);
+
+        sub(reg_work_amount, simd_w);
+        cmp(reg_work_amount, simd_w);
+        jge(vectorized_loop_start, T_NEAR);
+
+        L(vectorized_loop_end);
+
+        L(reminder_loop_start);
+
+        cmp(reg_work_amount, 0);
+        jle(reminder_loop_end, T_NEAR);
+
+        movss(xmm_src, ptr[reg_from]);
+        eltwise_injector_->compute_vector(xmm_src.getIdx());
+        movss(ptr[reg_to], xmm_src);
+
+        add(reg_from, sizeof(float));
+        add(reg_to, sizeof(float));
+
+        dec(reg_work_amount);
+        jmp(reminder_loop_start, T_NEAR);
+
+        L(reminder_loop_end);
+
+        postamble();
+
+        eltwise_injector_->prepare_table();
+
+        ker_ = (decltype(ker_))this->getCode();
+    }
+
+    ~jit_uni_kernel_fwd_f32() { delete eltwise_injector_; }
+
+private:
+    using Vmm = typename utils::conditional3<isa == sse42, Xmm,
+                isa == avx2, Ymm, Zmm>::type;
+
+    const int simd_w = cpu_isa_traits<isa>::vlen / sizeof(float);
+    const int vlen   = cpu_isa_traits<isa>::vlen;
+
+    Reg64 reg_from = rax;
+    Reg64 reg_to = r8;
+    Reg64 reg_work_amount = rsi;
+    Reg64 imm_addr64 = rbx;
+
+    Xmm xmm_src = Xmm(1);
+    Vmm vmm_src = Vmm(1);
+
+    jit_uni_eltwise_injector_f32<isa> *eltwise_injector_;
+};
+
+} /* namespace */
+
+template <cpu_isa_t isa>
+status_t jit_uni_eltwise_fwd_t<isa>::pd_t::init() {
+    using namespace alg_kind;
+
+    bool ok = true
+        && mayiuse(isa)
+        && is_fwd()
+        && utils::everyone_is(data_type::f32, desc()->data_desc.data_type)
+        && !has_zero_dim_memory()
+        && utils::one_of(desc()->alg_kind, eltwise_relu, eltwise_tanh,
+                eltwise_elu, eltwise_square, eltwise_abs, eltwise_sqrt,
+                eltwise_linear, eltwise_bounded_relu, eltwise_soft_relu,
+                eltwise_logistic)
+        && memory_desc_wrapper(src_md()).is_dense(true)
+        && IMPLICATION(!memory_desc_wrapper(src_md()).is_dense(false),
+                math::eltwise_fwd_preserves_zero(desc()->alg_kind, true))
+        && attr()->has_default_values();
+
+    return ok ? status::success : status::unimplemented;
+}
+
+template <cpu_isa_t isa>
+jit_uni_eltwise_fwd_t<isa>::jit_uni_eltwise_fwd_t(const pd_t *apd)
+    : cpu_primitive_t(apd), kernel_(nullptr) {
+    const auto &desc = *pd()->desc();
+    switch (desc.alg_kind) {
+    case alg_kind::eltwise_relu:
+        kernel_ = new jit_uni_relu_kernel_f32<isa>(desc); break;
+    default:
+        kernel_ = new jit_uni_kernel_fwd_f32<isa>(desc);
+    }
+}
+
+template <cpu_isa_t isa>
+jit_uni_eltwise_fwd_t<isa>::~jit_uni_eltwise_fwd_t()
+{ delete kernel_; }
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_fwd_t<isa>::execute_forward(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+
+    const size_t nelems = data_d.nelems(true);
+
+    src += data_d.offset0();
+    dst += data_d.offset0();
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        size_t start{0}, end{0};
+
+        const int cache_line = 16;
+
+        balance211(utils::div_up(nelems, cache_line), nthr, ithr, start, end);
+        start = nstl::min(nelems, start * cache_line);
+        end = nstl::min(nelems, end * cache_line);
+
+        auto arg = jit_args();
+        arg.from = &src[start];
+        arg.for_comparison = &src[start];
+        arg.to = &dst[start];
+        arg.work_amount = end - start;
+        if (arg.work_amount)
+            (*kernel_)(&arg);
+    });
+}
+
+template <cpu_isa_t isa>
+status_t jit_uni_eltwise_bwd_t<isa>::pd_t::init() {
+    bool ok = true
+        && !is_fwd()
+        && utils::one_of(desc()->alg_kind, alg_kind::eltwise_relu)
+        && src_md()->data_type == data_type::f32
+        && !has_zero_dim_memory()
+        && mayiuse(isa)
+        && memory_desc_wrapper(src_md()).is_dense()
+        && memory_desc_wrapper(diff_dst_md()) == memory_desc_wrapper(src_md())
+        && attr()->has_default_values();
+
+    return ok ? status::success : status::unimplemented;
+}
+
+template <cpu_isa_t isa>
+jit_uni_eltwise_bwd_t<isa>::jit_uni_eltwise_bwd_t(const pd_t *apd)
+    : cpu_primitive_t(apd), kernel_(nullptr) {
+    const auto &desc = *pd()->desc();
+    switch (desc.alg_kind) {
+    case alg_kind::eltwise_relu:
+        kernel_ = new jit_uni_relu_kernel_f32<isa>(desc); break;
+    default: assert(!"unknown eltwise alg_kind");
+    }
+}
+
+template <cpu_isa_t isa>
+jit_uni_eltwise_bwd_t<isa>::~jit_uni_eltwise_bwd_t()
+{ delete kernel_; }
+
+template <cpu_isa_t isa>
+void jit_uni_eltwise_bwd_t<isa>::execute_backward(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+    const memory_desc_wrapper diff_data_d(pd()->diff_src_md());
+
+    const size_t nelems = data_d.nelems();
+
+    src += data_d.offset0();
+    diff_dst += diff_data_d.offset0();
+    diff_src += diff_data_d.offset0();
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        size_t start{0}, end{0};
+
+        const int cache_line = 16;
+
+        balance211(utils::div_up(nelems, cache_line), nthr, ithr, start, end);
+        start = nstl::min(nelems, start * cache_line);
+        end = nstl::min(nelems, end * cache_line);
+
+        auto arg = jit_args();
+        arg.from = &diff_dst[start];
+        arg.to = &diff_src[start];
+        arg.for_comparison = &src[start];
+        arg.work_amount = end - start;
+        if (arg.work_amount)
+            (*kernel_)(&arg);
+    });
+}
+
+template struct jit_uni_eltwise_fwd_t<sse42>;
+template struct jit_uni_eltwise_bwd_t<sse42>;
+template struct jit_uni_eltwise_fwd_t<avx2>;
+template struct jit_uni_eltwise_bwd_t<avx2>;
+template struct jit_uni_eltwise_fwd_t<avx512_common>;
+template struct jit_uni_eltwise_bwd_t<avx512_common>;
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_eltwise.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_eltwise.hpp
new file mode 100644
index 0000000000..45436b9f46
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_eltwise.hpp
@@ -0,0 +1,193 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_UNI_ELTWISE_HPP
+#define CPU_JIT_UNI_ELTWISE_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_eltwise_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <cpu_isa_t isa>
+struct jit_uni_eltwise_injector_f32 {
+    using Vmm = typename utils::conditional3<isa == sse42, Xbyak::Xmm,
+            isa == avx2, Xbyak::Ymm, Xbyak::Zmm>::type;
+
+    jit_uni_eltwise_injector_f32(jit_generator *host, alg_kind_t alg,
+            float alpha, float beta, bool save_state = true,
+            Xbyak::Reg64 p_table = Xbyak::util::rax,
+            Xbyak::Opmask k_mask = Xbyak::Opmask(1))
+        : alg_(alg), alpha_(alpha), beta_(beta), h(host)
+        , save_state_(save_state), p_table(p_table), k_mask(k_mask)
+    {
+        using namespace alg_kind;
+        assert(utils::one_of(isa, sse42, avx2, avx512_common));
+        assert(utils::one_of(alg_, eltwise_relu, eltwise_tanh, eltwise_elu,
+                    eltwise_square, eltwise_abs, eltwise_sqrt, eltwise_linear,
+                    eltwise_bounded_relu, eltwise_soft_relu, eltwise_logistic));
+    }
+
+    // note that eltwise.scale is ignored
+    jit_uni_eltwise_injector_f32(jit_generator *host,
+            const post_ops_t::entry_t::eltwise_t &eltwise,
+            bool save_state = true, Xbyak::Reg64 p_table = Xbyak::util::rax,
+            Xbyak::Opmask k_mask = Xbyak::Opmask(1))
+        : jit_uni_eltwise_injector_f32(host, eltwise.alg, eltwise.alpha,
+                eltwise.beta, save_state, p_table, k_mask) {}
+
+    void compute_vector_range(size_t start_idx, size_t end_idx);
+    void compute_vector(size_t idx) { compute_vector_range(idx, idx + 1); }
+    void prepare_table(bool gen_table=true);
+    void load_table_addr() { h->mov(p_table, l_table); }
+
+    const alg_kind_t alg_;
+    const float alpha_;
+    const float beta_;
+
+    jit_generator * const h;
+
+    const bool save_state_;
+    const Xbyak::Reg64 p_table;
+    const Xbyak::Opmask k_mask;
+    Xbyak::Label l_table;
+
+private:
+    // if only the injector was inherited from jit_generator...
+    enum {
+        _cmp_le_os = jit_generator::_cmp_le_os,
+        _cmp_nle_us = jit_generator::_cmp_nle_us,
+        _op_floor = jit_generator::_op_floor,
+    };
+
+    size_t vlen = cpu_isa_traits<isa>::vlen;
+
+    const static size_t preserved_vecs_max = 5;
+
+    size_t vecs_to_preserve = 0;
+    size_t vecs_count = isa == avx512_common ? 32 : 16;
+    size_t preserved_vecs_count = 0;
+    size_t preserved_vec_idxs[preserved_vecs_max] = {0};
+    size_t start_idx_tail = 0;
+
+    Vmm vmm_mask, vmm_aux0, vmm_aux1, vmm_aux2, vmm_aux3, vmm_aux4;
+
+    Xbyak::Address table_val(int index)
+    { return h->ptr[p_table + index * vlen]; }
+
+    int aux_vecs_count(alg_kind_t alg);
+
+    void compute_body(size_t start_idx, size_t end_idx);
+    void injector_preamble(size_t start_idx, size_t end_idx);
+    void injector_preamble_tail(size_t start_idx);
+    void injector_postamble();
+    void assign_regs();
+
+    void exp_compute_vector(const Vmm &vmm_src);
+    void relu_compute_vector(const Vmm &vmm_src);
+    void relu_zero_ns_compute_vector(const Vmm &vmm_src);
+    void elu_compute_vector(const Vmm &vmm_src);
+    void tanh_compute_vector(const Vmm &vmm_src);
+    void square_compute_vector(const Vmm &vmm_src);
+    void abs_compute_vector(const Vmm &vmm_src);
+    void sqrt_compute_vector(const Vmm &vmm_src);
+    void linear_compute_vector(const Vmm &vmm_src);
+    void bounded_relu_compute_vector(const Vmm &vmm_src);
+    void soft_relu_compute_vector(const Vmm &vmm_src);
+    void logistic_compute_vector(const Vmm &vmm_src);
+
+    void relu_prepare_table();
+    void elu_prepare_table();
+    void soft_relu_prepare_table();
+    void abs_prepare_table();
+    void sqrt_prepare_table();
+    void linear_prepare_table();
+    void bounded_relu_prepare_table();
+};
+
+struct jit_uni_eltwise_kernel_f32;
+
+template <cpu_isa_t isa>
+struct jit_uni_eltwise_fwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_eltwise_fwd_pd_t {
+        using cpu_eltwise_fwd_pd_t::cpu_eltwise_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", isa, ""),
+                jit_uni_eltwise_fwd_t<isa>);
+
+        status_t init();
+    };
+
+    jit_uni_eltwise_fwd_t(const pd_t *apd);
+    ~jit_uni_eltwise_fwd_t();
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    jit_uni_eltwise_kernel_f32 *kernel_;
+};
+
+template <cpu_isa_t isa>
+struct jit_uni_eltwise_bwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_eltwise_bwd_pd_t {
+        using cpu_eltwise_bwd_pd_t::cpu_eltwise_bwd_pd_t;
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", isa, ""),
+                jit_uni_eltwise_bwd_t<isa>);
+
+        status_t init();
+    };
+
+    jit_uni_eltwise_bwd_t(const pd_t *apd);
+    ~jit_uni_eltwise_bwd_t();
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    jit_uni_eltwise_kernel_f32 *kernel_;
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_i8i8_pooling.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_i8i8_pooling.cpp
new file mode 100644
index 0000000000..a3ca6273a0
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_i8i8_pooling.cpp
@@ -0,0 +1,949 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "jit_uni_i8i8_pooling.hpp"
+
+#include <math.h>
+
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace Xbyak;
+
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::types;
+using namespace alg_kind;
+
+template <cpu_isa_t isa>
+struct jit_uni_i8i8_pooling_fwd_ker_t: public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_i8i8_pooling_fwd_ker_t)
+
+    struct call_params_t {
+        const char *src_i8;
+        const char *dst_i8;
+        size_t kw_range;
+        size_t kh_range;
+        float idivider;
+    };
+
+    using Vmm = typename cpu_isa_traits<isa>::Vmm;
+    Xmm xreg(int idx) const { return Xmm(idx); }
+    Ymm yreg(int idx) const { return Ymm(xreg(idx).getIdx()); }
+    Vmm vreg(int idx) const { return Vmm(xreg(idx).getIdx()); }
+
+    // In case of avx2 with data type i8 we need to use
+    // maskmovdqu instruction which has its destination hardcoded in rdi.
+    // Windows ABI: abi_param1 is rcx - nothing to do else
+    // Unix ABI: abi_param1 is rdi - copy it to rcx and use it as abi_param1
+    Reg64 reg_param      = rcx; // Our "unified abi_param1"
+    Reg64 reg_ptr_src_i8 = r8;
+    Reg64 reg_ptr_dst_i8 = r9;
+    Reg64 reg_ptr_maskmovdqu_dst = rdi; // store destination - must be rdi
+
+    Reg64 ki = r10;
+    Reg64 kj = r11;
+    Reg64 reg_kw = r12;
+    Reg64 reg_kh = r13;
+    Reg64 c_iter = r14;
+
+    Reg64 aux_reg_src_h = rax;
+    Reg64 aux_reg_src_w = rbx;
+
+    Reg64 reg_tmp = rdx;
+
+    Reg64 reg_mask = r15;
+
+    Opmask k_cmp_mask = Opmask(7);
+
+    Opmask mask(int idx) {
+        return Opmask(6 - idx);
+    }
+
+    // ref to any of XYZ-regs via xreg/yreg/vreg functions
+    Xmm xmm_tmp = xreg(0);     // temp to init vreg_tmp
+    Vmm vreg_tmp = vreg(0);    // max pooling : holds minimum values for data_type
+    Vmm vreg_zeros = vreg(1);
+
+    // only in case of <isa> == avx2
+    Vmm vreg_mask    = vreg(2); // full byte-mask
+    Xmm xreg_mask_lo = xreg(2); // low 128-bits part of byte-mask (alias for xmm part of vreg_mask)
+    Xmm xreg_mask_hi = xreg(3); // "max" - high 128-bits part of byte-mask (stored separately)
+    Xmm xreg_mask_q  = xreg(3); // "avg" - 1/4 part of the mask for s8/u8 operations
+    Vmm vreg_mask_q  = vreg(3); // "avg" - 1/4 part for non-zero tails
+
+    enum:int {vidx_base = isa == avx2 ? 4 : 2};
+    Vmm base_vr(int idx) const { return vreg(vidx_base + idx); }
+
+    size_t sizeof_src_dt() const { return data_type_size(jpp.src_dt); }
+    size_t sizeof_dst_dt() const { return data_type_size(jpp.dst_dt); }
+
+    /* max pooling */
+    Vmm vreg_src(int idx) const { return base_vr(idx); }            // [0    .. ur_c-1]
+    Vmm vreg_dst(int idx) const { return base_vr(jpp.ur_c + idx); } // [ur_c .. 2*ur_c-1]
+
+    /* avg pooling */
+    // s32 used for processing of s8/u8 data
+    // thus we need to take into account ratio of sizes s32/i8 = 4
+    static constexpr data_type_t avg_proc_dt = data_type::s32;
+    enum:int {
+        s32_to_i8_ratio = sizeof(typename prec_traits<avg_proc_dt>::type)
+                / sizeof(typename prec_traits<data_type::u8>::type),
+        max_num_ll =  s32_to_i8_ratio
+    };
+    Vmm vreg_src_s32(int jj, int ll) { return base_vr(3*max_num_ll*jj + ll + 0*max_num_ll); }  // ll: 0..4 [0..3]
+    Vmm vreg_dst_s32(int jj, int ll) { return base_vr(3*max_num_ll*jj + ll + 1*max_num_ll); }  // ll: 0..4 [4..7]
+    Vmm vreg_dst_f32(int jj, int ll) { return base_vr(3*max_num_ll*jj + ll + 2*max_num_ll); }  // ll: 0..4 [8..11]
+
+    void (*ker_)(const call_params_t *);
+    jit_pool_conf_t jpp;
+
+    void init_tmp_reg();
+    void init_mask();
+
+    void load_vreg_mask_q(int ll) {};
+
+    void load_src_max_op(int jj, int ll, size_t offset, bool masked, uint64_t msk);
+    void load_src_avg_op(int jj, int ll, size_t offset, bool masked, uint64_t msk);
+    void load_src(int jj, int ll, int c_tail);
+
+    void store_dst_max_op(int jj, int ll, size_t offset, bool masked, uint64_t msk);
+    void store_dst_avg_op(int jj, int ll, size_t offset, bool masked, uint64_t msk);
+    void store_dst(int jj, int ll, int c_tail);
+
+    void compute_avg_step(int ur_c, int c_tail);
+    void compute_max_op(const int jj);
+    void compute_max_step(int ur_c, int c_tail);
+    void compute_step(int ur_c, int c_tail);
+
+    void compute_c_block();
+    void generate();
+
+    static status_t init_conf(jit_pool_conf_t &jpp, const pooling_pd_t *ppd);
+
+    jit_uni_i8i8_pooling_fwd_ker_t(const jit_pool_conf_t &jpp_)
+           : jpp(jpp_) {
+        generate();
+        ker_ = reinterpret_cast<decltype(ker_)>(const_cast<uint8_t*>(
+                       getCode()));
+    }
+};
+
+template <>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx2>::load_vreg_mask_q(int ll) {
+
+    // extract ll-th part of mask (ll-th QWORD)
+    vpblendd(vreg_mask_q, vreg_zeros, vreg_mask, 0x3 << ll); // 0x3 - mask for 2 x DWORD
+
+    // Move mask from ll-th pos to 0-th pos
+    if (ll>0)
+        vpermq(vreg_mask_q, vreg_mask_q, ll);
+};
+
+template <>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx2>::load_src_max_op(int jj, int ll,
+        size_t offset, bool masked, uint64_t msk) {
+    using namespace data_type;
+
+    if (masked) {
+        if (jpp.src_dt == s32) {
+            vpblendd(vreg_src(jj), vreg_tmp, ptr[aux_reg_src_w + offset], static_cast<uint8_t>(msk));
+        } else {
+            vpblendvb(vreg_src(jj), vreg_tmp, ptr[aux_reg_src_w + offset], vreg_mask);
+        }
+    } else
+        vmovups(vreg_src(jj), ptr[aux_reg_src_w + offset]);
+};
+
+template <>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx512_core>::load_src_max_op(int jj, int ll,
+        size_t offset, bool masked, uint64_t msk) {
+    using namespace data_type;
+
+    if (masked) {
+        if (jpp.src_dt == s32)
+            vmovups(vreg_src(jj) | mask(0), ptr[aux_reg_src_w + offset]);
+        else
+            vmovdqu8(vreg_src(jj) | mask(0), ptr[aux_reg_src_w + offset]);
+    } else
+        vmovups(vreg_src(jj), ptr[aux_reg_src_w + offset]);
+};
+
+template <>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx2>::load_src_avg_op(int jj, int ll,
+        size_t offset, bool masked, uint64_t msk) {
+    using namespace data_type;
+
+    // Don't generate useless code
+    if (masked && !msk)
+        return;
+
+    auto load_i8 = [&](bool is_signed, const Vmm& vr_src) {
+
+        // Need to use mask of tail?
+        if (masked) {
+
+            // load ll-th part of mask into vreg_mask_q
+            load_vreg_mask_q(ll);
+
+            // Load by mask from mem into register vr_src
+            vpblendvb(vr_src, vreg_zeros, ptr[aux_reg_src_w + offset], vreg_mask_q);
+
+            // Conversion s8/u8 -> s32
+            if (is_signed)
+                vpmovsxbd(vr_src, vr_src);
+            else
+                vpmovzxbd(vr_src, vr_src);
+        } else {
+
+            // Load from mem into vr_src with conversion
+            if (is_signed)
+                vpmovsxbd(vr_src, ptr[aux_reg_src_w + offset]);
+            else
+                vpmovzxbd(vr_src, ptr[aux_reg_src_w + offset]);
+        }
+    };
+
+    switch (jpp.src_dt) {
+        case s32:
+            if (masked)
+                vpblendd(vreg_src_s32(jj, ll), vreg_zeros, ptr[aux_reg_src_w + offset],
+                    static_cast<uint8_t>(msk));
+            else
+                vmovups(vreg_src_s32(jj, ll), ptr[aux_reg_src_w + offset]);
+            break;
+        case s8:
+                load_i8(true, vreg_src_s32(jj, ll));
+            break;
+        case u8:
+                load_i8(false, vreg_src_s32(jj, ll));
+            break;
+        default: assert(!"unsupported src data type");
+    }
+};
+
+template <>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx512_core>::load_src_avg_op(int jj, int ll,
+        size_t offset, bool masked, uint64_t msk) {
+    using namespace data_type;
+
+    // Don't generate useless code
+    if (masked && !msk)
+        return;
+
+    const Vmm& vr_src = masked ?
+            vreg_src_s32(jj, ll) | mask(ll) :
+            vreg_src_s32(jj, ll);
+
+    switch (jpp.src_dt) {
+        case s32:
+            vmovups(vr_src, ptr[aux_reg_src_w + offset]);
+            break;
+        case s8:
+            vpmovsxbd(vr_src, ptr[aux_reg_src_w + offset]);
+            break;
+        case u8:
+            vpmovzxbd(vr_src, ptr[aux_reg_src_w + offset]);
+            break;
+        default: assert(!"unsupported src data type");
+    }
+};
+
+template <cpu_isa_t isa>
+void jit_uni_i8i8_pooling_fwd_ker_t<isa>::load_src(int jj, int ll, int c_tail) {
+    using namespace data_type;
+
+    int c_block = jpp.c_block;
+    int ur_c = jpp.ur_c;
+
+    switch (jpp.alg) {
+        case pooling_max: {
+            auto offset = jj*c_block*sizeof_src_dt();
+            bool masked = jj == ur_c - 1 && c_tail;
+            load_src_max_op(jj, ll, offset, masked, jpp.tail[0]);
+            break;
+        }
+        case pooling_avg_include_padding:
+        case pooling_avg_exclude_padding: {
+            auto offset = (ll*(c_block/max_num_ll) + jj*c_block)*sizeof_src_dt();
+            bool masked = jj == ur_c - 1 && c_tail;
+            load_src_avg_op(jj, ll, offset, masked, jpp.tail[ll]);
+            break;
+        }
+        default: assert(!"unsupported algorithm");
+    }
+}
+
+template <>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx2>::store_dst_max_op(int jj, int ll,
+        size_t offset, bool masked, uint64_t msk) {
+    using namespace data_type;
+
+    int c_block = jpp.c_block;
+
+    if (masked) {
+        switch (jpp.src_dt) {
+            case s32:
+                vpmaskmovd(ptr[reg_ptr_dst_i8 + offset], vreg_mask, vreg_dst(jj));
+                break;
+            case s8:
+            case u8: {
+                // Store low half by mask (bytes 0...15)
+                lea(reg_ptr_maskmovdqu_dst, ptr[reg_ptr_dst_i8 + offset]);
+                maskmovdqu(vreg_dst(jj), xreg_mask_lo);
+
+                // Do we need to store high half (bytes 16...31) ?
+                const uint64_t low_mask = (1ULL << (c_block/2))-1;
+                if (msk & ~low_mask) {
+                    vextracti128(Xmm(vreg_dst(jj).getIdx()), vreg_dst(jj), 1);
+                    add(reg_ptr_maskmovdqu_dst, c_block / 2);
+                    maskmovdqu(vreg_dst(jj), xreg_mask_hi);
+                }
+            } break;
+            default: assert(!"unsupported src data type");
+        }
+    } else
+        vmovups(ptr[reg_ptr_dst_i8 + offset], vreg_dst(jj));
+}
+
+template <>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx512_core>::store_dst_max_op(int jj, int ll,
+        size_t offset, bool masked, uint64_t msk) {
+    using namespace data_type;
+
+    if (masked) {
+        switch (jpp.src_dt) {
+            case s32:
+                vmovups(ptr[reg_ptr_dst_i8 + offset], vreg_dst(jj) | mask(0));
+                break;
+            case s8:
+            case u8:
+                vmovdqu8(ptr[reg_ptr_dst_i8 + offset], vreg_dst(jj) | mask(0));
+                break;
+            default: assert(!"unsupported src data type");
+        }
+    } else
+        vmovups(ptr[reg_ptr_dst_i8 + offset], vreg_dst(jj));
+}
+
+template <>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx2>::store_dst_avg_op(int jj, int ll,
+        size_t offset, bool masked, uint64_t msk){
+    using namespace data_type;
+
+    // Don't generate useless code
+    if (masked && !msk)
+        return;
+
+    auto s32_to_i8 = [&](bool is_signed, const Vmm& vr_dst) {
+
+        // conversion: s32 -> s16/u16 : {8 x s32}{8 x 0} -> {16 x s16/u16}
+        // Result QWORDs (qw0, qw1) permuted: {qw0, 0, qw1, 0}
+        if (is_signed)
+            vpackssdw(vr_dst, vr_dst, vreg_zeros);
+        else
+            vpackusdw(vr_dst, vr_dst, vreg_zeros);
+
+        // Permute qwords to restore original order
+        // {qw0, 0, qw1, 0} -> {qw0, qw1, 0, 0}
+        vpermq(vr_dst, vr_dst, 0x58);
+
+        // conversion: s16/u16 -> s8/u8 : {16 x s16/u16}{16 x 0} -> {32 x s8/u8}
+        // Target QWORD qw = {8 x s8/u8} has proper position: {qw, xx, xx, xx}
+        if (is_signed)
+            vpacksswb(vr_dst, vr_dst, vreg_zeros);
+        else
+            vpackuswb(vr_dst, vr_dst, vreg_zeros);
+
+    };
+
+    auto store_i8 = [&](bool is_signed, bool is_masked, const Vmm& vr_dst) {
+
+        // Conversion s32 -> s8/u8
+        s32_to_i8(is_signed, vr_dst);
+
+        // Need to use mask of tail?
+        if (is_masked) {
+            // load ll-th part of mask into vreg_mask_q
+            load_vreg_mask_q(ll);
+        }
+
+        // store 8 bytes
+        lea(reg_ptr_maskmovdqu_dst, ptr[reg_ptr_dst_i8 + offset]);
+        maskmovdqu(vr_dst, xreg_mask_q);
+    };
+
+    switch (jpp.dst_dt) {
+        case s32:
+            if (masked) {
+                vpmaskmovd(ptr[reg_ptr_dst_i8 + offset], vreg_mask, vreg_dst_s32(jj, ll));
+            } else
+                vmovups(ptr[reg_ptr_dst_i8 + offset], vreg_dst_s32(jj, ll));
+            break;
+        case s8:
+            store_i8(true, masked, vreg_dst_s32(jj, ll));
+            break;
+        case u8:
+            store_i8(false, masked, vreg_dst_s32(jj, ll));
+            break;
+        default: assert(!"unsuppotred dst data_type");
+    }
+}
+
+template <>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx512_core>::store_dst_avg_op(int jj, int ll,
+        size_t offset, bool masked, uint64_t msk) {
+    using namespace data_type;
+
+    // Don't generate useless code
+    if (masked && !msk)
+        return;
+
+    const Vmm& vr_dst = masked ?
+            vreg_dst_s32(jj, ll) | mask(ll) :
+            vreg_dst_s32(jj, ll);
+
+    switch (jpp.dst_dt) {
+        case s32:
+            vmovups(ptr[reg_ptr_dst_i8 + offset], vr_dst);
+            break;
+        case s8:
+            vpmovdb(ptr[reg_ptr_dst_i8 + offset], vr_dst);
+            break;
+        case u8:
+            vpmovusdb(ptr[reg_ptr_dst_i8 + offset], vr_dst);
+            break;
+        default: assert(!"unsupported dst data_type");
+    }
+}
+
+
+template <cpu_isa_t isa>
+void jit_uni_i8i8_pooling_fwd_ker_t<isa>::store_dst(int jj, int ll,
+        int c_tail) {
+    using namespace data_type;
+
+    int c_block = jpp.c_block;
+    int ur_c = jpp.ur_c;
+
+    switch(jpp.alg) {
+        case pooling_max: {
+            auto offset = jj*c_block*sizeof_dst_dt();
+            bool masked = jj == ur_c - 1 && c_tail;
+            store_dst_max_op(jj, ll, offset, masked, jpp.tail[ll]);
+            break;
+        }
+        case pooling_avg_include_padding:
+        case pooling_avg_exclude_padding: {
+            auto offset = (ll*(c_block/max_num_ll) + jj*c_block)*sizeof_dst_dt();
+            bool masked = jj == ur_c - 1 && c_tail;
+            store_dst_avg_op(jj, ll, offset, masked, jpp.tail[ll]);
+            break;
+        }
+        default: assert(!"unsupported pooling algorithm");
+    }
+}
+
+template <>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx2>::compute_max_op(const int jj)
+{
+    using namespace data_type;
+    switch (jpp.src_dt) {
+        case s32:
+            vpmaxsd(vreg_dst(jj), vreg_dst(jj), vreg_src(jj));
+            break;
+        case s8:
+            vpmaxsb(vreg_dst(jj), vreg_dst(jj), vreg_src(jj));
+            break;
+        case u8:
+            vpmaxub(vreg_dst(jj), vreg_dst(jj), vreg_src(jj));
+            break;
+        default: assert(!"unsupported src data type");
+    }
+}
+
+template <>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx512_core>::compute_max_op(const int jj)
+{
+    using namespace data_type;
+
+    // Compare
+    switch (jpp.src_dt) {
+        case s32:
+            vpcmpd(k_cmp_mask, vreg_dst(jj), vreg_src(jj), _cmp_lt_os);
+            break;
+        case s8:
+            vpcmpb(k_cmp_mask, vreg_dst(jj), vreg_src(jj), _cmp_lt_os);
+            break;
+        case u8:
+            vpcmpub(k_cmp_mask, vreg_dst(jj), vreg_src(jj), _cmp_lt_os);
+            break;
+        default: assert(!"unsupported src data type");
+    }
+
+    // move max values into vreg_dst
+    if (jpp.src_dt == s32)
+        vpblendmd(vreg_dst(jj) | k_cmp_mask, vreg_dst(jj), vreg_src(jj));
+    else
+        vpblendmb(vreg_dst(jj) | k_cmp_mask, vreg_dst(jj), vreg_src(jj));
+}
+
+
+template <cpu_isa_t isa>
+void jit_uni_i8i8_pooling_fwd_ker_t<isa>::compute_max_step(int ur_c, int c_tail)
+{
+    Label l_kw, l_kh;
+
+    int iw = jpp.iw;
+    int c = jpp.c;
+
+    for (int jj = 0; jj < ur_c; jj++)
+        vmovups(vreg_dst(jj), vreg_tmp);
+
+    mov(aux_reg_src_h, reg_ptr_src_i8);
+
+    xor_(kj, kj);
+    L(l_kh);
+    {
+        mov(aux_reg_src_w, aux_reg_src_h);
+        xor_(ki, ki);
+        L(l_kw);
+        {
+            for (int jj = 0; jj < ur_c; jj++) {
+                load_src(jj, 0, c_tail);
+                compute_max_op(jj);
+            }
+            add(aux_reg_src_w, c * sizeof_src_dt());
+            inc(ki);
+            cmp(ki, reg_kw);
+            jl(l_kw, T_NEAR);
+        }
+        add(aux_reg_src_h, iw * c * sizeof_src_dt());
+        inc(kj);
+        cmp(kj, reg_kh);
+        jl(l_kh, T_NEAR);
+    }
+
+    for (int jj = 0; jj < ur_c; jj++)
+        store_dst(jj, 0, c_tail);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_i8i8_pooling_fwd_ker_t<isa>::compute_avg_step(int ur_c, int c_tail)
+{
+    using namespace data_type;
+
+    Label l_kw, l_kh;
+
+    int iw = jpp.iw;
+    int c = jpp.c;
+
+    const int num_ll = data_type_size(avg_proc_dt)/data_type_size(jpp.src_dt);
+
+    for (int jj = 0; jj < ur_c; jj++) {
+        for (int ll = 0; ll < num_ll; ll++) {
+            bool masked = jj == ur_c - 1 && c_tail;
+            size_t msk = jpp.tail[ll];
+            if (!(masked && !msk)) {
+                uni_vpxor(vreg_src_s32(jj, ll), vreg_src_s32(jj, ll), vreg_src_s32(jj, ll));
+                uni_vpxor(vreg_dst_s32(jj, ll), vreg_dst_s32(jj, ll), vreg_dst_s32(jj, ll));
+            }
+        }
+    }
+
+    mov(aux_reg_src_h, reg_ptr_src_i8);
+
+    xor_(kj, kj);
+    L(l_kh);
+    {
+        mov(aux_reg_src_w, aux_reg_src_h);
+        xor_(ki, ki);
+        L(l_kw);
+        {
+            for (int jj = 0; jj < ur_c; jj++) {
+                for (int ll = 0; ll < num_ll; ll++) {
+                    bool masked = jj == ur_c - 1 && c_tail;
+                    size_t msk = jpp.tail[ll];
+                    if (!(masked && !msk)) {
+                        load_src(jj, ll, c_tail);
+                        vpaddd(vreg_dst_s32(jj, ll), vreg_dst_s32(jj, ll),
+                                vreg_src_s32(jj, ll));
+                    }
+                }
+            }
+            add(aux_reg_src_w, c * sizeof_src_dt());
+            inc(ki);
+            cmp(ki, reg_kw);
+            jl(l_kw, T_NEAR);
+        }
+        add(aux_reg_src_h, iw * c * sizeof_src_dt());
+        inc(kj);
+        cmp(kj, reg_kh);
+        jl(l_kh, T_NEAR);
+    }
+
+    for (int jj = 0; jj < ur_c; jj++) {
+        for (int ll = 0; ll < num_ll; ll++) {
+            bool masked = jj == ur_c - 1 && c_tail;
+            size_t msk = jpp.tail[ll];
+            if (!(masked && !msk)) {
+                vcvtdq2ps(vreg_dst_f32(jj, ll), vreg_dst_s32(jj, ll));
+                vfmadd132ps(vreg_dst_f32(jj, ll), vreg_zeros, vreg_tmp);
+                vcvtps2dq(vreg_dst_s32(jj, ll), vreg_dst_f32(jj, ll));
+                store_dst(jj, ll, c_tail);
+            }
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_i8i8_pooling_fwd_ker_t<isa>::compute_step(int ur_c, int c_tail) {
+    switch (jpp.alg) {
+        case pooling_max:
+            compute_max_step(ur_c, c_tail); break;
+        case pooling_avg_include_padding:
+        case pooling_avg_exclude_padding:
+            compute_avg_step(ur_c, c_tail); break;
+        default: assert(!"unsupported pooling algorithm");
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_i8i8_pooling_fwd_ker_t<isa>::compute_c_block(){
+    Label l_main_loop;
+
+    int nb_c = jpp.nb_c;
+    int c_block = jpp.c_block;
+    int ur_c = jpp.ur_c;
+    int ur_c_tail = jpp.ur_c_tail;
+    int c_steps = nb_c / ur_c;
+    int c_tail = jpp.c_tail;
+
+    xor_(c_iter, c_iter);
+    if (c_steps > 0) {
+        L(l_main_loop); {
+            compute_step(ur_c, 0);
+            add(reg_ptr_src_i8, ur_c*c_block*sizeof_src_dt());
+            add(reg_ptr_dst_i8, ur_c*c_block*sizeof_dst_dt());
+            inc(c_iter);
+            cmp(c_iter, c_steps);
+            jl(l_main_loop, T_NEAR);
+        }
+    }
+
+    if (ur_c_tail != 0) {
+        compute_step(ur_c_tail, c_tail);
+    }
+}
+
+template<>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx2>::init_mask() {
+    using namespace data_type;
+    using cpu_isa = cpu_isa_traits<avx2>;
+
+    // AVX2 mask initialization: mask stored in Ymm-regs
+    auto init = [&](uint64_t bit_mask, bool init_mask_q) {
+        const size_t QW_PER_VREG = cpu_isa::vlen / sizeof(uint64_t);
+
+        uint64_t vmask[QW_PER_VREG];
+        for (size_t i = 0; i < QW_PER_VREG; i++){
+
+            uint64_t qw_vmask=0ULL;
+            const size_t DBITS = 8*sizeof_src_dt();
+            const uint64_t VMSK = 1ULL << (DBITS-1);
+            const size_t D_PER_QW = (8*sizeof(qw_vmask))/DBITS;
+            for (size_t j = 0; j < D_PER_QW; j++) {
+                if (bit_mask & 1)
+                    qw_vmask |= VMSK << DBITS * j;
+                bit_mask >>= 1;
+            }
+            vmask[i] = qw_vmask;
+        }
+
+        // Put QWORDS with target mask into xmm regs
+        const int xdst_i[QW_PER_VREG] = {
+                xreg_mask_lo.getIdx(),
+                xreg_mask_lo.getIdx(),
+                xreg_mask_hi.getIdx(),
+                xreg_mask_hi.getIdx()
+        };
+        const int xsrc_i[QW_PER_VREG] = {
+                vreg_zeros.getIdx(),   // 0-th qword insert in zeros -> {qw0,  0}
+                xreg_mask_lo.getIdx(), // 1-st and 0-th merge        -> {qw0,qw1}
+                vreg_zeros.getIdx(),
+                xreg_mask_hi.getIdx()
+        };
+        const uint8 qw_dst_idx[QW_PER_VREG] = {0, 1, 0, 1}; // qword index in 128-bit xreg
+
+        for (size_t i = 0; i < QW_PER_VREG; i++) {
+            mov(reg_mask, vmask[i]);
+            vpinsrq(Xmm(xdst_i[i]), Xmm(xsrc_i[i]), reg_mask, qw_dst_idx[i]);
+        }
+
+        // Merge Low (xreg_mask_lo alias for vreg_mask.xreg)
+        // and High (xreg_mask_hi) into full vreg_mask
+        // vreg_mask -> {xreg_mask_hi, vreg_mask.xreg}
+        vinserti128(vreg_mask, vreg_mask, xreg_mask_hi, 1);
+
+        // Keep only low qword of mask in xreg_mask_q
+        if (init_mask_q) {
+            mov(reg_mask, vmask[0]);
+            vpinsrq(xreg_mask_q, Xmm(vreg_zeros.getIdx()), reg_mask, 0);
+        }
+    };
+
+    uint64_t tail_mask = (1ULL << jpp.c_tail) - 1;
+    switch (jpp.alg) {
+        case pooling_max:
+            // For "max" we need mask only in case of non-zero tail
+            if (tail_mask)
+                init(tail_mask, false);
+            break;
+        case pooling_avg_include_padding:
+        case pooling_avg_exclude_padding:
+            // For "avg" we need mask:
+            // - s32   - in case of the non-zero tail
+            // - s8/u8 - irrespective of the tail
+            switch (jpp.src_dt) {
+                case s32:
+                    if (tail_mask)
+                        init(tail_mask, false);
+                    break;
+                case s8:
+                case u8:
+                    init(tail_mask ? tail_mask : ~0ULL, tail_mask == 0);
+                    break;
+                default: assert(!"unsupported src data type");
+            }
+            break;
+        default: assert(!"unsupported pooling algorithm");
+    }
+}
+
+template<>
+void jit_uni_i8i8_pooling_fwd_ker_t<avx512_core>::init_mask() {
+
+    for (int ll = 0; ll < max_num_ll; ll++) {
+        mov(reg_mask, jpp.tail[ll]);
+        kmovq(mask(ll), reg_mask);
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_i8i8_pooling_fwd_ker_t<isa>::init_tmp_reg() {
+    using namespace data_type;
+
+    switch (jpp.alg) {
+        case pooling_avg_include_padding:
+        case pooling_avg_exclude_padding:
+            mov(reg_tmp, ptr[reg_param + offsetof(call_params_t, idivider)]);
+            movq(xmm_tmp, reg_tmp);
+            vpbroadcastd(vreg_tmp, xmm_tmp);
+            break;
+        case pooling_max:
+            switch (jpp.src_dt) {
+                case s32:
+                    mov(reg_tmp, nstl::numeric_limits<int32_t>::lowest());
+                    break;
+                case s8:
+                    mov(reg_tmp, nstl::numeric_limits<int8_t>::lowest());
+                    break;
+                case u8:
+                    mov(reg_tmp, nstl::numeric_limits<uint8_t>::lowest());
+                    break;
+                default: assert(!"unsupported src data_type");
+            }
+
+            movq(xmm_tmp, reg_tmp);
+            if (jpp.src_dt == s32)
+                vpbroadcastd(vreg_tmp, xmm_tmp);
+            else
+                vpbroadcastb(vreg_tmp, xmm_tmp);
+            break;
+        default: assert(!"unsupported pooling algorithm");
+    }
+
+}
+
+template <cpu_isa_t isa>
+void jit_uni_i8i8_pooling_fwd_ker_t<isa>::generate() {
+    preamble();
+
+#if !defined(_WIN32)
+    // Always use rcx as abi_param1 -
+    // see the note about maskmovdqu near reg_param.
+    mov(rcx, rdi);
+#endif
+
+#   define READ_PARAM(reg, field) \
+        mov(reg, ptr[reg_param + offsetof(call_params_t, field)])
+    READ_PARAM(reg_ptr_src_i8, src_i8);
+    READ_PARAM(reg_ptr_dst_i8, dst_i8);
+    READ_PARAM(reg_kw, kw_range);
+    READ_PARAM(reg_kh, kh_range);
+
+#   undef READ_PARAM
+
+    uni_vpxor(vreg_zeros, vreg_zeros, vreg_zeros);
+
+    init_mask();
+
+    init_tmp_reg();
+
+    compute_c_block();
+
+    postamble();
+}
+
+template <cpu_isa_t isa>
+status_t jit_uni_i8i8_pooling_fwd_ker_t<isa>::init_conf(jit_pool_conf_t &jpp,
+        const pooling_pd_t *ppd) {
+    if (!mayiuse(isa))
+        return status::unimplemented;
+
+    const auto &pd = *ppd->desc();
+    const memory_desc_wrapper src_d(ppd->src_md());
+    const memory_desc_wrapper dst_d(ppd->dst_md());
+
+    jpp.mb = src_d.dims()[0];
+    jpp.c = src_d.dims()[1];
+    jpp.ih = src_d.dims()[2];
+    jpp.iw = src_d.dims()[3];
+    jpp.oh = dst_d.dims()[2];
+    jpp.ow = dst_d.dims()[3];
+
+    jpp.stride_h = pd.strides[0];
+    jpp.stride_w = pd.strides[1];
+    jpp.kh = pd.kernel[0];
+    jpp.kw = pd.kernel[1];
+
+    jpp.t_pad = pd.padding[0][0];
+    jpp.l_pad = pd.padding[0][1];
+
+    jpp.alg = pd.alg_kind;
+
+    jpp.src_dt = pd.src_desc.data_type;
+    jpp.dst_dt = pd.dst_desc.data_type;
+
+    // data_type items per one vreg on the <isa>
+    //     isa == avx2    : 32 bytes -> 32 for s8/u8, 8 for s32
+    //     isa == avx512* : 64 bytes -> 64 for s8/u8, 16 for s32
+    int simd_w = cpu_isa_traits<isa>::vlen / data_type_size(jpp.src_dt);
+
+    jpp.c_block = simd_w;
+    jpp.c_tail = jpp.c % jpp.c_block;
+    jpp.nb_c = jpp.c / jpp.c_block;
+    jpp.ur_c = 1;
+    jpp.ur_c_tail = jpp.nb_c - (jpp.nb_c / jpp.ur_c)*jpp.ur_c +
+            (jpp.c_tail != 0);
+
+    size_t tail_mask = (1ULL << jpp.c_tail) - 1;
+
+    switch (jpp.alg) {
+        case pooling_max:
+            jpp.tail[0] = tail_mask;
+            jpp.tail[1] = 0;
+            jpp.tail[2] = 0;
+            jpp.tail[3] = 0;
+            break;
+        case pooling_avg_include_padding:
+        case pooling_avg_exclude_padding: {
+            // avg_proc_dt (s32) defines granularity (because u8/s8 processed as s32)
+            // avx2 : 8, avx512 : 16
+            const size_t msk_gran = cpu_isa_traits<isa>::vlen / data_type_size(avg_proc_dt);
+            const size_t msk_msk = (1ULL << msk_gran) - 1;
+            size_t m = tail_mask;
+            for (size_t ll = 0; ll < max_num_ll; ll++) {
+                jpp.tail[ll] = m & msk_msk;
+                m = m >> msk_gran;
+            }
+            break;
+        }
+        default: return status::unimplemented;
+    }
+
+    return status::success;
+}
+
+template <cpu_isa_t isa>
+status_t jit_uni_i8i8_pooling_fwd_t<isa>::pd_t::jit_conf() {
+    return jit_uni_i8i8_pooling_fwd_ker_t<isa>::init_conf(jpp_, this);
+}
+
+template <cpu_isa_t isa>
+jit_uni_i8i8_pooling_fwd_t<isa>::
+jit_uni_i8i8_pooling_fwd_t(const pd_t *apd)
+    : cpu_primitive_t(apd), ker_(nullptr)
+{ ker_ = new jit_uni_i8i8_pooling_fwd_ker_t<isa>(pd()->jpp_); }
+
+template <cpu_isa_t isa>
+jit_uni_i8i8_pooling_fwd_t<isa>::
+~jit_uni_i8i8_pooling_fwd_t() { delete ker_; }
+
+template <cpu_isa_t isa>
+void jit_uni_i8i8_pooling_fwd_t<isa>::execute_forward(
+        const exec_ctx_t &ctx) const {
+    auto src_i8 = CTX_IN_MEM(const char *, MKLDNN_ARG_SRC);
+    auto dst_i8 = CTX_OUT_MEM(char *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+
+    const auto &jpp = pd()->jpp_;
+
+    parallel_nd(jpp.mb, jpp.oh, jpp.ow,
+            [&](int n, int oh, int ow) {
+        const int ih = nstl::max(oh*jpp.stride_h - jpp.t_pad, 0);
+        const int iw = nstl::max(ow*jpp.stride_w - jpp.l_pad, 0);
+
+        const int kh_start = nstl::max(0, jpp.t_pad - oh * jpp.stride_h);
+        const int kh_end = nstl::min(jpp.kh,
+                jpp.ih + jpp.t_pad - oh * jpp.stride_h);
+        const int kw_start = nstl::max(0, jpp.l_pad - ow * jpp.stride_w);
+        const int kw_end = nstl::min(jpp.kw,
+                jpp.iw + jpp.l_pad - ow * jpp.stride_w);
+
+        auto p = typename jit_uni_i8i8_pooling_fwd_ker_t<isa>::call_params_t();
+        p.src_i8 = &src_i8[
+            src_d.blk_off(n, 0, ih, iw) * src_d.data_type_size()];
+        p.dst_i8 = &dst_i8[
+            dst_d.blk_off(n, 0, oh, ow) * dst_d.data_type_size()];
+        p.kw_range = (size_t)(kw_end - kw_start);
+        p.kh_range = (size_t)(kh_end - kh_start);
+        p.idivider = 1.0f / ((jpp.alg == pooling_avg_exclude_padding) ?
+            p.kh_range*p.kw_range : jpp.kw*jpp.kh);
+
+        ker_->ker_(&p);
+    });
+}
+
+// Explicit instantiation only for supported <isa> values.
+//
+template struct jit_uni_i8i8_pooling_fwd_ker_t<avx512_core>;
+template struct jit_uni_i8i8_pooling_fwd_t<avx512_core>;
+
+template struct jit_uni_i8i8_pooling_fwd_ker_t<avx2>;
+template struct jit_uni_i8i8_pooling_fwd_t<avx2>;
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_i8i8_pooling.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_i8i8_pooling.hpp
new file mode 100644
index 0000000000..d757679df5
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_i8i8_pooling.hpp
@@ -0,0 +1,89 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_UNI_I8I8_POOLING_HPP
+#define CPU_JIT_UNI_I8I8_POOLING_HPP
+
+#include "c_types_map.hpp"
+
+#include "cpu_pooling_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "cpu_isa_traits.hpp"
+#include "jit_primitive_conf.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <cpu_isa_t isa>
+struct jit_uni_i8i8_pooling_fwd_ker_t;
+
+template <cpu_isa_t isa>
+struct jit_uni_i8i8_pooling_fwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_pooling_fwd_pd_t {
+        using cpu_pooling_fwd_pd_t::cpu_pooling_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", isa, ""),
+                jit_uni_i8i8_pooling_fwd_t<isa>);
+
+        status_t init() {
+            bool ok = true
+                && mayiuse(isa)
+                && ndims() == 4
+                && set_default_params() == status::success
+                && desc()->prop_kind == prop_kind::forward_inference
+                && utils::one_of(desc()->alg_kind, alg_kind::pooling_max,
+                        alg_kind::pooling_avg_include_padding,
+                        alg_kind::pooling_avg_exclude_padding)
+                && utils::one_of(src_md()->data_type, data_type::s32,
+                        data_type::s8, data_type::u8)
+                && src_md()->data_type == dst_md()->data_type
+                && attr()->has_default_values()
+                && memory_desc_matches_tag(*src_md(), format_tag::nhwc)
+                && memory_desc_matches_tag(*dst_md(), format_tag::nhwc);
+            if (!ok) return status::unimplemented;
+
+            return jit_conf();
+        }
+
+        jit_pool_conf_t jpp_;
+
+    protected:
+        status_t jit_conf();
+    };
+
+    jit_uni_i8i8_pooling_fwd_t(const pd_t *apd);
+    ~jit_uni_i8i8_pooling_fwd_t();
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_uni_i8i8_pooling_fwd_ker_t<isa> *ker_;
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn.cpp
new file mode 100644
index 0000000000..2c5a8e8973
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn.cpp
@@ -0,0 +1,305 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_uni_lrn_kernel_f32.hpp"
+#include "jit_uni_lrn.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::utils;
+
+template <cpu_isa_t isa>
+jit_uni_lrn_fwd_t<isa>::jit_uni_lrn_fwd_t(const pd_t *apd)
+    : cpu_primitive_t(apd), ker_(nullptr)
+    , ker_first_(nullptr), ker_last_(nullptr)
+{
+    using namespace alg_kind;
+
+    const int C = pd()->C();
+    const int H = pd()->H();
+    const int W = pd()->W();
+    const int ls = pd()->desc()->local_size;
+    float A = pd()->desc()->lrn_alpha / ls;
+    float K = pd()->desc()->lrn_k;
+
+    auto pk = pd()->desc()->prop_kind;
+    auto ak = pd()->desc()->alg_kind;
+    auto dat_tag = pd()->dat_tag_;
+
+    if (dat_tag == nChw8c && ls == 5 && ak == lrn_across_channels) {
+        ker_ = new jit_uni_lrn_fwd_kernel_f32<isa>(
+                nchw8c_across(H, W, 0), A, K, pk);
+        ker_first_ = new jit_uni_lrn_fwd_kernel_f32<isa>(
+                nchw8c_across(H, W, -1), A, K, pk);
+        ker_last_ = new jit_uni_lrn_fwd_kernel_f32<isa>(
+                nchw8c_across(H, W, +1), A, K, pk);
+    } else if (dat_tag == nChw8c && ak == lrn_within_channel) {
+        /* within channel, local_size (x) local_size */
+        A /= ls; /* XXX: why? */
+        ker_ = new jit_uni_lrn_fwd_kernel_f32<isa>(
+                nchw8c_within(H, W, ls), A, K, pk);
+    } else if (dat_tag == nchw && ls == 5 && ak == lrn_across_channels) {
+        ker_ = new jit_uni_lrn_fwd_kernel_f32<isa>(
+                nchw_across(C, H*W, 0), A, K, pk);
+        int remind = (H*W) % VECTOR_LENGTH;
+        if (remind != 0) {
+            ker_last_ = new jit_uni_lrn_fwd_kernel_f32<isa>(
+                        nchw_across(C, H*W, remind), A, K, pk);
+        }
+    } else if (true /* XXX: why */) {
+        ker_ = new jit_uni_lrn_fwd_kernel_f32<isa>(nhwc_across(C), A, K, pk);
+    }
+}
+
+template <cpu_isa_t isa>
+jit_uni_lrn_fwd_t<isa>::~jit_uni_lrn_fwd_t()
+{ delete ker_; delete ker_first_; delete ker_last_; }
+
+template <cpu_isa_t isa>
+void jit_uni_lrn_fwd_t<isa>::execute_forward(const exec_ctx_t &ctx) const {
+    using namespace alg_kind;
+
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+    auto ws = CTX_OUT_MEM(data_t *, MKLDNN_ARG_WORKSPACE);
+
+    const int N = pd()->MB();
+    const int C = pd()->C();
+    const int HW = pd()->H() * pd()->W();
+    const int ls = pd()->desc()->local_size;
+
+    auto ak = pd()->desc()->alg_kind;
+    auto dat_tag = pd()->dat_tag_;
+
+    if (dat_tag == nChw8c && ls == 5 && ak == lrn_across_channels) {
+        parallel_nd(N, C / VECTOR_LENGTH, [&](int n, int c8) {
+            jit_args_fwd_t args;
+            args.src = &src[n*HW*C + c8 * HW * VECTOR_LENGTH];
+            args.dst = &dst[n*HW*C + c8 * HW * VECTOR_LENGTH];
+            args.scratch = &ws[n*HW*C + c8 * HW * VECTOR_LENGTH];
+            if (c8 == 0)
+                (*ker_first_)(&args);
+            else if (c8 == C / VECTOR_LENGTH - 1)
+                (*ker_last_)(&args);
+            else
+                (*ker_)(&args);
+        });
+    }
+    else if (dat_tag == nChw8c && ak == lrn_within_channel) {
+        parallel_nd(N, C / VECTOR_LENGTH, [&](int n, int c8) {
+            jit_args_fwd_t args;
+            args.src = &src[n*HW*C + c8 * HW * VECTOR_LENGTH];
+            args.dst = &dst[n*HW*C + c8 * HW * VECTOR_LENGTH];
+            args.scratch = &ws[n*HW*C + c8 * HW * VECTOR_LENGTH];
+            (*ker_)(&args);
+        });
+    }
+    else if (dat_tag == nchw && ls == 5 && ak == lrn_across_channels) {
+        parallel_nd(N, (HW + VECTOR_LENGTH - 1) / VECTOR_LENGTH,
+            [&](int n, int hw8) {
+            jit_args_fwd_t args;
+            args.src = &src[n*HW*C + hw8 * VECTOR_LENGTH];
+            args.dst = &dst[n*HW*C + hw8 * VECTOR_LENGTH];
+            args.scratch = &ws[n*HW*C + hw8 * VECTOR_LENGTH];
+            if ((hw8 + 1)*VECTOR_LENGTH > HW)
+                (*ker_last_)(&args);
+            else
+                (*ker_)(&args);
+        });
+    }
+    else { // nhwc
+        parallel_nd(N, HW, [&](int n, int hw) {
+            jit_args_fwd_t args;
+            args.src = &src[n*HW*C + hw * C];
+            args.dst = &dst[n*HW*C + hw * C];
+            args.scratch = &ws[n*HW*C + hw * C];
+            (*ker_)(&args);
+        });
+    }
+}
+
+template <cpu_isa_t isa>
+status_t jit_uni_lrn_fwd_t<isa>::pd_t::init() {
+    using namespace prop_kind;
+    using namespace alg_kind;
+
+    const memory_desc_wrapper data_d(src_md());
+    bool ok = true
+        && mayiuse(isa)
+        && is_fwd()
+        && everyone_is(data_type::f32, data_d.data_type())
+        && !has_zero_dim_memory()
+        && data_d.ndims() == 4
+        && data_d.dims()[1] % VECTOR_LENGTH == 0
+        && data_d.dims()[1] >= 2 * VECTOR_LENGTH
+        && desc()->lrn_beta == 0.75
+        && attr()->has_default_values();
+    if (!ok) return unimplemented;
+
+    if (desc_.prop_kind == forward_training) ws_md_ = *src_md();
+
+    dat_tag_ = memory_desc_matches_one_of_tag(*src_md(), nChw8c, nchw, nhwc);
+
+    bool args_ok_across = true
+        && desc()->alg_kind == lrn_across_channels
+        && desc()->local_size == 5
+        && one_of(dat_tag_, nChw8c, nchw, nhwc);
+
+    const int jit_max_local_size = 5; // bigger size triggers too big code size
+    bool args_ok_within = true
+        && desc()->alg_kind == lrn_within_channel
+        && desc()->local_size <= ( jit_max_local_size <= MAX_LOCAL_SIZE
+                                 ? jit_max_local_size : MAX_LOCAL_SIZE)
+        && data_d.dims()[2] >= desc()->local_size
+        && data_d.dims()[3] >= desc()->local_size
+        && one_of(dat_tag_, nChw8c);
+
+    return args_ok_across || args_ok_within ? success : unimplemented;
+}
+
+template <cpu_isa_t isa>
+jit_uni_lrn_bwd_t<isa>::jit_uni_lrn_bwd_t(const pd_t *apd)
+    : cpu_primitive_t(apd)
+    , ker_(nullptr), ker_first_(nullptr), ker_last_(nullptr)
+{
+    using namespace alg_kind;
+    const int C = pd()->C();
+    const int H = pd()->H();
+    const int W = pd()->W();
+    const int ls = pd()->desc()->local_size;
+    float A = pd()->desc()->lrn_alpha / ls;
+    float B = pd()->desc()->lrn_beta;
+
+    int use_h_parallelizm = 0;// XXX
+    if (C / VECTOR_LENGTH == 1) {
+        ker_ = new jit_uni_lrn_bwd_kernel_f32<isa>(
+            nchw8c_across(H, W, 3), A, B, use_h_parallelizm);
+    }
+    else {
+        ker_ = new jit_uni_lrn_bwd_kernel_f32<isa>(
+            nchw8c_across(H, W, 0), A, B, use_h_parallelizm);
+        ker_first_ = new jit_uni_lrn_bwd_kernel_f32<isa>(
+            nchw8c_across(H, W, -1), A, B, use_h_parallelizm);
+        ker_last_ = new jit_uni_lrn_bwd_kernel_f32<isa>(
+            nchw8c_across(H, W, +1), A, B, use_h_parallelizm);
+    }
+}
+
+template <cpu_isa_t isa>
+jit_uni_lrn_bwd_t<isa>::~jit_uni_lrn_bwd_t()
+{
+    delete ker_; delete ker_first_; delete ker_last_;
+}
+
+template <cpu_isa_t isa>
+void jit_uni_lrn_bwd_t<isa>::execute_backward(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto ws = CTX_IN_MEM(const data_t *, MKLDNN_ARG_WORKSPACE);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const int N = pd()->MB();
+    const int C = pd()->C();
+    const int H = pd()->H();
+    const int W = pd()->W();
+
+    int use_h_parallelizm = 0; // XXX
+    if (use_h_parallelizm) {
+        parallel_nd(N, C / VECTOR_LENGTH, H, [&](int n, int c8, int h) {
+            auto offset = n*C*H*W + c8*H*W*VECTOR_LENGTH
+                + h*W*VECTOR_LENGTH;
+            jit_args_bwd_t args;
+            args.src = &src[offset];
+            args.diff_dst = &diff_dst[offset];
+            args.scratch = &ws[offset];
+            args.diff_src = &diff_src[offset];
+            if (C / VECTOR_LENGTH == 1)
+                (*ker_)(&args);
+            else if (c8 == 0)
+                (*ker_first_)(&args);
+            else if (c8 == C / VECTOR_LENGTH - 1)
+                (*ker_last_)(&args);
+            else
+                (*ker_)(&args);
+        });
+    }
+    else {
+        parallel_nd(N, C / VECTOR_LENGTH, [&](int n, int c8) {
+            auto offset = n*C*H*W + c8*H*W*VECTOR_LENGTH;
+            jit_args_bwd_t args;
+            args.src = &src[offset];
+            args.diff_dst = &diff_dst[offset];
+            args.scratch = &ws[offset];
+            args.diff_src = &diff_src[offset];
+            if (C / VECTOR_LENGTH == 1)
+                (*ker_)(&args);
+            else if (c8 == 0)
+                (*ker_first_)(&args);
+            else if (c8 == C / VECTOR_LENGTH - 1)
+                (*ker_last_)(&args);
+            else
+                (*ker_)(&args);
+        });
+    }
+}
+
+template <cpu_isa_t isa>
+status_t jit_uni_lrn_bwd_t<isa>::pd_t::init() {
+    using namespace prop_kind;
+    using namespace alg_kind;
+
+    const memory_desc_wrapper data_d(src_md());
+    bool ok = true
+        && mayiuse(isa)
+        && !is_fwd()
+        && utils::everyone_is(data_type::f32, data_d.data_type())
+        && !has_zero_dim_memory()
+        && data_d.ndims() == 4
+        && data_d.dims()[1] % VECTOR_LENGTH == 0
+        && desc()->lrn_beta == 0.75
+        && attr()->has_default_values();
+    if (!ok) return unimplemented;
+
+    ws_md_ = *src_md();
+    if (!compare_ws(hint_fwd_pd_)) return unimplemented;
+
+    dat_tag_ = memory_desc_matches_one_of_tag(*src_md(), nChw8c);
+
+    bool args_ok_across = true
+        && desc()->alg_kind == lrn_across_channels
+        && desc()->local_size == 5
+        && utils::one_of(dat_tag_, nChw8c);
+
+    return args_ok_across ? success : unimplemented;
+}
+
+template struct jit_uni_lrn_fwd_t<sse42>;
+template struct jit_uni_lrn_fwd_t<avx2>;
+template struct jit_uni_lrn_bwd_t<avx2>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn.hpp
new file mode 100644
index 0000000000..333cd3396d
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn.hpp
@@ -0,0 +1,103 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_UNI_LRN_HPP
+#define CPU_JIT_UNI_LRN_HPP
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_isa_traits.hpp"
+#include "cpu_lrn_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <cpu_isa_t isa> struct jit_uni_lrn_fwd_kernel_f32;
+template <cpu_isa_t isa> struct jit_uni_lrn_bwd_kernel_f32;
+
+template <cpu_isa_t isa>
+struct jit_uni_lrn_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_lrn_fwd_pd_t {
+        using cpu_lrn_fwd_pd_t::cpu_lrn_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", isa, ""),
+                jit_uni_lrn_fwd_t<isa>);
+
+        status_t init();
+
+        format_tag_t dat_tag_;
+    };
+
+    jit_uni_lrn_fwd_t(const pd_t *apd);
+    ~jit_uni_lrn_fwd_t();
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_uni_lrn_fwd_kernel_f32<isa> *ker_, *ker_first_, *ker_last_;
+};
+
+template <cpu_isa_t isa>
+struct jit_uni_lrn_bwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_lrn_bwd_pd_t {
+        using cpu_lrn_bwd_pd_t::cpu_lrn_bwd_pd_t;
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", isa, ""),
+                jit_uni_lrn_bwd_t<isa>);
+
+        status_t init();
+
+        format_tag_t dat_tag_;
+    };
+
+    jit_uni_lrn_bwd_t(const pd_t *apd);
+    ~jit_uni_lrn_bwd_t();
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    jit_uni_lrn_bwd_kernel_f32<isa> *ker_, *ker_first_, *ker_last_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn_kernel_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn_kernel_f32.cpp
new file mode 100644
index 0000000000..89af47272c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn_kernel_f32.cpp
@@ -0,0 +1,1487 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "utils.hpp"
+
+#include "jit_uni_lrn_kernel_f32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace Xbyak;
+
+//////////////////////////////////////////////////////////////////////////////
+// forward kernel
+template<cpu_isa_t isa>
+void jit_uni_lrn_fwd_kernel_f32<isa>::within_body(
+        int hoff, int Hoff, int woff, int Woff, int stride,
+        Xbyak::Ymm ysum, Xbyak::Ymm ydst, Xbyak::Ymm ytmp, Xbyak::Ymm ysum2,
+        prop_kind_t pk)
+{
+    vxorps(ysum, ysum, ysum);
+    for (int i = hoff; i <= Hoff; ++i)
+    {
+        for (int j = woff; j <= Woff; ++j)
+        {
+            if (i == 0 && j == 0)
+            {
+                vmovups(ydst, ptr[src]);
+                vfmadd231ps(ysum, ydst, ydst);
+            }
+            else
+            {
+                vmovups(ytmp, ptr[src + (i*stride + j)*VECTOR_LENGTH*4]);
+                vfmadd231ps(ysum, ytmp, ytmp);
+            }
+        }
+    }
+    vfmadd132ps(ysum, yk, yalpha); // ysum <- ysum*yalpha+yk
+    vmovaps(ytmp, ysum);
+    if (pk != prop_kind::forward_inference)
+        vmovups(ptr[scratch], ytmp);
+    vmulps(ysum2, ysum, ysum);
+    vmulps(ysum, ysum, ysum2); // ysum = (ysum*yalpha+yk)^3;
+    vsqrtps(ysum, ysum);
+    vsqrtps(ysum, ysum); // ysum = (ysum*yalpha+yk)^0.75
+    vdivps(ydst, ydst, ysum); // ydst <- ydst / ysum
+    vmovups(ptr[dst], ydst);
+    add(src, 32);
+    add(dst, 32);
+    if (pk != prop_kind::forward_inference)
+        add(scratch, 32);
+}
+
+template<cpu_isa_t isa>
+void jit_uni_lrn_fwd_kernel_f32<isa>::within_body_sse42(
+    int hoff, int Hoff, int woff, int Woff, int stride, prop_kind_t pk)
+{
+    Xbyak::Xmm xtmp_lo = xmm12;
+    Xbyak::Xmm xtmp_hi = xmm13;
+    Xbyak::Xmm xsum_lo = xmm8;
+    Xbyak::Xmm xsum_hi = xmm9;
+    Xbyak::Xmm xdst_lo = xmm10;
+    Xbyak::Xmm xdst_hi = xmm11;
+    Xbyak::Xmm xsum2_lo = xmm14;
+    Xbyak::Xmm xsum2_hi = xmm15;
+
+    xorps(xsum_lo, xsum_lo);
+    xorps(xsum_hi, xsum_hi);
+    for (int i = hoff; i <= Hoff; ++i)
+    {
+        for (int j = woff; j <= Woff; ++j)
+        {
+            if (i == 0 && j == 0)
+            {
+                movups(xdst_lo, ptr[src]);
+                movups(xdst_hi, ptr[src + 4 * sizeof(float)]);
+                mulps(xdst_lo, xdst_lo);
+                mulps(xdst_hi, xdst_hi);
+                addps(xsum_lo, xdst_lo);
+                addps(xsum_hi, xdst_hi);
+            }
+            else
+            {
+                movups(xtmp_lo, ptr[src + (i*stride + j)*VECTOR_LENGTH * 4]);
+                movups(xtmp_hi, ptr[src + (i*stride + j)*VECTOR_LENGTH * 4 + 4 * sizeof(float)]);
+                mulps(xtmp_lo, xtmp_lo);
+                mulps(xtmp_hi, xtmp_hi);
+                addps(xsum_lo, xtmp_lo);
+                addps(xsum_hi, xtmp_hi);
+            }
+        }
+    }
+    mulps(xsum_lo, xalpha);
+    mulps(xsum_hi, xalpha);
+    addps(xsum_lo, xk);
+    addps(xsum_hi, xk); // xsum <- xsum*xalpha+xk
+    movaps(xtmp_lo, xsum_lo);
+    movaps(xtmp_hi, xsum_hi);
+    if (pk != prop_kind::forward_inference) {
+        movups(ptr[scratch], xtmp_lo);
+        movups(ptr[scratch + 4 * sizeof(float)], xtmp_hi);
+    }
+    movaps(xsum2_lo, xsum_lo);
+    movaps(xsum2_hi, xsum_hi);
+    mulps(xsum2_lo, xsum_lo);
+    mulps(xsum2_hi, xsum_hi);
+    mulps(xsum_lo, xsum2_lo);
+    mulps(xsum_hi, xsum2_hi); // xsum = (xsum*xalpha+xk)^3;
+
+    sqrtps(xsum_lo, xsum_lo);
+    sqrtps(xsum_hi, xsum_hi);
+    sqrtps(xsum_lo, xsum_lo);
+    sqrtps(xsum_hi, xsum_hi); // xsum = (xsum*xalpha+xk)^0.75
+
+    movups(xdst_lo, ptr[src]);
+    movups(xdst_hi, ptr[src + 4 * sizeof(float)]);
+    divps(xdst_lo, xsum_lo);
+    divps(xdst_hi, xsum_hi); // xdst <- xdst / xsum
+
+    movups(ptr[dst], xdst_lo);
+    movups(ptr[dst + 4 * sizeof(float)], xdst_hi);
+    add(src, 32);
+    add(dst, 32);
+    if (pk != prop_kind::forward_inference)
+        add(scratch, 32);
+}
+
+template <cpu_isa_t isa>
+jit_uni_lrn_fwd_kernel_f32<isa>::jit_uni_lrn_fwd_kernel_f32(
+        const struct nchw8c_within &J,
+        float A,
+        float K,
+        prop_kind_t pk,
+        void *code_ptr,
+        size_t code_size)
+        : jit_generator(code_ptr, code_size)
+        , alpha(A), k(K)
+{
+    Xbyak::Reg64 h = r9;
+    Xbyak::Reg64 w = r10;
+    Vmm ysum = Vmm(isa == avx2 ? 9 : 9);
+    Vmm ysum2 = Vmm(isa == avx2 ? 10 : 10);
+    Vmm ydst = Vmm(isa == avx2 ? 11 : 11);
+    Vmm ytmp = Vmm(isa == avx2 ? 12 : 12);
+
+    this->preamble();
+
+    mov(src, ptr[this->param1 + 0]);
+    mov(dst, ptr[this->param1 + 8]);
+    if (pk != prop_kind::forward_inference)
+        mov(scratch, ptr[this->param1 + 16]);
+
+    mov(imm_addr64, float2int(this->alpha));
+    movq(xalpha, imm_addr64);
+    if (isa == avx2) {
+        vbroadcastss(yalpha, xalpha);
+    } else {
+        shufps(xalpha, xalpha, 0);
+    }
+
+    mov(imm_addr64, float2int(this->k));
+    movq(xk, imm_addr64);
+    if (isa == avx2) {
+        vbroadcastss(yk, xk);
+    } else {
+        shufps(xk, xk, 0);
+    }
+
+    int s2 = (J.size - 1) / 2, S2 = J.size - s2 - 1;
+
+    for (int i = 0; i < s2; ++i)
+    {
+        Label label_t;
+        for (int j = 0; j < s2; ++j) {
+            if (isa == avx2) {
+                within_body(-i, S2, -j, S2, J.W, ysum, ydst, ytmp, ysum2, pk);
+            }
+            else {
+                within_body_sse42(-i, S2, -j, S2, J.W, pk);
+            }
+        }
+        mov(w, J.W - J.size + 1);
+        L(label_t);
+        if (isa == avx2) {
+            within_body(-i, S2, -s2, S2, J.W, ysum, ydst, ytmp, ysum2, pk);
+        } else {
+            within_body_sse42(-i, S2, -s2, S2, J.W, pk);
+        }
+        dec(w);
+        cmp(w, 0);
+        jne(label_t, T_NEAR);
+        for (int j = J.W - S2; j < J.W; ++j) {
+            if (isa == avx2) {
+                within_body(-i, S2, -s2, J.W - 1 - j, J.W,
+                    ysum, ydst, ytmp, ysum2, pk);
+            } else {
+                within_body_sse42(-i, S2, -s2, J.W - 1 - j, J.W, pk);
+            }
+        }
+    }
+
+    mov(h, J.H - J.size + 1);
+    Label lrn_loop_h;
+    L(lrn_loop_h);
+    for (int j = 0; j < s2; ++j) {
+        if (isa == avx2) {
+            within_body(-s2, S2, -j, S2, J.W, ysum, ydst, ytmp, ysum2, pk);
+        } else {
+            within_body_sse42(-s2, S2, -j, S2, J.W, pk);
+        }
+    }
+    mov(w, J.W - J.size + 1);
+    Label lrn_loop_w;
+    L(lrn_loop_w);
+    if (isa == avx2) {
+        within_body(-s2, S2, -s2, S2, J.W, ysum, ydst, ytmp, ysum2, pk);
+    } else {
+        within_body_sse42(-s2, S2, -s2, S2, J.W, pk);
+    }
+    dec(w);
+    cmp(w, 0);
+    jne(lrn_loop_w, T_NEAR);
+    for (int j = J.W - S2; j < J.W; ++j) {
+        if (isa == avx2) {
+            within_body(-s2, S2, -s2, J.W - 1 - j, J.W,
+                ysum, ydst, ytmp, ysum2, pk);
+        } else {
+            within_body_sse42(-s2, S2, -s2, J.W - 1 - j, J.W, pk);
+        }
+    }
+    dec(h);
+    cmp(h, 0);
+    jne(lrn_loop_h, T_NEAR);
+
+    for (int i = J.H - S2; i < J.H; ++i)
+    {
+        for (int j = 0; j < s2; ++j) {
+            if (isa == avx2) {
+                within_body(-s2, J.H - 1 - i, -j, S2, J.W,
+                    ysum, ydst, ytmp, ysum2, pk);
+            } else {
+                within_body_sse42(-s2, J.H - 1 - i, -j, S2, J.W, pk);
+            }
+        }
+
+        mov(w, J.W - J.size + 1);
+        Label label_b;
+        L(label_b);
+        if (isa == avx2) {
+            within_body(-s2, J.H - 1 - i, -s2, S2, J.W,
+                ysum, ydst, ytmp, ysum2, pk);
+        } else {
+            within_body_sse42(-s2, J.H - 1 - i, -s2, S2, J.W, pk);
+        }
+        dec(w);
+        cmp(w, 0);
+        jne(label_b, T_NEAR);
+
+        for (int j = J.W - S2; j < J.W; ++j) {
+            if (isa == avx2) {
+                within_body(-s2, J.H - 1 - i, -s2, J.W - 1 - j, J.W,
+                    ysum, ydst, ytmp, ysum2, pk);
+            } else {
+                within_body_sse42(-s2, J.H - 1 - i, -s2, J.W - 1 - j, J.W, pk);
+            }
+        }
+    }
+
+    this->postamble();
+
+    ker = reinterpret_cast<decltype(ker)>(const_cast<uint8_t*>(
+                this->getCode()));
+}
+
+template<>
+jit_uni_lrn_fwd_kernel_f32<avx2>::jit_uni_lrn_fwd_kernel_f32(
+        const struct nchw8c_across &J,
+        float A,
+        float K,
+        prop_kind_t pk,
+        void *code_ptr,
+        size_t code_size)
+        : jit_generator(code_ptr, code_size)
+        , alpha(A), k(K)
+{
+    Xbyak::Reg64 t = rsp;
+    Xbyak::Reg64 hw = r9;
+    Xbyak::Xmm xsrc_prev = xmm2;
+    Xbyak::Ymm ysrc = ymm3;
+    Xbyak::Ymm yc = ymm3;
+    Xbyak::Xmm xsrc_next = xmm4;
+    Xbyak::Ymm ya = ymm5;
+    Xbyak::Ymm yb = ymm6;
+    Xbyak::Ymm yd = ymm7;
+    Xbyak::Ymm ye = ymm8;
+    Xbyak::Ymm ysum = ymm9;
+    Xbyak::Ymm ysum2 = ymm10;
+    Xbyak::Ymm ydst = ymm11;
+    Xbyak::Ymm ybase = ymm12;
+
+    this->preamble();
+
+    mov(src, ptr[this->param1 + 0]);
+    mov(dst, ptr[this->param1 + 8]);
+    if (pk != prop_kind::forward_inference)
+        mov(scratch, ptr[this->param1 + 16]);
+    sub(t, 64);
+    mov(imm_addr64, float2int(this->alpha));
+    movq(xalpha, imm_addr64);
+    vbroadcastss(yalpha, xalpha);
+
+    mov(imm_addr64, float2int(this->k));
+    movq(xk, imm_addr64);
+    vbroadcastss(yk, xk);
+
+    if (J.version == -1)
+    {
+        vxorps(xsrc_prev, xsrc_prev, xsrc_prev);
+        vmovups(ptr[t + 0], xsrc_prev);
+    }
+    if (J.version == +1)
+    {
+        vxorps(xsrc_next, xsrc_next, xsrc_next);
+        vmovups(ptr[t + 48], xsrc_next);
+    }
+
+    mov(hw, J.H*J.W);
+
+    Label lrn_loop;
+    L(lrn_loop);
+
+    if (J.version != -1) vmovups(xsrc_prev, ptr[src - J.H*J.W * 32 + 16]);
+    vmovups(ysrc, ptr[src]);
+    if (J.version != +1) vmovups(xsrc_next, ptr[src + J.H*J.W * 32]);
+
+    if (J.version != -1) vmovups(ptr[t + 0], xsrc_prev);
+    vmovups(ptr[t + 16], ysrc);
+    if (J.version != +1) vmovups(ptr[t + 48], xsrc_next);
+
+    vmovups(ya, ptr[t + 16 - 8]);
+    vmovups(yb, ptr[t + 16 - 4]);
+    vmovups(yd, ptr[t + 16 + 4]);
+    vmovups(ye, ptr[t + 16 + 8]);
+    vmulps(ysum, yc, yc);
+    vfmadd231ps(ysum, ya, ya); // ysum <- ysum + ya*ya
+    vfmadd231ps(ysum, yb, yb);
+    vfmadd231ps(ysum, yd, yd);
+    vfmadd231ps(ysum, ye, ye);
+    vfmadd132ps(ysum, yk, yalpha); // ysum <- ysum*yalpha+yk
+
+    vmovaps(ybase, ysum);
+    if (pk != prop_kind::forward_inference)
+        vmovups(ptr[scratch], ybase);
+    vmulps(ysum2, ysum, ysum);
+    vmulps(ysum, ysum, ysum2); // ysum = ybase^3;
+    vsqrtps(ysum, ysum);
+    vsqrtps(ysum, ysum); // ysum = ybase^0.75
+    vdivps(ydst, ysrc, ysum); // ydst = ysrc / ysum
+    vmovups(ptr[dst], ydst);
+
+    add(src, 32);
+    add(dst, 32);
+    if (pk != prop_kind::forward_inference)
+        add(scratch, 32);
+    dec(hw);
+    cmp(hw, 0);
+    jne(lrn_loop, T_NEAR);
+
+    add(t, 64);
+    this->postamble();
+
+    ker = reinterpret_cast<decltype(ker)>(const_cast<uint8_t*>(
+                this->getCode()));
+}
+
+template<>
+jit_uni_lrn_fwd_kernel_f32<sse42>::jit_uni_lrn_fwd_kernel_f32(
+    const struct nchw8c_across &J,
+    float A,
+    float K,
+    prop_kind_t pk,
+    void *code_ptr,
+    size_t code_size)
+    : jit_generator(code_ptr, code_size)
+    , alpha(A), k(K)
+{
+    Xbyak::Reg64 t = rsp;
+    Xbyak::Reg64 hw = r9;
+
+    Xbyak::Xmm xsrc_lo = xmm2;
+    Xbyak::Xmm xsrc_hi = xmm3;
+    Xbyak::Xmm xc_lo = xmm4;
+    Xbyak::Xmm xc_hi = xmm5;
+    Xbyak::Xmm xsum_lo = xc_lo;
+    Xbyak::Xmm xsum_hi = xc_hi;
+    Xbyak::Xmm xsrc_prev = xmm6;
+    Xbyak::Xmm xsrc_next = xmm7;
+    Xbyak::Xmm xa_lo = xmm8;
+    Xbyak::Xmm xa_hi = xmm9;
+    Xbyak::Xmm xb_lo = xmm10;
+    Xbyak::Xmm xb_hi = xmm11;
+    Xbyak::Xmm xd_lo = xmm12;
+    Xbyak::Xmm xd_hi = xmm13;
+    Xbyak::Xmm xe_lo = xmm14;
+    Xbyak::Xmm xe_hi = xmm15;
+    Xbyak::Xmm xbase_lo = xmm14;
+    Xbyak::Xmm xbase_hi = xmm15;
+
+    this->preamble();
+
+    mov(src, ptr[this->param1 + 0]);
+    mov(dst, ptr[this->param1 + 8]);
+    if (pk != prop_kind::forward_inference)
+        mov(scratch, ptr[this->param1 + 16]);
+    sub(t, 64);
+    mov(imm_addr64, float2int(this->alpha));
+    movq(xalpha, imm_addr64);
+    shufps(xalpha, xalpha, 0);
+
+    mov(imm_addr64, float2int(this->k));
+    movq(xk, imm_addr64);
+    shufps(xk, xk, 0);
+
+    if (J.version == -1)
+    {
+        xorps(xsrc_prev, xsrc_prev);
+        movups(ptr[t + 0], xsrc_prev);
+    }
+    if (J.version == +1)
+    {
+        xorps(xsrc_next, xsrc_next);
+        movups(ptr[t + 48], xsrc_next);
+    }
+
+    mov(hw, J.H*J.W);
+    Label lrn_loop;
+    L(lrn_loop);
+
+    if (J.version != -1) movups(xsrc_prev, ptr[src - J.H*J.W * 32 + 16]);
+    movups(xsrc_lo, ptr[src]);
+    movups(xsrc_hi, ptr[src + 4 * sizeof(float)]);
+    if (J.version != +1) movups(xsrc_next, ptr[src + J.H*J.W * 32]);
+
+    if (J.version != -1) movups(ptr[t + 0], xsrc_prev);
+    movups(ptr[t + 16], xsrc_lo);
+    movups(ptr[t + 16 + 4 * sizeof(float)], xsrc_hi);
+    if (J.version != +1) movups(ptr[t + 48], xsrc_next);
+
+    movups(xa_lo, ptr[t + 16 - 8]);
+    movups(xa_hi, ptr[t + 16 - 8 + 4 * sizeof(float)]);
+    movups(xb_lo, ptr[t + 16 - 4]);
+    movups(xb_hi, ptr[t + 16 - 4 + 4 * sizeof(float)]);
+    movups(xd_lo, ptr[t + 16 + 4]);
+    movups(xd_hi, ptr[t + 16 + 4 + 4 * sizeof(float)]);
+    movups(xe_lo, ptr[t + 16 + 8]);
+    movups(xe_hi, ptr[t + 16 + 8 + 4 * sizeof(float)]);
+    movaps(xc_lo, xsrc_lo);
+    movaps(xc_hi, xsrc_hi);
+    mulps(xsum_lo, xc_lo);
+    mulps(xsum_hi, xc_hi);
+    mulps(xa_lo, xa_lo);
+    mulps(xa_hi, xa_hi);
+    addps(xsum_lo, xa_lo);
+    addps(xsum_hi, xa_hi); // xsum <- xsum + xa*xa
+    mulps(xb_lo, xb_lo);
+    mulps(xb_hi, xb_hi);
+    addps(xsum_lo, xb_lo);
+    addps(xsum_hi, xb_hi);
+    mulps(xd_lo, xd_lo);
+    mulps(xd_hi, xd_hi);
+    addps(xsum_lo, xd_lo);
+    addps(xsum_hi, xd_hi);
+    mulps(xe_lo, xe_lo);
+    mulps(xe_hi, xe_hi);
+    addps(xsum_lo, xe_lo);
+    addps(xsum_hi, xe_hi);
+
+    mulps(xsum_lo, xalpha);
+    mulps(xsum_hi, xalpha);
+    addps(xsum_lo, xk);
+    addps(xsum_hi, xk); // xsum <- xsum*xalpha+xk
+
+    movaps(xbase_lo, xsum_lo);
+    movaps(xbase_hi, xsum_hi);
+    if (pk != prop_kind::forward_inference) {
+        movups(ptr[scratch], xbase_lo);
+        movups(ptr[scratch + 4 * sizeof(float)], xbase_hi);
+    }
+    mulps(xsum_lo, xsum_lo);
+    mulps(xsum_hi, xsum_hi);
+    mulps(xsum_lo, xbase_lo);
+    mulps(xsum_hi, xbase_hi); // xsum = xbase^3;
+    sqrtps(xsum_lo, xsum_lo);
+    sqrtps(xsum_hi, xsum_hi);
+    sqrtps(xsum_lo, xsum_lo);
+    sqrtps(xsum_hi, xsum_hi); // xsum = xbase^0.75
+    divps(xsrc_lo, xsum_lo);
+    divps(xsrc_hi, xsum_hi); // xdst = xsrc / xsum
+    movups(ptr[dst], xsrc_lo);
+    movups(ptr[dst + 4 * sizeof(float)], xsrc_hi);
+
+    add(src, 32);
+    add(dst, 32);
+    if (pk != prop_kind::forward_inference)
+        add(scratch, 32);
+    dec(hw);
+    cmp(hw, 0);
+    jne(lrn_loop, T_NEAR);
+
+    add(t, 64);
+    this->postamble();
+
+    ker = reinterpret_cast<decltype(ker)>(const_cast<uint8_t*>(
+        this->getCode()));
+}
+
+template<>
+jit_uni_lrn_fwd_kernel_f32<avx2>::jit_uni_lrn_fwd_kernel_f32(
+    const struct nhwc_across &J,
+    float A,
+    float K,
+    prop_kind_t pk,
+    void *code_ptr,
+    size_t code_size)
+    : jit_generator(code_ptr, code_size)
+    , alpha(A), k(K)
+{
+    static const uint32_t mask[] = {
+        0, 0, 0x80000000, 0x80000000, 0x80000000, 0x80000000,
+        0x80000000, 0x80000000, 0x80000000, 0, 0
+    };
+
+    Xbyak::Reg64 c = r9;
+    Xbyak::Ymm ya = ymm2;
+    Xbyak::Ymm yb = ymm3;
+    Xbyak::Ymm yc = ymm4;
+    Xbyak::Ymm yd = ymm5;
+    Xbyak::Ymm ye = ymm6;
+    Xbyak::Ymm ysum = ymm7;
+    Xbyak::Ymm ydst = ymm8;
+    Xbyak::Ymm ybase = ymm9;
+    Xbyak::Ymm ymask = ymm10;
+
+    this->preamble();
+
+    mov(src, ptr[this->param1 + 0]);
+    mov(dst, ptr[this->param1 + 8]);
+    if (pk != prop_kind::forward_inference)
+        mov(scratch, ptr[this->param1 + 16]);
+    mov(imm_addr64, float2int(this->alpha));
+    movq(xalpha, imm_addr64);
+    vbroadcastss(yalpha, xalpha);
+
+    mov(imm_addr64, float2int(this->k));
+    movq(xk, imm_addr64);
+    vbroadcastss(yk, xk);
+
+    vxorps(ysum, ysum, ysum);
+
+    mov(imm_addr64, reinterpret_cast<size_t>(&mask[0]));
+    vmovups(ymask, ptr[imm_addr64]);
+    vmaskmovps(ya, ymask, ptr[src - 8]);
+    vfmadd231ps(ysum, ya, ya); // ysum <- ysum + ya^2+yb^2+yc^2+yd^2+ye^2
+
+    mov(imm_addr64, reinterpret_cast<size_t>(&mask[1]));
+    vmovups(ymask, ptr[imm_addr64]);
+    vmaskmovps(yb, ymask, ptr[src - 4]);
+    vfmadd231ps(ysum, yb, yb);
+
+    mov(c, J.C / 8 - 1);
+    Label lrn_loop;
+    L(lrn_loop);
+
+    vmovups(yc, ptr[src]);
+    vmovups(yd, ptr[src + 4]);
+    vmovups(ye, ptr[src + 8]);
+    vfmadd231ps(ysum, yc, yc);
+    vfmadd231ps(ysum, yd, yd);
+    vfmadd231ps(ysum, ye, ye);
+
+    vmovups(ydst, ysum);
+    vfmadd132ps(ydst, yk, yalpha); // ydst <- ysum*yalpha+yk
+
+    vmovaps(ybase, ydst);
+    if (pk != prop_kind::forward_inference)
+        vmovups(ptr[scratch], ybase);
+    vmulps(ydst, ydst, ydst);
+    vmulps(ydst, ydst, ybase); // ydst = (ysum*yalpha+yk)^3;
+    vsqrtps(ydst, ydst);
+    vsqrtps(ydst, ydst); // ydst = (ysum*yalpha+yk)^0.75
+
+    vdivps(ydst, yc, ydst); // ydst = ysrc / (ysum*yalpha+yk)^0.75
+    vmovups(ptr[dst], ydst);
+
+    vxorps(ysum, ysum, ysum);
+
+    add(src, 32);
+    add(dst, 32);
+    if (pk != prop_kind::forward_inference)
+        add(scratch, 32);
+
+    vmovups(ya, ptr[src - 8]);
+    vfmadd231ps(ysum, ya, ya);
+    vmovups(yb, ptr[src - 4]);
+    vfmadd231ps(ysum, yb, yb);
+
+    dec(c);
+    cmp(c, 0);
+    jne(lrn_loop, T_NEAR);
+
+    vmovups(yc, ptr[src]);
+    vfmadd231ps(ysum, yc, yc);
+
+    mov(imm_addr64, reinterpret_cast<size_t>(&mask[2]));
+    vmovups(ymask, ptr[imm_addr64]);
+    vmaskmovps(yd, ymask, ptr[src + 4]);
+    vfmadd231ps(ysum, yd, yd); // ysum <- ysum + ya^2+yb^2+yc^2+yd^2+ye^2
+
+    mov(imm_addr64, reinterpret_cast<size_t>(&mask[3]));
+    vmovups(ymask, ptr[imm_addr64]);
+    vmaskmovps(ye, ymask, ptr[src + 8]);
+    vfmadd231ps(ysum, ye, ye);
+
+    vmovups(ydst, ysum);
+    vfmadd132ps(ydst, yk, yalpha); // ydst <- ysum*yalpha+yk
+
+    vmovaps(ybase, ydst);
+    if (pk != prop_kind::forward_inference)
+        vmovups(ptr[scratch], ybase);
+    vmulps(ydst, ydst, ydst);
+    vmulps(ydst, ydst, ybase); // ydst = (ysum*yalpha+yk)^3;
+    vsqrtps(ydst, ydst);
+    vsqrtps(ydst, ydst); // ydst = (ysum*yalpha+yk)^0.75
+    vdivps(ydst, yc, ydst); // ydst = ysrc / (ysum*yalpha+yk)^0.75
+
+    vmovups(ptr[dst], ydst);
+
+    this->postamble();
+
+    ker = reinterpret_cast<decltype(ker)>(const_cast<uint8_t*>(
+                this->getCode()));
+}
+
+template<>
+jit_uni_lrn_fwd_kernel_f32<sse42>::jit_uni_lrn_fwd_kernel_f32(
+    const struct nhwc_across &J,
+    float A,
+    float K,
+    prop_kind_t pk,
+    void *code_ptr,
+    size_t code_size)
+    : jit_generator(code_ptr, code_size)
+    , alpha(A), k(K)
+{
+    static const uint32_t mask[] = {
+        0, 0, 0xffffffff, 0xffffffff, 0xffffffff, 0xffffffff,
+        0xffffffff, 0xffffffff, 0xffffffff, 0, 0
+    };
+
+    static uint32_t store[] = {
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+    };
+    Xbyak::Reg64 c = r9;
+
+    Xbyak::Xmm xdst_lo = xmm0;
+    Xbyak::Xmm xdst_hi = xmm1;
+    Xbyak::Xmm xa_lo = xmm2;
+    Xbyak::Xmm xa_hi = xmm3;
+    Xbyak::Xmm xb_lo = xmm2;
+    Xbyak::Xmm xb_hi = xmm3;
+    Xbyak::Xmm xc_lo = xmm4;
+    Xbyak::Xmm xc_hi = xmm5;
+    Xbyak::Xmm xd_lo = xmm6;
+    Xbyak::Xmm xd_hi = xmm7;
+    Xbyak::Xmm xe_lo = xmm8;
+    Xbyak::Xmm xe_hi = xmm9;
+    Xbyak::Xmm xsum_lo = xmm10;
+    Xbyak::Xmm xsum_hi = xmm11;
+    Xbyak::Xmm xmask_lo = xmm12;
+    Xbyak::Xmm xmask_hi = xmm13;
+    Xbyak::Xmm xbase_lo = xmm14;
+    Xbyak::Xmm xbase_hi = xmm15;
+
+    this->preamble();
+
+    mov(src, ptr[this->param1 + 0]);
+    mov(dst, ptr[this->param1 + 8]);
+    if (pk != prop_kind::forward_inference)
+        mov(scratch, ptr[this->param1 + 16]);
+    mov(imm_addr64, float2int(this->alpha));
+    movq(xalpha, imm_addr64);
+    shufps(xalpha, xalpha, 0);
+
+    mov(imm_addr64, float2int(this->k));
+    movq(xk, imm_addr64);
+    shufps(xk, xk, 0);
+
+    mov(store_addr, reinterpret_cast<size_t>(&store[0]));
+    and_(store_addr, -15);
+    movups(ptr[store_addr], xalpha);
+    movups(ptr[store_addr + 4 * sizeof(float)], xk);
+
+    xorps(xsum_lo, xsum_lo);
+    xorps(xsum_hi, xsum_hi);
+
+    mov(imm_addr64, reinterpret_cast<size_t>(&mask[0]));
+    movups(xmask_lo, ptr[imm_addr64]);
+    movups(xmask_hi, ptr[imm_addr64 + 4 * sizeof(float)]);
+    movups(xa_lo, ptr[src - 8]);
+    movups(xa_hi, ptr[src - 8 + 4 * sizeof(float)]);
+    andps(xa_lo, xmask_lo);
+    andps(xa_hi, xmask_hi);
+    mulps(xa_lo, xa_lo);
+    mulps(xa_hi, xa_hi);
+    addps(xsum_lo, xa_lo);
+    addps(xsum_hi, xa_hi); // xsum <- xsum + xa^2+xb^2+xc^2+xd^2+xe^2
+
+    mov(imm_addr64, reinterpret_cast<size_t>(&mask[1]));
+    movups(xmask_lo, ptr[imm_addr64]);
+    movups(xmask_hi, ptr[imm_addr64 + 4 * sizeof(float)]);
+    movups(xb_lo, ptr[src - 4]);
+    movups(xb_hi, ptr[src - 4 + 4 * sizeof(float)]);
+    andps(xb_lo, xmask_lo);
+    andps(xb_hi, xmask_hi);
+    mulps(xb_lo, xb_lo);
+    mulps(xb_hi, xb_hi);
+    addps(xsum_lo, xb_lo);
+    addps(xsum_hi, xb_hi);
+
+    mov(c, J.C / 8 - 1);
+    Label lrn_loop;
+    L(lrn_loop);
+
+    movups(xc_lo, ptr[src]);
+    movups(xc_hi, ptr[src + 4 * sizeof(float)]);
+    movups(xd_lo, ptr[src + 4]);
+    movups(xd_hi, ptr[src + 4 + 4 * sizeof(float)]);
+    movups(xe_lo, ptr[src + 8]);
+    movups(xe_hi, ptr[src + 8 + 4 * sizeof(float)]);
+    mulps(xc_lo, xc_lo);
+    mulps(xc_hi, xc_hi);
+    addps(xsum_lo, xc_lo);
+    addps(xsum_hi, xc_hi);
+    mulps(xd_lo, xd_lo);
+    mulps(xd_hi, xd_hi);
+    addps(xsum_lo, xd_lo);
+    addps(xsum_hi, xd_hi);
+    mulps(xe_lo, xe_lo);
+    mulps(xe_hi, xe_hi);
+    addps(xsum_lo, xe_lo);
+    addps(xsum_hi, xe_hi);
+
+    movaps(xdst_lo, xsum_lo);
+    movaps(xdst_hi, xsum_hi);
+    // xdst <- xsum*xalpha+xk
+    mulps(xdst_lo, ptr[store_addr]);
+    mulps(xdst_hi, ptr[store_addr]);
+    addps(xdst_lo, ptr[store_addr + 4 * sizeof(float)]);
+    addps(xdst_hi, ptr[store_addr + 4 * sizeof(float)]);
+
+    movaps(xbase_lo, xdst_lo);
+    movaps(xbase_hi, xdst_hi);
+    if (pk != prop_kind::forward_inference) {
+        movups(ptr[scratch], xbase_lo);
+        movups(ptr[scratch + 4 * sizeof(float)], xbase_hi);
+    }
+    mulps(xdst_lo, xdst_lo);
+    mulps(xdst_hi, xdst_hi);
+    mulps(xdst_lo, xbase_lo);
+    mulps(xdst_hi, xbase_hi); // xdst = (xsum*xalpha+xk)^3;
+    sqrtps(xdst_lo, xdst_lo);
+    sqrtps(xdst_hi, xdst_hi);
+    sqrtps(xdst_lo, xdst_lo);
+    sqrtps(xdst_hi, xdst_hi); // xdst = (xsum*xalpha+xk)^0.75
+
+    movups(xc_lo, ptr[src]);
+    movups(xc_hi, ptr[src + 4 * sizeof(float)]);
+    divps(xc_lo, xdst_lo);
+    divps(xc_hi, xdst_hi); // xdst = xsrc / (xsum*xalpha+xk)^0.75
+    movups(ptr[dst], xc_lo);
+    movups(ptr[dst + 4 * sizeof(float)], xc_hi);
+
+    xorps(xsum_lo, xsum_lo);
+    xorps(xsum_hi, xsum_hi);
+
+    add(src, 32);
+    add(dst, 32);
+    if (pk != prop_kind::forward_inference)
+        add(scratch, 32);
+
+    movups(xa_lo, ptr[src - 8]);
+    movups(xa_hi, ptr[src - 8 + 4 * sizeof(float)]);
+    mulps(xa_lo, xa_lo);
+    mulps(xa_hi, xa_hi);
+    addps(xsum_lo, xa_lo);
+    addps(xsum_hi, xa_hi);
+    movups(xb_lo, ptr[src - 4]);
+    movups(xb_hi, ptr[src - 4 + 4 * sizeof(float)]);
+    mulps(xb_lo, xb_lo);
+    mulps(xb_hi, xb_hi);
+    addps(xsum_lo, xb_lo);
+    addps(xsum_hi, xb_hi);
+
+    dec(c);
+    cmp(c, 0);
+    jne(lrn_loop, T_NEAR);
+
+    movups(xc_lo, ptr[src]);
+    movups(xc_hi, ptr[src + 4 * sizeof(float)]);
+    mulps(xc_lo, xc_lo);
+    mulps(xc_hi, xc_hi);
+    addps(xsum_lo, xc_lo);
+    addps(xsum_hi, xc_hi);
+
+    mov(imm_addr64, reinterpret_cast<size_t>(&mask[2]));
+    movups(xmask_lo, ptr[imm_addr64]);
+    movups(xmask_hi, ptr[imm_addr64 + 4 * sizeof(float)]);
+    movups(xd_lo, ptr[src + 4]);
+    movups(xd_hi, ptr[src + 4 + 4 * sizeof(float)]);
+    andps(xd_lo, xmask_lo);
+    andps(xd_hi, xmask_hi);
+    mulps(xd_lo, xd_lo);
+    mulps(xd_hi, xd_hi);
+    addps(xsum_lo, xd_lo);
+    addps(xsum_hi, xd_hi); // xsum <- xsum + xa^2+xb^2+xc^2+xd^2+xe^2
+
+    mov(imm_addr64, reinterpret_cast<size_t>(&mask[3]));
+    movups(xmask_lo, ptr[imm_addr64]);
+    movups(xmask_hi, ptr[imm_addr64 + 4 * sizeof(float)]);
+    movups(xe_lo, ptr[src + 8]);
+    movups(xe_hi, ptr[src + 8 + 4 * sizeof(float)]);
+    andps(xe_lo, xmask_lo);
+    andps(xe_hi, xmask_hi);
+    mulps(xe_lo, xe_lo);
+    mulps(xe_hi, xe_hi);
+    addps(xsum_lo, xe_lo);
+    addps(xsum_hi, xe_hi);
+
+    movups(xdst_lo, xsum_lo);
+    movups(xdst_hi, xsum_hi);
+    // xdst <- xsum*xalpha+xk
+    mulps(xdst_lo, ptr[store_addr]);
+    mulps(xdst_hi, ptr[store_addr]);
+    addps(xdst_lo, ptr[store_addr + 4 * sizeof(float)]);
+    addps(xdst_hi, ptr[store_addr + 4 * sizeof(float)]);
+
+    movaps(xbase_lo, xdst_lo);
+    movaps(xbase_hi, xdst_hi);
+    if (pk != prop_kind::forward_inference) {
+        movups(ptr[scratch], xbase_lo);
+        movups(ptr[scratch + 4 * sizeof(float)], xbase_hi);
+    }
+    mulps(xdst_lo, xdst_lo);
+    mulps(xdst_hi, xdst_hi);
+    mulps(xdst_lo, xbase_lo);
+    mulps(xdst_hi, xbase_hi); // xdst = (xsum*xalpha+xk)^3;
+    sqrtps(xdst_lo, xdst_lo);
+    sqrtps(xdst_hi, xdst_hi);
+    sqrtps(xdst_lo, xdst_lo);
+    sqrtps(xdst_hi, xdst_hi); // xdst = (xsum*xalpha+xk)^0.75
+    movups(xc_lo, ptr[src]);
+    movups(xc_hi, ptr[src + 4 * sizeof(float)]);
+    divps(xc_lo, xdst_lo);
+    divps(xc_hi, xdst_hi); // xdst = xsrc / (xsum*xalpha+xk)^0.75
+
+    movups(ptr[dst], xc_lo);
+    movups(ptr[dst + 4 * sizeof(float)], xc_hi);
+
+    this->postamble();
+
+    ker = reinterpret_cast<decltype(ker)>(const_cast<uint8_t*>(
+        this->getCode()));
+}
+
+template<>
+void jit_uni_lrn_fwd_kernel_f32<sse42>::nchw_body(
+    int tail, int HW, prop_kind_t pk,
+    Xbyak::Ymm ymask,
+    Xbyak::Ymm ya,
+    Xbyak::Ymm yb,
+    Xbyak::Ymm yc,
+    Xbyak::Ymm yd,
+    Xbyak::Ymm ye,
+    Xbyak::Ymm ysum) {}
+
+template<>
+void jit_uni_lrn_fwd_kernel_f32<avx2>::nchw_body(
+    int tail, int HW, prop_kind_t pk,
+    Xbyak::Ymm ymask,
+    Xbyak::Ymm ya,
+    Xbyak::Ymm yb,
+    Xbyak::Ymm yc,
+    Xbyak::Ymm yd,
+    Xbyak::Ymm ye,
+    Xbyak::Ymm ysum)
+{
+    Xbyak::Ymm ydst = ymm14;
+    Xbyak::Ymm ybase = ymm15;
+
+    vfmadd231ps(ysum, ye, ye);
+
+    vmovups(ydst, ysum);
+    vfmadd132ps(ydst, yk, yalpha); // ydst <- ysum*yalpha+yk
+
+    vmovaps(ybase, ydst);
+    if (pk != prop_kind::forward_inference)
+    {
+        if (tail != 0)
+            vmaskmovps(ptr[scratch], ymask, ybase);
+        else
+            vmovups(ptr[scratch], ybase);
+    }
+    vmulps(ydst, ydst, ydst);
+    vmulps(ydst, ydst, ybase); // ydst = (ysum*yalpha+yk)^3;
+    vsqrtps(ydst, ydst);
+    vsqrtps(ydst, ydst); // ydst = (ysum*yalpha+yk)^0.75
+    vdivps(ydst, yc, ydst); // ydst = ysrc / (ysum*yalpha+yk)^0.75
+
+    if (tail != 0)
+        vmaskmovps(ptr[dst], ymask, ydst);
+    else
+        vmovups(ptr[dst], ydst);
+
+
+    vfnmadd231ps(ysum, ya, ya);
+    vmovups(ya, yb);
+    vmovups(yb, yc);
+    vmovups(yc, yd);
+    vmovups(yd, ye);
+}
+
+template<>
+void jit_uni_lrn_fwd_kernel_f32<avx2>::nchw_tail_sse42(
+    int tail, Xbyak::Reg64 reg_dst, Xbyak::Xmm xtail_lo, Xbyak::Xmm xtail_hi)
+{}
+
+template<>
+void jit_uni_lrn_fwd_kernel_f32<sse42>::nchw_tail_sse42(
+    int tail, Xbyak::Reg64 reg_dst, Xbyak::Xmm xtail_lo, Xbyak::Xmm xtail_hi)
+{
+    Xbyak::Xmm xmm_tmp = xmm10;
+    movaps(xmm_tmp, xtail_lo);
+    size_t offset = 0;
+
+    if (tail > 4) {
+        movups(ptr[reg_dst], xtail_lo);
+        movaps(xmm_tmp, xtail_hi);
+        offset += 4 * sizeof(float);
+        tail -= 4;
+    }
+    movss(ptr[reg_dst + offset], xmm_tmp);
+    for (int i = 1; i < tail; i++)
+    {
+        psrldq(xmm_tmp, 4);
+        movss(ptr[reg_dst + offset + i * sizeof(float)], xmm_tmp);
+    }
+}
+
+template<>
+void jit_uni_lrn_fwd_kernel_f32<sse42>::nchw_body_sse42(
+    int tail, int HW, prop_kind_t pk,
+    Xbyak::Xmm xmask_lo, Xbyak::Xmm xmask_hi,
+    Xbyak::Xmm xe_lo, Xbyak::Xmm xe_hi,
+    Xbyak::Xmm xsum_lo, Xbyak::Xmm xsum_hi)
+{
+    Xbyak::Xmm xdst_lo = xmm0;
+    Xbyak::Xmm xdst_hi = xmm1;
+    Xbyak::Xmm xbase_lo = xmm6;
+    Xbyak::Xmm xbase_hi = xmm7;
+    Xbyak::Xmm xtmp_lo = xmm8;
+    Xbyak::Xmm xtmp_hi = xmm9;
+    Xbyak::Xmm xa_lo = xmm6;
+    Xbyak::Xmm xa_hi = xmm7;
+    Xbyak::Xmm xb_lo = xmm8;
+    Xbyak::Xmm xb_hi = xmm9;
+    Xbyak::Xmm xc_lo = xmm10;
+    Xbyak::Xmm xc_hi = xmm11;
+    Xbyak::Xmm xd_lo = xmm12;
+    Xbyak::Xmm xd_hi = xmm13;
+
+    // store xe
+    movaps(ptr[store_addr + 10 * 4 * sizeof(float)], xe_lo);
+    movaps(ptr[store_addr + 11 * 4 * sizeof(float)], xe_hi);
+
+    mulps(xe_lo, xe_lo);
+    mulps(xe_hi, xe_hi);
+    addps(xsum_lo, xe_lo);
+    addps(xsum_hi, xe_hi);
+
+    // xdst <- xsum*xalpha+xk
+    movaps(xdst_lo, xsum_lo);
+    movaps(xdst_hi, xsum_hi);
+    mulps(xdst_lo, ptr[store_addr + 0 * 4 * sizeof(float)]);
+    mulps(xdst_hi, ptr[store_addr + 0 * 4 * sizeof(float)]);
+    addps(xdst_lo, ptr[store_addr + 1 * 4 * sizeof(float)]);
+    addps(xdst_hi, ptr[store_addr + 1 * 4 * sizeof(float)]);
+
+    movaps(xbase_lo, xdst_lo);
+    movaps(xbase_hi, xdst_hi);
+    if (pk != prop_kind::forward_inference)
+    {
+        if (tail != 0) {
+            nchw_tail_sse42(tail, scratch, xbase_lo, xbase_hi);
+        }
+        else {
+            movups(ptr[scratch], xbase_lo);
+            movups(ptr[scratch + 4 * sizeof(float)], xbase_hi);
+        }
+    }
+    mulps(xdst_lo, xdst_lo);
+    mulps(xdst_hi, xdst_hi);
+    mulps(xdst_lo, xbase_lo);
+    mulps(xdst_hi, xbase_hi); // xdst = (xsum*xalpha+xk)^3;
+    sqrtps(xdst_lo, xdst_lo);
+    sqrtps(xdst_hi, xdst_hi);
+    sqrtps(xdst_lo, xdst_lo);
+    sqrtps(xdst_hi, xdst_hi); // xdst = (xsum*xalpha+xk)^0.75
+    movaps(xtmp_lo, ptr[store_addr + 6 * 4 * sizeof(float)]);
+    movaps(xtmp_hi, ptr[store_addr + 7 * 4 * sizeof(float)]);
+    divps(xtmp_lo, xdst_lo);
+    divps(xtmp_hi, xdst_hi); // xdst = xsrc / (xsum*xalpha+xk)^0.75
+    movaps(xdst_lo, xtmp_lo);
+    movaps(xdst_hi, xtmp_hi);
+
+    if (tail != 0) {
+        nchw_tail_sse42(tail, dst, xdst_lo, xdst_hi);
+    }
+    else {
+        movups(ptr[dst], xdst_lo);
+        movups(ptr[dst + 4 * sizeof(float)], xdst_hi);
+    }
+
+    movaps(xa_lo, ptr[store_addr + 2 * 4 * sizeof(float)]);
+    movaps(xa_hi, ptr[store_addr + 3 * 4 * sizeof(float)]);
+    mulps(xa_lo, xa_lo);
+    mulps(xa_hi, xa_hi);
+    subps(xsum_lo, xa_lo);
+    subps(xsum_hi, xa_hi);
+
+    // xa <- xb
+    movaps(xb_lo, ptr[store_addr + 4 * 4 * sizeof(float)]);
+    movaps(xb_hi, ptr[store_addr + 5 * 4 * sizeof(float)]);
+    movaps(ptr[store_addr + 2 * 4 * sizeof(float)], xb_lo);
+    movaps(ptr[store_addr + 3 * 4 * sizeof(float)], xb_hi);
+
+    // xb <- xc
+    movaps(xc_lo, ptr[store_addr + 6 * 4 * sizeof(float)]);
+    movaps(xc_hi, ptr[store_addr + 7 * 4 * sizeof(float)]);
+    movaps(ptr[store_addr + 4 * 4 * sizeof(float)], xc_lo);
+    movaps(ptr[store_addr + 5 * 4 * sizeof(float)], xc_hi);
+
+    // xc <- xd
+    movaps(xd_lo, ptr[store_addr + 8 * 4 * sizeof(float)]);
+    movaps(xd_hi, ptr[store_addr + 9 * 4 * sizeof(float)]);
+    movaps(ptr[store_addr + 6 * 4 * sizeof(float)], xd_lo);
+    movaps(ptr[store_addr + 7 * 4 * sizeof(float)], xd_hi);
+
+    // xd <- xe
+    movaps(xe_lo, ptr[store_addr + 10 * 4 * sizeof(float)]);
+    movaps(xe_hi, ptr[store_addr + 11 * 4 * sizeof(float)]);
+    movaps(ptr[store_addr + 8 * 4 * sizeof(float)], xe_lo);
+    movaps(ptr[store_addr + 9 * 4 * sizeof(float)], xe_hi);
+}
+
+template<>
+void jit_uni_lrn_fwd_kernel_f32<avx2>::nchw_body_sse42(
+    int tail, int HW, prop_kind_t pk,
+    Xbyak::Xmm xmask_lo, Xbyak::Xmm xmask_hi,
+    Xbyak::Xmm xe_lo, Xbyak::Xmm xe_hi,
+    Xbyak::Xmm xsum_lo, Xbyak::Xmm xsum_hi) {}
+
+template<>
+jit_uni_lrn_fwd_kernel_f32<avx2>::jit_uni_lrn_fwd_kernel_f32(
+    struct nchw_across J,
+    float A,
+    float K,
+    prop_kind_t pk,
+    void* code_ptr,
+    size_t code_size)
+    : jit_generator(code_ptr, code_size)
+    , alpha(A), k(K)
+{
+    static const uint32_t mask[] = {
+        0x80000000, 0x80000000, 0x80000000, 0x80000000, 0x80000000,
+        0x80000000, 0x80000000, 0, 0, 0, 0, 0, 0, 0
+    };
+    Xbyak::Reg64 c = r10;
+    Xbyak::Ymm ymask = ymm2;
+    Xbyak::Ymm ye = ymm3;
+    Xbyak::Ymm ya = ymm4;
+    Xbyak::Ymm yb = ymm5;
+    Xbyak::Ymm yc = ymm6;
+    Xbyak::Ymm yd = ymm7;
+    Xbyak::Ymm ysum = ymm8;
+
+    this->preamble();
+
+    if (J.tail != 0)
+    {
+        mov(imm_addr64, reinterpret_cast<size_t>(&mask[7 - J.tail]));
+        vmovups(ymask, ptr[imm_addr64]);
+    }
+    mov(imm_addr64, float2int(this->alpha));
+    movq(xalpha, imm_addr64);
+    vbroadcastss(yalpha, xalpha);
+
+    mov(imm_addr64, float2int(this->k));
+    movq(xk, imm_addr64);
+    vbroadcastss(yk, xk);
+
+    mov(src, ptr[this->param1 + 0]);
+    mov(dst, ptr[this->param1 + 8]);
+    if (pk != prop_kind::forward_inference)
+        mov(scratch, ptr[this->param1 + 16]);
+
+    vxorps(ya, ya, ya);
+    vxorps(yb, yb, yb);
+    if (J.tail != 0)
+        vmaskmovps(yc, ymask, ptr[src + J.HW * 0]);
+    else
+        vmovups(yc, ptr[src + J.HW * 0]);
+    if (J.tail != 0)
+        vmaskmovps(yd, ymask, ptr[src + J.HW * 4]);
+    else
+        vmovups(yd, ptr[src + J.HW * 4]);
+
+    vxorps(ysum, ysum, ysum);
+    vfmadd231ps(ysum, yc, yc); // ysum <- ysum + ya^2+yb^2+yc^2+yd^2+ye^2
+    vfmadd231ps(ysum, yd, yd);
+
+    mov(c, J.C - 2);
+    Label lrn_loop;
+    L(lrn_loop);
+
+    if (J.tail != 0)
+        vmaskmovps(ye, ymask, ptr[src + J.HW * 8]);
+    else
+        vmovups(ye, ptr[src + J.HW * 8]);
+
+    nchw_body(J.tail, J.HW, pk, ymask, ya, yb, yc, yd, ye, ysum);
+
+    add(src, J.HW * 4);
+    add(dst, J.HW * 4);
+    if (pk != prop_kind::forward_inference)
+        add(scratch, J.HW * 4);
+    dec(c);
+    cmp(c, 0);
+    jne(lrn_loop, T_NEAR);
+
+    vxorps(ye, ye, ye);
+
+    nchw_body(J.tail, J.HW, pk, ymask, ya, yb, yc, yd, ye, ysum);
+    add(src, J.HW * 4);
+    add(dst, J.HW * 4);
+    if (pk != prop_kind::forward_inference)
+        add(scratch, J.HW * 4);
+
+    nchw_body(J.tail, J.HW, pk, ymask, ya, yb, yc, yd, ye, ysum);
+
+    this->postamble();
+
+    ker = reinterpret_cast<decltype(ker)>(const_cast<uint8_t*>(
+                this->getCode()));
+}
+
+template<>
+jit_uni_lrn_fwd_kernel_f32<sse42>::jit_uni_lrn_fwd_kernel_f32(
+    struct nchw_across J,
+    float A,
+    float K,
+    prop_kind_t pk,
+    void* code_ptr,
+    size_t code_size)
+    : jit_generator(code_ptr, code_size)
+    , alpha(A), k(K)
+{
+    static const uint32_t mask[] = {
+        0xffffffff, 0xffffffff, 0xffffffff, 0xffffffff, 0xffffffff,
+        0xffffffff, 0xffffffff, 0, 0, 0, 0, 0, 0, 0
+    };
+
+    Xbyak::Reg64 c = r10;
+
+    Xbyak::Xmm xmask_lo = xmm2;
+    Xbyak::Xmm xmask_hi = xmm3;
+    Xbyak::Xmm xsum_lo = xmm4;
+    Xbyak::Xmm xsum_hi = xmm5;
+    Xbyak::Xmm xa_lo = xmm6;
+    Xbyak::Xmm xa_hi = xmm7;
+    Xbyak::Xmm xb_lo = xmm8;
+    Xbyak::Xmm xb_hi = xmm9;
+    Xbyak::Xmm xc_lo = xmm10;
+    Xbyak::Xmm xc_hi = xmm11;
+    Xbyak::Xmm xd_lo = xmm12;
+    Xbyak::Xmm xd_hi = xmm13;
+    Xbyak::Xmm xe_lo = xmm14;
+    Xbyak::Xmm xe_hi = xmm15;
+
+    this->preamble();
+
+    mov(src, ptr[this->param1 + 0]);
+    mov(dst, ptr[this->param1 + 8]);
+    if (pk != prop_kind::forward_inference)
+        mov(scratch, ptr[this->param1 + 16]);
+
+    sub(rsp, stack_space_needed);
+    mov(store_addr, rsp);
+    and_(store_addr, -15);
+
+    mov(imm_addr64, float2int(this->alpha));
+    movq(xalpha, imm_addr64);
+    shufps(xalpha, xalpha, 0);
+
+    mov(imm_addr64, float2int(this->k));
+    movq(xk, imm_addr64);
+    shufps(xk, xk, 0);
+
+    // put alpha and k into store (free up regs)
+    movaps(ptr[store_addr + 0 * 4 * sizeof(float)], xalpha);
+    movaps(ptr[store_addr + 1 * 4 * sizeof(float)], xk);
+
+    if (J.tail != 0)
+    {
+        mov(imm_addr64, reinterpret_cast<size_t>(&mask[7 - J.tail]));
+        movups(xmask_lo, ptr[imm_addr64]);
+        movups(xmask_hi, ptr[imm_addr64 + 4 * sizeof(float)]);
+    }
+    // init xa, xb
+    xorps(xa_lo, xa_lo);
+    xorps(xa_hi, xa_hi);
+    xorps(xb_lo, xb_lo);
+    xorps(xb_hi, xb_hi);
+
+    // read xc, xd
+    if (J.tail != 0) {
+        movups(xc_lo, ptr[src + J.HW * 0]);
+        movups(xc_hi, ptr[src + J.HW * 0 + 4 * sizeof(float)]);
+        andps(xc_lo, xmask_lo);
+        andps(xc_hi, xmask_hi);
+    }
+    else {
+        movups(xc_lo, ptr[src + J.HW * 0]);
+        movups(xc_hi, ptr[src + J.HW * 0 + 4 * sizeof(float)]);
+    }
+    if (J.tail != 0) {
+        movups(xd_lo, ptr[src + J.HW * 4]);
+        movups(xd_hi, ptr[src + J.HW * 4 + 4 * sizeof(float)]);
+        andps(xd_lo, xmask_lo);
+        andps(xd_hi, xmask_hi);
+    }
+    else {
+        movups(xd_lo, ptr[src + J.HW * 4]);
+        movups(xd_hi, ptr[src + J.HW * 4 + 4 * sizeof(float)]);
+    }
+
+    // put xa, xb, xc, xd into store to free-up regs
+    movaps(ptr[store_addr + 2 * 4 * sizeof(float)], xa_lo);
+    movaps(ptr[store_addr + 3 * 4 * sizeof(float)], xa_hi);
+    movaps(ptr[store_addr + 4 * 4 * sizeof(float)], xb_lo);
+    movaps(ptr[store_addr + 5 * 4 * sizeof(float)], xb_hi);
+    movaps(ptr[store_addr + 6 * 4 * sizeof(float)], xc_lo);
+    movaps(ptr[store_addr + 7 * 4 * sizeof(float)], xc_hi);
+    movaps(ptr[store_addr + 8 * 4 * sizeof(float)], xd_lo);
+    movaps(ptr[store_addr + 9 * 4 * sizeof(float)], xd_hi);
+
+    xorps(xsum_lo, xsum_lo);
+    xorps(xsum_hi, xsum_hi);
+    mulps(xc_lo, xc_lo);
+    mulps(xc_hi, xc_hi);
+    addps(xsum_lo, xc_lo);
+    addps(xsum_hi, xc_hi);
+    mulps(xd_lo, xd_lo);
+    mulps(xd_hi, xd_hi);
+    addps(xsum_lo, xd_lo);
+    addps(xsum_hi, xd_hi); // xsum <- xsum + xa^2+xb^2+xc^2+xd^2+xe^2
+
+    mov(c, J.C - 2);
+    Label lrn_loop;
+    L(lrn_loop);
+
+    if (J.tail != 0) {
+        movups(xe_lo, ptr[src + J.HW * 8]);
+        movups(xe_hi, ptr[src + J.HW * 8 + 4 * sizeof(float)]);
+        andps(xe_lo, xmask_lo);
+        andps(xe_hi, xmask_hi);
+    }
+    else {
+        movups(xe_lo, ptr[src + J.HW * 8]);
+        movups(xe_hi, ptr[src + J.HW * 8 + 4 * sizeof(float)]);
+    }
+
+    nchw_body_sse42(J.tail, J.HW, pk, xmask_lo, xmask_hi,
+        xe_lo, xe_hi,
+        xsum_lo, xsum_hi);
+
+    add(src, J.HW * 4);
+    add(dst, J.HW * 4);
+    if (pk != prop_kind::forward_inference)
+        add(scratch, J.HW * 4);
+    dec(c);
+    cmp(c, 0);
+    jne(lrn_loop, T_NEAR);
+
+    xorps(xe_lo, xe_lo);
+    xorps(xe_hi, xe_hi);
+
+    nchw_body_sse42(J.tail, J.HW, pk, xmask_lo, xmask_hi,
+        xe_lo, xe_hi,
+        xsum_lo, xsum_hi);
+    add(src, J.HW * 4);
+    add(dst, J.HW * 4);
+    if (pk != prop_kind::forward_inference)
+        add(scratch, J.HW * 4);
+
+    nchw_body_sse42(J.tail, J.HW, pk, xmask_lo, xmask_hi,
+        xe_lo, xe_hi,
+        xsum_lo, xsum_hi);
+
+    add(rsp, stack_space_needed);
+
+    this->postamble();
+
+    ker = reinterpret_cast<decltype(ker)>(const_cast<uint8_t*>(
+        this->getCode()));
+}
+
+//////////////////////////////////////////////////////////////////////////////
+// backward kernel
+template <cpu_isa_t isa>
+jit_uni_lrn_bwd_kernel_f32<isa>::jit_uni_lrn_bwd_kernel_f32(
+    const struct nchw8c_across &J,
+    float A,
+    float B,
+    int use_h_parallel,
+    void *code_ptr,
+    size_t code_size)
+    : jit_generator(code_ptr, code_size)
+    , nalphabeta(-2 * A*B)
+    , use_h_parallelizm(use_h_parallel)
+{
+    Xbyak::Reg64 t = rsp;
+    Xbyak::Reg64 hw = r10;
+
+    Xbyak::Xmm xsrc_prev = xmm1;
+    Xbyak::Xmm xws_prev = xmm2;
+    Xbyak::Xmm xdiffdst_prev = xmm3;
+    Xbyak::Ymm ysrc = ymm4;
+    Xbyak::Ymm yws = ymm5;
+    Xbyak::Ymm ydiffdst = ymm6;
+    Xbyak::Xmm xsrc_next = xmm7;
+    Xbyak::Xmm xws_next = xmm8;
+    Xbyak::Xmm xdiffdst_next = xmm9;
+    Xbyak::Ymm ya = ymm10;
+    Xbyak::Xmm xa = xmm10;
+    Xbyak::Ymm yb = ymm11;
+    Xbyak::Ymm yd = ymm12;
+    Xbyak::Ymm ye = ymm13;
+    Xbyak::Ymm ysum = ymm14;
+    Xbyak::Ymm ydiffsrc = ymm15;
+
+    this->preamble();
+
+    mov(src, ptr[this->param1 + 0]);
+    mov(diffdst, ptr[this->param1 + 8]);
+    mov(workspace, ptr[this->param1 + 16]);
+    mov(diffsrc, ptr[this->param1 + 24]);
+
+    sub(t, 64);
+    mov(imm_addr64, float2int(this->nalphabeta));
+    movq(xnalphabeta, imm_addr64);
+    vbroadcastss(ynalphabeta, xnalphabeta);
+
+    bool is_single = J.version == 3;
+    bool is_first = J.version == -1 || J.version == -2;
+    bool is_last = J.version == +1 || J.version == -2;
+
+    if (is_first || is_single) {
+        vxorps(xsrc_prev, xsrc_prev, xsrc_prev);
+        vmovups(ptr[t + 0], xsrc_prev);
+    }
+    if (is_last || is_single) {
+        vxorps(xsrc_next, xsrc_next, xsrc_next);
+        vmovups(ptr[t + 48], xsrc_next);
+    }
+    mov(hw, this->use_h_parallelizm ? J.W : J.H*J.W);
+    Label lrn_loop;
+    L(lrn_loop);
+    {
+        if (!is_first && !is_single) {
+            vmovups(xws_prev, ptr[workspace - J.H*J.W * 32 + 16]);
+            vmovups(xsrc_prev, ptr[src - J.H*J.W * 32 + 16]);
+            vmovups(xdiffdst_prev, ptr[diffdst - J.H*J.W * 32 + 16]);
+            vmulps(xa, xws_prev, xws_prev);
+            vmulps(xa, xa, xws_prev);
+            vsqrtps(xa, xa);
+            vsqrtps(xa, xa);
+            vmulps(xa, xa, xws_prev);
+            vdivps(xsrc_prev, xsrc_prev, xa);
+            vmulps(xdiffdst_prev, xdiffdst_prev, xsrc_prev);
+        }
+
+        vmovups(ysrc, ptr[src]);
+        vmovups(yws, ptr[workspace]);
+        vmovups(ydiffdst, ptr[diffdst]);
+        vmulps(ya, yws, yws);
+        vmulps(ya, ya, yws);
+        vsqrtps(ya, ya);
+        vsqrtps(ya, ya);
+        vdivps(ydiffsrc, ydiffdst, ya);
+        vdivps(ysum, ydiffsrc, yws);
+        vmulps(ysum, ysum, ysrc);
+
+        if (!is_last && !is_single) {
+            vmovups(xws_next, ptr[workspace + J.H*J.W * 32]);
+            vmovups(xsrc_next, ptr[src + J.H*J.W * 32]);
+            vmovups(xdiffdst_next, ptr[diffdst + J.H*J.W * 32]);
+            vmulps(xa, xws_next, xws_next);
+            vmulps(xa, xa, xws_next);
+            vsqrtps(xa, xa);
+            vsqrtps(xa, xa);
+            vmulps(xa, xa, xws_next);
+            vdivps(xsrc_next, xsrc_next, xa);
+            vdivps(xsrc_next, xsrc_next, xws_next);
+            vmulps(xdiffdst_next, xdiffdst_next, xsrc_next);
+        }
+
+        if (!is_first && !is_single) vmovups(ptr[t + 0], xdiffdst_prev);
+        vmovups(ptr[t + 16], ysum);
+        if (!is_last && !is_single) vmovups(ptr[t + 48], xdiffdst_next);
+
+        vmovups(ya, ptr[t + 16 - 8]);
+        vmovups(yb, ptr[t + 16 - 4]);
+        vaddps(ysum, ysum, ya);
+        vmulps(ysrc, ysrc, ynalphabeta);
+        vaddps(ysum, ysum, yb);
+
+        vmovups(yd, ptr[t + 16 + 4]);
+        vmovups(ye, ptr[t + 16 + 8]);
+        vaddps(ysum, ysum, yd);
+        vaddps(ysum, ysum, ye);
+
+        vfmadd231ps(ydiffsrc, ysum, ysrc);
+
+        vmovups(ptr[diffsrc], ydiffsrc);
+
+        add(src, 32);
+        add(diffsrc, 32);
+        add(diffdst, 32);
+        add(workspace, 32);
+
+        dec(hw);
+        cmp(hw, 0);
+        jne(lrn_loop, T_NEAR);
+    }
+
+    add(t, 64);
+    this->postamble();
+
+    ker = reinterpret_cast<decltype(ker)>(const_cast<uint8_t*>(
+        this->getCode()));
+}
+
+template struct jit_uni_lrn_fwd_kernel_f32<sse42>;
+template struct jit_uni_lrn_fwd_kernel_f32<avx2>;
+template struct jit_uni_lrn_bwd_kernel_f32<avx2>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn_kernel_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn_kernel_f32.hpp
new file mode 100644
index 0000000000..2b3ed43cd4
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_lrn_kernel_f32.hpp
@@ -0,0 +1,183 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_UNI_LRN_KERNEL_F32_HPP
+#define CPU_JIT_UNI_LRN_KERNEL_F32_HPP
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+
+#include "jit_generator.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace Xbyak;
+
+enum params { VECTOR_LENGTH = 8, MAX_LOCAL_SIZE = 32 };
+
+typedef struct {
+    const float *src;
+    float *dst, *scratch;
+} jit_args_fwd_t;
+
+typedef struct {
+    const float *src, *diff_dst, *scratch;
+    float *diff_src;
+} jit_args_bwd_t;
+
+struct nchw8c_across {
+    /*  version:
+    *  -1: channels 0..7,
+    *   1: channels C-8 .. C-1,
+    *   0: other channels
+    *   3: channels only for this kernel(without prev and next)
+    */
+    int H, W, version;
+    nchw8c_across(int h, int w, int v) : H(h), W(w), version(v) {}
+};
+
+struct nchw8c_within {
+    int H, W, size;
+    nchw8c_within(int h, int w, int s) : H(h), W(w), size(s) {}
+};
+
+struct nchw_across {
+    int C, HW, tail;
+    nchw_across(int c, int hw, int t) : C(c), HW(hw), tail(t) {}
+};
+
+struct nhwc_across {
+    int C;
+    nhwc_across(int c) : C(c) {}
+};
+
+template <cpu_isa_t isa>
+struct jit_uni_lrn_fwd_kernel_f32 : public jit_generator {
+    Xbyak::Reg64 src = rax;
+    Xbyak::Reg64 dst = r8;
+    Xbyak::Reg64 scratch = rdx;
+    Xbyak::Reg64 imm_addr64 = rbx;
+    Xbyak::Reg64 store_addr = rbp;
+
+    Xbyak::Xmm xalpha = xmm0;
+    Xbyak::Ymm yalpha = ymm0;
+    Xbyak::Xmm xk = xmm1;
+    Xbyak::Ymm yk = ymm1;
+
+    float alpha;
+    float k;
+
+    int stack_space_needed = 11 * 4 * sizeof(float) + 16;
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_lrn_fwd_kernel_f32)
+
+    /* cpu specific part */
+    using Vmm = typename utils::conditional<isa == avx2, Ymm, Zmm>::type;
+
+    jit_uni_lrn_fwd_kernel_f32(
+        const struct nchw8c_within &J,
+        float A,
+        float K,
+        prop_kind_t pk,
+        void *code_ptr = nullptr,
+        size_t code_size = 4 * Xbyak::DEFAULT_MAX_CODE_SIZE);
+    jit_uni_lrn_fwd_kernel_f32(
+        const struct nchw8c_across &J,
+        float A,
+        float K,
+        prop_kind_t pk,
+        void *code_ptr = nullptr,
+        size_t code_size = 1 * Xbyak::DEFAULT_MAX_CODE_SIZE);
+    jit_uni_lrn_fwd_kernel_f32(
+        const struct nhwc_across &J,
+        float A,
+        float K,
+        prop_kind_t pk,
+        void *code_ptr = nullptr,
+        size_t code_size = 1 * Xbyak::DEFAULT_MAX_CODE_SIZE);
+    jit_uni_lrn_fwd_kernel_f32(
+        struct nchw_across J,
+        float A,
+        float K,
+        prop_kind_t pk,
+        void* code_ptr = nullptr,
+        size_t code_size = 2 * Xbyak::DEFAULT_MAX_CODE_SIZE);
+
+    void within_body(
+        int hoff, int Hoff, int woff, int Woff, int stride,
+        Xbyak::Ymm ysum, Xbyak::Ymm ydst, Xbyak::Ymm ytmp, Xbyak::Ymm ysum2,
+        prop_kind_t pk);
+    void within_body_sse42(
+        int hoff, int Hoff, int woff, int Woff, int stride, prop_kind_t pk);
+
+
+    void nchw_body(int tail, int HW, prop_kind_t pk,
+        Xbyak::Ymm ymask,
+        Xbyak::Ymm ya,
+        Xbyak::Ymm yb,
+        Xbyak::Ymm yc,
+        Xbyak::Ymm yd,
+        Xbyak::Ymm ye,
+        Xbyak::Ymm ysum);
+    void nchw_body_sse42(int tail, int HW, prop_kind_t pk,
+        Xbyak::Xmm xmask_lo, Xbyak::Xmm xmask_hi,
+        Xbyak::Xmm xe_lo, Xbyak::Xmm xe_hi,
+        Xbyak::Xmm xsum_lo, Xbyak::Xmm xsum_hi);
+    void nchw_tail_sse42(int tail, Xbyak::Reg64 reg_dst,
+        Xbyak::Xmm xtail_lo, Xbyak::Xmm xtail_hi);
+
+    void operator()(jit_args_fwd_t *arg) { ker(arg); }
+    void(*ker)(jit_args_fwd_t *);
+};
+
+template <cpu_isa_t isa>
+struct jit_uni_lrn_bwd_kernel_f32 : public jit_generator {
+    Xbyak::Reg64 src = rax;
+    Xbyak::Reg64 diffsrc = r8;
+    Xbyak::Reg64 diffdst = r9;
+    Xbyak::Reg64 workspace = rdx;
+    Xbyak::Reg64 imm_addr64 = rsi;
+
+    Xbyak::Xmm xnalphabeta = xmm0;
+    Xbyak::Ymm ynalphabeta = ymm0;
+
+    float nalphabeta;
+
+    int use_h_parallelizm;
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_lrn_bwd_kernel_f32)
+
+    jit_uni_lrn_bwd_kernel_f32(
+        const struct nchw8c_across &J,
+        float A,
+        float B,
+        int use_h_parallel,
+        void *code_ptr = nullptr,
+        size_t code_size = 1 * Xbyak::DEFAULT_MAX_CODE_SIZE);
+
+    void operator()(jit_args_bwd_t *arg) { ker(arg); }
+    void(*ker)(jit_args_bwd_t *);
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pool_kernel_f32.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pool_kernel_f32.cpp
new file mode 100644
index 0000000000..bf8e609d23
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pool_kernel_f32.cpp
@@ -0,0 +1,699 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+* Copyright 2018 YANDEX LLC
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "utils.hpp"
+#include "cpu_pooling_pd.hpp"
+
+#include "jit_uni_pool_kernel_f32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace Xbyak;
+using namespace alg_kind;
+
+#define GET_OFF(field) offsetof(jit_pool_call_s, field)
+
+template <cpu_isa_t isa>
+status_t jit_uni_pool_kernel_f32<isa>::init_conf(jit_pool_conf_t &jpp,
+        const pooling_pd_t *ppd) {
+    const auto &pd = *ppd->desc();
+    const memory_desc_wrapper src_d(
+            ppd->is_fwd() ? ppd->src_md() : ppd->diff_src_md());
+    const memory_desc_wrapper dst_d(
+            ppd->is_fwd() ? ppd->dst_md() : ppd->diff_dst_md());
+
+    bool args_ok = true
+        && mayiuse(isa)
+        && utils::one_of(pd.alg_kind, pooling_max,
+                pooling_avg_include_padding,
+                pooling_avg_exclude_padding);
+    if (!args_ok) return status::unimplemented;
+
+    const int simd_w = isa == avx512_common ? 16 : 8;
+    const int ndims = src_d.ndims();
+
+    jpp.ndims = ndims;
+    jpp.mb = src_d.dims()[0];
+
+    jpp.c = utils::rnd_up(src_d.dims()[1], simd_w);
+    if (jpp.c > src_d.padded_dims()[1])
+        return status::unimplemented;
+
+    jpp.id = (ndims == 5) ? src_d.dims()[2] : 1;
+    jpp.ih = src_d.dims()[ndims-2];
+    jpp.iw = src_d.dims()[ndims-1];
+    jpp.od = (ndims == 5) ? dst_d.dims()[2] : 1;
+    jpp.oh = dst_d.dims()[ndims-2];
+    jpp.ow = dst_d.dims()[ndims-1];
+
+    jpp.stride_d = (ndims == 5 ) ? pd.strides[0] : 1;
+    jpp.stride_h = pd.strides[ndims-4];
+    jpp.stride_w = pd.strides[ndims-3];
+    jpp.kd = (ndims == 5) ? pd.kernel[0] : 1;
+    jpp.kh = pd.kernel[ndims-4];
+    jpp.kw = pd.kernel[ndims-3];
+
+    jpp.f_pad = (ndims == 5 ) ? pd.padding[0][0] : 0;
+    jpp.t_pad = pd.padding[0][ndims-4];
+    jpp.l_pad = pd.padding[0][ndims-3];
+
+    jpp.alg = pd.alg_kind;
+
+    jpp.is_training = pd.prop_kind == prop_kind::forward_training;
+    jpp.is_backward = pd.prop_kind == prop_kind::backward_data;
+    jpp.ind_dt = ppd->workspace_md()
+        ? ppd->workspace_md()->data_type : data_type::undef;
+
+    jpp.simple_alg = jpp.is_training
+        || IMPLICATION(jpp.is_backward, jpp.kd <= jpp.stride_d);
+
+    jpp.c_block = simd_w;
+
+    jpp.nb_c = jpp.c / jpp.c_block;
+    if (jpp.alg == pooling_max) {
+        jpp.ur_w = isa == avx512_common ? 16 : 4;
+        if (jpp.is_training)
+            jpp.ur_w = isa == avx512_common ? 9 : 3;
+        else if (jpp.is_backward)
+            jpp.ur_w = isa == avx512_common ? 6 : 3;
+    } else {
+        if (jpp.is_backward)
+            jpp.ur_w = isa == avx512_common ? 12 : 6;
+        else
+            jpp.ur_w = isa == avx512_common ? 24 : 12;
+    }
+    if (jpp.ow < jpp.ur_w) jpp.ur_w = jpp.ow;
+    if (jpp.l_pad > jpp.ur_w) return status::unimplemented;
+
+    jpp.ur_w_tail = jpp.ow % jpp.ur_w;
+
+    return status::success;
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_pool_kernel_f32<isa>::maybe_recalculate_divisor(int jj,
+        int ur_w, int pad_l, int pad_r) {
+    if (jpp.alg == pooling_avg_exclude_padding) {
+        int kw = jpp.kw;
+        int stride_w = jpp.stride_w;
+
+        int non_zero_kw = kw;
+        non_zero_kw -= nstl::max(0, pad_l - jj*stride_w);
+        non_zero_kw -= nstl::max(0, pad_r - (ur_w - 1 - jj)*stride_w);
+
+        if (non_zero_kw != prev_kw) {
+            mov(tmp_gpr, float2int((float)non_zero_kw));
+            movq(xmm_tmp, tmp_gpr);
+            uni_vbroadcastss(vmm_tmp, xmm_tmp);
+            uni_vmulps(vmm_tmp, vmm_tmp, vmm_ker_area_h);
+            prev_kw = non_zero_kw;
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_pool_kernel_f32<isa>::avg_step(int ur_w, int pad_l,
+        int pad_r) {
+
+    int iw = jpp.iw;
+    int kw = jpp.kw;
+    int stride_w = jpp.stride_w;
+    int c_block = jpp.c_block;
+    Label kd_label, kh_label;
+
+    for (int jj = 0; jj < ur_w; jj++) {
+        if (jpp.is_backward) {
+            uni_vmovups(vreg(jj), ptr[reg_output + sizeof(float)*jj*c_block]);
+            maybe_recalculate_divisor(jj, ur_w, pad_l, pad_r);
+            uni_vdivps(vreg(jj), vreg(jj), vmm_tmp);
+        } else {
+            uni_vpxor(vreg(jj), vreg(jj), vreg(jj));
+        }
+    }
+
+    if (jpp.simple_alg && jpp.ndims == 5) {
+        push(reg_input);
+        push(reg_output);
+        mov(aux_reg_input_d, reg_input);
+        mov(ki, ptr[reg_param + GET_OFF(kd_padding)]);
+        L(kd_label);
+        mov(aux_reg_input, aux_reg_input_d);
+    } else {
+        mov(aux_reg_input, reg_input);
+    }
+
+    xor_(kj, kj);
+    L(kh_label);
+    {
+        for (int ki = 0; ki < kw; ki++) {
+            int jj_start = nstl::max(0, pad_l - ki);
+            int jj_end = ur_w
+                - utils::div_up(nstl::max(0, ki + pad_r - (kw-1)), stride_w);
+            for (int jj = jj_start; jj  < jj_end; jj++) {
+                int aux_input_offset = (ki+jj*stride_w-pad_l)* c_block;
+                if (aux_input_offset > iw * c_block)
+                    continue;
+                int input_offset = sizeof(float)*aux_input_offset;
+                if (jpp.is_backward) {
+                    uni_vmovups(vreg(ur_w+jj),
+                                ptr[aux_reg_input + input_offset]);
+                    uni_vaddps(vreg(ur_w+jj), vreg(ur_w+jj), vreg(jj));
+                    uni_vmovups(vmmword[aux_reg_input + input_offset],
+                                vreg(ur_w+jj));
+                } else {
+                    uni_vaddps(vreg(jj), vreg(jj),
+                               ptr[aux_reg_input + input_offset]);
+                }
+            }
+        }
+        add(aux_reg_input,  sizeof(float) * iw * c_block);
+        inc(kj);
+        cmp(kj, reg_kh);
+        jl(kh_label, T_NEAR);
+    }
+
+    if (jpp.simple_alg && jpp.ndims == 5)
+    {
+        add(aux_reg_input_d,  sizeof(float) * jpp.ih * iw * c_block);
+        dec(ki);
+        cmp(ki, 0);
+        jg(kd_label, T_NEAR);
+        pop(reg_output);
+        pop(reg_input);
+    }
+
+    if (!jpp.is_backward) {
+        for (int jj = 0; jj < ur_w; jj++) {
+            maybe_recalculate_divisor(jj, ur_w, pad_l, pad_r);
+            uni_vdivps(vreg(jj), vreg(jj), vmm_tmp);
+            uni_vmovups(vmmword[reg_output + sizeof(float)*jj*c_block],
+                        vreg(jj));
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_pool_kernel_f32<isa>::max_step_fwd(int ur_w, int pad_l,
+        int pad_r) {
+    int iw = jpp.iw;
+    int kw = jpp.kw;
+    int stride_w = jpp.stride_w;
+    int c_block = jpp.c_block;
+    Label kd_label, kh_label;
+
+    mov(tmp_gpr, float2int(nstl::numeric_limits<float>::lowest()));
+    movq(xmm_tmp, tmp_gpr);
+    uni_vbroadcastss(vmm_tmp, xmm_tmp);
+
+    for (int jj = 0; jj < ur_w; jj++) {
+        uni_vmovups(vreg(jj), vmm_tmp);
+        if (jpp.is_training)
+            uni_vpxor(vreg(2*ur_w+jj), vreg(2*ur_w+jj), vreg(2*ur_w+jj));
+    }
+    if (jpp.is_training)
+    {
+        movq(xmm_tmp, reg_k_shift);
+        uni_vpbroadcastd(vmm_k_offset, xmm_tmp);
+    }
+
+    if (jpp.ndims == 5) {
+        push(reg_input);
+        push(reg_output);
+        mov(aux_reg_input_d, reg_input);
+        mov(ki, ptr[reg_param + GET_OFF(kd_padding)]);
+        L(kd_label);
+        mov(aux_reg_input, aux_reg_input_d);
+    } else {
+        mov(aux_reg_input, reg_input);
+    }
+    xor_(kj, kj);
+    L(kh_label);
+    {
+        for (int ki = 0; ki < kw; ki++) {
+            int jj_start = nstl::max(0, pad_l - ki);
+            int jj_end = ur_w
+                - utils::div_up(nstl::max(0, ki + pad_r - (kw-1)), stride_w);
+            for (int jj = jj_start; jj  < jj_end; jj++) {
+                int aux_input_offset = (ki+jj*stride_w-pad_l)* c_block;
+                if (aux_input_offset > iw * c_block)
+                    continue;
+                int input_offset = sizeof(float)*aux_input_offset;
+                uni_vmovups(vreg(ur_w+jj), ptr[aux_reg_input + input_offset]);
+                if (isa == sse42) {
+                    movups(vmm_mask, vreg(jj));
+                    cmpps(vmm_mask, vreg(ur_w+jj), _cmp_lt_os);
+                    blendvps(vreg(jj), vreg(ur_w+jj));
+                    if (jpp.is_training)
+                        blendvps(vreg(2*ur_w+jj), vmm_k_offset);
+                } else if (isa == avx) {
+                    vcmpps(vreg(3*ur_w+jj), vreg(jj), vreg(ur_w+jj),
+                           _cmp_lt_os);
+                    vblendvps(vreg(jj), vreg(jj), vreg(ur_w+jj),
+                              vreg(3*ur_w+jj));
+                    if (jpp.is_training)
+                        vblendvps(vreg(2*ur_w+jj), vreg(2*ur_w+jj),
+                                  vmm_k_offset, vreg(3*ur_w+jj));
+                } else {
+                    vcmpps(k_store_mask, vreg(jj), vreg(ur_w+jj), _cmp_lt_os);
+                    vblendmps(vreg(jj) | k_store_mask, vreg(jj), vreg(ur_w+jj));
+                    if (jpp.is_training)
+                        vblendmps(vreg(2*ur_w+jj) | k_store_mask,
+                                  vreg(2*ur_w+jj), vmm_k_offset);
+                }
+            }
+            if (jpp.is_training) {
+                if (isa == avx && !mayiuse(avx2)) {
+                    avx_vpadd1(vmm_k_offset, vmm_one, xmm_tmp);
+                } else {
+                    uni_vpaddd(vmm_k_offset, vmm_k_offset, vmm_one);
+                }
+            }
+        }
+        add(aux_reg_input,  sizeof(float) * iw * c_block);
+        inc(kj);
+        cmp(kj, reg_kh);
+        jl(kh_label, T_NEAR);
+    }
+
+    if (jpp.ndims == 5)
+    {
+        add(aux_reg_input_d,  sizeof(float) * jpp.ih * iw * c_block);
+        if (jpp.is_training) {
+            mov(tmp_gpr, ptr[reg_param + GET_OFF(kd_padding_shift)]);
+            movq(xmm_tmp, tmp_gpr);
+            uni_vpbroadcastd(vmm_tmp, xmm_tmp);
+            if (isa == avx && !mayiuse(avx2)) {
+                Xmm t(vmm_mask.getIdx());
+                avx_vpadd1(vmm_k_offset, xmm_tmp, t);
+            } else {
+                uni_vpaddd(vmm_k_offset, vmm_k_offset, vmm_tmp);
+            }
+        }
+
+        dec(ki);
+        cmp(ki, 0);
+        jg(kd_label, T_NEAR);
+        pop(reg_output);
+        pop(reg_input);
+    }
+
+    for (int jj = 0; jj < ur_w; jj++) {
+        uni_vmovups(vmmword[reg_output + sizeof(float)*jj*c_block], vreg(jj));
+        if (jpp.is_training) {
+            const size_t step_index
+                = jj * c_block * types::data_type_size(jpp.ind_dt);
+
+            auto x = xreg(2 * ur_w + jj);
+            if (jpp.ind_dt == data_type::u8) {
+                if (isa == sse42) {
+                    for (int i = 0; i < 4; ++i)
+                        pextrb(ptr[reg_index + step_index + i], x, 4*i);
+                } else if (isa == avx) {
+                    auto y = yreg(2 * ur_w + jj);
+                    if (jj == 0) {
+                        movd(xmm_tmp, reg_shuf_mask);
+                        uni_vpbroadcastd(vmm_tmp, xmm_tmp);
+                    }
+                    if (mayiuse(avx2)) {
+                        vpshufb(y, y, vmm_tmp);
+                        movd(ptr[reg_index + step_index], x);
+                        vperm2i128(y, y, y, 0x1u);
+                        movd(ptr[reg_index + step_index + 4], x);
+                    } else {
+                        Xmm t(vmm_mask.getIdx());
+                        vextractf128(t, y, 0);
+                        vpshufb(t, t, xmm_tmp);
+                        movd(ptr[reg_index + step_index], t);
+                        vextractf128(t, y, 1);
+                        vpshufb(t, t, xmm_tmp); // ymm_tmp[:128]==ymm_tmp[127:0]
+                        movd(ptr[reg_index + step_index + 4], t);
+                    }
+                } else {
+                    auto v = vreg(2 * ur_w + jj);
+                    vpmovusdb(x, v);
+                    vmovups(ptr[reg_index + step_index], v | k_index_mask);
+                }
+            } else {
+                uni_vmovups(ptr[reg_index + step_index], vreg(2*ur_w+jj));
+            }
+        }
+    }
+}
+
+template <cpu_isa_t isa>
+inline void jit_uni_pool_kernel_f32<isa>::max_step_bwd(int ur_w, int pad_l,
+        int pad_r) {
+
+    int iw = jpp.iw;
+    int kw = jpp.kw;
+    int stride_w = jpp.stride_w;
+    int c_block = jpp.c_block;
+    Label kd_label, kh_label;
+
+    for (int jj = 0; jj < ur_w; jj++) {
+        uni_vmovups(vreg(jj), ptr[reg_output + sizeof(float)*jj*c_block]);
+
+        const size_t step_index
+            = jj * c_block * types::data_type_size(jpp.ind_dt);
+        if (jpp.ind_dt == data_type::u8) {
+            if (isa == sse42) {
+                movd(xreg(ur_w+jj), ptr[reg_index + step_index]);
+                pmovzxbd(vreg(ur_w+jj), xreg(ur_w+jj));
+            } else if (isa == avx) {
+                movq(xreg(ur_w+jj), ptr[reg_index + step_index]);
+                if (!mayiuse(avx2)) {
+                    avx_pmovzxbd(vreg(ur_w+jj), xreg(ur_w+jj), xmm_tmp);
+                } else {
+                    vpmovzxbd(vreg(ur_w+jj), xreg(ur_w+jj));
+                }
+            } else {
+                vmovups(vreg(ur_w+jj) | k_index_mask,
+                        ptr[reg_index + step_index]);
+                vpmovzxbd(vreg(ur_w+jj), xreg(ur_w+jj));
+            }
+        } else {
+            uni_vmovups(vreg(ur_w+jj), ptr[reg_index + step_index]);
+        }
+    }
+    movq(xmm_tmp, reg_k_shift);
+    uni_vpbroadcastd(vmm_k_offset, xmm_tmp);
+
+    if (jpp.simple_alg && jpp.ndims == 5) {
+        push(reg_input);
+        push(reg_output);
+        if (isa == sse42) {
+            // Save rdi since it is used in maskmovdqu
+            assert(dst_ptr == rdi);
+            push(dst_ptr);
+        }
+        mov(aux_reg_input_d, reg_input);
+        mov(ki, ptr[reg_param + GET_OFF(kd_padding)]);
+        mov(reg_kd_pad_shift, ptr[reg_param + GET_OFF(kd_padding_shift)]);
+        L(kd_label);
+        mov(aux_reg_input, aux_reg_input_d);
+    } else {
+        mov(aux_reg_input, reg_input);
+    }
+
+    xor_(kj, kj);
+    L(kh_label);
+    {
+        for (int ki = 0; ki < kw; ki++) {
+            int jj_start = nstl::max(0, pad_l - ki);
+            int jj_end = ur_w
+                - utils::div_up(nstl::max(0, ki + pad_r - (kw-1)), stride_w);
+            for (int jj = jj_start; jj  < jj_end; jj++) {
+                int aux_input_offset = (ki+jj*stride_w-pad_l)* c_block;
+                if (aux_input_offset > iw * c_block)
+                    continue;
+                int input_offset = sizeof(float)*aux_input_offset;
+                uni_vmovups(vreg(2*ur_w+jj), ptr[aux_reg_input + input_offset]);
+                if (isa == sse42) {
+                    mov(dst_ptr, aux_reg_input);
+                    add(dst_ptr, input_offset);
+
+                    movups(vreg(3*ur_w+jj), vreg(ur_w+jj));
+                    pcmpeqd(vreg(3*ur_w+jj), vmm_k_offset);
+                    addps(vreg(2*ur_w+jj), vreg(jj));
+                    maskmovdqu(vreg(2*ur_w+jj), vreg(3*ur_w+jj));
+                } else if (isa == avx) {
+                    if (mayiuse(avx2)) {
+                        vpcmpeqd(vreg(3*ur_w+jj), vreg(ur_w+jj), vmm_k_offset);
+                    } else {
+                        avx_pcmpeqd(vreg(3*ur_w+jj), vreg(ur_w+jj), vmm_k_offset, xmm_tmp);
+                    }
+                    vaddps(vreg(2*ur_w+jj), vreg(2*ur_w+jj), vreg(jj));
+                    vmaskmovps(vmmword[aux_reg_input + input_offset],
+                            vreg(3*ur_w+jj), vreg(2*ur_w+jj));
+                } else {
+                    vpcmpeqd(k_store_mask, vreg(ur_w+jj), vmm_k_offset);
+                    vblendmps(vmm_tmp | k_store_mask | T_z, vreg(jj), vreg(jj));
+                    vaddps(vreg(2*ur_w+jj), vreg(2*ur_w+jj), vmm_tmp);
+                    vmovups(vmmword[aux_reg_input +
+                        sizeof(float)*aux_input_offset], vreg(2*ur_w+jj));
+                }
+            }
+            if (isa == avx && !mayiuse(avx2)) {
+                avx_vpadd1(vmm_k_offset, vmm_one, xmm_tmp);
+            } else {
+                uni_vpaddd(vmm_k_offset, vmm_k_offset, vmm_one);
+            }
+        }
+        add(aux_reg_input,  sizeof(float) * iw * c_block);
+        inc(kj);
+        cmp(kj, reg_kh);
+        jl(kh_label, T_NEAR);
+    }
+    if (jpp.simple_alg && jpp.ndims == 5)
+    {
+        add(aux_reg_input_d,  sizeof(float) * jpp.ih * iw * c_block);
+
+        mov(tmp_gpr, reg_kd_pad_shift);
+        movq(xmm_tmp, tmp_gpr);
+        uni_vpbroadcastd(vmm_tmp, xmm_tmp);
+        if (isa == avx && !mayiuse(avx2)) {
+            Xmm t(vmm_mask.getIdx());
+            avx_vpadd1(vmm_k_offset, vmm_tmp, t);
+        } else {
+            uni_vpaddd(vmm_k_offset, vmm_k_offset, vmm_tmp);
+        }
+
+        dec(ki);
+        cmp(ki, 0);
+        jg(kd_label, T_NEAR);
+        if (isa == sse42) {
+            // Save rdi since it is used in maskmovdqu
+            assert(dst_ptr == rdi);
+            pop(dst_ptr);
+        }
+        pop(reg_output);
+        pop(reg_input);
+    }
+}
+
+template <cpu_isa_t isa>
+void jit_uni_pool_kernel_f32<isa>::maybe_zero_diff_src() {
+    assert(jpp.c_block * sizeof(float) % cpu_isa_traits<isa>::vlen == 0);
+    Label l_skip, l_zero;
+
+    auto reg_oh = tmp_gpr;
+    mov(reg_oh, ptr[reg_param + GET_OFF(oh)]);
+    cmp(reg_oh, 0);
+    jz(l_skip, T_NEAR);
+
+    if (jpp.ndims == 5) {
+        mov(zero_size, ptr[reg_param + GET_OFF(oh)]);
+        mov(tmp_gpr, jpp.ih * jpp.iw * jpp.c_block * sizeof(float));
+        imul(zero_size, tmp_gpr);
+    }
+
+    auto vzero = vmm_tmp;
+    uni_vpxor(vzero, vzero, vzero);
+
+    auto reg_off = tmp_gpr;
+    xor_(reg_off, reg_off);
+
+    L(l_zero);
+    {
+        const int dim = jpp.iw * jpp.c_block * sizeof(float);
+        for (int i = 0; i < dim; i += cpu_isa_traits<isa>::vlen)
+            uni_vmovups(ptr[reg_input + reg_off + i], vzero);
+        add(reg_off, dim);
+        if (jpp.ndims == 5) cmp(reg_off, zero_size);
+        else cmp(reg_off, jpp.ih * dim);
+        jl(l_zero, T_NEAR);
+    }
+
+    L(l_skip);
+}
+
+template <cpu_isa_t isa>
+void jit_uni_pool_kernel_f32<isa>::generate() {
+
+    this->preamble();
+
+    int ow = jpp.ow;
+    int iw = jpp.iw;
+    int kw = jpp.kw;
+    int kh = jpp.kh;
+    int ur_w = jpp.ur_w;
+    int c_block = jpp.c_block;
+    int stride_w = jpp.stride_w;
+    int l_pad = jpp.l_pad;
+    int ur_w_tail = jpp.ur_w_tail;
+
+    int n_oi = ow / ur_w;
+
+    prev_kw = 0;
+
+    int vlen = cpu_isa_traits<isa>::vlen;
+
+#if defined(_WIN32)
+    // Always mimic the Unix ABI (see the note about maskmovdqu in the header
+    // file).
+    xor_(rdi, rcx);
+    xor_(rcx, rdi);
+    xor_(rdi, rcx);
+#endif
+
+    mov(reg_input, ptr[reg_param + GET_OFF(src)]);
+    mov(reg_output, ptr[reg_param + GET_OFF(dst)]);
+    if (jpp.alg == pooling_max && (jpp.is_training || jpp.is_backward))
+        mov(reg_index, ptr[reg_param + GET_OFF(indices)]);
+    mov(reg_kh, ptr[reg_param + GET_OFF(kh_padding)]);
+    mov(reg_k_shift, ptr[reg_param + GET_OFF(kh_padding_shift)]);
+    mov(reg_ker_area_h, ptr[reg_param + GET_OFF(ker_area_h)]);
+
+    if (jpp.is_backward)
+        maybe_zero_diff_src();
+
+    if (jpp.alg == pooling_max && (jpp.is_training || jpp.is_backward)) {
+        mov(tmp_gpr, 1);
+        movq(xmm_one, tmp_gpr);
+        uni_vpbroadcastd(vmm_one, xmm_one);
+
+        if (isa == avx) {
+            mov(reg_shuf_mask, 0x0c080400);
+        } else if (isa >= avx512_common) {
+            mov(tmp_gpr.cvt32(), 0x000f);
+            kmovw(k_index_mask, tmp_gpr.cvt32());
+        }
+    }
+
+    int r_pad  = nstl::max(0, ((ow-1)*stride_w) + kw - 1 - (iw + l_pad - 1));
+    int r_pad1 = (ur_w*n_oi - 1)*stride_w + kw - 1 - (iw + l_pad - 1);
+    if (r_pad1 > 0) n_oi--;
+
+    if (jpp.alg == pooling_avg_exclude_padding) {
+        movq(xmm_ker_area_h, reg_ker_area_h);
+        uni_vpbroadcastd(vmm_ker_area_h, xmm_ker_area_h);
+    }
+
+    if (jpp.alg == pooling_avg_include_padding) {
+        mov(tmp_gpr, float2int((float)(kw * kh * jpp.kd)));
+        movq(xmm_tmp, tmp_gpr);
+        uni_vpbroadcastd(vmm_tmp, xmm_tmp);
+    }
+    if (l_pad > 0) {
+        n_oi--;
+        if (n_oi < 0 && r_pad1 > 0) {
+            step(ur_w, l_pad, r_pad1);
+        } else  {
+            step(ur_w, l_pad, 0);
+        }
+
+        if (isa == sse42) {
+            if (n_oi < 0 && r_pad1 > 0) {
+                step_high_half(ur_w, l_pad, r_pad1);
+            } else  {
+                step_high_half(ur_w, l_pad, 0);
+            }
+        }
+
+        if (isa == sse42) {
+            add(reg_input, sizeof(float)*(ur_w*stride_w-l_pad)*c_block - vlen);
+            add(reg_output, sizeof(float)*ur_w*c_block - vlen);
+            if (jpp.alg == pooling_max && (jpp.is_training || jpp.is_backward))
+                add(reg_index, (2 * ur_w - 1) * c_block / 2
+                        * types::data_type_size(jpp.ind_dt));
+        } else {
+            add(reg_input, sizeof(float)*(ur_w*stride_w - l_pad)*c_block);
+            add(reg_output, sizeof(float)*ur_w*c_block);
+            if (jpp.alg == pooling_max && (jpp.is_training || jpp.is_backward))
+                add(reg_index, ur_w * c_block
+                        * types::data_type_size(jpp.ind_dt));
+        }
+    }
+
+    xor_(oi_iter, oi_iter);
+    if (n_oi > 0) {
+        Label ow_loop;
+        L(ow_loop); {
+            step(ur_w, 0, 0);
+
+            if (isa == sse42) {
+                step_high_half(ur_w, 0, 0);
+            }
+
+            if (isa == sse42) {
+                add(reg_input, sizeof(float)*ur_w*stride_w*c_block - vlen);
+                add(reg_output, sizeof(float)*ur_w*c_block - vlen);
+                if (jpp.alg == pooling_max &&
+                    (jpp.is_training || jpp.is_backward))
+                    add(reg_index, (2 * ur_w - 1) * c_block / 2
+                            * types::data_type_size(jpp.ind_dt));
+            } else {
+                add(reg_input, sizeof(float)*ur_w*stride_w*c_block);
+                add(reg_output, sizeof(float)*ur_w*c_block);
+                if (jpp.alg == pooling_max &&
+                    (jpp.is_training || jpp.is_backward))
+                    add(reg_index, ur_w * c_block
+                            * types::data_type_size(jpp.ind_dt));
+            }
+
+            inc(oi_iter);
+            cmp(oi_iter, n_oi);
+            jl(ow_loop, T_NEAR);
+        }
+    }
+
+    if (r_pad1 > 0 && n_oi >= 0) {
+        step(ur_w, 0, r_pad1);
+
+        if (isa == sse42) {
+            step_high_half(ur_w, 0, r_pad1);
+        }
+
+        if (isa == sse42) {
+            add(reg_input, sizeof(float)*ur_w*stride_w*c_block - vlen);
+            add(reg_output, sizeof(float)*ur_w*c_block - vlen);
+            if (jpp.alg == pooling_max && (jpp.is_training || jpp.is_backward))
+                add(reg_index, (2 * ur_w - 1) * c_block / 2
+                        * types::data_type_size(jpp.ind_dt));
+        } else {
+            add(reg_input, sizeof(float)*ur_w*stride_w*c_block);
+            add(reg_output, sizeof(float)*ur_w*c_block);
+            if (jpp.alg == pooling_max && (jpp.is_training || jpp.is_backward))
+                add(reg_index, ur_w * c_block
+                        * types::data_type_size(jpp.ind_dt));
+        }
+    }
+
+    if (ur_w_tail != 0) {
+        step(ur_w_tail, 0, r_pad);
+
+        if (isa == sse42) {
+            step_high_half(ur_w_tail, 0, r_pad);
+        }
+    }
+
+    this->postamble();
+}
+
+template struct jit_uni_pool_kernel_f32<sse42>;
+template struct jit_uni_pool_kernel_f32<avx>; // implements both <avx> and <avx2>
+template struct jit_uni_pool_kernel_f32<avx512_common>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pool_kernel_f32.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pool_kernel_f32.hpp
new file mode 100644
index 0000000000..992b526587
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pool_kernel_f32.hpp
@@ -0,0 +1,192 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+* Copyright 2018 YANDEX LLC
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_UNI_POOL_KERNEL_F32_HPP
+#define JIT_UNI_POOL_KERNEL_F32_HPP
+
+#include <cfloat>
+
+#include "c_types_map.hpp"
+#include "pooling_pd.hpp"
+#include "type_helpers.hpp"
+
+#include "jit_generator.hpp"
+#include "jit_primitive_conf.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace Xbyak;
+
+template <cpu_isa_t isa>
+struct jit_uni_pool_kernel_f32: public jit_generator {
+    jit_uni_pool_kernel_f32(jit_pool_conf_t ajpp): jpp(ajpp)
+    {
+        this->generate();
+        jit_ker = (decltype(jit_ker))this->getCode();
+    }
+
+    jit_pool_conf_t jpp;
+
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_pool_kernel_f32)
+
+    void operator()(jit_pool_call_s *arg) { jit_ker(arg); }
+    static status_t init_conf(jit_pool_conf_t &jbp, const pooling_pd_t *ppd);
+
+private:
+    using Vmm = typename utils::conditional3<isa == sse42, Xmm, isa == avx,
+                                             Ymm, Zmm>::type;
+    Xmm xreg(int idx) { return Xmm((isa == avx512_common ? 31 : 15) - idx); }
+    Ymm yreg(int idx) { return Ymm(xreg(idx).getIdx()); }
+    Vmm vreg(int idx) { return Vmm(xreg(idx).getIdx()); }
+
+    const AddressFrame &vmmword = (isa == sse42) ? xword :
+                                  (isa == avx) ? yword : zword;
+
+    Xmm vmm_mask = Xmm(0);
+    Xmm xmm_ker_area_h = Xmm(2);
+    Xmm xmm_one = Xmm(2);
+    Xmm xmm_tmp = Xmm(3);
+
+    Vmm vmm_ker_area_h = Vmm(2);
+    Vmm vmm_one = Vmm(2);
+    Vmm vmm_tmp = Vmm(3);
+
+    Vmm vmm_k_offset = Vmm(1);
+
+    Opmask k_index_mask = Opmask(6);
+    Opmask k_store_mask = Opmask(7);
+
+    // Here be some (tame) dragons. This kernel does not follow the regular
+    // OS-agnostic ABI pattern because when isa is sse42 it uses maskmovdqu
+    // instruction which has its destination hardcoded in rdi. Therefore:
+    // - all registers are hardcoded
+    // - on Windows rdi and rcx are swapped to mimic the Unix x86_64 ABI
+    //
+    // While this is only required by the backward pass, the quirk above
+    // is applied to the forward pass as well to keep things simpler.
+
+    using reg64_t = const Xbyak::Reg64;
+    reg64_t reg_param      = rdi; // Always mimic the Unix ABI
+    reg64_t reg_input      = r8;
+    reg64_t aux_reg_input  = r9;
+    reg64_t reg_index      = r10;
+    reg64_t reg_output     = r12;
+    reg64_t reg_kd_pad_shift = r13;
+    reg64_t dst_ptr        = rdi; // Must be rdi due to maskmovdqu
+
+    reg64_t kj      = r14;
+    reg64_t oi_iter = r15;
+    reg64_t reg_kh  = rax;
+    reg64_t reg_k_shift  = rbx;
+    reg64_t tmp_gpr = rcx; // Must be rcx because rdi is used above
+    reg64_t reg_ker_area_h = rdx;
+
+    reg64_t zero_size = r15;
+    reg64_t ki = r12;
+    reg64_t aux_reg_input_d = r8;
+
+    Xbyak::Reg32 reg_shuf_mask = esi;
+
+    int prev_kw;
+    void (*jit_ker)(jit_pool_call_s *);
+
+    void maybe_recalculate_divisor(int jj, int ur_w, int pad_l, int pad_r);
+    void avg_step(int ur_w, int pad_l, int pad_r);
+    void max_step_fwd(int ur_w, int pad_l, int pad_r);
+    void max_step_bwd(int ur_w, int pad_l, int pad_r);
+
+    void maybe_zero_diff_src();
+
+    void step(int ur_w, int pad_l, int pad_r) {
+        if (jpp.alg == alg_kind::pooling_max) {
+            if(jpp.is_backward)
+                max_step_bwd(ur_w, pad_l, pad_r);
+            else
+                max_step_fwd(ur_w, pad_l, pad_r);
+        }
+        else
+            avg_step(ur_w, pad_l, pad_r);
+    }
+
+    void step_high_half(int ur_w, int pad_l, int pad_r) {
+        add(reg_input, sizeof(float) * 4);
+        add(reg_output, sizeof(float) * 4);
+        if (jpp.alg == alg_kind::pooling_max &&
+                (jpp.is_training || jpp.is_backward))
+            add(reg_index, types::data_type_size(jpp.ind_dt) * 4);
+
+        step(ur_w, pad_l, pad_r);
+    }
+
+    void generate();
+
+    void avx_vpadd1(const Ymm& y0, const Xmm& x1, const Xmm& xtmp) {
+        assert(y0.getIdx() != x1.getIdx());
+        vextractf128(xtmp, y0, 0);
+        vpaddd(xtmp, xtmp, x1);
+        vinsertf128(y0, y0, xtmp, 0);
+        vextractf128(xtmp, y0, 1);
+        vpaddd(xtmp, xtmp, x1);
+        vinsertf128(y0, y0, xtmp, 1);
+    }
+
+    void avx_vpadd1(const Xmm& x0, const Xmm& x1, const Xmm&) {
+        assert(false /*function should not be used*/);
+        paddd(x0, x1);
+    }
+
+    void avx_pmovzxbd(const Ymm& y0, const Xmm& x1, const Xmm& xtmp) {
+        Xmm x0(y0.getIdx());
+        pshufd(xmm_tmp, x1, 1);
+        pmovzxbd(x0, x1);
+        pmovzxbd(xmm_tmp, xmm_tmp);
+        vinsertf128(y0, y0, xmm_tmp, 1);
+    }
+
+    void avx_pmovzxbd(const Xmm& x0, const Xmm& x1, const Xmm&) {
+        assert(false /*function should not be used*/);
+        pmovzxbd(x0, x1);
+    }
+
+    void avx_pcmpeqd(const Ymm& y0, const Ymm& y1, const Ymm& y2, const Xmm& xtmp) {
+        assert(y0.getIdx() != y1.getIdx());
+        assert(y0.getIdx() != y2.getIdx());
+        Xmm x0(y0.getIdx());
+        Xmm x2(y2.getIdx());
+        vextractf128(x0, y1, 1);
+        vextractf128(xtmp, y2, 1);
+        pcmpeqd(xtmp, x0);
+        vextractf128(x0, y1, 0);
+        pcmpeqd(x0, x2);
+        vinsertf128(y0, y0, xtmp, 1);
+    }
+
+    void avx_pcmpeqd(const Xmm& x0, const Xmm& x1, const Xmm&, const Xmm&) {
+        assert(false /*function should not be used*/);
+        pcmpeqd(x0, x1);
+    }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pooling.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pooling.cpp
new file mode 100644
index 0000000000..afbcf996d8
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pooling.cpp
@@ -0,0 +1,264 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn_types.h"
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "nstl.hpp"
+
+#include "jit_uni_pooling.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <cpu_isa_t isa>
+void jit_uni_pooling_fwd_t<isa>::execute_forward(const data_t *src,
+        data_t *dst, char *indices) const {
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper indices_d(pd()->workspace_md());
+    const size_t ind_dt_size = indices
+        ? types::data_type_size(indices_d.data_type()) : 0;
+
+    const auto &jpp = pd()->jpp_;
+
+    auto ker = [&](int n, int b_c, int oh) {
+        auto arg = jit_pool_call_s();
+
+        const int ij = oh * jpp.stride_h;
+        const int i_t_overflow = nstl::max(0, jpp.t_pad-ij);
+        const int i_b_overflow = nstl::max(jpp.ih, ij+jpp.kh-jpp.t_pad)-jpp.ih;
+        const int ih = nstl::max(ij - jpp.t_pad, 0);
+
+        arg.src = &src[src_d.blk_off(n, b_c, ih)];
+        arg.dst = &dst[dst_d.blk_off(n, b_c, oh)];
+        if (indices) {
+            const size_t ind_off = indices_d.blk_off(n, b_c, oh);
+            arg.indices = &indices[ind_off * ind_dt_size];
+        }
+        arg.oh = oh == 0;
+        arg.kh_padding = jpp.kh - i_t_overflow - i_b_overflow;
+        arg.kh_padding_shift = i_t_overflow*jpp.kw;
+        arg.kw_padding = 0;
+        arg.ker_area_h = (float)(jpp.kh -
+            nstl::max(0, oh*jpp.stride_h - jpp.t_pad + jpp.kh - jpp.ih) -
+            nstl::max(0, jpp.t_pad - oh*jpp.stride_h));
+        (*kernel_)(&arg);
+    };
+
+    parallel_nd(jpp.mb, jpp.nb_c, jpp.oh,
+        [&](int n, int b_c, int oh) {
+        ker(n, b_c, oh);
+    });
+}
+
+template <cpu_isa_t isa>
+void jit_uni_pooling_fwd_t<isa>::execute_forward_3d(const data_t *src,
+        data_t *dst, char *indices) const {
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper indices_d(pd()->workspace_md());
+    const size_t ind_dt_size = indices
+        ? types::data_type_size(indices_d.data_type()) : 0;
+
+    const auto &jpp = pd()->jpp_;
+
+    auto ker = [&](int n, int b_c, int od, int oh, int id, int d_t_overflow,
+            int d_b_overflow) {
+        auto arg = jit_pool_call_s();
+
+        const int ij = oh * jpp.stride_h;
+        const int i_t_overflow = nstl::max(0, jpp.t_pad-ij);
+        const int i_b_overflow = nstl::max(jpp.ih, ij+jpp.kh-jpp.t_pad)-jpp.ih;
+        const int ih = nstl::max(ij - jpp.t_pad, 0);
+
+        arg.src = &src[src_d.blk_off(n, b_c, id, ih)];
+        arg.dst = &dst[dst_d.blk_off(n, b_c, od, oh)];
+        if (indices) {
+            const size_t ind_off = indices_d.blk_off(n, b_c, od, oh);
+            arg.indices = &indices[ind_off * ind_dt_size];
+        }
+        arg.oh = (oh + od == 0);
+        arg.kd_padding = jpp.kd - d_t_overflow - d_b_overflow;
+        arg.kh_padding = jpp.kh - i_t_overflow - i_b_overflow;
+        arg.kh_padding_shift = i_t_overflow*jpp.kw + d_t_overflow*jpp.kw*jpp.kh;
+        arg.kd_padding_shift = (i_t_overflow + i_b_overflow)*jpp.kw;
+        arg.kw_padding = 0;
+        arg.ker_area_h = (float)(jpp.kh -
+            nstl::max(0, oh*jpp.stride_h - jpp.t_pad + jpp.kh - jpp.ih) -
+            nstl::max(0, jpp.t_pad - oh*jpp.stride_h)) * (jpp.kd -
+            nstl::max(0, od*jpp.stride_d - jpp.f_pad + jpp.kd - jpp.id) -
+            nstl::max(0, jpp.f_pad - od*jpp.stride_d));
+
+
+        (*kernel_)(&arg);
+    };
+
+    parallel_nd(jpp.mb, jpp.nb_c, jpp.od,
+        [&](int n, int b_c, int od) {
+        const int ik = od * jpp.stride_d;
+        const int d_t_overflow = nstl::max(0, jpp.f_pad-ik);
+        const int d_b_overflow = nstl::max(jpp.id, ik+jpp.kd-jpp.f_pad)
+            -jpp.id;
+        const int id = nstl::max(ik - jpp.f_pad, 0);
+        for (int oh = 0; oh < jpp.oh; ++oh) {
+            ker(n, b_c, od, oh, id, d_t_overflow, d_b_overflow);
+        }
+    });
+}
+
+template <cpu_isa_t isa>
+void jit_uni_pooling_bwd_t<isa>::execute_backward(const data_t *diff_dst,
+        const char *indices, data_t *diff_src) const {
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper indices_d(pd()->workspace_md());
+    const size_t ind_dt_size = indices
+        ? types::data_type_size(indices_d.data_type()) : 0;
+
+    const auto &jpp = pd()->jpp_;
+
+    auto ker = [&](int n, int b_c, int oh) {
+        auto arg = jit_pool_call_s();
+
+        const int ij = oh * jpp.stride_h;
+        const int i_t_overflow = nstl::max(0, jpp.t_pad-ij);
+        const int i_b_overflow = nstl::max(jpp.ih, ij+jpp.kh-jpp.t_pad)-jpp.ih;
+        const int ih = nstl::max(ij - jpp.t_pad, 0);
+
+        arg.src = &diff_src[diff_src_d.blk_off(n, b_c, ih)];
+        arg.dst = &diff_dst[diff_dst_d.blk_off(n, b_c, oh)];
+        if (indices) {
+            const size_t ind_off = indices_d.blk_off(n, b_c, oh);
+            arg.indices = &indices[ind_off * ind_dt_size];
+        }
+        arg.oh = (oh == 0);
+        arg.kh_padding = jpp.kh - i_t_overflow - i_b_overflow;
+        arg.kh_padding_shift = i_t_overflow*jpp.kw;
+        arg.kw_padding = 0;
+        arg.ker_area_h = (float)(jpp.kh -
+            nstl::max(0, oh*jpp.stride_h - jpp.t_pad + jpp.kh - jpp.ih) -
+            nstl::max(0, jpp.t_pad - oh*jpp.stride_h));
+
+        (*kernel_)(&arg);
+    };
+
+    parallel_nd(jpp.mb, jpp.nb_c, [&](int n, int b_c) {
+        for (int oh = 0; oh < jpp.oh; ++oh) {
+            ker(n, b_c, oh);
+        }
+    });
+}
+
+template <cpu_isa_t isa>
+void jit_uni_pooling_bwd_t<isa>::execute_backward_3d(const data_t *diff_dst,
+        const char *indices, data_t *diff_src) const {
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper indices_d(pd()->workspace_md());
+    const size_t ind_dt_size = indices
+        ? types::data_type_size(indices_d.data_type()) : 0;
+
+    const auto &jpp = pd()->jpp_;
+
+    auto ker = [&](int n, int b_c, int od, int oh, int id, int d_t_overflow,
+            int d_b_overflow, int zero_size, int kd) {
+        auto arg = jit_pool_call_s();
+
+        const int ij = oh * jpp.stride_h;
+        const int i_t_overflow = nstl::max(0, jpp.t_pad-ij);
+        const int i_b_overflow = nstl::max(jpp.ih, ij+jpp.kh-jpp.t_pad)-jpp.ih;
+        const int ih = nstl::max(ij - jpp.t_pad, 0);
+
+        arg.src = &diff_src[diff_src_d.blk_off(n, b_c, id + kd, ih)];
+        arg.dst = &diff_dst[diff_dst_d.blk_off(n, b_c, od, oh)];
+        if (indices) {
+            const size_t ind_off = indices_d.blk_off(n, b_c, od, oh);
+            arg.indices = &indices[ind_off * ind_dt_size];
+        }
+        arg.oh = zero_size;
+        arg.kd_padding = jpp.kd - d_t_overflow - d_b_overflow;
+        arg.kh_padding = jpp.kh - i_t_overflow - i_b_overflow;
+        arg.kh_padding_shift = i_t_overflow*jpp.kw + d_t_overflow*jpp.kw*jpp.kh
+            + kd * jpp.kw * jpp.kh;
+        arg.kd_padding_shift = (i_t_overflow + i_b_overflow)*jpp.kw;
+        arg.kw_padding = 0;
+        arg.ker_area_h = (float)(jpp.kh -
+            nstl::max(0, oh*jpp.stride_h - jpp.t_pad + jpp.kh - jpp.ih) -
+            nstl::max(0, jpp.t_pad - oh*jpp.stride_h)) * (jpp.kd -
+            nstl::max(0, od*jpp.stride_d - jpp.f_pad + jpp.kd - jpp.id) -
+            nstl::max(0, jpp.f_pad - od*jpp.stride_d));
+
+        (*kernel_)(&arg);
+    };
+
+    if (jpp.simple_alg) {
+
+        parallel_nd(jpp.mb, jpp.nb_c, jpp.od,
+            [&](int n, int b_c, int od) {
+            const int ik = od * jpp.stride_d;
+            const int d_t_overflow = nstl::max(0, jpp.f_pad - ik);
+            const int d_b_overflow = nstl::max(jpp.id, ik + jpp.kd
+                    - jpp.f_pad) - jpp.id;
+            const int id = nstl::max(ik - jpp.f_pad, 0);
+            int zero_s = jpp.stride_d - d_t_overflow - (nstl::max(
+                    jpp.id, ik + jpp.stride_d - jpp.f_pad) - jpp.id);
+            for (int oh = 0; oh < jpp.oh; ++oh) {
+                ker(n, b_c, od, oh, id, d_t_overflow, d_b_overflow,
+                        (oh == 0) ? zero_s : 0, 0);
+            }
+        });
+    } else {
+        ptrdiff_t nelems = (ptrdiff_t)jpp.mb * (ptrdiff_t)jpp.c
+            * (ptrdiff_t)jpp.id * (ptrdiff_t)jpp.ih * (ptrdiff_t)jpp.iw;
+
+        parallel_nd(nelems, [&](ptrdiff_t i) { diff_src[i] = 0.f; });
+
+        for (int kd = 0; kd < jpp.kd; ++kd) {
+            parallel_nd(jpp.mb, jpp.nb_c, [&](int n, int b_c) {
+                for (int od = 0; od < jpp.od; ++od) {
+                    const int ik = od * jpp.stride_d;
+                    const int d_t_overflow = nstl::max(0, jpp.f_pad-ik);
+                    const int d_b_overflow = nstl::max(jpp.id, ik + jpp.kd
+                            - jpp.f_pad) - jpp.id;
+                    if (kd >= jpp.kd - d_t_overflow - d_b_overflow)
+                        continue;
+                    const int id = nstl::max(ik - jpp.f_pad, 0);
+                    for (int oh = 0; oh < jpp.oh; ++oh) {
+                        ker(n, b_c, od, oh, id, d_t_overflow, d_b_overflow,
+                                0, kd);
+                    }
+                }
+            });
+        }
+    }
+}
+
+
+template struct jit_uni_pooling_fwd_t<sse42>;
+template struct jit_uni_pooling_bwd_t<sse42>;
+template struct jit_uni_pooling_fwd_t<avx>;
+template struct jit_uni_pooling_bwd_t<avx>;
+template struct jit_uni_pooling_fwd_t<avx512_common>;
+template struct jit_uni_pooling_bwd_t<avx512_common>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pooling.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pooling.hpp
new file mode 100644
index 0000000000..57bebacdee
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_pooling.hpp
@@ -0,0 +1,182 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_JIT_UNI_POOLING_HPP
+#define CPU_JIT_UNI_POOLING_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_pooling_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "jit_uni_pool_kernel_f32.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <cpu_isa_t isa>
+struct jit_uni_pooling_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_pooling_fwd_pd_t {
+        using cpu_pooling_fwd_pd_t::cpu_pooling_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", isa, ""),
+                jit_uni_pooling_fwd_t<isa>);
+
+        status_t init() {
+            using namespace utils;
+
+            bool ok = true
+                && set_default_params() == status::success
+                && is_fwd()
+                && !has_zero_dim_memory()
+                && everyone_is(data_type::f32,
+                        src_md()->data_type,
+                        dst_md()->data_type)
+                && attr()->has_default_values()
+                && memory_desc_matches_tag(*src_md(), desired_fmt_tag())
+                && memory_desc_matches_tag(*dst_md(), desired_fmt_tag());
+            if (!ok) return status::unimplemented;
+
+            bool is_training = desc_.prop_kind == prop_kind::forward_training;
+            if (desc()->alg_kind == alg_kind::pooling_max && is_training)
+                init_default_ws();
+
+            return jit_uni_pool_kernel_f32<isa>::init_conf(jpp_, this);
+        }
+
+        format_tag_t desired_fmt_tag() {
+            using namespace format_tag;
+            return ndims() == 4
+                ? isa == avx512_common ? nChw16c : nChw8c
+                : isa == avx512_common ? nCdhw16c : nCdhw8c;
+        }
+
+        jit_pool_conf_t jpp_;
+    };
+
+    jit_uni_pooling_fwd_t(const pd_t *apd): cpu_primitive_t(apd)
+    { kernel_ = new jit_uni_pool_kernel_f32<isa>(pd()->jpp_); }
+
+    ~jit_uni_pooling_fwd_t() { delete kernel_; }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+        auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+        auto ws = CTX_OUT_MEM(char *, MKLDNN_ARG_WORKSPACE);
+
+        if (pd()->ndims() == 5)
+            execute_forward_3d(src, dst, ws);
+        else
+            execute_forward(src, dst, ws);
+
+        return status::success;
+    }
+
+private:
+    void execute_forward(const data_t *src, data_t *dst, char *indices) const;
+    void execute_forward_3d(const data_t *src, data_t *dst,
+            char *indices) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    jit_uni_pool_kernel_f32<isa> *kernel_;
+};
+
+template <cpu_isa_t isa>
+struct jit_uni_pooling_bwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_pooling_bwd_pd_t {
+        using cpu_pooling_bwd_pd_t::cpu_pooling_bwd_pd_t;
+
+        DECLARE_COMMON_PD_T(
+                JIT_IMPL_NAME_HELPER("jit:", isa, ""),
+                jit_uni_pooling_bwd_t<isa>);
+
+        status_t init() {
+            using namespace utils;
+
+            bool ok = true
+                && set_default_params() == status::success
+                && !is_fwd()
+                && !has_zero_dim_memory()
+                && everyone_is(data_type::f32,
+                        diff_src_md()->data_type,
+                        diff_dst_md()->data_type)
+                && attr()->has_default_values()
+                && memory_desc_matches_tag(*diff_dst_md(), desired_fmt_tag())
+                && memory_desc_matches_tag(*diff_src_md(), desired_fmt_tag());
+            if (!ok) return status::unimplemented;
+
+            if (desc()->alg_kind == alg_kind::pooling_max) {
+                init_default_ws();
+                if (!compare_ws(hint_fwd_pd_))
+                    return status::unimplemented;
+            }
+
+            return jit_uni_pool_kernel_f32<isa>::init_conf(jpp_, this);
+        }
+
+        format_tag_t desired_fmt_tag() {
+            using namespace format_tag;
+            return ndims()
+                ? isa == avx512_common ? nChw16c : nChw8c
+                : isa == avx512_common ? nCdhw16c : nCdhw8c;
+        }
+
+        jit_pool_conf_t jpp_;
+    };
+
+    jit_uni_pooling_bwd_t(const pd_t *apd): cpu_primitive_t(apd)
+    { kernel_ = new jit_uni_pool_kernel_f32<isa>(pd()->jpp_); }
+
+    ~jit_uni_pooling_bwd_t() { delete kernel_; }
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+        auto ws = CTX_IN_MEM(const char *, MKLDNN_ARG_WORKSPACE);
+        auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+        if (pd()->ndims() == 5)
+            execute_backward_3d(diff_dst, ws, diff_src);
+        else
+            execute_backward(diff_dst, ws, diff_src);
+
+        return status::success;
+    }
+
+private:
+    void execute_backward(const data_t *diff_dst, const char *indices,
+            data_t *diff_src) const;
+    void execute_backward_3d(const data_t *diff_dst, const char *indices,
+            data_t *diff_src) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    jit_uni_pool_kernel_f32<isa> *kernel_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder.cpp
new file mode 100644
index 0000000000..98796503b7
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder.cpp
@@ -0,0 +1,1006 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory_desc_wrapper.hpp"
+#include "mkldnn_debug.h"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+
+#include "cpu_primitive.hpp"
+#include "cpu_reorder_pd.hpp"
+#include "jit_uni_reorder.hpp"
+
+#include "jit_generator.hpp"
+
+// #define TR_DEBUG
+#if defined(TR_DEBUG)
+#define DEBUg(...) do { __VA_ARGS__ } while (0)
+#else
+#define DEBUg(...)
+#endif
+#define DEBUG(...) DEBUg(__VA_ARGS__)
+
+#ifdef _WIN32
+/* seems like s_addr is a reserved macro on Windows */
+#undef s_addr
+#endif
+
+using namespace Xbyak;
+using namespace mkldnn::impl::types;
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace tr {
+
+/** Minimal reasonable/desirable kernel size.
+ * The constant might be used to determine how a problem should be split
+ * between kernel and threading driver. */
+const size_t ker_prb_size_min = 64;
+
+/* kernel */
+struct jit_uni_reorder_kernel_f32: public kernel_t, public jit_generator {
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_reorder_kernel_f32)
+
+    enum {
+        len_unroll_max = 256,
+        ndims_jit_loop_max = 3,
+    };
+
+    struct simple_impl_desc_t {
+        int ndims_full_unroll;
+        int len_last_dim_unroll;
+        int len_unroll;
+    };
+
+    static bool simple_impl_desc_init(const prb_t &prb,
+            simple_impl_desc_t *desc) {
+        const int ndims = prb.ndims;
+
+        int ndims_full_unroll = 0;
+        int len_last_dim_unroll = 1;
+        int len_unroll = 1;
+
+        for (int d = 0; d < ndims; ++d) {
+            auto &node = prb.nodes[d];
+            if (len_unroll * node.n <= len_unroll_max) {
+                ndims_full_unroll++;
+                len_unroll *= node.n;
+            } else {
+                len_last_dim_unroll = len_unroll_max / len_unroll;
+                while (node.n % len_last_dim_unroll)
+                    --len_last_dim_unroll;
+                len_unroll *= len_last_dim_unroll;
+                break;
+            }
+        }
+
+        if (prb.ndims - ndims_full_unroll > ndims_jit_loop_max)
+            return false;
+
+        if (desc) {
+            desc->ndims_full_unroll = ndims_full_unroll;
+            desc->len_last_dim_unroll = len_last_dim_unroll;
+            desc->len_unroll = len_unroll;
+        }
+
+        return true;
+    }
+
+    static bool applicable(const prb_t &p) {
+        using namespace data_type;
+
+        bool ok = true
+            && p.ndims > 0
+            && utils::one_of(p.itype, f32, s32, s8, u8)
+            && utils::one_of(p.otype, f32, s32, s8, u8)
+            && utils::everyone_is(0, p.ioff, p.ooff) /* do we need this? */
+            && utils::one_of(p.beta, 0.f, 1.f) /* anything else? */
+            && simple_impl_desc_init(p, nullptr)
+            && mayiuse(sse42)
+            && IMPLICATION(!utils::everyone_is(f32, p.itype, p.otype),
+                    mayiuse(avx));
+        if (!ok) return false;
+
+        const ptrdiff_t max_stride = (1LL<<31) - 1;
+        for (int d = 0; d < p.ndims; ++d) {
+            const ptrdiff_t cms = max_stride / p.nodes[d].n;
+            bool strides_ok = true
+                && p.nodes[d].is < cms / (int)data_type_size(p.itype)
+                && p.nodes[d].os < cms / (int)data_type_size(p.otype);
+            if (!strides_ok) return false;
+        }
+
+        return true;
+    }
+
+    int n(int d) { assert(d < prb_.ndims); return (int)prb_.nodes[d].n; }
+    int is(int d) { assert(d < prb_.ndims); return (int)prb_.nodes[d].is; }
+    int os(int d) { assert(d < prb_.ndims); return (int)prb_.nodes[d].os; }
+    int ss(int d) { assert(d < prb_.ndims); return (int)prb_.nodes[d].ss; }
+
+    Address i_addr(int i_off)
+    { return ptr[reg_ptr_in + reg_off_in + i_off * itype_sz]; }
+
+    Address o_addr(int o_off)
+    { return ptr[reg_ptr_out + reg_off_out + o_off * otype_sz]; }
+
+    Address s_addr(int s_off)
+    { return ptr[reg_ptr_scale + reg_off_scale + s_off * stype_sz]; }
+
+    void step(int off, int prev_i_off, int prev_o_off, int prev_s_off,
+            int &i_off, int &o_off, int &s_off, int step_size = 1) {
+        i_off = prev_i_off;
+        o_off = prev_o_off;
+        s_off = prev_s_off;
+
+        if (off == 0) return;
+
+        int start_dim = 0, dims_prod = 1;
+        for (; start_dim < prb_.ndims && dims_prod != step_size; ++start_dim)
+            dims_prod *= n(start_dim);
+        assert(start_dim < prb_.ndims);
+        off /= step_size;
+
+        for (int d = start_dim; d < prb_.ndims; ++d) {
+            i_off += is(d);
+            o_off += os(d);
+            s_off += ss(d);
+
+            if (off % n(d)) break;
+
+            i_off += - n(d) * is(d);
+            o_off += - n(d) * os(d);
+            s_off += - n(d) * ss(d);
+            off /= n(d);
+
+            if (off == 0) break; /* FIXME: is it really required? */
+        }
+    }
+
+    void step(int off, int prev_i_off, int prev_o_off, int &i_off, int &o_off,
+            int step_size = 1) {
+        int dummy = 0;
+        step(off, prev_i_off, prev_o_off, dummy, i_off, o_off, dummy,
+                step_size);
+    }
+
+    void tr8x8_avx2(int i_off, int o_off) {
+        for (int i = 0; i < 8; i++)
+            vmovups(Ymm(i), i_addr(i_off + i * 8));
+
+        for (int i = 0; i < 8 / 2; i++) {
+            vunpcklps(Ymm(8 + i), Ymm(2 * i), Ymm(2 * i + 1));
+            vunpckhps(Ymm(i), Ymm(2 * i), Ymm(2 * i + 1));
+        }
+
+        const unsigned int lfloat = 0x44;
+        const unsigned int ufloat = 0xee;
+        for (int i = 0; i < 8 / 2; i++) {
+            int j = i % 2 == 0 ? 8 + i : i - 1;
+            vshufps(Ymm(8 / 2 + 2 * i), Ymm(j), Ymm(j + 1), lfloat);
+            vshufps(Ymm(8 / 2 + 2 * i + 1), Ymm(j), Ymm(j + 1), ufloat);
+        }
+
+        const unsigned int lquad = 0x20;
+        for (int i = 0; i < 8 / 2; i++)
+            vperm2f128(Ymm(i), Ymm(8 / 2 + i), Ymm(8 + i), lquad);
+
+        const unsigned int uquad = 0x31;
+        for (int i = 8 / 2; i < 8; i++)
+            vperm2f128(Ymm(i), Ymm(i), Ymm(8 / 2 + i), uquad);
+
+        for (int i = 0; i < 8; i++)
+            vmovups(o_addr(o_off + i * 8), Ymm(i));
+    }
+
+    bool process_unroll_tr8x8(int len) {
+        bool can_do = true
+            && mayiuse(avx2)
+            && prb_.ndims >= 2
+            && utils::everyone_is(4, itype_sz, otype_sz)
+            && utils::everyone_is(8, n(0), n(1))
+            && utils::everyone_is(1, os(0), is(1))
+            && utils::everyone_is(8, os(1), is(0))
+            && prb_.scale_type == scale_type_t::NONE
+            && prb_.beta == 0.f;
+        if (!can_do) return false;
+
+        const int step_size = n(0) * n(1);
+        int i_off = 0, o_off = 0;
+        for (int off = 0; off < len; off += step_size) {
+            step(off, i_off, o_off, i_off, o_off, step_size);
+            tr8x8_avx2(i_off, o_off);
+        }
+
+        return true;
+    }
+
+    template <cpu_isa_t isa>
+    bool process_direct_copy(int len) {
+        using namespace data_type;
+
+        using Vmm = typename cpu_isa_traits<isa>::Vmm;
+        const int simd_w = cpu_isa_traits<isa>::vlen / itype_sz;
+
+        bool can_do = true
+            && mayiuse(isa)
+            && utils::everyone_is(1, os(0), is(0))
+            && (false
+                    || prb_.itype == prb_.otype
+                    || (prb_.itype == s32 && prb_.otype == f32)
+                    || (prb_.itype == f32 && prb_.otype == s32)
+                    )
+            && len % simd_w == 0
+            && n(0) % len == 0
+            && prb_.scale_type == scale_type_t::NONE
+            && prb_.beta == 0.f;
+        if (!can_do) return false;
+
+        for (int off = 0; off < len;) {
+            const int unroll = nstl::min(16, (len - off) / simd_w);
+
+            for (int ur = 0; ur < unroll; ++ur)
+                uni_vmovups(Vmm(ur), i_addr(off + ur * simd_w));
+
+            if (prb_.itype != prb_.otype) {
+                for (int ur = 0; ur < unroll; ++ur) {
+                    if (prb_.itype == s32 && prb_.otype == f32)
+                        uni_vcvtdq2ps(Vmm(ur), Vmm(ur));
+                    else if (prb_.itype == f32 && prb_.otype == s32)
+                        uni_vcvtps2dq(Vmm(ur), Vmm(ur));
+                    else assert(!"unreachable");
+                }
+            }
+
+            for (int ur = 0; ur < unroll; ++ur)
+                uni_vmovups(o_addr(off + ur * simd_w), Vmm(ur));
+
+            off += unroll * simd_w;
+        }
+
+        return true;
+    }
+
+    void process_unroll_generic_step(int reg_unroll, const int *i_off,
+            const int *o_off, const int *s_off) {
+        using namespace data_type;
+
+        auto cvt2ps = [=](const Xmm &dst, const Operand &src, data_type_t idt) {
+            Xmm dst_pure = Xmm(dst.getIdx());
+            switch (idt) {
+            case f32:
+                if (src.isMEM() || src.getIdx() != dst.getIdx())
+                    vmovups(dst, src);
+                break;
+            case s32: vcvtdq2ps(dst, src); break;
+            case s8: vpmovsxbd(dst, src); vcvtdq2ps(dst_pure, dst); break;
+            case u8: vpmovzxbd(dst, src); vcvtdq2ps(dst_pure, dst); break;
+            default: assert(!"unreachable");
+            }
+        };
+
+        auto cvt2int = [=](const Xmm &xmm, data_type_t odt, data_type_t idt) {
+            switch (odt) {
+            case s32:
+                if (idt == f32) vcvtps2dq(xmm, xmm);
+                else if (idt == s8) vpmovsxbd(xmm, xmm);
+                else if (idt == u8) vpmovzxbd(xmm, xmm);
+                break;
+            case s8:
+                if (idt == f32) vcvtps2dq(xmm, xmm);
+                if (idt == f32 || idt == s32) {
+                    if (mayiuse(avx512_core)) {
+                        vpmovsdb(xmm, xmm);
+                    } else {
+                        vpackssdw(xmm, xmm, xmm_zero);
+                        vpacksswb(xmm, xmm, xmm_zero);
+                    }
+                }
+                if (idt == u8) vpminub(xmm, xmm, xmm_4x127b);
+                break;
+            case u8:
+                if (idt == f32) vcvtps2dq(xmm, xmm);
+                if (idt == f32 || idt == s32) {
+                    if (mayiuse(avx512_core)) {
+                        vpmaxsd(xmm, xmm, xmm_zero);
+                        vpmovusdb(xmm, xmm);
+                    } else {
+                        vpackssdw(xmm, xmm, xmm_zero);
+                        vpackuswb(xmm, xmm, xmm_zero);
+                    }
+                }
+                if (idt == s8) vpmaxsb(xmm, xmm, xmm_zero);
+                break;
+            default: assert(!"unreachable");
+            }
+        };
+
+        auto load = [=](const Xmm &xmm, const Address &addr, int size) {
+            switch (size) {
+            case 16: movups(xmm, addr); break;
+            case 4: movss(xmm, addr); break;
+            case 1: pinsrb(xmm, addr, 0x0); break;
+            default: assert(!"unreachable");
+            }
+        };
+
+        auto store = [=](const Address &addr, const Xmm &xmm, int size) {
+            switch (size) {
+            case 16: movups(addr, xmm); break;
+            case 4: movss(addr, xmm); break;
+            case 1: pextrb(addr, xmm, 0x0); break;
+            default: assert(!"unreachable");
+            }
+        };
+
+        /* check whether loading 4 values at once is possible */
+        bool can_load_xmm = mayiuse(avx) && reg_unroll % 4 == 0;
+        for (int ur = 1; ur < reg_unroll; ++ur)
+            if (i_off[ur] != i_off[ur - 1] + 1)
+                can_load_xmm = false;
+        const int load_step = can_load_xmm ? 4 : 1;
+
+        /* check whether storing 4 values at once is possible */
+        bool can_store_xmm = reg_unroll % 4 == 0;
+        for (int ur = 1; ur < reg_unroll; ++ur)
+            if (o_off[ur] != o_off[ur - 1] + 1)
+                can_store_xmm = false;
+        const int ur_step = can_store_xmm ? 4 : 1;
+
+        const bool interim_f32 = false
+            || utils::one_of(f32, prb_.itype, prb_.otype)
+            || prb_.scale_type != scale_type_t::NONE
+            || prb_.beta != 0.f;
+
+        if (!can_load_xmm && can_store_xmm) {
+            assert(ur_step == 4);
+            /* load with stride */
+            for (int ur = 0; ur < reg_unroll; ur += ur_step) {
+                for (int r = 0; r < ur_step; ++r) {
+                    if (itype_sz == 4)
+                        pinsrd(Xmm(ur), i_addr(i_off[ur + r]), r);
+                    else
+                        pinsrb(Xmm(ur), i_addr(i_off[ur + r]), r);
+                }
+            }
+        } else {
+            for (int ur = 0; ur < reg_unroll; ur += load_step)
+                load(Xmm(ur), i_addr(i_off[ur]), load_step * itype_sz);
+        }
+
+        /* xmm[:] <-- (f32)xmm[:] */
+        if (interim_f32) {
+            const int cvt_step = nstl::max(load_step, ur_step);
+            for (int ur = 0; ur < reg_unroll; ur += cvt_step)
+                cvt2ps(Xmm(ur), Xmm(ur), prb_.itype);
+        }
+
+        if (can_load_xmm && !can_store_xmm) {
+            const bool fast_return = true // transposition on the fly
+                && prb_.scale_type != scale_type_t::MANY
+                && prb_.beta == 0.f;
+            if (fast_return) {
+                for (int ur = 0; ur < reg_unroll; ur += load_step) {
+                    if (prb_.scale_type == scale_type_t::COMMON)
+                        mulps(Xmm(ur), xmm_scale);
+                    if (prb_.otype != f32)
+                        cvt2int(Xmm(ur), prb_.otype,
+                                interim_f32 ? f32 : prb_.itype);
+                    for (int r = 0; r < load_step; ++r) {
+                        if (otype_sz == 4)
+                            pextrd(o_addr(o_off[ur + r]), Xmm(ur), r);
+                        else
+                            pextrb(o_addr(o_off[ur + r]), Xmm(ur), r);
+                    }
+                }
+                return;
+            }
+
+            /* scatter elements of xmm into 4 xmms */
+            if (itype_sz == 4 || interim_f32) {
+                for (int ur = 0; ur < reg_unroll; ur += load_step)
+                    for (int r = 1; r < load_step; ++r)
+                        vshufps(Xmm(ur + r), Xmm(ur), Xmm(ur), r);
+            } else {
+                for (int ur = 0; ur < reg_unroll; ur += load_step)
+                    for (int r = 1; r < load_step; ++r)
+                        vpalignr(Xmm(ur + r), Xmm(ur), Xmm(ur), r);
+            }
+        }
+
+        /* scale and beta processing */
+        if (can_store_xmm) {
+            /* xmm <-- scale * xmm[:] */
+            if (prb_.scale_type == scale_type_t::COMMON) {
+                for (int ur = 0; ur < reg_unroll; ur += ur_step)
+                    mulps(Xmm(ur), xmm_scale);
+            } else if (prb_.scale_type == scale_type_t::MANY) {
+                enum class scale_load_type_t { bcast, load, gather };
+
+                for (int ur = 0; ur < reg_unroll; ur += ur_step) {
+                    scale_load_type_t scale_load_type =
+                        scale_load_type_t::bcast; // the best case
+
+                    for (int r = ur + 1; r < ur + ur_step; ++r)
+                        if (s_off[r] != s_off[r - 1] + 0)
+                            scale_load_type = scale_load_type_t::load;
+
+                    if (scale_load_type == scale_load_type_t::bcast) {
+                        movss(xmm_scale, s_addr(s_off[ur]));
+                        shufps(xmm_scale, xmm_scale, 0x0);
+                        mulps(Xmm(ur), xmm_scale);
+                        continue;
+                    }
+
+                    // bcast doesn't work, the next try -- load
+                    for (int r = ur + 1; r < ur + ur_step; ++r)
+                        if (s_off[r] != s_off[r - 1] + 1)
+                            scale_load_type = scale_load_type_t::gather;
+
+                    if (scale_load_type == scale_load_type_t::load) {
+                        movups(xmm_scale, s_addr(s_off[ur]));
+                        mulps(Xmm(ur), xmm_scale);
+                        continue;
+                    }
+
+                    // load doesn't work as well
+                    // so gather the scale factors one by one
+                    for (int r = ur; r < ur + ur_step; ++r)
+                        pinsrd(xmm_scale, s_addr(s_off[r]), r - ur);
+                    mulps(Xmm(ur), xmm_scale);
+                }
+            }
+
+            /* dst <-- beta * dst + xmm[:] */
+            assert(prb_.beta == 0.f || prb_.beta == 1.f);
+            if (prb_.beta == 1.f) {
+                for (int ur = 0; ur < reg_unroll; ur += ur_step) {
+                    if (prb_.otype == f32) {
+                        /* non VEX instructions do not support unaligned
+                         * memory for instructions other than movups. */
+                        if (mayiuse(avx)) {
+                            vaddps(Xmm(ur), o_addr(o_off[ur]));
+                        } else {
+                            /* register xmm(1) is unused */
+                            movups(Xmm(1), o_addr(o_off[ur]));
+                            addps(Xmm(ur), Xmm(1));
+                        }
+                    } else {
+                        cvt2ps(Xmm(1), o_addr(o_off[ur]), prb_.otype);
+                        vaddps(Xmm(ur), Xmm(1));
+                    }
+                }
+            }
+        } else {
+            /* xmm[0] <-- scale * xmm[0] */
+            if (prb_.scale_type == scale_type_t::COMMON) {
+                for (int ur = 0; ur < reg_unroll; ur += ur_step)
+                    mulss(Xmm(ur), xmm_scale);
+            } else if (prb_.scale_type == scale_type_t::MANY) {
+                for (int ur = 0; ur < reg_unroll; ur += ur_step) {
+                    mulss(Xmm(ur), s_addr(s_off[ur]));
+                }
+            }
+
+            /* dst <-- beta * dst + xmm[0] */
+            assert(prb_.beta == 0.f || prb_.beta == 1.f);
+            if (prb_.beta == 1.f) {
+                for (int ur = 0; ur < reg_unroll; ur += ur_step) {
+                    if (prb_.otype == f32) {
+                        addss(Xmm(ur), o_addr(o_off[ur]));
+                    } else {
+                        if (prb_.otype == s32) {
+                            vmovss(xmm_tmp, o_addr(o_off[ur]));
+                        } else if (utils::one_of(prb_.otype, s8, u8)) {
+                            pinsrb(xmm_tmp, o_addr(o_off[ur]), 0x0);
+                        } else {
+                            assert(!"unsupported o_type");
+                        }
+                        cvt2ps(xmm_tmp, xmm_tmp, prb_.otype);
+                        addps(Xmm(ur), xmm_tmp);
+                    }
+                }
+            }
+        }
+
+        for (int ur = 0; ur < reg_unroll; ur += ur_step) {
+            if (prb_.otype != f32)
+                cvt2int(Xmm(ur), prb_.otype, interim_f32 ? f32 : prb_.itype);
+            store(o_addr(o_off[ur]), Xmm(ur), ur_step * otype_sz);
+        }
+    }
+
+    void process_unroll_generic(int len) {
+        const int blk = 8;
+
+        int i_off[2 * blk] = {0};
+        int o_off[2 * blk] = {0};
+        int s_off[2 * blk] = {0};
+
+        int curr = 0; // will switch between 0 and 1
+
+        for (int off = 0; off < len; off += blk) {
+            const int reg_unroll = nstl::min(off + blk, len) - off;
+
+            /* compute offsets */
+            for (int ur = off != 0 ? 0 : 1; ur < reg_unroll; ++ur) {
+                const int ur_c = curr * blk + ur;
+                const int ur_p = (ur_c - 1 + 2 * blk) % (2 * blk); // prev ur
+                step(off + ur,
+                        i_off[ur_p], o_off[ur_p], s_off[ur_p],
+                        i_off[ur_c], o_off[ur_c], s_off[ur_c]);
+            }
+
+            process_unroll_generic_step(reg_unroll, i_off + curr * blk,
+                    o_off + curr * blk, s_off + curr * blk);
+
+            curr = 1 - curr;
+        }
+    }
+
+    void loop_begin(Label &l, Reg64 reg_cnt, int len) {
+        mov(reg_cnt, len);
+        L(l);
+    }
+
+    void loop_end(Label &l, Reg64 reg_cnt, int len,
+            int i_step, int o_step, int s_step) {
+        add(reg_off_in, i_step * itype_sz);
+        add(reg_off_out, o_step * otype_sz);
+        if (prb_.scale_type == scale_type_t::MANY)
+            add(reg_off_scale, s_step * stype_sz);
+        dec(reg_cnt);
+        jnz(l);
+
+        sub(reg_off_in, len * i_step * itype_sz);
+        sub(reg_off_out, len * o_step * otype_sz);
+        if (prb_.scale_type == scale_type_t::MANY)
+            sub(reg_off_scale, len * s_step * stype_sz);
+    }
+
+    bool simple_impl() {
+        simple_impl_desc_t d;
+        if (!simple_impl_desc_init(prb_, &d)) return false;
+
+        const int nfu = d.ndims_full_unroll;
+        const int ldu = d.len_last_dim_unroll;
+        const int n_jit_loops = prb_.ndims - d.ndims_full_unroll;
+        assert(n_jit_loops <= ndims_jit_loop_max);
+
+        xor_(reg_off_in, reg_off_in);
+        xor_(reg_off_out, reg_off_out);
+        if (prb_.scale_type == scale_type_t::MANY)
+            xor_(reg_off_scale, reg_off_scale);
+
+        Label l_loop[3];
+        Reg64 reg_cnt[3] = {r15, r14, r13};
+
+        if (n_jit_loops > 2)
+            loop_begin(l_loop[2], reg_cnt[2], n(nfu + 2));
+
+        if (n_jit_loops > 1)
+            loop_begin(l_loop[1], reg_cnt[1], n(nfu + 1));
+
+        if (n_jit_loops > 0)
+            loop_begin(l_loop[0], reg_cnt[0], n(nfu + 0) / ldu);
+
+        const bool optimized = false
+            || process_direct_copy<avx>(d.len_unroll)
+            || process_direct_copy<sse42>(d.len_unroll)
+            || process_unroll_tr8x8(d.len_unroll);
+        if (!optimized)
+            process_unroll_generic(d.len_unroll);
+
+        if (n_jit_loops > 0)
+            loop_end(l_loop[0], reg_cnt[0],
+                    n(nfu + 0) / ldu, is(nfu + 0) * ldu, os(nfu + 0) * ldu,
+                    ss(nfu + 0) * ldu);
+
+        if (n_jit_loops > 1)
+            loop_end(l_loop[1], reg_cnt[1],
+                    n(nfu + 1), is(nfu + 1), os(nfu + 1), ss(nfu + 1));
+
+        if (n_jit_loops > 2)
+            loop_end(l_loop[2], reg_cnt[2],
+                    n(nfu + 2), is(nfu + 2), os(nfu + 2), ss(nfu + 2));
+
+        return true;
+    }
+
+    void impl() {
+        if (simple_impl()) return;
+        assert(!"no implementation available");
+    }
+
+    jit_uni_reorder_kernel_f32(const desc_t &desc)
+        : kernel_t(desc), jit_generator() {
+        itype_sz = data_type_size(prb_.itype);
+        otype_sz = data_type_size(prb_.otype);
+        stype_sz = sizeof(float);
+
+        preamble();
+#       define PARAM(x) ptr[abi_param1 + offsetof(call_param_t, x)]
+        if (prb_.scale_type == scale_type_t::COMMON) {
+            auto reg_ptr_scale_tmp = reg_ptr_in;
+            mov(reg_ptr_scale_tmp, PARAM(scale));
+            movups(xmm_scale, ptr[reg_ptr_scale_tmp]);
+        } else if (prb_.scale_type == scale_type_t::MANY) {
+            mov(reg_ptr_scale, PARAM(scale));
+        }
+        mov(reg_ptr_in, PARAM(in));
+        mov(reg_ptr_out, PARAM(out));
+#       undef PARAM
+
+        if (mayiuse(avx)) {
+            vxorps(xmm_zero, xmm_zero, xmm_zero);
+
+            if (prb_.itype == data_type::u8 && prb_.otype == data_type::s8) {
+                mov(reg_tmp.cvt32(), 0x7f7f7f7f);
+                movd(xmm_4x127b, reg_tmp.cvt32());
+            }
+        }
+
+        impl();
+        postamble();
+        ker_ = (void (*)(const call_param_t *))getCode();
+    }
+
+private:
+    int itype_sz;
+    int otype_sz;
+    int stype_sz;
+
+    Reg64 reg_ptr_in = rsi;
+    Reg64 reg_ptr_out = rdx;
+    Reg64 reg_ptr_scale = abi_not_param1;
+
+    Reg64 reg_off_in = r8;
+    Reg64 reg_off_out = r9;
+    Reg64 reg_off_scale = r10;
+
+    Reg64 reg_tmp = rax;
+
+    Xmm xmm_scale = xmm15;
+    Xmm xmm_zero = xmm14;
+    Xmm xmm_4x127b = xmm13; // TODO: unite with xmm_zero
+    Xmm xmm_tmp = xmm12;
+};
+
+status_t kernel_t::desc_init(kernel_t::desc_t &desc, const prb_t &prb,
+        int ndims_ker_max) {
+    desc.prb = prb;
+    desc.prb.ioff = desc.prb.ooff = 0;
+
+    if (ndims_ker_max > prb.ndims)
+        return status::invalid_arguments;
+
+    auto ndims_ker_max_f = [&]() {
+        size_t cur_size = 1;
+        for (int d = 0; d < prb.ndims; cur_size *= prb.nodes[d++].n)
+            if (cur_size >= ker_prb_size_min) return d;
+        return prb.ndims;
+    };
+
+    if (ndims_ker_max <= 0)
+        ndims_ker_max = ndims_ker_max_f();
+
+    /* traverse through kernel implementations */
+    /* TODO: find a better way to do that... */
+    desc.id = 0;
+    for (int ndims_ker = ndims_ker_max; ndims_ker > 0; --ndims_ker) {
+        desc.prb.ndims = ndims_ker;
+        if (jit_uni_reorder_kernel_f32::applicable(desc.prb))
+            return status::success;
+    }
+
+    return status::unimplemented;
+}
+
+kernel_t *kernel_t::create(const kernel_t::desc_t &desc) {
+    switch (desc.id) {
+    case 0: return new jit_uni_reorder_kernel_f32(desc);
+    default: assert(!"unknown kernel id"); return nullptr;
+    }
+
+    return nullptr;
+}
+
+}
+
+static void prb_block_for_cache(tr::prb_t &prb) {
+    if (prb.nodes[0].is % 64 == 0 && prb.nodes[0].n > 16) {
+        /** an attempt to use caches more efficient and
+         * address the 4K-aliasing issue */
+        /* TODO: improve the logic around here */
+        int j = 1;
+        for (; j < prb.ndims && prb.nodes[j].is != 1; ++j);
+        if (j == prb.ndims) return;
+
+        /* it makes sense to re-prioritize sequential read over
+         * sequential write if the former would not trash the
+         * cache, i.e. is == 1 and os % 2^smth != 0. Smth is
+         * set to 2 at the moment */
+        const int move_to = prb.nodes[j].os % 4 != 0 ? 0 : 1;
+        if (j == move_to) return;
+
+        if (prb.nodes[j].n > 16 && prb.nodes[j].n % 16 == 0)
+            prb_node_split(prb, j, 16);
+
+        prb_node_move(prb, j, move_to);
+        DEBUG({ printf("cache: "); prb_dump(prb); });
+    }
+}
+
+/** finds the maximum number of dimension the kernel should process and
+ * optionally splits one of the dimension to achieve better balance between
+ * parallel driver and the kernel. */
+static void prb_thread_kernel_balance(tr::prb_t &prb, int &ndims_ker_max) {
+    size_t sz_total = 1;
+    for (int d = 0; d < prb.ndims; ++d)
+        sz_total *= prb.nodes[d].n;
+
+    /* sz_drv_min is the minimal size for the parallel
+     * driver required for good parallelization */
+    const size_t sz_drv_min = nstl::min<size_t>(
+            16 * mkldnn_get_max_threads(),
+            utils::div_up(sz_total, 1024));
+
+    /* kdims -- # of dimensions processed by a kernel
+     * sz_ker_cur -- product of the dimension processed by a kernel
+     * sz_drv_cur -- product of the dimension processed by a driver */
+
+    int kdims = prb.ndims;
+    size_t sz_drv_cur = 1;
+    for (; kdims > 1 && sz_drv_cur < sz_drv_min; --kdims)
+        sz_drv_cur *= prb.nodes[kdims - 1].n;
+
+    size_t sz_ker_cur = 1;
+    for (int d = 0; d < kdims; ++d)
+        sz_ker_cur *= prb.nodes[d].n;
+
+    /* Initially kdims is chosen so that sz_drv_cur >= sz_drv_min.
+     *
+     * It might happen that for chosen kdims the sz_ker_cur is too small
+     * (less than tr::ker_prb_size_min). In that case try to split the
+     * innermost driver dimension into two, to increase sz_ker_cur. */
+    bool want_borrow_ker_from_drv = true
+        && kdims < prb.ndims
+        && sz_ker_cur < tr::ker_prb_size_min
+        && sz_drv_cur > sz_drv_min;
+    if (want_borrow_ker_from_drv) {
+        /* sz_want_borrow is the minimal sz, so that:
+         *  o) sz_ker_cur * sz_want_borrow >= tr::ker_prb_size_min
+         *  o) current innermost driver dimension is divisible by
+         *     sz_want_borrow (so that we can evenly split that
+         *     dimension into two)
+         *
+         *  In the worst case the minimal sz_want_borrow is equal
+         *  to the innermost driver dimension itself. In that case
+         *  we will sacrifice it in favor of kernel (is it fine?). */
+        size_t sz_want_borrow
+            = utils::div_up(tr::ker_prb_size_min, sz_ker_cur);
+        for (; prb.nodes[kdims].n % sz_want_borrow; ++sz_want_borrow);
+        if (sz_want_borrow != prb.nodes[kdims].n)
+            prb_node_split(prb, kdims, sz_want_borrow);
+        kdims += 1;
+    }
+
+    /* On the other hand it might happen that for chosen kdims
+     * the sz_drv_cur is too small (less than sz_drv_min). In that case
+     * try to split the outermost kernel dimension into two, to increase
+     * sz_drv_cur. */
+    bool want_borrow_drv_from_ker = true
+        && sz_ker_cur > tr::ker_prb_size_min
+        && sz_drv_cur < sz_drv_min;
+    if (want_borrow_drv_from_ker) {
+        size_t sz_want_borrow = utils::div_up(sz_drv_min, sz_drv_cur);
+        for (; prb.nodes[kdims - 1].n % sz_want_borrow; ++sz_want_borrow);
+        if (sz_want_borrow != prb.nodes[kdims - 1].n)
+            prb_node_split(prb, kdims - 1,
+                    prb.nodes[kdims - 1].n / sz_want_borrow);
+    }
+
+    ndims_ker_max = kdims;
+
+    if (want_borrow_ker_from_drv || want_borrow_drv_from_ker) {
+        DEBUG({ printf("split: "); prb_dump(prb);
+                printf("ndims_ker_max = %d\n", ndims_ker_max); });
+    }
+}
+
+struct jit_uni_reorder_t : public cpu_primitive_t {
+    struct pd_t : public cpu_reorder_pd_t {
+        using cpu_reorder_pd_t::cpu_reorder_pd_t;
+
+        DECLARE_COMMON_PD_T("jit:uni", jit_uni_reorder_t);
+
+        static status_t create(reorder_pd_t **reorder_pd,
+                engine_t *engine, const primitive_attr_t *attr,
+                engine_t *src_engine, const memory_desc_t *src_md,
+                engine_t *dst_engine, const memory_desc_t *dst_md) {
+            auto prb = tr::prb_t();
+
+            status_t prb_init_status = prb_init(prb, *src_md, *dst_md, attr);
+            if (prb_init_status != status::success) return prb_init_status;
+
+            DEBUG({ printf("init : "); prb_dump(prb); });
+            prb_normalize(prb);
+            DEBUG({ printf("norm : "); prb_dump(prb); });
+            prb_simplify(prb);
+            DEBUG({ printf("smpl : "); prb_dump(prb); });
+
+            prb_block_for_cache(prb);
+
+            int ndims_ker_max;
+            prb_thread_kernel_balance(prb, ndims_ker_max);
+
+            tr::kernel_t::desc_t ker_desc;
+            status_t ker_init_status
+                = tr::kernel_t::desc_init(ker_desc, prb, ndims_ker_max);
+            if (ker_init_status != status::success) return ker_init_status;
+
+            const int ndims_driver = prb.ndims - ker_desc.prb.ndims;
+            if (ndims_driver > jit_uni_reorder_t::ndims_driver_max)
+                return status::unimplemented;
+
+            DEBUG({ printf("ker  : "); prb_dump(ker_desc.prb); });
+
+            auto _pd = new pd_t(engine, attr, src_engine, src_md, dst_engine,
+                    dst_md);
+            if (_pd == nullptr) return status::out_of_memory;
+            if (_pd->init() != status::success) {
+                delete _pd;
+                return status::unimplemented;
+            }
+            _pd->prb_ = prb;
+            _pd->ker_desc_ = ker_desc;
+            return safe_ptr_assign<reorder_pd_t>(*reorder_pd, _pd);
+        }
+
+        tr::prb_t prb_;
+        tr::kernel_t::desc_t ker_desc_;
+    };
+
+    jit_uni_reorder_t(const pd_t *apd): cpu_primitive_t(apd) {
+        kernel_ = tr::kernel_t::create(pd()->ker_desc_);
+        assert(kernel_);
+    }
+    ~jit_uni_reorder_t() { delete kernel_; }
+
+    void omp_driver_0d(int off, const char *in, char *out,
+            const float *scale) const {
+        tr::call_param_t c{in, out, scale};
+        (*kernel_)(&c);
+    }
+
+    void omp_driver_1d(int ithr, int nthr, int off, const char *in, char *out,
+            const float *scale) const {
+        const tr::node_t *ns = pd()->prb_.nodes + off;
+        for_nd(ithr, nthr, (ptrdiff_t)ns[0].n, [&](ptrdiff_t d0) {
+            auto c = tr::call_param_t();
+            c.in = in + d0 * ns[0].is * data_type_size(pd()->prb_.itype);
+            c.out = out + d0 * ns[0].os * data_type_size(pd()->prb_.otype);
+            c.scale = scale + d0 * ns[0].ss;
+            (*kernel_)(&c);
+        });
+    }
+
+    void omp_driver_2d(int ithr, int nthr, int off, const char *in, char *out,
+            const float *scale) const {
+        const tr::node_t *ns = pd()->prb_.nodes + off;
+        for_nd(ithr, nthr, (ptrdiff_t)ns[1].n, (ptrdiff_t)ns[0].n,
+                [&](ptrdiff_t d1, ptrdiff_t d0) {
+            auto c = tr::call_param_t();
+            c.in = in + (d0 * ns[0].is + d1 * ns[1].is)
+                * data_type_size(pd()->prb_.itype);
+            c.out = out + (d0 * ns[0].os + d1 * ns[1].os)
+                * data_type_size(pd()->prb_.otype);
+            c.scale = scale + d0 * ns[0].ss + d1 * ns[1].ss;
+            (*kernel_)(&c);
+        });
+    }
+
+    void omp_driver_3d(int ithr, int nthr, int off, const char *in, char *out,
+            const float *scale) const {
+        const tr::node_t *ns = pd()->prb_.nodes + off;
+        for_nd(ithr, nthr, (ptrdiff_t)ns[2].n, (ptrdiff_t)ns[1].n,
+                (ptrdiff_t)ns[0].n,
+                [&](ptrdiff_t d2, ptrdiff_t d1, ptrdiff_t d0) {
+            auto c = tr::call_param_t();
+            c.in = in + (d0 * ns[0].is + d1 * ns[1].is + d2 * ns[2].is)
+                * data_type_size(pd()->prb_.itype);
+            c.out = out + (d0 * ns[0].os + d1 * ns[1].os + d2 * ns[2].os)
+                * data_type_size(pd()->prb_.otype);
+            c.scale = scale + d0 * ns[0].ss + d1 * ns[1].ss + d2 * ns[2].ss;
+            (*kernel_)(&c);
+        });
+    }
+
+    void omp_driver_4d(int ithr, int nthr, int off, const char *in, char *out,
+            const float *scale) const {
+        const tr::node_t *ns = pd()->prb_.nodes + off;
+        for_nd(ithr, nthr, (ptrdiff_t)ns[3].n, (ptrdiff_t)ns[2].n,
+                (ptrdiff_t)ns[1].n, (ptrdiff_t)ns[0].n,
+                [&](ptrdiff_t d3, ptrdiff_t d2, ptrdiff_t d1, ptrdiff_t d0) {
+            auto c = tr::call_param_t();
+            c.in = in + (d0 * ns[0].is + d1 * ns[1].is + d2 * ns[2].is
+                    + d3 * ns[3].is) * data_type_size(pd()->prb_.itype);
+            c.out = out + (d0 * ns[0].os + d1 * ns[1].os + d2 * ns[2].os
+                    + d3 * ns[3].os) * data_type_size(pd()->prb_.otype);
+            c.scale = scale + d0 * ns[0].ss + d1 * ns[1].ss + d2 * ns[2].ss
+                + d3 * ns[3].ss;
+            (*kernel_)(&c);
+        });
+    }
+
+    void omp_driver(const char *in, char *out, const float *scale) const {
+        in += pd()->prb_.ioff * data_type_size(pd()->prb_.itype);
+        out += pd()->prb_.ooff * data_type_size(pd()->prb_.otype);
+
+        DEBUG({ printf("prb : "); tr::prb_dump(pd()->prb_); });
+        DEBUG({ printf("ker : "); tr::prb_dump(pd()->ker_desc_.prb); });
+
+        int ndims = pd()->prb_.ndims;
+        int ndims_ker = pd()->ker_desc_.prb.ndims;
+        assert(ndims - ndims_ker <= ndims_driver_max);
+
+        if (ndims - ndims_ker == 0) {
+            omp_driver_0d(ndims_ker, in, out, scale);
+        } else {
+            parallel(0, [&](const int ithr, const int nthr) {
+                switch (ndims - ndims_ker) {
+                case 1: omp_driver_1d(ithr, nthr, ndims_ker, in, out, scale); break;
+                case 2: omp_driver_2d(ithr, nthr, ndims_ker, in, out, scale); break;
+                case 3: omp_driver_3d(ithr, nthr, ndims_ker, in, out, scale); break;
+                case 4: omp_driver_4d(ithr, nthr, ndims_ker, in, out, scale); break;
+                default: assert(!"unimplemented");
+                }
+            });
+        }
+    }
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        auto in = CTX_IN_MEM(const char *, MKLDNN_ARG_FROM);
+        auto out = CTX_OUT_MEM(char *, MKLDNN_ARG_TO);
+
+        omp_driver(in, out, pd()->attr()->output_scales_.scales_);
+
+        return status::success;
+    }
+
+    enum { ndims_driver_max = 4 };
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    tr::kernel_t *kernel_;
+};
+
+status_t jit_uni_reorder_create(reorder_pd_t **reorder_pd,
+        engine_t *engine, const primitive_attr_t *attr,
+        engine_t *src_engine, const memory_desc_t *src_md,
+        engine_t *dst_engine, const memory_desc_t *dst_md) {
+    return jit_uni_reorder_t::pd_t::create(reorder_pd, engine, attr,
+            src_engine, src_md, dst_engine, dst_md);
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder.hpp
new file mode 100644
index 0000000000..0746ea61d3
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder.hpp
@@ -0,0 +1,127 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef _JIT_UNI_REORDER_HPP
+#define _JIT_UNI_REORDER_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+
+#include "cpu_primitive.hpp"
+#include "cpu_reorder_pd.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace tr {
+
+constexpr int max_ndims = MKLDNN_MAX_NDIMS;
+
+struct node_t {
+    size_t n;
+    ptrdiff_t is; // input stride
+    ptrdiff_t os; // output stride
+    ptrdiff_t ss; // scale stride
+};
+
+enum class scale_type_t { NONE, COMMON, MANY };
+
+struct prb_t {
+    data_type_t itype;
+    data_type_t otype;
+    int ndims;
+    node_t nodes[max_ndims];
+    ptrdiff_t ioff;
+    ptrdiff_t ooff;
+    scale_type_t scale_type;
+    float beta;
+};
+
+status_t prb_init(prb_t &prb, const memory_desc_t &imd,
+        const memory_desc_t &omd, const primitive_attr_t *attr);
+
+/** sorts the problem nodes so that output strides come in ascending order */
+void prb_normalize(prb_t &p);
+
+/** folds nodes together if possible */
+void prb_simplify(prb_t &p);
+
+/** splits the node dim into two of sizes n1 and n / n1
+ * @warning n must be multiple of n1 */
+void prb_node_split(prb_t &p, int dim, size_t n1);
+
+/** swaps d0 and d1 nodes */
+void prb_node_swap(prb_t &p, int d0, int d1);
+
+/** moves node d0 to the d1 position.
+ * nodes (d0, d1] are shifted to the left if d0 < d1 or
+ * to the right if d0 > d1 */
+void prb_node_move(prb_t &p, int d0, int d1);
+
+/** dumps the problem to stdout */
+void prb_dump(const prb_t &p);
+
+struct call_param_t {
+    const void *in;
+    void *out;
+    const float *scale;
+};
+
+struct kernel_t {
+    struct desc_t {
+        int id;
+        prb_t prb;
+    };
+
+    kernel_t(const desc_t &desc): desc_(desc), ker_(nullptr) {}
+    void operator()(const call_param_t *c) const { assert(ker_); ker_(c); }
+    virtual ~kernel_t() {}
+
+    /** inits kernel descriptor:
+     *      desc            -- kernel descriptor (output)
+     *      prb             -- transposition problem (input)
+     *      ndims_ker_max   -- limit the maximum number of dimensions kernel
+     *                         will process (optional, 0 -- no limitation) */
+    static status_t desc_init(desc_t &desc, const prb_t &prb,
+            int ndims_ker_max = 0);
+
+    /** creates kernel for the problem described in desc */
+    static kernel_t *create(const desc_t &desc);
+
+protected:
+    const desc_t desc_;
+    const prb_t &prb_ = desc_.prb;
+    void (*ker_)(const call_param_t *);
+};
+
+/* TODO: add trans_t class */
+
+}
+
+/* for cpu reorder list */
+status_t jit_uni_reorder_create(reorder_pd_t **reorder_pd,
+        engine_t *engine, const primitive_attr_t *attr,
+        engine_t *src_engine, const memory_desc_t *src_md,
+        engine_t *dst_engine, const memory_desc_t *dst_md);
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder_utils.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder_utils.cpp
new file mode 100644
index 0000000000..69b7a33604
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_uni_reorder_utils.cpp
@@ -0,0 +1,313 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory_desc_wrapper.hpp"
+#include "mkldnn_debug.h"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "jit_uni_reorder.hpp"
+
+using namespace mkldnn::impl::types;
+using namespace mkldnn::impl::status;
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace tr {
+
+/** ad-hoc structure to describe blocked memory layout */
+struct layout_desc_t {
+    data_type_t dt;
+    int ndims;
+    dims_t id;
+    dims_t dims;
+    strides_t strides;
+};
+
+status_t cvt_mem_desc_to_layout_desc(const memory_desc_t &md_,
+        layout_desc_t &ld) {
+    const auto md = memory_desc_wrapper(md_);
+
+    bool ok = true
+        && md.is_blocking_desc()
+        && md.extra().flags == 0;
+    if (!ok) return invalid_arguments;
+
+    const auto &bd = md.blocking_desc();
+
+    ld.ndims = 0;
+    ld.dt = md.data_type();
+
+    auto P = [&ld](int id, int dim, ptrdiff_t stride) {
+        assert((size_t)ld.ndims < sizeof(ld.dims) / sizeof(ld.dims[0]));
+        ld.id[ld.ndims] = id;
+        ld.dims[ld.ndims] = dim;
+        ld.strides[ld.ndims] = stride;
+        ++ld.ndims;
+    };
+
+    dims_t blocks;
+    md.compute_blocks(blocks);
+
+    for (int d = 0; d < md.ndims(); ++d) {
+        const int ld_ndims_start = ld.ndims;
+        if (blocks[d] != 1) {
+            stride_t stride = 1;
+            for (int iblk = bd.inner_nblks - 1; iblk >= 0; --iblk) {
+                if (bd.inner_idxs[iblk] == d)
+                    P(d, bd.inner_blks[iblk], stride);
+                stride *= bd.inner_blks[iblk];
+            }
+        }
+        P(d, md.padded_dims()[d] / blocks[d], bd.strides[d]);
+
+        // TODO: NOW: revisit, do we need a reverse?
+        // TODO: NOW: consider using strides instead of block sizes in md
+        // reverse the order of dims
+        for (int ld_d = 0; ld_d < (ld.ndims - ld_ndims_start) / 2; ++ld_d) {
+            const int idx0 = ld_ndims_start + ld_d;
+            const int idx1 = ld.ndims - 1 - ld_d;
+            nstl::swap(ld.dims[idx0], ld.dims[idx1]);
+            nstl::swap(ld.strides[idx0], ld.strides[idx1]);
+        }
+    }
+
+    return success;
+}
+
+status_t prb_init(prb_t &p, const memory_desc_t &imd, const memory_desc_t &omd,
+        const primitive_attr_t *attr) {
+    auto im_d = memory_desc_wrapper(imd);
+    auto om_d = memory_desc_wrapper(omd);
+
+    bool ok = true
+        && im_d.is_blocking_desc()
+        && om_d.is_blocking_desc()
+        && !im_d.has_zero_dim()
+        && !om_d.has_zero_dim();
+    if (!ok)
+        return unimplemented;
+
+    dims_t iblocks, oblocks;
+    im_d.compute_blocks(iblocks);
+    om_d.compute_blocks(oblocks);
+
+    /* padding_dim consistency check */
+    for (int d = 0; d < im_d.ndims(); ++d) {
+        const auto pdim = im_d.padded_dims()[d];
+        bool ok = true
+            && pdim == om_d.padded_dims()[d]
+            && pdim % iblocks[d] == 0
+            && pdim % oblocks[d] == 0;
+            if (!ok) return unimplemented;
+    }
+
+    layout_desc_t ild, old;
+    status_t status = cvt_mem_desc_to_layout_desc(imd, ild);
+    if (status != success) return status;
+    status = cvt_mem_desc_to_layout_desc(omd, old);
+    if (status != success) return status;
+
+    p.itype = ild.dt;
+    p.otype = old.dt;
+
+    p.scale_type = attr->output_scales_.has_default_values()
+        ? scale_type_t::NONE
+        : (attr->output_scales_.mask_ == 0
+                ? scale_type_t::COMMON
+                : scale_type_t::MANY);
+
+    ptrdiff_t ss[max_ndims] = {0};
+    if (p.scale_type == scale_type_t::MANY) {
+        ptrdiff_t last_ss = 1;
+        for (int d = old.ndims - 1; d >=0; --d) {
+            assert((d == 0 || old.id[d - 1] <= old.id[d])
+                    && "logical dimensions should be in ascending order");
+            if (attr->output_scales_.mask_ & (1 << old.id[d])) {
+                ss[d] = last_ss;
+                last_ss *= old.dims[d];
+            }
+        }
+    }
+
+    int ndims = 0;
+
+    int i_pos = 0; /* state for input  -- current dimension */
+    int o_pos = 0; /* state for output -- current dimension */
+
+    while (i_pos < ild.ndims && o_pos < old.ndims) {
+        assert(ild.id[i_pos] == old.id[o_pos]);
+        if (ild.id[i_pos] != old.id[o_pos])
+            return runtime_error;
+
+        assert(ndims < max_ndims);
+        if (ndims == max_ndims)
+            return runtime_error;
+
+        if (ild.dims[i_pos] == old.dims[o_pos]) {
+            p.nodes[ndims].n = ild.dims[i_pos];
+            p.nodes[ndims].is = ild.strides[i_pos];
+            p.nodes[ndims].os = old.strides[o_pos];
+            p.nodes[ndims].ss = ss[o_pos];
+            ++ndims;
+            ++i_pos;
+            ++o_pos;
+        } else if (ild.dims[i_pos] < old.dims[o_pos]) {
+            assert(old.dims[o_pos] % ild.dims[i_pos] == 0);
+            int factor = old.dims[o_pos] / ild.dims[i_pos];
+            p.nodes[ndims].n = ild.dims[i_pos];
+            p.nodes[ndims].is = ild.strides[i_pos];
+            p.nodes[ndims].os = old.strides[o_pos] * factor;
+            p.nodes[ndims].ss = ss[o_pos] * factor;
+            ++ndims;
+            ++i_pos;
+            old.dims[o_pos] = factor;
+        } else if (ild.dims[i_pos] > old.dims[o_pos]) {
+            assert(ild.dims[i_pos] % old.dims[o_pos] == 0);
+            int factor = ild.dims[i_pos] / old.dims[o_pos];
+            p.nodes[ndims].n = old.dims[o_pos];
+            p.nodes[ndims].is = ild.strides[i_pos] * factor;
+            p.nodes[ndims].os = old.strides[o_pos];
+            p.nodes[ndims].ss = ss[o_pos];
+            ++ndims;
+            ++o_pos;
+            ild.dims[i_pos] = factor;
+        }
+    }
+    p.ndims = ndims;
+
+    dims_t zero_pos = {0};
+    p.ioff = memory_desc_wrapper(imd).off_v(zero_pos);
+    p.ooff = memory_desc_wrapper(omd).off_v(zero_pos);
+
+    const int sum_idx = attr->post_ops_.find(primitive_kind::sum);
+    p.beta = sum_idx == -1 ? 0.f : attr->post_ops_.entry_[sum_idx].sum.scale;
+
+    return success;
+}
+
+void prb_normalize(prb_t &p) {
+    for (int d = 0; d < p.ndims; ++d) {
+        int min_pos = d;
+        for (int j = d + 1; j < p.ndims; ++j) {
+            bool new_min = false
+                || p.nodes[j].os < p.nodes[min_pos].os
+                || (true
+                        && p.nodes[j].os == p.nodes[min_pos].os
+                        && p.nodes[j].n < p.nodes[min_pos].n);
+            if (new_min) min_pos = j;
+        }
+        if (min_pos != d)
+            nstl::swap(p.nodes[d], p.nodes[min_pos]);
+    }
+}
+
+void prb_simplify(prb_t &p) {
+#if defined(__GNUC__) && __GNUC__ >= 4
+/* GCC produces bogus array subscript is above array bounds warning for
+ * the `p.nodes[j - 1] = p.nodes[j]` line below, so disable it for now. */
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Warray-bounds"
+#endif
+    for (int d = 0; d < p.ndims - 1; ++d) {
+        auto &this_node = p.nodes[d + 0];
+        auto &next_node = p.nodes[d + 1];
+        const bool fold = false
+            || next_node.n == (size_t)1 // trivial case, just drop next node
+            || (true // or real folding if possible
+                    && next_node.is == (ptrdiff_t)this_node.n * this_node.is
+                    && next_node.os == (ptrdiff_t)this_node.n * this_node.os
+                    && next_node.ss == (ptrdiff_t)this_node.n * this_node.ss);
+        if (fold) {
+            this_node.n *= next_node.n;
+            for (int j = d + 2; j < p.ndims; ++j)
+                p.nodes[j - 1] = p.nodes[j];
+            --p.ndims;
+            --d; // make another try
+        }
+    }
+#if defined(__GNUC__) && __GNUC__ >= 4
+#pragma GCC diagnostic pop
+#endif
+}
+
+void prb_node_split(prb_t &p, int dim, size_t n1) {
+    assert(dim < p.ndims);
+    assert(p.ndims < max_ndims);
+    assert(p.nodes[dim].n % n1 == 0);
+
+    p.ndims += 1;
+
+    for (int d = p.ndims; d > dim + 1; --d)
+        p.nodes[d] = p.nodes[d - 1];
+
+    p.nodes[dim + 1].n = p.nodes[dim].n / n1;
+    p.nodes[dim + 1].is = p.nodes[dim].is * n1;
+    p.nodes[dim + 1].os = p.nodes[dim].os * n1;
+    p.nodes[dim + 1].ss = p.nodes[dim].ss * n1;
+
+    p.nodes[dim].n = n1;
+}
+
+void prb_node_swap(prb_t &p, int d0, int d1) {
+    assert(d0 < p.ndims);
+    assert(d1 < p.ndims);
+    assert(p.ndims < max_ndims);
+
+    if (d0 == d1) return;
+
+    nstl::swap(p.nodes[d0], p.nodes[d1]);
+}
+
+void prb_node_move(prb_t &p, int d0, int d1) {
+    assert(d0 < p.ndims);
+    assert(d1 < p.ndims);
+    assert(p.ndims < max_ndims);
+
+    if (d0 == d1) return;
+
+    node_t node = p.nodes[d0];
+
+    if (d0 < d1)
+        for (int d = d0; d < d1; ++d)
+            p.nodes[d] = p.nodes[d + 1];
+    else
+        for (int d = d0; d > d1; --d)
+            p.nodes[d] = p.nodes[d - 1];
+
+    p.nodes[d1] = node;
+}
+
+void prb_dump(const prb_t &p) {
+    printf("@@@ type:%s:%s ndims:%d ", mkldnn_dt2str(p.itype),
+            mkldnn_dt2str(p.otype), p.ndims);
+    for (int d = 0; d < p.ndims; ++d)
+        printf("[%zu:%td:%td:%td]",
+                p.nodes[d].n, p.nodes[d].is, p.nodes[d].os, p.nodes[d].ss);
+    printf(" off:%zu:%zu\n", p.ioff, p.ooff);
+}
+
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jit_utils.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jit_utils.cpp
new file mode 100644
index 0000000000..08747aa89c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jit_utils.cpp
@@ -0,0 +1,115 @@
+/*******************************************************************************
+* Copyright 2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <mutex>
+
+#include "utils.hpp"
+
+#ifndef MKLDNN_ENABLE_JIT_PROFILING
+#define MKLDNN_ENABLE_JIT_PROFILING 1
+#endif
+
+#ifndef MKLDNN_ENABLE_JIT_DUMP
+#define MKLDNN_ENABLE_JIT_DUMP 1
+#endif
+
+#if MKLDNN_ENABLE_JIT_PROFILING
+#include "jitprofiling/jitprofiling.h"
+#endif
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+namespace jit_utils {
+
+// WARNING: These functions are not thread safe and must be protected by a
+// mutex
+
+void dump_jit_code(const void *code, size_t code_size, const char *code_name)
+{
+#if MKLDNN_ENABLE_JIT_DUMP
+    if (code && jit_dump_enabled()) {
+        static int counter = 0;
+#define MAX_FNAME_LEN 256
+        char fname[MAX_FNAME_LEN + 1];
+        // TODO (Roma): support prefix for code / linux perf dumps
+        snprintf(fname, MAX_FNAME_LEN, "mkldnn_dump_%s.%d.bin", code_name,
+                counter);
+        counter++;
+
+        FILE *fp = fopen(fname, "w+");
+        // Failure to dump code is not fatal
+        if (fp) {
+            size_t unused = fwrite(code, code_size, 1, fp);
+            UNUSED(unused);
+            fclose(fp);
+        }
+    }
+#undef MAX_FNAME_LEN
+#else
+    UNUSED(code);
+    UNUSED(code_size);
+    UNUSED(code_name);
+#endif
+}
+
+void register_jit_code_vtune(const void *code, size_t code_size,
+        const char *code_name, const char *source_file_name)
+{
+#if MKLDNN_ENABLE_JIT_PROFILING
+    if (iJIT_IsProfilingActive() == iJIT_SAMPLING_ON) {
+        auto jmethod = iJIT_Method_Load();
+        jmethod.method_id = iJIT_GetNewMethodID(); // XXX: not thread-safe
+        jmethod.method_name = (char *)code_name; // XXX: dropping const
+        jmethod.class_file_name = NULL;
+        jmethod.source_file_name = (char *)source_file_name; // XXX: dropping const
+        jmethod.method_load_address = (void *)code;
+        jmethod.method_size = (unsigned int)code_size;
+
+        iJIT_NotifyEvent(iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED,
+                (void*)&jmethod);
+    }
+#else
+    UNUSED(code);
+    UNUSED(code_size);
+    UNUSED(code_name);
+    UNUSED(source_file_name);
+#endif
+}
+
+void register_jit_code(const void *code, size_t code_size,
+        const char *code_name, const char *source_file_name)
+{
+    // The #ifdef guards are required to avoid generating a function that only
+    // consists of lock and unlock code
+#if MKLDNN_ENABLE_JIT_PROFILING || MKLDNN_ENABLE_JIT_DUMP
+    static std::mutex m;
+    std::lock_guard<std::mutex> guard(m);
+
+    dump_jit_code(code, code_size, code_name);
+    register_jit_code_vtune(code, code_size, code_name, source_file_name);
+#else
+    UNUSED(code);
+    UNUSED(code_size);
+    UNUSED(code_name);
+    UNUSED(source_file_name);
+#endif
+}
+
+}
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jit_utils.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jit_utils.hpp
new file mode 100644
index 0000000000..2f52dba4ac
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jit_utils.hpp
@@ -0,0 +1,32 @@
+/*******************************************************************************
+* Copyright 2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef JIT_SUPPORT_HPP
+#define JIT_SUPPORT_HPP
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+namespace jit_utils {
+
+void register_jit_code(const void *code, size_t code_size,
+        const char *code_name, const char *source_file_name);
+
+}
+}
+}
+}
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/LICENSE.BSD b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/LICENSE.BSD
new file mode 100644
index 0000000000..4fd21cea57
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/LICENSE.BSD
@@ -0,0 +1,27 @@
+Copyright (c) 2011, Intel Corporation
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/README.md b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/README.md
new file mode 100644
index 0000000000..fc67c4f134
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/README.md
@@ -0,0 +1 @@
+This code is from [Intel SEAPI library](https://github.com/intel/IntelSEAPI)
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/ittnotify_config.h b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/ittnotify_config.h
new file mode 100644
index 0000000000..edbf4a15f0
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/ittnotify_config.h
@@ -0,0 +1,595 @@
+/* <copyright>
+
+  Contact Information:
+  http://software.intel.com/en-us/articles/intel-vtune-amplifier-xe/
+
+  BSD LICENSE
+
+  Copyright (c) 2005-2014 Intel Corporation. All rights reserved.
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions
+  are met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the
+      distribution.
+    * Neither the name of Intel Corporation nor the names of its
+      contributors may be used to endorse or promote products derived
+      from this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+  A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+  OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+  LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+  DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+  THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+</copyright> */
+#ifndef _ITTNOTIFY_CONFIG_H_
+#define _ITTNOTIFY_CONFIG_H_
+
+/** @cond exclude_from_documentation */
+#ifndef ITT_OS_WIN
+#  define ITT_OS_WIN   1
+#endif /* ITT_OS_WIN */
+
+#ifndef ITT_OS_LINUX
+#  define ITT_OS_LINUX 2
+#endif /* ITT_OS_LINUX */
+
+#ifndef ITT_OS_MAC
+#  define ITT_OS_MAC   3
+#endif /* ITT_OS_MAC */
+
+#ifndef ITT_OS_FREEBSD
+#  define ITT_OS_FREEBSD   4
+#endif /* ITT_OS_FREEBSD */
+
+#ifndef ITT_OS
+#  if defined WIN32 || defined _WIN32
+#    define ITT_OS ITT_OS_WIN
+#  elif defined( __APPLE__ ) && defined( __MACH__ )
+#    define ITT_OS ITT_OS_MAC
+#  elif defined( __FreeBSD__ )
+#    define ITT_OS ITT_OS_FREEBSD
+#  else
+#    define ITT_OS ITT_OS_LINUX
+#  endif
+#endif /* ITT_OS */
+
+#ifndef ITT_PLATFORM_WIN
+#  define ITT_PLATFORM_WIN 1
+#endif /* ITT_PLATFORM_WIN */
+
+#ifndef ITT_PLATFORM_POSIX
+#  define ITT_PLATFORM_POSIX 2
+#endif /* ITT_PLATFORM_POSIX */
+
+#ifndef ITT_PLATFORM_MAC
+#  define ITT_PLATFORM_MAC 3
+#endif /* ITT_PLATFORM_MAC */
+
+#ifndef ITT_PLATFORM_FREEBSD
+#  define ITT_PLATFORM_FREEBSD 4
+#endif /* ITT_PLATFORM_FREEBSD */
+
+#ifndef ITT_PLATFORM
+#  if ITT_OS==ITT_OS_WIN
+#    define ITT_PLATFORM ITT_PLATFORM_WIN
+#  elif ITT_OS==ITT_OS_MAC
+#    define ITT_PLATFORM ITT_PLATFORM_MAC
+#  elif ITT_OS==ITT_OS_FREEBSD
+#    define ITT_PLATFORM ITT_PLATFORM_FREEBSD
+#  else
+#    define ITT_PLATFORM ITT_PLATFORM_POSIX
+#  endif
+#endif /* ITT_PLATFORM */
+
+#if defined(_UNICODE) && !defined(UNICODE)
+#define UNICODE
+#endif
+
+#include <stddef.h>
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+#include <tchar.h>
+#else  /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+#include <stdint.h>
+#if defined(UNICODE) || defined(_UNICODE)
+#include <wchar.h>
+#endif /* UNICODE || _UNICODE */
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+
+#ifndef ITTAPI_CDECL
+#  if ITT_PLATFORM==ITT_PLATFORM_WIN
+#    define ITTAPI_CDECL __cdecl
+#  else /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+#    if defined _M_IX86 || defined __i386__
+#      define ITTAPI_CDECL __attribute__ ((cdecl))
+#    else  /* _M_IX86 || __i386__ */
+#      define ITTAPI_CDECL /* actual only on x86 platform */
+#    endif /* _M_IX86 || __i386__ */
+#  endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+#endif /* ITTAPI_CDECL */
+
+#ifndef STDCALL
+#  if ITT_PLATFORM==ITT_PLATFORM_WIN
+#    define STDCALL __stdcall
+#  else /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+#    if defined _M_IX86 || defined __i386__
+#      define STDCALL __attribute__ ((stdcall))
+#    else  /* _M_IX86 || __i386__ */
+#      define STDCALL /* supported only on x86 platform */
+#    endif /* _M_IX86 || __i386__ */
+#  endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+#endif /* STDCALL */
+
+#define ITTAPI    ITTAPI_CDECL
+#define LIBITTAPI ITTAPI_CDECL
+
+/* TODO: Temporary for compatibility! */
+#define ITTAPI_CALL    ITTAPI_CDECL
+#define LIBITTAPI_CALL ITTAPI_CDECL
+
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+/* use __forceinline (VC++ specific) */
+#define ITT_INLINE           __forceinline
+#define ITT_INLINE_ATTRIBUTE /* nothing */
+#else  /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+/*
+ * Generally, functions are not inlined unless optimization is specified.
+ * For functions declared inline, this attribute inlines the function even
+ * if no optimization level was specified.
+ */
+#ifdef __STRICT_ANSI__
+#define ITT_INLINE           static
+#define ITT_INLINE_ATTRIBUTE __attribute__((unused))
+#else  /* __STRICT_ANSI__ */
+#define ITT_INLINE           static inline
+#define ITT_INLINE_ATTRIBUTE __attribute__((always_inline, unused))
+#endif /* __STRICT_ANSI__ */
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+/** @endcond */
+
+#ifndef ITT_ARCH_IA32
+#  define ITT_ARCH_IA32  1
+#endif /* ITT_ARCH_IA32 */
+
+#ifndef ITT_ARCH_IA32E
+#  define ITT_ARCH_IA32E 2
+#endif /* ITT_ARCH_IA32E */
+
+#ifndef ITT_ARCH_ARM
+#  define ITT_ARCH_ARM  4
+#endif /* ITT_ARCH_ARM */
+
+#ifndef ITT_ARCH_PPC64
+#  define ITT_ARCH_PPC64  5
+#endif /* ITT_ARCH_PPC64 */
+
+#ifndef ITT_ARCH
+#  if defined _M_IX86 || defined __i386__
+#    define ITT_ARCH ITT_ARCH_IA32
+#  elif defined _M_X64 || defined _M_AMD64 || defined __x86_64__
+#    define ITT_ARCH ITT_ARCH_IA32E
+#  elif defined _M_IA64 || defined __ia64__
+#    define ITT_ARCH ITT_ARCH_IA64
+#  elif defined _M_ARM || defined __arm__
+#    define ITT_ARCH ITT_ARCH_ARM
+#  elif defined __powerpc64__
+#    define ITT_ARCH ITT_ARCH_PPC64
+#  endif
+#endif
+
+#ifdef __cplusplus
+#  define ITT_EXTERN_C extern "C"
+#  define ITT_EXTERN_C_BEGIN extern "C" {
+#  define ITT_EXTERN_C_END }
+#else
+#  define ITT_EXTERN_C /* nothing */
+#  define ITT_EXTERN_C_BEGIN /* nothing */
+#  define ITT_EXTERN_C_END /* nothing */
+#endif /* __cplusplus */
+
+#define ITT_TO_STR_AUX(x) #x
+#define ITT_TO_STR(x)     ITT_TO_STR_AUX(x)
+
+#define __ITT_BUILD_ASSERT(expr, suffix) do { \
+    static char __itt_build_check_##suffix[(expr) ? 1 : -1]; \
+    __itt_build_check_##suffix[0] = 0; \
+} while(0)
+#define _ITT_BUILD_ASSERT(expr, suffix)  __ITT_BUILD_ASSERT((expr), suffix)
+#define ITT_BUILD_ASSERT(expr)           _ITT_BUILD_ASSERT((expr), __LINE__)
+
+#define ITT_MAGIC { 0xED, 0xAB, 0xAB, 0xEC, 0x0D, 0xEE, 0xDA, 0x30 }
+
+/* Replace with snapshot date YYYYMMDD for promotion build. */
+#define API_VERSION_BUILD    20151119
+
+#ifndef API_VERSION_NUM
+#define API_VERSION_NUM 0.0.0
+#endif /* API_VERSION_NUM */
+
+#define API_VERSION "ITT-API-Version " ITT_TO_STR(API_VERSION_NUM) \
+                                " (" ITT_TO_STR(API_VERSION_BUILD) ")"
+
+/* OS communication functions */
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+#include <windows.h>
+typedef HMODULE           lib_t;
+typedef DWORD             TIDT;
+typedef CRITICAL_SECTION  mutex_t;
+#define MUTEX_INITIALIZER { 0 }
+#define strong_alias(name, aliasname) /* empty for Windows */
+#else  /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+#include <dlfcn.h>
+#if defined(UNICODE) || defined(_UNICODE)
+#include <wchar.h>
+#endif /* UNICODE */
+#ifndef _GNU_SOURCE
+#define _GNU_SOURCE 1 /* need for PTHREAD_MUTEX_RECURSIVE */
+#endif /* _GNU_SOURCE */
+#ifndef __USE_UNIX98
+#define __USE_UNIX98 1 /* need for PTHREAD_MUTEX_RECURSIVE, on SLES11.1 with gcc 4.3.4 wherein pthread.h missing dependency on __USE_XOPEN2K8 */
+#endif /*__USE_UNIX98*/
+#include <pthread.h>
+typedef void*             lib_t;
+typedef pthread_t         TIDT;
+typedef pthread_mutex_t   mutex_t;
+#define MUTEX_INITIALIZER PTHREAD_MUTEX_INITIALIZER
+#define _strong_alias(name, aliasname) \
+            extern __typeof (name) aliasname __attribute__ ((alias (#name)));
+#define strong_alias(name, aliasname) _strong_alias(name, aliasname)
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+#define __itt_get_proc(lib, name) GetProcAddress(lib, name)
+#define __itt_mutex_init(mutex)   InitializeCriticalSection(mutex)
+#define __itt_mutex_lock(mutex)   EnterCriticalSection(mutex)
+#define __itt_mutex_unlock(mutex) LeaveCriticalSection(mutex)
+#define __itt_load_lib(name)      LoadLibraryA(name)
+#define __itt_unload_lib(handle)  FreeLibrary(handle)
+#define __itt_system_error()      (int)GetLastError()
+#define __itt_fstrcmp(s1, s2)     lstrcmpA(s1, s2)
+#define __itt_fstrnlen(s, l)      strnlen_s(s, l)
+#define __itt_fstrcpyn(s1, b, s2, l) strncpy_s(s1, b, s2, l)
+#define __itt_fstrdup(s)          _strdup(s)
+#define __itt_thread_id()         GetCurrentThreadId()
+#define __itt_thread_yield()      SwitchToThread()
+#ifndef ITT_SIMPLE_INIT
+ITT_INLINE long
+__itt_interlocked_increment(volatile long* ptr) ITT_INLINE_ATTRIBUTE;
+ITT_INLINE long __itt_interlocked_increment(volatile long* ptr)
+{
+    return InterlockedIncrement(ptr);
+}
+#endif /* ITT_SIMPLE_INIT */
+
+#define DL_SYMBOLS (1)
+#define PTHREAD_SYMBOLS (1)
+
+#else /* ITT_PLATFORM!=ITT_PLATFORM_WIN */
+#define __itt_get_proc(lib, name) dlsym(lib, name)
+#define __itt_mutex_init(mutex)   {\
+    pthread_mutexattr_t mutex_attr;                                         \
+    int error_code = pthread_mutexattr_init(&mutex_attr);                   \
+    if (error_code)                                                         \
+        __itt_report_error(__itt_error_system, "pthread_mutexattr_init",    \
+                           error_code);                                     \
+    error_code = pthread_mutexattr_settype(&mutex_attr,                     \
+                                           PTHREAD_MUTEX_RECURSIVE);        \
+    if (error_code)                                                         \
+        __itt_report_error(__itt_error_system, "pthread_mutexattr_settype", \
+                           error_code);                                     \
+    error_code = pthread_mutex_init(mutex, &mutex_attr);                    \
+    if (error_code)                                                         \
+        __itt_report_error(__itt_error_system, "pthread_mutex_init",        \
+                           error_code);                                     \
+    error_code = pthread_mutexattr_destroy(&mutex_attr);                    \
+    if (error_code)                                                         \
+        __itt_report_error(__itt_error_system, "pthread_mutexattr_destroy", \
+                           error_code);                                     \
+}
+#define __itt_mutex_lock(mutex)   pthread_mutex_lock(mutex)
+#define __itt_mutex_unlock(mutex) pthread_mutex_unlock(mutex)
+#define __itt_load_lib(name)      dlopen(name, RTLD_LAZY)
+#define __itt_unload_lib(handle)  dlclose(handle)
+#define __itt_system_error()      errno
+#define __itt_fstrcmp(s1, s2)     strcmp(s1, s2)
+
+/* makes customer code define safe APIs for SDL_STRNLEN_S and SDL_STRNCPY_S */
+#ifdef SDL_STRNLEN_S
+#define __itt_fstrnlen(s, l)      SDL_STRNLEN_S(s, l)
+#else
+#define __itt_fstrnlen(s, l)      strlen(s)
+#endif /* SDL_STRNLEN_S */
+#ifdef SDL_STRNCPY_S
+#define __itt_fstrcpyn(s1, b, s2, l) SDL_STRNCPY_S(s1, b, s2, l)
+#else
+#define __itt_fstrcpyn(s1, b, s2, l) strncpy(s1, s2, l)
+#endif /* SDL_STRNCPY_S */
+
+#define __itt_fstrdup(s)          strdup(s)
+#define __itt_thread_id()         pthread_self()
+#define __itt_thread_yield()      sched_yield()
+#if ITT_ARCH==ITT_ARCH_IA64
+#ifdef __INTEL_COMPILER
+#define __TBB_machine_fetchadd4(addr, val) __fetchadd4_acq((void *)addr, val)
+#else  /* __INTEL_COMPILER */
+/* TODO: Add Support for not Intel compilers for IA-64 architecture */
+#endif /* __INTEL_COMPILER */
+#elif ITT_ARCH==ITT_ARCH_IA32 || ITT_ARCH==ITT_ARCH_IA32E /* ITT_ARCH!=ITT_ARCH_IA64 */
+ITT_INLINE long
+__TBB_machine_fetchadd4(volatile void* ptr, long addend) ITT_INLINE_ATTRIBUTE;
+ITT_INLINE long __TBB_machine_fetchadd4(volatile void* ptr, long addend)
+{
+    long result;
+    __asm__ __volatile__("lock\nxadd %0,%1"
+                          : "=r"(result),"=m"(*(int*)ptr)
+                          : "0"(addend), "m"(*(int*)ptr)
+                          : "memory");
+    return result;
+}
+#elif ITT_ARCH==ITT_ARCH_ARM || ITT_ARCH==ITT_ARCH_PPC64
+#define __TBB_machine_fetchadd4(addr, val) __sync_fetch_and_add(addr, val)
+#endif /* ITT_ARCH==ITT_ARCH_IA64 */
+#ifndef ITT_SIMPLE_INIT
+ITT_INLINE long
+__itt_interlocked_increment(volatile long* ptr) ITT_INLINE_ATTRIBUTE;
+ITT_INLINE long __itt_interlocked_increment(volatile long* ptr)
+{
+    return __TBB_machine_fetchadd4(ptr, 1) + 1L;
+}
+#endif /* ITT_SIMPLE_INIT */
+
+void* dlopen(const char*, int) __attribute__((weak));
+void* dlsym(void*, const char*) __attribute__((weak));
+int dlclose(void*) __attribute__((weak));
+#define DL_SYMBOLS (dlopen && dlsym && dlclose)
+
+int pthread_mutex_init(pthread_mutex_t*, const pthread_mutexattr_t*) __attribute__((weak));
+int pthread_mutex_lock(pthread_mutex_t*) __attribute__((weak));
+int pthread_mutex_unlock(pthread_mutex_t*) __attribute__((weak));
+int pthread_mutex_destroy(pthread_mutex_t*) __attribute__((weak));
+int pthread_mutexattr_init(pthread_mutexattr_t*) __attribute__((weak));
+int pthread_mutexattr_settype(pthread_mutexattr_t*, int) __attribute__((weak));
+int pthread_mutexattr_destroy(pthread_mutexattr_t*) __attribute__((weak));
+pthread_t pthread_self(void) __attribute__((weak));
+#define PTHREAD_SYMBOLS (pthread_mutex_init && pthread_mutex_lock && pthread_mutex_unlock && pthread_mutex_destroy && pthread_mutexattr_init && pthread_mutexattr_settype && pthread_mutexattr_destroy && pthread_self)
+
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+
+typedef enum {
+    __itt_collection_normal = 0,
+    __itt_collection_paused = 1
+} __itt_collection_state;
+
+typedef enum {
+    __itt_thread_normal  = 0,
+    __itt_thread_ignored = 1
+} __itt_thread_state;
+
+#pragma pack(push, 8)
+
+typedef struct ___itt_thread_info
+{
+    const char* nameA; /*!< Copy of original name in ASCII. */
+#if defined(UNICODE) || defined(_UNICODE)
+    const wchar_t* nameW; /*!< Copy of original name in UNICODE. */
+#else  /* UNICODE || _UNICODE */
+    void* nameW;
+#endif /* UNICODE || _UNICODE */
+    TIDT               tid;
+    __itt_thread_state state;   /*!< Thread state (paused or normal) */
+    int                extra1;  /*!< Reserved to the runtime */
+    void*              extra2;  /*!< Reserved to the runtime */
+    struct ___itt_thread_info* next;
+} __itt_thread_info;
+
+#include "ittnotify_types.h" /* For __itt_group_id definition */
+
+typedef struct ___itt_api_info_20101001
+{
+    const char*    name;
+    void**         func_ptr;
+    void*          init_func;
+    __itt_group_id group;
+}  __itt_api_info_20101001;
+
+typedef struct ___itt_api_info
+{
+    const char*    name;
+    void**         func_ptr;
+    void*          init_func;
+    void*          null_func;
+    __itt_group_id group;
+}  __itt_api_info;
+
+typedef struct __itt_counter_info
+{
+    const char* nameA;  /*!< Copy of original name in ASCII. */
+#if defined(UNICODE) || defined(_UNICODE)
+    const wchar_t* nameW; /*!< Copy of original name in UNICODE. */
+#else  /* UNICODE || _UNICODE */
+    void* nameW;
+#endif /* UNICODE || _UNICODE */
+    const char* domainA;  /*!< Copy of original name in ASCII. */
+#if defined(UNICODE) || defined(_UNICODE)
+    const wchar_t* domainW; /*!< Copy of original name in UNICODE. */
+#else  /* UNICODE || _UNICODE */
+    void* domainW;
+#endif /* UNICODE || _UNICODE */
+    int type;
+    long index;
+    int   extra1; /*!< Reserved to the runtime */
+    void* extra2; /*!< Reserved to the runtime */
+    struct __itt_counter_info* next;
+}  __itt_counter_info_t;
+
+struct ___itt_domain;
+struct ___itt_string_handle;
+
+typedef struct ___itt_global
+{
+    unsigned char          magic[8];
+    unsigned long          version_major;
+    unsigned long          version_minor;
+    unsigned long          version_build;
+    volatile long          api_initialized;
+    volatile long          mutex_initialized;
+    volatile long          atomic_counter;
+    mutex_t                mutex;
+    lib_t                  lib;
+    void*                  error_handler;
+    const char**           dll_path_ptr;
+    __itt_api_info*        api_list_ptr;
+    struct ___itt_global*  next;
+    /* Joinable structures below */
+    __itt_thread_info*     thread_list;
+    struct ___itt_domain*  domain_list;
+    struct ___itt_string_handle* string_list;
+    __itt_collection_state state;
+    __itt_counter_info_t* counter_list;
+} __itt_global;
+
+#pragma pack(pop)
+
+#define NEW_THREAD_INFO_W(gptr,h,h_tail,t,s,n) { \
+    h = (__itt_thread_info*)malloc(sizeof(__itt_thread_info)); \
+    if (h != NULL) { \
+        h->tid    = t; \
+        h->nameA  = NULL; \
+        h->nameW  = n ? _wcsdup(n) : NULL; \
+        h->state  = s; \
+        h->extra1 = 0;    /* reserved */ \
+        h->extra2 = NULL; /* reserved */ \
+        h->next   = NULL; \
+        if (h_tail == NULL) \
+            (gptr)->thread_list = h; \
+        else \
+            h_tail->next = h; \
+    } \
+}
+
+#define NEW_THREAD_INFO_A(gptr,h,h_tail,t,s,n) { \
+    h = (__itt_thread_info*)malloc(sizeof(__itt_thread_info)); \
+    if (h != NULL) { \
+        h->tid    = t; \
+        h->nameA  = n ? __itt_fstrdup(n) : NULL; \
+        h->nameW  = NULL; \
+        h->state  = s; \
+        h->extra1 = 0;    /* reserved */ \
+        h->extra2 = NULL; /* reserved */ \
+        h->next   = NULL; \
+        if (h_tail == NULL) \
+            (gptr)->thread_list = h; \
+        else \
+            h_tail->next = h; \
+    } \
+}
+
+#define NEW_DOMAIN_W(gptr,h,h_tail,name) { \
+    h = (__itt_domain*)malloc(sizeof(__itt_domain)); \
+    if (h != NULL) { \
+        h->flags  = 1;    /* domain is enabled by default */ \
+        h->nameA  = NULL; \
+        h->nameW  = name ? _wcsdup(name) : NULL; \
+        h->extra1 = 0;    /* reserved */ \
+        h->extra2 = NULL; /* reserved */ \
+        h->next   = NULL; \
+        if (h_tail == NULL) \
+            (gptr)->domain_list = h; \
+        else \
+            h_tail->next = h; \
+    } \
+}
+
+#define NEW_DOMAIN_A(gptr,h,h_tail,name) { \
+    h = (__itt_domain*)malloc(sizeof(__itt_domain)); \
+    if (h != NULL) { \
+        h->flags  = 1;    /* domain is enabled by default */ \
+        h->nameA  = name ? __itt_fstrdup(name) : NULL; \
+        h->nameW  = NULL; \
+        h->extra1 = 0;    /* reserved */ \
+        h->extra2 = NULL; /* reserved */ \
+        h->next   = NULL; \
+        if (h_tail == NULL) \
+            (gptr)->domain_list = h; \
+        else \
+            h_tail->next = h; \
+    } \
+}
+
+#define NEW_STRING_HANDLE_W(gptr,h,h_tail,name) { \
+    h = (__itt_string_handle*)malloc(sizeof(__itt_string_handle)); \
+    if (h != NULL) { \
+        h->strA   = NULL; \
+        h->strW   = name ? _wcsdup(name) : NULL; \
+        h->extra1 = 0;    /* reserved */ \
+        h->extra2 = NULL; /* reserved */ \
+        h->next   = NULL; \
+        if (h_tail == NULL) \
+            (gptr)->string_list = h; \
+        else \
+            h_tail->next = h; \
+    } \
+}
+
+#define NEW_STRING_HANDLE_A(gptr,h,h_tail,name) { \
+    h = (__itt_string_handle*)malloc(sizeof(__itt_string_handle)); \
+    if (h != NULL) { \
+        h->strA   = name ? __itt_fstrdup(name) : NULL; \
+        h->strW   = NULL; \
+        h->extra1 = 0;    /* reserved */ \
+        h->extra2 = NULL; /* reserved */ \
+        h->next   = NULL; \
+        if (h_tail == NULL) \
+            (gptr)->string_list = h; \
+        else \
+            h_tail->next = h; \
+    } \
+}
+
+#define NEW_COUNTER_W(gptr,h,h_tail,name,domain,type) { \
+    h = (__itt_counter_info_t*)malloc(sizeof(__itt_counter_info_t)); \
+    if (h != NULL) { \
+        h->nameA   = NULL; \
+        h->nameW   = name ? _wcsdup(name) : NULL; \
+        h->domainA   = NULL; \
+        h->domainW   = name ? _wcsdup(domain) : NULL; \
+        h->type = type; \
+        h->index = 0; \
+        h->next   = NULL; \
+        if (h_tail == NULL) \
+            (gptr)->counter_list = h; \
+        else \
+            h_tail->next = h; \
+    } \
+}
+
+#define NEW_COUNTER_A(gptr,h,h_tail,name,domain,type) { \
+    h = (__itt_counter_info_t*)malloc(sizeof(__itt_counter_info_t)); \
+    if (h != NULL) { \
+        h->nameA   = name ? __itt_fstrdup(name) : NULL; \
+        h->nameW   = NULL; \
+        h->domainA   = domain ? __itt_fstrdup(domain) : NULL; \
+        h->domainW   = NULL; \
+        h->type = type; \
+        h->index = 0; \
+        h->next   = NULL; \
+        if (h_tail == NULL) \
+            (gptr)->counter_list = h; \
+        else \
+            h_tail->next = h; \
+    } \
+}
+
+#endif /* _ITTNOTIFY_CONFIG_H_ */
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/ittnotify_types.h b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/ittnotify_types.h
new file mode 100644
index 0000000000..99fbc24054
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/ittnotify_types.h
@@ -0,0 +1,94 @@
+/* <copyright>
+
+  Contact Information:
+  http://software.intel.com/en-us/articles/intel-vtune-amplifier-xe/
+
+  BSD LICENSE
+
+  Copyright (c) 2005-2014 Intel Corporation. All rights reserved.
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions
+  are met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the
+      distribution.
+    * Neither the name of Intel Corporation nor the names of its
+      contributors may be used to endorse or promote products derived
+      from this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+  A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+  OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+  LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+  DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+  THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+</copyright> */
+
+#ifndef _ITTNOTIFY_TYPES_H_
+#define _ITTNOTIFY_TYPES_H_
+
+typedef enum ___itt_group_id
+{
+    __itt_group_none      = 0,
+    __itt_group_legacy    = 1<<0,
+    __itt_group_control   = 1<<1,
+    __itt_group_thread    = 1<<2,
+    __itt_group_mark      = 1<<3,
+    __itt_group_sync      = 1<<4,
+    __itt_group_fsync     = 1<<5,
+    __itt_group_jit       = 1<<6,
+    __itt_group_model     = 1<<7,
+    __itt_group_splitter_min = 1<<7,
+    __itt_group_counter   = 1<<8,
+    __itt_group_frame     = 1<<9,
+    __itt_group_stitch    = 1<<10,
+    __itt_group_heap      = 1<<11,
+    __itt_group_splitter_max = 1<<12,
+    __itt_group_structure = 1<<12,
+    __itt_group_suppress = 1<<13,
+    __itt_group_arrays    = 1<<14,
+    __itt_group_all       = -1
+} __itt_group_id;
+
+#pragma pack(push, 8)
+
+typedef struct ___itt_group_list
+{
+    __itt_group_id id;
+    const char*    name;
+} __itt_group_list;
+
+#pragma pack(pop)
+
+#define ITT_GROUP_LIST(varname) \
+    static __itt_group_list varname[] = {       \
+        { __itt_group_all,       "all"       }, \
+        { __itt_group_control,   "control"   }, \
+        { __itt_group_thread,    "thread"    }, \
+        { __itt_group_mark,      "mark"      }, \
+        { __itt_group_sync,      "sync"      }, \
+        { __itt_group_fsync,     "fsync"     }, \
+        { __itt_group_jit,       "jit"       }, \
+        { __itt_group_model,     "model"     }, \
+        { __itt_group_counter,   "counter"   }, \
+        { __itt_group_frame,     "frame"     }, \
+        { __itt_group_stitch,    "stitch"    }, \
+        { __itt_group_heap,      "heap"      }, \
+        { __itt_group_structure, "structure" }, \
+        { __itt_group_suppress,  "suppress"  }, \
+        { __itt_group_arrays,    "arrays"    }, \
+        { __itt_group_none,      NULL        }  \
+    }
+
+#endif /* _ITTNOTIFY_TYPES_H_ */
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/jitprofiling.c b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/jitprofiling.c
new file mode 100644
index 0000000000..15f4b9929b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/jitprofiling.c
@@ -0,0 +1,293 @@
+/* <copyright>
+
+  Contact Information:
+  http://software.intel.com/en-us/articles/intel-vtune-amplifier-xe/
+
+  BSD LICENSE
+
+  Copyright (c) 2005-2014 Intel Corporation. All rights reserved.
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions
+  are met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the
+      distribution.
+    * Neither the name of Intel Corporation nor the names of its
+      contributors may be used to endorse or promote products derived
+      from this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+  A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+  OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+  LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+  DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+  THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+</copyright> */
+
+#include "ittnotify_config.h"
+
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+#include <windows.h>
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+#if ITT_PLATFORM != ITT_PLATFORM_MAC && ITT_PLATFORM != ITT_PLATFORM_FREEBSD
+#include <malloc.h>
+#endif
+#include <stdlib.h>
+
+#include "jitprofiling.h"
+
+static const char rcsid[] = "\n@(#) $Revision: 471937 $\n";
+
+#define DLL_ENVIRONMENT_VAR             "VS_PROFILER"
+
+#ifndef NEW_DLL_ENVIRONMENT_VAR
+#if ITT_ARCH==ITT_ARCH_IA32
+#define NEW_DLL_ENVIRONMENT_VAR	        "INTEL_JIT_PROFILER32"
+#else
+#define NEW_DLL_ENVIRONMENT_VAR	        "INTEL_JIT_PROFILER64"
+#endif
+#endif /* NEW_DLL_ENVIRONMENT_VAR */
+
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+#define DEFAULT_DLLNAME                 "JitPI.dll"
+HINSTANCE m_libHandle = NULL;
+#elif ITT_PLATFORM==ITT_PLATFORM_MAC
+#define DEFAULT_DLLNAME                 "libJitPI.dylib"
+void* m_libHandle = NULL;
+#else
+#define DEFAULT_DLLNAME                 "libJitPI.so"
+void* m_libHandle = NULL;
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+
+/* default location of JIT profiling agent on Android */
+#define ANDROID_JIT_AGENT_PATH  "/data/intel/libittnotify.so"
+
+/* the function pointers */
+typedef unsigned int(JITAPI *TPInitialize)(void);
+static TPInitialize FUNC_Initialize=NULL;
+
+typedef unsigned int(JITAPI *TPNotify)(unsigned int, void*);
+static TPNotify FUNC_NotifyEvent=NULL;
+
+static iJIT_IsProfilingActiveFlags executionMode = iJIT_NOTHING_RUNNING;
+
+/* end collector dll part. */
+
+/* loadiJIT_Funcs() : this function is called just in the beginning
+ * and is responsible to load the functions from BistroJavaCollector.dll
+ * result:
+ *  on success: the functions loads, iJIT_DLL_is_missing=0, return value = 1
+ *  on failure: the functions are NULL, iJIT_DLL_is_missing=1, return value = 0
+ */
+static int loadiJIT_Funcs(void);
+
+/* global representing whether the collector can't be loaded */
+static int iJIT_DLL_is_missing = 0;
+
+ITT_EXTERN_C int JITAPI
+iJIT_NotifyEvent(iJIT_JVM_EVENT event_type, void *EventSpecificData)
+{
+    int ReturnValue = 0;
+
+    /* initialization part - the collector has not been loaded yet. */
+    if (!FUNC_NotifyEvent)
+    {
+        if (iJIT_DLL_is_missing)
+            return 0;
+
+        if (!loadiJIT_Funcs())
+            return 0;
+    }
+
+    if (event_type == iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED ||
+        event_type == iJVM_EVENT_TYPE_METHOD_UPDATE)
+    {
+        if (((piJIT_Method_Load)EventSpecificData)->method_id == 0)
+            return 0;
+    }
+    else if (event_type == iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED_V2)
+    {
+        if (((piJIT_Method_Load_V2)EventSpecificData)->method_id == 0)
+            return 0;
+    }
+    else if (event_type == iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED_V3)
+    {
+        if (((piJIT_Method_Load_V3)EventSpecificData)->method_id == 0)
+            return 0;
+    }
+    else if (event_type == iJVM_EVENT_TYPE_METHOD_INLINE_LOAD_FINISHED)
+    {
+        if (((piJIT_Method_Inline_Load)EventSpecificData)->method_id == 0 ||
+            ((piJIT_Method_Inline_Load)EventSpecificData)->parent_method_id == 0)
+            return 0;
+    }
+
+    ReturnValue = (int)FUNC_NotifyEvent(event_type, EventSpecificData);
+
+    return ReturnValue;
+}
+
+ITT_EXTERN_C iJIT_IsProfilingActiveFlags JITAPI iJIT_IsProfilingActive()
+{
+    if (!iJIT_DLL_is_missing)
+    {
+        loadiJIT_Funcs();
+    }
+
+    return executionMode;
+}
+
+/* This function loads the collector dll and the relevant functions.
+ * on success: all functions load,     iJIT_DLL_is_missing = 0, return value = 1
+ * on failure: all functions are NULL, iJIT_DLL_is_missing = 1, return value = 0
+ */
+static int loadiJIT_Funcs()
+{
+    static int bDllWasLoaded = 0;
+    char *dllName = (char*)rcsid; /* !! Just to avoid unused code elimination */
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+    DWORD dNameLength = 0;
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+
+    if(bDllWasLoaded)
+    {
+        /* dll was already loaded, no need to do it for the second time */
+        return 1;
+    }
+
+    /* Assumes that the DLL will not be found */
+    iJIT_DLL_is_missing = 1;
+    FUNC_NotifyEvent = NULL;
+
+    if (m_libHandle)
+    {
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+        FreeLibrary(m_libHandle);
+#else  /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+        dlclose(m_libHandle);
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+        m_libHandle = NULL;
+    }
+
+    /* Try to get the dll name from the environment */
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+    dNameLength = GetEnvironmentVariableA(NEW_DLL_ENVIRONMENT_VAR, NULL, 0);
+    if (dNameLength)
+    {
+        DWORD envret = 0;
+        dllName = (char*)malloc(sizeof(char) * (dNameLength + 1));
+        if(dllName != NULL)
+        {
+            envret = GetEnvironmentVariableA(NEW_DLL_ENVIRONMENT_VAR,
+                                             dllName, dNameLength);
+            if (envret)
+            {
+                /* Try to load the dll from the PATH... */
+                m_libHandle = LoadLibraryExA(dllName,
+                                             NULL, LOAD_WITH_ALTERED_SEARCH_PATH);
+            }
+            free(dllName);
+        }
+    } else {
+        /* Try to use old VS_PROFILER variable */
+        dNameLength = GetEnvironmentVariableA(DLL_ENVIRONMENT_VAR, NULL, 0);
+        if (dNameLength)
+        {
+            DWORD envret = 0;
+            dllName = (char*)malloc(sizeof(char) * (dNameLength + 1));
+            if(dllName != NULL)
+            {
+                envret = GetEnvironmentVariableA(DLL_ENVIRONMENT_VAR,
+                                                 dllName, dNameLength);
+                if (envret)
+                {
+                    /* Try to load the dll from the PATH... */
+                    m_libHandle = LoadLibraryA(dllName);
+                }
+                free(dllName);
+            }
+        }
+    }
+#else  /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+    dllName = getenv(NEW_DLL_ENVIRONMENT_VAR);
+    if (!dllName)
+        dllName = getenv(DLL_ENVIRONMENT_VAR);
+#if defined(__ANDROID__) || defined(ANDROID)
+    if (!dllName)
+        dllName = ANDROID_JIT_AGENT_PATH;
+#endif
+    if (dllName)
+    {
+        /* Try to load the dll from the PATH... */
+        m_libHandle = dlopen(dllName, RTLD_LAZY);
+    }
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+
+    if (!m_libHandle)
+    {
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+        m_libHandle = LoadLibraryA(DEFAULT_DLLNAME);
+#else  /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+        m_libHandle = dlopen(DEFAULT_DLLNAME, RTLD_LAZY);
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+    }
+
+    /* if the dll wasn't loaded - exit. */
+    if (!m_libHandle)
+    {
+        iJIT_DLL_is_missing = 1; /* don't try to initialize
+                                  * JIT agent the second time
+                                  */
+        return 0;
+    }
+
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+    FUNC_NotifyEvent = (TPNotify)GetProcAddress(m_libHandle, "NotifyEvent");
+#else  /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+    FUNC_NotifyEvent = (TPNotify)dlsym(m_libHandle, "NotifyEvent");
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+    if (!FUNC_NotifyEvent)
+    {
+        FUNC_Initialize = NULL;
+        return 0;
+    }
+
+#if ITT_PLATFORM==ITT_PLATFORM_WIN
+    FUNC_Initialize = (TPInitialize)GetProcAddress(m_libHandle, "Initialize");
+#else  /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+    FUNC_Initialize = (TPInitialize)dlsym(m_libHandle, "Initialize");
+#endif /* ITT_PLATFORM==ITT_PLATFORM_WIN */
+    if (!FUNC_Initialize)
+    {
+        FUNC_NotifyEvent = NULL;
+        return 0;
+    }
+
+    executionMode = (iJIT_IsProfilingActiveFlags)FUNC_Initialize();
+
+    bDllWasLoaded = 1;
+    iJIT_DLL_is_missing = 0; /* DLL is ok. */
+
+    return 1;
+}
+
+ITT_EXTERN_C unsigned int JITAPI iJIT_GetNewMethodID()
+{
+    static unsigned int methodID = 1;
+
+    if (methodID == 0)
+        return 0;  /* ERROR : this is not a valid value */
+
+    return methodID++;
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/jitprofiling.h b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/jitprofiling.h
new file mode 100644
index 0000000000..bf0489b1a1
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/jit_utils/jitprofiling/jitprofiling.h
@@ -0,0 +1,673 @@
+/* <copyright>
+
+  Contact Information:
+  http://software.intel.com/en-us/articles/intel-vtune-amplifier-xe/
+
+  BSD LICENSE
+
+  Copyright (c) 2005-2014 Intel Corporation. All rights reserved.
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions
+  are met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in
+      the documentation and/or other materials provided with the
+      distribution.
+    * Neither the name of Intel Corporation nor the names of its
+      contributors may be used to endorse or promote products derived
+      from this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+  A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+  OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+  LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+  DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+  THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+</copyright> */
+
+#ifndef __JITPROFILING_H__
+#define __JITPROFILING_H__
+
+/**
+ * @brief JIT Profiling APIs
+ *
+ * The JIT Profiling API is used to report information about just-in-time
+ * generated code that can be used by performance tools. The user inserts
+ * calls in the code generator to report information before JIT-compiled
+ * code goes to execution. This information is collected at runtime and used
+ * by tools like Intel(R) VTune(TM) Amplifier to display performance metrics
+ * associated with JIT-compiled code.
+ *
+ * These APIs can be used to\n
+ * - **Profile trace-based and method-based JIT-compiled
+ * code**. Some examples of environments that you can profile with these APIs:
+ * dynamic JIT compilation of JavaScript code traces, JIT execution in OpenCL(TM)
+ * software technology, Java/.NET managed execution environments, and custom
+ * ISV JIT engines.
+ * @code
+ * #include <jitprofiling.h>
+ *
+ * if (iJIT_IsProfilingActive != iJIT_SAMPLING_ON) {
+ *     return;
+ * }
+ *
+ * iJIT_Method_Load jmethod = {0};
+ * jmethod.method_id = iJIT_GetNewMethodID();
+ * jmethod.method_name = "method_name";
+ * jmethod.class_file_name = "class_name";
+ * jmethod.source_file_name = "source_file_name";
+ * jmethod.method_load_address = code_addr;
+ * jmethod.method_size = code_size;
+ *
+ * iJIT_NotifyEvent(iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED, (void*)&jmethod);
+ * iJIT_NotifyEvent(iJVM_EVENT_TYPE_SHUTDOWN, NULL);
+ * @endcode
+ *
+ *  * Expected behavior:
+ *    * If any iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED event overwrites an
+ *      already reported method, then such a method becomes invalid and its
+ *      memory region is treated as unloaded. VTune Amplifier displays the metrics
+ *      collected by the method until it is overwritten.
+ *    * If supplied line number information contains multiple source lines for
+ *      the same assembly instruction (code location), then VTune Amplifier picks up
+ *      the first line number.
+ *    * Dynamically generated code can be associated with a module name.
+ *      Use the iJIT_Method_Load_V2 structure.\n
+ *      Clarification of some cases:
+ *        * If you register a function with the same method ID multiple times,
+ *          specifying different module names, then the VTune Amplifier picks up
+ *          the module name registered first. If you want to distinguish the same
+ *          function between different JIT engines, supply different method IDs for
+ *          each function. Other symbolic information (for example, source file)
+ *          can be identical.
+ *
+ * - **Analyze split functions** (multiple joint or disjoint code regions
+ * belonging to the same function) **including re-JIT**
+ * with potential overlapping of code regions in time, which is common in
+ * resource-limited environments.
+ * @code
+ * #include <jitprofiling.h>
+ *
+ * unsigned int method_id = iJIT_GetNewMethodID();
+ *
+ * iJIT_Method_Load a = {0};
+ * a.method_id = method_id;
+ * a.method_load_address = 0x100;
+ * a.method_size = 0x20;
+ *
+ * iJIT_Method_Load b = {0};
+ * b.method_id = method_id;
+ * b.method_load_address = 0x200;
+ * b.method_size = 0x30;
+ *
+ * iJIT_NotifyEvent(iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED, (void*)&a);
+ * iJIT_NotifyEvent(iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED, (void*)&b);
+ * @endcode
+ *
+ *  * Expected behaviour:
+ *      * If a iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED event overwrites an
+ *        already reported method, then such a method becomes invalid and
+ *        its memory region is treated as unloaded.
+ *      * All code regions reported with the same method ID are considered as
+ *        belonging to the same method. Symbolic information (method name,
+ *        source file name) will be taken from the first notification, and all
+ *        subsequent notifications with the same method ID will be processed
+ *        only for line number table information. So, the VTune Amplifier will map
+ *        samples to a source line using the line number table from the current
+ *        notification while taking the source file name from the very first one.\n
+ *        Clarification of some cases:\n
+ *          * If you register a second code region with a different source file
+ *          name and the same method ID, then this information will be saved and
+ *          will not be considered as an extension of the first code region, but
+ *          VTune Amplifier will use the source file of the first code region and map
+ *          performance metrics incorrectly.
+ *          * If you register a second code region with the same source file as
+ *          for the first region and the same method ID, then the source file will be
+ *          discarded but VTune Amplifier will map metrics to the source file correctly.
+ *          * If you register a second code region with a null source file and
+ *          the same method ID, then provided line number info will be associated
+ *          with the source file of the first code region.
+ *
+ * - **Explore inline functions** including multi-level hierarchy of
+ * nested inline methods which shows how performance metrics are distributed through them.
+ * @code
+ * #include <jitprofiling.h>
+ *
+ *  //                                    method_id   parent_id
+ *  //   [-- c --]                          3000        2000
+ *  //                  [---- d -----]      2001        1000
+ *  //  [---- b ----]                       2000        1000
+ *  // [------------ a ----------------]    1000         n/a
+ *
+ * iJIT_Method_Load a = {0};
+ * a.method_id = 1000;
+ *
+ * iJIT_Method_Inline_Load b = {0};
+ * b.method_id = 2000;
+ * b.parent_method_id = 1000;
+ *
+ * iJIT_Method_Inline_Load c = {0};
+ * c.method_id = 3000;
+ * c.parent_method_id = 2000;
+ *
+ * iJIT_Method_Inline_Load d = {0};
+ * d.method_id = 2001;
+ * d.parent_method_id = 1000;
+ *
+ * iJIT_NotifyEvent(iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED, (void*)&a);
+ * iJIT_NotifyEvent(iJVM_EVENT_TYPE_METHOD_INLINE_LOAD_FINISHED, (void*)&b);
+ * iJIT_NotifyEvent(iJVM_EVENT_TYPE_METHOD_INLINE_LOAD_FINISHED, (void*)&c);
+ * iJIT_NotifyEvent(iJVM_EVENT_TYPE_METHOD_INLINE_LOAD_FINISHED, (void*)&d);
+ * @endcode
+ *
+ *  * Requirements:
+ *      * Each inline (iJIT_Method_Inline_Load) method should be associated
+ *        with two method IDs: one for itself; one for its immediate parent.
+ *      * Address regions of inline methods of the same parent method cannot
+ *        overlap each other.
+ *      * Execution of the parent method must not be started until it and all
+ *        its inline methods are reported.
+ *  * Expected behaviour:
+ *      * In case of nested inline methods an order of
+ *        iJVM_EVENT_TYPE_METHOD_INLINE_LOAD_FINISHED events is not important.
+ *      * If any event overwrites either inline method or top parent method,
+ *        then the parent, including inline methods, becomes invalid and its memory
+ *        region is treated as unloaded.
+ *
+ * **Life time of allocated data**\n
+ * The client sends an event notification to the agent with event-specific
+ * data, which is a structure. The pointers in the structure refer to memory
+ * allocated by the client, which responsible for releasing it. The pointers are
+ * used by the iJIT_NotifyEvent method to copy client's data in a trace file,
+ * and they are not used after the iJIT_NotifyEvent method returns.
+ */
+
+/**
+ * @defgroup jitapi JIT Profiling
+ * @ingroup internal
+ * @{
+ */
+
+/**
+ * @brief Enumerator for the types of notifications
+ */
+typedef enum iJIT_jvm_event
+{
+    iJVM_EVENT_TYPE_SHUTDOWN = 2,               /**<\brief Send this to shutdown the agent.
+                                                 * Use NULL for event data. */
+
+    iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED = 13,  /**<\brief Send when dynamic code is
+                                                 * JIT compiled and loaded into
+                                                 * memory by the JIT engine, but
+                                                 * before the code is executed.
+                                                 * Use iJIT_Method_Load as event
+                                                 * data. */
+/** @cond exclude_from_documentation */
+    iJVM_EVENT_TYPE_METHOD_UNLOAD_START,    /**<\brief Send when compiled dynamic
+                                             * code is being unloaded from memory.
+                                             * Use iJIT_Method_Load as event data.*/
+/** @endcond */
+
+    iJVM_EVENT_TYPE_METHOD_UPDATE,   /**<\brief Send to provide new content for
+                                      * a previously reported dynamic code.
+                                      * The previous content will be invalidated
+                                      * starting from the time of the notification.
+                                      * Use iJIT_Method_Load as event data but
+                                      * required fields are following:
+                                      * - method_id    identify the code to update.
+                                      * - method_load_address    specify start address
+                                      *                          within identified code range
+                                      *                          where update should be started.
+                                      * - method_size            specify length of updated code
+                                      *                          range. */
+
+
+    iJVM_EVENT_TYPE_METHOD_INLINE_LOAD_FINISHED, /**<\brief Send when an inline dynamic
+                                                  * code is JIT compiled and loaded
+                                                  * into memory by the JIT engine,
+                                                  * but before the parent code region
+                                                  * starts executing.
+                                                  * Use iJIT_Method_Inline_Load as event data.*/
+
+/** @cond exclude_from_documentation */
+    iJVM_EVENT_TYPE_METHOD_UPDATE_V2,
+/** @endcond */
+
+    iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED_V2 = 21, /**<\brief Send when a dynamic code is
+                                                   * JIT compiled and loaded into
+                                                   * memory by the JIT engine, but
+                                                   * before the code is executed.
+                                                   * Use iJIT_Method_Load_V2 as event data. */
+
+    iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED_V3       /**<\brief Send when a dynamic code is
+                                                   * JIT compiled and loaded into
+                                                   * memory by the JIT engine, but
+                                                   * before the code is executed.
+                                                   * Use iJIT_Method_Load_V3 as event data. */
+} iJIT_JVM_EVENT;
+
+/**
+ * @brief Enumerator for the agent's mode
+ */
+typedef enum _iJIT_IsProfilingActiveFlags
+{
+    iJIT_NOTHING_RUNNING           = 0x0000,    /**<\brief The agent is not running;
+                                                 * iJIT_NotifyEvent calls will
+                                                 * not be processed. */
+    iJIT_SAMPLING_ON               = 0x0001,    /**<\brief The agent is running and
+                                                 * ready to process notifications. */
+} iJIT_IsProfilingActiveFlags;
+
+/**
+ * @brief Description of a single entry in the line number information of a code region.
+ * @details A table of line number entries gives information about how the reported code region
+ * is mapped to source file.
+ * Intel(R) VTune(TM) Amplifier uses line number information to attribute
+ * the samples (virtual address) to a line number. \n
+ * It is acceptable to report different code addresses for the same source line:
+ * @code
+ *   Offset LineNumber
+ *      1       2
+ *      12      4
+ *      15      2
+ *      18      1
+ *      21      30
+ *
+ *  VTune Amplifier constructs the following table using the client data
+ *
+ *   Code subrange  Line number
+ *      0-1             2
+ *      1-12            4
+ *      12-15           2
+ *      15-18           1
+ *      18-21           30
+ * @endcode
+ */
+typedef struct _LineNumberInfo
+{
+    unsigned int Offset;     /**<\brief Offset from the begining of the code region. */
+    unsigned int LineNumber; /**<\brief Matching source line number offset (from beginning of source file). */
+
+} *pLineNumberInfo, LineNumberInfo;
+
+/**
+ * @brief Enumerator for the code architecture.
+ */
+typedef enum _iJIT_CodeArchitecture
+{
+    iJIT_CA_NATIVE = 0, /**<\brief Native to the process architecture that is calling it. */
+
+    iJIT_CA_32,         /**<\brief 32-bit machine code. */
+
+    iJIT_CA_64          /**<\brief 64-bit machine code. */
+
+} iJIT_CodeArchitecture;
+
+#pragma pack(push, 8)
+
+/**
+ * @brief Description of a JIT-compiled method
+ * @details When you use the iJIT_Method_Load structure to describe
+ *  the JIT compiled method, use iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED
+ *  as an event type to report it.
+ */
+typedef struct _iJIT_Method_Load
+{
+    unsigned int method_id; /**<\brief Unique method ID. Cannot be 0.
+                             *  You must either use the API function
+                             *  iJIT_GetNewMethodID to get a valid and unique
+                             *  method ID, or else manage ID uniqueness
+                             *  and correct range by yourself.\n
+                             *  You must use the same method ID for all code
+                             *  regions of the same method, otherwise different
+                             *  method IDs specify different methods. */
+
+    char* method_name; /**<\brief The name of the method. It can be optionally
+                        *  prefixed with its class name and appended with
+                        *  its complete signature. Can't be NULL. */
+
+    void* method_load_address; /**<\brief The start virtual address of the method code
+                                *  region. If NULL, data provided with
+                                *  event are not accepted. */
+
+    unsigned int method_size; /**<\brief The code size of the method in memory.
+                               *  If 0, then data provided with the event are not
+                               *  accepted. */
+
+    unsigned int line_number_size; /**<\brief The number of entries in the line number
+                                    *  table.0 if none. */
+
+    pLineNumberInfo line_number_table; /**<\brief Pointer to the line numbers info
+                                        *  array. Can be NULL if
+                                        *  line_number_size is 0. See
+                                        *  LineNumberInfo Structure for a
+                                        *  description of a single entry in
+                                        *  the line number info array */
+
+    unsigned int class_id; /**<\brief This field is obsolete. */
+
+    char* class_file_name; /**<\brief Class name. Can be NULL.*/
+
+    char* source_file_name; /**<\brief Source file name. Can be NULL.*/
+
+} *piJIT_Method_Load, iJIT_Method_Load;
+
+/**
+ * @brief Description of a JIT-compiled method
+ * @details When you use the iJIT_Method_Load_V2 structure to describe
+ *  the JIT compiled method, use iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED_V2
+ *  as an event type to report it.
+ */
+typedef struct _iJIT_Method_Load_V2
+{
+    unsigned int method_id; /**<\brief Unique method ID. Cannot be 0.
+                             *  You must either use the API function
+                             *  iJIT_GetNewMethodID to get a valid and unique
+                             *  method ID, or else manage ID uniqueness
+                             *  and correct range by yourself.\n
+                             *  You must use the same method ID for all code
+                             *  regions of the same method, otherwise different
+                             *  method IDs specify different methods. */
+
+    char* method_name; /**<\brief The name of the method. It can be optionally
+                        *  prefixed with its class name and appended with
+                        *  its complete signature. Can't be  NULL. */
+
+    void* method_load_address; /**<\brief The start virtual address of the method code
+                                *  region. If NULL, then data provided with the
+                                *  event are not accepted. */
+
+    unsigned int method_size; /**<\brief The code size of the method in memory.
+                               *  If 0, then data provided with the event are not
+                               *  accepted. */
+
+    unsigned int line_number_size; /**<\brief The number of entries in the line number
+                                    *  table. 0 if none. */
+
+    pLineNumberInfo line_number_table; /**<\brief Pointer to the line numbers info
+                                        *  array. Can be NULL if
+                                        *  line_number_size is 0. See
+                                        *  LineNumberInfo Structure for a
+                                        *  description of a single entry in
+                                        *  the line number info array. */
+
+    char* class_file_name; /**<\brief Class name. Can be NULL. */
+
+    char* source_file_name; /**<\brief Source file name. Can be NULL. */
+
+    char* module_name; /**<\brief Module name. Can be NULL.
+                           The module name can be useful for distinguishing among
+                           different JIT engines. VTune Amplifier will display
+                           reported methods grouped by specific module. */
+
+} *piJIT_Method_Load_V2, iJIT_Method_Load_V2;
+
+/**
+ * @brief Description of a JIT-compiled method
+ * @details The iJIT_Method_Load_V3 structure is the same as iJIT_Method_Load_V2
+ *  with a newly introduced 'arch' field that specifies architecture of the code region.
+ *  When you use the iJIT_Method_Load_V3 structure to describe
+ *  the JIT compiled method, use iJVM_EVENT_TYPE_METHOD_LOAD_FINISHED_V3
+ *  as an event type to report it.
+ */
+typedef struct _iJIT_Method_Load_V3
+{
+    unsigned int method_id; /**<\brief Unique method ID. Cannot be 0.
+                             *  You must either use the API function
+                             *  iJIT_GetNewMethodID to get a valid and unique
+                             *  method ID, or manage ID uniqueness
+                             *  and correct range by yourself.\n
+                             *  You must use the same method ID for all code
+                             *  regions of the same method, otherwise they are
+                             *  treated as regions of different methods. */
+
+    char* method_name; /**<\brief The name of the method. It can be optionally
+                        *  prefixed with its class name and appended with
+                        *  its complete signature. Cannot be  NULL. */
+
+    void* method_load_address; /**<\brief The start virtual address of the method code
+                                *  region. If NULL, then data provided with the
+                                *  event are not accepted. */
+
+    unsigned int method_size; /**<\brief The code size of the method in memory.
+                               *  If 0, then data provided with the event are not
+                               *  accepted. */
+
+    unsigned int line_number_size; /**<\brief The number of entries in the line number
+                                    *  table. 0 if none. */
+
+    pLineNumberInfo line_number_table; /**<\brief Pointer to the line numbers info
+                                        *  array. Can be NULL if
+                                        *  line_number_size is 0. See
+                                        *  LineNumberInfo Structure for a
+                                        *  description of a single entry in
+                                        *  the line number info array. */
+
+    char* class_file_name; /**<\brief Class name. Can be NULL. */
+
+    char* source_file_name; /**<\brief Source file name. Can be NULL. */
+
+    char* module_name; /**<\brief Module name. Can be NULL.
+                        *  The module name can be useful for distinguishing among
+                        *  different JIT engines. VTune Amplifier will display
+                        *  reported methods grouped by specific module. */
+
+    iJIT_CodeArchitecture module_arch; /**<\brief Architecture of the method's code region.
+                                        *  By default, it is the same as the process
+                                        *  architecture that is calling it.
+                                        *  For example, you can use it if your 32-bit JIT
+                                        *  engine generates 64-bit code.
+                                        *
+                                        *  If JIT engine reports both 32-bit and 64-bit types
+                                        *  of methods then VTune Amplifier splits the methods
+                                        *  with the same module name but with different
+                                        *  architectures in two different modules. VTune Amplifier
+                                        *  modifies the original name provided with a 64-bit method
+                                        *  version by ending it with '(64)' */
+
+} *piJIT_Method_Load_V3, iJIT_Method_Load_V3;
+
+/**
+ * @brief Description of an inline JIT-compiled method
+ * @details When you use the_iJIT_Method_Inline_Load structure to describe
+ *  the JIT compiled method, use iJVM_EVENT_TYPE_METHOD_INLINE_LOAD_FINISHED
+ *  as an event type to report it.
+ */
+typedef struct _iJIT_Method_Inline_Load
+{
+    unsigned int method_id; /**<\brief Unique method ID. Cannot be 0.
+                             *  You must either use the API function
+                             *  iJIT_GetNewMethodID to get a valid and unique
+                             *  method ID, or else manage ID uniqueness
+                             *  and correct range by yourself. */
+
+    unsigned int parent_method_id; /**<\brief Unique immediate parent's method ID.
+                                    *  Cannot be 0.
+                                    *  You must either use the API function
+                                    *  iJIT_GetNewMethodID to get a valid and unique
+                                    *  method ID, or else manage ID uniqueness
+                                    *  and correct range by yourself. */
+
+    char* method_name; /**<\brief The name of the method. It can be optionally
+                        *  prefixed with its class name and appended with
+                        *  its complete signature. Can't be NULL. */
+
+    void* method_load_address;  /** <\brief The virtual address on which the method
+                                 *  is inlined. If NULL, then data provided with
+                                 *  the event are not accepted. */
+
+    unsigned int method_size; /**<\brief The code size of the method in memory.
+                               *  If 0, then data provided with the event are not
+                               *  accepted. */
+
+    unsigned int line_number_size; /**<\brief The number of entries in the line number
+                                    *  table. 0 if none. */
+
+    pLineNumberInfo line_number_table; /**<\brief Pointer to the line numbers info
+                                        *  array. Can be NULL if
+                                        *  line_number_size is 0. See
+                                        *  LineNumberInfo Structure for a
+                                        *  description of a single entry in
+                                        *  the line number info array */
+
+    char* class_file_name; /**<\brief Class name. Can be NULL.*/
+
+    char* source_file_name; /**<\brief Source file name. Can be NULL.*/
+
+} *piJIT_Method_Inline_Load, iJIT_Method_Inline_Load;
+
+/** @cond exclude_from_documentation */
+/**
+ * @brief Description of a segment type
+ * @details Use the segment type to specify a type of data supplied
+ * with the iJVM_EVENT_TYPE_METHOD_UPDATE_V2 event to be applied to
+ * a certain code trace.
+ */
+typedef enum _iJIT_SegmentType
+{
+    iJIT_CT_UNKNOWN = 0,
+
+    iJIT_CT_CODE,           /**<\brief Executable code. */
+
+    iJIT_CT_DATA,           /**<\brief Data (not executable code).
+                             * VTune Amplifier uses the format string
+                             * (see iJIT_Method_Update) to represent
+                             * this data in the VTune Amplifier GUI */
+
+    iJIT_CT_KEEP,           /**<\brief Use the previous markup for the trace.
+                             * Can be used for the following
+                             * iJVM_EVENT_TYPE_METHOD_UPDATE_V2 events,
+                             * if the type of the previously reported segment
+                             * type is the same. */
+    iJIT_CT_EOF
+} iJIT_SegmentType;
+
+/**
+ * @brief Description of a dynamic update of the content within JIT-compiled method
+ * @details The JIT engine may generate the methods that are updated at runtime
+ * partially by mixed (data + executable code) content. When you use the iJIT_Method_Update
+ * structure to describe the update of the content within a JIT-compiled method,
+ * use iJVM_EVENT_TYPE_METHOD_UPDATE_V2 as an event type to report it.
+ *
+ * On the first Update event, VTune Amplifier copies the original code range reported by
+ * the iJVM_EVENT_TYPE_METHOD_LOAD event, then modifies it with the supplied bytes and
+ * adds the modified range to the original method. For next update events, VTune Amplifier
+ * does the same but it uses the latest modified version of a code region for update.
+ * Eventually, VTune Amplifier GUI displays multiple code ranges for the method reported by
+ * the iJVM_EVENT_TYPE_METHOD_LOAD event.
+ * Notes:
+ * - Multiple update events with different types for the same trace are allowed
+ *   but they must be reported for the same code ranges.
+ *   Example,
+ * @code
+ *                      [-- data---]        Allowed
+ *          [-- code --]                    Allowed
+ *        [code]                            Ignored
+ *                      [-- data---]        Allowed
+ *          [-- code --]                    Allowed
+ *      [------------ trace ---------]
+ * @endcode
+ * - The types of previously reported events can be changed but they must be reported
+ *   for the same code ranges.
+ *   Example,
+ * @code
+ *          [-- data---]                    Allowed
+ *          [-- code --]                    Allowed
+ *          [-- data---]                    Allowed
+ *          [-- code --]                    Allowed
+ *      [------------ trace ---------]
+ * @endcode
+ */
+
+typedef struct _iJIT_Method_Update
+{
+    void* load_address;         /**<\brief Start address of the update within a method */
+
+    unsigned int size;          /**<\brief The update size */
+
+    iJIT_SegmentType type;      /**<\brief Type of the update */
+
+    const char* data_format;    /**<\brief C string that contains a format string
+                                 * that follows the same specifications as format in printf.
+                                 * The format string is used for iJIT_CT_CODE only
+                                 * and cannot be NULL.
+                                 * Format can be changed on the fly. */
+} *piJIT_Method_Update, iJIT_Method_Update;
+
+/** @endcond */
+
+#pragma pack(pop)
+
+/** @cond exclude_from_documentation */
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+#ifndef JITAPI_CDECL
+#  if defined WIN32 || defined _WIN32
+#    define JITAPI_CDECL __cdecl
+#  else /* defined WIN32 || defined _WIN32 */
+#    if defined _M_IX86 || defined __i386__
+#      define JITAPI_CDECL __attribute__ ((cdecl))
+#    else  /* _M_IX86 || __i386__ */
+#      define JITAPI_CDECL /* actual only on x86_64 platform */
+#    endif /* _M_IX86 || __i386__ */
+#  endif /* defined WIN32 || defined _WIN32 */
+#endif /* JITAPI_CDECL */
+
+#define JITAPI JITAPI_CDECL
+/** @endcond */
+
+/**
+ * @brief Generates a new unique method ID.
+ *
+ * You must use this API to obtain unique and valid method IDs for methods or
+ * traces reported to the agent if you don't have your own mechanism to generate
+ * unique method IDs.
+ *
+ * @return a new unique method ID. When out of unique method IDs, this API
+ * returns 0, which is not an accepted value.
+ */
+unsigned int JITAPI iJIT_GetNewMethodID(void);
+
+/**
+ * @brief Returns the current mode of the agent.
+ *
+ * @return iJIT_SAMPLING_ON, indicating that agent is running, or
+ * iJIT_NOTHING_RUNNING if no agent is running.
+ */
+iJIT_IsProfilingActiveFlags JITAPI iJIT_IsProfilingActive(void);
+
+/**
+ * @brief Reports infomation about JIT-compiled code to the agent.
+ *
+ * The reported information is used to attribute samples obtained from any
+ * Intel(R) VTune(TM) Amplifier collector. This API needs to be called
+ * after JIT compilation and before the first entry into the JIT-compiled
+ * code.
+ *
+ * @param[in] event_type - type of the data sent to the agent
+ * @param[in] EventSpecificData - pointer to event-specific data
+ *
+ * @returns 1 on success, otherwise 0.
+ */
+int JITAPI iJIT_NotifyEvent(iJIT_JVM_EVENT event_type, void *EventSpecificData);
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+/** @endcond */
+
+/** @} jitapi group */
+
+#endif /* __JITPROFILING_H__ */
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.cpp
new file mode 100644
index 0000000000..ef4c42bacf
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.cpp
@@ -0,0 +1,317 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <math.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+#include "nstl.hpp"
+
+#include "nchw_pooling.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t data_type>
+void nchw_pooling_fwd_t<data_type>::execute_forward(
+        const exec_ctx_t &ctx) const {
+    using namespace alg_kind;
+
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+    auto ws = CTX_OUT_MEM(unsigned char *, MKLDNN_ARG_WORKSPACE);
+
+    const memory_desc_wrapper ws_d(pd()->workspace_md());
+    const data_type_t ws_dt = ws ? ws_d.data_type() : data_type::undef;
+
+    const int MB = pd()->MB();
+    const int C = pd()->C();
+    const int OD = pd()->OD();
+    const int OH = pd()->OH();
+    const int OW = pd()->OW();
+    const int ID = pd()->ID();
+    const int IH = pd()->IH();
+    const int IW = pd()->IW();
+    const int KD = pd()->KD();
+    const int KH = pd()->KH();
+    const int KW = pd()->KW();
+    const int SD = pd()->KSD();
+    const int SH = pd()->KSH();
+    const int SW = pd()->KSW();
+    const int padF = pd()->padFront();
+    const int padT = pd()->padT();
+    const int padL = pd()->padL();
+
+    auto alg = pd()->desc()->alg_kind;
+
+    auto apply_offset = [=](int index, int offset) {
+        return (index > offset) ? index - offset : 0;
+    };
+
+    auto set_ws = [=](int mb, int c, int od, int oh, int ow, int value) {
+        if (ws) {
+            assert(ws_dt == data_type::u8 || ws_dt == data_type::s32);
+            size_t ws_offset
+                = (size_t)OW * OH * OD * C * mb
+                + (size_t)OW * OH * OD * c
+                + (size_t)OW * OH * od
+                + (size_t)OW * oh
+                + (size_t)ow;
+            if (ws_dt == data_type::u8) {
+                assert(0 <= value && value <= 255);
+                ws[ws_offset] = value;
+            } else
+                reinterpret_cast<int *>(ws)[ws_offset] = value;
+        }
+    };
+
+    auto ker_max = [=](data_t *d, int mb, int c, int od, int oh, int ow) {
+        for (int kd = 0; kd < KD; ++kd) {
+            for (int kh = 0; kh < KH; ++kh) {
+                for (int kw = 0; kw < KW; ++kw) {
+                    const int id = od * SD - padF + kd;
+                    const int ih = oh * SH - padT + kh;
+                    const int iw = ow * SW - padL + kw;
+
+                    if (id < 0 || id >= ID) continue;
+                    if (ih < 0 || ih >= IH) continue;
+                    if (iw < 0 || iw >= IW) continue;
+
+                    auto src_offset
+                        = (size_t)IW * IH * ID * C * mb
+                        + (size_t)IW * IH * ID * c
+                        + (size_t)IW * IH * id
+                        + (size_t)IW * ih
+                        + (size_t)iw;
+                    auto s = src[src_offset];
+                    if (s > d[0]) {
+                        d[0] = s;
+                        set_ws(mb, c, od, oh, ow, kd*KH*KW + kh*KW + kw);
+                    }
+                }
+            }
+        }
+    };
+
+    auto ker_avg = [=](data_t *d, int mb, int c, int od, int oh, int ow) {
+        auto id_start = apply_offset(od*SD, padF);
+        auto ih_start = apply_offset(oh*SH, padT);
+        auto iw_start = apply_offset(ow*SW, padL);
+        auto id_end = nstl::min(od*SD - padF + KD, ID);
+        auto ih_end = nstl::min(oh*SH - padT + KH, IH);
+        auto iw_end = nstl::min(ow*SW - padL + KW, IW);
+
+        auto num_summands = (alg == pooling_avg_include_padding) ? KD*KW*KH
+            : (id_end - id_start)*(ih_end - ih_start)*(iw_end - iw_start);
+
+        for (int id = id_start; id < id_end; ++id) {
+            for (int ih = ih_start; ih < ih_end; ++ih) {
+                for (int iw = iw_start; iw < iw_end; ++iw) {
+                    auto src_offset
+                        = (size_t)IW * IH * ID * C * mb
+                        + (size_t)IW * IH * ID * c
+                        + (size_t)IW * IH * id
+                        + (size_t)IW * ih
+                        + (size_t)iw;
+                    d[0] += src[src_offset];
+                }
+            }
+        }
+
+        d[0] = math::out_round<data_t>((float)d[0] / num_summands);
+    };
+
+
+    if (pd()->desc()->alg_kind == pooling_max) {
+        parallel_nd(MB, C, OD, OH, OW,
+            [&](int mb, int c, int od, int oh, int ow) {
+            size_t dst_offset
+                = (size_t)OW * OH * OD * C * mb
+                + (size_t)OW * OH * OD * c
+                + (size_t)OW * OH * od
+                + (size_t)OW * oh
+                + (size_t)ow;
+            data_t *d = &dst[dst_offset];
+            d[0] = nstl::numeric_limits<data_t>::lowest();
+            set_ws(mb, c, od, oh, ow, 0);
+            ker_max(d, mb, c, od, oh, ow);
+        });
+    } else {
+        parallel_nd(MB, C, OD, OH, OW,
+            [&](int mb, int c, int od, int oh, int ow) {
+            size_t dst_offset
+                = (size_t)OW * OH * OD * C * mb
+                + (size_t)OW * OH * OD * c
+                + (size_t)OW * OH * od
+                + (size_t)OW * oh
+                + (size_t)ow;
+            data_t *d = &dst[dst_offset];
+            d[0] = 0;
+            ker_avg(d, mb, c, od, oh, ow);
+        });
+    }
+}
+
+template <impl::data_type_t data_type>
+void nchw_pooling_bwd_t<data_type>::execute_backward(
+        const exec_ctx_t &ctx) const {
+    using namespace alg_kind;
+
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto ws = CTX_IN_MEM(const unsigned char *, MKLDNN_ARG_WORKSPACE);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper ws_d(pd()->workspace_md());
+
+    const int MB = pd()->MB();
+    const int C = pd()->C();
+    const int OD = pd()->OD();
+    const int OH = pd()->OH();
+    const int OW = pd()->OW();
+    const int ID = pd()->ID();
+    const int IH = pd()->IH();
+    const int IW = pd()->IW();
+    const int KD = pd()->KD();
+    const int KH = pd()->KH();
+    const int KW = pd()->KW();
+    const int SD = pd()->KSD();
+    const int SH = pd()->KSH();
+    const int SW = pd()->KSW();
+    const int padF = pd()->padFront();
+    const int padT = pd()->padT();
+    const int padL = pd()->padL();
+
+    const bool is_3d = pd()->desc()->diff_src_desc.ndims == 5;
+
+    auto alg = pd()->desc()->alg_kind;
+
+    auto apply_offset = [=](int index, int offset) {
+        return (index > offset) ? index - offset : 0;
+    };
+
+    auto ker_zero = [=](int mb, int c) {
+        size_t diff_src_offset = (size_t)mb*C*ID*IH*IW + (size_t)c*ID*IH*IW;
+        for (int id = 0; id < ID; ++id) {
+            for (int ih = 0; ih < IH; ++ih) {
+                for (int iw = 0; iw < IW; ++iw) {
+                    diff_src[diff_src_offset++] = 0;
+                }
+            }
+        }
+    };
+
+    auto ker_max = [=](const data_t *d, int mb, int c, int od, int oh, int ow) {
+        auto b_c = ws_d.blocking_desc().inner_nblks == 0
+            ? 1 : ws_d.blocking_desc().inner_blks[0];
+        auto ws_offset = is_3d
+            ? ws_d.blk_off(mb, c / b_c, od, oh, ow) + c % b_c
+            : ws_d.blk_off(mb, c / b_c, oh, ow) + c % b_c;
+
+        const int index = ws_d.data_type() == data_type::u8
+            ? (int)ws[ws_offset] : ((const int *)ws)[ws_offset];
+        const int kw = index % KW;
+        const int kh = (index / KW) % KH;
+        const int kd = (index / KW) / KH;
+
+        const int id = od * SD - padF + kd;
+        const int ih = oh * SH - padT + kh;
+        const int iw = ow * SW - padL + kw;
+
+        // If padding area could fit the kernel,
+        // then input displacement would be out of bounds.
+        // No need to back propagate there as padding is
+        // virtual in pooling_max case.
+        if (id < 0 || id >= ID)
+            return;
+        if (ih < 0 || ih >= IH)
+            return;
+        if (iw < 0 || iw >= IW)
+            return;
+
+        size_t diff_src_offset =
+            (size_t)mb*C*ID*IH*IW + (size_t)c*ID*IH*IW + (size_t)id*IH*IW
+            + (size_t)ih*IW + (size_t)iw;
+        diff_src[diff_src_offset] += d[0];
+    };
+
+    auto ker_avg = [=](const data_t *d, int mb, int c, int od, int oh, int ow) {
+        auto id_start = apply_offset(od*SD, padF);
+        auto ih_start = apply_offset(oh*SH, padT);
+        auto iw_start = apply_offset(ow*SW, padL);
+        auto id_end = nstl::min(od*SD - padF + KD, ID);
+        auto ih_end = nstl::min(oh*SH - padT + KH, IH);
+        auto iw_end = nstl::min(ow*SW - padL + KW, IW);
+
+        size_t num_summands = (alg == pooling_avg_include_padding)
+            ? (size_t)KW*KH*KD
+            : (size_t)(id_end - id_start)*(ih_end - ih_start)
+                *(iw_end - iw_start);
+
+        for (int id = id_start; id < id_end; ++id) {
+            for (int ih = ih_start; ih < ih_end; ++ih) {
+                for (int iw = iw_start; iw < iw_end; ++iw) {
+                    size_t diff_src_offset = (size_t)mb*C*ID*IH*IW
+                        + (size_t)c*ID*IH*IW + (size_t)id*IH*IW
+                        + (size_t)ih*IW + (size_t)iw;
+                    diff_src[diff_src_offset] += d[0] / num_summands;
+                }
+            }
+        }
+    };
+
+    if (pd()->desc()->alg_kind == pooling_max) {
+        parallel_nd(MB, C, [&](int mb, int c) {
+            size_t diff_dst_offset = (size_t)mb*C*OD*OH*OW
+                + (size_t)c*OD*OH*OW;
+            ker_zero(mb, c);
+            for (int od = 0; od < OD; ++od) {
+                for (int oh = 0; oh < OH; ++oh) {
+                    for (int ow = 0; ow < OW; ++ow) {
+                        const data_t *d = &diff_dst[diff_dst_offset++];
+                        ker_max(d, mb, c, od, oh, ow);
+                    }
+                }
+            }
+        });
+    } else {
+        parallel_nd(MB, C, [&](int mb, int c) {
+            size_t diff_dst_offset = (size_t)mb*C*OD*OH*OW
+                + (size_t)c*OD*OH*OW;
+            ker_zero(mb, c);
+            for (int od = 0; od < OD; ++od) {
+                for (int oh = 0; oh < OH; ++oh) {
+                    for (int ow = 0; ow < OW; ++ow) {
+                        const data_t *d = &diff_dst[diff_dst_offset++];
+                        ker_avg(d, mb, c, od, oh, ow);
+                    }
+                }
+            }
+        });
+    }
+}
+
+template struct nchw_pooling_fwd_t<data_type::f32>;
+template struct nchw_pooling_bwd_t<data_type::f32>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.hpp
new file mode 100644
index 0000000000..bbdd04f6b9
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/nchw_pooling.hpp
@@ -0,0 +1,147 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_NCHW_POOLING_HPP
+#define CPU_NCHW_POOLING_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_pooling_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t data_type>
+struct nchw_pooling_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_pooling_fwd_pd_t {
+        using cpu_pooling_fwd_pd_t::cpu_pooling_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T("nchw_pooling:any", nchw_pooling_fwd_t);
+
+        status_t init() {
+            const format_tag_t desired_fmt_tag =
+                ndims() == 4 ? format_tag::nchw : format_tag::ncdhw;
+
+            bool ok = true
+                && set_default_params() == status::success
+                && is_fwd()
+                && utils::one_of(desc()->alg_kind, alg_kind::pooling_max,
+                        alg_kind::pooling_avg_include_padding,
+                        alg_kind::pooling_avg_exclude_padding)
+                && !has_zero_dim_memory()
+                && utils::everyone_is(data_type, src_md()->data_type,
+                        dst_md()->data_type)
+                && attr()->has_default_values()
+                && memory_desc_matches_tag(*src_md(), desired_fmt_tag)
+                && memory_desc_matches_tag(*dst_md(), desired_fmt_tag);
+            if (!ok) return status::unimplemented;
+
+            bool is_training = desc_.prop_kind == prop_kind::forward_training;
+            if (desc()->alg_kind == alg_kind::pooling_max && is_training)
+                init_default_ws();
+
+            return status::success;
+        }
+    };
+
+    nchw_pooling_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t data_type>
+struct nchw_pooling_bwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_pooling_bwd_pd_t {
+        using cpu_pooling_bwd_pd_t::cpu_pooling_bwd_pd_t;
+
+        DECLARE_COMMON_PD_T("nchw:any", nchw_pooling_bwd_t);
+
+        status_t init() {
+            const format_tag_t desired_fmt_tag =
+                ndims() == 4 ? format_tag::nchw : format_tag::ncdhw;
+
+            bool ok = true
+                && set_default_params() == status::success
+                && !is_fwd()
+                && utils::one_of(desc()->alg_kind, alg_kind::pooling_max,
+                        alg_kind::pooling_avg_include_padding,
+                        alg_kind::pooling_avg_exclude_padding)
+                && !has_zero_dim_memory()
+                && utils::everyone_is(data_type,
+                        diff_dst_md()->data_type,
+                        diff_src_md()->data_type)
+                && attr()->has_default_values()
+                && memory_desc_matches_tag(*diff_dst_md(), desired_fmt_tag)
+                && memory_desc_matches_tag(*diff_src_md(), desired_fmt_tag);
+            if (!ok) return status::unimplemented;
+
+            if (desc()->alg_kind == alg_kind::pooling_max) {
+                bool ws_ok = true
+                    && hint_fwd_pd_
+                    && hint_fwd_pd_->workspace_md();
+                if (!ws_ok)
+                    return status::unimplemented;
+
+                const auto &ws_blk =
+                    hint_fwd_pd_->workspace_md()->format_desc.blocking;
+                ws_ok = ws_ok
+                    && ws_blk.inner_nblks < 1
+                    && IMPLICATION(ws_blk.inner_nblks == 1,
+                            ws_blk.inner_idxs[0] == 1);
+                if (!ws_ok)
+                    return status::unimplemented;
+
+                ws_md_ = *hint_fwd_pd_->workspace_md();
+            }
+
+            return status::success;
+        }
+    };
+
+    nchw_pooling_bwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ncsp_batch_normalization.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ncsp_batch_normalization.cpp
new file mode 100644
index 0000000000..c0e93fefe4
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ncsp_batch_normalization.cpp
@@ -0,0 +1,382 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <math.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+
+#include "cpu_batch_normalization_utils.hpp"
+#include "jit_generator.hpp"
+
+#include "ncsp_batch_normalization.hpp"
+
+// clang 6 and 7 generate incorrect code with OMP_SIMD in some particular cases
+#if (defined __clang_major__) && (__clang_major__ >= 6)
+#define SAFE_TO_USE_OMP_SIMD 0
+#else
+#define SAFE_TO_USE_OMP_SIMD 1
+#endif
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace memory_tracking::names;
+
+void ncsp_batch_normalization_fwd_t::execute_forward(
+        const exec_ctx_t &ctx) const {
+    const bool calculate_stats = !pd()->stats_is_src();
+    const bool save_stats = pd()->is_training();
+    const bool is_training = pd()->is_training();
+    const bool fuse_bn_relu = pd()->fuse_bn_relu();
+
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto scaleshift = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SCALE_SHIFT);
+
+    auto scratchpad = this->scratchpad(ctx);
+    auto *ws_reduce = scratchpad.get<data_t>(key_bnorm_reduction);
+
+    data_t *mean, *variance;
+    if (!calculate_stats) {
+        mean = const_cast<data_t *>(
+                CTX_IN_MEM(const data_t *, MKLDNN_ARG_MEAN));
+        variance = const_cast<data_t *>(
+                CTX_IN_MEM(const data_t *, MKLDNN_ARG_VARIANCE));
+    } else {
+        if (save_stats) {
+            mean = CTX_OUT_MEM(data_t *, MKLDNN_ARG_MEAN);
+            variance = CTX_OUT_MEM(data_t *, MKLDNN_ARG_VARIANCE);
+        } else {
+            mean = scratchpad.get<data_t>(key_bnorm_tmp_mean);
+            variance = scratchpad.get<data_t>(key_bnorm_tmp_var);
+        }
+    }
+
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+    auto ws = CTX_OUT_MEM(uint8_t *, MKLDNN_ARG_WORKSPACE);
+
+    const float eps = pd()->desc()->batch_norm_epsilon;
+    const bool use_scaleshift = pd()->use_scaleshift();
+    const bool with_relu = pd()->with_relu_post_op();
+    auto maybe_post_op
+            = [&](data_t res) { return (with_relu && res < 0) ? 0 : res; };
+    const bool has_spatial = utils::one_of(pd()->ndims(), 4, 5);
+    dim_t SP = (has_spatial) ? pd()->H() * pd()->W() * pd()->D() : 1;
+    dim_t N = pd()->MB();
+    dim_t C = pd()->C();
+
+    int nthr = mkldnn_get_max_threads();
+    size_t l3_size_ = get_cache_size(3, true) * nthr / 2;
+    size_t data_size = N * C * SP * sizeof(data_t);
+    bool do_blocking = (data_size >= l3_size_ / 2 && l3_size_ > 0);
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        int C_ithr = 0, C_nthr = 0;
+        int N_ithr = 0, N_nthr = 0;
+        int S_ithr = 0, S_nthr = 0;
+
+        dim_t C_blk_gl_s = 0, C_blk_gl_e = 0, C_blk_s = 0, C_blk_e = 0;
+        dim_t N_s = 0, N_e = 0;
+        dim_t S_s = 0, S_e = 0;
+
+        dim_t C_blks_per_iter = 1;
+        int64_t iters = 1;
+
+        if (do_blocking) {
+            size_t working_set_size = N * SP * sizeof(data_t);
+            bnorm_utils::cache_balance(
+                    working_set_size, C, C_blks_per_iter, iters);
+        } else
+            C_blks_per_iter = C;
+        int64_t last_iter_blks = C - (iters - 1) * C_blks_per_iter;
+        bool spatial_thr_allowed
+                = bnorm_utils::thread_balance(do_blocking, true, ithr, nthr, N,
+                        C_blks_per_iter, SP, C_ithr, C_nthr, C_blk_s, C_blk_e,
+                        N_ithr, N_nthr, N_s, N_e, S_ithr, S_nthr, S_s, S_e);
+        balance211(C_blks_per_iter, nthr, ithr, C_blk_gl_s, C_blk_gl_e);
+        int SP_N_ithr = N_ithr * S_nthr + S_ithr;
+        int SP_N_nthr = N_nthr * S_nthr;
+        for (int64_t it = 0; it < iters; ++it) {
+            if (it == iters - 1 && iters > 1) {
+                // On the last iteration the access pattern to ws_reduce
+                // might change (due to re-balance on C). So sync the
+                // threads if they are not synced by the algorithm.
+                if (SP_N_nthr == 1 && mkldnn_thr_syncable())
+                    mkldnn_thr_barrier();
+
+                S_s = S_e = C_blk_s = C_blk_e = N_s = N_e = 0;
+                spatial_thr_allowed = bnorm_utils::thread_balance(do_blocking,
+                        spatial_thr_allowed, ithr, nthr, N, last_iter_blks, SP,
+                        C_ithr, C_nthr, C_blk_s, C_blk_e, N_ithr, N_nthr, N_s,
+                        N_e, S_ithr, S_nthr, S_s, S_e);
+                balance211(last_iter_blks, nthr, ithr, C_blk_gl_s, C_blk_gl_e);
+                SP_N_ithr = N_ithr * S_nthr + S_ithr;
+                SP_N_nthr = N_nthr * S_nthr;
+            }
+            size_t C_off = it * C_blks_per_iter;
+            // On the last iteration the access pattern to ws_reduce
+            // might change (due to re-balance on C). Since sync is not always
+            // possible (in case of TBB) use different parts of ws for each
+            // iteration if threads are not synced by the algorithm.
+            size_t ws_iter_off = (mkldnn_thr_syncable() ? 0 : 1) * C_off;
+
+            if (calculate_stats) {
+                data_t *mean_blk = mean + C_off;
+                data_t *variance_blk = variance + C_off;
+                for (dim_t c = C_blk_s; c < C_blk_e; c++) {
+                    size_t off = (c + C_off) * SP;
+                    data_t sum = 0;
+                    for (dim_t n = N_s; n < N_e; ++n)
+                        PRAGMA_OMP_SIMD(reduction(+ : sum))
+                        for (dim_t sp = S_s; sp < S_e; ++sp) {
+                            sum += src[off + n * C * SP + sp];
+                        }
+                    ws_reduce[ws_iter_off + SP_N_ithr * C_blks_per_iter + c]
+                        = sum;
+                }
+
+                if (SP_N_nthr > 1) mkldnn_thr_barrier();
+
+                for (dim_t c = C_blk_gl_s; c < C_blk_gl_e; c++) {
+                    mean_blk[c] = 0.;
+                    for (dim_t n = 0; n < SP_N_nthr; n++)
+                        mean_blk[c] += ws_reduce[ws_iter_off
+                                + n * C_blks_per_iter + c];
+                    mean_blk[c] /= (N * SP);
+                }
+
+                if (SP_N_nthr > 1) mkldnn_thr_barrier();
+
+                for (dim_t c = C_blk_s; c < C_blk_e; c++) {
+                    size_t off = c + C_off;
+                    data_t sum = 0.;
+                    for (dim_t n = N_s; n < N_e; ++n)
+                        PRAGMA_OMP_SIMD(reduction(+ : sum))
+                        for (dim_t sp = S_s; sp < S_e; ++sp) {
+                            data_t m = src[off * SP + n * C * SP + sp]
+                                    - mean[off];
+                            sum += m * m;
+                        }
+                    ws_reduce[ws_iter_off + SP_N_ithr * C_blks_per_iter + c]
+                        = sum;
+                }
+
+                if (SP_N_nthr > 1) mkldnn_thr_barrier();
+
+                for (dim_t c = C_blk_gl_s; c < C_blk_gl_e; c++) {
+                    variance_blk[c] = 0.;
+                    for (dim_t n = 0; n < SP_N_nthr; n++)
+                        variance_blk[c] += ws_reduce[ws_iter_off
+                                + n * C_blks_per_iter + c];
+                    variance_blk[c] /= (N * SP);
+                }
+
+                if (SP_N_nthr > 1) mkldnn_thr_barrier();
+            }
+
+            for (dim_t c = C_blk_s; c < C_blk_e; c++) {
+                size_t off = c + C_off;
+                data_t sqrt_variance
+                        = static_cast<data_t>(sqrtf(variance[off] + eps));
+                data_t sm = (use_scaleshift ? scaleshift[off] : 1.0f) / sqrt_variance;
+                data_t sv = use_scaleshift ? scaleshift[C + off] : 0;
+                for (dim_t n = N_s; n < N_e; ++n)
+#if SAFE_TO_USE_OMP_SIMD
+                    PRAGMA_OMP_SIMD()
+#endif
+                    for (dim_t sp = S_s; sp < S_e; ++sp) {
+                        size_t d_off = off * SP + n * C * SP + sp;
+                        data_t bn_res
+                                = sm * (src[d_off] - mean[off]) + sv;
+                        if (fuse_bn_relu) {
+                            if (bn_res <= 0) {
+                                bn_res = 0;
+                                if (is_training)
+                                    ws[d_off] = 0;
+                            } else {
+                                if (is_training)
+                                    ws[d_off] = 1;
+                            }
+                        }
+                        dst[d_off] = maybe_post_op(bn_res);
+                    }
+            }
+        }
+    });
+}
+
+void ncsp_batch_normalization_bwd_t::execute_backward(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto mean = CTX_IN_MEM(const data_t *, MKLDNN_ARG_MEAN);
+    auto variance = CTX_IN_MEM(const data_t *, MKLDNN_ARG_VARIANCE);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto scaleshift = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SCALE_SHIFT);
+    auto ws = CTX_IN_MEM(const uint8_t *, MKLDNN_ARG_WORKSPACE);
+
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+    auto diff_scaleshift = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SCALE_SHIFT);
+
+    auto scratchpad = this->scratchpad(ctx);
+    auto *ws_reduce = scratchpad.get<data_t>(key_bnorm_reduction);
+
+    if (diff_scaleshift == nullptr)
+        diff_scaleshift = scratchpad.get<data_t>(key_bnorm_tmp_diff_ss);
+
+    const bool has_spatial = utils::one_of(pd()->ndims(), 4, 5);
+    dim_t SP = (has_spatial) ? pd()->H() * pd()->W() * pd()->D() : 1;
+    dim_t C = pd()->C(), N = pd()->MB();
+    const bool use_scaleshift = pd()->use_scaleshift();
+    const float eps = pd()->desc()->batch_norm_epsilon;
+    const bool calculate_diff_stats = !pd()->use_global_stats();
+    const bool fuse_bn_relu = pd()->fuse_bn_relu();
+
+    int nthr = mkldnn_get_max_threads();
+    size_t l3_size_ = get_cache_size(3, true) * nthr / 2;
+    size_t data_size = N * C * SP * sizeof(data_t);
+    bool do_blocking = (data_size >= l3_size_ / 2 && l3_size_ > 0);
+
+    parallel(0, [&](const int ithr, const int nthr) {
+        int C_ithr = 0, C_nthr = 0;
+        int N_ithr = 0, N_nthr = 0;
+        int S_ithr = 0, S_nthr = 0;
+
+        dim_t C_blk_gl_s = 0, C_blk_gl_e = 0, C_blk_s = 0, C_blk_e = 0;
+        dim_t N_s = 0, N_e = 0;
+        dim_t S_s = 0, S_e = 0;
+
+        dim_t C_blks_per_iter = 1;
+        int64_t iters = 1;
+
+        if (do_blocking) {
+            size_t working_set_size = 2 * N * SP * sizeof(data_t);
+            bnorm_utils::cache_balance(
+                    working_set_size, C, C_blks_per_iter, iters);
+        } else
+            C_blks_per_iter = C;
+        int64_t last_iter_blks = C - (iters - 1) * C_blks_per_iter;
+        bool spatial_thr_allowed
+                = bnorm_utils::thread_balance(do_blocking, true, ithr, nthr, N,
+                        C_blks_per_iter, SP, C_ithr, C_nthr, C_blk_s, C_blk_e,
+                        N_ithr, N_nthr, N_s, N_e, S_ithr, S_nthr, S_s, S_e);
+        balance211(C_blks_per_iter, nthr, ithr, C_blk_gl_s, C_blk_gl_e);
+        int SP_N_ithr = N_ithr * S_nthr + S_ithr;
+        int SP_N_nthr = N_nthr * S_nthr;
+
+        for (int64_t it = 0; it < iters; ++it) {
+            if (it == iters - 1 && iters > 1) {
+                // On the last iteration the access pattern to ws_reduce
+                // might change (due to re-balance on C). So sync the
+                // threads if they are not synced by the algorithm.
+                if (SP_N_nthr == 1 && mkldnn_thr_syncable())
+                    mkldnn_thr_barrier();
+
+                C_blk_s = C_blk_e = N_s = N_e = 0;
+                spatial_thr_allowed = bnorm_utils::thread_balance(do_blocking,
+                        spatial_thr_allowed, ithr, nthr, N, last_iter_blks, SP,
+                        C_ithr, C_nthr, C_blk_s, C_blk_e, N_ithr, N_nthr, N_s,
+                        N_e, S_ithr, S_nthr, S_s, S_e);
+                balance211(last_iter_blks, nthr, ithr, C_blk_gl_s, C_blk_gl_e);
+                SP_N_ithr = N_ithr * S_nthr + S_ithr;
+                SP_N_nthr = N_nthr * S_nthr;
+            }
+            size_t C_off = it * C_blks_per_iter;
+            // On the last iteration the access pattern to ws_reduce
+            // might change (due to re-balance on C). Since sync is not always
+            // possible (in case of TBB) use different parts of ws for each
+            // iteration if threads are not synced by the algorithm.
+            size_t ws_iter_off = (mkldnn_thr_syncable() ? 0 : 1) * 2 * C_off;
+
+            data_t *diff_gamma_blk = diff_scaleshift + C_off;
+            data_t *diff_beta_blk = diff_scaleshift + C + C_off;
+            for (dim_t c = C_blk_s; c < C_blk_e; c++) {
+                size_t off = c + C_off;
+                data_t diff_gamma = 0.0, diff_beta = 0.0;
+                data_t v_mean = mean[off];
+                for (dim_t n = N_s; n < N_e; ++n)
+                    PRAGMA_OMP_SIMD(reduction(+ : diff_gamma, diff_beta))
+                    for (dim_t sp = S_s; sp < S_e; ++sp) {
+                        const size_t d_off = off * SP + n * C * SP + sp;
+                        data_t dd;
+                        if (fuse_bn_relu)
+                            dd = (!ws[d_off]) ? 0 : diff_dst[d_off];
+                        else
+                            dd = diff_dst[d_off];
+                        diff_gamma += (src[d_off] - v_mean) * dd;
+                        diff_beta += dd;
+                    }
+                ws_reduce[ws_iter_off + SP_N_ithr * C_blks_per_iter + c]
+                    = diff_gamma;
+                ws_reduce[ws_iter_off + SP_N_nthr * C_blks_per_iter
+                        + SP_N_ithr * C_blks_per_iter + c] = diff_beta;
+            }
+
+            if (SP_N_nthr > 1) mkldnn_thr_barrier();
+
+            for (dim_t c = C_blk_gl_s; c < C_blk_gl_e; c++) {
+                data_t sqrt_variance = static_cast<data_t>(
+                        1.0f / sqrtf(variance[c + C_off] + eps));
+                diff_gamma_blk[c] = 0.;
+                diff_beta_blk[c] = 0.;
+                for (dim_t n = 0; n < SP_N_nthr; n++) {
+                    diff_gamma_blk[c] += ws_reduce[ws_iter_off
+                            + n * C_blks_per_iter + c];
+                    diff_beta_blk[c] += ws_reduce[ws_iter_off
+                            + SP_N_nthr * C_blks_per_iter + n * C_blks_per_iter
+                            + c];
+                }
+                diff_gamma_blk[c] *= sqrt_variance;
+            }
+
+            if (SP_N_nthr > 1) mkldnn_thr_barrier();
+
+            for (dim_t c = C_blk_s; c < C_blk_e; c++) {
+                size_t off = c + C_off;
+                data_t gamma = use_scaleshift ? scaleshift[off] : 1;
+                data_t sqrt_variance
+                        = static_cast<data_t>(1.0f / sqrtf(variance[off] + eps));
+                data_t v_mean = mean[off];
+                for (dim_t n = N_s; n < N_e; ++n)
+#if SAFE_TO_USE_OMP_SIMD
+                    PRAGMA_OMP_SIMD()
+#endif
+                    for (dim_t sp = S_s; sp < S_e; ++sp) {
+                        const size_t d_off = off * SP + n * C * SP + sp;
+
+                        data_t v_diff_src;
+                        if (fuse_bn_relu)
+                            v_diff_src = (!ws[d_off]) ? 0 : diff_dst[d_off];
+                        else
+                            v_diff_src = diff_dst[d_off];
+                        if (calculate_diff_stats) {
+                            v_diff_src -= diff_beta_blk[c] / (SP * N)
+                                    + (src[d_off] - v_mean) * diff_gamma_blk[c]
+                                            * sqrt_variance / (SP * N);
+                        }
+                        v_diff_src *= gamma * sqrt_variance;
+                        diff_src[d_off] = v_diff_src;
+                    }
+            }
+        }
+    });
+}
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ncsp_batch_normalization.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ncsp_batch_normalization.hpp
new file mode 100644
index 0000000000..97ca3b003f
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ncsp_batch_normalization.hpp
@@ -0,0 +1,160 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_NCSP_BATCH_NORMALIZATION_HPP
+#define CPU_NCSP_BATCH_NORMALIZATION_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_batch_normalization_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct ncsp_batch_normalization_fwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_batch_normalization_fwd_pd_t {
+        using cpu_batch_normalization_fwd_pd_t::cpu_batch_normalization_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ncsp_bnorm:any", ncsp_batch_normalization_fwd_t);
+
+        status_t init() {
+            using namespace data_type;
+            using namespace prop_kind;
+            using namespace format_tag;
+
+            bool ok = true
+                && is_fwd()
+                && !has_zero_dim_memory()
+                && src_md()->data_type == f32
+                && IMPLICATION(use_scaleshift(), weights_md()->data_type == f32)
+                && memory_desc_matches_one_of_tag(*src_md(), ncdhw, nchw, nc)
+                && (attr()->has_default_values() || this->with_relu_post_op());
+            if (!ok) return status::unimplemented;
+
+            if (is_training() && fuse_bn_relu()) init_default_ws(8);
+
+            init_scratchpad();
+
+            return status::success;
+        }
+
+    private:
+        void init_scratchpad() {
+            using namespace memory_tracking::names;
+            auto scratchpad = scratchpad_registry().registrar();
+            if (!stats_is_src()) {
+                scratchpad.book(key_bnorm_reduction,
+                        sizeof(data_t) * C() * mkldnn_get_max_threads());
+
+                if (!is_training()) {
+                    scratchpad.book(key_bnorm_tmp_mean, sizeof(data_t) * C());
+                    scratchpad.book(key_bnorm_tmp_var, sizeof(data_t) * C());
+                }
+            }
+        }
+    };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    ncsp_batch_normalization_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    ~ncsp_batch_normalization_fwd_t() {}
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+struct ncsp_batch_normalization_bwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_batch_normalization_bwd_pd_t {
+        using cpu_batch_normalization_bwd_pd_t::cpu_batch_normalization_bwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ncsp_bnorm:any", ncsp_batch_normalization_bwd_t);
+
+        status_t init() {
+            using namespace data_type;
+            using namespace format_tag;
+
+            bool ok = true
+                && is_bwd()
+                && !has_zero_dim_memory()
+                && utils::everyone_is(f32, src_md()->data_type,
+                        diff_src_md()->data_type)
+                && IMPLICATION(use_scaleshift(),
+                        utils::everyone_is(f32,
+                            weights_md()->data_type,
+                            diff_weights_md()->data_type))
+                && memory_desc_matches_one_of_tag(*src_md(), ncdhw, nchw, nc)
+                && memory_desc_matches_one_of_tag(*diff_src_md(), ncdhw, nchw, nc)
+                && attr()->has_default_values();
+            if (!ok) return status::unimplemented;
+
+            if (fuse_bn_relu()) {
+                init_default_ws(8);
+                if (!compare_ws(hint_fwd_pd_))
+                    return status::unimplemented;
+            }
+
+            init_scratchpad();
+
+            return status::success;
+        }
+
+    private:
+        void init_scratchpad() {
+            using namespace memory_tracking::names;
+            auto scratchpad = scratchpad_registry().registrar();
+            scratchpad.book(key_bnorm_reduction,
+                    sizeof(data_t) * 2 * C() * mkldnn_get_max_threads());
+            if (!(use_scaleshift() && desc()->prop_kind == prop_kind::backward))
+                scratchpad.book(key_bnorm_tmp_diff_ss,
+                        sizeof(data_t) * 2 * C());
+        }
+    };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    ncsp_batch_normalization_bwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    ~ncsp_batch_normalization_bwd_t() {}
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/nhwc_pooling.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/nhwc_pooling.cpp
new file mode 100644
index 0000000000..38cfb28dce
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/nhwc_pooling.cpp
@@ -0,0 +1,392 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <math.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+#include "nstl.hpp"
+
+#include "nhwc_pooling.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+#define MEM_D(name) name##_d
+
+#define DECLARE_READ_STRIDES(name)                                             \
+    const size_t name##_n_stride = MEM_D(name).blocking_desc().strides[0];     \
+    const size_t name##_d_stride = (!is_3d)                                    \
+                                 ? 0                                           \
+                                 : MEM_D(name).blocking_desc().strides[2];     \
+    const size_t name##_h_stride = (!is_3d)                                    \
+                                 ? MEM_D(name).blocking_desc().strides[2]      \
+                                 : MEM_D(name).blocking_desc().strides[3];     \
+    const size_t name##_w_stride = (!is_3d)                                    \
+                                 ? MEM_D(name).blocking_desc().strides[3]      \
+                                 : MEM_D(name).blocking_desc().strides[4];
+
+namespace nhwc_pooling {
+    size_t strided_offset(const int _n, const size_t _sn,
+                          const int _d, const size_t _sd,
+                          const int _h, const size_t _sh,
+                          const int _w, const size_t _sw)
+    {
+        return   _n * _sn
+               + _d * _sd
+               + _h * _sh
+               + _w * _sw;
+    }
+}
+
+template <impl::data_type_t data_type>
+void nhwc_pooling_fwd_t<data_type>::array_div_by_const(const int n,
+        const data_t *src, const size_t num, data_t *dst) const
+{
+    for (int i = 0; i < n; ++i)
+    {
+        float ftmp = (float)src[i];
+        ftmp = ftmp / num;
+        dst[i] = math::out_round<data_t>(ftmp);
+    }
+}
+
+template <impl::data_type_t data_type>
+void nhwc_pooling_fwd_t<data_type>::array_add(const int n, const data_t *src,
+        data_t *dst) const
+{
+    for (int i = 0;  i < n; ++i)
+    {
+        dst[i] += src[i];
+    }
+}
+
+template <impl::data_type_t data_type>
+void nhwc_pooling_fwd_t<data_type>::execute_forward(
+        const exec_ctx_t &ctx) const {
+    using namespace alg_kind;
+    using namespace prop_kind;
+    using namespace nhwc_pooling;
+
+    auto alg = pd()->desc()->alg_kind;
+
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+    auto ws = CTX_OUT_MEM(unsigned char *, MKLDNN_ARG_WORKSPACE);
+
+    const memory_desc_wrapper MEM_D(src)(pd()->src_md());
+    const memory_desc_wrapper MEM_D(dst)(pd()->dst_md());
+    const memory_desc_wrapper MEM_D(ws)(pd()->workspace_md());
+
+    const int ID = pd()->ID();
+    const int IH = pd()->IH();
+    const int IW = pd()->IW();
+    const int KD = pd()->KD();
+    const int KH = pd()->KH();
+    const int KW = pd()->KW();
+    const int SD = pd()->KSD();
+    const int SH = pd()->KSH();
+    const int SW = pd()->KSW();
+    const int padF = pd()->padFront();
+    const int padT = pd()->padT();
+    const int padL = pd()->padL();
+    const int MB = pd()->MB();
+    const int OC = pd()->C();
+    const int OD = pd()->OD();
+    const int OH = pd()->OH();
+    const int OW = pd()->OW();
+
+    const bool is_3d = pd()->desc()->src_desc.ndims == 5;
+    const data_type_t ws_dt = ws ? ws_d.data_type() : data_type::undef;
+
+    DECLARE_READ_STRIDES(src);
+    DECLARE_READ_STRIDES(dst);
+
+    auto apply_offset = [=](int index, int offset) {
+        return (index > offset) ? index - offset : 0;
+    };
+
+    parallel_nd(MB, OD, OH, OW,
+        [&](int mb, int od, int oh, int ow) {
+        size_t dst_offset_init = strided_offset(mb, dst_n_stride,
+                                                od, dst_d_stride,
+                                                oh, dst_h_stride,
+                                                ow, dst_w_stride);
+        if (alg == pooling_max) {
+            size_t ws_offset_init = 0;
+            if (ws)
+            {
+                DECLARE_READ_STRIDES(ws);
+                ws_offset_init = strided_offset(mb, ws_n_stride,
+                                                od, ws_d_stride,
+                                                oh, ws_h_stride,
+                                                ow, ws_w_stride);
+            }
+            // Note: GCC 4.8.5 won't vectorize below
+            // simple loops unless they are singled out
+            // into separate helper routines:
+            //    array_nhwc_initialize, array_nhwc_max
+            if (!ws)
+                array_nhwc_initialize<false>(OC, dst + dst_offset_init,
+                                    ws, ws_offset_init, ws_dt);
+            else
+                array_nhwc_initialize<true>(OC, dst + dst_offset_init,
+                                    ws, ws_offset_init, ws_dt);
+
+
+            for (int kd = 0; kd < KD; ++kd)
+            for (int kh = 0; kh < KH; ++kh)
+            for (int kw = 0; kw < KW; ++kw) {
+                const int id = od * SD - padF + kd;
+                const int ih = oh * SH - padT + kh;
+                const int iw = ow * SW - padL + kw;
+
+                if (id < 0 || id >= ID)
+                    continue;
+                if (ih < 0 || ih >= IH)
+                    continue;
+                if (iw < 0 || iw >= IW)
+                    continue;
+
+                size_t src_offset_init = strided_offset(mb, src_n_stride,
+                                                        id, src_d_stride,
+                                                        ih, src_h_stride,
+                                                        iw, src_w_stride);
+
+                if (!ws)
+                    array_nhwc_max<false>(OC,
+                       dst + dst_offset_init,
+                       src + src_offset_init,
+                       ws, ws_offset_init,
+                       ws_dt,
+                       kd * KH * KW + kh * KW + kw
+                    );
+                else
+                    array_nhwc_max<true>(OC,
+                       dst + dst_offset_init,
+                       src + src_offset_init,
+                       ws, ws_offset_init,
+                       ws_dt,
+                       kd * KH * KW + kh * KW + kw
+                    );
+            }
+        } else {
+            // pooling_avg
+            auto d = dst + dst_offset_init;
+
+            utils::array_set(d, 0, OC);
+
+            auto id_start = apply_offset(od * SD, padF);
+            auto ih_start = apply_offset(oh * SH, padT);
+            auto iw_start = apply_offset(ow * SW, padL);
+            auto id_end = nstl::min(od * SD - padF + KD, ID);
+            auto ih_end = nstl::min(oh * SH - padT + KH, IH);
+            auto iw_end = nstl::min(ow * SW - padL + KW, IW);
+
+            // it is cheaper to actually count this in a loop
+            // as the typical kernel is small
+            size_t num_summands = 0;
+
+            for (int id = id_start; id < id_end; ++id)
+            for (int ih = ih_start; ih < ih_end; ++ih)
+            for (int iw = iw_start; iw < iw_end; ++iw) {
+                size_t src_offset_init = strided_offset(mb, src_n_stride,
+                                                        id, src_d_stride,
+                                                        ih, src_h_stride,
+                                                        iw, src_w_stride);
+                auto s = src + src_offset_init;
+
+                // need to move the loop to separate function
+                // for GCC 4.8.5 to vectorize
+                array_add(OC, s, d);
+
+                num_summands++;
+            }
+
+            num_summands = (alg == pooling_avg_include_padding) ?
+                    KW * KH * KD : num_summands;
+
+            // need to move the loop to separate function
+            // for GCC 4.8.5 to vectorize
+            array_div_by_const(OC, d, num_summands, d);
+        }
+    });
+}
+
+template <impl::data_type_t data_type>
+void nhwc_pooling_bwd_t<data_type>::execute_backward(
+        const exec_ctx_t &ctx) const {
+    using namespace alg_kind;
+    using namespace nhwc_pooling;
+
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto ws = CTX_IN_MEM(const unsigned char *, MKLDNN_ARG_WORKSPACE);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper MEM_D(diff_src)(pd()->diff_src_md());
+    const memory_desc_wrapper MEM_D(diff_dst)(pd()->diff_dst_md());
+    const memory_desc_wrapper MEM_D(ws)(pd()->workspace_md());
+
+    const int ID = pd()->ID();
+    const int IH = pd()->IH();
+    const int IW = pd()->IW();
+    const int KD = pd()->KD();
+    const int KH = pd()->KH();
+    const int KW = pd()->KW();
+    const int SD = pd()->KSD();
+    const int SH = pd()->KSH();
+    const int SW = pd()->KSW();
+    const int OC = pd()->C();
+    const int padF = pd()->padFront();
+    const int padT = pd()->padT();
+    const int padL = pd()->padL();
+    const int OD = pd()->OD();
+    const int OH = pd()->OH();
+    const int OW = pd()->OW();
+
+    const bool is_3d = pd()->desc()->diff_src_desc.ndims == 5;
+    auto alg = pd()->desc()->alg_kind;
+
+    DECLARE_READ_STRIDES(diff_src);
+    DECLARE_READ_STRIDES(diff_dst);
+
+    auto apply_offset = [=](int index, int offset) {
+        return (index > offset) ? index - offset : 0;
+    };
+
+    const int MB = pd()->MB();
+
+    parallel_nd(MB, ID, IH, IW,
+        [&](int mb, int id, int ih, int iw) {
+        size_t src_offset_init = strided_offset(mb, diff_src_n_stride,
+                                                id, diff_src_d_stride,
+                                                ih, diff_src_h_stride,
+                                                iw, diff_src_w_stride);
+
+        // check if kernel windows are disjoint, in this case there's no
+        // update needed and we just write there once, no initialization
+        // required.
+        if (!(KD == SD && KH == SH && KW == SW))
+            for (int oc = 0; oc < OC; ++oc)
+                diff_src[src_offset_init + oc] = data_type_t(0);
+
+        // Find out which output cells may correspond to current
+        // input position. Current input postition divided by
+        // stride, with integer divide rounding down, is the
+        // right-most output.
+        // Left-most output may be computed if we decrement input
+        // by (kernel_size - 1) and then do the same division by
+        // stride.
+        int od_left  = nstl::max((id + padF - KD + 1) / SD,  0);
+        int oh_left  = nstl::max((ih + padT - KH + 1) / SH,  0);
+        int ow_left  = nstl::max((iw + padL - KW + 1) / SW,  0);
+        // Notice +1 here to preserve the C loop "less than"
+        // condition for continuing the for loop.
+        int od_right = nstl::min((id + padF) / SD + 1     , OD);
+        int oh_right = nstl::min((ih + padT) / SH + 1     , OH);
+        int ow_right = nstl::min((iw + padL) / SW + 1     , OW);
+
+        for (int od = od_left; od < od_right; ++od)
+        for (int oh = oh_left; oh < oh_right; ++oh)
+        for (int ow = ow_left; ow < ow_right; ++ow) {
+            const int kd = id - od*SD + padF;
+            const int kh = ih - oh*SH + padT;
+            const int kw = iw - ow*SW + padL;
+
+            if (kd < 0 || kd >= KD)
+                continue;
+            if (kh < 0 || kh >= KH)
+                continue;
+            if (kw < 0 || kw >= KW)
+                continue;
+
+            size_t dst_offset_init = strided_offset(mb, diff_dst_n_stride,
+                                                    od, diff_dst_d_stride,
+                                                    oh, diff_dst_h_stride,
+                                                    ow, diff_dst_w_stride);
+
+            if (alg == pooling_max) {
+                DECLARE_READ_STRIDES(ws);
+                size_t ws_offset_init = strided_offset(mb, ws_n_stride,
+                                                       od, ws_d_stride,
+                                                       oh, ws_h_stride,
+                                                       ow, ws_w_stride);
+                const int index = kd * KH * KW + kh * KW + kw;
+
+                PRAGMA_OMP_SIMD()
+                for (int oc = 0; oc < OC; ++oc) {
+                    const int index_from_ws =
+                                    (MEM_D(ws).data_type() == data_type::u8)
+                                    ? (int)ws[ws_offset_init + oc]
+                                    : ((int *)ws)[ws_offset_init + oc];
+
+                    const data_t d = diff_dst[dst_offset_init + oc];
+
+                    // Check if kernel windows are disjoint, in this case
+                    // there's no update needed and we just write there once
+                    // otherwise we add value to the contents.
+                    if (!(KD == SD && KH == SH && KW == SW))
+                        diff_src[src_offset_init + oc] +=
+                                                   (index_from_ws == index)
+                                                   ? d
+                                                   : data_type_t(0);
+                    else
+                        diff_src[src_offset_init + oc] =
+                                                   (index_from_ws == index)
+                                                   ? d
+                                                   : data_type_t(0);
+                }
+            } else {
+                // pooling_avg
+                auto id_start = apply_offset(od*SD, padF);
+                auto ih_start = apply_offset(oh*SH, padT);
+                auto iw_start = apply_offset(ow*SW, padL);
+                auto id_end = nstl::min(od*SD - padF + KD, ID);
+                auto ih_end = nstl::min(oh*SH - padT + KH, IH);
+                auto iw_end = nstl::min(ow*SW - padL + KW, IW);
+
+                auto num_summands = (alg == pooling_avg_include_padding)
+                  ? KW*KH*KD
+                  : (ih_end - ih_start)*(iw_end - iw_start)*(id_end - id_start);
+
+                PRAGMA_OMP_SIMD()
+                for (int oc = 0; oc < OC; ++oc) {
+                    const data_t d = diff_dst[dst_offset_init + oc];
+                    // Check if kernel windows are disjoint, in this case
+                    // there's no update needed and we just write there once
+                    // otherwise we add value to the contents.
+                    if (!(KD == SD && KH == SH && KW == SW))
+                      diff_src[src_offset_init + oc] += d / num_summands;
+                    else
+                      diff_src[src_offset_init + oc] = d / num_summands;
+                }
+            }
+        }
+    });
+}
+
+template struct nhwc_pooling_fwd_t<data_type::f32>;
+template struct nhwc_pooling_bwd_t<data_type::f32>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/nhwc_pooling.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/nhwc_pooling.hpp
new file mode 100644
index 0000000000..7e33b6869f
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/nhwc_pooling.hpp
@@ -0,0 +1,210 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_NHWC_POOLING_HPP
+#define CPU_NHWC_POOLING_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_pooling_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace nhwc_pooling {
+size_t strided_offset(const int _n, const size_t _sn, const int _d,
+        const size_t _sd, const int _h, const size_t _sh, const int _w,
+        const size_t _sw);
+}
+
+template <impl::data_type_t data_type>
+struct nhwc_pooling_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_pooling_fwd_pd_t {
+        using cpu_pooling_fwd_pd_t::cpu_pooling_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T("nhwc_pooling:any", nhwc_pooling_fwd_t);
+
+        status_t init() {
+            const format_tag_t desired_fmt_tag =
+                ndims() == 4 ? format_tag::nhwc : format_tag::ndhwc;
+
+            bool ok = true
+                && set_default_params() == status::success
+                && is_fwd()
+                && utils::one_of(desc()->alg_kind, alg_kind::pooling_max,
+                        alg_kind::pooling_avg_include_padding,
+                        alg_kind::pooling_avg_exclude_padding)
+                && utils::everyone_is(data_type,
+                        src_md()->data_type,
+                        dst_md()->data_type)
+                && attr()->has_default_values()
+                && memory_desc_matches_tag(*src_md(), desired_fmt_tag)
+                && memory_desc_matches_tag(*dst_md(), desired_fmt_tag);
+            if (!ok) return status::unimplemented;
+
+            bool is_training = desc_.prop_kind == prop_kind::forward_training;
+            if (desc()->alg_kind == alg_kind::pooling_max && is_training)
+                init_default_ws();
+
+            return status::success;
+        }
+    };
+
+    nhwc_pooling_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    void array_div_by_const(const int n, const data_t *src, const size_t num,
+            data_t *dst) const;
+    void array_add(const int n, const data_t *src, data_t *dst) const;
+
+    template <bool use_workspace>
+    void array_nhwc_max(const int n, data_t *dst, const data_t *src,
+            unsigned char *ws, const size_t ws_offset, const data_type_t ws_dt,
+            const int index) const {
+        assert(!((use_workspace == false) ^ (!ws))); // ensure ws pointer exists
+        PRAGMA_OMP_SIMD()
+        for (int oc = 0; oc < n; ++oc) {
+            auto s = src[oc];
+            data_t mv = dst[oc];
+
+            // update index of maximum
+#if defined __INTEL_COMPILER
+            if ((use_workspace) && (s > mv)) {
+                assert(ws_dt == data_type::u8 || ws_dt == data_type::s32);
+                if (ws_dt == data_type::u8) {
+                    assert(0 <= index && index <= 255);
+                    ws[ws_offset + oc] = index;
+                } else
+                    reinterpret_cast<int *>(ws)[ws_offset + oc] = index;
+            }
+#else
+            // Need to add explicit predicates for GCC to vectorize this.
+            // And although the resulting code is ugly, it is still 4 times
+            // faster than scalar
+            if (use_workspace) {
+                assert(ws_dt == data_type::u8 || ws_dt == data_type::s32);
+
+                if (ws_dt == data_type::u8) {
+                    assert(0 <= index && index <= 255);
+                    unsigned char predicate = (s > mv) ? 0xff : 0;
+                    unsigned char current_value = ws[ws_offset + oc];
+                    current_value = (predicate & (unsigned char)index)
+                        | ((~predicate) & current_value);
+                    ws[ws_offset + oc] = current_value;
+                } else {
+                    auto wint = reinterpret_cast<int *>(ws);
+                    unsigned int predicate = (s > mv) ? 0xffffffff : 0;
+                    unsigned int current_value = wint[ws_offset + oc];
+                    current_value = (predicate & (unsigned int)index)
+                        | ((~predicate) & current_value);
+                    wint[ws_offset + oc] = current_value;
+                }
+            }
+#endif
+            // update maximum
+            dst[oc] = nstl::max(s, mv);
+        }
+    }
+
+    template <bool use_workspace>
+    void array_nhwc_initialize(const int n, data_t *dst, unsigned char *ws,
+            const size_t ws_offset, const data_type_t ws_dt) const {
+        assert(!((use_workspace == false) ^ (!ws))); // ensure ws pointer exists
+        for (int oc = 0; oc < n; ++oc) {
+            if (use_workspace) {
+                assert(ws_dt == data_type::u8 || ws_dt == data_type::s32);
+                if (ws_dt == data_type::u8) {
+                    ws[ws_offset + oc] = 0;
+                } else
+                    reinterpret_cast<int *>(ws)[ws_offset + oc] = 0;
+            }
+            dst[oc] = nstl::numeric_limits<data_t>::lowest();
+        }
+    }
+
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t data_type>
+struct nhwc_pooling_bwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_pooling_bwd_pd_t {
+        using cpu_pooling_bwd_pd_t::cpu_pooling_bwd_pd_t;
+
+        DECLARE_COMMON_PD_T("nhwc:any", nhwc_pooling_bwd_t);
+
+        status_t init() {
+            const format_tag_t desired_fmt_tag =
+                ndims() == 4 ? format_tag::nchw : format_tag::ncdhw;
+
+            bool ok = true
+                && set_default_params() == status::success
+                && !is_fwd()
+                && utils::one_of(desc()->alg_kind, alg_kind::pooling_max,
+                        alg_kind::pooling_avg_include_padding,
+                        alg_kind::pooling_avg_exclude_padding)
+                && utils::everyone_is(data_type,
+                        diff_dst_md()->data_type,
+                        diff_src_md()->data_type)
+                && attr()->has_default_values()
+                && memory_desc_matches_tag(*diff_dst_md(), desired_fmt_tag)
+                && memory_desc_matches_tag(*diff_src_md(), desired_fmt_tag);
+            if (!ok) return status::unimplemented;
+
+            if (desc()->alg_kind == alg_kind::pooling_max) {
+                init_default_ws();
+                if (!compare_ws(hint_fwd_pd_))
+                    return status::unimplemented;
+            }
+
+            return status::success;
+        }
+    };
+
+    nhwc_pooling_bwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}// namespace cpu
+}// namespace impl
+}// namespace mkldnn
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/nspc_batch_normalization.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/nspc_batch_normalization.cpp
new file mode 100644
index 0000000000..e20333e66f
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/nspc_batch_normalization.cpp
@@ -0,0 +1,288 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <math.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+
+#include "cpu_batch_normalization_utils.hpp"
+#include "jit_generator.hpp"
+
+#include "nspc_batch_normalization.hpp"
+
+// clang 6 and 7 generate incorrect code with OMP_SIMD in some particular cases
+#if (defined __clang_major__) && (__clang_major__ >= 6)
+#define SAFE_TO_USE_OMP_SIMD 0
+#else
+#define SAFE_TO_USE_OMP_SIMD 1
+#endif
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace memory_tracking::names;
+
+void nspc_batch_normalization_fwd_t::execute_forward(
+        const exec_ctx_t &ctx) const {
+    const bool save_stats = pd()->is_training();
+    const bool is_training = pd()->is_training();
+    const bool fuse_bn_relu = pd()->fuse_bn_relu();
+    const bool calculate_stats = !pd()->stats_is_src();
+    const bool with_relu = pd()->with_relu_post_op();
+
+    auto scratchpad = this->scratchpad(ctx);
+    auto tmp_mean = scratchpad.get<data_t>(key_bnorm_tmp_mean);
+    auto tmp_var = scratchpad.get<data_t>(key_bnorm_tmp_var);
+    auto *ws_reduce = scratchpad.get<data_t>(key_bnorm_reduction);
+
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto scaleshift = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SCALE_SHIFT);
+
+    data_t *mean, *variance;
+    if (!calculate_stats) {
+        mean = const_cast<data_t *>(
+                CTX_IN_MEM(const data_t *, MKLDNN_ARG_MEAN));
+        variance = const_cast<data_t *>(
+                CTX_IN_MEM(const data_t *, MKLDNN_ARG_VARIANCE));
+    } else {
+        if (save_stats) {
+            mean = CTX_OUT_MEM(data_t *, MKLDNN_ARG_MEAN);
+            variance = CTX_OUT_MEM(data_t *, MKLDNN_ARG_VARIANCE);
+        } else {
+            mean = tmp_mean;
+            variance = tmp_var;
+        }
+    }
+
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+    auto ws = CTX_OUT_MEM(uint8_t *, MKLDNN_ARG_WORKSPACE);
+
+    const dim_t N = pd()->MB();
+    const dim_t C = pd()->C();
+    const dim_t SP = pd()->H() * pd()->W() * pd()->D();
+
+    const float eps = pd()->desc()->batch_norm_epsilon;
+    const bool use_scaleshift = pd()->use_scaleshift();
+    auto maybe_post_op
+            = [&](data_t res) { return (with_relu && res < 0) ? 0 : res; };
+
+    assert(mkldnn_thr_syncable());
+    parallel(0, [&](const int ithr, const int nthr) {
+        dim_t N_s = 0, N_e = 0, C_s = 0, C_e = 0;
+        balance211(N, nthr, ithr, N_s, N_e);
+        balance211(C, nthr, ithr, C_s, C_e);
+        data_t *mean_loc = tmp_mean + nstl::max(C, (dim_t)16) * ithr;
+        data_t *variance_loc = tmp_var + nstl::max(C, (dim_t)16) * ithr;
+
+        if (calculate_stats) {
+            for (dim_t c = 0; c < C; c++)
+                ws_reduce[C * ithr + c] = 0.;
+
+            for (dim_t n = N_s; n < N_e; n++)
+                for (dim_t sp = 0; sp < SP; sp++)
+                    PRAGMA_OMP_SIMD()
+                    for (dim_t c = 0; c < C; c++)
+                        ws_reduce[C * ithr + c] += src[(size_t)n * SP * C
+                            + sp * C + c];
+
+            mkldnn_thr_barrier();
+
+            for (dim_t c = C_s; c < C_e; c++) {
+                mean[c] = 0;
+                for (dim_t n = 0; n < nthr; n++)
+                    mean[c] += ws_reduce[C * n + c];
+                mean[c] /= SP * N;
+            }
+
+            mkldnn_thr_barrier();
+
+            for (dim_t c = 0; c < C; c++) {
+                mean_loc[c] = mean[c];
+                ws_reduce[C * ithr + c] = 0.;
+            }
+
+            for (dim_t n = N_s; n < N_e; n++)
+                for (dim_t sp = 0; sp < SP; sp++)
+                    PRAGMA_OMP_SIMD()
+                    for (dim_t c = 0; c < C; c++) {
+                        data_t m = src[(size_t)n * SP * C + sp * C + c]
+                            - mean_loc[c];
+                        ws_reduce[C * ithr + c] += m * m;
+                    }
+
+            mkldnn_thr_barrier();
+
+            for (dim_t c = C_s; c < C_e; c++) {
+                variance[c] = 0;
+                for (dim_t n = 0; n < nthr; n++)
+                    variance[c] += ws_reduce[C * n + c];
+                variance[c] /= SP * N;
+            }
+
+            mkldnn_thr_barrier();
+
+            for (dim_t c = 0; c < C; c++)
+                variance_loc[c] = variance[c];
+        } else {
+            variance_loc = variance;
+            mean_loc = mean;
+        }
+
+        for (dim_t n = N_s; n < N_e; n++) {
+            for (dim_t sp = 0; sp < SP; sp++) {
+#if SAFE_TO_USE_OMP_SIMD
+                PRAGMA_OMP_SIMD()
+#endif
+                for (dim_t c = 0; c < C; c++) {
+                    data_t sqrt_variance = static_cast<data_t>(
+                            sqrtf(variance_loc[c] + eps));
+                    data_t sm = (use_scaleshift ? scaleshift[c] : 1.0f) / sqrt_variance;
+                    data_t sv = use_scaleshift ? scaleshift[C + c] : 0;
+                    size_t d_off = (size_t)n * SP * C + sp * C + c;
+                    data_t bn_res = sm * (src[d_off] - mean_loc[c]) + sv;
+                    if (fuse_bn_relu) {
+                        if (bn_res <= 0) {
+                            bn_res = 0;
+                            if (is_training)
+                                ws[d_off] = 0;
+                        } else {
+                            if (is_training)
+                                ws[d_off] = 1;
+                        }
+                    }
+                    dst[d_off] = maybe_post_op(bn_res);
+                }
+            }
+        }
+    });
+}
+
+void nspc_batch_normalization_bwd_t::execute_backward(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto mean = CTX_IN_MEM(const data_t *, MKLDNN_ARG_MEAN);
+    auto variance = CTX_IN_MEM(const data_t *, MKLDNN_ARG_VARIANCE);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto scaleshift = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SCALE_SHIFT);
+    auto ws = CTX_IN_MEM(const uint8_t *, MKLDNN_ARG_WORKSPACE);
+
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+    auto diff_scaleshift = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SCALE_SHIFT);
+
+    auto scratchpad = this->scratchpad(ctx);
+    auto tmp_diff_ss = scratchpad.get<data_t>(key_bnorm_tmp_diff_ss);
+
+    if (diff_scaleshift == nullptr)
+        diff_scaleshift = tmp_diff_ss;
+
+    const dim_t N = pd()->MB();
+    const dim_t C = pd()->C();
+    const dim_t SP = pd()->D() * pd()->H() * pd()->W();
+    data_t *diff_gamma = diff_scaleshift, *diff_beta = diff_scaleshift + C;
+    auto *ws_reduce = scratchpad.get<data_t>(key_bnorm_reduction);
+
+    const float eps = pd()->desc()->batch_norm_epsilon;
+    const bool use_scaleshift = pd()->use_scaleshift();
+    const bool calculate_diff_stats = !pd()->use_global_stats();
+    const bool fuse_bn_relu = pd()->fuse_bn_relu();
+
+    assert(mkldnn_thr_syncable());
+    parallel(0, [&](const int ithr, const int nthr) {
+        dim_t N_s = 0, N_e = 0, C_s = 0, C_e = 0;
+        balance211(N, nthr, ithr, N_s, N_e);
+        balance211(C, nthr, ithr, C_s, C_e);
+
+        data_t *diff_gamma_loc = tmp_diff_ss + 2 * C + C * ithr;
+        data_t *diff_beta_loc = tmp_diff_ss + 2 * C + C * (nthr + ithr);
+
+        for (dim_t c = 0; c < C; c++) {
+            ws_reduce[C * ithr + c] = 0.;
+            ws_reduce[C * nthr + C * ithr + c] = 0.;
+        }
+
+        for (dim_t n = N_s; n < N_e; n++)
+            for (dim_t sp = 0; sp < SP; sp++)
+#if SAFE_TO_USE_OMP_SIMD
+                PRAGMA_OMP_SIMD()
+#endif
+                for (dim_t c = 0; c < C; c++) {
+                    const size_t d_off = (size_t)n * SP * C + sp * C + c;
+                    data_t dd;
+                    if (fuse_bn_relu)
+                        dd = (!ws[d_off]) ? 0 : diff_dst[d_off];
+                    else
+                        dd = diff_dst[d_off];
+                    ws_reduce[C * ithr + c] += (src[d_off] - mean[c]) * dd;
+                    ws_reduce[C * nthr + C * ithr + c] += dd;
+                }
+
+        mkldnn_thr_barrier();
+
+        for (dim_t c = C_s; c < C_e; c++) {
+            data_t sqrt_variance
+                    = static_cast<data_t>(1.0f / sqrtf(variance[c] + eps));
+            diff_gamma[c] = 0;
+            diff_beta[c] = 0;
+            for (dim_t n = 0; n < nthr; n++) {
+                diff_gamma[c] += ws_reduce[C * n + c];
+                diff_beta[c] += ws_reduce[C * nthr + C * n + c];
+            }
+            diff_gamma[c] *= sqrt_variance;
+        }
+
+        mkldnn_thr_barrier();
+
+        for (dim_t c = 0; c < C; c++) {
+            diff_gamma_loc[c] = diff_gamma[c];
+            diff_beta_loc[c] = diff_beta[c];
+        }
+
+        for (dim_t n = N_s; n < N_e; n++) {
+            for (dim_t sp = 0; sp < SP; sp++) {
+#if SAFE_TO_USE_OMP_SIMD
+                PRAGMA_OMP_SIMD()
+#endif
+                for (dim_t c = 0; c < C; c++) {
+                    const size_t d_off = (size_t)n * SP * C + sp * C + c;
+                    data_t gamma = use_scaleshift ? scaleshift[c] : 1;
+                    data_t sqrt_variance
+                            = static_cast<data_t>(1.0f / sqrtf(variance[c] + eps));
+                    data_t v_diff_src;
+                    if (fuse_bn_relu)
+                        v_diff_src = (!ws[d_off]) ? 0 : diff_dst[d_off];
+                    else
+                        v_diff_src = diff_dst[d_off];
+                    if (calculate_diff_stats) {
+                        v_diff_src -= diff_beta_loc[c] / (SP * N)
+                                + (src[d_off] - mean[c]) * diff_gamma_loc[c]
+                                        * sqrt_variance / (SP * N);
+                    }
+                    v_diff_src *= gamma * sqrt_variance;
+                    diff_src[d_off] = v_diff_src;
+                }
+            }
+        }
+    });
+}
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/nspc_batch_normalization.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/nspc_batch_normalization.hpp
new file mode 100644
index 0000000000..aad86b05a7
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/nspc_batch_normalization.hpp
@@ -0,0 +1,169 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_NSPC_BATCH_NORMALIZATION_HPP
+#define CPU_NSPC_BATCH_NORMALIZATION_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_batch_normalization_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct nspc_batch_normalization_fwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_batch_normalization_fwd_pd_t {
+        pd_t(engine_t *engine, const batch_normalization_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const batch_normalization_fwd_pd_t *hint_fwd_pd)
+            : cpu_batch_normalization_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+        {}
+
+        DECLARE_COMMON_PD_T("nspc_bnorm:any", nspc_batch_normalization_fwd_t);
+
+        status_t init() {
+            using namespace data_type;
+            using namespace prop_kind;
+
+            bool ok = true
+                /* the algorithm requires barriers while switching
+                 * between parallelization over N and C dimensions */
+                && mkldnn_thr_syncable()
+                && is_fwd()
+                && !has_zero_dim_memory()
+                && src_md()->data_type == f32
+                && IMPLICATION(use_scaleshift(), weights_md()->data_type == f32)
+                && memory_desc_matches_tag(*src_md(), format_tag::nhwc)
+                && (attr()->has_default_values() || this->with_relu_post_op());
+            if (!ok) return status::unimplemented;
+
+            if (is_training() && fuse_bn_relu()) init_default_ws(8);
+
+            init_scratchpad();
+
+            return status::success;
+        }
+
+    private:
+        void init_scratchpad() {
+            using namespace memory_tracking::names;
+            auto scratchpad = scratchpad_registry().registrar();
+            if (!stats_is_src()) {
+                dim_t sz = nstl::max<dim_t>(C(), 16) * mkldnn_get_max_threads();
+                scratchpad.book(key_bnorm_reduction, sizeof(data_t) * sz);
+                scratchpad.book(key_bnorm_tmp_mean, sizeof(data_t) * sz);
+                scratchpad.book(key_bnorm_tmp_var, sizeof(data_t) * sz);
+            }
+        }
+    };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    nspc_batch_normalization_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    ~nspc_batch_normalization_fwd_t() {}
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+struct nspc_batch_normalization_bwd_t : public cpu_primitive_t {
+    struct pd_t : public cpu_batch_normalization_bwd_pd_t {
+        pd_t(engine_t *engine, const batch_normalization_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const batch_normalization_fwd_pd_t *hint_fwd_pd)
+            : cpu_batch_normalization_bwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+        {}
+
+        DECLARE_COMMON_PD_T("nspc_bnorm:any", nspc_batch_normalization_bwd_t);
+
+        status_t init() {
+            using namespace data_type;
+            using namespace prop_kind;
+
+            bool ok = true
+                /* the algorithm requires barriers while switching
+                 * between parallelization over N and C dimensions */
+                && mkldnn_thr_syncable()
+                && is_bwd()
+                && !has_zero_dim_memory()
+                && utils::everyone_is(f32, src_md()->data_type,
+                        diff_src_md()->data_type)
+                && IMPLICATION(use_scaleshift(),
+                        utils::everyone_is(f32,
+                            weights_md()->data_type,
+                            diff_weights_md()->data_type))
+                && memory_desc_matches_tag(*src_md(), format_tag::nhwc)
+                && memory_desc_matches_tag(*diff_src_md(), format_tag::nhwc)
+                && attr()->has_default_values();
+            if (!ok) return status::unimplemented;
+
+            if (fuse_bn_relu()) {
+                init_default_ws(8);
+                if (!compare_ws(hint_fwd_pd_))
+                    return status::unimplemented;
+            }
+
+            init_scratchpad();
+
+            return status::success;
+        }
+
+    private:
+        void init_scratchpad() {
+            using namespace memory_tracking::names;
+            auto scratchpad = scratchpad_registry().registrar();
+            scratchpad.book(key_bnorm_reduction,
+                    sizeof(data_t) * 2 * C() * mkldnn_get_max_threads());
+            scratchpad.book(key_bnorm_tmp_diff_ss, sizeof(data_t) * 2 * C()
+                    * (mkldnn_get_max_threads() + 1));
+        }
+    };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    nspc_batch_normalization_bwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    ~nspc_batch_normalization_bwd_t() {}
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp
new file mode 100644
index 0000000000..d79b1a034b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.cpp
@@ -0,0 +1,265 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <math.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "mkldnn_thread.hpp"
+#include "simple_q10n.hpp"
+
+#include "ref_batch_normalization.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t data_type>
+void ref_batch_normalization_fwd_t<data_type>::execute_forward(
+        const exec_ctx_t &ctx) const {
+    /* fast return */
+    if (this->pd()->has_zero_dim_memory()) return;
+
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto scaleshift = CTX_IN_MEM(const float *, MKLDNN_ARG_SCALE_SHIFT);
+
+    auto mean = pd()->stats_is_src()
+        ? const_cast<float *>(CTX_IN_MEM(const float *, MKLDNN_ARG_MEAN))
+        : CTX_OUT_MEM(float *, MKLDNN_ARG_MEAN);
+    auto variance = pd()->stats_is_src()
+        ? const_cast<float *>(CTX_IN_MEM(const float *, MKLDNN_ARG_VARIANCE))
+        : CTX_OUT_MEM(float *, MKLDNN_ARG_VARIANCE);
+
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+    auto ws = CTX_OUT_MEM(uint8_t *, MKLDNN_ARG_WORKSPACE);
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+    const memory_desc_wrapper scaleshift_d(pd()->weights_md());
+
+    const dim_t N = pd()->MB();
+    const dim_t C = pd()->C();
+    dim_t H = 1, W = 1, D = 1;
+    const bool has_spatial = utils::one_of(data_d.ndims(), 4, 5);
+    if (has_spatial) {
+        D = pd()->D();
+        H = pd()->H();
+        W = pd()->W();
+    }
+
+    const float eps = pd()->desc()->batch_norm_epsilon;
+    const bool use_scaleshift = pd()->use_scaleshift();;
+    const bool save_stats = pd()->is_training();
+    const bool is_training = pd()->is_training();
+    const bool fuse_bn_relu = pd()->fuse_bn_relu();
+    const bool calculate_stats = !pd()->stats_is_src();
+
+    const bool with_relu = pd()->with_relu_post_op();
+    auto maybe_post_op = [&](float res) {
+        return (with_relu && res < 0.0f) ? 0.0f : res;
+    };
+    const bool is_3d = data_d.ndims() == 5;
+
+    auto data_offset = [&](const memory_desc_wrapper &data_d, dim_t n, dim_t c,
+            dim_t d, dim_t h, dim_t w) {
+        if (has_spatial) {
+            if (is_3d)
+                return data_d.off(n, c, d, h, w);
+            else
+                return data_d.off(n, c, h, w);
+        } else
+            return data_d.off(n, c);
+    };
+
+    parallel_nd(C, [&](dim_t c) {
+        float v_mean = calculate_stats ? 0 : mean[c];
+        float v_variance = calculate_stats ? 0 : variance[c];
+
+        if (calculate_stats) {
+            for (dim_t n = 0; n < N; ++n)
+            for (dim_t d = 0; d < D; ++d)
+            for (dim_t h = 0; h < H; ++h)
+            for (dim_t w = 0; w < W; ++w)
+                v_mean += src[data_offset(data_d, n, c, d, h, w)];
+            v_mean /= W*N*H*D;
+
+            for (dim_t n = 0; n < N; ++n)
+            for (dim_t d = 0; d < D; ++d)
+            for (dim_t h = 0; h < H; ++h)
+            for (dim_t w = 0; w < W; ++w) {
+                float m = src[data_offset(data_d, n, c, d, h, w)] - v_mean;
+                v_variance += m*m;
+            }
+            v_variance /= W*H*N*D;
+        }
+
+        float sqrt_variance = sqrtf(v_variance + eps);
+        float sm = (use_scaleshift
+            ? scaleshift[scaleshift_d.off(0, c)]
+            : 1.0f) / sqrt_variance;
+        float sv = use_scaleshift ? scaleshift[scaleshift_d.off(1, c)] : 0;
+
+        for (dim_t n = 0; n < N; ++n)
+        for (dim_t d = 0; d < D; ++d)
+        for (dim_t h = 0; h < H; ++h)
+        for (dim_t w = 0; w < W; ++w) {
+            auto d_off = data_offset(data_d,n,c,d,h,w);
+            float bn_res = sm * ((float)src[d_off] - v_mean) + sv;
+            if (fuse_bn_relu) {
+                if (bn_res <= 0) {
+                    bn_res = 0;
+                    if (is_training)
+                        ws[d_off] = 0;
+                } else {
+                    if (is_training)
+                        ws[d_off] = 1;
+                }
+            }
+            if (data_type == data_type::s8) {
+                dst[d_off] = qz_a1b0<float, data_t>()(maybe_post_op(bn_res));
+            } else {
+                dst[d_off] = static_cast<data_t>(maybe_post_op(bn_res));
+            }
+        }
+
+        if (calculate_stats) {
+            if (save_stats) {
+                mean[c] = v_mean;
+                variance[c] = v_variance;
+            }
+        }
+    });
+}
+
+template struct ref_batch_normalization_fwd_t<data_type::f32>;
+template struct ref_batch_normalization_fwd_t<data_type::s8>;
+
+template <impl::data_type_t data_type>
+void ref_batch_normalization_bwd_t<data_type>::execute_backward(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto mean = CTX_IN_MEM(const data_t *, MKLDNN_ARG_MEAN);
+    auto variance = CTX_IN_MEM(const data_t *, MKLDNN_ARG_VARIANCE);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto scaleshift = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SCALE_SHIFT);
+    auto ws = CTX_IN_MEM(const uint8_t *, MKLDNN_ARG_WORKSPACE);
+
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+    auto diff_scaleshift = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SCALE_SHIFT);
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+    const memory_desc_wrapper diff_data_d(pd()->diff_src_md());
+    const memory_desc_wrapper scaleshift_d(pd()->weights_md());
+    const memory_desc_wrapper diff_scaleshift_d(pd()->diff_weights_md());
+
+    const dim_t C = pd()->C();
+
+    /* fast return */
+    if (this->pd()->has_zero_dim_memory()) {
+        if (diff_scaleshift) {
+            for (dim_t c = 0; c < C; ++c) {
+                diff_scaleshift[diff_scaleshift_d.off(0, c)] = 0;
+                diff_scaleshift[diff_scaleshift_d.off(1, c)] = 0;
+            }
+        }
+        return;
+    }
+
+    const dim_t N = pd()->MB();
+    dim_t H = 1, W = 1, D = 1;
+    const bool has_spatial = utils::one_of(data_d.ndims(), 4, 5);
+    if (has_spatial) {
+        D = pd()->D();
+        H = pd()->H();
+        W = pd()->W();
+    }
+
+    const float eps = pd()->desc()->batch_norm_epsilon;
+    const bool use_scaleshift = pd()->use_scaleshift();
+    const bool calculate_diff_stats = !pd()->use_global_stats();
+    const bool fuse_bn_relu = pd()->fuse_bn_relu();
+
+    const bool is_3d = data_d.ndims() == 5;
+
+    auto data_offset = [&](const memory_desc_wrapper &data_d, dim_t n, dim_t c,
+            dim_t d, dim_t h, dim_t w) {
+        if (has_spatial) {
+            if (is_3d)
+                return data_d.off(n, c, d, h, w);
+            else
+                return data_d.off(n, c, h, w);
+        } else
+            return data_d.off(n, c);
+    };
+
+    parallel_nd(C, [&](dim_t c) {
+        data_t v_mean = mean[c];
+        data_t v_variance = variance[c];
+        data_t sqrt_variance = static_cast<data_t>(1.0f / sqrtf(v_variance + eps));
+        data_t gamma = use_scaleshift ? scaleshift[scaleshift_d.off(0, c)] : 1;
+        data_t diff_gamma = data_t(0);
+        data_t diff_beta = data_t(0);
+        diff_gamma = 0.0;
+        diff_beta = 0.0;
+
+        for (dim_t n = 0; n < N; ++n)
+        for (dim_t d = 0; d < D; ++d)
+        for (dim_t h = 0; h < H; ++h)
+        for (dim_t w = 0; w < W; ++w) {
+            const size_t s_off = data_offset(data_d, n, c, d, h, w);
+            data_t dd = diff_dst[data_offset(diff_data_d, n, c, d, h, w)];
+            if (fuse_bn_relu && !ws[s_off])
+                dd = 0;
+
+            diff_gamma += (src[s_off] - v_mean) * dd;
+            diff_beta += dd;
+        }
+        diff_gamma *= sqrt_variance;
+
+        if (diff_scaleshift) {
+            diff_scaleshift[diff_scaleshift_d.off(0, c)] = diff_gamma;
+            diff_scaleshift[diff_scaleshift_d.off(1, c)] = diff_beta;
+        }
+
+        for (dim_t n = 0; n < N; ++n)
+        for (dim_t d = 0; d < D; ++d)
+        for (dim_t h = 0; h < H; ++h)
+        for (dim_t w = 0; w < W; ++w) {
+            const size_t s_off = data_offset(data_d, n, c, d, h, w);
+            const size_t dd_off = data_offset(diff_data_d, n, c, d, h, w);
+            data_t dd = diff_dst[dd_off];
+            if (fuse_bn_relu && !ws[s_off])
+                dd = 0;
+
+            data_t v_diff_src = dd;
+            if (calculate_diff_stats) {
+                v_diff_src -= diff_beta/(D*W*H*N) +
+                    (src[s_off] - v_mean) *
+                    diff_gamma*sqrt_variance/(D*W*H*N);
+            }
+            v_diff_src *= gamma*sqrt_variance;
+            diff_src[dd_off] = v_diff_src;
+        }
+    });
+}
+
+template struct ref_batch_normalization_bwd_t<data_type::f32>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.hpp
new file mode 100644
index 0000000000..aa9f74125a
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_batch_normalization.hpp
@@ -0,0 +1,127 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REF_BATCH_NORMALIZATION_HPP
+#define CPU_REF_BATCH_NORMALIZATION_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_batch_normalization_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t data_type>
+struct ref_batch_normalization_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_batch_normalization_fwd_pd_t {
+        pd_t(engine_t *engine, const batch_normalization_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const batch_normalization_fwd_pd_t *hint_fwd_pd)
+            : cpu_batch_normalization_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+        {}
+
+        DECLARE_COMMON_PD_T("ref:any", ref_batch_normalization_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && src_md()->data_type == data_type
+                && IMPLICATION(use_scaleshift(),
+                        weights_md()->data_type == data_type::f32)
+                && (attr()->has_default_values() || with_relu_post_op());
+            if (!ok) return status::unimplemented;
+
+            if (src_md()->data_type == data_type::s8 && !stats_is_src())
+                return status::unimplemented;
+
+            if (is_training() && fuse_bn_relu()) init_default_ws(8);
+
+            return status::success;
+        }
+    };
+
+    ref_batch_normalization_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t data_type>
+struct ref_batch_normalization_bwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_batch_normalization_bwd_pd_t {
+        pd_t(engine_t *engine, const batch_normalization_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const batch_normalization_fwd_pd_t *hint_fwd_pd)
+            : cpu_batch_normalization_bwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+        {}
+
+        DECLARE_COMMON_PD_T("ref:any", ref_batch_normalization_bwd_t);
+
+        status_t init() {
+            bool ok = true
+                && is_bwd()
+                && utils::everyone_is(data_type, src_md()->data_type,
+                        diff_src_md()->data_type)
+                && IMPLICATION(use_scaleshift(), utils::everyone_is(data_type,
+                            weights_md()->data_type,
+                            diff_weights_md()->data_type))
+                && attr()->has_default_values();
+            if (!ok) return status::unimplemented;
+
+            if (fuse_bn_relu()) {
+                init_default_ws(8);
+                if (!compare_ws(hint_fwd_pd_))
+                    return status::unimplemented;
+            }
+
+            return status::success;
+        }
+    };
+
+    ref_batch_normalization_bwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_concat.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_concat.hpp
new file mode 100644
index 0000000000..4c534b5508
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_concat.hpp
@@ -0,0 +1,97 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef REF_CONCAT_HPP
+#define REF_CONCAT_HPP
+
+#include "reorder_pd.hpp"
+
+#include "cpu_concat_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct ref_concat_t: public cpu_primitive_t {
+    struct pd_t: public cpu_concat_pd_t {
+        using cpu_concat_pd_t::cpu_concat_pd_t;
+
+        pd_t(const pd_t &rhs): cpu_concat_pd_t(rhs) {
+            for (size_t i = 0; i < rhs.reorder_pds_.size(); ++i)
+                reorder_pds_.push_back(
+                        (const reorder_pd_t *)rhs.reorder_pds_[i]->clone());
+        }
+        ~pd_t() { for (auto &rpd: reorder_pds_) delete rpd; }
+
+        DECLARE_CONCAT_PD_T("ref:any", ref_concat_t);
+
+        status_t init() {
+            bool ok = cpu_concat_pd_t::init() == status::success;
+            if (!ok) return status::unimplemented;
+
+            for (int i = 0; i < n_; ++i) {
+                auto r_impls = engine_->get_reorder_implementation_list();
+                for (auto r = r_impls; *r; ++r) {
+                    const primitive_attr_t attr; /* alpha == 1. */
+                    reorder_pd_t *r_pd = nullptr;
+                    if ((*r)(&r_pd, engine_, &attr, engine_, src_md(i),
+                                engine_, src_image_md(i)) == status::success) {
+                        r_pd->init_info();
+                        reorder_pds_.push_back(r_pd);
+                        break;
+                    }
+                }
+            }
+
+            ok = reorder_pds_.size() == (size_t)n_;
+            return ok ? status::success : status::unimplemented;
+        }
+
+        nstl::vector<const reorder_pd_t *> reorder_pds_;
+    };
+
+    ref_concat_t(const pd_t *apd): cpu_primitive_t(apd) {
+        const int n = pd()->n_inputs();
+        reorders_.resize(n);
+        for (int i = 0; i < n; ++i)
+            pd()->reorder_pds_[i]->create_primitive(&reorders_[i]);
+    }
+
+    ~ref_concat_t() { for (auto &r: reorders_) delete r; }
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        const auto n = pd()->n_inputs();
+        for (int i = 0; i < n; ++i) {
+            exec_args_t r_args;
+            r_args[MKLDNN_ARG_SRC] = ctx.args().at(MKLDNN_ARG_MULTIPLE_SRC + i);
+            r_args[MKLDNN_ARG_DST] = ctx.args().at(MKLDNN_ARG_DST);
+            exec_ctx_t r_ctx(ctx.stream(), std::move(r_args));
+            reorders_[i]->execute(r_ctx);
+        }
+        return status::success;
+    }
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    nstl::vector<primitive_t *> reorders_;
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp
new file mode 100644
index 0000000000..c0a979c4cf
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.cpp
@@ -0,0 +1,395 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+#include "mkldnn_traits.hpp"
+#include "type_helpers.hpp"
+
+#include "ref_convolution.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using math::saturate;
+using math::get_bias;
+
+template <data_type_t src_type, data_type_t wei_type,
+         data_type_t dst_type, data_type_t acc_type>
+void ref_convolution_fwd_t<src_type, wei_type, dst_type, acc_type>::
+execute_forward(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    const bool with_groups = pd()->with_groups();
+
+    const int G = pd()->G();
+    const int MB = pd()->MB();
+    const int OD = pd()->OD();
+    const int OH = pd()->OH();
+    const int OW = pd()->OW();
+    const int ID = pd()->ID();
+    const int IH = pd()->IH();
+    const int IW = pd()->IW();
+
+    const int OC = pd()->OC() / G;
+    const int IC = pd()->IC() / G;
+    const int KD = pd()->KD();
+    const int KH = pd()->KH();
+    const int KW = pd()->KW();
+
+    const int KSD = pd()->KSD();
+    const int KSH = pd()->KSH();
+    const int KSW = pd()->KSW();
+
+    const int KDD = pd()->KDD();
+    const int KDH = pd()->KDH();
+    const int KDW = pd()->KDW();
+
+    const int padFront = pd()->padFront();
+    const int padT = pd()->padT();
+    const int padL = pd()->padL();
+
+    const bool with_relu = 0; // TODO: change if support post_ops
+    const float nslope = 0.f;
+
+    const int ndims = pd()->desc()->src_desc.ndims;
+
+    auto ker = [=](int g, int mb, int oc, int od, int oh,
+            int ow) {
+        acc_data_t d = 0;
+        for (int ic = 0; ic < IC; ++ic)
+        for (int kd = 0; kd < KD; ++kd)
+        for (int kh = 0; kh < KH; ++kh)
+        for (int kw = 0; kw < KW; ++kw) {
+            const int id = od * KSD - padFront + kd * (1 + KDD);
+            const int ih = oh * KSH - padT + kh * (1 + KDH);
+            const int iw = ow * KSW - padL + kw * (1 + KDW);
+
+            if (id < 0 || id >= ID) continue;
+            if (ih < 0 || ih >= IH) continue;
+            if (iw < 0 || iw >= IW) continue;
+
+            if (ndims == 5)
+                d += (acc_data_t)src[src_d.off(mb, g*IC + ic, id, ih, iw)]
+                    * (with_groups
+                    ? weights[weights_d.off(g, oc, ic, kd, kh, kw)]
+                    : weights[weights_d.off(oc, ic, kd, kh, kw)]);
+            else if (ndims == 4)
+                d += (acc_data_t)src[src_d.off(mb, g*IC + ic, ih, iw)]
+                    * (with_groups
+                    ? weights[weights_d.off(g, oc, ic, kh, kw)]
+                    : weights[weights_d.off(oc, ic, kh, kw)]);
+            else if (ndims == 3)
+                d += (acc_data_t)src[src_d.off(mb, g*IC + ic, iw)]
+                    * (with_groups
+                    ? weights[weights_d.off(g, oc, ic, kw)]
+                    : weights[weights_d.off(oc, ic, kw)]);
+           else
+               assert(false);
+
+        }
+        return d;
+    };
+
+    parallel_nd(G, MB, OC, OD, OH, OW,
+        [&](int g, int mb, int oc, int od, int oh, int ow) {
+        float a = bias
+            ? get_bias(bias, bias_d.off(g * OC + oc),
+                    pd()->desc()->bias_desc.data_type)
+            : 0;
+        a += ker(g, mb, oc, od, oh, ow);
+        if (with_relu && a < 0)
+            a = a * nslope;
+        if (ndims == 5)
+            dst[dst_d.off(mb, g*OC + oc, od, oh, ow)] = saturate<dst_data_t>(a);
+        else if (ndims == 4)
+            dst[dst_d.off(mb, g*OC + oc, oh, ow)] = saturate<dst_data_t>(a);
+        else if (ndims == 3)
+            dst[dst_d.off(mb, g*OC + oc, ow)] = saturate<dst_data_t>(a);
+        else
+            assert(false);
+   });
+}
+
+template <data_type_t diff_src_type, data_type_t wei_type,
+         data_type_t diff_dst_type, data_type_t acc_type>
+void ref_convolution_bwd_data_t<diff_src_type, wei_type, diff_dst_type,
+     acc_type>::execute_backward_data(const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto diff_src = CTX_OUT_MEM(diff_src_data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    const bool with_groups = pd()->with_groups();
+
+    const int G = pd()->G();
+    const int MB = pd()->MB();
+    const int OD = pd()->OD();
+    const int OH = pd()->OH();
+    const int OW = pd()->OW();
+    const int ID = pd()->ID();
+    const int IH = pd()->IH();
+    const int IW = pd()->IW();
+
+    const int OC = pd()->OC() / G;
+    const int IC = pd()->IC() / G;
+    const int KD = pd()->KD();
+    const int KH = pd()->KH();
+    const int KW = pd()->KW();
+
+    const int KSD = pd()->KSD();
+    const int KSH = pd()->KSH();
+    const int KSW = pd()->KSW();
+
+    const int KDD = pd()->KDD();
+    const int KDH = pd()->KDH();
+    const int KDW = pd()->KDW();
+
+    const int padFront = pd()->padFront();
+    const int padT = pd()->padT();
+    const int padL = pd()->padL();
+
+    const int ndims = pd()->desc()->diff_src_desc.ndims;
+
+    auto ker = [=](int g, int mb, int ic, int id, int ih,
+            int iw) {
+        acc_data_t d = 0;
+        for (int oc = 0; oc < OC; ++oc)
+        for (int kd = 0; kd < KD; ++kd)
+        for (int kh = 0; kh < KH; ++kh)
+        for (int kw = 0; kw < KW; ++kw) {
+            if (iw + padL < kw * (1 + KDW)
+                || ih + padT < kh * (1 + KDH)
+                || id + padFront < kd * (1 + KDD))
+                continue;
+            int ow = iw - kw * (1 + KDW) + padL;
+            int oh = ih - kh * (1 + KDH) + padT;
+            int od = id - kd * (1 + KDD) + padFront;
+            if (ow % KSW != 0 || oh % KSH != 0 || od % KSD != 0)
+                continue;
+
+            ow /= KSW;
+            oh /= KSH;
+            od /= KSD;
+
+            if (od < OD && oh < OH && ow < OW) {
+                if (ndims == 5)
+                    d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC
+                        + oc, od, oh, ow)] * (with_groups
+                        ? weights[weights_d.off(g, oc, ic, kd, kh, kw)]
+                        : weights[weights_d.off(oc, ic, kd, kh, kw)]);
+                else if (ndims == 4)
+                    d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC
+                        + oc, oh, ow)] * (with_groups
+                        ? weights[weights_d.off(g, oc, ic, kh, kw)]
+                        : weights[weights_d.off(oc, ic, kh, kw)]);
+                else if (ndims == 3)
+                    d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC
+                        + oc, ow)] * (with_groups
+                        ? weights[weights_d.off(g, oc, ic, kw)]
+                        : weights[weights_d.off(oc, ic, kw)]);
+                else
+                    assert(false);
+            }
+        }
+        return d;
+    };
+
+    parallel_nd(G, MB, IC, ID, IH, IW,
+        [&](int g, int mb, int ic, int id, int ih, int iw) {
+        auto ds_idx = (ndims == 5)
+            ? diff_src_d.off(mb, g*IC + ic, id, ih, iw)
+            : (ndims == 4)
+            ? diff_src_d.off(mb, g*IC + ic, ih, iw)
+            : diff_src_d.off(mb, g*IC + ic, iw);
+        float a = bias
+            ? get_bias(bias, bias_d.off(g * IC + ic),
+                    pd()->desc()->bias_desc.data_type)
+            : 0;
+        a += ker(g, mb, ic, id, ih, iw);
+        diff_src[ds_idx] = saturate<diff_src_data_t>(a);
+    });
+}
+
+template <data_type_t src_type, data_type_t diff_wei_type,
+         data_type_t diff_dst_type, data_type_t acc_type>
+void ref_convolution_bwd_weights_t<src_type, diff_wei_type, diff_dst_type,
+     acc_type>::execute_backward_weights(const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST);
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto diff_weights = CTX_OUT_MEM(diff_wei_data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
+    auto diff_bias = CTX_OUT_MEM(diff_wei_data_t *, MKLDNN_ARG_DIFF_BIAS);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
+    const memory_desc_wrapper diff_bias_d(pd()->diff_weights_md(1));
+
+    const bool with_groups = pd()->with_groups();
+
+    const int G = pd()->G();
+    const int MB = pd()->MB();
+    const int OD = pd()->OD();
+    const int OH = pd()->OH();
+    const int OW = pd()->OW();
+    const int ID = pd()->ID();
+    const int IH = pd()->IH();
+    const int IW = pd()->IW();
+
+    const int OC = pd()->OC() / G;
+    const int IC = pd()->IC() / G;
+    const int KD = pd()->KD();
+    const int KH = pd()->KH();
+    const int KW = pd()->KW();
+
+    const int KSD = pd()->KSD();
+    const int KSH = pd()->KSH();
+    const int KSW = pd()->KSW();
+
+    const int KDD = pd()->KDD();
+    const int KDH = pd()->KDH();
+    const int KDW = pd()->KDW();
+
+    const int padFront = pd()->padFront();
+    const int padT = pd()->padT();
+    const int padL = pd()->padL();
+
+    const int ndims = pd()->desc()->src_desc.ndims;
+
+auto ker = [=](acc_data_t &d, int g, int oc, int ic, int kd, int kh, int kw) {
+        for (int mb = 0; mb < MB; ++mb)
+        for (int od = 0; od < OD; ++od)
+        for (int oh = 0; oh < OH; ++oh)
+        for (int ow = 0; ow < OW; ++ow) {
+            if (ow*KSW + kw * (1 + KDW) < padL
+                || oh*KSH + kh * (1 + KDH) < padT
+                || od*KSD + kd * (1 + KDD) < padFront
+                || ow*KSW + kw * (1 + KDW) >= IW + padL
+                || oh*KSH + kh * (1 + KDH) >= IH + padT
+                || od*KSD + kd * (1 + KDD) >= ID + padFront)
+                continue;
+
+            int id = od*KSD - padFront + kd * (1 + KDD);
+            int ih = oh*KSH - padT + kh * (1 + KDH);
+            int iw = ow*KSW - padL + kw * (1 + KDW);
+            if (ndims == 5)
+                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, od,
+                    oh, ow)] * src[src_d.off(mb, g*IC + ic, id, ih, iw)];
+            else if (ndims == 4)
+                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, oh, ow)]
+                    * src[src_d.off(mb, g*IC + ic, ih, iw)];
+            else if (ndims == 3)
+                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, ow)]
+                    * src[src_d.off(mb, g*IC + ic, iw)];
+            else
+                assert(false);
+        }
+    };
+
+    auto ker_bias = [=](acc_data_t &d, int g, int oc) {
+        for (int mb = 0; mb < MB; ++mb)
+        for (int od = 0; od < OD; ++od)
+        for (int oh = 0; oh < OH; ++oh)
+        for (int ow = 0; ow < OW; ++ow) {
+            if (ndims == 5)
+                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, od, oh,
+                     ow)];
+            else if (ndims == 4)
+                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, oh,
+                     ow)];
+            else if (ndims == 3)
+                d += (acc_data_t)diff_dst[diff_dst_d.off(mb, g*OC + oc, ow)];
+            else
+                assert(false);
+        }
+    };
+
+    parallel_nd(G, OC, [&](int g, int oc) {
+        if (diff_bias) {
+            // XXX: loss of precision when bias is a float...
+            acc_data_t db = 0;
+            ker_bias(db, g, oc);
+            diff_bias[diff_bias_d.off(g*OC+oc)]
+                = saturate<diff_wei_data_t>(db);
+        }
+
+        for (int ic = 0; ic < IC; ++ic)
+        for (int kd = 0; kd < KD; ++kd)
+        for (int kh = 0; kh < KH; ++kh)
+        for (int kw = 0; kw < KW; ++kw) {
+            acc_data_t dw = 0;
+            ker(dw, g, oc, ic, kd, kh, kw);
+
+            if (ndims == 5) {
+                auto idx = with_groups
+                    ? diff_weights_d.off(g, oc, ic, kd, kh, kw)
+                    : diff_weights_d.off(oc, ic, kd, kh, kw);
+                    diff_weights[idx] = saturate<diff_wei_data_t>(dw);
+            } else if (ndims == 4) {
+                auto idx = with_groups
+                    ? diff_weights_d.off(g, oc, ic, kh, kw)
+                    : diff_weights_d.off(oc, ic, kh, kw);
+                    diff_weights[idx] = saturate<diff_wei_data_t>(dw);
+            } else if (ndims == 3) {
+                auto idx = with_groups
+                    ? diff_weights_d.off(g, oc, ic, kw)
+                    : diff_weights_d.off(oc, ic, kw);
+                    diff_weights[idx] = saturate<diff_wei_data_t>(dw);
+            } else {
+                 assert(false);
+            }
+        }
+    });
+}
+
+using namespace data_type;
+
+template struct ref_convolution_fwd_t<f32>;
+
+template struct ref_convolution_fwd_t<u8, s8, f32, s32>;
+template struct ref_convolution_fwd_t<u8, s8, s32, s32>;
+template struct ref_convolution_fwd_t<u8, s8, s8, s32>;
+template struct ref_convolution_fwd_t<u8, s8, u8, s32>;
+
+template struct ref_convolution_bwd_data_t<f32, f32, f32, f32>;
+
+template struct ref_convolution_bwd_data_t<f32, s8, u8, s32>;
+template struct ref_convolution_bwd_data_t<s32, s8, u8, s32>;
+template struct ref_convolution_bwd_data_t<s8, s8, u8, s32>;
+template struct ref_convolution_bwd_data_t<u8, s8, u8, s32>;
+
+template struct ref_convolution_bwd_weights_t<f32, f32, f32, f32>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.hpp
new file mode 100644
index 0000000000..7c83d0c6d4
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_convolution.hpp
@@ -0,0 +1,194 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REF_CONVOLUTION_HPP
+#define CPU_REF_CONVOLUTION_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t src_type,
+         impl::data_type_t wei_type = src_type,
+         impl::data_type_t dst_type = src_type,
+         impl::data_type_t acc_type = dst_type>
+struct ref_convolution_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_fwd_pd_t {
+        using cpu_convolution_fwd_pd_t::cpu_convolution_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_convolution_fwd_t);
+
+        status_t init() {
+            using namespace data_type;
+
+            bool ok = true
+                && is_fwd()
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(src_type, wei_type, data_type::undef,
+                        dst_type, acc_type)
+                && IMPLICATION(with_bias(), true
+                        && IMPLICATION(src_type == u8,
+                            utils::one_of(bias_md_.data_type, f32, s32, s8, u8))
+                        && IMPLICATION(src_type == f32,
+                            bias_md_.data_type == f32))
+                && set_default_formats()
+                && attr()->has_default_values();
+            return ok ? status::success : status::unimplemented;
+        }
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+            auto dat_tag = utils::pick(ndims() - 3, ncw, nchw, ncdhw);
+            auto wei_tag = with_groups()
+                ? utils::pick(ndims() - 3, goiw, goihw, goidhw)
+                : utils::pick(ndims() - 3, oiw, oihw, oidhw);
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    ref_convolution_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<wei_type>::type wei_data_t;
+    typedef typename prec_traits<dst_type>::type dst_data_t;
+    typedef typename prec_traits<acc_type>::type acc_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t diff_src_type, impl::data_type_t wei_type,
+         impl::data_type_t diff_dst_type,
+         impl::data_type_t acc_type = diff_src_type>
+struct ref_convolution_bwd_data_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_bwd_data_pd_t {
+        using cpu_convolution_bwd_data_pd_t::cpu_convolution_bwd_data_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_convolution_bwd_data_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_data
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(diff_src_type, wei_type, data_type::undef,
+                        diff_dst_type, acc_type)
+                && set_default_formats()
+                && attr()->has_default_values();
+
+            return ok ? status::success : status::unimplemented;
+        }
+
+        virtual bool support_bias() const override { return true; }
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+            auto dat_tag = utils::pick(ndims() - 3, ncw, nchw, ncdhw);
+            auto wei_tag = with_groups()
+                ? utils::pick(ndims() - 3, goiw, goihw, goidhw)
+                : utils::pick(ndims() - 3, oiw, oihw, oidhw);
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    ref_convolution_bwd_data_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    typedef typename prec_traits<diff_src_type>::type diff_src_data_t;
+    typedef typename prec_traits<wei_type>::type wei_data_t;
+    typedef typename prec_traits<diff_dst_type>::type diff_dst_data_t;
+    typedef typename prec_traits<acc_type>::type acc_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_data(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_data(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t src_type, impl::data_type_t diff_wei_type,
+         impl::data_type_t diff_dst_type,
+         impl::data_type_t acc_type = diff_wei_type>
+struct ref_convolution_bwd_weights_t: public cpu_primitive_t {
+    struct pd_t: public cpu_convolution_bwd_weights_pd_t {
+        using cpu_convolution_bwd_weights_pd_t::cpu_convolution_bwd_weights_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_convolution_bwd_weights_t);
+
+        status_t init() {
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_weights
+                && set_default_alg_kind(alg_kind::convolution_direct)
+                && expect_data_types(src_type, diff_wei_type, diff_wei_type,
+                        diff_dst_type, acc_type)
+                && set_default_formats()
+                && attr()->has_default_values();
+            return ok ? status::success : status::unimplemented;
+        }
+
+    protected:
+        bool set_default_formats() {
+            using namespace format_tag;
+            auto dat_tag = utils::pick(ndims() - 3, ncw, nchw, ncdhw);
+            auto wei_tag = with_groups()
+                ? utils::pick(ndims() - 3, goiw, goihw, goidhw)
+                : utils::pick(ndims() - 3, oiw, oihw, oidhw);
+            return set_default_formats_common(dat_tag, wei_tag, dat_tag);
+        }
+    };
+
+    ref_convolution_bwd_weights_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<diff_wei_type>::type diff_wei_data_t;
+    typedef typename prec_traits<diff_dst_type>::type diff_dst_data_t;
+    typedef typename prec_traits<acc_type>::type acc_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_weights(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_weights(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_deconvolution.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_deconvolution.cpp
new file mode 100644
index 0000000000..541a303aab
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_deconvolution.cpp
@@ -0,0 +1,199 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "mkldnn_thread.hpp"
+#include "mkldnn_traits.hpp"
+#include "math_utils.hpp"
+
+#include "ref_deconvolution.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+void ref_deconvolution_fwd_t::compute_fwd_bias(const data_t *bias,
+        data_t *dst) const {
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+
+    const int G = pd()->G();
+    const int MB = pd()->MB();
+    const int OH = pd()->OH();
+    const int OW = pd()->OW();
+    const int OD = pd()->OD();
+    const int OC = pd()->OC() / G;
+    const int ndims = pd()->desc()->src_desc.ndims;
+
+    parallel_nd(MB, G, OC, OD, OH, OW,
+        [&](int mb, int g, int oc, int od, int oh, int ow) {
+            auto b = bias[g * OC + oc];
+            switch (ndims) {
+            case 5: dst[dst_d.off(mb, g * OC + oc, od, oh, ow)] += b; break;
+            case 4: dst[dst_d.off(mb, g * OC + oc, oh, ow)] += b; break;
+            case 3: dst[dst_d.off(mb, g * OC + oc, ow)] += b; break;
+            default: assert(!"invalid dimension size");
+            }
+    });
+}
+
+void ref_deconvolution_fwd_t::compute_fwd_bias_ncdhw(const data_t *bias,
+        data_t *dst) const {
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+
+    const int MB = pd()->MB();
+    const int OC = pd()->OC();
+    const int SP = pd()->OW()*pd()->OH()*pd()->OD();
+
+    parallel_nd(MB, OC, [&](int mb, int oc) {
+        PRAGMA_OMP_SIMD()
+        for (int sp = 0; sp < SP; ++sp) {
+            auto offset = (size_t)(mb * OC + oc) * SP + sp;
+            dst[offset] += bias[oc];
+        }
+    });
+}
+
+template <int blksize>
+void ref_deconvolution_fwd_t::compute_fwd_bias_nCdhwXc(const data_t *bias,
+        data_t *dst) const {
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+
+    const int MB = pd()->MB();
+    const int OC = pd()->OC();
+    const int SP = pd()->OW() * pd()->OH() * pd()->OD();
+
+    const ptrdiff_t stride_mb = dst_d.blocking_desc().strides[0];
+
+    parallel_nd(MB, utils::div_up(OC, blksize), SP,
+        [&](int mb, int oc_blk, int sp) {
+        int oc = oc_blk * blksize;
+        auto offset = mb * stride_mb + oc * SP + sp * blksize;
+        const int blk = nstl::min(blksize, OC - oc);
+
+        PRAGMA_OMP_SIMD()
+        for (int i = 0; i < blk; ++i)
+            dst[offset + i] += bias[oc + i];
+    });
+}
+
+void ref_deconvolution_bwd_weights_t::compute_bwd_bias(const data_t *diff_dst,
+        data_t *diff_bias) const {
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+
+    const int G = pd()->G();
+    const int MB = pd()->MB();
+    const int OH = pd()->OH();
+    const int OW = pd()->OW();
+    const int OC = pd()->OC() / G;
+    const int OD = pd()->OD();
+    const int ndims = pd()->desc()->src_desc.ndims;
+
+    parallel_nd(G, OC, [&](int g, int oc) {
+        data_t db = 0;
+        for (int mb = 0; mb < MB; ++mb) {
+            for (int od = 0; od < OD; ++od) {
+                for (int oh = 0; oh < OH; ++oh) {
+                    for (int ow = 0; ow < OW; ++ow) {
+                        switch (ndims) {
+                        case 5:
+                            db += diff_dst[diff_dst_d.off(
+                                    mb, g * OC + oc, od, oh, ow)];
+                            break;
+                        case 4:
+                            db += diff_dst[diff_dst_d.off(
+                                    mb, g * OC + oc, oh, ow)];
+                            break;
+                        case 3:
+                            db += diff_dst[diff_dst_d.off(mb, g * OC + oc, ow)];
+                            break;
+                        default: assert(!"invalid dimension size");
+                        }
+                    }
+                }
+            }
+        }
+        diff_bias[g * OC + oc] = db;
+    });
+}
+
+void ref_deconvolution_bwd_weights_t::compute_bwd_bias_ncdhw(
+        const data_t *diff_dst, data_t *diff_bias) const {
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+
+    const int OC = pd()->OC();
+    const int MB = pd()->MB();
+    const int SP = pd()->OH()*pd()->OW()*pd()->OD();
+
+    parallel_nd(OC, [&](int oc) {
+        data_t db = 0;
+        for (int mb = 0; mb < MB; ++mb) {
+            PRAGMA_OMP_SIMD()
+            for (int sp = 0; sp < SP; ++sp) {
+                auto offset = (size_t)(mb * OC + oc) * SP + sp;
+                db += diff_dst[offset];
+            }
+        }
+        diff_bias[oc] = db;
+    });
+}
+
+template <int blksize>
+void ref_deconvolution_bwd_weights_t::compute_bwd_bias_nCdhwXc(
+        const data_t *diff_dst, data_t *diff_bias) const {
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+
+    const int OC = pd()->OC();
+    const int MB = pd()->MB();
+    const int SP = pd()->OH() * pd()->OW() * pd()->OD();
+
+    const ptrdiff_t stride_mb = diff_dst_d.blocking_desc().strides[0];
+
+    parallel_nd(utils::div_up(OC, blksize), [&](int ocb) {
+        data_t db[blksize] = {0};
+
+        for (int mb = 0; mb < MB; ++mb) {
+            for (int sp = 0; sp < SP; ++sp) {
+                auto offset = mb * stride_mb + (ocb * SP + sp) * blksize;
+
+                PRAGMA_OMP_SIMD()
+                for (int i = 0; i < blksize; ++i)
+                    db[i] += diff_dst[offset+i];
+            }
+        }
+
+        const int blk = nstl::min(blksize, OC - ocb * blksize);
+
+        PRAGMA_OMP_SIMD()
+        for (int i = 0; i < blk; ++i)
+            diff_bias[ocb * blksize + i] = db[i];
+    });
+}
+
+template void ref_deconvolution_fwd_t::compute_fwd_bias_nCdhwXc<8>(
+        const data_t *diff_dst, data_t *diff_bias) const;
+template void ref_deconvolution_fwd_t::compute_fwd_bias_nCdhwXc<16>(
+        const data_t *diff_dst, data_t *diff_bias) const;
+template void ref_deconvolution_bwd_weights_t::compute_bwd_bias_nCdhwXc<8>(
+        const data_t *diff_dst, data_t *diff_bias) const;
+template void ref_deconvolution_bwd_weights_t::compute_bwd_bias_nCdhwXc<16>(
+        const data_t *diff_dst, data_t *diff_bias) const;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_deconvolution.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_deconvolution.hpp
new file mode 100644
index 0000000000..d61903c32d
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_deconvolution.hpp
@@ -0,0 +1,502 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REF_DECONVOLUTION_HPP
+#define CPU_REF_DECONVOLUTION_HPP
+
+#include <assert.h>
+#include <string.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+#include "primitive_iterator.hpp"
+
+#include "cpu_convolution_pd.hpp"
+#include "cpu_deconvolution_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+static status_t compute_blocked_format(bool with_groups,
+        const memory_desc_t *oi_md, memory_desc_t *io_md)
+{
+    /* Computes blocking for *i*o* format from *o*i* format */
+
+    bool sanity_check_ok = true
+        && oi_md->ndims == io_md->ndims
+        && oi_md->format_kind == format_kind::blocked;
+    if (!sanity_check_ok) return status::invalid_arguments;
+
+    const blocking_desc_t &oi_blk = oi_md->format_desc.blocking;
+    blocking_desc_t io_blk = io_md->format_desc.blocking;
+
+    io_md->format_kind = format_kind::blocked;
+    io_blk = oi_blk;
+
+    const int ID_OC = 0 + with_groups;
+    const int ID_IC = 1 + with_groups;
+
+    nstl::swap(io_blk.strides[ID_OC], io_blk.strides[ID_IC]);
+    for (int i_blk = 0; i_blk < io_blk.inner_nblks; ++i_blk) {
+        if (utils::one_of(io_blk.inner_idxs[i_blk], ID_OC, ID_IC)) {
+            io_blk.inner_idxs[i_blk] =
+                (io_blk.inner_idxs[i_blk] == ID_OC ? ID_IC : ID_OC);
+        }
+    }
+
+    return memory_desc_init_by_blocking_desc(*io_md, io_blk);
+}
+
+static status_t conv_descr_create(const deconvolution_desc_t *dd,
+        convolution_desc_t *cd)
+{
+    using namespace prop_kind;
+    alg_kind_t alg_kind = dd->alg_kind == alg_kind::deconvolution_direct
+        ? alg_kind::convolution_direct : alg_kind::convolution_winograd;
+
+    const memory_desc_t *src_md, *dst_md, *d_weights_d;
+    prop_kind_t prop_kind;
+    memory_desc_t c_weights_d;
+    if (utils::one_of(dd->prop_kind, forward_training, forward_inference)) {
+        prop_kind = backward_data;
+        src_md = &dd->dst_desc;
+        dst_md = &dd->src_desc;
+        d_weights_d = &dd->weights_desc;
+    } else if (dd->prop_kind == backward_data) {
+        prop_kind = forward_training;
+        src_md = &dd->diff_dst_desc;
+        dst_md = &dd->diff_src_desc;
+        d_weights_d = &dd->weights_desc;
+    } else {
+        prop_kind = dd->prop_kind;
+        src_md = &dd->diff_dst_desc;
+        dst_md = &dd->src_desc;
+        d_weights_d = &dd->diff_weights_desc;
+    }
+
+    const bool with_groups = d_weights_d->ndims == src_md->ndims + 1;
+
+    /* create weights desc for convolution */
+    c_weights_d = *d_weights_d;
+
+    const int ID_OC = 0 + with_groups;
+    const int ID_IC = 1 + with_groups;
+
+    nstl::swap(c_weights_d.dims[ID_OC], c_weights_d.dims[ID_IC]);
+    nstl::swap(c_weights_d.padded_dims[ID_OC], c_weights_d.padded_dims[ID_IC]);
+    nstl::swap(c_weights_d.padded_offsets[ID_OC], c_weights_d.padded_offsets[ID_IC]);
+
+    if (c_weights_d.format_kind != format_kind::any)
+        CHECK(compute_blocked_format(with_groups, d_weights_d, &c_weights_d));
+
+    return conv_desc_init(cd, prop_kind, alg_kind, src_md, &c_weights_d,
+            prop_kind != backward_weights ? &dd->bias_desc : nullptr,
+            dst_md, dd->strides, dd->dilates,
+            dd->padding[0], dd->padding[1], dd->padding_kind);
+}
+
+struct ref_deconvolution_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_deconvolution_fwd_pd_t {
+        pd_t(engine_t *engine,
+                const deconvolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const deconvolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_deconvolution_fwd_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , conv_pd_(nullptr)
+        {}
+
+        pd_t(const pd_t &other)
+            : cpu_deconvolution_fwd_pd_t(other)
+            , conv_pd_(other.conv_pd_->clone())
+            , conv_supports_bias_(other.conv_supports_bias_)
+            , dst_tag_(other.dst_tag_)
+        {}
+
+        ~pd_t() { delete conv_pd_; }
+
+        DECLARE_COMMON_PD_T(conv_pd_->name(), ref_deconvolution_fwd_t);
+
+        status_t init_convolution() {
+            using namespace types;
+
+            convolution_desc_t cd;
+            CHECK(conv_descr_create(desc(), &cd));
+
+            mkldnn_primitive_desc_iterator it(engine_, (op_desc_t *)&cd,
+                &attr_, nullptr);
+            while (++it != it.end()) {
+                conv_pd_ = *it;
+                conv_supports_bias_ =
+                    static_cast<cpu_convolution_bwd_data_pd_t *>(conv_pd_)
+                    ->support_bias();
+                bool output_f32 = utils::everyone_is(data_type::f32,
+                        desc()->accum_data_type, desc()->dst_desc.data_type);
+
+                bool ok = true
+                    && conv_pd_->weights_md()->extra.flags == 0
+                    /* deconv reference code can process only f32 bias */
+                    && IMPLICATION(with_bias(),
+                            conv_supports_bias_ || output_f32);
+                if (ok) return status::success;
+
+                delete conv_pd_;
+            }
+            conv_pd_ = nullptr;
+            return status::unimplemented;
+        }
+
+        status_t init() {
+            using namespace format_tag;
+            bool ok = true
+                && is_fwd()
+                && utils::one_of(desc()->alg_kind,
+                        alg_kind::deconvolution_direct,
+                        alg_kind::deconvolution_winograd)
+                && attr()->post_ops_.has_default_values();
+
+            if (ok) {
+                CHECK(init_convolution());
+                if (weights_md_.format_kind == format_kind::any) {
+                    CHECK(compute_blocked_format(with_groups(),
+                                conv_pd_->weights_md(), &desc_.weights_desc));
+                    weights_md_ = desc_.weights_desc;
+                }
+                if (src_md_.format_kind == format_kind::any)
+                    src_md_ = *conv_pd_->diff_dst_md();
+                if (dst_md_.format_kind == format_kind::any)
+                    dst_md_ = *conv_pd_->diff_src_md();
+                if (bias_md_.format_kind == format_kind::any)
+                    CHECK(memory_desc_init_by_tag(bias_md_, x));
+
+                dst_tag_ = memory_desc_matches_one_of_tag(dst_md_,
+                        utils::pick(ndims() - 3, ncw, nchw, ncdhw),
+                        utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c),
+                        utils::pick(ndims() - 3, nCw16c, nChw16c, nCdhw16c));
+
+                return status::success;
+            }
+
+            return status::unimplemented;
+        }
+
+        virtual void init_scratchpad_md() override {
+            scratchpad_md_ = *conv_pd_->scratchpad_md();
+        }
+
+        primitive_desc_t *conv_pd_;
+        bool conv_supports_bias_;
+        format_tag_t dst_tag_;
+    };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    ref_deconvolution_fwd_t(const pd_t *apd): cpu_primitive_t(apd)
+    { pd()->conv_pd_->create_primitive((primitive_t **)&conv_p_); }
+    ~ref_deconvolution_fwd_t() { delete conv_p_; }
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        const auto &args = ctx.args();
+        exec_args_t conv_args;
+        conv_args[MKLDNN_ARG_DIFF_DST] = args.at(MKLDNN_ARG_SRC);
+        conv_args[MKLDNN_ARG_WEIGHTS] = args.at(MKLDNN_ARG_WEIGHTS);
+        if (pd()->with_bias() && pd()->conv_supports_bias_)
+            conv_args[MKLDNN_ARG_BIAS] = args.at(MKLDNN_ARG_BIAS);
+        conv_args[MKLDNN_ARG_DIFF_SRC] = args.at(MKLDNN_ARG_DST);
+        if (!types::is_zero_md(pd()->scratchpad_md()))
+            conv_args[MKLDNN_ARG_SCRATCHPAD] = args.at(MKLDNN_ARG_SCRATCHPAD);
+        const exec_ctx_t conv_ctx(ctx.stream(), std::move(conv_args));
+
+        conv_p_->execute(conv_ctx);
+
+        if (pd()->with_bias() && !pd()->conv_supports_bias_) {
+            using namespace format_tag;
+
+            auto bias = CTX_IN_MEM(const data_t *, MKLDNN_ARG_BIAS);
+            auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+            switch (pd()->dst_tag_) {
+            case ncdhw: case nchw: case ncw:
+                compute_fwd_bias_ncdhw(bias, dst);
+                break;
+            case nCdhw8c: case nChw8c: case nCw8c:
+                compute_fwd_bias_nCdhwXc<8>(bias, dst);
+                break;
+            case nCdhw16c: case nChw16c: case nCw16c:
+                compute_fwd_bias_nCdhwXc<16>(bias, dst);
+                break;
+            default:
+                compute_fwd_bias(bias, dst);
+                break;
+            }
+        }
+        return status::success;
+    }
+
+private:
+    void compute_fwd_bias(const data_t *bias, data_t *dst) const;
+    void compute_fwd_bias_ncdhw(const data_t *bias, data_t *dst) const;
+    template <int blksize> void compute_fwd_bias_nCdhwXc(const data_t *bias,
+            data_t *dst) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    primitive_t *conv_p_;
+};
+
+struct ref_deconvolution_bwd_data_t: public cpu_primitive_t {
+    struct pd_t: public cpu_deconvolution_bwd_data_pd_t {
+        pd_t(engine_t *engine, const deconvolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const deconvolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_deconvolution_bwd_data_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , conv_pd_(nullptr)
+        {}
+
+        pd_t(const pd_t &other)
+            : cpu_deconvolution_bwd_data_pd_t(other)
+            , conv_pd_(other.conv_pd_->clone()) {}
+
+        ~pd_t() { delete conv_pd_; }
+
+        DECLARE_COMMON_PD_T(conv_pd_->name(), ref_deconvolution_bwd_data_t);
+
+        status_t init_convolution() {
+            using namespace types;
+
+            convolution_desc_t cd;
+            status_t status = conv_descr_create(desc(), &cd);
+            if (status != status::success) return status;
+
+            mkldnn_primitive_desc_iterator it(engine_, (op_desc_t *)&cd,
+                    &attr_, nullptr);
+            while (++it != it.end()) {
+                conv_pd_ = *it;
+                if (conv_pd_->weights_md()->extra.flags == 0)
+                    return status::success;
+                delete conv_pd_;
+            }
+
+            return status::unimplemented;
+        }
+
+        status_t init() {
+            using namespace data_type;
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_data
+                && utils::everyone_is(data_type::f32,
+                        desc()->diff_src_desc.data_type,
+                        desc()->weights_desc.data_type,
+                        desc()->diff_dst_desc.data_type)
+                && utils::one_of(desc()->alg_kind,
+                        alg_kind::deconvolution_direct,
+                        alg_kind::deconvolution_winograd);
+
+            if (ok) {
+                CHECK(init_convolution());
+                if (weights_md_.format_kind == format_kind::any) {
+                    CHECK(compute_blocked_format(with_groups(),
+                                conv_pd_->weights_md(), &desc_.weights_desc));
+                    weights_md_ = desc_.weights_desc;
+                }
+                if (diff_src_md_.format_kind == format_kind::any)
+                    diff_src_md_ = *conv_pd_->dst_md();
+                if (diff_dst_md_.format_kind == format_kind::any)
+                    diff_dst_md_ = *conv_pd_->src_md();
+
+                return status::success;
+            }
+
+            return status::unimplemented;
+        }
+
+        virtual void init_scratchpad_md() override {
+            scratchpad_md_ = *conv_pd_->scratchpad_md();
+        }
+
+        primitive_desc_t *conv_pd_;
+    };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    ref_deconvolution_bwd_data_t(const pd_t *apd): cpu_primitive_t(apd)
+    { pd()->conv_pd_->create_primitive((primitive_t **)&conv_p_); }
+    ~ref_deconvolution_bwd_data_t() { delete conv_p_; }
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        const auto &args = ctx.args();
+        exec_args_t conv_args;
+        conv_args[MKLDNN_ARG_SRC] = args.at(MKLDNN_ARG_DIFF_DST);
+        conv_args[MKLDNN_ARG_WEIGHTS] = args.at(MKLDNN_ARG_WEIGHTS);
+        conv_args[MKLDNN_ARG_DST] = args.at(MKLDNN_ARG_DIFF_SRC);
+        if (!types::is_zero_md(pd()->scratchpad_md()))
+            conv_args[MKLDNN_ARG_SCRATCHPAD] = args.at(MKLDNN_ARG_SCRATCHPAD);
+        const exec_ctx_t conv_ctx(ctx.stream(), std::move(conv_args));
+
+        conv_p_->execute(conv_ctx);
+        return status::success;
+    }
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    primitive_t *conv_p_;
+};
+
+struct ref_deconvolution_bwd_weights_t: public cpu_primitive_t {
+    struct pd_t: public cpu_deconvolution_bwd_weights_pd_t {
+        pd_t(engine_t *engine,
+                const deconvolution_desc_t *adesc,
+                const primitive_attr_t *attr,
+                const deconvolution_fwd_pd_t *hint_fwd_pd)
+            : cpu_deconvolution_bwd_weights_pd_t(engine, adesc, attr, hint_fwd_pd)
+            , conv_pd_(nullptr)
+        {}
+
+        pd_t(const pd_t &other)
+            : cpu_deconvolution_bwd_weights_pd_t(other)
+            , conv_pd_(other.conv_pd_->clone())
+            , dst_tag_(other.dst_tag_)
+        {}
+
+        ~pd_t() { delete conv_pd_; }
+
+        DECLARE_COMMON_PD_T(conv_pd_->name(), ref_deconvolution_bwd_weights_t);
+
+        status_t init_convolution() {
+            using namespace types;
+
+            convolution_desc_t cd;
+            status_t status = conv_descr_create(desc(), &cd);
+            if (status != status::success) return status;
+
+            mkldnn_primitive_desc_iterator it(engine_, (op_desc_t *)&cd,
+                    &attr_, nullptr);
+            while (++it != it.end()) {
+                conv_pd_ = *it;
+                if (conv_pd_->diff_weights_md()->extra.flags == 0)
+                    return status::success;
+                delete conv_pd_;
+            }
+            return status::unimplemented;
+        }
+
+        status_t init() {
+            using namespace format_tag;
+            bool ok = true
+                && desc()->prop_kind == prop_kind::backward_weights
+                && utils::everyone_is(data_type::f32,
+                        desc()->src_desc.data_type,
+                        desc()->diff_weights_desc.data_type,
+                        desc()->diff_dst_desc.data_type)
+                && utils::one_of(desc()->alg_kind,
+                        alg_kind::deconvolution_direct,
+                        alg_kind::deconvolution_winograd)
+                && attr()->has_default_values();
+            if (ok) {
+                CHECK(init_convolution());
+                if (diff_weights_md_.format_kind == format_kind::any) {
+                    CHECK(compute_blocked_format(with_groups(),
+                                conv_pd_->diff_weights_md(),
+                                &desc_.diff_weights_desc));
+                    diff_weights_md_ = desc_.diff_weights_desc;
+                }
+                if (src_md_.format_kind == format_kind::any)
+                    src_md_ = *conv_pd_->diff_dst_md();
+                if (diff_dst_md_.format_kind == format_kind::any)
+                    diff_dst_md_ = *conv_pd_->src_md();
+                if (diff_bias_md_.format_kind == format_kind::any)
+                    CHECK(memory_desc_init_by_tag(diff_bias_md_, x));
+
+                dst_tag_ = memory_desc_matches_one_of_tag(diff_dst_md_,
+                        utils::pick(ndims() - 3, ncw, nchw, ncdhw),
+                        utils::pick(ndims() - 3, nCw8c, nChw8c, nCdhw8c),
+                        utils::pick(ndims() - 3, nCw16c, nChw16c, nCdhw16c));
+
+                return status::success;
+            }
+
+            return status::unimplemented;
+        }
+
+        virtual void init_scratchpad_md() override {
+            scratchpad_md_ = *conv_pd_->scratchpad_md();
+        }
+
+        primitive_desc_t *conv_pd_;
+        format_tag_t dst_tag_;
+    };
+
+    typedef typename prec_traits<data_type::f32>::type data_t;
+
+    ref_deconvolution_bwd_weights_t(const pd_t *apd): cpu_primitive_t(apd)
+    { pd()->conv_pd_->create_primitive((primitive_t **)&conv_p_); }
+    ~ref_deconvolution_bwd_weights_t() { delete conv_p_; }
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        const auto &args = ctx.args();
+        exec_args_t conv_args;
+        conv_args[MKLDNN_ARG_DIFF_DST] = args.at(MKLDNN_ARG_SRC);
+        conv_args[MKLDNN_ARG_SRC] = args.at(MKLDNN_ARG_DIFF_DST);
+        conv_args[MKLDNN_ARG_DIFF_WEIGHTS] = args.at(MKLDNN_ARG_DIFF_WEIGHTS);
+        if (!types::is_zero_md(pd()->scratchpad_md()))
+            conv_args[MKLDNN_ARG_SCRATCHPAD] = args.at(MKLDNN_ARG_SCRATCHPAD);
+        const exec_ctx_t conv_ctx(ctx.stream(), std::move(conv_args));
+
+        status_t status = conv_p_->execute(conv_ctx);
+        if (status != status::success) return status;
+
+        if (pd()->with_bias()) {
+            using namespace format_tag;
+
+            auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+            auto diff_bias = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_BIAS);
+
+            switch (pd()->dst_tag_) {
+            case ncdhw: case nchw: case ncw:
+                compute_bwd_bias_ncdhw(diff_dst, diff_bias);
+                break;
+            case nCdhw8c: case nChw8c: case nCw8c:
+                compute_bwd_bias_nCdhwXc<8>(diff_dst, diff_bias);
+                break;
+            case nCdhw16c: case nChw16c: case nCw16c:
+                compute_bwd_bias_nCdhwXc<16>(diff_dst, diff_bias);
+                break;
+            default:
+                compute_bwd_bias(diff_dst, diff_bias);
+                break;
+            }
+        }
+        return status::success;
+    }
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    void compute_bwd_bias(const data_t *diff_dst, data_t *diff_bias) const;
+    void compute_bwd_bias_ncdhw(const data_t *diff_dst,
+            data_t *diff_bias) const;
+    template <int blksize> void compute_bwd_bias_nCdhwXc(
+            const data_t *diff_dst, data_t *diff_bias) const;
+
+    primitive_t *conv_p_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_eltwise.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_eltwise.cpp
new file mode 100644
index 0000000000..7beee8d323
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_eltwise.cpp
@@ -0,0 +1,297 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+
+#include "ref_eltwise.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace alg_kind;
+using namespace math;
+
+ref_eltwise_scalar_fwd_t::ref_eltwise_scalar_fwd_t(alg_kind_t alg, float alpha,
+        float beta): alg_(alg), alpha_(alpha), beta_(beta) {
+    assert(utils::one_of(alg_, eltwise_relu, eltwise_tanh, eltwise_elu,
+                eltwise_square, eltwise_abs, eltwise_sqrt, eltwise_linear,
+                eltwise_bounded_relu, eltwise_soft_relu, eltwise_logistic));
+}
+
+ref_eltwise_scalar_fwd_t::ref_eltwise_scalar_fwd_t(
+        const post_ops_t::entry_t::eltwise_t &eltwise)
+    : ref_eltwise_scalar_fwd_t(eltwise.alg, eltwise.alpha, eltwise.beta) {}
+
+float ref_eltwise_scalar_fwd_t::compute_scalar(float s) {
+    switch (alg_) {
+        case eltwise_relu: return relu_fwd(s, alpha_);
+        case eltwise_tanh: return tanh_fwd(s);
+        case eltwise_elu: return elu_fwd(s, alpha_);
+        case eltwise_square: return square_fwd(s);
+        case eltwise_abs: return abs_fwd(s);
+        case eltwise_sqrt: return sqrt_fwd(s);
+        case eltwise_linear: return linear_fwd(s, alpha_, beta_);
+        case eltwise_bounded_relu: return bounded_relu_fwd(s, alpha_);
+        case eltwise_soft_relu: return soft_relu_fwd(s);
+        case eltwise_logistic: return logistic_fwd(s);
+        default: assert(!"unknown eltwise alg_kind");
+    }
+
+    return 0.f;
+}
+
+template <impl::data_type_t data_type>
+void ref_eltwise_fwd_t<data_type>::execute_forward_nCspBc_padded(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+    const blocking_desc_t &blk = data_d.blocking_desc();
+    const int block = blk.inner_blks[0];
+
+    const int MB = pd()->MB();
+    const int C = pd()->C() / block;
+    const int C_PADDED = data_d.padded_dims()[1] / block;
+    const int tail = pd()->C() % block;
+    const int SP = pd()->D() * pd()->H() * pd()->W();
+    const auto alg_kind = pd()->desc()->alg_kind;
+    const float alpha = pd()->desc()->alpha;
+    const float beta = pd()->desc()->beta;
+
+    auto ker = [=] (data_t &d, data_t s) {
+        switch (alg_kind) {
+            case eltwise_linear: d = linear_fwd(s, alpha, beta); break;
+            case eltwise_bounded_relu:
+                d = bounded_relu_fwd(s, alpha); break;
+            case eltwise_soft_relu: d = soft_relu_fwd(s); break;
+            case eltwise_logistic: d = logistic_fwd(s); break;
+            default: assert(!"unknown eltwise alg_kind");
+        }
+    };
+
+    // FIXME: integer overflow?
+
+    parallel_nd(MB, C_PADDED, SP,
+        [&](int n, int c, int sp) {
+        auto d_off = (n*C_PADDED*SP + c*SP + sp) * block;
+        if (c < C) {
+            for (int v = 0; v < block; v++)
+                ker(dst[d_off + v], src[d_off + v]);
+        } else {
+            for (int v = 0; v < tail; v++)
+                ker(dst[d_off + v], src[d_off + v]);
+        }
+    });
+}
+
+template <impl::data_type_t data_type>
+void ref_eltwise_fwd_t<data_type>::execute_forward_generic(
+        const exec_ctx_t &ctx) const {
+    /* fast return */
+    if (pd()->has_zero_dim_memory()) return;
+
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+
+    const int MB = pd()->MB();
+    const int C = pd()->C();
+    const int D = pd()->D();
+    const int H = pd()->H();
+    const int W = pd()->W();
+    const auto alg_kind = pd()->desc()->alg_kind;
+    const float alpha = pd()->desc()->alpha;
+    const float beta = pd()->desc()->beta;
+    const bool is_3d = pd()->desc()->data_desc.ndims == 5;
+
+    parallel_nd(MB, C, D, H, W,
+        [&](int n, int c, int id, int h, int w) {
+        auto d_off = is_3d
+            ? data_d.off(n, c, id, h, w) : data_d.off(n, c, h, w);
+        data_t s = src[d_off];
+        data_t &d = dst[d_off];
+        switch (alg_kind) {
+            case eltwise_relu: d = relu_fwd(s, alpha); break;
+            case eltwise_tanh: d = tanh_fwd(s); break;
+            case eltwise_elu: d = elu_fwd(s, alpha); break;
+            case eltwise_square: d = square_fwd(s); break;
+            case eltwise_abs: d = abs_fwd(s); break;
+            case eltwise_sqrt: d = sqrt_fwd(s); break;
+            case eltwise_linear: d = linear_fwd(s, alpha, beta); break;
+            case eltwise_bounded_relu:
+                d = bounded_relu_fwd(s, alpha); break;
+            case eltwise_soft_relu: d = soft_relu_fwd(s); break;
+            case eltwise_logistic: d = logistic_fwd(s); break;
+            default: assert(!"unknown eltwise alg_kind");
+        }
+    });
+}
+
+template <impl::data_type_t data_type>
+void ref_eltwise_fwd_t<data_type>::execute_forward_dense(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+
+    const ptrdiff_t nelems = static_cast<ptrdiff_t>(data_d.nelems(true));
+    const auto alg_kind = pd()->desc()->alg_kind;
+    const float alpha = pd()->desc()->alpha;
+    const float beta  = pd()->desc()->beta;
+
+    src += data_d.offset0();
+    dst += data_d.offset0();
+
+    if (alg_kind == eltwise_relu) {
+        // a fast path for relu as the most popular activation
+        parallel_nd(nelems, [&](ptrdiff_t e) {
+            dst[e] = relu_fwd(src[e], alpha);
+        });
+        return;
+    }
+
+    parallel_nd(nelems, [&](ptrdiff_t e) {
+        const data_t s = src[e];
+        data_t &d = dst[e];
+
+        switch (alg_kind) {
+        case eltwise_tanh: d = tanh_fwd(s); break;
+        case eltwise_elu: d = elu_fwd(s, alpha); break;
+        case eltwise_square: d = square_fwd(s); break;
+        case eltwise_abs: d = abs_fwd(s); break;
+        case eltwise_sqrt: d = sqrt_fwd(s); break;
+        case eltwise_linear: d = linear_fwd(s, alpha, beta); break;
+        case eltwise_bounded_relu: d = bounded_relu_fwd(s, alpha); break;
+        case eltwise_soft_relu: d = soft_relu_fwd(s); break;
+        case eltwise_logistic: d = logistic_fwd(s); break;
+        default: assert(!"unknown eltwise alg_kind");
+        }
+    });
+}
+
+template <impl::data_type_t data_type>
+void ref_eltwise_bwd_t<data_type>::execute_backward_generic(
+        const exec_ctx_t &ctx) const {
+    /* fast return */
+    if (pd()->has_zero_dim_memory()) return;
+
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+    const memory_desc_wrapper diff_data_d(pd()->diff_src_md());
+
+    const int MB = pd()->MB();
+    const int C = pd()->C();
+    const int D = pd()->D();
+    const int H = pd()->H();
+    const int W = pd()->W();
+    const auto alg_kind = pd()->desc()->alg_kind;
+    const float alpha = pd()->desc()->alpha;
+    const float beta = pd()->desc()->beta;
+    const bool is_3d = pd()->desc()->data_desc.ndims == 5;
+
+    parallel_nd(MB, C, D, H, W,
+        [&](int n, int c, int d, int h, int w) {
+        auto data_off = is_3d
+            ? data_d.off(n, c, d, h, w) : data_d.off(n, c, h, w);
+        auto diff_data_off = is_3d
+            ? diff_data_d.off(n, c, d, h, w)
+            : diff_data_d.off(n, c, h, w);
+        data_t s = src[data_off];
+        data_t dd = diff_dst[diff_data_off];
+        data_t &ds = diff_src[diff_data_off];
+        switch (alg_kind) {
+            case eltwise_relu: ds = relu_bwd(dd, s, alpha); break;
+            case eltwise_tanh: ds = tanh_bwd(dd, s); break;
+            case eltwise_elu: ds = elu_bwd(dd, s, alpha); break;
+            case eltwise_square: ds = square_bwd(dd, s); break;
+            case eltwise_abs: ds = abs_bwd(dd, s); break;
+            case eltwise_sqrt: ds = sqrt_bwd(dd, s); break;
+            case eltwise_linear:
+                ds = linear_bwd(dd, s, alpha, beta); break;
+            case eltwise_bounded_relu:
+                ds = bounded_relu_bwd(dd, s, alpha); break;
+            case eltwise_soft_relu: ds = soft_relu_bwd(dd, s); break;
+            case eltwise_logistic: ds = logistic_bwd(dd, s); break;
+            default: assert(!"unknown eltwise alg_kind");
+        }
+    });
+}
+
+template <impl::data_type_t data_type>
+void ref_eltwise_bwd_t<data_type>::execute_backward_dense(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+    const memory_desc_wrapper diff_data_d(pd()->diff_src_md());
+
+    const ptrdiff_t nelems = static_cast<ptrdiff_t>(data_d.nelems(true));
+    const auto alg_kind = pd()->desc()->alg_kind;
+    const float alpha = pd()->desc()->alpha;
+    const float beta = pd()->desc()->beta;
+
+    src += data_d.offset0();
+    diff_dst += diff_data_d.offset0();
+    diff_src += diff_data_d.offset0();
+
+    parallel_nd(nelems, [&](ptrdiff_t e) {
+        const data_t dd = diff_dst[e];
+        const data_t s = src[e];
+        data_t &ds = diff_src[e];
+
+        switch (alg_kind) {
+        case eltwise_relu: ds = relu_bwd(dd, s, alpha); break;
+        case eltwise_tanh: ds = tanh_bwd(dd, s); break;
+        case eltwise_elu: ds = elu_bwd(dd, s, alpha); break;
+        case eltwise_square: ds = square_bwd(dd, s); break;
+        case eltwise_abs: ds = abs_bwd(dd, s); break;
+        case eltwise_sqrt: ds = sqrt_bwd(dd, s); break;
+        case eltwise_linear: ds = linear_bwd(dd, s, alpha, beta); break;
+        case eltwise_bounded_relu: ds = bounded_relu_bwd(dd, s, alpha); break;
+        case eltwise_soft_relu: ds = soft_relu_bwd(dd, s); break;
+        case eltwise_logistic: ds = logistic_bwd(dd, s); break;
+        default: assert(!"unknown eltwise alg_kind");
+        }
+    });
+}
+
+template struct ref_eltwise_fwd_t<data_type::f32>;
+template struct ref_eltwise_fwd_t<data_type::s32>;
+template struct ref_eltwise_fwd_t<data_type::s8>;
+template struct ref_eltwise_fwd_t<data_type::u8>;
+
+template struct ref_eltwise_bwd_t<data_type::f32>;
+template struct ref_eltwise_bwd_t<data_type::s32>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_eltwise.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_eltwise.hpp
new file mode 100644
index 0000000000..8f4ab35413
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_eltwise.hpp
@@ -0,0 +1,168 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REF_ELTWISE_HPP
+#define CPU_REF_ELTWISE_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_eltwise_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct ref_eltwise_scalar_fwd_t {
+public:
+    ref_eltwise_scalar_fwd_t(alg_kind_t alg, float alpha, float beta);
+
+    // note that eltwise.scale is ignored
+    ref_eltwise_scalar_fwd_t(const post_ops_t::entry_t::eltwise_t &eltwise);
+
+    float compute_scalar(float s);
+
+    const alg_kind_t alg_;
+    const float alpha_;
+    const float beta_;
+};
+
+template <impl::data_type_t data_type>
+struct ref_eltwise_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_eltwise_fwd_pd_t {
+        using cpu_eltwise_fwd_pd_t::cpu_eltwise_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_eltwise_fwd_t);
+
+        status_t init() {
+            using namespace utils;
+
+            auto src_d = memory_desc_wrapper(src_md());
+
+            use_dense_ = false
+                || src_d.is_dense()
+                || (src_d.is_dense(true) && is_zero_preserved());
+
+            use_nCspBc_padded_ = !use_dense_
+                && src_d.blocking_desc().inner_nblks == 1
+                && one_of(src_d.blocking_desc().inner_blks[0], 8, 16)
+                && src_d.blocking_desc().inner_idxs[0] == 1
+                && src_d.only_padded_dim(1)
+                && src_d.is_dense(true);
+
+            if (has_zero_dim_memory())
+                use_dense_ = use_nCspBc_padded_ = false;
+
+            const bool use_generic = !use_dense_ && !use_nCspBc_padded_;
+
+            bool ok = true
+                && is_fwd()
+                && everyone_is(data_type, desc()->data_desc.data_type)
+                && IMPLICATION(use_generic, one_of(src_d.ndims(), 4, 5))
+                && attr()->has_default_values();
+            if (!ok) return status::unimplemented;
+
+            return status::success;
+        }
+
+        bool use_dense_, use_nCspBc_padded_;
+    };
+
+    ref_eltwise_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        if (pd()->use_dense_)
+            execute_forward_dense(ctx);
+        else if (pd()->use_nCspBc_padded_)
+            execute_forward_nCspBc_padded(ctx);
+        else
+            execute_forward_generic(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward_nCspBc_padded(const exec_ctx_t &ctx) const;
+    void execute_forward_dense(const exec_ctx_t &ctx) const;
+    void execute_forward_generic(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t data_type>
+struct ref_eltwise_bwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_eltwise_bwd_pd_t {
+        using cpu_eltwise_bwd_pd_t::cpu_eltwise_bwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_eltwise_bwd_t);
+
+        status_t init() {
+            using namespace utils;
+
+            bool ok = true
+                && !is_fwd()
+                && everyone_is(data_type,
+                        desc()->data_desc.data_type,
+                        desc()->diff_data_desc.data_type)
+                && attr()->has_default_values();
+            if (!ok) return status::unimplemented;
+
+            auto diff_dst_d = memory_desc_wrapper(diff_dst_md());
+            const bool same_fmt_ = diff_dst_d == memory_desc_wrapper(src_md());
+
+            use_dense_ = true
+                && same_fmt_
+                && diff_dst_d.is_dense(true)
+                && is_zero_preserved()
+                && !has_zero_dim_memory();
+            const bool use_generic = !use_dense_;
+
+            if (use_generic && !one_of(diff_dst_d.ndims(), 4, 5))
+                return status::unimplemented;
+
+            return status::success;
+        }
+
+        bool use_dense_;
+    };
+
+    ref_eltwise_bwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        if (pd()->use_dense_)
+            execute_backward_dense(ctx);
+        else
+            execute_backward_generic(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_dense(const exec_ctx_t &ctx) const;
+    void execute_backward_generic(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_inner_product.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_inner_product.cpp
new file mode 100644
index 0000000000..c807a9ffd0
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_inner_product.cpp
@@ -0,0 +1,285 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "mkldnn_thread.hpp"
+#include "mkldnn_traits.hpp"
+#include "math_utils.hpp"
+
+#include "ref_inner_product.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using math::saturate;
+using math::get_bias;
+
+template <data_type_t src_type, data_type_t wei_type, data_type_t dst_type,
+         data_type_t acc_type>
+void ref_inner_product_fwd_t<src_type, wei_type, dst_type, acc_type>::
+execute_forward(const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto bias = CTX_IN_MEM(const char *, MKLDNN_ARG_BIAS);
+    auto dst = CTX_OUT_MEM(dst_data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper bias_d(pd()->weights_md(1));
+
+    const int MB = pd()->MB();
+    const int OC = pd()->OC();
+    const int IC = pd()->IC();
+
+    const bool src_has_spatial = utils::one_of(src_d.ndims(), 3, 4, 5);
+    const int ndims = src_d.ndims() - 2;
+
+    const auto &post_ops = pd()->attr()->post_ops_;
+    const bool do_relu = post_ops.len_ == 1;
+    const float nslope = do_relu ? post_ops.entry_[0].eltwise.alpha : 0.f;
+
+    auto ker_has_spatial = [=](int mb, int oc) {
+        acc_data_t d = 0;
+        const int KD = pd()->KD();
+        const int KH = pd()->KH();
+        const int KW = pd()->KW();
+        for (int ic = 0; ic < IC; ++ic) {
+            for (int kd = 0; kd < KD; ++kd) {
+                for (int kh = 0; kh < KH; ++kh) {
+                    for (int kw = 0; kw < KW; ++kw) {
+                        switch (ndims) {
+                        case 3:
+                            d += (acc_data_t)src[src_d.off(mb, ic, kd, kh, kw)]
+                                    * weights[weights_d.off(
+                                              oc, ic, kd, kh, kw)];
+                            break;
+                        case 2:
+                            d += (acc_data_t)src[src_d.off(mb, ic, kh, kw)]
+                                    * weights[weights_d.off(oc, ic, kh, kw)];
+                            break;
+                        case 1:
+                            d += (acc_data_t)src[src_d.off(mb, ic, kw)]
+                                    * weights[weights_d.off(oc, ic, kw)];
+                            break;
+                        default: assert(!"unsupported ndims size");
+                        }
+                    }
+                }
+            }
+        }
+        return d;
+    };
+
+    auto ker_no_spatial = [=](int mb, int oc) {
+        acc_data_t d = 0;
+        for (int ic = 0; ic < IC; ++ic) {
+            d += (acc_data_t)src[src_d.off(mb, ic)]
+                * weights[weights_d.off(oc, ic)];
+        }
+        return d;
+    };
+
+    parallel_nd(MB, OC, [&](int mb, int oc) {
+        float a = bias
+            ? get_bias(bias, bias_d.off(oc), pd()->desc()->bias_desc.data_type)
+            : 0;
+        if (src_has_spatial)
+            a += ker_has_spatial(mb, oc);
+        else
+            a += ker_no_spatial(mb, oc);
+        if (do_relu && a < (acc_data_t)0)
+            a *= nslope;
+        dst[dst_d.off(mb, oc)] = saturate<dst_data_t>(a);
+    });
+}
+
+using namespace data_type;
+template struct ref_inner_product_fwd_t<f32>;
+template struct ref_inner_product_fwd_t<u8, s8, f32, s32>;
+template struct ref_inner_product_fwd_t<u8, s8, s32, s32>;
+template struct ref_inner_product_fwd_t<u8, s8, s8, s32>;
+template struct ref_inner_product_fwd_t<u8, s8, u8, s32>;
+
+template <data_type_t diff_src_type, data_type_t wei_type,
+         data_type_t diff_dst_type, data_type_t acc_type>
+void ref_inner_product_bwd_data_t<diff_src_type, wei_type, diff_dst_type,
+     acc_type>::execute_backward_data(const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, MKLDNN_ARG_DIFF_DST);
+    auto weights = CTX_IN_MEM(const wei_data_t *, MKLDNN_ARG_WEIGHTS);
+    auto diff_src = CTX_OUT_MEM(diff_src_data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper weights_d(pd()->weights_md(0));
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+
+    const int MB = pd()->MB();
+    const int OC = pd()->OC();
+    const int IC = pd()->IC();
+
+    const bool diff_src_has_spatial
+            = utils::one_of(diff_src_d.ndims(), 3, 4, 5);
+    const int ndims = diff_src_d.ndims() - 2;
+
+    parallel_nd(MB, IC, [&](int mb, int ic) {
+        if (diff_src_has_spatial) {
+            const int KD = pd()->KD();
+            const int KH = pd()->KH();
+            const int KW = pd()->KW();
+            for (int kd = 0; kd < KD; ++kd)
+            for (int kh = 0; kh < KH; ++kh)
+            for (int kw = 0; kw < KW; ++kw) {
+                acc_data_t ds = acc_data_t(0);
+                for (int oc = 0; oc < OC; ++oc) {
+                    switch (ndims) {
+                    case 3:
+                        ds += (acc_data_t)(diff_dst[diff_dst_d.off(mb, oc)]
+                                * weights[weights_d.off(oc, ic, kd, kh, kw)]);
+                        break;
+                    case 2:
+                        ds += (acc_data_t)(diff_dst[diff_dst_d.off(mb, oc)]
+                                * weights[weights_d.off(oc, ic, kh, kw)]);
+                        break;
+                    case 1:
+                        ds += (acc_data_t)(diff_dst[diff_dst_d.off(mb, oc)]
+                                * weights[weights_d.off(oc, ic, kw)]);
+                        break;
+                    default: assert(!"unsupported ndims size");
+                    }
+                }
+                switch (ndims) {
+                case 3:
+                    diff_src[diff_src_d.off(mb, ic, kd, kh, kw)]
+                            = (diff_src_data_t)ds;
+                    break;
+                case 2:
+                    diff_src[diff_src_d.off(mb, ic, kh, kw)]
+                            = (diff_src_data_t)ds;
+                    break;
+                case 1:
+                    diff_src[diff_src_d.off(mb, ic, kw)] = (diff_src_data_t)ds;
+                    break;
+                default: assert(!"unsupported ndims size");
+                }
+            }
+        } else {
+            acc_data_t ds = acc_data_t(0);
+            for (int oc = 0; oc < OC; ++oc) {
+                ds += (acc_data_t)(diff_dst[diff_dst_d.off(mb, oc)] *
+                    weights[weights_d.off(oc, ic)]);
+            }
+            diff_src[diff_src_d.off(mb, ic)] = (diff_src_data_t)ds;
+        }
+    });
+}
+
+template struct ref_inner_product_bwd_data_t<f32, f32, f32, f32>;
+
+template <impl::data_type_t data_type>
+void ref_inner_product_bwd_weights_t<data_type>::execute_backward_weights(
+        const exec_ctx_t &ctx) const {
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_weights = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_WEIGHTS);
+    auto diff_bias = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_BIAS);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
+    const memory_desc_wrapper diff_bias_d(pd()->diff_weights_md(1));
+
+    const int MB = pd()->MB();
+    const int OC = pd()->OC();
+    const int IC = pd()->IC();
+
+    const bool src_has_spatial = utils::one_of(src_d.ndims(), 3, 4 ,5);
+    const int ndims = src_d.ndims() - 2;
+
+    parallel_nd(OC, IC, [&](int oc, int ic) {
+        if (src_has_spatial) {
+            const int KD = pd()->KD();
+            const int KH = pd()->KH();
+            const int KW = pd()->KW();
+            for (int kd = 0; kd < KD; ++kd) {
+                for (int kh = 0; kh < KH; ++kh) {
+                    for (int kw = 0; kw < KW; ++kw) {
+                        data_t *dw(nullptr);
+                        switch (ndims) {
+                        case 3:
+                            dw = &diff_weights[diff_weights_d.off(
+                                    oc, ic, kd, kh, kw)];
+                            break;
+                        case 2:
+                            dw = &diff_weights[diff_weights_d.off(
+                                    oc, ic, kh, kw)];
+                            break;
+                        case 1:
+                            dw = &diff_weights[diff_weights_d.off(oc, ic, kw)];
+                            break;
+                        default: assert(!"unsupported ndims size");
+                        }
+                        *dw = data_t(0);
+                        for (int mb = 0; mb < MB; ++mb) {
+                            switch (ndims) {
+                            case 3:
+                                *dw += diff_dst[diff_dst_d.off(mb, oc)]
+                                        * src[src_d.off(mb, ic, kd, kh, kw)];
+                                break;
+                            case 2:
+                                *dw += diff_dst[diff_dst_d.off(mb, oc)]
+                                        * src[src_d.off(mb, ic, kh, kw)];
+                                break;
+                            case 1:
+                                *dw += diff_dst[diff_dst_d.off(mb, oc)]
+                                        * src[src_d.off(mb, ic, kw)];
+                                break;
+                            default: assert(!"unsupported ndims size");
+                            }
+                        }
+                    }
+                }
+            }
+        } else {
+            data_t *dw = &diff_weights[diff_weights_d.off(oc, ic)];
+            *dw = data_t(0);
+            for (int mb = 0; mb < MB; ++mb) {
+                *dw += diff_dst[diff_dst_d.off(mb, oc)] *
+                    src[src_d.off(mb, ic)];
+            }
+        }
+    });
+
+    if (diff_bias) {
+        diff_bias += diff_bias_d.offset0();
+
+        parallel_nd(OC, [&](int oc) {
+            data_t *db = &diff_bias[oc];
+            *db = data_t(0);
+            for (int mb = 0; mb < MB; ++mb)
+                *db += diff_dst[diff_dst_d.off(mb, oc)];
+        });
+    }
+}
+
+template struct ref_inner_product_bwd_weights_t<data_type::f32>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_inner_product.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_inner_product.hpp
new file mode 100644
index 0000000000..bf87dbd514
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_inner_product.hpp
@@ -0,0 +1,159 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REF_INNER_PRODUCT_HPP
+#define CPU_REF_INNER_PRODUCT_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_inner_product_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t src_type, impl::data_type_t wei_type = src_type,
+         impl::data_type_t dst_type = src_type,
+         impl::data_type_t acc_type = dst_type>
+struct ref_inner_product_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_inner_product_fwd_pd_t {
+        using cpu_inner_product_fwd_pd_t::cpu_inner_product_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_inner_product_fwd_t);
+
+        status_t init() {
+            using namespace data_type;
+
+            bool ok = true
+                && set_default_params() == status::success
+                && is_fwd()
+                && src_md()->data_type == src_type
+                && weights_md()->data_type == wei_type
+                && desc()->accum_data_type == acc_type
+                && dst_md()->data_type == dst_type
+                && IMPLICATION(with_bias(), utils::one_of(
+                            weights_md(1)->data_type, f32, s32, s8, u8))
+                && attr()->output_scales_.has_default_values()
+                && attr()->post_ops_.len_ <= 1
+                && IMPLICATION(attr()->post_ops_.len_ == 1,
+                        attr()->post_ops_.entry_[0].is_relu(true, false));
+            return ok ? status::success : status::unimplemented;
+        }
+    };
+
+    ref_inner_product_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<wei_type>::type wei_data_t;
+    typedef typename prec_traits<dst_type>::type dst_data_t;
+    typedef typename prec_traits<acc_type>::type acc_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t diff_src_type, impl::data_type_t wei_type,
+         impl::data_type_t diff_dst_type,
+         impl::data_type_t acc_type = diff_src_type>
+struct ref_inner_product_bwd_data_t: public cpu_primitive_t {
+    struct pd_t: public cpu_inner_product_bwd_data_pd_t {
+        using cpu_inner_product_bwd_data_pd_t::cpu_inner_product_bwd_data_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_inner_product_bwd_data_t);
+
+        status_t init() {
+            bool ok = true
+                && set_default_params() == status::success
+                && desc()->prop_kind == prop_kind::backward_data
+                && diff_src_md()->data_type == diff_src_type
+                && weights_md()->data_type == wei_type
+                && desc()->accum_data_type == acc_type
+                && diff_dst_md()->data_type == diff_dst_type
+                && attr()->has_default_values();
+            return ok ? status::success : status::unimplemented;
+        }
+    };
+
+    ref_inner_product_bwd_data_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    typedef typename prec_traits<diff_src_type>::type diff_src_data_t;
+    typedef typename prec_traits<wei_type>::type wei_data_t;
+    typedef typename prec_traits<diff_dst_type>::type diff_dst_data_t;
+    typedef typename prec_traits<acc_type>::type acc_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_data(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_data(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t data_type>
+struct ref_inner_product_bwd_weights_t: public cpu_primitive_t {
+    struct pd_t: public cpu_inner_product_bwd_weights_pd_t {
+        using cpu_inner_product_bwd_weights_pd_t::cpu_inner_product_bwd_weights_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_inner_product_bwd_weights_t);
+
+        status_t init() {
+            bool ok = true
+                && set_default_params() == status::success
+                && desc()->prop_kind == prop_kind::backward_weights
+                && utils::everyone_is(data_type,
+                        src_md()->data_type,
+                        diff_dst_md()->data_type,
+                        diff_weights_md()->data_type)
+                && IMPLICATION(with_bias(),
+                        data_type == diff_weights_md(1)->data_type)
+                && attr()->has_default_values();
+            return ok ? status::success : status::unimplemented;
+        }
+    };
+
+    ref_inner_product_bwd_weights_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward_weights(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_weights(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_lrn.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_lrn.cpp
new file mode 100644
index 0000000000..325e97963b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_lrn.cpp
@@ -0,0 +1,252 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <math.h>
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+
+#include "ref_lrn.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+static inline float fast_negative_powf(float omega, float beta) {
+    float Y;
+/*
+ * Y = omega^(-3/4) =
+ * = 1.0f / sqrtf(omega) * sqrtf(1.0f / sqrtf(omega))
+ * = sqrtf(1.0f / sqrtf(omega)) * 1.0f / sqrtf(omega)
+ * = sqrtf(1.0f / sqrtf(omega)) / sqrtf(omega)
+ * = sqrtf(1.0f / sqrtf(omega) / omega)
+ * = sqrtf(1.0f / (sqrtf(omega) * omega))
+ */
+    if (beta == 0.75f) {
+        Y = sqrtf(1.0f / (sqrtf(omega) * omega));
+    } else {
+        Y = 1.0f / powf(omega, beta);
+    }
+    return Y;
+};
+
+template <impl::data_type_t data_type>
+template <impl::format_tag_t tag>
+void ref_lrn_fwd_t<data_type>::execute_forward(const exec_ctx_t &ctx) const {
+    using namespace alg_kind;
+    using namespace format_tag;
+
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+
+    const int C = pd()->C();
+    const int H = pd()->H();
+    const int W = pd()->W();
+    const size_t stride_mb = data_d.blocking_desc().strides[0];
+    const bool across_channels = pd()->desc()->alg_kind == lrn_across_channels;
+    constexpr int blksize = tag == nChw16c ? 16 : 8;
+
+    auto data_off = [&](int mb, int c, int h, int w) -> size_t {
+        switch (tag) {
+        case nChw16c:
+        case nChw8c: return mb * stride_mb + c / blksize * H * W * blksize
+                     + h * W * blksize + w * blksize + c % blksize;
+        case nchw: return mb * stride_mb + c * H * W + h * W + w;
+        case nhwc: return mb * stride_mb + h * W * C + w * C + c;
+        default: return data_d.off(mb, c, h, w);
+        }
+    };
+
+    auto ker = [=](data_t *d, int mb, int oc, int oh, int ow) {
+        const float alpha = static_cast<float>(pd()->desc()->lrn_alpha);
+        const float beta = static_cast<float>(pd()->desc()->lrn_beta);
+        const float k = static_cast<float>(pd()->desc()->lrn_k);
+
+        const int size = pd()->desc()->local_size;
+        const int half_size = (size - 1) / 2;
+
+        float sum = 0;
+        if (across_channels) {
+            const int c_st = nstl::max(oc - half_size + 0, 0);
+            const int c_en = nstl::min(oc + half_size + 1, C);
+
+            for (int c = c_st; c < c_en; ++c) {
+                const float s = src[data_off(mb, c, oh, ow)];
+                sum += s * s;
+            }
+        } else {
+            int h_st = nstl::max(oh - half_size + 0, 0);
+            int h_en = nstl::min(oh + half_size + 1, H);
+            int w_st = nstl::max(ow - half_size + 0, 0);
+            int w_en = nstl::min(ow + half_size + 1, W);
+            for (int h = h_st; h < h_en; ++h) {
+                for (int w = w_st; w < w_en; ++w) {
+                    const float s = src[data_off(mb, oc, h, w)];
+                    sum += s * s;
+                }
+            }
+        }
+        const int summands = across_channels ? size : size * size;
+        sum = k + alpha * sum / summands;
+        size_t off = data_off(mb, oc, oh, ow);
+        d[0] = static_cast<data_t>(src[off] * fast_negative_powf(sum, beta));
+    };
+
+    const int MB = pd()->MB();
+    if (tag == nChw16c || tag == nChw8c) {
+        parallel_nd(MB, utils::div_up(C, blksize), H, W,
+            [&](int mb, int c_blk, int h, int w) {
+            int c = c_blk * blksize;
+            const size_t off = mb * stride_mb + c * H * W
+                + (h * W + w) * blksize;
+            PRAGMA_OMP_SIMD()
+            for (int cc = 0; cc < nstl::min(blksize, C - c); ++cc)
+                ker(&dst[off + cc], mb, c + cc, h, w);
+        });
+    } else if (tag == nhwc) {
+        parallel_nd(MB, H, W, C,
+            [&](int mb, int h, int w, int c) {
+            const size_t off = mb * stride_mb + h * W * C + w * C + c;
+            ker(&dst[off], mb, c, h, w);
+        });
+    } else {
+        parallel_nd(MB, C, H, W,
+            [&](int mb, int c, int h, int w) {
+            const size_t off = data_off(mb, c, h, w);
+            ker(&dst[off], mb, c, h, w);
+        });
+    }
+}
+
+template <impl::data_type_t data_type>
+template <mkldnn_format_tag_t tag>
+void ref_lrn_bwd_t<data_type>::execute_backward(const exec_ctx_t &ctx) const {
+    using namespace alg_kind;
+    using namespace format_tag;
+
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+
+    const int MB = pd()->MB();
+    const int C = pd()->C();
+    const int H = pd()->H();
+    const int W = pd()->W();
+    const size_t stride_mb = data_d.blocking_desc().strides[0];
+    constexpr int blksize = tag == nChw16c ? 16 : 8;
+
+    const float alpha = static_cast<float>(pd()->desc()->lrn_alpha);
+    const float beta = static_cast<float>(pd()->desc()->lrn_beta);
+    const float k = static_cast<float>(pd()->desc()->lrn_k);
+    const int kernel_size = pd()->desc()->local_size;
+    const int half_ksize = (kernel_size - 1) / 2;
+
+    auto data_off = [&](int mb, int c, int h, int w) -> size_t {
+        switch (tag) {
+        case nChw16c:
+        case nChw8c: return mb * stride_mb + c/blksize * H * W * blksize
+                     + h * W * blksize + w * blksize + c%blksize;
+        case nchw: return mb * stride_mb + c * H * W + h * W + w;
+        case nhwc: return mb * stride_mb + h * W * C + w * C + c;
+        default: return data_d.off(mb, c, h, w);
+        }
+    };
+
+    auto ker = [=](data_t *d, int mb, int oc, int oh, int ow) {
+        const int c_st = nstl::max(oc - half_ksize + 0, 0);
+        const int c_en = nstl::min(oc + half_ksize + 1, C);
+
+        float A = 0, B = 0, omega_mid = 0;
+        for (int c = c_st; c < c_en; c++) {
+            float sum = 0.0;
+            const int i_st = nstl::max(c - half_ksize, 0);
+            const int i_en = nstl::min(c + kernel_size - half_ksize, C);
+
+            for (int i = i_st; i < i_en; ++i) {
+                const float value = src[data_off(mb, i, oh, ow)];
+                sum += value * value;
+            }
+            const float omega = static_cast<float>(k + sum * alpha / kernel_size);
+            if (c == oc) omega_mid = omega;
+            float t = src[data_off(mb, c, oh, ow)]
+                   * fast_negative_powf(omega, beta);
+            B += 1.0f / omega * t * diff_dst[data_off(mb, c, oh, ow)];
+        }
+
+        const size_t off = data_off(mb, oc, oh, ow);
+        A = fast_negative_powf(omega_mid, beta) * diff_dst[off];
+        B *= src[off];
+        B *= (2.0f * alpha * beta) / kernel_size;
+        *d = static_cast<data_t>(A - B); // final cast down to data_t
+    };
+
+    if (tag == nChw16c || tag == nChw8c) {
+        parallel_nd(MB, utils::div_up(C, blksize), H, W,
+            [&](int mb, int c_blk, int h, int w) {
+            int c = c_blk * blksize;
+            const size_t off = mb * stride_mb + c * H * W +
+                (h * W + w) * blksize;
+            PRAGMA_OMP_SIMD()
+            for (int cc = 0; cc < nstl::min(blksize, C - c); ++cc)
+                ker(&diff_src[off + cc], mb, c + cc, h, w);
+        });
+    } else if (tag == nhwc) {
+        parallel_nd(MB, H, W, C,
+            [&](int mb, int h, int w, int c) {
+            const size_t off = mb * stride_mb + h * W * C + w * C + c;
+            ker(&diff_src[off], mb, c, h, w);
+        });
+    } else {
+        parallel_nd(MB, C, H, W,
+            [&](int mb, int c, int h, int w) {
+            const size_t off = data_off(mb, c, h, w);
+            ker(&diff_src[off], mb, c, h, w);
+        });
+    }
+}
+
+template void ref_lrn_fwd_t<data_type::f32>::
+execute_forward<format_tag::nChw16c>(const exec_ctx_t &ctx) const;
+template void ref_lrn_fwd_t<data_type::f32>::
+execute_forward<format_tag::nChw8c>(const exec_ctx_t &ctx) const;
+template void ref_lrn_fwd_t<data_type::f32>::
+execute_forward<format_tag::nchw>(const exec_ctx_t &ctx) const;
+template void ref_lrn_fwd_t<data_type::f32>::
+execute_forward<format_tag::nhwc>(const exec_ctx_t &ctx) const;
+template void ref_lrn_fwd_t<data_type::f32>::
+execute_forward<format_tag::any>(const exec_ctx_t &ctx) const;
+template void ref_lrn_bwd_t<data_type::f32>::
+execute_backward<format_tag::nChw16c>(const exec_ctx_t &ctx) const;
+template void ref_lrn_bwd_t<data_type::f32>::
+execute_backward<format_tag::nChw8c>(const exec_ctx_t &ctx) const;
+template void ref_lrn_bwd_t<data_type::f32>::
+execute_backward<format_tag::nchw>(const exec_ctx_t &ctx) const;
+template void ref_lrn_bwd_t<data_type::f32>::
+execute_backward<format_tag::nhwc>(const exec_ctx_t &ctx) const;
+template void ref_lrn_bwd_t<data_type::f32>::
+execute_backward<format_tag::any>(const exec_ctx_t &ctx) const;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_lrn.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_lrn.hpp
new file mode 100644
index 0000000000..f25cfb7fae
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_lrn.hpp
@@ -0,0 +1,136 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REF_LRN_HPP
+#define CPU_REF_LRN_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_lrn_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t data_type>
+struct ref_lrn_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_lrn_fwd_pd_t {
+        using cpu_lrn_fwd_pd_t::cpu_lrn_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_lrn_fwd_t);
+
+        status_t init() {
+            using namespace format_tag;
+
+            bool ok = true
+                && is_fwd()
+                && src_md()->data_type == data_type
+                && attr()->has_default_values();
+            if (!ok) return status::unimplemented;
+
+            dat_tag_ = memory_desc_matches_one_of_tag(
+                    *src_md(), nChw16c, nChw8c, nchw, nhwc);
+
+            return status::success;
+        }
+
+        format_tag_t dat_tag_;
+    };
+
+    ref_lrn_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        using namespace format_tag;
+        switch (pd()->dat_tag_) {
+        case nChw16c: execute_forward<nChw16c>(ctx); break;
+        case nChw8c: execute_forward<nChw8c>(ctx); break;
+        case nchw: execute_forward<nchw>(ctx); break;
+        case nhwc: execute_forward<nhwc>(ctx); break;
+        default: execute_forward<any>(ctx);
+        }
+        return status::success;
+    }
+
+private:
+    template<format_tag_t tag>
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t data_type>
+struct ref_lrn_bwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_lrn_bwd_pd_t {
+        using cpu_lrn_bwd_pd_t::cpu_lrn_bwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_lrn_bwd_t);
+
+        status_t init() {
+            using namespace format_tag;
+            using namespace alg_kind;
+
+            bool ok = true
+                && !is_fwd()
+                && utils::one_of(desc()->alg_kind, lrn_across_channels
+                        /*, lrn_within_channel */) // not supported yet
+                && utils::everyone_is(data_type,
+                        src_md()->data_type,
+                        diff_src_md()->data_type)
+                && attr()->has_default_values();
+            if (!ok) return status::unimplemented;
+
+            dat_tag_ = memory_desc_matches_one_of_tag(
+                    *src_md(), nChw16c, nChw8c, nchw, nhwc);
+
+            return status::success;
+        }
+
+        format_tag_t dat_tag_;
+    };
+
+    ref_lrn_bwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        using namespace format_tag;
+        switch (pd()->dat_tag_) {
+        case nChw16c: execute_backward<nChw16c>(ctx); break;
+        case nChw8c: execute_backward<nChw8c>(ctx); break;
+        case nchw: execute_backward<nchw>(ctx); break;
+        case nhwc: execute_backward<nhwc>(ctx); break;
+        default: execute_backward<any>(ctx);
+        }
+        return status::success;
+    }
+
+private:
+    template<format_tag_t tag>
+    void execute_backward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_pooling.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_pooling.cpp
new file mode 100644
index 0000000000..65b934e123
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_pooling.cpp
@@ -0,0 +1,381 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <math.h>
+
+#include "c_types_map.hpp"
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+
+#include "ref_pooling.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <data_type_t data_type, data_type_t acc_type>
+void ref_pooling_fwd_t<data_type, acc_type>::execute_forward(
+        const exec_ctx_t &ctx) const {
+    using namespace alg_kind;
+    using namespace prop_kind;
+
+    auto alg = pd()->desc()->alg_kind;
+
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+    auto ws = CTX_OUT_MEM(unsigned char *, MKLDNN_ARG_WORKSPACE);
+
+    const memory_desc_wrapper src_d(pd()->src_md());
+    const memory_desc_wrapper dst_d(pd()->dst_md());
+    const memory_desc_wrapper ws_d(pd()->workspace_md());
+    const data_type_t ws_dt = ws ? ws_d.data_type() : data_type::undef;
+
+    const int ID = pd()->ID();
+    const int IH = pd()->IH();
+    const int IW = pd()->IW();
+    const int KD = pd()->KD();
+    const int KH = pd()->KH();
+    const int KW = pd()->KW();
+    const int SD = pd()->KSD();
+    const int SH = pd()->KSH();
+    const int SW = pd()->KSW();
+    const int padF = pd()->padFront();
+    const int padT = pd()->padT();
+    const int padL = pd()->padL();
+
+    const bool is_3d = pd()->desc()->src_desc.ndims == 5;
+
+    auto apply_offset = [=](int index, int offset) {
+        return (index > offset) ? index - offset : 0;
+    };
+
+    auto set_ws = [=](int mb, int oc, int od, int oh, int ow, int value) {
+        if (ws) {
+            assert(ws_dt == data_type::u8 || ws_dt == data_type::s32);
+            size_t offset = is_3d
+                ? ws_d.off(mb, oc, od, oh, ow) : ws_d.off(mb, oc, oh, ow);;
+            if (ws_dt == data_type::u8) {
+                assert(0 <= value && value <= 255);
+                ws[offset] = value;
+            } else
+                reinterpret_cast<int *>(ws)[offset] = value;
+        }
+    };
+
+    auto ker_max = [=](data_t *d, int mb, int oc, int oh, int ow) {
+        for (int kh = 0; kh < KH; ++kh) {
+            for (int kw = 0; kw < KW; ++kw) {
+                const int ih = oh * SH - padT + kh;
+                const int iw = ow * SW - padL + kw;
+
+                if (ih < 0 || ih >= IH) continue;
+                if (iw < 0 || iw >= IW) continue;
+
+                auto s = src[src_d.off(mb, oc, ih, iw)];
+                if (s > d[0]) {
+                    d[0] = s;
+                    set_ws(mb, oc, 1, oh, ow, kh*KW + kw);
+                }
+            }
+        }
+    };
+
+    auto ker_avg = [=](data_t *d, int mb, int oc, int oh, int ow) {
+        auto ih_start = apply_offset(oh*SH, padT);
+        auto iw_start = apply_offset(ow*SW, padL);
+        auto ih_end = nstl::min(oh*SH - padT + KH, IH);
+        auto iw_end = nstl::min(ow*SW - padL + KW, IW);
+
+        auto num_summands = (alg == pooling_avg_include_padding) ? KW*KH
+            : (ih_end - ih_start)*(iw_end - iw_start);
+
+        acc_data_t dst = 0;
+        for (int ih = ih_start; ih < ih_end; ++ih) {
+            for (int iw = iw_start; iw < iw_end; ++iw) {
+                dst += src[src_d.off(mb, oc, ih, iw)];
+            }
+        }
+
+        d[0] = math::out_round<data_t>((float)dst / num_summands);
+    };
+
+    auto ker_max_3d = [=](data_t *d, int mb, int oc, int od, int oh, int ow) {
+        for (int kd = 0; kd < KD; ++kd) {
+            for (int kh = 0; kh < KH; ++kh) {
+                for (int kw = 0; kw < KW; ++kw) {
+                    const int id = od * SD - padF + kd;
+                    const int ih = oh * SH - padT + kh;
+                    const int iw = ow * SW - padL + kw;
+
+                    if (id < 0 || id >= ID) continue;
+                    if (ih < 0 || ih >= IH) continue;
+                    if (iw < 0 || iw >= IW) continue;
+
+                    auto s = src[src_d.off(mb, oc, id, ih, iw)];
+                    if (s > d[0]) {
+                        d[0] = s;
+                        set_ws(mb, oc, od, oh, ow, kd * KH * KW + kh*KW + kw);
+                    }
+                }
+            }
+        }
+    };
+
+    auto ker_avg_3d = [=](data_t *d, int mb, int oc, int od, int oh, int ow) {
+        auto id_start = apply_offset(od*SD, padF);
+        auto ih_start = apply_offset(oh*SH, padT);
+        auto iw_start = apply_offset(ow*SW, padL);
+        auto id_end = nstl::min(od*SD - padF + KD, ID);
+        auto ih_end = nstl::min(oh*SH - padT + KH, IH);
+        auto iw_end = nstl::min(ow*SW - padL + KW, IW);
+
+        auto num_summands = (alg == pooling_avg_include_padding) ? KW*KH*KD
+            : (ih_end - ih_start)*(iw_end - iw_start)*(id_end - id_start);
+
+        acc_data_t dst = 0;
+        for (int id = id_start; id < id_end; ++id) {
+            for (int ih = ih_start; ih < ih_end; ++ih) {
+                for (int iw = iw_start; iw < iw_end; ++iw) {
+                    dst += src[src_d.off(mb, oc, id, ih, iw)];
+                }
+            }
+        }
+
+        d[0] = math::out_round<data_t>((float)dst / num_summands);
+    };
+
+    const int MB = pd()->MB();
+    const int OC = pd()->C();
+    const int OD = pd()->OD();
+    const int OH = pd()->OH();
+    const int OW = pd()->OW();
+
+    if (alg == pooling_max) {
+        parallel_nd(MB, OC, OD, OH, OW,
+            [&](int mb, int oc, int od, int oh, int ow) {
+            data_t *d = is_3d
+                ? &dst[dst_d.off(mb, oc, od, oh, ow)]
+                : &dst[dst_d.off(mb, oc, oh, ow)];
+                d[0] = nstl::numeric_limits<data_t>::lowest();
+                set_ws(mb, oc, od, oh, ow, 0);
+                if (is_3d) ker_max_3d(d, mb, oc, od, oh, ow);
+                else ker_max(d, mb, oc, oh, ow);
+        });
+    } else {
+        parallel_nd(MB, OC, OD, OH, OW,
+            [&](int mb, int oc, int od, int oh, int ow) {
+            data_t *d = is_3d
+                ? &dst[dst_d.off(mb, oc, od, oh, ow)]
+                : &dst[dst_d.off(mb, oc, oh, ow)];
+            d[0] = 0;
+            if (is_3d) ker_avg_3d(d, mb, oc, od, oh, ow);
+            else ker_avg(d, mb, oc, oh, ow);
+        });
+    }
+}
+
+template <data_type_t data_type, data_type_t acc_type>
+void ref_pooling_bwd_t<data_type, acc_type>::execute_backward(
+        const exec_ctx_t &ctx) const {
+    using namespace alg_kind;
+
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto ws = CTX_IN_MEM(const unsigned char *, MKLDNN_ARG_WORKSPACE);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
+    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());
+    const memory_desc_wrapper ws_d(pd()->workspace_md());
+
+    const int ID = pd()->ID();
+    const int IH = pd()->IH();
+    const int IW = pd()->IW();
+    const int KD = pd()->KD();
+    const int KH = pd()->KH();
+    const int KW = pd()->KW();
+    const int SD = pd()->KSD();
+    const int SH = pd()->KSH();
+    const int SW = pd()->KSW();
+    const int padF = pd()->padFront();
+    const int padT = pd()->padT();
+    const int padL = pd()->padL();
+
+    const bool is_3d = pd()->desc()->diff_src_desc.ndims == 5;
+
+    auto alg = pd()->desc()->alg_kind;
+
+    auto apply_offset = [=](int index, int offset) {
+        return (index > offset) ? index - offset : 0;
+    };
+
+    auto ker_zero = [=](int _mb, int _oc) {
+        for (int ih = 0; ih < IH; ++ih) {
+            for (int iw = 0; iw < IW; ++iw) {
+                diff_src[diff_src_d.off(_mb, _oc, ih, iw)] = data_type_t(0);
+            }
+        }
+    };
+
+    auto ker_max = [=](const data_t *d, int mb, int oc, int oh, int ow) {
+        const size_t ws_off = ws_d.off(mb, oc, oh, ow);
+        const int index = ws_d.data_type() == data_type::u8
+            ? (int)ws[ws_off] : ((int *)ws)[ws_off];
+        const int kw = index % KW;
+        const int kh = index / KW;
+        const int ih = oh * SH - padT + kh;
+        const int iw = ow * SW - padL + kw;
+
+        // If padding area could fit the kernel,
+        // then input displacement would be out of bounds.
+        // No need to back propagate there as padding is
+        // virtual in pooling_max case.
+        if (ih < 0 || ih >= IH)
+            return;
+        if (iw < 0 || iw >= IW)
+            return;
+
+        diff_src[diff_src_d.off(mb, oc, ih, iw)] += d[0];
+    };
+
+    auto ker_avg = [=](const data_t *d, int mb, int oc, int oh, int ow) {
+        auto ih_start = apply_offset(oh*SH, padT);
+        auto iw_start = apply_offset(ow*SW, padL);
+        auto ih_end = nstl::min(oh*SH - padT + KH, IH);
+        auto iw_end = nstl::min(ow*SW - padL + KW, IW);
+
+        auto num_summands = (alg == pooling_avg_include_padding) ? KW*KH
+            : (ih_end - ih_start)*(iw_end - iw_start);
+
+        for (int ih = ih_start; ih < ih_end; ++ih) {
+            for (int iw = iw_start; iw < iw_end; ++iw) {
+                diff_src[diff_src_d.off(mb, oc, ih, iw)] += d[0] / num_summands;
+            }
+        }
+    };
+
+    auto ker_zero_3d = [=](int _mb, int _oc) {
+        for (int id = 0; id < ID; ++id) {
+            for (int ih = 0; ih < IH; ++ih) {
+                for (int iw = 0; iw < IW; ++iw) {
+                    diff_src[diff_src_d.off(_mb, _oc, id, ih, iw)] =
+                        data_type_t(0);
+                }
+            }
+        }
+    };
+
+    auto ker_max_3d = [=](const data_t *d, int mb, int oc, int od, int oh,
+            int ow) {
+        const size_t ws_off = ws_d.off(mb, oc, od, oh, ow);
+        const int index = ws_d.data_type() == data_type::u8
+            ? (int)ws[ws_off] : ((int *)ws)[ws_off];
+        const int kw = index % KW;
+        const int kh = (index / KW) % KH;
+        const int kd = (index / KW) / KH;
+        const int id = od * SD - padF + kd;
+        const int ih = oh * SH - padT + kh;
+        const int iw = ow * SW - padL + kw;
+
+        // If padding area could fit the kernel,
+        // then input displacement would be out of bounds.
+        // No need to back propagate there as padding is
+        // virtual in pooling_max case.
+        if (id < 0 || id >= ID)
+            return;
+        if (ih < 0 || ih >= IH)
+            return;
+        if (iw < 0 || iw >= IW)
+            return;
+
+        diff_src[diff_src_d.off(mb, oc, id, ih, iw)] += d[0];
+    };
+
+    auto ker_avg_3d = [=](const data_t *d, int mb, int oc, int od, int oh,
+            int ow) {
+        auto id_start = apply_offset(od*SD, padF);
+        auto ih_start = apply_offset(oh*SH, padT);
+        auto iw_start = apply_offset(ow*SW, padL);
+        auto id_end = nstl::min(od*SD - padF + KD, ID);
+        auto ih_end = nstl::min(oh*SH - padT + KH, IH);
+        auto iw_end = nstl::min(ow*SW - padL + KW, IW);
+
+        auto num_summands = (alg == pooling_avg_include_padding) ? KW*KH*KD
+            : (ih_end - ih_start)*(iw_end - iw_start)*(id_end - id_start);
+
+        for (int id = id_start; id < id_end; ++id)
+        for (int ih = ih_start; ih < ih_end; ++ih)
+        for (int iw = iw_start; iw < iw_end; ++iw) {
+            diff_src[diff_src_d.off(mb, oc, id, ih, iw)] += d[0] / num_summands;
+        }
+    };
+
+    const int MB = pd()->MB();
+    const int OC = pd()->C();
+    const int OD = pd()->OD();
+    const int OH = pd()->OH();
+    const int OW = pd()->OW();
+
+    if (pd()->desc()->alg_kind == alg_kind::pooling_max) {
+        parallel_nd(MB, OC, [&](int mb, int oc) {
+            if (is_3d) ker_zero_3d(mb, oc);
+            else ker_zero(mb, oc);
+            for (int od = 0; od < OD; ++od) {
+                for (int oh = 0; oh < OH; ++oh) {
+                    for (int ow = 0; ow < OW; ++ow) {
+                        const data_t *d = is_3d
+                            ? &diff_dst[diff_dst_d.off(mb, oc, od, oh, ow)]
+                            : &diff_dst[diff_dst_d.off(mb, oc, oh, ow)];
+                        if (is_3d) ker_max_3d(d, mb, oc, od, oh, ow);
+                        else ker_max(d, mb, oc, oh, ow);
+                    }
+                }
+            }
+        });
+    } else {
+        parallel_nd(MB, OC, [&](int mb, int oc) {
+            if (is_3d) ker_zero_3d(mb, oc);
+            else ker_zero(mb, oc);
+            for (int od = 0; od < OD; ++od) {
+                for (int oh = 0; oh < OH; ++oh) {
+                    for (int ow = 0; ow < OW; ++ow) {
+                        const data_t *d = is_3d
+                            ? &diff_dst[diff_dst_d.off(mb, oc, od, oh, ow)]
+                            : &diff_dst[diff_dst_d.off(mb, oc, oh, ow)];
+                        if (is_3d) ker_avg_3d(d, mb, oc, od, oh, ow);
+                        else ker_avg(d, mb, oc, oh, ow);
+                    }
+                }
+            }
+        });
+    }
+}
+
+template struct ref_pooling_fwd_t<data_type::f32>;
+template struct ref_pooling_fwd_t<data_type::s32>;
+template struct ref_pooling_fwd_t<data_type::s8, data_type::s32>;
+template struct ref_pooling_fwd_t<data_type::u8, data_type::s32>;
+
+template struct ref_pooling_bwd_t<data_type::f32>;
+template struct ref_pooling_bwd_t<data_type::s32>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_pooling.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_pooling.hpp
new file mode 100644
index 0000000000..e43ceaa82b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_pooling.hpp
@@ -0,0 +1,119 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REF_POOLING_HPP
+#define CPU_REF_POOLING_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_pooling_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t data_type, impl::data_type_t acc_type = data_type>
+struct ref_pooling_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_pooling_fwd_pd_t {
+        using cpu_pooling_fwd_pd_t::cpu_pooling_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_pooling_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && set_default_params() == status::success
+                && is_fwd()
+                && utils::everyone_is(data_type, src_md()->data_type,
+                        dst_md()->data_type)
+                && desc()->accum_data_type == acc_type
+                && attr()->has_default_values();
+            if (!ok) return status::unimplemented;
+
+            bool is_training = desc_.prop_kind == prop_kind::forward_training;
+            if (desc()->alg_kind == alg_kind::pooling_max && is_training)
+                init_default_ws();
+
+            return status::success;
+        }
+    };
+
+    ref_pooling_fwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    typedef typename prec_traits<data_type>::type data_t;
+    typedef typename prec_traits<acc_type>::type acc_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_forward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <impl::data_type_t data_type, impl::data_type_t acc_type = data_type>
+struct ref_pooling_bwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_pooling_bwd_pd_t {
+        using cpu_pooling_bwd_pd_t::cpu_pooling_bwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_pooling_bwd_t);
+
+        status_t init() {
+            bool ok = true
+                && set_default_params() == status::success
+                && !is_fwd()
+                && utils::everyone_is(data_type, diff_dst_md()->data_type,
+                        diff_src_md()->data_type)
+                && attr()->has_default_values();
+            if (!ok) return status::unimplemented;
+
+            if (desc()->alg_kind == alg_kind::pooling_max) {
+                init_default_ws();
+                if (!compare_ws(hint_fwd_pd_))
+                    return status::unimplemented;
+            }
+
+            return status::success;
+        }
+    };
+
+    ref_pooling_bwd_t(const pd_t *apd): cpu_primitive_t(apd) {}
+    typedef typename prec_traits<data_type>::type data_t;
+    typedef typename prec_traits<acc_type>::type acc_data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_backward(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_shuffle.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_shuffle.cpp
new file mode 100644
index 0000000000..af27743110
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_shuffle.cpp
@@ -0,0 +1,153 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <math.h>
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+
+#include "ref_shuffle.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace format_tag;
+
+template <int data_type_size>
+template <mkldnn_format_tag_t tag>
+void ref_shuffle_t<data_type_size>::execute_(const exec_ctx_t &ctx) const {
+    using namespace prop_kind;
+    using namespace utils;
+
+    const memory_desc_wrapper data_d(pd()->data_md());
+
+    auto i_arg = pd()->is_fwd() ? MKLDNN_ARG_SRC : MKLDNN_ARG_DIFF_DST;
+    auto o_arg = pd()->is_fwd() ? MKLDNN_ARG_DST : MKLDNN_ARG_DIFF_SRC;
+    auto input = CTX_IN_MEM(const data_t *, i_arg);
+    auto output = CTX_OUT_MEM(data_t *, o_arg);
+
+    const int axis = pd()->axis();
+    const int axis_size = pd()->axis_size();
+
+    const int MB = pd()->MB();
+    const int C = pd()->C();
+    int H = 1, W = 1, D = 1, HW = 1, SP = 1;
+    const bool has_spatial = utils::one_of(data_d.ndims(), 3, 4 ,5);
+    if (has_spatial)
+    {
+        D = pd()->D();
+        H = pd()->H();
+        W = pd()->W();
+        HW = H * W;
+        SP = D * HW;
+    }
+    const size_t stride_mb = data_d.blocking_desc().strides[0];
+    constexpr int blksize = one_of(tag, nChw16c, nCdhw16c) ? 16 : 8;
+
+    if (axis == 1 && one_of(tag, nChw16c, nChw8c, nCdhw16c, nCdhw16c)) {
+#if MKLDNN_THR == MKLDNN_THR_OMP
+#       pragma omp parallel for collapse(3) schedule(static)
+        for (int mb = 0; mb < MB; ++mb)
+        for (int cb = 0; cb < C; cb += blksize)
+        for (int sp = 0; sp < SP; ++sp) {
+            const size_t off = mb * stride_mb + sp * blksize;
+            const size_t output_off = off + cb * SP;
+            PRAGMA_OMP_SIMD()
+            for (int cc = 0; cc < nstl::min(blksize, C - cb); ++cc)
+            {
+                int input_c = rev_transposed_[cb + cc];
+                const size_t input_off = off + input_c / blksize * SP * blksize
+                                           + input_c % blksize;
+                output[output_off + cc] = input[input_off];
+            }
+        }
+#else
+        parallel_nd(MB, utils::div_up(C, blksize), SP, [&](int mb, int c,
+                  int sp) {
+            const size_t off = mb * stride_mb + sp * blksize;
+            const int cb = c * blksize;
+            const size_t output_off = off + cb * SP;
+            for (int cc = 0; cc < nstl::min(blksize, C - cb); ++cc)
+            {
+                int input_c = rev_transposed_[cb + cc];
+                const size_t input_off = off + input_c / blksize * SP * blksize
+                                           + input_c % blksize;
+                output[output_off + cc] = input[input_off];
+            }
+        });
+#endif
+    } else if (axis == 1 && one_of(tag, nhwc, ndhwc)) {
+        parallel_nd(MB, SP, [&](int mb, int sp) {
+            const size_t off = mb * stride_mb + sp * C;
+            PRAGMA_OMP_SIMD()
+            for (int c = 0; c < C; ++c)
+                output[off + c] = input[off + rev_transposed_[c]];
+        });
+    } else if (axis == 1 && one_of(tag, nchw, ncdhw)) {
+        parallel_nd(MB, C, [&](int mb, int c) {
+            const size_t output_off = mb * stride_mb + c * SP;
+            const size_t input_off = mb * stride_mb + rev_transposed_[c] * SP;
+            PRAGMA_OMP_SIMD()
+            for (int sp = 0; sp < SP; ++sp) {
+                output[output_off + sp] = input[input_off + sp];
+            }
+        });
+    } else {
+        auto dims = pd()->desc()->data_desc.dims;
+        auto ndims = pd()->desc()->data_desc.ndims;
+        const size_t outer_size = utils::array_product(dims, axis);
+        const size_t inner_size = utils::array_product(dims + axis + 1,
+                                         ndims - axis - 1);
+        const size_t dim = axis_size * inner_size;
+
+        parallel_nd(outer_size, axis_size, inner_size, [&](size_t ou, int a,
+               size_t in)
+        {
+            const size_t off = ou * dim + in;
+            auto &o = output[data_d.off_l(off + a * inner_size)];
+            o = input[data_d.off_l(off + rev_transposed_[a] * inner_size)];
+        });
+    }
+}
+
+template void ref_shuffle_t<4>::execute_<nCdhw16c>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<4>::execute_<nChw16c>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<4>::execute_<nCdhw8c>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<4>::execute_<nChw8c>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<4>::execute_<ncdhw>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<4>::execute_<nchw>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<4>::execute_<ndhwc>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<4>::execute_<nhwc>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<4>::execute_<any>(const exec_ctx_t &ctx) const;
+
+template void ref_shuffle_t<1>::execute_<nCdhw16c>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<1>::execute_<nChw16c>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<1>::execute_<nCdhw8c>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<1>::execute_<nChw8c>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<1>::execute_<ncdhw>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<1>::execute_<nchw>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<1>::execute_<ndhwc>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<1>::execute_<nhwc>(const exec_ctx_t &ctx) const;
+template void ref_shuffle_t<1>::execute_<any>(const exec_ctx_t &ctx) const;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_shuffle.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_shuffle.hpp
new file mode 100644
index 0000000000..5e09a1a69b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_shuffle.hpp
@@ -0,0 +1,111 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REF_SHUFFLE_HPP
+#define CPU_REF_SHUFFLE_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_shuffle_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template<int data_type_size>
+struct ref_shuffle_t : public cpu_primitive_t {
+    using shuffle_class = ref_shuffle_t<data_type_size>;
+
+    struct pd_t: public cpu_shuffle_pd_t {
+        using cpu_shuffle_pd_t::cpu_shuffle_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", shuffle_class);
+
+        status_t init() {
+            using namespace format_tag;
+
+            bool ok = true
+                 && data_type_size
+                    == types::data_type_size(data_md()->data_type);
+            if (!ok) return status::unimplemented;
+
+            if (ndims() == 5) {
+                dat_tag_ = memory_desc_matches_one_of_tag(
+                        *data_md(), nCdhw16c, nCdhw8c, ncdhw, ndhwc);
+            } else if (ndims() == 4) {
+                dat_tag_ = memory_desc_matches_one_of_tag(
+                        *data_md(), nChw16c, nChw8c, nchw, nhwc);
+            } else
+                dat_tag_ = any;
+
+            return status::success;
+        }
+
+        format_tag_t dat_tag_;
+    };
+
+    ref_shuffle_t(const pd_t *apd): cpu_primitive_t(apd) {
+        const int axis_size = pd()->axis_size();
+        const int group_size = pd()->group_size();
+        const int transpose_row = pd()->is_fwd() ? group_size
+                                                 : axis_size / group_size;
+        const int transpose_col = pd()->is_fwd() ? axis_size / group_size
+                                                 : group_size;
+        rev_transposed_ = (int *)malloc(axis_size * sizeof(int), 64);
+        parallel_nd(transpose_col, transpose_row, [&](int i, int j) {
+            rev_transposed_[j * transpose_col + i] = i * transpose_row + j;
+        });
+    }
+
+    ~ref_shuffle_t() { free(rev_transposed_); }
+
+    typedef typename typesize_traits<data_type_size>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        using namespace format_tag;
+        switch (pd()->dat_tag_) {
+        case nCdhw16c: execute_<nCdhw16c>(ctx); break;
+        case nChw16c:  execute_<nChw16c>(ctx); break;
+        case nCdhw8c:  execute_<nCdhw8c>(ctx); break;
+        case nChw8c:   execute_<nChw8c>(ctx); break;
+        case ncdhw:    execute_<ncdhw>(ctx); break;
+        case nchw:     execute_<nchw>(ctx); break;
+        case ndhwc:    execute_<ndhwc>(ctx); break;
+        case nhwc:     execute_<nhwc>(ctx); break;
+        default:       execute_<any>(ctx); break;
+        }
+        return status::success;
+    }
+
+private:
+    template<format_tag_t tag>
+    void execute_(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    int *rev_transposed_;
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.cpp
new file mode 100644
index 0000000000..36d5237f56
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.cpp
@@ -0,0 +1,264 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <assert.h>
+#include <float.h>
+#include <math.h>
+
+#include "c_types_map.hpp"
+#include "mkldnn_thread.hpp"
+#include "type_helpers.hpp"
+
+#include "ref_softmax.hpp"
+#include "gemm/os_blas.hpp"
+
+#ifdef USE_MKL
+#include "mkl_vml_functions.h"
+#endif
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t data_type>
+void ref_softmax_fwd_t<data_type>::execute_forward_dense(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    parallel_nd(outer_size_, [&](int ou) {
+        const data_t *src_data = src + ou * channels_;
+        data_t *dst_data = dst + ou * channels_;
+        data_t scalar = 0;
+
+        _max(channels_, src_data, &scalar);
+        _sub(channels_, scalar, src_data, dst_data);
+        _exp(channels_, dst_data, dst_data);
+        _sum(channels_, dst_data, &scalar);
+        _scal(channels_, data_t(1)/scalar, dst_data);
+    });
+}
+
+template <impl::data_type_t data_type>
+void ref_softmax_fwd_t<data_type>::execute_forward_generic(
+        const exec_ctx_t &ctx) const {
+    auto src = CTX_IN_MEM(const data_t *, MKLDNN_ARG_SRC);
+    auto dst = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    data_t space_max_val = 0, space_denom_val = 0;
+    data_t *space_max = &space_max_val, *space_denom = &space_denom_val;
+    if (inner_size_ > 1) {
+        using namespace memory_tracking::names;
+        space_max = scratchpad(ctx).template get<data_t>(key_softmax_reduction);
+        space_denom = space_max + inner_size_;
+    }
+
+    const memory_desc_wrapper data_d(pd()->src_md());
+    const size_t dim = channels_ * inner_size_;
+
+    for (int ou = 0; ou < outer_size_; ou++) {
+        utils::array_set(space_max, -FLT_MAX, inner_size_);
+        utils::array_set(space_denom, 0, inner_size_);
+
+        for (int c = 0; c < channels_; c++) {
+            for(int in = 0; in < inner_size_; in++) {
+                size_t off = data_d.off_l(ou * dim + c * inner_size_ + in);
+                space_max[in] = nstl::max(space_max[in], src[off]);
+            }
+        }
+
+        for (int c = 0; c < channels_; c++) {
+            for(int in = 0; in < inner_size_; in++) {
+                size_t off = data_d.off_l(ou * dim + c * inner_size_ + in);
+                space_denom[in] += dst[off] = exp(src[off] - space_max[in]);
+            }
+        }
+
+        for (int c = 0; c < channels_; c++) {
+            for (int in = 0; in < inner_size_; in++) {
+                size_t off = data_d.off_l(ou * dim + c * inner_size_ + in);
+                dst[off] /= space_denom[in];
+            }
+        }
+    }
+}
+
+template <impl::data_type_t data_type>
+void ref_softmax_fwd_t<data_type>::_max(int n, const data_t *x,
+        data_t *max_data) const {
+// Intel(R) C++ Compiler generates the maxps + shuffle pattern
+// for the max search which works faster
+#if !defined(__INTEL_COMPILER)
+    // The code below makes a compiler to generate maxps instruction
+    // rather than maxss, which is generated for the 'else' code path
+    auto max_wrapper = [](data_t a, data_t b) { return nstl::max(a, b); };
+    auto min_wrapper = [](int a, int b) { return nstl::min(a, b); };
+
+    constexpr int unroll_factor = 32;
+    data_t max_values[unroll_factor];
+
+    if (n < unroll_factor) {
+        data_t max_val = x[0];
+        for (int i = 1; i < n; i++) {
+            max_val = max_wrapper(max_val, x[i]);
+        }
+        max_data[0] = max_val;
+        return;
+    }
+    for (int i = 0; i < unroll_factor; i++) {
+        max_values[i] = x[i];
+    }
+    for (int i = unroll_factor; i < n; i += unroll_factor) {
+        int offset = min_wrapper(i, n - unroll_factor);
+        for (int j = 0; j < unroll_factor; j++) {
+            max_values[j] = max_wrapper(max_values[j], x[offset + j]);
+        }
+    }
+    data_t max_val = max_values[0];
+    for (int i = 1; i < unroll_factor; i++) {
+        max_val = max_wrapper(max_val, max_values[i]);
+    }
+    max_data[0] = max_val;
+#else
+    max_data[0] = x[0];
+    for (int c = 1; c < n; ++c)
+        max_data[0] = nstl::max(max_data[0], x[c]);
+#endif
+}
+
+template <impl::data_type_t data_type>
+void ref_softmax_fwd_t<data_type>::_sub(int n, data_t alpha, const data_t *x,
+        data_t *y) const {
+    constexpr int unroll_factor = 32;
+    int tail = n % unroll_factor;
+    for (int i = 0; i < n - tail; i += unroll_factor) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < unroll_factor; j++) {
+            y[i + j] = x[i + j] - alpha;
+        }
+    }
+    PRAGMA_OMP_SIMD()
+    for (int i = n - tail; i < n; i++) {
+        y[i] = x[i] - alpha;
+    }
+}
+
+template <impl::data_type_t data_type>
+void ref_softmax_fwd_t<data_type>::_exp(int n, const data_t *a,
+        data_t *r) const {
+#ifdef USE_MKL
+    if (data_type == data_type::f32) {
+        vsExp(n, a, r);
+        return;
+    }
+#endif
+    parallel_nd(n, [&](int c) { r[c] = expf(a[c]); });
+}
+
+template <impl::data_type_t data_type>
+void ref_softmax_fwd_t<data_type>::_sum(int n, const data_t *x,
+        data_t *sum_data) const {
+#ifdef USE_CBLAS
+    // Here we are summing x's eg. e^z , which are positives
+    // so we can use BLAS ASUM
+    if (data_type == data_type::f32) {
+        sum_data[0] = cblas_sasum(n, x, 1);
+        return;
+    }
+#endif
+    data_t tsum = static_cast<data_t>(0);
+    PRAGMA_OMP_SIMD(reduction(+ : tsum))
+    for (int c = 0; c < n; ++c)
+        tsum += x[c];
+    sum_data[0] = tsum;
+}
+
+template <impl::data_type_t data_type>
+void ref_softmax_fwd_t<data_type>::_scal(int n, data_t alpha, data_t *x) const {
+#ifdef USE_CBLAS
+    if (data_type == data_type::f32) {
+        cblas_sscal(n, alpha, x, 1);
+        return;
+    }
+#endif
+    parallel_nd(n, [&](int c) { x[c] *= alpha; });
+}
+
+template struct ref_softmax_fwd_t<data_type::f32>;
+
+
+// NC/NCHW softmax for along final axe (1 for NC, 3 for NCHW)
+template <impl::data_type_t data_type>
+void ref_softmax_bwd_t<data_type>::execute_backward_dense(
+        const exec_ctx_t &ctx) const {
+    auto dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DST);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    parallel_nd(outer_size_, [&](int ou) {
+        data_t sbr = 0;
+        size_t off = channels_*ou;
+        for (int c = 0; c < channels_; c++) {
+            size_t loff = off + c;
+            data_t ldata = dst[loff];
+            sbr += diff_dst[loff]*ldata;
+            diff_src[loff] = ldata;
+        }
+
+        for(int c=0; c < channels_ ; ++c) {
+          size_t loff = off + c;
+          diff_src[loff] *= (diff_dst[loff] - sbr);
+        }
+    });
+}
+
+template <impl::data_type_t data_type>
+void ref_softmax_bwd_t<data_type>::execute_backward_generic(
+        const exec_ctx_t &ctx) const {
+    auto dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DST);
+    auto diff_dst = CTX_IN_MEM(const data_t *, MKLDNN_ARG_DIFF_DST);
+    auto diff_src = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DIFF_SRC);
+
+    const memory_desc_wrapper diff_d(pd()->diff_src_md());
+    const memory_desc_wrapper data_d(pd()->dst_md());
+
+    const size_t dim = channels_ * inner_size_;
+
+    parallel_nd(outer_size_, [&](int ou) {
+        for (int in = 0; in < inner_size_; in++) {
+            data_t sbr = 0;
+            for (int c = 0; c < channels_; c++) {
+                size_t off_diff = diff_d.off_l(ou * dim + c * inner_size_ + in);
+                size_t off_data = diff_d.off_l(ou * dim + c * inner_size_ + in);
+                sbr += diff_dst[off_diff] * dst[off_data];
+            }
+
+            for(int c=0; c < channels_ ; ++c) {
+              size_t off_diff = diff_d.off_l(ou * dim + c * inner_size_ + in);
+              size_t off_data = data_d.off_l(ou * dim + c * inner_size_ + in);
+              diff_src[off_diff] = dst[off_data] * (diff_dst[off_diff] - sbr);
+            }
+        }
+    });
+}
+
+template struct ref_softmax_bwd_t<data_type::f32>;
+
+}
+}
+}
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.hpp
new file mode 100644
index 0000000000..5cb74d8007
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_softmax.hpp
@@ -0,0 +1,186 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REF_SOFTMAX_HPP
+#define CPU_REF_SOFTMAX_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "cpu_softmax_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <impl::data_type_t data_type>
+struct ref_softmax_fwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_softmax_fwd_pd_t {
+        using cpu_softmax_fwd_pd_t::cpu_softmax_fwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_softmax_fwd_t);
+
+        status_t init() {
+            bool ok = true
+                && is_fwd()
+                && src_md()->data_type == data_type
+                && attr()->has_default_values();
+            if (!ok) return status::unimplemented;
+
+            init_scratchpad();
+
+            return status::success;
+        }
+
+    private:
+        void init_scratchpad() {
+            const int inner_size = utils::array_product(
+                    desc()->data_desc.dims + desc()->softmax_axis + 1,
+                    desc()->data_desc.ndims - desc()->softmax_axis - 1);
+
+            if (inner_size > 1) {
+                auto scratchpad = scratchpad_registry().registrar();
+                scratchpad.book(memory_tracking::names::key_softmax_reduction,
+                        sizeof(data_t) * 2 * inner_size);
+            }
+        }
+    };
+
+    ref_softmax_fwd_t(const pd_t *apd): cpu_primitive_t(apd)
+    {
+        auto ndims = pd()->desc()->data_desc.ndims;
+        auto dims = pd()->desc()->data_desc.dims;
+        auto axis = pd()->desc()->softmax_axis;
+
+        outer_size_ = utils::array_product(dims, axis);
+        channels_ = dims[axis];
+        inner_size_ = utils::array_product(dims + axis + 1, ndims - axis - 1);
+
+        const memory_desc_wrapper data_d(pd()->src_md());
+
+        bool no_axis_blocking = true;
+        for (int iblk = 0; iblk < data_d.blocking_desc().inner_nblks; ++iblk)
+            if (data_d.blocking_desc().inner_idxs[iblk] == axis)
+                no_axis_blocking = false;
+
+        use_dense_ = inner_size_ == 1 && data_d.is_dense()
+            && no_axis_blocking
+            && data_d.blocking_desc().strides[axis] == 1;
+    }
+
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        if (use_dense_)
+            execute_forward_dense(ctx);
+        else
+            execute_forward_generic(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_forward_dense(const exec_ctx_t &ctx) const;
+    void execute_forward_generic(const exec_ctx_t &ctx) const;
+
+    void _max(int n, const data_t *x, data_t *max_data) const;
+    void _sub(int n, data_t alpha, const data_t *x, data_t *y) const;
+    void _exp(int n, const data_t *a, data_t *r) const;
+    void _sum(int n, const data_t *x, data_t *sum_data) const;
+    void _scal(int n, data_t alpha, data_t *x) const;
+
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    bool use_dense_;
+    int outer_size_, channels_, inner_size_;
+};
+
+template <impl::data_type_t data_type>
+struct ref_softmax_bwd_t: public cpu_primitive_t {
+    struct pd_t: public cpu_softmax_bwd_pd_t {
+        using cpu_softmax_bwd_pd_t::cpu_softmax_bwd_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", ref_softmax_bwd_t);
+
+        status_t init() {
+            bool ok = true
+                && !is_fwd()
+                && utils::everyone_is(data_type,
+                        dst_md()->data_type,
+                        diff_src_md()->data_type)
+                && attr()->has_default_values();
+            if (!ok) return status::unimplemented;
+
+            return status::success;
+        }
+    };
+
+    ref_softmax_bwd_t(const pd_t *apd): cpu_primitive_t(apd) {
+        auto dims = pd()->desc()->diff_desc.dims;
+        auto axis = pd()->desc()->softmax_axis;
+        auto ndims = pd()->desc()->diff_desc.ndims;
+
+        outer_size_ = utils::array_product(dims, axis);
+        channels_ = dims[axis];
+        inner_size_ = utils::array_product(dims + axis + 1, ndims - axis - 1);
+
+        const memory_desc_wrapper data_d(pd()->dst_md());
+        const memory_desc_wrapper diff_d(pd()->diff_dst_md());
+
+        bool no_axis_blocking = true;
+        for (int iblk = 0; iblk < diff_d.blocking_desc().inner_nblks; ++iblk)
+            if (diff_d.blocking_desc().inner_idxs[iblk] == axis)
+                no_axis_blocking = false;
+
+        use_dense_ = true
+            && inner_size_ == 1
+            && diff_d == data_d
+            && diff_d.is_dense()
+            && no_axis_blocking
+            && diff_d.blocking_desc().strides[axis] == 1;
+    }
+
+    typedef typename prec_traits<data_type>::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        if (use_dense_)
+            execute_backward_dense(ctx);
+        else
+            execute_backward_generic(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_backward_dense(const exec_ctx_t &ctx) const;
+    void execute_backward_generic(const exec_ctx_t &ctx) const;
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    bool use_dense_;
+    int outer_size_, channels_, inner_size_;
+};
+
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/ref_sum.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/ref_sum.hpp
new file mode 100644
index 0000000000..3b2a75d99b
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/ref_sum.hpp
@@ -0,0 +1,101 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef REF_SUM_HPP
+#define REF_SUM_HPP
+
+#include "reorder_pd.hpp"
+
+#include "cpu_sum_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct ref_sum_t: public cpu_primitive_t {
+    struct pd_t: public cpu_sum_pd_t {
+        using cpu_sum_pd_t::cpu_sum_pd_t;
+
+        pd_t(const pd_t &rhs): cpu_sum_pd_t(rhs) {
+            for (size_t i = 0; i < rhs.reorder_pds_.size(); ++i)
+                reorder_pds_.push_back(
+                       (const reorder_pd_t *)rhs.reorder_pds_[i]->clone());
+        }
+
+        ~pd_t() { for (auto &rpd: reorder_pds_) delete rpd; }
+
+        DECLARE_SUM_PD_T("ref:any", ref_sum_t);
+
+        status_t init() {
+            bool ok = cpu_sum_pd_t::init() == status::success;
+            if (!ok) return status::unimplemented;
+
+            for (int i = 0; i < n_; ++i) {
+                auto r_impls = engine_->get_reorder_implementation_list();
+                for (auto r = r_impls; *r; ++r) {
+                    primitive_attr_t attr;
+                    attr.output_scales_.set(scales_[i]);
+                    if (i != 0) attr.post_ops_.append_sum(1.0);
+
+                    reorder_pd_t *r_pd;
+                    if ((*r)(&r_pd, engine_, &attr, engine_, src_md(i),
+                                engine_, dst_md()) == status::success) {
+                        r_pd->init_info();
+                        reorder_pds_.push_back(r_pd);
+                        break;
+                    }
+                }
+            }
+
+            ok = reorder_pds_.size() == (size_t)n_;
+            return ok ? status::success : status::unimplemented;
+        }
+
+        nstl::vector<const reorder_pd_t *> reorder_pds_;
+    };
+
+    ref_sum_t(const pd_t *apd): cpu_primitive_t(apd) {
+        const int n = pd()->n_inputs();
+        reorders_.resize(n);
+        for (int i = 0; i < n; ++i)
+            pd()->reorder_pds_[i]->create_primitive(&reorders_[i]);
+    }
+
+    ~ref_sum_t() { for (auto &r: reorders_) delete r; }
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        const auto n = pd()->n_inputs();
+        for (int i = 0; i < n; ++i) {
+            exec_args_t r_args;
+            r_args[MKLDNN_ARG_SRC] = ctx.args().at(MKLDNN_ARG_MULTIPLE_SRC + i);
+            r_args[MKLDNN_ARG_DST] = ctx.args().at(MKLDNN_ARG_DST);
+            exec_ctx_t r_ctx(ctx.stream(), std::move(r_args));
+            reorders_[i]->execute(r_ctx);
+        }
+        return status::success;
+    }
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    nstl::vector<primitive_t *> reorders_;
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_common.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_common.cpp
new file mode 100644
index 0000000000..537084db91
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_common.cpp
@@ -0,0 +1,90 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/*
+ * Common for RNN and LSTM cell execution
+ */
+#include "ref_rnn.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+using namespace rnn_utils;
+
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+rnn_cell_execution_sig(
+        (_ref_rnn_common_t<aprop, src_type, weights_type>::cell_execution)) {
+    if (!rnn.merge_gemm_layer) {
+        (this->*gemm_layer_func)('N', 'N', rnn.n_gates * rnn.dic, rnn.mb,
+                rnn.slc, 1.0, w_layer_[0], rnn.weights_layer_ld,
+                states_t_lm1_, rnn.states_ws_ld, 0.0, ws_gates_,
+                rnn.gates_ws_ld);
+    }
+    (this->*gemm_iter_func)('N', 'N', rnn.n_gates * rnn.dic, rnn.mb, rnn.sic,
+            1.0, w_iter_[0], rnn.weights_iter_ld, states_tm1_l_,
+            rnn.states_ws_ld, 1.0, ws_gates_, rnn.gates_ws_ld);
+
+    if (rnn_postgemm_ != nullptr)
+        rnn_postgemm_->execute<src_data_t, acc_data_t>(rnn, ws_gates_, states_t_l_, c_states_t_l_,
+            states_tm1_l_, c_states_tm1_l_, diff_states_t_l_,
+            diff_states_t_lp1_, diff_states_tp1_l_, bias_[0], ws_grid_,
+            ws_cell_);
+    else
+        (this->*elemwise_func)(rnn, ws_gates_, states_t_l_, c_states_t_l_,
+                states_tm1_l_, c_states_tm1_l_, diff_states_t_l_,
+                diff_states_t_lp1_, diff_states_tp1_l_, bias_[0], ws_grid_,
+                ws_cell_);
+}
+template rnn_cell_execution_sig(ref_rnn_fwd_f32_t::cell_execution);
+template rnn_cell_execution_sig(ref_rnn_fwd_u8s8_t::cell_execution);
+
+template <>
+rnn_cell_execution_sig(ref_rnn_bwd_f32_t::cell_execution) {
+    ws_diff_states_aoc_t diff_states_t_l(rnn, diff_states_t_l_);
+    (this->*elemwise_func)(rnn, ws_gates_, states_t_l_, c_states_t_l_,
+            states_tm1_l_, c_states_tm1_l_, diff_states_t_l_,
+            diff_states_t_lp1_, diff_states_tp1_l_, bias_[0], ws_grid_,
+            ws_cell_);
+
+    /// bwd by data on the cell
+    (this->*gemm_iter_func)('N', 'N', rnn.sic, rnn.mb, rnn.n_gates * rnn.dic,
+            1.0, w_iter_[0], rnn.weights_iter_ld, ws_gates_, rnn.gates_ws_ld,
+            0.0, diff_states_t_l_, rnn.states_ws_ld);
+
+    if (!rnn.merge_gemm_layer) {
+        (this->*gemm_layer_func)('N', 'N', rnn.slc, rnn.mb,
+                rnn.n_gates * rnn.dic, 1.0, w_layer_[0],
+                rnn.weights_layer_ld, ws_gates_, rnn.gates_ws_ld, 0.0,
+                &diff_states_t_l(rnn.n_states, 0, 0), rnn.states_ws_ld);
+
+        /// bwd by weights on the cell
+        gemm('N', 'T', rnn.n_gates * rnn.dic, rnn.slc, rnn.mb, 1.0, ws_gates_,
+                rnn.gates_ws_ld, states_t_lm1_, rnn.states_ws_ld, 1.0,
+                diff_w_layer_, rnn.diff_weights_layer_ld);
+    }
+
+    if (!rnn.merge_gemm_iter)
+        gemm('N', 'T', rnn.n_gates * rnn.dic, rnn.sic, rnn.mb, 1.0, ws_gates_,
+                rnn.gates_ws_ld, states_tm1_l_, rnn.states_ws_ld, 1.0,
+                diff_w_iter_, rnn.diff_weights_iter_ld);
+
+    /// bwd by bias we just accumulate diffs from the gates
+    gates_reduction(rnn, ws_gates_, diff_bias_);
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_gru.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_gru.cpp
new file mode 100644
index 0000000000..e1a61d4c62
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_gru.cpp
@@ -0,0 +1,180 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/*
+ * Cell execution GRU
+ */
+
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+
+#include "ref_rnn.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::math;
+using namespace rnn_utils;
+
+#define AOC array_offset_calculator
+template <>
+rnn_cell_execution_sig(ref_rnn_fwd_f32_t::cell_execution_gru) {
+    ws_gates_aoc_t ws_gates(rnn, ws_gates_);
+    bias_aoc_t bias(rnn, bias_[0]);
+    ws_states_aoc_t states_t_l(rnn, states_t_l_);
+    ws_states_aoc_t states_tm1_l(rnn, states_tm1_l_);
+
+    // 1. gemm Wx[0-2],x
+    if (!rnn.merge_gemm_layer) {
+        (this->*gemm_layer_func)('N', 'N', rnn.n_gates * rnn.dic, rnn.mb,
+                rnn.slc, 1.0, w_layer_[0], rnn.weights_layer_ld,
+                states_t_lm1_, rnn.states_ws_ld, 0.0, ws_gates_,
+                rnn.gates_ws_ld);
+    }
+
+    // 2. gemm Wh[0-1],h
+    (this->*gemm_iter_func)('N', 'N', (rnn.n_gates - 1) * rnn.dic, rnn.mb,
+            rnn.sic, 1.0, w_iter_[0], rnn.weights_iter_ld, states_tm1_l_,
+            rnn.states_ws_ld, 1.0, ws_gates_, rnn.gates_ws_ld);
+
+    // 3. activation zt and rt + elemwise multiplication rt,ht-1
+    parallel_nd(rnn.mb, [&](int i) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < rnn.dic; j++) {
+            ws_gates(i, 0, j) = logistic_fwd(ws_gates(i, 0, j) + bias(0, j));
+            ws_gates(i, 1, j) = logistic_fwd(ws_gates(i, 1, j) + bias(1, j));
+            states_t_l(i, j) = states_tm1_l(i, j) * ws_gates(i, 1, j);
+        }
+    });
+
+    // 4. gemm Wh[2],h~t
+    (this->*gemm_iter_func)('N', 'N', rnn.dic, rnn.mb, rnn.sic, 1.0, w_iter_[1],
+            rnn.weights_iter_ld, states_t_l_, rnn.states_ws_ld, 1.0,
+            &(ws_gates(0, 2, 0)), rnn.gates_ws_ld);
+
+    // 5. activation h~t + calculate ht
+    parallel_nd(rnn.mb, [&](int i) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < rnn.dic; j++) {
+            ws_gates(i, 2, j) = tanh_fwd(ws_gates(i, 2, j) + bias(2, j));
+            states_t_l(i, j) = states_tm1_l(i, j) * ws_gates(i, 0, j)
+                    + (1.0f - ws_gates(i, 0, j)) * ws_gates(i, 2, j);
+        }
+    });
+}
+
+template <>
+rnn_cell_execution_sig(ref_rnn_fwd_u8s8_t::cell_execution_gru) {
+    assert(!"GRU int8 is not supported");
+}
+
+template <>
+rnn_cell_execution_sig(ref_rnn_bwd_f32_t::cell_execution_gru) {
+    ws_gates_aoc_t ws_gates(rnn, ws_gates_);
+    ws_states_aoc_t states_t_l(rnn, states_t_l_);
+    ws_states_aoc_t states_tm1_l(rnn, states_tm1_l_);
+    ws_diff_w_iter_aoc_t diff_w_iter(rnn, diff_w_iter_);
+    ws_diff_states_aoc_t diff_states_t_l(rnn, diff_states_t_l_);
+    ws_diff_states_aoc_t diff_states_tp1_l(rnn, diff_states_tp1_l_);
+    ws_diff_states_aoc_t diff_states_t_lp1(rnn, diff_states_t_lp1_);
+
+    // use state memory for intermediate computations
+    // TODO: use cell ws for that
+    float *dhG1_ = &(diff_states_t_l(rnn.n_states, 0, 0));
+    float *hG1_ = dhG1_;
+    AOC<float, 2> dhG1(dhG1_, rnn.states_nld, rnn.states_ws_ld);
+    AOC<float, 2> hG1(hG1_, rnn.states_nld, rnn.states_ws_ld);
+
+    // 1. calculate dG2, dG1, and part of dht-1
+    // dG2^ = dh * (1 - G0) * (1 - G2^2)
+    // dG0^ = dh * (ht-1 - G2) * u * (1 - G0)
+    // dht-1 (part) = dh * G0
+    parallel_nd(rnn.mb, [&](int i) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < rnn.dic; j++) {
+            float h = states_tm1_l(i, j);
+            float dHt = diff_states_tp1_l(0, i, j)
+                    + diff_states_t_lp1(rnn.n_states, i, j);
+            float dG2 = (1.0f - ws_gates(i, 0, j)) * dHt
+                    * one_m_square(ws_gates(i, 2, j));
+            float dG0 = (h - ws_gates(i, 2, j)) * dHt
+                    * x_m_square(ws_gates(i, 0, j));
+
+            diff_states_t_l(0, i, j) = dHt * ws_gates(i, 0, j);
+            ws_gates(i, 0, j) = dG0;
+            ws_gates(i, 2, j) = dG2;
+        }
+    });
+
+    // 2. calculate intermediate d(hG1)
+    // d(hG1) = dG2 * W2h^t
+    (this->*gemm_iter_func)('N', 'N', rnn.sic, rnn.mb, rnn.dic, 1.0, w_iter_[1],
+            rnn.weights_iter_ld, &(ws_gates(0, 2, 0)), rnn.gates_ws_ld, 0.0,
+            dhG1_, rnn.states_ws_ld);
+
+    // 3. calculate dG1^ and part of dht-1
+    // dG1^ = d(hG1) * h * G1 * (1 - G1)
+    // dht-1 (part) += d(hG1) * G1
+    // h * G1 (required for dWh)
+    parallel_nd(rnn.mb, [&](int i) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < rnn.dic; j++) {
+            float h = states_tm1_l(i, j);
+            float G1 = ws_gates(i, 1, j);
+            diff_states_t_l(0, i, j) += dhG1(i, j) * G1;
+            ws_gates(i, 1, j) = dhG1(i, j) * h * x_m_square(G1);
+            hG1(i, j) = G1 * h;
+        }
+    });
+
+    // 4. calculate diff weights
+    // dWh1 += dG1 * h, dWh2 += dG2 * h, dWh3 += dG3 * (G1(*)h)
+    gemm('N', 'T', (rnn.n_gates - 1) * rnn.dic, rnn.sic, rnn.mb, 1.0, ws_gates_,
+            rnn.gates_ws_ld, states_tm1_l_, rnn.states_ws_ld, 1.0, diff_w_iter_,
+            rnn.diff_weights_iter_ld);
+    gemm('N', 'T', rnn.dic, rnn.sic, rnn.mb, 1.0, &(ws_gates(0, 2, 0)),
+            rnn.gates_ws_ld, hG1_, rnn.states_ws_ld, 1.0,
+            &(diff_w_iter(0, 2, 0)), rnn.diff_weights_iter_ld);
+
+    // 5. calculate diff states
+    // dht-1 += dG1 * W1h + dG0 * W0h
+    (this->*gemm_iter_func)('N', 'N', rnn.sic, rnn.mb,
+            (rnn.n_gates - 1) * rnn.dic, 1.0, w_iter_[0],
+            rnn.weights_iter_ld, ws_gates_, rnn.gates_ws_ld, 1.0,
+            diff_states_t_l_, rnn.states_ws_ld);
+
+    if (!rnn.merge_gemm_layer) {
+        // dWx += [dG0 dG1 dG2] * [x]
+        gemm('N', 'T', rnn.n_gates * rnn.dic, rnn.slc, rnn.mb, 1.0, ws_gates_,
+                rnn.gates_ws_ld, states_t_lm1_, rnn.states_ws_ld, 1.0,
+                diff_w_layer_, rnn.diff_weights_layer_ld);
+        // dx = dG2 * W2x + dG1 * W1x + dG0 * W0x
+        (this->*gemm_layer_func)('N', 'N', rnn.slc, rnn.mb,
+                rnn.n_gates * rnn.dic, 1.0, w_layer_[0],
+                rnn.weights_layer_ld, ws_gates_, rnn.gates_ws_ld, 0.0,
+                &(diff_states_t_l(rnn.n_states, 0, 0)), rnn.states_ws_ld);
+    }
+
+    // 6. calculate diff bias
+    gates_reduction(rnn, ws_gates_, diff_bias_);
+}
+#undef AOC
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_gru_lbr.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_gru_lbr.cpp
new file mode 100644
index 0000000000..8dea8c90a4
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_gru_lbr.cpp
@@ -0,0 +1,170 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/*
+ * Cell execution GRU with linear before reset
+ */
+
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+
+#include "ref_rnn.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::math;
+using namespace rnn_utils;
+#define AOC array_offset_calculator
+
+template <>
+rnn_elemwise_sig(ref_rnn_fwd_f32_t::gru_lbr_elemwise) {
+    ws_gates_aoc_t ws_gates(rnn, ws_gates_);
+    bias_aoc_t bias(rnn, bias_);
+    ws_states_aoc_t states_t_l(rnn, states_t_l_);
+    ws_states_aoc_t states_tm1_l(rnn, states_tm1_l_);
+    ws_gates_aoc_t ws_gemm_state(rnn, ws_cell_);
+    AOC<float, 2> ws_Wh_b(ws_grid_, rnn.mb, rnn.dic);
+
+    parallel_nd(rnn.mb, [&](int i) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < rnn.dic; j++) {
+            float Wh_b = ws_gemm_state(i, 2, j) + bias(3, j);
+            ws_gates(i, 0, j) = logistic_fwd(
+                    ws_gates(i, 0, j) + ws_gemm_state(i, 0, j) + bias(0, j));
+            ws_gates(i, 1, j) = logistic_fwd(
+                    ws_gates(i, 1, j) + ws_gemm_state(i, 1, j) + bias(1, j));
+            ws_gates(i, 2, j) = tanh_fwd(
+                    ws_gates(i, 2, j) + ws_gates(i, 1, j) * Wh_b + bias(2, j));
+            states_t_l(i, j) = states_tm1_l(i, j) * ws_gates(i, 0, j)
+                    + (1.0f - ws_gates(i, 0, j)) * ws_gates(i, 2, j);
+            if (rnn.is_training)
+                ws_Wh_b(i, j) = Wh_b;
+        }
+    });
+}
+
+template <>
+rnn_elemwise_sig(ref_rnn_fwd_u8s8_t::gru_lbr_elemwise) {
+    assert(!"GRU LBR int8 is not supported");
+}
+
+template <>
+rnn_cell_execution_sig(ref_rnn_fwd_f32_t::cell_execution_gru_lbr) {
+    if (!rnn.merge_gemm_layer) {
+        (this->*gemm_layer_func)('N', 'N', rnn.n_gates * rnn.dic, rnn.mb,
+                rnn.slc, 1.0, w_layer_[0], rnn.weights_layer_ld,
+                states_t_lm1_, rnn.states_ws_ld, 0.0, ws_gates_,
+                rnn.gates_ws_ld);
+    }
+    (this->*gemm_iter_func)('N', 'N', rnn.n_gates * rnn.dic, rnn.mb, rnn.sic,
+            1.0, w_iter_[0], rnn.weights_iter_ld, states_tm1_l_,
+            rnn.states_ws_ld, 0.0, ws_cell_, rnn.gates_ws_ld);
+    (this->*elemwise_func)(rnn, ws_gates_, states_t_l_, c_states_t_l_,
+            states_tm1_l_, c_states_tm1_l_, diff_states_t_l_,
+            diff_states_t_lp1_, diff_states_tp1_l_, bias_[0], ws_grid_,
+            ws_cell_);
+}
+
+template <>
+rnn_cell_execution_sig(ref_rnn_fwd_u8s8_t::cell_execution_gru_lbr) {
+    assert(!"GRU LBR int8 is not supported");
+}
+
+template <>
+rnn_elemwise_sig(ref_rnn_bwd_f32_t::gru_lbr_elemwise) {
+    ws_gates_aoc_t ws_gates(rnn, ws_gates_);
+    ws_states_aoc_t states_tm1_l(rnn, states_tm1_l_);
+    ws_diff_states_aoc_t diff_states_t_l(rnn, diff_states_t_l_);
+    ws_diff_states_aoc_t diff_states_tp1_l(rnn, diff_states_tp1_l_);
+    ws_diff_states_aoc_t diff_states_t_lp1(rnn, diff_states_t_lp1_);
+    ws_gates_aoc_t ws_gates_r(rnn, ws_cell_);
+    AOC<float, 2> ws_Wh_b(ws_grid_, rnn.mb, rnn.dic);
+
+    // 1. calculate dG1 dG2 dG3
+    // dG0 = (dht - G2) * dht * (1 - G0) * G0
+    // dG1 = (W*h + b) * dG2 * (1 - G1) * G1
+    // dG2 = (1 - G0) * dht * (1 - G2*G2)
+    parallel_nd(rnn.mb, [&](int i) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < rnn.dic; j++) {
+            float h = states_tm1_l(i, j);
+            float dHt = diff_states_tp1_l(0, i, j)
+                    + diff_states_t_lp1(rnn.n_states, i, j);
+            float dG0 = (h - ws_gates(i, 2, j)) * dHt
+                    * x_m_square(ws_gates(i, 0, j));
+            float dG2 = (1.0f - ws_gates(i, 0, j))
+                    * one_m_square(ws_gates(i, 2, j)) * dHt;
+            float dG1 = ws_Wh_b(i, j) * dG2 * x_m_square(ws_gates(i, 1, j));
+
+            diff_states_t_l(0, i, j) = dHt * ws_gates(i, 0, j);
+            ws_gates(i, 2, j) = dG2;
+            ws_gates_r(i, 2, j) = dG2 * ws_gates(i, 1, j);
+            ws_gates(i, 0, j) = ws_gates_r(i, 0, j) = dG0;
+            ws_gates(i, 1, j) = ws_gates_r(i, 1, j) = dG1;
+        }
+    });
+}
+
+template <>
+rnn_cell_execution_sig(ref_rnn_bwd_f32_t::cell_execution_gru_lbr) {
+    ws_gates_aoc_t ws_gates_r(rnn, ws_cell_);
+    ws_diff_states_aoc_t diff_states_t_l(rnn, diff_states_t_l_);
+
+    (this->*elemwise_func)(rnn, ws_gates_, states_t_l_, c_states_t_l_,
+            states_tm1_l_, c_states_tm1_l_, diff_states_t_l_,
+            diff_states_t_lp1_, diff_states_tp1_l_, bias_[0], ws_grid_,
+            ws_cell_);
+
+    if (!rnn.merge_gemm_layer) {
+        //  dx = dG * Wx^t
+        (this->*gemm_layer_func)('N', 'N', rnn.slc, rnn.mb,
+                rnn.n_gates * rnn.dic, 1.0, w_layer_[0],
+                rnn.weights_layer_ld, ws_gates_, rnn.gates_ws_ld, 0.0,
+                &diff_states_t_l(rnn.n_states, 0, 0), rnn.states_ws_ld);
+        // dWx +=  dG^t * x
+        gemm('N', 'T', rnn.n_gates * rnn.dic, rnn.slc, rnn.mb, 1.0, ws_gates_,
+                rnn.gates_ws_ld, states_t_lm1_, rnn.states_ws_ld, 1.0,
+                diff_w_layer_, rnn.diff_weights_layer_ld);
+    }
+    // dh +=  dGr * Wh^t
+    (this->*gemm_iter_func)('N', 'N', rnn.sic, rnn.mb, rnn.n_gates * rnn.dic,
+            1.0, w_iter_[0], rnn.weights_iter_ld, ws_cell_, rnn.gates_ws_ld,
+            1.0, diff_states_t_l_, rnn.states_ws_ld);
+
+    // dWh += dGr^t * h
+    gemm('N', 'T', rnn.n_gates * rnn.dic, rnn.sic, rnn.mb, 1.0, ws_cell_,
+            rnn.gates_ws_ld, states_tm1_l_, rnn.states_ws_ld, 1.0, diff_w_iter_,
+            rnn.diff_weights_layer_ld);
+
+    // db1-3 += e * dG
+    // db4 += e * (r * dG2)
+    gates_reduction(rnn, ws_gates_, diff_bias_);
+
+    parallel_nd(rnn.dic, [&](int j) {
+        for (int i = 0; i < rnn.mb; i++) {
+            diff_bias_[3 * rnn.dic + j] += ws_gates_r(i, 2, j);
+        }
+    });
+}
+
+#undef AOC
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_lstm.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_lstm.cpp
new file mode 100644
index 0000000000..a15ba00d4c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_lstm.cpp
@@ -0,0 +1,143 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/*
+ * Cell execution LSTM
+ */
+
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+
+#include "../simple_q10n.hpp"
+#include "ref_rnn.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::math;
+using namespace rnn_utils;
+
+template <>
+rnn_elemwise_sig(ref_rnn_fwd_f32_t::lstm_elemwise) {
+    ws_gates_aoc_t ws_gates(rnn, ws_gates_);
+    bias_aoc_t bias(rnn, bias_);
+    ws_states_aoc_t states_t_l(rnn, states_t_l_);
+    ws_states_aoc_t c_states_t_l(rnn, c_states_t_l_);
+    ws_states_aoc_t c_states_tm1_l(rnn, c_states_tm1_l_);
+
+    parallel_nd(rnn.mb, [&](int i) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < rnn.dic; j++) {
+            ws_gates(i, 0, j) = logistic_fwd(ws_gates(i, 0, j) + bias(0, j));
+            ws_gates(i, 1, j) = logistic_fwd(ws_gates(i, 1, j) + bias(1, j));
+            ws_gates(i, 2, j) = tanh_fwd(ws_gates(i, 2, j) + bias(2, j));
+            ws_gates(i, 3, j) = logistic_fwd(ws_gates(i, 3, j) + bias(3, j));
+
+            float tmp = ws_gates(i, 1, j) * c_states_tm1_l(i, j)
+                    + ws_gates(i, 0, j) * ws_gates(i, 2, j);
+            states_t_l(i, j) = ws_gates(i, 3, j) * tanh_fwd(tmp);
+            c_states_t_l(i, j) = tmp;
+        }
+    });
+}
+
+template <>
+rnn_elemwise_sig(ref_rnn_fwd_u8s8_t::lstm_elemwise) {
+    ws_gates_aoc_s32_t ws_gates_s32(rnn, ws_gates_);
+    bias_aoc_t bias(rnn, bias_);
+    ws_states_aoc_u8_t states_t_l(rnn, states_t_l_);
+    ws_states_aoc_t c_states_t_l(rnn, c_states_t_l_);
+    ws_states_aoc_t c_states_tm1_l(rnn, c_states_tm1_l_);
+
+    float *weights_scales = pd()->attr()->rnn_weights_qparams_.scales_;
+    float data_shift = pd()->attr()->rnn_data_qparams_.shift_;
+    float data_scale = pd()->attr()->rnn_data_qparams_.scale_;
+
+    auto q_d = [&](float f) {
+        float qf = f * data_scale + data_shift;
+        return qz_a1b0<float, src_data_t>()(qf);
+    };
+
+    auto deq_w = [&](acc_data_t s, int gate, int j) {
+        return pd()->attr()->rnn_weights_qparams_.mask_ == 0 ?
+                saturate<float>(s) * (1.f / (weights_scales[0] * data_scale)) :
+                saturate<float>(s) * (1.f / (weights_scales[gate * rnn.dic + j]
+                                                   * data_scale));
+    };
+
+    parallel_nd(rnn.mb, [&](int i) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < rnn.dic; j++) {
+            float G0 = logistic_fwd<float>(
+                    deq_w(ws_gates_s32(i, 0, j), 0, j) + bias(0, j));
+            float G1 = logistic_fwd<float>(
+                    deq_w(ws_gates_s32(i, 1, j), 1, j) + bias(1, j));
+            float G2 = tanh_fwd<float>(
+                    deq_w(ws_gates_s32(i, 2, j), 2, j) + bias(2, j));
+            float G3 = logistic_fwd<float>(
+                    deq_w(ws_gates_s32(i, 3, j), 3, j) + bias(3, j));
+            float tmp = G1 * c_states_tm1_l(i, j) + G0 * G2;
+            states_t_l(i, j) = q_d(G3 * tanh_fwd(tmp));
+            c_states_t_l(i, j) = tmp;
+        }
+    });
+}
+
+template <>
+rnn_elemwise_sig(ref_rnn_bwd_f32_t::lstm_elemwise) {
+    ws_gates_aoc_t ws_gates(rnn, ws_gates_);
+    bias_aoc_t bias(rnn, bias_);
+    ws_states_aoc_t c_states_t_l(rnn, c_states_t_l_);
+    ws_states_aoc_t c_states_tm1_l(rnn, c_states_tm1_l_);
+    ws_diff_states_aoc_t diff_states_t_l(rnn, diff_states_t_l_);
+    ws_diff_states_aoc_t diff_states_tp1_l(rnn, diff_states_tp1_l_);
+    ws_diff_states_aoc_t diff_states_t_lp1(rnn, diff_states_t_lp1_);
+
+    parallel_nd(rnn.mb, [&](int i) {
+        PRAGMA_OMP_SIMD()
+        for (int j = 0; j < rnn.dic; j++) {
+            float Ct = c_states_t_l(i, j);
+            /// @todo save it in the workspace in fwd pass or recompute it to
+            /// save bw
+            float tanhCt = tanh_fwd(Ct);
+            // we have 2 incoming diffs on Ht
+            float dHt = diff_states_tp1_l(0, i, j)
+                    + diff_states_t_lp1(rnn.n_states, i, j);
+            float dCt = diff_states_tp1_l(1, i, j)
+                    + one_m_square(tanhCt) * ws_gates(i, 3, j) * dHt;
+
+            float dG1 = c_states_tm1_l(i, j) * dCt
+                    * x_m_square(ws_gates(i, 1, j));
+            float dG0 = ws_gates(i, 2, j) * dCt * x_m_square(ws_gates(i, 0, j));
+            float dG3 = tanhCt * dHt * x_m_square(ws_gates(i, 3, j));
+            float dG2
+                    = ws_gates(i, 0, j) * dCt * one_m_square(ws_gates(i, 2, j));
+
+            diff_states_t_l(1, i, j) = dCt * ws_gates(i, 1, j);
+
+            ws_gates(i, 0, j) = dG0;
+            ws_gates(i, 1, j) = dG1;
+            ws_gates(i, 2, j) = dG2;
+            ws_gates(i, 3, j) = dG3;
+        }
+    });
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_rnn.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_rnn.cpp
new file mode 100644
index 0000000000..4536e8dfad
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cell_rnn.cpp
@@ -0,0 +1,113 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/*
+ * Cell execution of Vanilla RNN
+ */
+
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+
+#include "ref_rnn.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::math;
+using namespace rnn_utils;
+
+template <>
+float activation<alg_kind::eltwise_relu, prop_kind::forward>(
+        float dd, float s, float alpha, float cliping) {
+    return relu_fwd<float>(s, alpha);
+}
+
+template <>
+float activation<alg_kind::eltwise_relu, prop_kind::backward>(
+        float dd, float s, float alpha, float cliping) {
+    return relu_bwd<float>(dd, s, alpha);
+}
+
+template <>
+float activation<alg_kind::eltwise_tanh, prop_kind::forward>(
+        float dd, float s, float alpha, float cliping) {
+    return tanh_fwd<float>(s);
+}
+
+template <>
+float activation<alg_kind::eltwise_tanh, prop_kind::backward>(
+        float dd, float s, float alpha, float cliping) {
+    return dd * one_m_square<float>(s);
+}
+
+template <>
+float activation<alg_kind::eltwise_logistic, prop_kind::forward>(
+        float dd, float s, float alpha, float cliping) {
+    return logistic_fwd<float>(s);
+}
+
+template <>
+float activation<alg_kind::eltwise_logistic, prop_kind::backward>(
+        float dd, float s, float alpha, float cliping) {
+    return dd * x_m_square<float>(s);
+}
+
+template <>
+rnn_elemwise_sig(ref_rnn_fwd_f32_t::rnn_elemwise) {
+    ws_gates_aoc_t ws_gates(rnn, ws_gates_);
+    bias_aoc_t bias(rnn, bias_);
+    ws_states_aoc_t states_t_l(rnn, states_t_l_);
+    ws_states_aoc_t states_tm1_l(rnn, states_tm1_l_);
+
+    parallel_nd(rnn.mb, [&](int i) {
+        for (int j = 0; j < rnn.dic; j++) {
+            const float h
+                    = activation_func(0, ws_gates(i, 0, j) + bias(0, j), 0, 0);
+            ws_gates(i, 0, j) = states_t_l(i, j) = h;
+        }
+    });
+}
+
+template <>
+rnn_elemwise_sig(ref_rnn_fwd_u8s8_t::rnn_elemwise) {
+    assert(!"VANILLA RNN int8 is not supported");
+}
+
+template <>
+rnn_elemwise_sig(ref_rnn_bwd_f32_t::rnn_elemwise) {
+    ws_gates_aoc_t ws_gates(rnn, ws_gates_);
+    bias_aoc_t bias(rnn, bias_);
+    ws_states_aoc_t states_t_l(rnn, states_t_l_);
+    ws_states_aoc_t states_tm1_l(rnn, states_tm1_l_);
+    ws_diff_states_aoc_t diff_states_t_l(rnn, diff_states_t_l_);
+    ws_diff_states_aoc_t diff_states_tp1_l(rnn, diff_states_tp1_l_);
+    ws_diff_states_aoc_t diff_states_t_lp1(rnn, diff_states_t_lp1_);
+
+    parallel_nd(rnn.mb, [&](int i) {
+        for (int j = 0; j < rnn.dic; ++j) {
+            const float dH = diff_states_t_lp1(rnn.n_states, i, j)
+                    + diff_states_tp1_l(0, i, j);
+            auto g = ws_gates(i, 0, j);
+            ws_gates(i, 0, j) = activation_func(dH, g, 0, 0);
+        }
+    });
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cpu_rnn_pd.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cpu_rnn_pd.hpp
new file mode 100644
index 0000000000..b39427caf9
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/cpu_rnn_pd.hpp
@@ -0,0 +1,191 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_RNN_PD_HPP
+#define CPU_RNN_PD_HPP
+
+#include "c_types_map.hpp"
+#include "nstl.hpp"
+#include "rnn_pd.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+#include "rnn_utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct cpu_rnn_fwd_pd_t : public rnn_fwd_pd_t {
+    using rnn_fwd_pd_t::rnn_fwd_pd_t;
+
+protected:
+    status_t set_default_params() {
+        using namespace format_tag;
+        if (src_layer_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(src_layer_md_, tnc));
+        if (dst_layer_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(dst_layer_md_, tnc));
+
+        // Optional parameters
+        if (with_src_iter() && src_iter_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(src_iter_md_, ldsnc));
+        if (with_bias() && bias_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(bias_md_, ldgo));
+        if (with_dst_iter() && dst_iter_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(dst_iter_md_, ldsnc));
+
+        return status::success;
+    }
+
+    status_t check_layout_consistency() {
+        using namespace format_tag;
+        using namespace data_type;
+        using namespace types;
+
+        auto is_blocked = [&](memory_desc_t md, int ndims) {
+            return md.format_kind == format_kind::blocked && md.ndims == ndims;
+        };
+
+        bool ok = true;
+        ok = ok && is_blocked(src_layer_md_, 3)
+                && is_blocked(dst_layer_md_, 3);
+        ok = ok && IMPLICATION(!is_zero_md(&src_iter_md_),
+                           is_blocked(src_iter_md_, 5))
+                && IMPLICATION(!is_zero_md(&dst_iter_md_),
+                           is_blocked(dst_iter_md_, 5));
+
+        if (weights_layer_md_.format_kind == format_kind::rnn_packed)
+            ok = ok && (weights_layer_md_.format_desc.rnn_packed_desc.format
+                               == mkldnn_ldigo_p);
+        else
+            ok = ok && rnn_utils::is_ldigo(&weights_layer_md_);
+
+        if (weights_iter_md_.format_kind == format_kind::rnn_packed)
+            ok = ok && (weights_iter_md_.format_desc.rnn_packed_desc.format
+                               == mkldnn_ldigo_p);
+        else
+            ok = ok && rnn_utils::is_ldigo(&weights_iter_md_);
+
+        ok = ok && IMPLICATION(!is_zero_md(&bias_md_),
+                           memory_desc_matches_tag(bias_md_, ldgo));
+
+        /* Int8 is supported only for packed weights */
+        data_type_t weights_iter_dt = weights_iter_md_.data_type;
+        data_type_t weights_layer_dt = weights_layer_md_.data_type;
+        ok = ok && IMPLICATION(
+                           weights_iter_dt == s8, weights_iter_md_.format_kind
+                                   == format_kind::rnn_packed);
+        ok = ok && IMPLICATION(
+                           weights_layer_dt == s8, weights_layer_md_.format_kind
+                                   == format_kind::rnn_packed);
+
+        return ok ? status::success : status::unimplemented;
+    }
+};
+
+struct cpu_rnn_bwd_pd_t : public rnn_bwd_pd_t {
+    using rnn_bwd_pd_t::rnn_bwd_pd_t;
+
+protected:
+    status_t set_default_params() {
+        using namespace format_tag;
+        if (src_layer_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(src_layer_md_, tnc));
+        if (dst_layer_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(dst_layer_md_, tnc));
+
+        if (diff_src_layer_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(diff_src_layer_md_, tnc));
+        if (diff_weights_layer_md_.format_kind == format_kind::any) {
+            CHECK(memory_desc_init_by_tag(diff_weights_layer_md_, ldigo));
+            CHECK(rnn_utils::set_good_strides(diff_weights_layer_md_, ldigo));
+        }
+        if (diff_weights_iter_md_.format_kind == format_kind::any) {
+            CHECK(memory_desc_init_by_tag(diff_weights_iter_md_, ldigo));
+            CHECK(rnn_utils::set_good_strides(diff_weights_iter_md_, ldigo));
+        }
+        if (diff_dst_layer_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(diff_dst_layer_md_, tnc));
+
+        // Optional parameters
+        if (with_src_iter() && src_iter_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(src_iter_md_, ldsnc));
+        if (with_bias() && bias_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(bias_md_, ldgo));
+        if (with_dst_iter() && dst_iter_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(dst_iter_md_, ldsnc));
+
+        if (with_src_iter() && diff_src_iter_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(diff_src_iter_md_, ldsnc));
+        if (with_bias() && diff_bias_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(diff_bias_md_, ldgo));
+        if (with_dst_iter() && diff_dst_iter_md_.format_kind == format_kind::any)
+            CHECK(memory_desc_init_by_tag(diff_dst_iter_md_, ldsnc));
+
+        return status::success;
+    }
+
+    status_t check_layout_consistency() {
+        using namespace format_tag;
+        using namespace types;
+
+        auto is_blocked = [&](memory_desc_t md, int ndims) {
+            return md.format_kind == format_kind::blocked && md.ndims == ndims;
+        };
+
+        bool ok = true;
+        ok = ok && is_blocked(src_layer_md_, 3)
+                && is_blocked(dst_layer_md_, 3);
+        ok = ok && IMPLICATION(!is_zero_md(&src_iter_md_),
+                           is_blocked(src_iter_md_, 5))
+                && IMPLICATION(!is_zero_md(&dst_iter_md_),
+                           is_blocked(dst_iter_md_, 5));
+
+        if (weights_layer_md_.format_kind == format_kind::rnn_packed)
+            ok = ok && (weights_layer_md_.format_desc.rnn_packed_desc.format
+                               == mkldnn_ldgoi_p);
+        else
+            ok = ok && rnn_utils::is_ldgoi(&weights_layer_md_);
+
+        if (weights_iter_md_.format_kind == format_kind::rnn_packed)
+            ok = ok && (weights_iter_md_.format_desc.rnn_packed_desc.format
+                               == mkldnn_ldgoi_p);
+        else
+            ok = ok && rnn_utils::is_ldgoi(&weights_iter_md_);
+
+        ok = ok && IMPLICATION(!is_zero_md(&bias_md_),
+                           memory_desc_matches_tag(bias_md_, ldgo));
+
+        ok = ok && is_blocked(diff_src_layer_md_, 3)
+                && is_blocked(diff_dst_layer_md_, 3);
+        ok = ok && IMPLICATION(!is_zero_md(&diff_src_iter_md_),
+                           is_blocked(diff_src_iter_md_, 5))
+                && IMPLICATION(!is_zero_md(&diff_dst_iter_md_),
+                           is_blocked(diff_dst_iter_md_, 5));
+
+        ok = ok && rnn_utils::is_ldigo(&diff_weights_layer_md_)
+                && rnn_utils::is_ldigo(&diff_weights_iter_md_);
+        ok = ok && IMPLICATION(!is_zero_md(&diff_bias_md_),
+                           memory_desc_matches_tag(diff_bias_md_, ldgo));
+
+        return ok ? status::success : status::unimplemented;
+    }
+};
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/jit_uni_rnn_postgemm.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/jit_uni_rnn_postgemm.hpp
new file mode 100644
index 0000000000..09445648aa
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/jit_uni_rnn_postgemm.hpp
@@ -0,0 +1,401 @@
+/*******************************************************************************
+* Copyright 2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/*
+ * Cell execution LSTM
+ */
+
+#include "rnn_utils.hpp"
+#include "../jit_generator.hpp"
+#include "../jit_uni_eltwise.hpp"
+#include "c_types_map.hpp"
+#include "utils.hpp"
+
+#include "mkldnn_thread.hpp"
+
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+struct jit_uni_rnn_postgemm_kernel : public jit_generator {
+
+    typedef void (*kernel_t)(void *gates_, const void *bias, void *states_t_l_,
+                     void *c_states_t_l_, void *c_states_tm1_l_);
+
+    jit_uni_rnn_postgemm_kernel(const rnn_utils::rnn_conf_t &rnn, const primitive_attr_t *attr): rnn_(rnn), attr_(attr){}
+
+    virtual void init() = 0;
+
+template <typename src_data_t, typename acc_data_t>
+    rnn_elemwise_sig(execute) {
+        rnn_utils::ws_gates_aoc<acc_data_t> ws_gates(rnn, ws_gates_);
+        rnn_utils::bias_aoc_t bias(rnn, bias_);
+        rnn_utils::ws_states_aoc<src_data_t> states_t_l(rnn, states_t_l_);
+        rnn_utils::ws_states_aoc_t c_states_t_l(rnn, c_states_t_l_);
+        rnn_utils::ws_states_aoc_t c_states_tm1_l(rnn, c_states_tm1_l_);
+
+        // Todo: add parallelization on dic for the batch 1 case
+        // Assumption: the kernel runs a loop on dic elements
+        parallel_nd(rnn.mb, [&](int i) {
+                auto b_ = &bias(0, 0);
+                auto g_ = &ws_gates(i, 0, 0);
+                auto s_tl_ = &states_t_l(i, 0);
+                auto c_tl_ = &c_states_t_l(i, 0);
+                auto c_tm1l_ = &c_states_tm1_l(i, 0);
+                kernel_(g_, b_, s_tl_, c_tm1l_, c_tl_);
+            });
+    }
+
+protected:
+    kernel_t kernel_;
+    const rnn_utils::rnn_conf_t &rnn_;
+    const primitive_attr_t *attr_;
+};
+
+template <cpu_isa_t isa, impl::data_type_t src_data_t>
+struct jit_uni_lstm_postgemm_kernel_fwd: public jit_uni_rnn_postgemm_kernel
+{
+    DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_lstm_postgemm_kernel_fwd)
+
+    typedef typename utils::conditional<src_data_t == data_type::u8, int32_t,
+            float>::type acc_data_t;
+    typedef typename utils::conditional<isa == avx512_core,
+            jit_uni_eltwise_injector_f32<avx512_common>,
+            jit_uni_eltwise_injector_f32<isa>>::type injector_t;
+
+    jit_uni_lstm_postgemm_kernel_fwd(const rnn_utils::rnn_conf_t &rnn, const primitive_attr_t *attr)
+    : jit_uni_rnn_postgemm_kernel(rnn, attr){}
+
+    void init() override {
+        // we use rax for both constant tables as they use the same table
+        sigmoid_injector_ = new injector_t(this,
+                alg_kind::eltwise_logistic, 0.0f, 0.0f, true, rax);
+        tanh_injector_ = new injector_t(this,
+                alg_kind::eltwise_tanh, 0.0f, 0.0f, true, rax);
+        generate();
+        kernel_ = (kernel_t) this->getCode();
+    }
+
+protected:
+    injector_t *sigmoid_injector_;
+    injector_t *tanh_injector_;
+
+    // register size in bytes
+    using Vmm = typename jit_uni_eltwise_injector_f32<isa>::Vmm;
+    size_t vlen = cpu_isa_traits<isa>::vlen;
+    size_t vlen_dst = (src_data_t == data_type::u8) ? vlen/4 : vlen;
+    size_t cstate_dt_size = sizeof(float);
+    size_t hstate_dt_size = (src_data_t == data_type::u8) ? sizeof(uint8_t) : sizeof(float);
+    size_t gate_dt_size = (src_data_t == data_type::u8) ? sizeof(uint32_t) : sizeof(float);
+    size_t qscale_dt_size = sizeof(float);
+    size_t bias_dt_size = sizeof(float);
+
+    void generate() {
+        using namespace Xbyak;
+
+        int mask = attr_->rnn_weights_qparams_.mask_;
+        float *weights_scales = attr_->rnn_weights_qparams_.scales_;
+        float data_scale = attr_->rnn_data_qparams_.scale_;
+        float data_shift = attr_->rnn_data_qparams_.shift_;
+
+        // Labels declaration
+        Label vector_loop_start_label, vector_loop_end_label;
+        Label rem_loop_start_label, rem_loop_end_label;
+        Label table_label;
+
+        // Register map
+        Reg64 loop_cnt(r11);  // loop counter
+        Reg64 table_reg(rbx); // table is used for data scale and shifts
+        Reg64 weights_scales_reg(r13);
+        // We skip vmm0 as it can be used by the injector for masks on sse4.2
+        Vmm G0(1), G1(2), G2(3), G3(4), tmp1_vmm(5), tmp2_vmm(6), zero_vmm(7);
+
+        // constant table map
+        Address dscale_off_addr = ptr[table_reg];
+        Address dshift_off_addr = ptr[table_reg + vlen];
+        Address ymm_perm_mask_addr = ptr[table_reg + 2*vlen];
+        Address zmm_perm_mask_addr = ptr[table_reg + 2*vlen + cpu_isa_traits<avx>::vlen];
+
+        // quantize from float to u8
+        auto q_d = [&](Vmm f, Vmm tmp_vmm) {
+            uni_vpxor(tmp_vmm, tmp_vmm, tmp_vmm);
+            uni_vmulps(f, f, dscale_off_addr); // apply scale
+            uni_vaddps(f, f, dshift_off_addr); // apply shift
+            uni_vcvtps2dq(f, f); // convert to int32
+            uni_vpackssdw(f, f, tmp_vmm); // convert from s32 to s16
+            uni_vpackuswb(f, f, tmp_vmm); // convert from s16 to u8 with saturation
+            // Note that the results are interleaved by 128 bit chunks, so we need to merge them together
+            switch (vlen) {
+            case 64:  { //avx512
+                Zmm fz(f.getIdx()), tmpz(tmp_vmm.getIdx());
+                uni_vmovups(tmpz, zmm_perm_mask_addr);
+                vpermd(fz, tmpz, fz);
+                break; }
+            case 32: { //avx
+                Ymm fy(f.getIdx()), tmpy(tmp_vmm.getIdx());
+                uni_vmovups(tmpy, ymm_perm_mask_addr);
+                vpermd(fy, tmpy, fy);
+                break; }
+            case 16: // sse: nothing to do
+                break;
+            default: assert(!"Unsupported case");
+            };
+        };
+
+        auto fast_recip =[&](Vmm s, Vmm tmp, bool packed) {
+            if (packed)
+                uni_vrcpps(tmp, s);
+            else
+                uni_vrcpss(tmp, s); // prevent divide by zero
+            // we add one Newton iteration
+            uni_vmulps(s, s, tmp);
+            uni_vmulps(s, s, tmp); // s <- s * tmp^2
+            uni_vaddps(tmp, tmp, tmp);
+            uni_vsubps(tmp, tmp, s);
+            uni_vmovups(s, tmp); // s <- 2 * tmp - s * tmp^2
+        };
+
+        // dequantize from s32 to float
+        auto deq_w = [&](Vmm s, Vmm tmp1, Vmm tmp2, int gate, bool packed) {
+            // TODO: if mask is 0 precompute mul and inverse
+            if (mask == 0)
+                uni_vbroadcastss(tmp1, ptr[weights_scales_reg]);
+            else
+                uni_vmovups(tmp1, ptr[weights_scales_reg + gate * rnn_.dic * qscale_dt_size]);
+            uni_vcvtdq2ps(s, s);
+            uni_vmulps(tmp1, tmp1, dscale_off_addr);
+            fast_recip(tmp1, tmp2, packed);
+            uni_vmulps(s, s, tmp1);
+        };
+
+        // We start code generations here
+        preamble();
+
+        // extract addresses passed as parameter
+#ifdef _WIN32
+        auto addr_ws_gates_reg = abi_param1;
+        auto addr_bias_reg = abi_param2;
+        auto addr_states_t_l_reg = abi_param3;
+        auto addr_c_states_tm1_l_reg = abi_param4;
+        auto addr_c_states_t_l_reg = r10;
+        // Here we cannot use rbp to have initial stack pointer so we
+        // use rsp and offset it with the size of pushed registers in
+        // preamble
+        mov(addr_c_states_t_l_reg, ptr[rsp + get_size_of_abi_save_regs() + 40]);
+#else
+        auto addr_ws_gates_reg = abi_param1;
+        auto addr_bias_reg = abi_param2;
+        auto addr_states_t_l_reg = abi_param3;
+        auto addr_c_states_tm1_l_reg = abi_param4;
+        auto addr_c_states_t_l_reg = abi_param5;
+#endif
+
+        // initialize registers with addresses and constants
+        mov(table_reg, table_label);
+        mov(weights_scales_reg, size_t(weights_scales));
+        // both sigmoid and tanh use the same table so load address just once in rax
+        sigmoid_injector_->load_table_addr();
+
+        mov(loop_cnt, rnn_.dic * gate_dt_size);
+        cmp(loop_cnt, vlen);
+        jl(vector_loop_end_label, Xbyak::CodeGenerator::T_NEAR);
+
+        L(vector_loop_start_label);
+        {
+            // load G0 G1 G2 G3
+            uni_vmovups(G0, ptr[addr_ws_gates_reg + 0 * rnn_.dic * gate_dt_size]);
+            uni_vmovups(G1, ptr[addr_ws_gates_reg + 1 * rnn_.dic * gate_dt_size]);
+            uni_vmovups(G2, ptr[addr_ws_gates_reg + 2 * rnn_.dic * gate_dt_size]);
+            uni_vmovups(G3, ptr[addr_ws_gates_reg + 3 * rnn_.dic * gate_dt_size]);
+
+            // dequantize the gates from s32 to f32 if needed
+            if (src_data_t == data_type::u8){
+                deq_w(G0, tmp1_vmm, tmp2_vmm, 0, true);
+                deq_w(G1, tmp1_vmm, tmp2_vmm, 1, true);
+                deq_w(G2, tmp1_vmm, tmp2_vmm, 2, true);
+                deq_w(G3, tmp1_vmm, tmp2_vmm, 3, true);
+            }
+
+            // add biases
+            uni_vaddps(G0, G0, ptr[addr_bias_reg + 0 * rnn_.dic * bias_dt_size]);
+            uni_vaddps(G1, G1, ptr[addr_bias_reg + 1 * rnn_.dic * bias_dt_size]);
+            uni_vaddps(G2, G2, ptr[addr_bias_reg + 2 * rnn_.dic * bias_dt_size]);
+            uni_vaddps(G3, G3, ptr[addr_bias_reg + 3 * rnn_.dic * bias_dt_size]);
+
+            // inject eltwise code
+            sigmoid_injector_->compute_vector(G0.getIdx());
+            sigmoid_injector_->compute_vector(G1.getIdx());
+            tanh_injector_->compute_vector(G2.getIdx());
+            sigmoid_injector_->compute_vector(G3.getIdx());
+
+            // compute c_states_t_l = G1 * c_tm1_l + G0 * G2
+            uni_vmovups(tmp1_vmm, ptr[addr_c_states_tm1_l_reg]);
+            uni_vmulps(tmp1_vmm, tmp1_vmm, G1);
+            uni_vfmadd231ps(tmp1_vmm, G0, G2);
+            uni_vmovups(ptr[addr_c_states_t_l_reg], tmp1_vmm);
+
+            // states_t_l = G3 * tanh(c_states_t_l)
+            tanh_injector_->compute_vector(tmp1_vmm.getIdx());
+            uni_vmulps(tmp1_vmm, tmp1_vmm, G3);
+
+            // if int8, we quantize the resulting state
+            if (src_data_t == data_type::u8)
+                q_d(tmp1_vmm, tmp2_vmm);
+
+            // write back the result
+            if(vlen_dst == vlen)
+                uni_vmovups(ptr[addr_states_t_l_reg], tmp1_vmm);
+            else
+                // we write only 1/4 of the register
+                switch(vlen_dst){
+                case 16: uni_vmovups(ptr[addr_states_t_l_reg], Xmm(tmp1_vmm.getIdx())); break;
+                case 8: uni_vmovsd(ptr[addr_states_t_l_reg], Xmm(tmp1_vmm.getIdx())); break;
+                case 4: uni_vmovss(ptr[addr_states_t_l_reg], Xmm(tmp1_vmm.getIdx())); break;
+                default:
+                    assert(!"Unsuported vector length for quantization");
+                }
+
+            // increment address pointers
+            add(addr_ws_gates_reg, vlen);
+            add(addr_bias_reg, vlen);
+            add(addr_states_t_l_reg, vlen_dst);
+            add(addr_c_states_tm1_l_reg, vlen);
+            add(addr_c_states_t_l_reg, vlen);
+            if (mask != 0)
+                add(weights_scales_reg, vlen);
+
+            // increment loop counter
+            sub(loop_cnt, vlen);
+            cmp(loop_cnt, vlen);
+            jge(vector_loop_start_label);
+        }
+        L(vector_loop_end_label);
+
+        cmp(loop_cnt, 0);
+        je(rem_loop_end_label, Xbyak::CodeGenerator::T_NEAR);
+        // Same code as above, we just use movuss for accessing inputs
+        // TODO: smarter handling of tails with Zmm -> Ymm -> Xmm -> scalar
+        L(rem_loop_start_label);
+        {
+            // remaping registers to Xmms
+            Xmm G0s(G0.getIdx()), G1s(G1.getIdx()), G2s(G2.getIdx()), G3s(G3.getIdx());
+            Xmm tmp1s_vmm(tmp1_vmm.getIdx());
+
+            // load G0 G1 G2 G3
+            uni_vmovss(G0s, ptr[addr_ws_gates_reg + 0 * rnn_.dic * gate_dt_size]);
+            uni_vmovss(G1s, ptr[addr_ws_gates_reg + 1 * rnn_.dic * gate_dt_size]);
+            uni_vmovss(G2s, ptr[addr_ws_gates_reg + 2 * rnn_.dic * gate_dt_size]);
+            uni_vmovss(G3s, ptr[addr_ws_gates_reg + 3 * rnn_.dic * gate_dt_size]);
+
+            // dequantize the gates from s32 to f32 if needed
+            if (src_data_t == data_type::u8){
+                deq_w(G0, tmp1_vmm, tmp2_vmm, 0, false);
+                deq_w(G1, tmp1_vmm, tmp2_vmm, 1, false);
+                deq_w(G2, tmp1_vmm, tmp2_vmm, 2, false);
+                deq_w(G3, tmp1_vmm, tmp2_vmm, 3, false);
+            }
+
+            // add biases
+            uni_vmovss(tmp1s_vmm, ptr[addr_bias_reg + 0 * rnn_.dic * bias_dt_size]);
+            uni_vaddps(G0s, G0s, tmp1s_vmm);
+            uni_vmovss(tmp1s_vmm, ptr[addr_bias_reg + 1 * rnn_.dic * bias_dt_size]);
+            uni_vaddps(G1s, G1s, tmp1s_vmm);
+            uni_vmovss(tmp1s_vmm, ptr[addr_bias_reg + 2 * rnn_.dic * bias_dt_size]);
+            uni_vaddps(G2s, G2s, tmp1s_vmm);
+            uni_vmovss(tmp1s_vmm, ptr[addr_bias_reg + 3 * rnn_.dic * bias_dt_size]);
+            uni_vaddps(G3s, G3s, tmp1s_vmm);
+
+            // inject eltwise code
+            sigmoid_injector_->compute_vector(G0s.getIdx());
+            sigmoid_injector_->compute_vector(G1s.getIdx());
+            tanh_injector_->compute_vector(G2s.getIdx());
+            sigmoid_injector_->compute_vector(G3s.getIdx());
+
+            // compute c_states_t_l = G1 * c_tm1_l + G0s * G2
+            uni_vmovups(tmp1s_vmm, ptr[addr_c_states_tm1_l_reg]);
+            uni_vmulps(tmp1s_vmm, tmp1s_vmm, G1s);
+            uni_vfmadd231ps(tmp1s_vmm, G0s, G2s);
+            uni_vmovss(ptr[addr_c_states_t_l_reg], tmp1s_vmm);
+
+            // states_t_l = G3 * tanh(c_states_t_l)
+            tanh_injector_->compute_vector(tmp1s_vmm.getIdx());
+            uni_vmulps(tmp1s_vmm, tmp1s_vmm, G3s);
+
+            // if int8, we quantize the resulting state
+            if (src_data_t == data_type::u8)
+                q_d(tmp1_vmm, tmp2_vmm);
+
+            // write back the result
+            if(vlen_dst == vlen)
+                uni_vmovups(ptr[addr_states_t_l_reg], tmp1s_vmm);
+            else
+                // we write only 1/4 of the register
+                switch(vlen_dst){
+                case 16: uni_vmovups(ptr[addr_states_t_l_reg], Xmm(tmp1s_vmm.getIdx())); break;
+                case 8: uni_vmovsd(ptr[addr_states_t_l_reg], Xmm(tmp1s_vmm.getIdx())); break;
+                case 4: uni_vmovss(ptr[addr_states_t_l_reg], Xmm(tmp1s_vmm.getIdx())); break;
+                default:
+                    assert(!"Unsuported vector length for quantization");
+                }
+
+            // increment address pointers
+            add(addr_ws_gates_reg, gate_dt_size);
+            add(addr_bias_reg, bias_dt_size);
+            add(addr_states_t_l_reg, hstate_dt_size);
+            add(addr_c_states_tm1_l_reg, cstate_dt_size);
+            add(addr_c_states_t_l_reg, cstate_dt_size);
+            if (mask != 0)
+                add(weights_scales_reg, qscale_dt_size);
+
+            // increment loop counter
+            sub(loop_cnt, gate_dt_size);
+            cmp(loop_cnt, 0);
+            jg(rem_loop_start_label);
+
+        }
+        L(rem_loop_end_label);
+
+        postamble();
+
+        // Again, only one table is needed and shared between sigmoid and tanh
+        sigmoid_injector_->prepare_table(false);
+        tanh_injector_->prepare_table(true);
+
+        L(table_label);
+        {
+            for (size_t i = 0; i < vlen / sizeof(float); i++) dd(float2int(data_scale));
+            for (size_t i = 0; i < vlen / sizeof(float); i++) dd(float2int(data_shift));
+            // perm mask for ymm
+            dd(0); dd(4); dd(2); dd(3); dd(1); dd(5); dd(6); dd(7);
+            // perm mask for zmm
+            dd(0); dd(4); dd(8); dd(12); dd(1); dd(5); dd(6); dd(7);
+            dd(2); dd(9); dd(10); dd(11); dd(3); dd(12); dd(13); dd(14);
+        }
+    }
+
+};
+
+template struct jit_uni_lstm_postgemm_kernel_fwd<sse42, data_type::f32>;
+template struct jit_uni_lstm_postgemm_kernel_fwd<avx2, data_type::f32>;
+template struct jit_uni_lstm_postgemm_kernel_fwd<avx512_core, data_type::f32>;
+
+template struct jit_uni_lstm_postgemm_kernel_fwd<sse42, data_type::u8>;
+template struct jit_uni_lstm_postgemm_kernel_fwd<avx2, data_type::u8>;
+template struct jit_uni_lstm_postgemm_kernel_fwd<avx512_core, data_type::u8>;
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.cpp
new file mode 100644
index 0000000000..ead536816c
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.cpp
@@ -0,0 +1,788 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/*
+  General architecture
+
+  for diff states, we have n_states + 1 as we have n_states diff
+  to propagate to the previous iteration and 1 states to propagate
+  to the previous layer
+  index 0 is dh for cell(t-1, l) to consume
+  index 1 is dc for cell(t-1, l) to consume
+  index 2 is dh for cell(t, l-1) to consume
+  this indexing enables to have the same indexing for states in elemwise
+  function
+  only the cell execution function should be impacted
+
+ */
+
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+
+#include "ref_rnn.hpp"
+#include "../gemm/gemm.hpp"
+#include "../simple_q10n.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::utils;
+using namespace mkldnn::impl::memory_tracking::names;
+using namespace rnn_utils;
+#define AOC array_offset_calculator
+
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+void _ref_rnn_common_t<aprop, src_type, weights_type>::gates_reduction(
+        const rnn_conf_t &rnn, const acc_data_t *ws_gates_,
+        float *diff_bias_) const {
+    auto body = [&](int i, int k) {
+        for (int j = 0; j < rnn.mb; j++)
+            diff_bias_[i * rnn.dic + k]
+                    += ws_gates_[j * rnn.gates_ws_ld + i * rnn.dic + k];
+    };
+
+    // @todo block k on simd-width
+#if MKLDNN_THR == MKLDNN_THR_OMP && _OPENMP >= 201307 \
+    /* icc 17.0 has a problem with simd collapse */ \
+    && !((defined __INTEL_COMPILER) && (__INTEL_COMPILER == 1700))
+#pragma omp parallel for simd collapse(2)
+    for (int i = 0; i < rnn.n_gates; i++)
+        for (int k = 0; k < rnn.dic; k++)
+            body(i, k);
+#else
+    parallel_nd(rnn.n_gates, rnn.dic, body);
+#endif
+}
+
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+rnn_gemm_sig((_ref_rnn_common_t<aprop, src_type, weights_type>::gemm)) {
+    assert(ldA * ldB * ldC != 0);
+    extended_sgemm(&transA, &transB, &m, &n, &k, &alpha, a_, &ldA, b_, &ldB,
+            &beta, c_, &ldC, nullptr, pd()->rnn_.use_jit_gemm);
+}
+
+template <>
+rnn_gemm_sig((ref_rnn_fwd_u8s8_t::gemm)) {
+    assert(!"non packed gemm is disabled for int8");
+}
+
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+rnn_gemm_sig((_ref_rnn_common_t<aprop, src_type, weights_type>::packed_gemm)) {
+#if (USE_MKL_PACKED_GEMM)
+    assert(transA == 'N');
+    cblas_sgemm_compute(CblasColMajor, CblasPacked,
+            (transB == 'T') ? CblasTrans : CblasNoTrans, m, n, k, a_, ldA, b_,
+            ldB, beta, c_, ldC);
+#else
+    UNUSED(transA);
+    UNUSED(transB);
+    UNUSED(m);
+    UNUSED(n);
+    UNUSED(k);
+    UNUSED(alpha);
+    UNUSED(ldA);
+    UNUSED(b_);
+    UNUSED(ldB);
+    UNUSED(beta);
+    UNUSED(c_);
+    UNUSED(ldC);
+    assert(!"packed gemm is disabled");
+#endif
+}
+
+template <>
+rnn_gemm_sig((ref_rnn_fwd_u8s8_t::packed_gemm)) {
+#if (USE_MKL_PACKED_GEMM)
+    int8_t offseta = 0, offsetb = 0;
+    int32_t offsetc = 0;
+    cblas_gemm_s8u8s32_compute(CblasColMajor, (CBLAS_TRANSPOSE)CblasPacked,
+            CblasNoTrans, CblasFixOffset, m, n, k, alpha, a_, ldA, offseta, b_,
+            ldB, offsetb, beta, c_, ldC, &offsetc);
+#else
+    UNUSED(transA);
+    UNUSED(transB);
+    UNUSED(m);
+    UNUSED(n);
+    UNUSED(k);
+    UNUSED(alpha);
+    UNUSED(ldA);
+    UNUSED(b_);
+    UNUSED(ldB);
+    UNUSED(beta);
+    UNUSED(c_);
+    UNUSED(ldC);
+    assert(!"packed gemm is disabled");
+#endif
+}
+
+//*************** Grid computations strategy: linear ***************//
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+rnn_grid_execution_sig(
+        (_ref_rnn_common_t<aprop, src_type, weights_type>::linear_execution)) {
+    AOC<src_data_t, 4> ws_states(ws_states_, rnn.n_layer + 1, rnn.n_dir,
+            rnn.n_iter + 1, rnn.states_nld * rnn.states_ws_ld);
+    AOC<float, 4> ws_c_states(ws_c_states_, rnn.n_layer + 1, rnn.n_dir,
+            rnn.n_iter + 1, rnn.states_nld * rnn.states_ws_ld);
+    AOC<float, 5> ws_diff_states(ws_diff_states_, rnn.n_layer + 1, rnn.n_dir,
+            (rnn.n_states + 1), rnn.n_iter + 1,
+            rnn.states_nld * rnn.states_ws_ld);
+    AOC<acc_data_t, 4> ws_gates(ws_gates_, rnn.n_layer, rnn.n_dir, rnn.n_iter,
+            rnn.gates_nld * rnn.gates_ws_ld);
+    AOC<weights_data_t *, 3> weights_input(
+            weights_layer_, rnn.n_layer, rnn.n_dir, rnn.n_parts_weights_layer);
+    AOC<weights_data_t *, 3> weights_states(
+            weights_states_, rnn.n_layer, rnn.n_dir, rnn.n_parts_weights_iter);
+    AOC<float*, 3> bias(
+        bias_, rnn.n_layer, rnn.n_dir, rnn.n_parts_bias);
+    AOC<float, 3> diff_weights_layer(diff_weights_layer_, rnn.n_layer,
+            rnn.n_dir,
+            rnn.diff_weights_layer_nld * rnn.diff_weights_layer_ld);
+    AOC<float, 3> diff_weights_iter(diff_weights_iter_, rnn.n_layer, rnn.n_dir,
+            rnn.diff_weights_iter_nld * rnn.diff_weights_iter_ld);
+    AOC<float, 3> diff_bias(
+            diff_bias_, rnn.n_layer, rnn.n_dir, rnn.n_bias * rnn.dic);
+    AOC<float, 4> ws_grid(
+            ws_grid_, rnn.n_layer, rnn.n_dir, rnn.n_iter, (int)rnn.ws_per_cell);
+
+    // We run the grid of computation
+    for (int dir = 0; dir < rnn.n_dir; dir++) {
+        for (int j = 0; j < rnn.n_layer; j++) {
+            int lay = (aprop == prop_kind::forward) ? j : rnn.n_layer - j - 1;
+
+            if ((aprop == prop_kind::forward) && rnn.merge_gemm_layer) {
+                (this->*gemm_layer_func)('N', 'N', rnn.n_gates * rnn.dic,
+                        rnn.mb * rnn.n_iter, rnn.slc, 1.0,
+                        weights_input(lay, dir, 0), rnn.weights_iter_ld,
+                        &(ws_states(lay, dir, 1, 0)), rnn.states_ws_ld, 0.0,
+                        &(ws_gates(lay, dir, 0, 0)), rnn.gates_ws_ld);
+            }
+
+            for (int i = 0; i < rnn.n_iter; i++) {
+                int iter = (aprop == prop_kind::forward) ? i : rnn.n_iter - i - 1;
+                (this->*cell_func)(rnn,
+                        &(ws_states(lay + 1, dir, iter + 1, 0)),
+                        &(ws_c_states(lay + 1, dir, iter + 1, 0)),
+                        &(ws_diff_states(lay, dir, 0, iter, 0)),
+                        &(weights_input(lay, dir, 0)),
+                        &(weights_states(lay, dir, 0)),
+                        &(bias(lay, dir, 0)),
+                        &(ws_states(lay, dir, iter + 1, 0)),
+                        &(ws_states(lay + 1, dir, iter, 0)),
+                        &(ws_c_states(lay + 1, dir, iter, 0)),
+                        &(ws_diff_states(lay + 1, dir, 0, iter, 0)),
+                        &(ws_diff_states(lay, dir, 0, iter + 1, 0)),
+                        &(diff_weights_layer(lay, dir, 0)),
+                        &(diff_weights_iter(lay, dir, 0)),
+                        &(diff_bias(lay, dir, 0)),
+                        &(ws_gates(lay, dir, iter, 0)),
+                        &(ws_grid(lay, dir, iter, 0)),
+                        ws_cell_);
+            }
+
+            if ((aprop == prop_kind::backward) && rnn.merge_gemm_layer) {
+                (this->*gemm_layer_func)('N', 'N', rnn.slc, rnn.mb * rnn.n_iter,
+                        rnn.n_gates * rnn.dic, 1.0, weights_input(lay, dir, 0),
+                        rnn.weights_layer_ld,
+                        (src_data_t *)(&(ws_gates(lay, dir, 0, 0))),
+                        rnn.gates_ws_ld, 0.0,
+                        (acc_data_t *)(&(ws_diff_states(
+                                lay, dir, rnn.n_states, 0, 0))),
+                        rnn.states_ws_ld);
+                gemm('N', 'T', rnn.n_gates * rnn.dic, rnn.slc,
+                        rnn.mb * rnn.n_iter, 1.0,
+                        (weights_data_t *)(&(ws_gates(lay, dir, 0, 0))),
+                        rnn.gates_ws_ld,
+                        (src_data_t *)(&(ws_states(lay, dir, 1, 0))),
+                        rnn.states_ws_ld, 1.0,
+                        (acc_data_t *)(&(diff_weights_layer(lay, dir, 0))),
+                        rnn.diff_weights_layer_ld);
+            }
+            if ((aprop == prop_kind::backward) && rnn.merge_gemm_iter) {
+                gemm('N', 'T', rnn.n_gates * rnn.dic, rnn.sic,
+                        rnn.mb * rnn.n_iter, 1.0,
+                        (weights_data_t *)(&(ws_gates(lay, dir, 0, 0))),
+                        rnn.gates_ws_ld,
+                        (src_data_t *)(&(ws_states(lay + 1, dir, 0, 0))),
+                        rnn.states_ws_ld, 1.0,
+                        (acc_data_t *)(&(diff_weights_iter(lay, dir, 0))),
+                        rnn.diff_weights_iter_ld);
+            }
+        }
+    }
+}
+
+//********* GRID computations strategy: utility functions **********//
+
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+void _ref_rnn_common_t<aprop, src_type, weights_type>::copy_init_layer(
+        const rnn_conf_t &rnn, src_data_t *__restrict ws_states_,
+        float *__restrict ws_diff_states_, const src_data_t *__restrict xt_,
+        const float *__restrict diff_dst_layer_) const {
+
+    AOC<src_data_t, 4> ws_states(
+            ws_states_, rnn.n_dir, rnn.n_iter + 1, rnn.mb, rnn.states_ws_ld);
+    auto xt_d = memory_desc_wrapper(pd()->src_md(0));
+
+    parallel_nd(rnn.n_iter, rnn.mb, [&](int it, int b) {
+        auto xxt = xt_ + xt_d.blk_off(it, b);
+        src_data_t *ws_l2r_ptr = &(ws_states(0, it + 1, b, 0));
+        src_data_t *ws_r2l_ptr = &(ws_states(rnn.n_dir - 1, rnn.n_iter - it, b, 0));
+        if (rnn.exec_dir != r2l)
+            for (int c = 0; c < rnn.slc; c++)
+                ws_l2r_ptr[c] = xxt[c];
+        if (rnn.exec_dir != l2r)
+            for (int c = 0; c < rnn.slc; c++)
+                ws_r2l_ptr[c] = xxt[c];
+    });
+}
+
+template <>
+void ref_rnn_bwd_f32_t::copy_init_layer(const rnn_conf_t &rnn,
+        src_data_t *ws_states_, float *ws_diff_states_, const src_data_t *xt_,
+        const float *diff_dst_layer_) const {
+    AOC<float, 6> ws_diff_states(ws_diff_states_, rnn.n_layer + 1, rnn.n_dir,
+            (rnn.n_states + 1), rnn.n_iter + 1, rnn.mb, rnn.states_ws_ld);
+    auto diff_dst_layer_d = memory_desc_wrapper(pd()->diff_dst_md(0));
+
+    switch (rnn.exec_dir) {
+    case bi_concat:
+        parallel_nd(rnn.n_iter, rnn.mb, [&](int it, int b) {
+            auto diff_dst_layer_x
+                    = diff_dst_layer_ + diff_dst_layer_d.blk_off(it, b);
+            for (int s = 0; s < rnn.dic; s++) {
+                ws_diff_states(rnn.n_layer, 0, rnn.n_states, it, b, s)
+                        = diff_dst_layer_x[s];
+                ws_diff_states(
+                        rnn.n_layer, 1, rnn.n_states, rnn.n_iter - it - 1, b, s)
+                        = diff_dst_layer_x[rnn.dic + s];
+            }
+        });
+        break;
+    case bi_sum:
+        parallel_nd(rnn.n_iter, rnn.mb, [&](int it, int b) {
+            auto diff_dst_layer_x
+                    = diff_dst_layer_ + diff_dst_layer_d.blk_off(it, b);
+            for (int s = 0; s < rnn.dic; s++) {
+                ws_diff_states(rnn.n_layer, 0, rnn.n_states, it, b, s)
+                        = diff_dst_layer_x[s];
+                ws_diff_states(
+                        rnn.n_layer, 1, rnn.n_states, rnn.n_iter - it - 1, b, s)
+                        = diff_dst_layer_x[s];
+            }
+        });
+        break;
+    case l2r:
+        parallel_nd(rnn.n_iter, rnn.mb, [&](int it, int b) {
+            auto diff_dst_layer_x
+                    = diff_dst_layer_ + diff_dst_layer_d.blk_off(it, b);
+            for (int s = 0; s < rnn.dic; s++) {
+                ws_diff_states(rnn.n_layer, 0, rnn.n_states, it, b, s)
+                        = diff_dst_layer_x[s];
+            }
+        });
+        break;
+    case r2l:
+        parallel_nd(rnn.n_iter, rnn.mb, [&](int it, int b) {
+            auto diff_dst_layer_x = diff_dst_layer_
+                    + diff_dst_layer_d.blk_off(rnn.n_iter - it - 1, b);
+            for (int s = 0; s < rnn.dic; s++) {
+                ws_diff_states(rnn.n_layer, 0, rnn.n_states, it, b, s)
+                        = diff_dst_layer_x[s];
+            }
+        });
+        break;
+    default: assert(!"Unsupported direction"); break;
+    }
+}
+
+/* For int8 configuration, input iteration states may be of types f32 or u8
+ * Internally h_state is always stored in u8 and c_state is always stored in f32
+ * If input states are of type u8 then h state is copied and c state is dequantized
+ * If input states are of type f32 then h state is quantized and c_state is copied
+ * */
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+template <typename input_data_t>
+void _ref_rnn_common_t<aprop, src_type, weights_type>::copy_init_iter(
+        const rnn_conf_t &rnn, src_data_t *__restrict ws_states_,
+        float *__restrict ws_c_states_, float *__restrict ws_diff_states_,
+        const input_data_t *__restrict firstit_states_,
+        const float *__restrict diff_dst_iter_) const {
+    AOC<src_data_t, 5> ws_states(ws_states_, rnn.n_layer + 1, rnn.n_dir,
+            rnn.n_iter + 1, rnn.mb, rnn.states_ws_ld);
+    AOC<float, 5> ws_c_states(ws_c_states_, rnn.n_layer + 1, rnn.n_dir,
+            rnn.n_iter + 1, rnn.mb, rnn.states_ws_ld);
+    float data_shift = pd()->attr()->rnn_data_qparams_.shift_;
+    float data_scale = pd()->attr()->rnn_data_qparams_.scale_;
+
+    const bool quantize = pd()->with_src_iter()
+        && pd()->src_md(1)->data_type == data_type::f32
+        && rnn.dt_conf != all_f32;
+    auto maybe_q = [&](input_data_t f) {
+        if (quantize) {
+            float qf = f * data_scale + data_shift;
+            return qz_a1b0<float, src_data_t>()(qf);
+        } else
+            return (src_data_t)f;
+    };
+
+    const bool dequantize = pd()->with_src_iter()
+        && pd()->src_md(1)->data_type == data_type::u8;
+    auto maybe_deq = [&](input_data_t s) {
+        if (dequantize)
+            return (((float)s - data_shift) / data_scale);
+        else
+            return (float)s;
+    };
+    auto firstit_states_d = memory_desc_wrapper(pd()->src_md(1));
+    if (firstit_states_) {
+        parallel_nd(
+                rnn.n_layer, rnn.n_dir, rnn.mb, [&](int lay, int dir, int b) {
+                    for (int s = 0; s < rnn.sic; s++)
+                        ws_states(lay + 1, dir, 0, b, s) = maybe_q(
+                                firstit_states_[firstit_states_d.blk_off(
+                                        lay, dir, 0, b, s)]);
+                    if (pd()->cell_kind() == alg_kind::vanilla_lstm)
+                        for (int s = 0; s < rnn.sic; s++)
+                            ws_c_states(lay + 1, dir, 0, b, s) = maybe_deq(
+                                    firstit_states_[firstit_states_d.blk_off(
+                                            lay, dir, 1, b, s)]);
+                });
+    } else {
+        parallel_nd(
+                rnn.n_layer, rnn.n_dir, rnn.mb, [&](int lay, int dir, int b) {
+                    for (int j = 0; j < rnn.sic; j++) {
+                        ws_states(lay + 1, dir, 0, b, j) = (src_data_t)0;
+                        ws_c_states(lay + 1, dir, 0, b, j) = 0.0f;
+                    }
+        });
+    }
+}
+
+template <>
+template <typename input_data_t>
+void ref_rnn_bwd_f32_t::copy_init_iter(const rnn_conf_t &rnn,
+        src_data_t *ws_states_, float *ws_c_states_, float *ws_diff_states_,
+        const input_data_t *firstit_states_,
+        const float *diff_dst_iter_) const {
+    AOC<float, 6> ws_diff_states(ws_diff_states_, rnn.n_layer + 1, rnn.n_dir,
+            rnn.n_states + 1, rnn.n_iter + 1, rnn.mb, rnn.states_ws_ld);
+    auto diff_dst_iter_d = memory_desc_wrapper(pd()->diff_dst_md(1));
+    if (diff_dst_iter_) {
+        parallel_nd(rnn.n_layer, rnn.n_dir, rnn.n_states, rnn.mb,
+                [&](int lay, int dir, int state, int b) {
+                    array_copy(&(ws_diff_states(
+                                       lay, dir, state, rnn.n_iter, b, 0)),
+                            diff_dst_iter_
+                                    + diff_dst_iter_d.blk_off(
+                                              lay, dir, state, b),
+                            rnn.dic);
+                });
+    } else {
+        parallel_nd(rnn.n_layer, rnn.n_dir, rnn.n_states, rnn.mb,
+                [&](int lay, int dir, int state, int i) {
+                    for (int j = 0; j < rnn.dic; j++)
+                        ws_diff_states(lay, dir, state, rnn.n_iter, i, j)
+                                = 0.0f;
+                });
+    }
+}
+
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+template <typename dst_data_t>
+void _ref_rnn_common_t<aprop, src_type, weights_type>::copy_res_layer(
+        const rnn_conf_t &rnn, dst_data_t *dst_layer_, float *diff_src_layer,
+        const src_data_t *ws_states_, const float *ws_diff_states_) const {
+
+    auto dst_layer_d = memory_desc_wrapper(pd()->dst_md(0));
+    AOC<const src_data_t, 5> ws_states(ws_states_, rnn.n_layer + 1, rnn.n_dir,
+            rnn.n_iter + 1, rnn.mb, rnn.states_ws_ld);
+    float shift = (pd()->attr()->rnn_data_qparams_.shift_);
+    float scale = (pd()->attr()->rnn_data_qparams_.scale_);
+
+    const bool dequantize = pd()->dst_md(0)->data_type == data_type::f32
+            && rnn.dt_conf != all_f32;
+    auto maybe_deq = [&](src_data_t s) {
+        if (dequantize)
+            return (dst_data_t)(((float)s - shift) / scale);
+        else
+            return (dst_data_t)s;
+    };
+    parallel_nd(rnn.n_iter, rnn.mb, [&](int it, int b) {
+        int dir = 0;
+        if (rnn.exec_dir != r2l) {
+            for (int s = 0; s < rnn.dic; s++) {
+                dst_layer_[dst_layer_d.blk_off(it, b, dir * rnn.dic + s)]
+                        = maybe_deq(ws_states(rnn.n_layer, dir, it + 1, b, s));
+            }
+            dir = 1;
+        }
+        if (rnn.exec_dir != l2r) {
+            for (int s = 0; s < rnn.dic; s++)
+                switch (rnn.exec_dir) {
+                case bi_sum:
+                    dst_layer_[dst_layer_d.blk_off(it, b, s)]
+                            += maybe_deq(ws_states(
+                                    rnn.n_layer, dir, rnn.n_iter - it, b, s));
+                    break;
+                default:
+                    dst_layer_[dst_layer_d.blk_off(it, b, dir * rnn.dic + s)]
+                            = maybe_deq(ws_states(
+                                    rnn.n_layer, dir, rnn.n_iter - it, b, s));
+                }
+        }
+    });
+}
+
+template <>
+template <typename dst_data_t>
+void ref_rnn_bwd_f32_t::copy_res_layer(
+        const rnn_conf_t &rnn, dst_data_t *dst_layer_, float *diff_src_layer_,
+        const src_data_t *ws_states_, const float *ws_diff_states_) const {
+    auto diff_src_layer_d = memory_desc_wrapper(pd()->diff_src_md(0));
+    AOC<const float, 6> ws_diff_states(ws_diff_states_, rnn.n_layer + 1,
+            rnn.n_dir, rnn.n_states + 1, rnn.n_iter + 1, rnn.mb,
+            rnn.states_ws_ld);
+
+    parallel_nd(rnn.n_iter, rnn.mb, [&](int it, int b) {
+        int dir = 0;
+        for (int s = 0; s < rnn.slc; s++) {
+            float *dst_addr = diff_src_layer_
+                    + diff_src_layer_d.blk_off(
+                              (rnn.exec_dir == r2l) ? rnn.n_iter - 1 - it : it,
+                              b, dir * rnn.slc + s);
+            float res = ws_diff_states(0, 0, rnn.n_states, it, b, s);
+            if (rnn.n_dir - 1)
+                res += ws_diff_states(
+                        0, 1, rnn.n_states, rnn.n_iter - 1 - it, b, s);
+            dst_addr[0] = res;
+        }
+    });
+}
+
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+template <typename output_data_t>
+void _ref_rnn_common_t<aprop, src_type, weights_type>::copy_res_iter(
+        const rnn_conf_t &rnn, output_data_t *dst_iter_, float *diff_src_iter_,
+        const src_data_t *ws_states_, float *ws_c_states_,
+        const float *ws_diff_states_) const {
+    auto dst_iter_d = memory_desc_wrapper(pd()->dst_md(1));
+    AOC<const src_data_t, 5> ws_states(ws_states_, rnn.n_layer + 1, rnn.n_dir,
+            rnn.n_iter + 1, rnn.mb, rnn.states_ws_ld);
+    AOC<const float, 5> ws_c_states(ws_c_states_, rnn.n_layer + 1, rnn.n_dir,
+            rnn.n_iter + 1, rnn.mb, rnn.states_ws_ld);
+    float data_shift = pd()->attr()->rnn_data_qparams_.shift_;
+    float data_scale = pd()->attr()->rnn_data_qparams_.scale_;
+
+    const bool quantize = pd()->with_dst_iter()
+        && pd()->dst_md(1)->data_type == data_type::u8
+        && rnn.dt_conf != all_f32;
+    auto maybe_q = [&](float f) {
+        if (quantize) {
+            float qf = f * data_scale + data_shift;
+            return qz_a1b0<float, output_data_t>()(qf);
+        } else
+            return (output_data_t)f;
+    };
+
+    const bool dequantize = pd()->with_dst_iter()
+        && pd()->dst_md(1)->data_type == data_type::f32
+        && rnn.dt_conf != all_f32;
+    auto maybe_deq = [&](src_data_t s) {
+        if (dequantize)
+            return (output_data_t)(((float)s - data_shift) / data_scale);
+        else
+            return (output_data_t)s;
+    };
+    if (dst_iter_) {
+        parallel_nd(rnn.n_layer, rnn.n_dir, rnn.mb,
+                [&](int lay, int dir, int b) {
+            for (int s = 0; s < rnn.dic; s++) {
+                dst_iter_[dst_iter_d.blk_off(lay, dir, 0, b, s)]
+                        = maybe_deq(ws_states(lay + 1, dir, rnn.n_iter, b, s));
+            }
+            if (pd()->cell_kind() == alg_kind::vanilla_lstm)
+                    for (int s = 0; s < rnn.dic; s++) {
+                        dst_iter_[dst_iter_d.blk_off(lay, dir, 1, b, s)]
+                                = maybe_q(ws_c_states(
+                                        lay + 1, dir, rnn.n_iter, b, s));
+                    }
+            });
+    }
+}
+
+template <>
+template <typename output_data_t>
+void ref_rnn_bwd_f32_t::copy_res_iter(
+        const rnn_conf_t &rnn, output_data_t *dst_iter_, float *diff_src_iter_,
+        const src_data_t *ws_states_, float *ws_c_states_,
+        const float *ws_diff_states_) const {
+    auto diff_src_iter_d = memory_desc_wrapper(pd()->diff_src_md(1));
+    AOC<const float, 6> ws_diff_states(ws_diff_states_, rnn.n_layer + 1,
+            rnn.n_dir, rnn.n_states + 1, rnn.n_iter + 1, rnn.mb,
+            rnn.states_ws_ld);
+    if (diff_src_iter_) {
+        parallel_nd(rnn.n_layer, rnn.n_dir, rnn.n_states, rnn.mb,
+                [&](int lay, int dir, int state, int b) {
+                    for (int s = 0; s < rnn.sic; s++) {
+                        diff_src_iter_[diff_src_iter_d.blk_off(
+                                lay, dir, state, b, s)]
+                                = ws_diff_states(lay, dir, state, 0, b, s);
+                    }
+                });
+    }
+}
+
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+rnn_bias_prepare_sig((_ref_rnn_common_t<aprop, src_type, weights_type>::bias_prepare)) {
+    /* Original set of bias provided by the user */
+    AOC<const float, 5> b(
+            b_, rnn.n_layer, rnn.n_dir, rnn.n_bias * rnn.dic);
+    /* Array of pointers initialized in packing */
+    AOC<float *, 3> bias(bias_, rnn.n_layer, rnn.n_dir, rnn.n_parts_bias);
+    AOC<float, 3> scratch_bias(
+            scratch_bias_, rnn.n_layer, rnn.n_dir, rnn.n_bias * rnn.dic);
+
+    if (rnn.copy_bias) {
+        parallel_nd(rnn.n_layer * rnn.n_dir * rnn.n_bias * rnn.dic,
+                [&](size_t i) { scratch_bias_[i] = b_[i]; });
+    }
+
+    for (int i = 0; i < rnn.n_layer; i++) {
+        for (int d = 0; d < rnn.n_dir; d++) {
+            int offset_bias = 0;
+            for (int p = 0; p < rnn.n_parts_bias; p++) {
+                bias(i, d, p) = rnn.copy_bias
+                        ? (float *) &scratch_bias(i, d, offset_bias)
+                        : (float *) &b(i, d, offset_bias);
+                offset_bias += rnn.parts_bias[p] * rnn.dic;
+            }
+        }
+    }
+
+}
+
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+rnn_bias_finalize_sig(
+        (_ref_rnn_common_t<aprop, src_type, weights_type>::bias_finalize)) {
+    if (rnn.dt_conf != all_f32) {
+        float data_shift = pd()->attr()->rnn_data_qparams_.shift_;
+        float data_scale = pd()->attr()->rnn_data_qparams_.scale_;
+        float *weights_scales = pd()->attr()->rnn_weights_qparams_.scales_;
+        bool scale_per_oc = pd()->attr()->rnn_weights_qparams_.mask_ != 0;
+        for (int i = 0; i < rnn.n_layer * rnn.n_dir; i++)
+            for (int j = 0; j < rnn.n_bias * rnn.dic; j++) {
+                size_t off = i * rnn.n_bias * rnn.dic + j;
+                float weights_scale
+                        = scale_per_oc ? weights_scales[j] : weights_scales[0];
+                scratch_bias_[off] -= (w_iter_comp[off] + w_layer_comp[off])
+                        * data_shift / (weights_scale * data_scale);
+            }
+    }
+}
+
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+rnn_weights_assign_sig((_ref_rnn_common_t<aprop, src_type,
+        weights_type>::assign_packed_weights)) {
+    assert(md->format_kind == format_kind::rnn_packed);
+    const auto packed_desc = md->format_desc.rnn_packed_desc;
+    AOC<weights_data_t *, 3> weights(weights_,
+            rnn.n_layer, rnn.n_dir, packed_desc.n_parts);
+
+    size_t offset_packed = 0;
+    for (int l = 0; l < rnn.n_layer; l++)
+        for (int d = 0; d < rnn.n_dir; d++) {
+            for (int p = 0; p < packed_desc.n_parts; p++) {
+                weights(l, d, p) = (weights_data_t *)&w_[offset_packed];
+                offset_packed
+                    += packed_desc.part_pack_size[p] / sizeof(weights_data_t);
+            }
+        }
+}
+
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+rnn_weights_assign_sig(
+        (_ref_rnn_common_t<aprop, src_type, weights_type>::assign_weights)) {
+    assert(md->format_kind == format_kind::blocked);
+    const auto &blk = md->format_desc.blocking;
+    /* Original set of weights provided by the user */
+    AOC<const weights_data_t, 3> w(w_,
+            rnn.n_layer, rnn.n_dir, (int)blk.strides[1]);
+    /* Array of pointers for each part of weights */
+    AOC<weights_data_t *, 3> weights(weights_, rnn.n_layer, rnn.n_dir, n_parts);
+
+    for (int i = 0; i < rnn.n_layer; i++)
+        for (int d = 0; d < rnn.n_dir; d++) {
+            size_t offset_weights = 0;
+            for (int p = 0; p < n_parts; p++) {
+                weights(i, d, p) = (weights_data_t *)&w(i, d, offset_weights);
+                offset_weights += gates_per_part[p] * blk.strides[3];
+            }
+        }
+}
+
+//********************* Execution function *********************//
+template <prop_kind_t aprop, data_type_t src_type, data_type_t weights_type>
+void _ref_rnn_common_t<aprop, src_type, weights_type>::execute_(
+        const exec_ctx_t &ctx) const {
+    const rnn_conf_t &rnn = this->pd()->rnn_;
+    auto input = CTX_IN_MEM(const src_data_t *, MKLDNN_ARG_SRC_LAYER);
+    auto states = CTX_IN_MEM(const char *, MKLDNN_ARG_SRC_ITER);
+    auto layer_weights_n_comp = CTX_IN_MEM(const char *, MKLDNN_ARG_WEIGHTS_LAYER);
+    auto iter_weights_n_comp = CTX_IN_MEM(const char *, MKLDNN_ARG_WEIGHTS_ITER);
+    auto bias = CTX_IN_MEM(const float *, MKLDNN_ARG_BIAS);
+
+    auto dst_last_layer = rnn.is_fwd
+        ? CTX_OUT_MEM(char *, MKLDNN_ARG_DST_LAYER)
+        : const_cast<char *>(CTX_IN_MEM(const char *, MKLDNN_ARG_DST_LAYER));
+    auto dst_last_iter = rnn.is_fwd
+        ? CTX_OUT_MEM(char *, MKLDNN_ARG_DST_ITER)
+        : const_cast<char *>(CTX_IN_MEM(const char *, MKLDNN_ARG_DST_ITER));
+
+    auto diff_dst_layer = CTX_IN_MEM(const float *, MKLDNN_ARG_DIFF_DST_LAYER);
+    auto diff_dst_iter = CTX_IN_MEM(const float *, MKLDNN_ARG_DIFF_DST_ITER);
+
+    auto w_layer = reinterpret_cast<const weights_data_t *>(layer_weights_n_comp);
+    auto w_iter = reinterpret_cast<const weights_data_t *>(iter_weights_n_comp);
+    auto w_iter_comp = reinterpret_cast<const float *>(
+            iter_weights_n_comp + rnn.weights_iter_comp_offset);
+    auto w_layer_comp = reinterpret_cast<const float *>(
+            layer_weights_n_comp + rnn.weights_layer_comp_offset);
+
+    auto scratchpad = this->scratchpad(ctx);
+
+    auto ptr_wei_layer
+            = scratchpad.template get<weights_data_t *>(key_rnn_ptrs_wei_layer);
+    auto ptr_wei_iter
+            = scratchpad.template get<weights_data_t *>(key_rnn_ptrs_wei_iter);
+    auto ptr_bias =
+        scratchpad.template get<float *>(key_rnn_ptrs_bia);
+
+    // fetchihg buffers from the workspace
+    // if no workspace was provided we use the scratchpad
+    char *scratch_ptr = scratchpad.template get<char>(key_rnn_space);
+    char *ws_ptr = nullptr;
+    if (rnn.use_workspace)
+        ws_ptr = rnn.is_fwd
+            ? CTX_OUT_MEM(char *, MKLDNN_ARG_WORKSPACE)
+            : const_cast<char *>(CTX_IN_MEM(const char *, MKLDNN_ARG_WORKSPACE));
+
+    char *base_ptr = rnn.use_workspace ? ws_ptr : scratch_ptr;
+    acc_data_t *ws_gates = (acc_data_t *)(base_ptr + ws_gates_offset_);
+    src_data_t *ws_states = (src_data_t *)(base_ptr + ws_states_offset_);
+    float *ws_c_states = (float *)(base_ptr + ws_c_states_offset_);
+    float *ws_diff_states = (float *)(base_ptr + ws_diff_states_offset_);
+    float *ws_grid = (float *)(base_ptr + ws_grid_comp_offset_);
+    float *ws_cell = (float *)(base_ptr + ws_cell_comp_offset_);
+
+    auto diff_src_layer = CTX_OUT_MEM(float *, MKLDNN_ARG_DIFF_SRC_LAYER);
+    auto diff_src_iter = CTX_OUT_MEM(float *, MKLDNN_ARG_DIFF_SRC_ITER);
+
+    auto diff_weights_layer = CTX_OUT_MEM(float *, MKLDNN_ARG_DIFF_WEIGHTS_LAYER);
+    auto diff_weights_iter = CTX_OUT_MEM(float *, MKLDNN_ARG_DIFF_WEIGHTS_ITER);
+    auto diff_bias = CTX_OUT_MEM(float *, MKLDNN_ARG_DIFF_BIAS);
+
+    // Fetching extra buffers from scratchpad
+    float *ws_bias = (float *)(scratch_ptr + ws_bias_offset_);
+
+    // initialize diff_states to 0
+    if (aprop == prop_kind::backward)
+        array_set(ws_diff_states, 0.0f, rnn.ws_diff_states_size / sizeof(float));
+
+    /* Pack(if using packed gemm API) or copy(if input arrays have bad leading
+     * dimension */
+    (this->*bias_preparation_func)(rnn, ptr_bias, bias, ws_bias);
+
+    (this->*weights_iter_assign_func)(rnn, pd()->weights_md(1),
+            rnn.weights_iter_nld, rnn.weights_iter_ld, rnn.dic,
+            rnn.sic, rnn.n_parts_weights_iter, rnn.parts_weights_iter,
+            rnn.part_weights_iter_pack_size, ptr_wei_iter, w_iter,
+            ptr_bias, bias, ws_bias);
+    (this->*weights_layer_assign_func)(rnn, pd()->weights_md(0),
+            rnn.weights_layer_nld, rnn.weights_layer_ld, rnn.dic, rnn.slc,
+            rnn.n_parts_weights_layer, rnn.parts_weights_layer,
+            rnn.part_weights_layer_pack_size, ptr_wei_layer, w_layer, ptr_bias,
+            bias, ws_bias);
+
+    (this->*bias_finalization_func)(rnn, ws_bias, w_iter_comp, w_layer_comp);
+
+    // we first need to copy the initial states and input into ws
+    copy_init_layer(rnn, ws_states, ws_diff_states, input, diff_dst_layer);
+    if (rnn.dt_conf == f32u8f32u8 || rnn.dt_conf == f32u8f32f32
+            || rnn.dt_conf == all_f32)
+        copy_init_iter(rnn, ws_states, ws_c_states, ws_diff_states,
+                (const float *)states, diff_dst_iter);
+    else if (rnn.dt_conf == u8u8u8u8 || rnn.dt_conf == u8u8u8f32)
+        copy_init_iter(rnn, ws_states, ws_c_states, ws_diff_states,
+                (const uint8_t *)states, diff_dst_iter);
+    else
+        assert(!"unimplemented");
+
+    // run the execution on the grid
+    (this->*grid_computation)(rnn, ptr_wei_layer, ptr_wei_iter, ptr_bias,
+            ws_states, ws_c_states, ws_diff_states, ws_gates, ws_cell, ws_grid,
+            diff_weights_layer, diff_weights_iter, diff_bias);
+
+    // Finally we copy the results to the result buffers
+    if (rnn.dt_conf == u8u8u8f32 || rnn.dt_conf == f32u8f32f32
+            || rnn.dt_conf == all_f32)
+        copy_res_layer(rnn, (float *)dst_last_layer, diff_src_layer, ws_states,
+                ws_diff_states);
+    else if (rnn.dt_conf == u8u8u8u8 || rnn.dt_conf == f32u8f32u8)
+        copy_res_layer(rnn, (uint8_t *)dst_last_layer, diff_src_layer,
+                ws_states, ws_diff_states);
+    else
+        assert(!"unimplemented");
+
+    if (rnn.dt_conf == f32u8f32u8 || rnn.dt_conf == f32u8f32f32
+            || rnn.dt_conf == all_f32)
+        copy_res_iter(rnn, (float *)dst_last_iter, diff_src_iter, ws_states,
+                ws_c_states, ws_diff_states);
+    else if (rnn.dt_conf == u8u8u8u8 || rnn.dt_conf == u8u8u8f32)
+        copy_res_iter(rnn, (uint8_t *)dst_last_iter, diff_src_iter, ws_states,
+                ws_c_states, ws_diff_states);
+    else
+        assert(!"unimplemented");
+};
+
+/* Fix for MSVS warning C4661 */
+template<> rnn_cell_execution_sig(ref_rnn_fwd_f32_t::cell_execution);
+template<> rnn_cell_execution_sig(ref_rnn_fwd_u8s8_t::cell_execution);
+template<> rnn_cell_execution_sig(ref_rnn_bwd_f32_t::cell_execution);
+template<> rnn_cell_execution_sig(ref_rnn_fwd_f32_t::cell_execution_gru);
+template<> rnn_cell_execution_sig(ref_rnn_fwd_u8s8_t::cell_execution_gru);
+template<> rnn_cell_execution_sig(ref_rnn_bwd_f32_t::cell_execution_gru);
+template<> rnn_cell_execution_sig(ref_rnn_fwd_f32_t::cell_execution_gru_lbr);
+template<> rnn_cell_execution_sig(ref_rnn_fwd_u8s8_t::cell_execution_gru_lbr);
+template<> rnn_cell_execution_sig(ref_rnn_bwd_f32_t::cell_execution_gru_lbr);
+template<> rnn_elemwise_sig(ref_rnn_fwd_f32_t::rnn_elemwise);
+template<> rnn_elemwise_sig(ref_rnn_fwd_u8s8_t::rnn_elemwise);
+template<> rnn_elemwise_sig(ref_rnn_bwd_f32_t::rnn_elemwise);
+template<> rnn_elemwise_sig(ref_rnn_fwd_f32_t::lstm_elemwise);
+template<> rnn_elemwise_sig(ref_rnn_fwd_u8s8_t::lstm_elemwise);
+template<> rnn_elemwise_sig(ref_rnn_bwd_f32_t::lstm_elemwise);
+template<> rnn_elemwise_sig(ref_rnn_fwd_f32_t::gru_lbr_elemwise);
+template<> rnn_elemwise_sig(ref_rnn_fwd_u8s8_t::gru_lbr_elemwise);
+template<> rnn_elemwise_sig(ref_rnn_bwd_f32_t::gru_lbr_elemwise);
+
+template struct _ref_rnn_common_t<prop_kind::forward, data_type::f32, data_type::f32>;
+template struct _ref_rnn_common_t<prop_kind::forward, data_type::u8, data_type::s8>;
+template struct _ref_rnn_common_t<prop_kind::backward, data_type::f32, data_type::f32>;
+
+#undef AOC
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.hpp
new file mode 100644
index 0000000000..6f449a9016
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/ref_rnn.hpp
@@ -0,0 +1,328 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_REF_RNN_HPP
+#define CPU_REF_RNN_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "memory_tracking.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+#include "../cpu_isa_traits.hpp"
+#include "../gemm/os_blas.hpp"
+
+#include "cpu_rnn_pd.hpp"
+#include "../cpu_primitive.hpp"
+#include "rnn_utils.hpp"
+#include "jit_uni_rnn_postgemm.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <alg_kind_t alg_kind, prop_kind_t prop_kind>
+float activation(float s, float alpha, float cliping, float dd);
+
+template <prop_kind_t aprop, impl::data_type_t src_type,
+        impl::data_type_t weights_type>
+struct _ref_rnn_common_t : public cpu_primitive_t {
+    typedef typename prec_traits<src_type>::type src_data_t;
+    typedef typename prec_traits<weights_type>::type weights_data_t;
+    typedef typename utils::conditional<src_type == data_type::u8, int32_t,
+            float>::type acc_data_t;
+
+    using class_name = _ref_rnn_common_t<aprop, src_type, weights_type>;
+
+    typedef rnn_elemwise_sig((class_name::*elemwise_f));
+    typedef rnn_cell_execution_sig((class_name::*cell_execution_f));
+    typedef rnn_grid_execution_sig((class_name::*grid_execution_f));
+
+    typedef rnn_gemm_sig((class_name::*gemm_t));
+    typedef rnn_bias_prepare_sig((class_name::*bias_prepare_t));
+    typedef rnn_bias_finalize_sig((class_name::*bias_finalize_t));
+    typedef rnn_weights_assign_sig((class_name::*weights_assign_t));
+
+    using base_pd_t =
+            typename utils::conditional<false || aprop == prop_kind::forward,
+                    cpu_rnn_fwd_pd_t, cpu_rnn_bwd_pd_t>::type;
+
+    struct pd_t : public base_pd_t {
+        using base_pd_t::base_pd_t;
+
+        DECLARE_COMMON_PD_T("ref:any", class_name);
+
+        status_t init() {
+            using namespace prop_kind;
+            using namespace utils;
+            using namespace format_tag;
+            using namespace rnn_utils;
+            const alg_kind_t cell_kind = this->desc()->cell_desc.cell_kind;
+
+            data_type_t src_layer_dt = this->desc()->src_layer_desc.data_type;
+            data_type_t weights_iter_dt
+                    = this->desc()->weights_iter_desc.data_type;
+            data_type_t weights_layer_dt
+                    = this->desc()->weights_layer_desc.data_type;
+
+            bool ok = true
+                    && one_of(cell_kind, alg_kind::vanilla_rnn,
+                               alg_kind::vanilla_lstm, alg_kind::vanilla_gru,
+                               alg_kind::gru_linear_before_reset)
+                    && IMPLICATION(aprop == prop_kind::forward,
+                               one_of(this->desc()->prop_kind, forward_training,
+                                           forward_inference))
+                    && IMPLICATION(aprop == backward,
+                               one_of(this->desc()->prop_kind, backward))
+                    && src_layer_dt == src_type
+                    && everyone_is(
+                               weights_type, weights_iter_dt, weights_layer_dt)
+                    && this->set_default_params() == status::success
+                    && this->with_bias();
+            if (!ok)
+                return status::unimplemented;
+
+            init_conf(rnn_, *this->desc(), this->src_md(0), this->src_md(1),
+                    this->weights_md(0), this->weights_md(1), this->dst_md(0));
+
+            if (rnn_.dt_conf == all_f32)
+                ok = ok && this->attr()->has_default_values();
+
+            // Set weights descriptors to desired format
+            memory_desc_t new_weights_layer_md = *this->weights_md(0);
+            CHECK(set_expected_desc(rnn_, new_weights_layer_md, false));
+            if (this->weights_layer_md_.format_kind == format_kind::any) {
+                this->weights_layer_md_ = new_weights_layer_md;
+            } else if (this->weights_layer_md_.format_kind
+                    == format_kind::rnn_packed) {
+                if (this->weights_layer_md_ != new_weights_layer_md)
+                    return status::unimplemented;
+            }
+
+            memory_desc_t new_weights_iter_md = *this->weights_md(1);
+            CHECK(set_expected_desc(rnn_, new_weights_iter_md, true));
+            if (this->weights_iter_md_.format_kind == format_kind::any) {
+                this->weights_iter_md_ = new_weights_iter_md;
+            } else if (this->weights_iter_md_.format_kind
+                    == format_kind::rnn_packed) {
+                if (this->weights_iter_md_ != new_weights_iter_md)
+                    return status::unimplemented;
+            }
+
+            CHECK(this->check_layout_consistency());
+
+            set_conf(rnn_, *this->desc(), this->weights_md(0),
+                    this->weights_md(1), this->diff_weights_md(0),
+                    this->diff_weights_md(1));
+
+            size_t scratchpad_sz{0}, ws_sz{0};
+            get_scratchpad_and_workspace_sizes(rnn_, scratchpad_sz, ws_sz);
+
+            // initialize the workspace if needed
+            if (rnn_.is_training) {
+                dims_t ws_dims = { (int)ws_sz };
+                mkldnn_memory_desc_init_by_tag(&this->ws_md_, 1, ws_dims,
+                        data_type::u8, format_tag::x);
+            }
+
+            init_scratchpad(scratchpad_sz);
+
+            return status::success;
+        }
+
+        rnn_utils::rnn_conf_t rnn_;
+
+    private:
+        void init_scratchpad(size_t scratchpad_sz) {
+            using namespace memory_tracking::names;
+            auto scratchpad = this->scratchpad_registry().registrar();
+            scratchpad.book(key_rnn_space, sizeof(float) * scratchpad_sz, 4096);
+
+            int max_nparts = this->cell_kind() == alg_kind::vanilla_gru ? 2 : 1;
+            int ptr_wei_sz = rnn_.n_layer * rnn_.n_dir * max_nparts;
+            scratchpad.book(key_rnn_ptrs_wei_layer,
+                    sizeof(float *) * ptr_wei_sz);
+            scratchpad.book(key_rnn_ptrs_wei_iter,
+                    sizeof(float *) * ptr_wei_sz);
+            scratchpad.book(key_rnn_ptrs_bia,
+                    sizeof(float *) * ptr_wei_sz);
+        }
+    };
+
+    _ref_rnn_common_t(const pd_t *apd)
+        : cpu_primitive_t(apd, true), rnn_postgemm_(nullptr) {
+        /// @todo set max_feature_size assuming that we limit the number of
+        /// iterations and layer to one if slc != dic and sic != dic
+        /// respectively
+
+        bias_preparation_func = &class_name::bias_prepare;
+        bias_finalization_func = &class_name::bias_finalize;
+
+        auto set_gemm_funcs
+                = [](bool packed_gemm, gemm_t &g, weights_assign_t &a) {
+                      if (packed_gemm) {
+                          g = &class_name::packed_gemm;
+                          a = &class_name::assign_packed_weights;
+                      } else {
+                          g = &class_name::gemm;
+                          a = &class_name::assign_weights;
+                      }
+                  };
+        set_gemm_funcs(pd()->rnn_.use_iter_packed_gemm, gemm_iter_func,
+                weights_iter_assign_func);
+
+        set_gemm_funcs(pd()->rnn_.use_layer_packed_gemm, gemm_layer_func,
+                weights_layer_assign_func);
+
+        switch (pd()->cell_kind()) {
+        case alg_kind::vanilla_lstm:
+            cell_func = &class_name::cell_execution;
+            if (aprop == prop_kind::forward) {
+                if (mayiuse(avx512_core))
+                    rnn_postgemm_ = new jit_uni_lstm_postgemm_kernel_fwd<avx512_core, src_type>(
+                        pd()->rnn_, pd()->attr());
+                else if (mayiuse(avx2))
+                    rnn_postgemm_ = new jit_uni_lstm_postgemm_kernel_fwd<avx2, src_type>(
+                        pd()->rnn_, pd()->attr());
+                else if (mayiuse(sse42))
+                    rnn_postgemm_ = new jit_uni_lstm_postgemm_kernel_fwd<sse42, src_type>(
+                        pd()->rnn_, pd()->attr());
+                assert(rnn_postgemm_ != nullptr);
+                rnn_postgemm_->init();
+            }
+            elemwise_func = &class_name::lstm_elemwise;
+            break;
+        case alg_kind::vanilla_rnn: // @todo switch on cell kind
+            cell_func = &class_name::cell_execution;
+            elemwise_func = &class_name::rnn_elemwise;
+            switch (pd()->activation_kind()) {
+            case alg_kind::eltwise_relu:
+                activation_func = &activation<alg_kind::eltwise_relu, aprop>;
+                break;
+            case alg_kind::eltwise_tanh:
+                activation_func = &activation<alg_kind::eltwise_tanh, aprop>;
+                break;
+            case alg_kind::eltwise_logistic:
+                activation_func = &activation<alg_kind::eltwise_logistic, aprop>;
+                break;
+            default: break;
+            }
+            break;
+        case alg_kind::vanilla_gru:
+            cell_func = &class_name::cell_execution_gru;
+            break;
+        case alg_kind::gru_linear_before_reset:
+            cell_func = &class_name::cell_execution_gru_lbr;
+            elemwise_func = &class_name::gru_lbr_elemwise;
+            break;
+        default: break;
+        }
+
+        grid_computation = &class_name::linear_execution;
+
+        size_t scratchpad_size, workspace_size;
+        rnn_utils::set_offsets(pd()->rnn_, ws_gates_offset_, ws_states_offset_,
+                ws_c_states_offset_, ws_diff_states_offset_,
+                ws_grid_comp_offset_, ws_cell_comp_offset_,
+                ws_bias_offset_, scratchpad_size, workspace_size);
+    }
+
+    ~_ref_rnn_common_t() {}
+
+    // typedef typename prec_traits::type data_t;
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        execute_(ctx);
+        return status::success;
+    }
+
+private:
+    void execute_(const exec_ctx_t &ctx) const;
+    rnn_grid_execution_sig(linear_execution);
+    rnn_cell_execution_sig(cell_execution);
+    rnn_cell_execution_sig(cell_execution_gru);
+    rnn_cell_execution_sig(cell_execution_gru_lbr);
+    rnn_elemwise_sig(rnn_elemwise);
+    rnn_elemwise_sig(lstm_elemwise);
+    rnn_elemwise_sig(gru_lbr_elemwise);
+    rnn_gemm_sig(gemm);
+    rnn_gemm_sig(packed_gemm);
+    rnn_bias_prepare_sig(bias_prepare);
+    rnn_bias_finalize_sig(bias_finalize);
+    rnn_weights_assign_sig(assign_weights);
+    rnn_weights_assign_sig(assign_packed_weights);
+
+    float (*activation_func)(float dd, float s, float alpha, float cliping);
+
+    void copy_init_layer(const rnn_utils::rnn_conf_t &rnn,
+            src_data_t *ws_states_, float *ws_diff_states_,
+            const src_data_t *xt_, const float *diff_dst_layer) const;
+
+    template <typename input_data_t>
+    void copy_init_iter(const rnn_utils::rnn_conf_t &rnn,
+            src_data_t *ws_states_, float *ws_c_states, float *ws_diff_states_,
+            const input_data_t *firstit_states_,
+            const float *diff_dst_iter) const;
+
+    template <typename dst_data_t>
+    void copy_res_layer(const rnn_utils::rnn_conf_t &rnn,
+            dst_data_t *dst_layer_, float *diff_src_layer,
+            const src_data_t *ws_states_, const float *ws_diff_states_) const;
+
+    template <typename output_data_t>
+    void copy_res_iter(const rnn_utils::rnn_conf_t &rnn,
+            output_data_t *dst_iter_, float *diff_src_iter,
+            const src_data_t *ws_states_, float *ws_c_states,
+            const float *ws_diff_states_) const;
+
+    void gates_reduction(const rnn_utils::rnn_conf_t &rnn,
+            const acc_data_t *ws_gates_, float *diff_bias_) const;
+
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+
+    size_t ws_gates_offset_;
+    size_t ws_states_offset_;
+    size_t ws_c_states_offset_;
+    size_t ws_bias_offset_;
+    size_t ws_diff_states_offset_;
+    size_t ws_grid_comp_offset_;
+    size_t ws_cell_comp_offset_;
+    jit_uni_rnn_postgemm_kernel *rnn_postgemm_;
+
+    grid_execution_f grid_computation;
+    cell_execution_f cell_func;
+
+    bias_prepare_t bias_preparation_func;
+    bias_finalize_t bias_finalization_func;
+    weights_assign_t weights_layer_assign_func;
+    weights_assign_t weights_iter_assign_func;
+
+    gemm_t gemm_layer_func;
+    gemm_t gemm_iter_func;
+    elemwise_f elemwise_func;
+};
+
+using ref_rnn_fwd_f32_t = _ref_rnn_common_t<prop_kind::forward, data_type::f32, data_type::f32>;
+using ref_rnn_bwd_f32_t = _ref_rnn_common_t<prop_kind::backward, data_type::f32, data_type::f32>;
+using ref_rnn_fwd_u8s8_t = _ref_rnn_common_t<prop_kind::forward, data_type::u8, data_type::s8>;
+}
+}
+}
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_reorders.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_reorders.hpp
new file mode 100644
index 0000000000..597c63e3f8
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_reorders.hpp
@@ -0,0 +1,380 @@
+/*******************************************************************************
+ * Copyright 2018 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *******************************************************************************/
+
+#ifndef CPU_RNN_REORDERS_HPP
+#define CPU_RNN_REORDERS_HPP
+
+#include <assert.h>
+
+#include "type_helpers.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+#include "simple_q10n.hpp"
+#include "cpu_reorder_pd.hpp"
+#include "../gemm/os_blas.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <data_type_t type_i, data_type_t type_o>
+struct rnn_data_reorder_t : public cpu_primitive_t {
+    struct pd_t : public cpu_reorder_pd_t {
+        using cpu_reorder_pd_t::cpu_reorder_pd_t;
+
+        DECLARE_COMMON_PD_T("rnn_data_reorder", rnn_data_reorder_t);
+
+        static status_t create(reorder_pd_t **reorder_pd,
+                engine_t *engine, const primitive_attr_t *attr,
+                engine_t *src_engine, const memory_desc_t *src_md,
+                engine_t *dst_engine, const memory_desc_t *dst_md) {
+            const memory_desc_wrapper id(src_md), od(dst_md);
+            bool args_ok = true
+                    && id.data_type() == type_i
+                    && od.data_type() == type_o
+                    && id.matches_one_of_tag(format_tag::tnc, format_tag::ldsnc)
+                    && od == id;
+            if (!args_ok) return status::invalid_arguments;
+
+            auto _pd = new pd_t(engine, attr, src_engine, src_md, dst_engine,
+                    dst_md);
+            if (_pd == nullptr) return out_of_memory;
+            if (_pd->init() != success) { delete _pd; return unimplemented; }
+            return safe_ptr_assign<reorder_pd_t>(*reorder_pd, _pd);
+        }
+    };
+
+private:
+    typedef typename prec_traits<type_i>::type in_data_t;
+    typedef typename prec_traits<type_o>::type out_data_t;
+
+    rnn_data_reorder_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        auto input = CTX_IN_MEM(const in_data_t *, MKLDNN_ARG_FROM);
+        auto output = CTX_OUT_MEM(out_data_t *, MKLDNN_ARG_TO);
+        const memory_desc_wrapper &input_d = pd()->src_md();
+        const memory_desc_wrapper &output_d = pd()->dst_md();
+        const size_t nelems = input_d.nelems();
+        const float scale = pd()->attr()->rnn_data_qparams_.scale_;
+        const float shift = pd()->attr()->rnn_data_qparams_.shift_;
+
+        parallel_nd(nelems, [&](size_t i) {
+            float in = (float)input[input_d.off_l(i)] * scale + shift;
+            output[output_d.off_l(i)] = qz_a1b0<float, out_data_t>()(in);
+        });
+
+        return status::success;
+    }
+
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <data_type_t type_i, data_type_t type_o>
+struct rnn_weights_reorder_t : public cpu_primitive_t {
+    struct pd_t : public cpu_reorder_pd_t {
+        using cpu_reorder_pd_t::cpu_reorder_pd_t;
+
+        DECLARE_COMMON_PD_T("rnn_weights_reorder", rnn_weights_reorder_t);
+
+        static status_t create(reorder_pd_t **reorder_pd,
+                engine_t *engine, const primitive_attr_t *attr,
+                engine_t *src_engine, const memory_desc_t *src_md,
+                engine_t *dst_engine, const memory_desc_t *dst_md) {
+#if !USE_MKL_PACKED_GEMM
+            return status::unimplemented;
+#endif
+            const memory_desc_wrapper id(src_md), od(dst_md);
+            bool args_ok = true
+                    && id.data_type() == type_i
+                    && od.data_type() == type_o
+                    && od.format_kind() == format_kind::rnn_packed
+                    && od.rnn_packed_desc().format == mkldnn_ldigo_p
+                    && od.rnn_packed_desc().n_parts == 1
+                    && attr != nullptr;
+            if (!args_ok) return status::invalid_arguments;
+
+            format_tag_t itag = id.matches_one_of_tag(
+                    format_tag::ldigo, format_tag::ldgoi);
+            if (itag == format_tag::undef) return status::invalid_arguments;
+
+            const int mask = attr->rnn_weights_qparams_.mask_;
+            if (!utils::one_of(mask, 0, 3)) return status::unimplemented;
+
+            auto _pd = new pd_t(engine, attr, src_engine, src_md, dst_engine,
+                    dst_md);
+            if (_pd == nullptr) return out_of_memory;
+            _pd->itag_ = itag;
+            if (_pd->init() != success) { delete _pd; return unimplemented; }
+            return safe_ptr_assign<reorder_pd_t>(*reorder_pd, _pd);
+        }
+
+        status_t init() {
+            status_t status = cpu_reorder_pd_t::init();
+            if (status != status::success) return status;
+
+            init_scratchpad();
+
+            return status::success;
+        }
+
+        format_tag_t itag_;
+
+    private:
+        void init_scratchpad() {
+            const memory_desc_wrapper id(src_md());
+            const size_t nelems = id.nelems();
+            const auto &dims = id.dims();
+
+            using namespace memory_tracking::names;
+            auto scratchpad = scratchpad_registry().registrar();
+            size_t quantization_size = sizeof(int8_t) * nelems;
+            size_t reduction_size = itag_ == ldigo
+                    ? sizeof(int32_t) * mkldnn_get_max_threads() * dims[0]
+                            * dims[1] * dims[3] * dims[4]
+                    : 0;
+            scratchpad.book(
+                    key_reorder_rnn_weights_quantization, quantization_size);
+            scratchpad.book(key_reorder_rnn_weights_reduction, reduction_size);
+        }
+    };
+
+private:
+    typedef typename prec_traits<type_i>::type in_data_t;
+    typedef typename prec_traits<type_o>::type out_data_t;
+
+    rnn_weights_reorder_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+#if USE_MKL_PACKED_GEMM
+        auto input = CTX_IN_MEM(const in_data_t *, MKLDNN_ARG_FROM);
+        auto output = CTX_OUT_MEM(char *, MKLDNN_ARG_TO);
+        const memory_desc_wrapper &input_d = pd()->src_md();
+        const memory_desc_wrapper &output_d = pd()->dst_md();
+        const auto &dims = input_d.dims();
+
+        const int L = dims[0];
+        const int D = dims[1];
+        const int I = dims[2];
+        const int G = dims[3];
+        const int O = dims[4];
+
+        const bool is_igo = pd()->itag_ == format_tag::ldigo;
+
+        /* Quantize input & compute compensation */
+        auto quantized = (int8_t * __restrict)scratchpad(ctx).template get<void>(
+                memory_tracking::names::key_reorder_rnn_weights_quantization);
+        auto reduction = (int32_t * __restrict)scratchpad(ctx).template get<void>(
+                memory_tracking::names::key_reorder_rnn_weights_reduction);
+        float *comp = reinterpret_cast<float *>(
+                output + output_d.rnn_packed_desc().offset_compensation);
+        const float *scales = pd()->attr()->rnn_weights_qparams_.scales_;
+        const int mask = pd()->attr()->rnn_weights_qparams_.mask_;
+
+        if (is_igo) {
+            int nthr = mkldnn_get_max_threads();
+            int LD_nthr = nstl::min(L * D, nthr);
+            int I_nthr = nstl::min(I, nthr / LD_nthr);
+            parallel(nthr, [&](const int ithr, const int nthr) {
+                int LD_ithr = -1, LD_s = -1, LD_e = -1;
+                int I_ithr = -1, I_s = -1, I_e = -1;
+                if (ithr < LD_nthr * I_nthr) {
+                    LD_ithr = ithr % LD_nthr;
+                    I_ithr = ithr / LD_nthr;
+                    balance211(L * D, LD_nthr, LD_ithr, LD_s, LD_e);
+                    balance211(I, I_nthr, I_ithr, I_s, I_e);
+                }
+                int32_t *comp_ithr = reduction + I_ithr * L * D * G * O;
+                for (int ld = LD_s; ld < LD_e; ld++) {
+                    for (int go = 0; go < G * O; go++)
+                        comp_ithr[ld * G * O + go] = 0;
+                    for (int i = I_s; i < I_e; i++) {
+                        PRAGMA_OMP_SIMD()
+                        for (int go = 0; go < G * O; go++) {
+                            const float s = scales[(mask == 0) ? 0 : go];
+                            int8_t q = qz_b0<in_data_t, out_data_t>()(
+                                    input[ld * I * G * O + i * G * O + go], s);
+                            quantized[ld * I * G * O + i * G * O + go]
+                                    = (int32_t)q;
+                            comp_ithr[ld * G * O + go] += (int32_t)q;
+                        }
+                    }
+                }
+            });
+            parallel_nd(L * D * G * O,
+                    [&](int s) { comp[s] = saturate<float>(reduction[s]); });
+            for (int i = 1; i < I_nthr; i++) {
+                parallel_nd(L * D * G * O, [&](int s) {
+                    comp[s] += saturate<float>(
+                            reduction[i * L * D * G * O + s]);
+                });
+            }
+        } else {
+            parallel_nd(L * D, G * O, [&](int ld, int go) {
+                int32_t compensation = 0;
+                const float s = scales[(mask == 0) ? 0 : go];
+                PRAGMA_OMP_SIMD()
+                for (int i = 0; i < I; i++) {
+                    int8_t q = qz_b0<in_data_t, out_data_t>()(
+                            input[ld * G * O * I + go * I + i], s);
+                    compensation += (int32_t)q;
+                    quantized[ld * G * O * I + go * I + i] = q;
+                }
+                comp[ld * G * O + go] = saturate<float>(compensation);
+            });
+        }
+
+        /* Pack */
+        auto off_igo = [&](int l, int d, int i, int g, int o) {
+            return l * D * I * G * O + d * I * G * O + i * G * O + g * O + o;
+        };
+        auto off_goi = [&](int l, int d, int i, int g, int o) {
+            return l * D * G * O * I + d * G * O * I + g * O * I + o * I + i;
+        };
+        int n_parts = output_d.rnn_packed_desc().n_parts;
+        const size_t *size_packed_cell
+                = output_d.rnn_packed_desc().part_pack_size;
+        const int *parts = output_d.rnn_packed_desc().parts;
+        const int n = output_d.rnn_packed_desc().n;
+        char *to_pack = output;
+        for (int l = 0; l < L; l++) {
+            for (int d = 0; d < D; d++) {
+                for (int p = 0; p < n_parts; p++) {
+                    int g = (p > 0) ? parts[p - 1] : 0;
+                    int m_p = parts[p] * O;
+                    int k_p = I;
+                    cblas_gemm_s8u8s32_pack(CblasColMajor, CblasAMatrix,
+                            is_igo ? CblasNoTrans : CblasTrans, m_p, n, k_p,
+                            &quantized[is_igo ? off_igo(l, d, 0, g, 0) :
+                                                off_goi(l, d, g, 0, 0)],
+                            is_igo ? G * O : I, to_pack);
+                    to_pack += size_packed_cell[p];
+                }
+            }
+        }
+#endif
+        return status::success;
+    }
+
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+template <>
+struct rnn_weights_reorder_t<data_type::f32, data_type::f32>
+        : public cpu_primitive_t {
+    struct pd_t : public cpu_reorder_pd_t {
+        using cpu_reorder_pd_t::cpu_reorder_pd_t;
+
+        DECLARE_COMMON_PD_T("rnn_weights_reorder", rnn_weights_reorder_t);
+
+        static status_t create(reorder_pd_t **reorder_pd,
+                engine_t *engine, const primitive_attr_t *attr,
+                engine_t *src_engine, const memory_desc_t *src_md,
+                engine_t *dst_engine, const memory_desc_t *dst_md) {
+#if !USE_MKL_PACKED_GEMM
+            return status::unimplemented;
+#endif
+            const memory_desc_wrapper id(src_md), od(dst_md);
+            bool args_ok = true
+                    && id.data_type() == data_type::f32
+                    && od.data_type() == data_type::f32
+                    && od.format_kind() == format_kind::rnn_packed
+                    && utils::one_of(od.rnn_packed_desc().format,
+                        mkldnn_ldigo_p, mkldnn_ldgoi_p)
+                    && attr->has_default_values();
+            if (!args_ok) return status::invalid_arguments;
+
+            format_tag_t itag = id.matches_one_of_tag(
+                    format_tag::ldigo, format_tag::ldgoi);
+            if (itag == format_tag::undef) return status::invalid_arguments;
+
+            const int mask = attr->rnn_weights_qparams_.mask_;
+            if (!utils::one_of(mask, 0, 3)) return status::unimplemented;
+
+            auto _pd = new pd_t(engine, attr, src_engine, src_md, dst_engine,
+                    dst_md);
+            if (_pd == nullptr) return out_of_memory;
+            if (_pd->init() != success) { delete _pd; return unimplemented; }
+            _pd->itag_ = itag;
+            return safe_ptr_assign<reorder_pd_t>(*reorder_pd, _pd);
+        }
+
+        format_tag_t itag_;
+    };
+
+private:
+    rnn_weights_reorder_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+#if USE_MKL_PACKED_GEMM
+        auto input = CTX_IN_MEM(const float *, MKLDNN_ARG_FROM);
+        auto output = CTX_OUT_MEM(float *, MKLDNN_ARG_TO);
+        const memory_desc_wrapper &input_d = pd()->src_md();
+        const memory_desc_wrapper &output_d = pd()->dst_md();
+        const auto &dims = input_d.dims();
+        const rnn_packed_desc_t &rnn_pdata = output_d.rnn_packed_desc();
+        const int L = dims[0];
+        const int D = dims[1];
+        const int I = dims[2];
+        const int G = dims[3];
+        const int O = dims[4];
+
+        /* Pack */
+        bool cross_case = false
+            || (pd()->itag_ == format_tag::ldigo
+                    && rnn_pdata.format == mkldnn_ldgoi_p)
+            || (pd()->itag_ == format_tag::ldgoi
+                    && rnn_pdata.format == mkldnn_ldigo_p);
+        auto trans = cross_case ? CblasTrans : CblasNoTrans;
+        int n_parts = rnn_pdata.n_parts;
+        const size_t *size_packed_cell = rnn_pdata.part_pack_size;
+        const int *parts = rnn_pdata.parts;
+        const int n = rnn_pdata.n;
+
+        const bool is_igo = pd()->itag_ == format_tag::ldigo;
+        auto off_igo = [&](int l, int d, int i, int g, int o) {
+            return l * D * I * G * O + d * I * G * O + i * G * O + g * O + o;
+        };
+        auto off_goi = [&](int l, int d, int i, int g, int o) {
+            return l * D * G * O * I + d * G * O * I + g * O * I + o * I + i;
+        };
+        for (int l = 0; l < L; l++) {
+            for (int d = 0; d < D; d++) {
+                for (int p = 0; p < n_parts; p++) {
+                    int g = (p > 0) ? parts[p - 1] : 0;
+                    int m_p = is_igo ? parts[p] * O : I;
+                    int k_p = is_igo ? I : parts[p] * O;
+                    int ld = is_igo ? G * O : I;
+                    cblas_sgemm_pack(CblasColMajor, CblasAMatrix, trans, m_p, n,
+                            k_p, 1.0f, &input[is_igo ? off_igo(l, d, 0, g, 0) :
+                                                       off_goi(l, d, 0, g, 0)],
+                            ld, output);
+                    output += size_packed_cell[p] / sizeof(float);
+                }
+            }
+        }
+#endif
+        return status::success;
+    }
+
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+} // namespace cpu
+} // namespace impl
+} // namespace mkldnn
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_utils.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_utils.cpp
new file mode 100644
index 0000000000..1d60415cbc
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_utils.cpp
@@ -0,0 +1,426 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "c_types_map.hpp"
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+
+#include "ref_rnn.hpp"
+#include "rnn_utils.hpp"
+#include "type_helpers.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::utils;
+using namespace rnn_utils;
+using namespace format_tag;
+using namespace rnn_packed_format;
+using namespace data_type;
+
+bool rnn_utils::is_ldigo(const memory_desc_wrapper &md) {
+    if (md.format_kind() != format_kind::blocked)
+        return false;
+
+    auto blk = md.blocking_desc();
+    auto str = blk.strides;
+    auto dims = md.dims();
+    return md.ndims() == 5 && blk.inner_nblks == 0 && str[4] == 1
+            && str[3] == dims[4] && str[1] == str[2] * dims[2]
+            && str[0] == str[1] * dims[1];
+};
+
+bool rnn_utils::is_ldgoi(const memory_desc_wrapper &md) {
+    if (md.format_kind() != format_kind::blocked)
+        return false;
+
+    auto blk = md.blocking_desc();
+    auto str = blk.strides;
+    auto dims = md.dims();
+    return md.ndims() == 5 && blk.inner_nblks == 0 && str[2] == 1
+            && str[3] == dims[4] * str[4] && str[1] == str[3] * dims[3]
+            && str[0] == str[1] * dims[1];
+};
+
+void rnn_utils::init_conf(rnn_conf_t &rnn, const rnn_desc_t &rd,
+        const memory_desc_wrapper &src_layer_d,
+        const memory_desc_wrapper &src_iter_d,
+        const memory_desc_wrapper &weights_layer_d,
+        const memory_desc_wrapper &weights_iter_d,
+        const memory_desc_wrapper &dst_layer_d) {
+    rnn.is_fwd = utils::one_of(rd.prop_kind, prop_kind::forward_training,
+            prop_kind::forward_inference);
+    rnn.is_training = utils::one_of(
+            rd.prop_kind, prop_kind::forward_training, prop_kind::backward);
+    rnn.is_lbr = rd.cell_desc.cell_kind == mkldnn_gru_linear_before_reset;
+
+    switch (rd.direction) {
+    case mkldnn_unidirectional_left2right: rnn.exec_dir = l2r; break;
+    case mkldnn_unidirectional_right2left: rnn.exec_dir = r2l; break;
+    case mkldnn_bidirectional_concat: rnn.exec_dir = bi_concat; break;
+    case mkldnn_bidirectional_sum: rnn.exec_dir = bi_sum; break;
+    default: break;
+    }
+
+    if (everyone_is(f32, src_layer_d.data_type(), dst_layer_d.data_type(),
+                weights_layer_d.data_type()))
+        rnn.dt_conf = all_f32;
+    else if (dst_layer_d.data_type() == u8) {
+        if (IMPLICATION(src_iter_d.md_, src_iter_d.data_type() == u8))
+            rnn.dt_conf = u8u8u8u8;
+        else
+            rnn.dt_conf = f32u8f32u8;
+    } else {
+        if (IMPLICATION(src_iter_d.md_, src_iter_d.data_type() == u8))
+            rnn.dt_conf = u8u8u8f32;
+        else
+            rnn.dt_conf = f32u8f32f32;
+    }
+
+    rnn.n_layer = weights_layer_d.dims()[0];
+    rnn.n_iter = src_layer_d.dims()[0];
+    rnn.n_dir = weights_layer_d.dims()[1];
+    rnn.n_gates = weights_layer_d.dims()[3];
+    rnn.n_states = mkldnn_rnn_cell_get_states_count(&rd.cell_desc);
+    rnn.n_bias = rnn.n_gates + rnn.is_lbr;
+    rnn.mb = src_layer_d.dims()[1];
+    rnn.sic = weights_iter_d.dims()[2];
+    rnn.slc = weights_layer_d.dims()[2];
+    rnn.dic = weights_layer_d.dims()[4];
+    rnn.dlc = dst_layer_d.dims()[2];
+
+    rnn.gates_ld = rnn.dic * rnn.n_gates;
+    rnn.gates_nld = rnn.mb;
+    rnn.states_nld = rnn.mb;
+
+    /* Set the correct number of weights parts */
+    bool is_orig_gru = rd.cell_desc.cell_kind == alg_kind::vanilla_gru;
+    rnn.n_parts_weights_layer = 1;
+    rnn.parts_weights_layer[0] = rnn.n_gates;
+    rnn.parts_weights_layer[1] = 0;
+
+    rnn.n_parts_weights_iter = is_orig_gru ? 2 : 1;
+    rnn.parts_weights_iter[0] = is_orig_gru ? 2 : rnn.n_gates;
+    rnn.parts_weights_iter[1] = is_orig_gru ? 1 : 0;
+
+    rnn.n_parts_bias = 1;
+    rnn.parts_bias[0] = rnn.n_bias;
+    rnn.parts_bias[1] = 0;
+
+    /* Decide wich gemm implementation to use: packed/nonpacked jit/cblas
+     * and if to mergre gemm across iterations */
+    bool is_int8 = rnn.dt_conf != all_f32;
+    rnn.merge_gemm_layer = ((rnn.is_fwd && rnn.mb < 128) || !rnn.is_fwd)
+            || is_int8;
+    bool is_gru = utils::one_of(rd.cell_desc.cell_kind, alg_kind::vanilla_gru,
+            alg_kind::gru_linear_before_reset);
+    rnn.merge_gemm_iter = !(rnn.is_fwd || is_gru) || is_int8;
+    bool is_inference = !rnn.is_training;
+
+    rnn.use_jit_gemm = !mayiuse(avx512_mic)
+            && ((is_inference && (rnn.n_layer > 1 || rnn.mb < 100))
+                || (rnn.is_training && rnn.dic < 500));
+
+    /* Decide to copy bias */
+    rnn.copy_bias = rnn.dt_conf != all_f32;
+
+#if USE_MKL_PACKED_GEMM
+    rnn.use_layer_packed_gemm
+            = (weights_layer_d.format_kind() == format_kind::any
+                      && rnn.slc > 760 && rnn.dic > 760 && is_inference)
+            || is_int8; // packed gemm is the only supported option for int8
+    rnn.use_iter_packed_gemm
+            = (weights_iter_d.format_kind() == format_kind::any && rnn.sic > 760
+                      && rnn.dic > 760 && is_inference)
+            || is_int8;
+#else
+    rnn.use_layer_packed_gemm = false;
+    rnn.use_iter_packed_gemm = false;
+#endif
+
+    /* Set packed gemm sizes */
+    if (rnn.use_layer_packed_gemm) {
+        rnn.weights_layer_pack_size = 0;
+        for (int p = 0; p < rnn.n_parts_weights_layer; p++) {
+            int m_p = rnn.is_fwd
+                ? (rnn.parts_weights_layer[p] * rnn.dic)
+                : rnn.slc;
+            int k_p = rnn.is_fwd
+                ? rnn.slc
+                : (rnn.parts_weights_layer[p] * rnn.dic);
+            int n_p = rnn.merge_gemm_layer ? rnn.mb * rnn.n_iter : rnn.mb;
+
+#if USE_MKL_PACKED_GEMM
+            if (rnn.dt_conf == all_f32)
+                rnn.part_weights_layer_pack_size[p] = cblas_sgemm_pack_get_size(
+                        CblasAMatrix, m_p, n_p, k_p);
+            else
+                rnn.part_weights_layer_pack_size[p]
+                        = cblas_gemm_s8u8s32_pack_get_size(
+                                CblasAMatrix, m_p, n_p, k_p);
+#else
+            UNUSED(m_p);
+            UNUSED(k_p);
+            UNUSED(n_p);
+            rnn.part_weights_layer_pack_size[p] = 0;
+#endif
+            rnn.weights_layer_pack_size += rnn.n_layer * rnn.n_dir
+                    * rnn.part_weights_layer_pack_size[p];
+        }
+        rnn.weights_layer_comp_offset = rnn.weights_layer_pack_size;
+        rnn.weights_layer_pack_size += rnn.dt_conf == all_f32 ? 0 : rnn.n_layer
+                        * rnn.n_dir * rnn.n_gates * rnn.dlc * sizeof(float);
+    }
+
+    if (rnn.use_iter_packed_gemm) {
+        rnn.weights_iter_pack_size = 0;
+        for (int p = 0; p < rnn.n_parts_weights_iter; p++) {
+            int m_p = rnn.is_fwd ? (rnn.parts_weights_iter[p] * rnn.dic) :
+                                   rnn.sic;
+            int k_p = rnn.is_fwd ? rnn.sic :
+                                   (rnn.parts_weights_iter[p] * rnn.dic);
+            int n_p = rnn.merge_gemm_iter ? rnn.mb * rnn.n_iter : rnn.mb;
+
+#if USE_MKL_PACKED_GEMM
+            if (rnn.dt_conf == all_f32)
+                rnn.part_weights_iter_pack_size[p] = cblas_sgemm_pack_get_size(
+                        CblasAMatrix, m_p, n_p, k_p);
+            else
+                rnn.part_weights_iter_pack_size[p]
+                        = cblas_gemm_s8u8s32_pack_get_size(
+                                CblasAMatrix, m_p, n_p, k_p);
+#else
+            UNUSED(m_p);
+            UNUSED(k_p);
+            UNUSED(n_p);
+            rnn.part_weights_iter_pack_size[p] = 0;
+#endif
+            rnn.weights_iter_pack_size += rnn.n_layer * rnn.n_dir
+                    * rnn.part_weights_iter_pack_size[p];
+        }
+        rnn.weights_iter_comp_offset = rnn.weights_iter_pack_size;
+        rnn.weights_iter_pack_size += rnn.dt_conf == all_f32 ? 0 : rnn.n_layer
+                        * rnn.n_dir * rnn.n_gates * rnn.dic * sizeof(float);
+    }
+
+}
+
+void rnn_utils::set_conf(rnn_conf_t &rnn, const rnn_desc_t &rd,
+        const memory_desc_wrapper &weights_layer_d,
+        const memory_desc_wrapper &weights_iter_d,
+        const memory_desc_wrapper &diff_weights_layer_d,
+        const memory_desc_wrapper &diff_weights_iter_d) {
+
+    /* Set leading dimensions for input weights arrays depending on input format
+     */
+    rnn.weights_layer_is_packed
+            = weights_layer_d.format_kind() == format_kind::rnn_packed;
+    rnn.weights_iter_is_packed
+            = weights_iter_d.format_kind() == format_kind::rnn_packed;
+
+    auto set_dims = [&](const memory_desc_wrapper &md, int &ld, int &nld) {
+        ld = 0; nld = 0;
+        if (md.is_blocking_desc()) {
+            if (is_ldigo(md)) {
+                ld = (int)md.blocking_desc().strides[2];
+                nld = md.dims()[2];
+            } else if (is_ldgoi(md)) {
+                ld = (int)md.blocking_desc().strides[4];
+                nld = md.dims()[3] * md.dims()[4];
+            } else
+                assert(!"unsupported weights format");
+        }
+    };
+    set_dims(weights_layer_d, rnn.weights_layer_ld, rnn.weights_layer_nld);
+    set_dims(weights_iter_d, rnn.weights_iter_ld, rnn.weights_iter_nld);
+    if (!rnn.is_fwd) {
+        set_dims(diff_weights_layer_d, rnn.diff_weights_layer_ld,
+                rnn.diff_weights_layer_nld);
+        set_dims(diff_weights_iter_d, rnn.diff_weights_iter_ld,
+                rnn.diff_weights_iter_nld);
+    }
+
+    int sizeof_states_dt
+            = rnn.dt_conf == all_f32 ? sizeof(float) : sizeof(uint8_t);
+    rnn.states_ws_ld
+            = get_good_ld(nstl::max(rnn.slc, nstl::max(rnn.sic, rnn.dic)),
+                sizeof_states_dt);
+    rnn.gates_ws_ld = get_good_ld(rnn.gates_ld, sizeof(float));
+
+    /* Set workspace sizes to store:
+     * states to copmute a pass
+     * diff states to copmute bwd pass (training only)
+     * intermediate results from the gates
+     */
+    rnn.use_workspace = rnn.is_training;
+    rnn.ws_states_size = (size_t)(rnn.n_layer + 1) * rnn.n_dir
+            * (rnn.n_iter + 1) * rnn.mb * rnn.states_ws_ld * sizeof_states_dt;
+    bool is_lstm = rd.cell_desc.cell_kind == mkldnn_vanilla_lstm;
+    rnn.ws_c_states_size = is_lstm
+            ? (size_t)(rnn.n_layer + 1) * rnn.n_dir * (rnn.n_iter + 1) * rnn.mb
+                    * rnn.states_ws_ld * sizeof(float)
+            : 0;
+    rnn.ws_diff_states_size = rnn.is_training
+            ? (size_t)(rnn.n_layer + 1) * rnn.n_dir * (rnn.n_iter + 1)
+                    * (rnn.n_states + 1) * rnn.mb * rnn.states_ws_ld
+                    * sizeof(float)
+            : (size_t)0;
+    rnn.ws_gates_size = (size_t)rnn.n_layer * rnn.n_dir * rnn.n_iter * rnn.mb
+            * rnn.gates_ws_ld * sizeof(float);
+
+    /* set other sizes */
+    rnn.ws_per_cell = (size_t)rnn.is_lbr * rnn.mb * rnn.dic * sizeof(float);
+    rnn.ws_cell_comp_size
+            = rnn.is_lbr || rnn.dt_conf != all_f32
+                ? (size_t) rnn.gates_nld * rnn.gates_ws_ld * sizeof(float)
+                : 0;
+    rnn.ws_grid_comp_size = (size_t)rnn.is_lbr * rnn.is_training * rnn.n_layer
+            * rnn.n_dir * rnn.n_iter * rnn.ws_per_cell * sizeof(float);
+    rnn.ws_bias_size = (size_t)rnn.n_layer * rnn.n_dir * rnn.n_bias * rnn.dic
+            * sizeof(float);
+}
+
+int rnn_utils::get_good_ld(int dim, int sizeof_dt) {
+    // we want matrices leading dimentions to be 64-byte aligned,
+    // and not divisible by 256 to avoid 4K aliasing effects
+    int ld = rnd_up(dim, 64 / sizeof_dt);
+    return (ld % 256 == 0) ? ld + 64 / sizeof_dt : ld;
+}
+
+void rnn_utils::set_offsets(const rnn_conf_t &rnn, size_t &ws_gates_offset,
+        size_t &ws_states_offset, size_t &ws_c_states_offset,
+        size_t &ws_diff_states_offset, size_t &ws_grid_comp_offset,
+        size_t &ws_cell_comp_offset, size_t &ws_bias_offset,
+        size_t &scratchpad_size, size_t &workspace_size) {
+
+    const size_t page_size = 4096; // 2097152;
+    size_t current_offset;
+    /* Mandatory workspaces: go to workspace if use_workspace, scratchpad
+     * otherwise */
+    current_offset = 0; // assumes the workspace base pointer is page aligned
+    ws_gates_offset = current_offset;
+    current_offset += rnn.ws_gates_size;
+
+    current_offset = utils::rnd_up(current_offset, page_size);
+    ws_states_offset = current_offset;
+    current_offset += rnn.ws_states_size;
+
+    current_offset = utils::rnd_up(current_offset, page_size);
+    ws_c_states_offset = current_offset;
+    current_offset += rnn.ws_c_states_size;
+
+    current_offset = utils::rnd_up(current_offset, page_size);
+    ws_diff_states_offset = current_offset;
+    current_offset += rnn.ws_diff_states_size;
+
+    current_offset = utils::rnd_up(current_offset, page_size);
+    ws_grid_comp_offset = current_offset;
+    current_offset += rnn.ws_grid_comp_size;
+
+    current_offset = utils::rnd_up(current_offset, page_size);
+    ws_cell_comp_offset = current_offset;
+    current_offset += rnn.ws_cell_comp_size;
+
+    workspace_size = rnn.use_workspace ? current_offset : 0;
+
+    /* Optional scratchpads */
+    // Assumes the scratchpad base pointer is page aligned.
+    // If use_workspace, the following goes to scratchpad alone,
+    // otherwise, all goes to scratchpad and continue incrementing offset
+    current_offset = rnn.use_workspace ? 0 : current_offset;
+
+    if (rnn.copy_bias) {
+        current_offset = utils::rnd_up(current_offset, page_size);
+        ws_bias_offset = current_offset;
+        current_offset += rnn.ws_bias_size;
+    }
+
+    scratchpad_size = current_offset;
+}
+
+void rnn_utils::get_scratchpad_and_workspace_sizes(const rnn_conf_t &rnn,
+        size_t &scratchpad_size, size_t &workspace_size) {
+    size_t ws_gates_offset, ws_states_offset, ws_c_states_offset,
+            ws_diff_states_offset, ws_grid_comp_offset, ws_cell_comp_offset,
+            ws_bias_offset;
+    set_offsets(rnn, ws_gates_offset, ws_states_offset, ws_diff_states_offset,
+            ws_c_states_offset, ws_grid_comp_offset, ws_cell_comp_offset,
+            ws_bias_offset, scratchpad_size, workspace_size);
+}
+
+status_t rnn_utils::set_good_strides(
+        memory_desc_t &weights_md, format_tag_t tag) {
+    auto &strides = weights_md.format_desc.blocking.strides;
+    auto dims = weights_md.dims;
+
+    if (tag == ldigo) {
+        strides[2] = rnn_utils::get_good_ld((int)strides[2],
+                (int)types::data_type_size(weights_md.data_type));
+        strides[1] = dims[2] * strides[2];
+        strides[0] = dims[1] * strides[1];
+    } else if (tag == ldgoi) {
+        strides[4] = rnn_utils::get_good_ld((int)strides[4],
+                (int)types::data_type_size(weights_md.data_type));
+        strides[3] = dims[4] * strides[4];
+        strides[1] = dims[3] * strides[3];
+        strides[0] = dims[1] * strides[1];
+    } else
+        return status::unimplemented;
+
+    return status::success;
+}
+
+status_t rnn_utils::set_expected_desc(rnn_conf_t &rnn,
+        memory_desc_t &weights_md, bool is_iter) {
+    using namespace format_tag;
+    bool use_packed_gemm = is_iter
+        ? rnn.use_iter_packed_gemm
+        : rnn.use_layer_packed_gemm;
+    if (use_packed_gemm) {
+        weights_md.format_kind = format_kind::rnn_packed;
+        rnn_packed_desc_t &rnn_pdata = weights_md.format_desc.rnn_packed_desc;
+        rnn_pdata.format = rnn.is_fwd ? mkldnn_ldigo_p : mkldnn_ldgoi_p;
+        if (is_iter) {
+            rnn_pdata.n = rnn.mb;
+            rnn_pdata.n_parts = rnn.n_parts_weights_iter;
+            array_copy(rnn_pdata.parts, rnn.parts_weights_iter,
+                    MKLDNN_RNN_MAX_N_PARTS);
+            array_copy(rnn_pdata.part_pack_size,
+                    rnn.part_weights_iter_pack_size, MKLDNN_RNN_MAX_N_PARTS);
+            rnn_pdata.offset_compensation = rnn.weights_iter_comp_offset;
+            rnn_pdata.size = rnn.weights_iter_pack_size;
+        } else {
+            rnn_pdata.n = rnn.merge_gemm_layer ? rnn.n_iter * rnn.mb : rnn.mb;
+            rnn_pdata.n_parts = rnn.n_parts_weights_layer;
+            array_copy(rnn_pdata.parts, rnn.parts_weights_layer,
+                    MKLDNN_RNN_MAX_N_PARTS);
+            array_copy(rnn_pdata.part_pack_size,
+                    rnn.part_weights_layer_pack_size, MKLDNN_RNN_MAX_N_PARTS);
+            rnn_pdata.offset_compensation = rnn.weights_layer_comp_offset;
+            rnn_pdata.size = rnn.weights_layer_pack_size;
+        }
+    } else {
+        CHECK(memory_desc_init_by_tag(weights_md, rnn.is_fwd ? ldigo : ldgoi));
+        // Adjust strides for good leading dimension in GEMM
+        CHECK(set_good_strides(weights_md, rnn.is_fwd ? ldigo : ldgoi));
+    }
+    return status::success;
+}
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_utils.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_utils.hpp
new file mode 100644
index 0000000000..99eb787a64
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/rnn/rnn_utils.hpp
@@ -0,0 +1,225 @@
+/*******************************************************************************
+* Copyright 2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef RNN_UTILS_HPP
+#define RNN_UTILS_HPP
+
+#include "mkldnn.h"
+
+#include "cpu_rnn_pd.hpp"
+
+
+#define rnn_elemwise_sig(f)                                               \
+    void f(const rnn_utils::rnn_conf_t &rnn, acc_data_t *ws_gates_,   \
+            src_data_t *states_t_l_, float *c_states_t_l_,            \
+            src_data_t *states_tm1_l_, float *c_states_tm1_l_,        \
+            float *diff_states_t_l_, float *diff_states_t_lp1_,       \
+            float *diff_states_tp1_l_, float *bias_, float *ws_grid_, \
+            float *ws_cell_) const
+
+#define rnn_cell_execution_sig(f)                                             \
+    void f(const rnn_utils::rnn_conf_t &rnn, src_data_t *states_t_l_,     \
+            float *c_states_t_l_, float *diff_states_t_l_,                \
+            weights_data_t **w_layer_, weights_data_t **w_iter_,          \
+            float **bias_, src_data_t *states_t_lm1_,                     \
+            src_data_t *states_tm1_l_, float *c_states_tm1_l_,            \
+            float *diff_states_t_lp1_, float *diff_states_tp1_l_,         \
+            float *diff_w_layer_, float *diff_w_iter_, float *diff_bias_, \
+            acc_data_t *ws_gates_, float *ws_grid_, float *ws_cell_) const
+
+#define rnn_grid_execution_sig(f)                                                 \
+    void f(const rnn_utils::rnn_conf_t &rnn, weights_data_t **weights_layer_, \
+            weights_data_t **weights_states_, float **bias_,                  \
+            src_data_t *ws_states_, float *ws_c_states_,                      \
+            float *ws_diff_states_, acc_data_t *ws_gates_, float *ws_cell_,   \
+            float *ws_grid_, float *diff_weights_layer_,                      \
+            float *diff_weights_iter_, float *diff_bias_) const
+
+#define rnn_gemm_sig(f)                                                     \
+    void f(const char transA, const char transB, int m, int n, int k,   \
+            const float alpha, const weights_data_t *a_, const int ldA, \
+            const src_data_t *b_, const int ldB, const float beta,      \
+            acc_data_t *c_, const int ldC) const
+
+#define rnn_bias_prepare_sig(f)                                                  \
+    void f(const rnn_utils::rnn_conf_t &rnn, float **bias_, const float *b_, \
+            float *scratch_bias_) const
+
+#define rnn_bias_finalize_sig(f)                                       \
+    void f(const rnn_utils::rnn_conf_t &rnn, float *scratch_bias_, \
+            const float *w_iter_comp, const float *w_layer_comp) const
+
+#define rnn_weights_assign_sig(f)                                                \
+    void f(const rnn_utils::rnn_conf_t &rnn, const memory_desc_t *md, int nld,   \
+            int ld, int OC_size, int IC_size, const int n_parts,             \
+            const int *gates_per_part, const size_t *part_weights_pack_size, \
+            weights_data_t **weights_, const weights_data_t *w_,             \
+            float **bias_, const float *b_, float *scratch_bias_) const
+
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+namespace rnn_utils {
+
+using namespace mkldnn::impl::utils;
+
+enum execution_direction_t {
+    l2r,
+    r2l,
+    bi_concat,
+    bi_sum,
+};
+
+enum data_type_conf_t {
+    all_f32,
+    u8u8u8f32,
+    f32u8f32f32,
+    u8u8u8u8,
+    f32u8f32u8
+};
+
+struct rnn_conf_t {
+    execution_direction_t exec_dir;
+    data_type_conf_t dt_conf;
+    int n_layer, n_iter, n_dir, n_gates, n_states;
+    int mb;
+    int slc, sic, dic, dlc;
+    int gates_ld, gates_nld, gates_ws_ld;
+    int n_parts_weights_layer, parts_weights_layer[MKLDNN_RNN_MAX_N_PARTS];
+    int n_parts_weights_iter, parts_weights_iter[MKLDNN_RNN_MAX_N_PARTS];
+    int n_bias, n_parts_bias, parts_bias[MKLDNN_RNN_MAX_N_PARTS];
+    size_t part_weights_iter_pack_size[MKLDNN_RNN_MAX_N_PARTS],
+            part_weights_layer_pack_size[MKLDNN_RNN_MAX_N_PARTS];
+    bool weights_layer_is_packed, weights_iter_is_packed;
+    /* Size of packed data in bytes */
+    size_t weights_layer_comp_offset, weights_layer_pack_size,
+        weights_iter_comp_offset, weights_iter_pack_size;
+
+    bool copy_bias;
+    int weights_layer_ld, weights_layer_nld;
+    int diff_weights_layer_ld, diff_weights_layer_nld;
+    int weights_iter_ld, weights_iter_nld;
+    int diff_weights_iter_ld, diff_weights_iter_nld;
+    int states_nld, states_ws_ld;
+    int weights_iter_compensation_size, weights_layer_compensation_size;
+    bool is_fwd, is_training, is_lbr;
+    bool use_workspace;
+
+    /* Size of workspace for each tensor in bytes */
+    size_t ws_gates_size, ws_states_size, ws_c_states_size, ws_diff_states_size,
+            ws_cell_comp_size, ws_grid_comp_size, ws_per_cell, ws_bias_size;
+    bool merge_gemm_iter, merge_gemm_layer, use_jit_gemm, use_layer_packed_gemm,
+        use_iter_packed_gemm;
+};
+
+bool is_ldigo(const memory_desc_wrapper &md);
+bool is_ldgoi(const memory_desc_wrapper &md);
+
+int get_good_ld(int dim, int sizeof_dt);
+
+void init_conf(rnn_conf_t &rnn, const rnn_desc_t &rd,
+        const memory_desc_wrapper &src_layer_d,
+        const memory_desc_wrapper &src_iter_d,
+        const memory_desc_wrapper &weights_layer_d,
+        const memory_desc_wrapper &weights_iter_d,
+        const memory_desc_wrapper &dst_layer_d);
+
+void set_conf(rnn_conf_t &rnn, const rnn_desc_t &rd,
+        const memory_desc_wrapper &weights_layer_d,
+        const memory_desc_wrapper &weights_iter_d,
+        const memory_desc_wrapper &diff_weights_layer_d,
+        const memory_desc_wrapper &diff_weights_iter_d);
+
+void set_offsets(const rnn_conf_t &rnn, size_t &ws_gates_offset,
+        size_t &ws_h_state_offset, size_t &ws_c_state_offset,
+        size_t &ws_diff_states_offset, size_t &ws_grid_comp_offset,
+        size_t &ws_cell_comp_offset, size_t &ws_bias_offset,
+        size_t &scratchpad_size, size_t &workspace_size);
+
+void get_scratchpad_and_workspace_sizes(const rnn_conf_t &rnn,
+        size_t &scratchpad_size, size_t &workspace_size);
+status_t set_expected_desc(
+        rnn_conf_t &rnn, memory_desc_t &weights_md, bool is_iter);
+status_t set_good_strides(memory_desc_t &weights_md, format_tag_t tag);
+
+template <typename T>
+struct ws_gates_aoc {
+    ws_gates_aoc(const rnn_conf_t &rnn, T *data)
+        : gates_(data, rnn.gates_nld, rnn.gates_ws_ld), DIC_(rnn.dic) {}
+    T &operator()(int batch, int gate, int dic) {
+        return gates_(batch, gate * DIC_ + dic);
+    }
+
+private:
+    mkldnn::impl::utils::array_offset_calculator<T, 2> gates_;
+    int DIC_;
+};
+using ws_gates_aoc_t = ws_gates_aoc<float>;
+using ws_gates_aoc_s32_t = ws_gates_aoc<int32_t>;
+
+struct bias_aoc_t {
+    bias_aoc_t(const rnn_conf_t &rnn, const float *data)
+        : bias_(data, rnn.n_bias, rnn.dic) {}
+    const float &operator()(int bias_n, int dic) { return bias_(bias_n, dic); }
+
+private:
+    mkldnn::impl::utils::array_offset_calculator<const float, 2> bias_;
+};
+
+template <typename T>
+struct ws_states_aoc {
+    ws_states_aoc(const rnn_conf_t &rnn, T *data)
+        : state_(data, rnn.states_nld, rnn.states_ws_ld) {}
+    T &operator()(int batch, int dic) { return state_(batch, dic); }
+
+private:
+    mkldnn::impl::utils::array_offset_calculator<T, 2> state_;
+};
+using ws_states_aoc_t = ws_states_aoc<float>;
+using ws_states_aoc_u8_t = ws_states_aoc<uint8_t>;
+
+struct ws_diff_states_aoc_t {
+    ws_diff_states_aoc_t(const rnn_conf_t &rnn, float *data)
+        : diff_states_(data, rnn.n_states + 1, rnn.n_iter + 1, rnn.states_nld,
+                  rnn.states_ws_ld) {}
+    float &operator()(int state_n, int batch, int dic) {
+        return diff_states_(state_n, 0, batch, dic);
+    }
+
+private:
+    mkldnn::impl::utils::array_offset_calculator<float, 4> diff_states_;
+};
+
+struct ws_diff_w_iter_aoc_t {
+    ws_diff_w_iter_aoc_t(const rnn_conf_t &rnn, float *data)
+        : diff_weights_iter_(
+                  data, rnn.diff_weights_iter_nld, rnn.diff_weights_iter_ld)
+        , DIC_(rnn.dic) {}
+    float &operator()(int sic, int gate, int dic) {
+        return diff_weights_iter_(sic, gate * DIC_ + dic);
+    }
+
+private:
+    mkldnn::impl::utils::array_offset_calculator<float, 2> diff_weights_iter_;
+    int DIC_;
+};
+}
+}
+}
+}
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/simple_concat.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/simple_concat.cpp
new file mode 100644
index 0000000000..0420f87aa5
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/simple_concat.cpp
@@ -0,0 +1,126 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn_thread.hpp"
+
+#include "simple_concat.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace memory_tracking::names;
+
+template <data_type_t data_type>
+status_t simple_concat_t<data_type>::execute(const exec_ctx_t &ctx) const {
+    auto scratchpad = this->scratchpad(ctx);
+    auto iptrs = scratchpad.template get<const data_t *>(key_concat_iptrs);
+    auto optrs = scratchpad.template get<data_t *>(key_concat_optrs);
+    auto nelems_to_copy = scratchpad.template get<dim_t>(key_concat_nelems);
+    auto is = scratchpad.template get<strides_t>(key_concat_istrides);
+
+    const int num_arrs = pd()->n_inputs();
+    const int *perm = pd()->perm_, *iperm = pd()->iperm_;
+    const int concat_dim = pd()->concat_dim();
+    auto o_base_ptr = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    for (int a = 0; a < num_arrs; ++a) {
+        const memory_desc_wrapper i_d(pd()->src_md(a));
+        const memory_desc_wrapper o_d(pd()->src_image_md(a));
+
+        iptrs[a] = CTX_IN_MEM(const data_t *, MKLDNN_ARG_MULTIPLE_SRC + a)
+            + i_d.blk_off(0);
+        optrs[a] = o_base_ptr + o_d.blk_off(0);
+        nelems_to_copy[a] = pd()->nelems_to_concat(i_d);
+        for (int i = 0; i < MKLDNN_MAX_NDIMS; i++) {
+            if (i < perm[concat_dim])
+                is[a][i] = size_t(i_d.blocking_desc().strides[iperm[i]]);
+            else
+                is[a][i] = 0;
+        }
+    }
+
+    const memory_desc_wrapper o_d(pd()->src_image_md(0));
+
+    strides_t os = { 0 };
+    for (int i = 0; i < perm[concat_dim]; i++)
+        os[i] = o_d.blocking_desc().strides[iperm[i]];
+
+    dims_t phys_dims;
+    for (size_t i = 0; i < sizeof(phys_dims)/sizeof(phys_dims[0]); i++)
+        phys_dims[i] = (i < (size_t)perm[concat_dim])
+            ?  o_d.dims()[iperm[i]] / pd()->blocks_[iperm[i]] : 1;
+
+    if (perm[concat_dim] == 0) {
+        for (int a = 0; a < num_arrs; ++a) {
+            const data_t *i = &iptrs[a][0];
+            data_t *o = &optrs[a][0];
+            parallel_nd((ptrdiff_t)nelems_to_copy[a],
+                    [&](ptrdiff_t e) { o[e] = i[e]; });
+        }
+    } else {
+        parallel_nd(phys_dims[0], phys_dims[1], phys_dims[2], phys_dims[3],
+            phys_dims[4], num_arrs,
+            [&](dim_t n0, dim_t n1, dim_t n2, dim_t n3, dim_t n4, int a) {
+            // XXX: this code may access uninitialized values in is[*][0-4] --
+            // that's why we have to set them to zero although this is
+            // probably benign
+            size_t in_off = is[a][0] * n0 + is[a][1] * n1 + is[a][2] * n2
+                    + is[a][3] * n3 + is[a][4] * n4;
+            size_t out_off = os[0] * n0 + os[1] * n1 + os[2] * n2
+                    + os[3] * n3 + os[4] * n4;
+            const data_t *i = &iptrs[a][in_off];
+            data_t *o = &optrs[a][out_off];
+#if defined(__GNUC__) && !defined(__INTEL_COMPILER)
+            // The code below performs data copying: o[e] = i[e]
+            // and uses a workaround to make GNU compilers optimize it
+            uint8_t *ptro = reinterpret_cast<uint8_t *>(o);
+            const uint8_t *ptri = reinterpret_cast<const uint8_t *>(i);
+            const dim_t main_part =
+                nelems_to_copy[a] * sizeof(data_t) / sizeof(uint32_t);
+            const dim_t tail_part =
+                nelems_to_copy[a] % sizeof(data_t) / sizeof(uint32_t);
+
+            PRAGMA_OMP_SIMD()
+            for (dim_t e = 0; e < main_part; ++e) {
+                *(reinterpret_cast<uint32_t *>(ptro))
+                    = *(reinterpret_cast<const uint32_t *>(ptri));
+                ptro += sizeof(uint32_t);
+                ptri += sizeof(uint32_t);
+            }
+            for (dim_t e = 0; e < tail_part; ++e) {
+                *ptro = *ptri;
+                ++ptro;
+                ++ptri;
+            }
+#else
+            PRAGMA_OMP_SIMD()
+            for (dim_t e = 0; e < nelems_to_copy[a]; ++e) o[e] = i[e];
+#endif
+        });
+    }
+
+    return status::success;
+}
+
+template struct simple_concat_t<data_type::f32>;
+template struct simple_concat_t<data_type::u8>;
+template struct simple_concat_t<data_type::s8>;
+template struct simple_concat_t<data_type::s32>;
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/simple_concat.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/simple_concat.hpp
new file mode 100644
index 0000000000..5177275452
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/simple_concat.hpp
@@ -0,0 +1,155 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef SIMPLE_CONCAT_HPP
+#define SIMPLE_CONCAT_HPP
+
+#include "memory_tracking.hpp"
+
+#include "cpu_concat_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <data_type_t data_type>
+struct simple_concat_t: public cpu_primitive_t {
+    struct pd_t: public cpu_concat_pd_t {
+        using cpu_concat_pd_t::cpu_concat_pd_t;
+
+        pd_t(const pd_t &rhs): cpu_concat_pd_t(rhs) {
+            int ndims = rhs.dst_md_.ndims;
+            utils::array_copy(perm_, rhs.perm_, ndims);
+            utils::array_copy(iperm_, rhs.iperm_, ndims);
+            utils::array_copy(blocks_, rhs.blocks_, ndims);
+        }
+
+        DECLARE_CONCAT_PD_T("simple:any", simple_concat_t);
+
+        status_t init() {
+            const memory_desc_wrapper dst_d(dst_md());
+            bool ok = true
+                && cpu_concat_pd_t::init() == status::success
+                && dst_d.ndims() <= 6;
+            if (!ok) return status::unimplemented;
+
+            for (size_t i = 0; i < src_mds_.size(); ++i) {
+                const memory_desc_wrapper i_d(&src_mds_[i]);
+                const memory_desc_wrapper o_d(&src_image_mds_[i]);
+
+                const int ignore_strides = 0;
+
+                ok = ok
+                    && utils::everyone_is(data_type, i_d.data_type(),
+                            o_d.data_type())
+                    && utils::everyone_is(format_kind::blocked,
+                            i_d.format_kind(), o_d.format_kind())
+                    && types::blocking_desc_is_equal(i_d.blocking_desc(),
+                            o_d.blocking_desc(), ignore_strides)
+                    && types::blocking_desc_is_equal(i_d.blocking_desc(),
+                            dst_d.blocking_desc(), ignore_strides)
+                    && !i_d.is_additional_buffer();
+                if (!ok) return status::unimplemented;
+            }
+
+            dst_d.compute_blocks(blocks_);
+            format_perm();
+
+            // start dim is the first dimension after which the concatenation
+            // would happen contiguously
+            const int start_dim = perm_[concat_dim()];
+
+            // check that contiguous part is indeed contiguous (i.e. dense)
+            if (nelems_to_concat(dst_d) !=
+                    dst_d.padded_dims()[concat_dim()] / blocks_[concat_dim()]
+                    * dst_d.blocking_desc().strides[concat_dim()])
+                return status::unimplemented;
+
+            // check that all inputs have the same strides for the
+            // contiguous part [concat_dim .. ndims] for the *major* dims.
+            // the block part is already checked above
+            for (size_t i = 0; i < src_mds_.size(); ++i) {
+                const memory_desc_wrapper i_d(&src_mds_[i]);
+                for (int d = start_dim; d < dst_d.ndims(); ++d) {
+                    if (dst_d.blocking_desc().strides[iperm_[d]]
+                            != i_d.blocking_desc().strides[iperm_[d]])
+                        return status::unimplemented;
+                }
+            }
+
+            init_scratchpad();
+
+            return status::success;
+        }
+
+        int perm_[MKLDNN_MAX_NDIMS];
+        int iperm_[MKLDNN_MAX_NDIMS];
+        dims_t blocks_;
+
+        dim_t nelems_to_concat(const memory_desc_wrapper &data_d) const {
+            const int ndims = data_d.ndims();
+
+            dim_t nelems = 1;
+            for (int i = perm_[concat_dim()]; i < ndims; i++)
+                nelems *= data_d.dims()[iperm_[i]] / blocks_[iperm_[i]];
+            for (int i = 0; i < ndims; i++)
+                nelems *= blocks_[i];
+
+            return nelems;
+        }
+
+    private:
+        void format_perm() {
+            const memory_desc_wrapper dst_d(dst_md());
+            const int ndims = dst_d.ndims();
+
+            strides_t strides;
+            utils::array_copy(strides, dst_d.blocking_desc().strides, ndims);
+            for (int i = 0; i < ndims; i++) iperm_[i] = i;
+
+            utils::simultaneous_sort(strides, iperm_, ndims,
+                    [](stride_t a, stride_t b) { return b - a; });
+
+            for (int i = 0; i < ndims; i++) perm_[iperm_[i]] = i;
+        }
+
+        void init_scratchpad() {
+            using namespace memory_tracking::names;
+            auto scratchpad = scratchpad_registry().registrar();
+            scratchpad.book(key_concat_iptrs, sizeof(data_t *) * n_inputs());
+            scratchpad.book(key_concat_optrs, sizeof(data_t *) * n_inputs());
+            scratchpad.book(key_concat_nelems, sizeof(dim_t) * n_inputs());
+            scratchpad.book(key_concat_istrides,
+                    sizeof(strides_t) * n_inputs());
+        }
+    };
+
+    simple_concat_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override;
+
+    typedef typename prec_traits<data_type>::type data_t;
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/simple_q10n.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/simple_q10n.hpp
new file mode 100644
index 0000000000..e6c3b8d7af
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/simple_q10n.hpp
@@ -0,0 +1,98 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_SIMPLE_Q10N_HPP
+#define CPU_SIMPLE_Q10N_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "math_utils.hpp"
+#include "nstl.hpp"
+#include "type_helpers.hpp"
+#include "utils.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::math;
+
+template <typename out_t>
+inline out_t round_and_saturate(float f)
+{ return math::saturate<out_t>(out_round<int>(f)); }
+
+/* Quantization with alpha == 1 and beta == 0 */
+template <typename in_t, typename out_t, typename enabled = void>
+struct qz_a1b0 {
+    out_t operator()(in_t in)
+    { return round_and_saturate<out_t>((float)in); }
+};
+
+template <typename in_t, typename out_t>
+struct qz_a1b0<in_t, out_t,
+    typename utils::enable_if<true
+        && nstl::is_integral<in_t>::value
+        && !is_subset<in_t, out_t>::value
+    >::type> {
+    out_t operator()(in_t in) { return math::saturate<out_t>(in); }
+};
+
+template <typename in_t, typename out_t>
+struct qz_a1b0<in_t, out_t,
+    typename utils::enable_if<is_subset<in_t, out_t>::value>::type> {
+    out_t operator()(in_t in) { return (out_t)in; }
+};
+
+/* Quantization with alpha == 1 */
+template <typename in_t, typename out_t> struct qz_a1 {
+    out_t operator()(in_t in, out_t out, float beta)
+    { return round_and_saturate<out_t>((float)in + beta * out); }
+};
+
+template <typename in_t> struct qz_a1<in_t, float> {
+    float operator()(in_t in, float out, float beta)
+    { return (float)in + beta * out; }
+};
+
+/* Quantization with beta == 0 */
+template <typename in_t, typename out_t> struct qz_b0 {
+    out_t operator()(in_t in, float alpha)
+    { return round_and_saturate<out_t>(alpha * in); }
+};
+
+template <typename in_t> struct qz_b0<in_t, float> {
+    float operator()(in_t in, float alpha) { return alpha * in; }
+};
+
+/* Quantization */
+template <typename in_t, typename out_t> struct qz {
+    out_t operator()(in_t in, out_t out, float alpha, float beta) {
+        return round_and_saturate<out_t>(
+                alpha * in + (beta ? beta * out : 0));
+    }
+};
+
+template <typename in_t> struct qz<in_t, float> {
+    float operator()(in_t in, float out, float alpha, float beta)
+    { return alpha * in + (beta ? beta * out : 0); }
+};
+
+}
+}
+}
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/simple_reorder.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/simple_reorder.hpp
new file mode 100644
index 0000000000..ff845f5bd3
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/simple_reorder.hpp
@@ -0,0 +1,1022 @@
+/*******************************************************************************
+* Copyright 2016-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef CPU_SIMPLE_REORDER_HPP
+#define CPU_SIMPLE_REORDER_HPP
+
+#include <assert.h>
+
+#include "c_types_map.hpp"
+#include "type_helpers.hpp"
+#include "math_utils.hpp"
+#include "mkldnn_thread.hpp"
+#include "utils.hpp"
+
+#include "tag_traits.hpp"
+#include "cpu_reorder_pd.hpp"
+#include "cpu_primitive.hpp"
+
+#include "simple_q10n.hpp"
+#include "cpu_isa_traits.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+using namespace mkldnn::impl::status;
+using namespace mkldnn::impl::format_tag;
+using namespace mkldnn::impl::data_type;
+
+using bd = block_dim_t;
+using ib = inner_blk_t;
+
+using namespace mkldnn::impl::utils;
+using math::saturate;
+
+template<impl::data_type_t type>
+using data_t = typename prec_traits<type>::type;
+
+template<impl::data_type_t type_i, impl::data_type_t type_o>
+using _qz_a1b0 = qz_a1b0<data_t<type_i>, data_t<type_o>>;
+
+template<impl::data_type_t type_i, impl::data_type_t type_o>
+using _qz = qz<data_t<type_i>, data_t<type_o>>;
+
+namespace fmt_order {
+    const bool keep = true;
+    const bool reverse = false;
+    const bool any = keep;
+}
+
+namespace spec {
+struct direct_copy {};
+struct direct_copy_except_dim_0 {};
+struct reference {};
+struct conv_s8s8 {};
+}
+
+#define SIMPLE_REORDER_TEMPL_DECL \
+    impl::data_type_t type_i, impl::format_tag_t tag_i, \
+    impl::data_type_t type_o, impl::format_tag_t tag_o, bool order_keep
+#define SIMPLE_REORDER_TEMPL_CALL \
+    type_i, tag_i, type_o, tag_o, order_keep
+
+#define DECLARE_COMMON_PARAMS() \
+        const memory_desc_wrapper &input_d = pd->src_md(); \
+        const memory_desc_wrapper &output_d = pd->dst_md(); \
+        const float alpha = pd->alpha(); MAYBE_UNUSED(alpha); \
+        const float beta = pd->beta(); MAYBE_UNUSED(beta);
+
+/* specific reorders: common template */
+template <SIMPLE_REORDER_TEMPL_DECL, typename spec = void>
+struct simple_reorder_impl {};
+
+namespace {
+inline bool simple_fmt_check(bool order_keep, impl::format_tag_t tag_i,
+        impl::format_tag_t tag_o, const memory_desc_wrapper &input_d,
+        const memory_desc_wrapper &output_d) {
+    return input_d.matches_tag(order_keep ? tag_i : tag_o)
+        && output_d.matches_tag(order_keep ? tag_o : tag_i);
+}
+inline bool simple_attr_check(const primitive_attr_t *attr, bool many_scales_support) {
+    if (many_scales_support)
+        return true;
+    return IMPLICATION(attr, attr->output_scales_.mask_ == 0);
+}
+}
+
+/* specific reorders: implementation */
+template <SIMPLE_REORDER_TEMPL_DECL>
+struct simple_reorder_impl<SIMPLE_REORDER_TEMPL_CALL,
+typename utils::enable_if<tag_i == any && (false
+    || tag_o == hwio
+    || tag_o == hwigo)
+    , spec::conv_s8s8>::type>
+{
+    static bool is_applicable(const memory_desc_wrapper &input_d,
+            const memory_desc_wrapper &output_d, const primitive_attr_t *attr)
+    {
+        const size_t D_mask = utils::array_product(input_d.dims(),
+                                math::ilog2q(attr->output_scales_.mask_ + 1));
+        const int oc = (input_d.dims()[tag_o == hwigo + 0]);
+        const int g = (tag_o == hwigo) ? (input_d.dims()[0]) : 1;
+
+        return output_d.matches_tag(tag_o)
+            && (output_d.extra().flags & memory_extra_flags::compensation_conv_s8s8)
+            && (input_d.data_type() == f32 || input_d.data_type() == s8)
+            && output_d.data_type() == s8
+            && (D_mask == 1 || D_mask == (size_t)g * oc);
+    }
+
+    static status_t execute(const cpu_reorder_pd_t *pd,
+        const data_t<type_i> *input, data_t<type_o> *output) {
+        DECLARE_COMMON_PARAMS();
+
+        static constexpr bool w_groups = tag_o == hwigo;
+
+        const auto &dims = input_d.dims();
+        const auto &pdims = output_d.padded_dims();
+
+        const int G = w_groups ? dims[0] : 1;
+        const int OC = dims[w_groups + 0];
+        const int IC = dims[w_groups + 1];
+        const int H = dims[w_groups + 2];
+        const int W = dims[w_groups + 3];
+
+        const float *scales = pd->attr()->output_scales_.scales_;
+        const size_t D_mask = utils::array_product(input_d.dims(),
+                math::ilog2q(pd->attr()->output_scales_.mask_ + 1));
+
+        assert(output_d.extra().flags
+                & memory_extra_flags::compensation_conv_s8s8);
+        float adj_scale =
+            (output_d.extra().flags & memory_extra_flags::scale_adjust)
+            ? output_d.extra().scale_adjust : 1.f;
+
+        size_t offset = G * pdims[w_groups + 0] * pdims[w_groups + 1] * H * W;
+        int32_t *cp = reinterpret_cast<int32_t *>(output + offset);
+
+        parallel_nd(G, OC, [&](int g, int oc) {
+            cp[g * OC + oc] = 0;
+            for (int ic = 0; ic < IC; ic++)
+            for (int h = 0; h < H; h++)
+            for (int w = 0; w < W; w++) {
+                auto i = input[input_d.blk_off<!w_groups>(g, oc, ic, h, w)];
+                auto &o = output[output_d.blk_off<!w_groups>(g, oc, ic, h, w)];
+                const float s = scales[(D_mask == 1) ? 0 : g * OC + oc];
+
+                o = qz_b0<data_t<type_i>, data_t<type_o>>()(
+                    i, s * adj_scale);
+                cp[g * OC + oc] -= (int32_t)o;
+            }
+            cp [g * OC + oc] *= 128;
+        });
+        return success;
+    }
+};
+
+template <SIMPLE_REORDER_TEMPL_DECL>
+struct simple_reorder_impl<SIMPLE_REORDER_TEMPL_CALL,
+    typename utils::enable_if<
+          (tag_i == goiw && tag_o == gOIw4i16o4i)
+       || (tag_i == oiw && tag_o == OIw4i16o4i)
+       || (tag_i == goihw && tag_o == gOIhw4i16o4i)
+       || (tag_i == oihw && tag_o == OIhw4i16o4i)
+       || (tag_i == goihw && tag_o == gOIhw2i8o4i)
+       || (tag_i == goihw && tag_o == gOIhw4o4i)
+    , spec::conv_s8s8>::type>
+{
+    static bool is_applicable(const memory_desc_wrapper &input_d,
+            const memory_desc_wrapper &output_d, const primitive_attr_t *attr)
+    {
+        const size_t D_mask = utils::array_product(input_d.dims(),
+                                math::ilog2q(attr->output_scales_.mask_ + 1));
+        const bool w_groups = !utils::one_of(tag_o, OIw4i16o4i, OIhw4i16o4i);
+        const int oc = (input_d.dims()[w_groups ? 1 : 0]);
+        const int g = w_groups ? input_d.dims()[0] : 1;
+
+        return input_d.matches_tag(tag_i)
+            && output_d.matches_tag(tag_o)
+            && (output_d.extra().flags & memory_extra_flags::compensation_conv_s8s8)
+            && (input_d.data_type() == f32 || input_d.data_type() == s8)
+            && output_d.data_type() == s8
+            && (D_mask == 1 || D_mask == (size_t)g * oc);
+    }
+
+    static status_t execute(const cpu_reorder_pd_t *pd,
+        const data_t<type_i> *input, data_t<type_o> *output) {
+        DECLARE_COMMON_PARAMS();
+
+        static constexpr bool w_groups =
+            !utils::one_of(tag_o, OIw4i16o4i, OIhw4i16o4i);
+        constexpr int is_1d =
+            utils::one_of(tag_o, gOIw4i16o4i, OIw4i16o4i);
+        constexpr int blksize = tag_traits<tag_o>::inner_blks == ib::_4b4c
+            ? 4
+            : tag_traits<tag_o>::inner_blks == ib::_2c8b4c
+            ? 8
+            : 16;
+
+        const auto &_g_oihw_d = order_keep ? input_d : output_d;
+        const auto &dims = input_d.dims();
+        const auto &pdims = order_keep
+            ? output_d.padded_dims()
+            : input_d.padded_dims();
+
+        const int G = w_groups ? dims[0] : 1;
+        const int OC = dims[w_groups + 0];
+        const int NB_OC = pdims[w_groups + 0] / blksize;
+        const int IC = dims[w_groups + 1];
+        const int NB_IC = pdims[w_groups + 1] / blksize;
+        const int H = is_1d ? 1 : dims[w_groups + 2];
+        const int W = dims[w_groups + 3 - is_1d];
+
+        const float *scales = pd->attr()->output_scales_.scales_;
+        const size_t D_mask = utils::array_product(input_d.dims(),
+                            math::ilog2q(pd->attr()->output_scales_.mask_ + 1));
+
+        assert(output_d.extra().flags
+                & memory_extra_flags::compensation_conv_s8s8);
+        float adj_scale =
+            (output_d.extra().flags & memory_extra_flags::scale_adjust)
+            ? output_d.extra().scale_adjust : 1.f;
+
+        auto ker = [&](const data_t<type_i> *inp, data_t<type_o> *out,
+            int32_t *c, const float *s, const int oc_block, const int ic_block) {
+#           define index AB_or_BC_blk_off<tag_traits<tag_o>::inner_blks>
+
+            for (int ic = 0; ic < ic_block; ++ic) {
+            for (int oc = 0; oc < oc_block; ++oc) {
+                const auto _g_oihw_off =
+                    oc * _g_oihw_d.blocking_desc().strides[w_groups + 0]
+                  + ic * _g_oihw_d.blocking_desc().strides[w_groups + 1];
+                out[index(oc, ic)]
+                    = qz_b0<data_t<type_i>, data_t<type_o>>()(
+                            inp[_g_oihw_off], s[oc] * adj_scale);
+                c[oc] -= (128 * (int32_t)(out[index(oc, ic)]));
+            }
+            }
+#           undef index
+        };
+
+        constexpr int i_mult = blksize;
+        constexpr int o_mult = 1;
+
+        size_t offset = G * pdims[w_groups+0] * pdims[w_groups+1] * H * W;
+        int32_t *cp = reinterpret_cast<int32_t *>(output + offset);
+        parallel_nd(G * NB_OC * blksize, [&](int i) {
+            cp[i] = 0;
+        });
+
+#       define wei_blk_off(md, g, o, i, h, w) \
+        (is_1d ? (md).blk_off<!w_groups>(g, o, i, w) \
+         : (md).blk_off<!w_groups>(g, o, i, h, w))
+
+        parallel_nd(G, NB_OC, [&](int g, int O) {
+            for (int I = 0; I < NB_IC; I++)
+                for (int h = 0; h < H; h++)
+                for (int w = 0; w < W; w++) {
+                    auto i = &input[wei_blk_off(
+                            input_d, g, i_mult * O, i_mult * I, h, w)];
+                    auto o = &output[wei_blk_off(
+                            output_d, g, o_mult * O, o_mult * I, h, w)];
+                    const int oc_block = nstl::min(blksize, OC - O * blksize);
+                    const int ic_block = nstl::min(blksize, IC - I * blksize);
+
+                    int _offset = (g * NB_OC + O) * blksize;
+                    ker(i, o, (order_keep) ? &cp[_offset] : nullptr,
+                            &scales[(D_mask == 1) ? 0 : _offset],
+                                        oc_block, ic_block);
+                }
+        });
+
+#       undef wei_blk_off
+
+        return success;
+    }
+};
+
+template <SIMPLE_REORDER_TEMPL_DECL>
+struct simple_reorder_impl<SIMPLE_REORDER_TEMPL_CALL,
+    typename utils::enable_if<false
+    ||(tag_i == goiw && tag_o == Goiw16g)
+    ||(tag_i == goihw && tag_o == Goihw16g)
+    , spec::conv_s8s8>::type>
+{
+    static bool is_applicable(const memory_desc_wrapper &input_d,
+            const memory_desc_wrapper &output_d, const primitive_attr_t *attr) {
+        const size_t D_mask = utils::array_product(input_d.dims(),
+                            math::ilog2q(attr->output_scales_.mask_ + 1));
+        const int oc = input_d.dims()[1];
+        const int g = input_d.dims()[0];
+
+        return true
+            && order_keep
+            && input_d.matches_tag(tag_i)
+            && output_d.matches_tag(tag_o)
+            && (output_d.extra().flags & memory_extra_flags::compensation_conv_s8s8)
+            && (input_d.data_type() == f32 || input_d.data_type() == s8)
+            && output_d.data_type() == s8
+            && (D_mask == 1 || D_mask == (size_t)g * oc);
+    }
+
+    static status_t execute(const cpu_reorder_pd_t *pd,
+            const data_t<type_i> *input, data_t<type_o> *output) {
+        DECLARE_COMMON_PARAMS();
+
+        constexpr bool is_1d = tag_i == goiw;
+        constexpr int blksize = 16;
+
+        const auto &dims = input_d.dims();
+        const auto &pdims = output_d.padded_dims();
+        const int G = dims[0];
+        const int Gp = pdims[0];
+        const int OC = dims[1];
+        const int IC = dims[2];
+        const int H = is_1d ? 1 : dims[3];
+        const int W = dims[4 - is_1d];
+
+        const size_t D_mask = utils::array_product(input_d.dims(),
+                            math::ilog2q(pd->attr()->output_scales_.mask_ + 1));
+        const float *scales = pd->attr()->output_scales_.scales_;
+
+        assert(output_d.extra().flags
+                & memory_extra_flags::compensation_conv_s8s8);
+        float adj_scale =
+            (output_d.extra().flags & memory_extra_flags::scale_adjust)
+            ? output_d.extra().scale_adjust : 1.f;
+
+        auto ker = [&](const data_t<type_i> *inp, data_t<type_o> *out,
+                int32_t *cp, const float *s, const int g_block) {
+            PRAGMA_OMP_SIMD()
+            for (int g = 0; g < g_block; g++) {
+                const auto i_off = g * input_d.blocking_desc().strides[0];
+                out[g] = qz_b0<data_t<type_i>, data_t<type_o>>()(
+                        inp[i_off], s[g * OC] * adj_scale);
+                cp[g * OC] -= 128 * (int32_t)(out[g]);
+            }
+        };
+
+        size_t cp_offset = output_d.size() - output_d.additional_buffer_size();
+        int32_t *cp = reinterpret_cast<int32_t *>(output + cp_offset);
+        parallel_nd((Gp/blksize) * OC, [&](int ib) {
+            PRAGMA_OMP_SIMD()
+            for (int i = 0; i < blksize; i++)
+                cp[ib * blksize + i] = 0;
+        });
+
+#       define wei_blk_off(md, g, o, i, h, w) \
+        (is_1d ? (md).blk_off(g, o, i, w) : (md).blk_off(g, o, i, h, w))
+
+        parallel_nd(Gp/blksize, OC, [&](int gb, int O) {
+                for (int I = 0; I < IC; I++) {
+                    for (int h = 0; h < H; h++)
+                    for (int w = 0; w < W; w++)
+                    {
+                        const int g_block = nstl::min(G - gb * blksize, blksize);
+                        const auto inp = &input[wei_blk_off(
+                                input_d, gb * blksize, O, I, h, w)];
+                        const auto out = &output[wei_blk_off(
+                                output_d, gb, O, I, h, w)];
+                        int offset = gb * blksize + O;
+                        ker(inp, out, &cp[offset],
+                            &scales[(D_mask == 1) ? 0 : offset], g_block);
+                   }
+               }
+        });
+
+#       undef wei_blk_off
+
+        return success;
+    }
+};
+
+/* reorders with tail support */
+
+template <SIMPLE_REORDER_TEMPL_DECL>
+struct simple_reorder_impl<SIMPLE_REORDER_TEMPL_CALL,
+typename utils::enable_if<false
+    || (tag_i == nCdhw8c && tag_o == nCdhw16c)
+    || (tag_i == nChw8c && tag_o == nChw16c)
+    || (tag_i == nCw8c && tag_o == nCw16c)
+    >::type>
+{
+    static bool is_applicable(const memory_desc_wrapper &input_d,
+            const memory_desc_wrapper &output_d, const primitive_attr_t *attr)
+    {
+        return simple_fmt_check(order_keep, tag_i, tag_o, input_d, output_d)
+            && simple_attr_check(attr, false);
+    }
+
+    static status_t execute(const cpu_reorder_pd_t *pd,
+        const data_t<type_i> *input, data_t<type_o> *output) {
+        DECLARE_COMMON_PARAMS();
+
+        constexpr int is_1d = tag_i == nCw8c;
+        constexpr int is_3d = tag_i == nCdhw8c;
+        constexpr int blksize_16 = 16;
+        constexpr int blksize_8 = 8;
+        constexpr int ic_mult = order_keep ? 2 : 1;
+        constexpr int oc_mult = order_keep ? 1 : 2;
+
+        const auto &dims = input_d.dims();
+        const auto &pdims = order_keep ? output_d.padded_dims()
+                                       : input_d.padded_dims();
+
+        const int C = dims[1];
+        const int D = is_3d ? dims[2] : 1;
+        const int H = is_1d ? 1 : dims[2 + is_3d];
+        const int W = dims[3 + is_3d - is_1d];
+
+        auto ker = [&](const data_t<type_i> *i, data_t<type_o> *o,
+            const int block_16) {
+            const int nb = (block_16 - 1) / blksize_8 + 1;
+            if (alpha == 1.0 && beta == 0.0) {
+                for (int b = 0; b < nb; ++b) {
+                    const ptrdiff_t i_off = order_keep ? b : b * blksize_8;
+                    const ptrdiff_t o_off = order_keep ? b * blksize_8 : b;
+                    const int block_8 = nstl::min(blksize_8,
+                                                  block_16 - b * blksize_8);
+                    for (int c = 0; c < block_8; ++c) {
+                        o[o_off + c] = _qz_a1b0<type_i, type_o>()(
+                                i[i_off + c]);
+                    }
+                }
+            } else {
+                for (int b = 0; b < nb; ++b) {
+                    const ptrdiff_t i_off = order_keep ? b : b * blksize_8;
+                    const ptrdiff_t o_off = order_keep ? b * blksize_8 : b;
+                    const int block_8 = nstl::min(blksize_8,
+                                                  block_16 - b * blksize_8);
+                    for (int c = 0; c < block_8; ++c) {
+                        o[o_off + c] = _qz<type_i, type_o>()(i[i_off + c],
+                                o[o_off + c], alpha, beta);
+                    }
+                }
+            }
+        };
+
+#       define data_blk_off(md, n, c, d, h, w) \
+        ( is_1d ? (md).blk_off(n, c, w) \
+          : is_3d ? (md).blk_off(n, c, d, h, w) : (md).blk_off(n, c, h, w))
+
+        parallel_nd(dims[0], pdims[1] / blksize_16, D, H, W,
+            [&](int n, int nb_c, int d, int h, int w) {
+            auto i = &input[data_blk_off(input_d, n, ic_mult * nb_c, d, h, w)];
+            auto o = &output[data_blk_off(output_d, n, oc_mult * nb_c, d, h, w)];
+            const int block_16 = nstl::min(blksize_16, C - nb_c * blksize_16);
+            ker(i, o, block_16);
+        });
+
+#       undef data_blk_off
+
+        return success;
+    }
+};
+
+#define PLAIN_TO_BLOCKED_IS_APPLICABLE() \
+    static bool is_applicable(const memory_desc_wrapper &input_d, \
+        const memory_desc_wrapper &output_d, const primitive_attr_t *attr) { \
+        return simple_attr_check(attr, false) && (order_keep \
+                ? output_d.matches_tag(tag_o) && input_d.is_plain() \
+                : input_d.matches_tag(tag_o) && output_d.is_plain()); \
+    }
+
+template <SIMPLE_REORDER_TEMPL_DECL>
+struct simple_reorder_impl<SIMPLE_REORDER_TEMPL_CALL,
+typename utils::enable_if<tag_i == any
+    && (tag_traits<tag_o>::block_dims == bd::_A
+            || tag_traits<tag_o>::block_dims == bd::_B)
+    && tag_traits<tag_o>::ndims >= 3
+    && tag_traits<tag_o>::ndims <= 6
+    >::type>
+{
+    PLAIN_TO_BLOCKED_IS_APPLICABLE();
+
+    static status_t execute(const cpu_reorder_pd_t *pd,
+        const data_t<type_i> *input, data_t<type_o> *output) {
+        DECLARE_COMMON_PARAMS();
+
+        const auto &flat_d = order_keep ? input_d : output_d;
+        const auto &block_d = order_keep ? output_d : input_d;
+        const auto &dims = input_d.dims();
+        const auto &pdims = block_d.padded_dims();
+
+        constexpr int ndims = tag_traits<tag_o>::ndims;
+        constexpr int blk_idx = tag_traits<tag_o>::block_dims == bd::_A ? 0 : 1;
+
+        const dim_t H0 = dims[0];
+        const dim_t H1 = dims[1];
+        const dim_t M0 = ndims >= 6 ? dims[ndims - 4] : 1;
+        const dim_t M1 = ndims >= 5 ? dims[ndims - 3] : 1;
+        const dim_t M2 = ndims >= 4 ? dims[ndims - 2] : 1;
+        const dim_t L = dims[ndims - 1];
+        const dim_t l_blk_stride = block_d.blocking_desc().strides[ndims - 1];
+
+        constexpr int blksize = false ? 0
+            : utils::one_of(tag_traits<tag_o>::inner_blks, ib::_4a, ib::_4b) ? 4
+            : utils::one_of(tag_traits<tag_o>::inner_blks, ib::_8a, ib::_8b) ? 8
+            : 16;
+
+        auto ker = [&](const data_t<type_i> *i, data_t<type_o> *o, int block) {
+            if (alpha == 1.0 && beta == 0.0) {
+                for (int l = 0; l < L; ++l)
+                for (int blk = 0; blk < block; ++blk) {
+                    const dim_t flat_off = 0
+                        + blk * flat_d.blocking_desc().strides[blk_idx]
+                        + l * flat_d.blocking_desc().strides[ndims - 1];
+                    if (order_keep) {
+                        o[l * l_blk_stride + blk] = _qz_a1b0<type_i, type_o>()(
+                                i[flat_off]);
+                    } else {
+                        o[flat_off] = _qz_a1b0<type_i, type_o>()(
+                                i[l * l_blk_stride + blk]);
+                    }
+                }
+            } else {
+                for (int l = 0; l < L; ++l)
+                for (int blk = 0; blk < block; ++blk) {
+                    const dim_t flat_off = 0
+                        + blk * flat_d.blocking_desc().strides[blk_idx]
+                        + l * flat_d.blocking_desc().strides[ndims - 1];
+                    if (order_keep) {
+                        o[l * l_blk_stride + blk] = _qz<type_i, type_o>()(
+                                i[flat_off], o[l * blksize + blk],
+                                alpha, beta);
+                    } else {
+                        o[flat_off] = _qz<type_i, type_o>()(
+                                i[l * l_blk_stride + blk], o[flat_off],
+                                alpha, beta);
+                    }
+                }
+            }
+        };
+
+#       define off(md, h0, h1, m0, m1, m2) \
+        (ndims >= 6 ? (md).blk_off(h0, h1, m0, m1, m2) \
+         : ndims >= 5 ? (md).blk_off(h0, h1, m1, m2) \
+         : ndims >= 4 ? (md).blk_off(h0, h1, m2) \
+         : /* ndims >= 3 ? */ (md).blk_off(h0, h1))
+
+        constexpr int i_mult = order_keep ? blksize : 1;
+        constexpr int o_mult = order_keep ? 1 : blksize;
+
+        if (blk_idx == 0) {
+            const dim_t BH0 = pdims[0] / blksize;
+            parallel_nd(BH0, H1, M0, M1, M2,
+                [&](dim_t bh0, dim_t h1, dim_t m0, dim_t m1, dim_t m2) {
+                auto i = &input[off(input_d, bh0 * i_mult, h1, m0, m1, m2)];
+                auto o = &output[off(output_d, bh0 * o_mult, h1, m0, m1, m2)];
+                const int block = nstl::min<int>(blksize, H0 - bh0 * blksize);
+                ker(i, o, block);
+            });
+        } else if (blk_idx == 1) {
+            const dim_t BH1 = pdims[1] / blksize;
+            parallel_nd(H0, BH1, M0, M1, M2,
+                [&](dim_t h0, dim_t bh1, dim_t m0, dim_t m1, dim_t m2) {
+                auto i = &input[off(input_d, h0, bh1 * i_mult, m0, m1, m2)];
+                auto o = &output[off(output_d, h0, bh1 * o_mult, m0, m1, m2)];
+                const int block = nstl::min<int>(blksize, H1 - bh1 * blksize);
+                ker(i, o, block);
+            });
+        } else {
+            assert(!"unimplemented");
+        }
+
+#       undef off
+
+        return success;
+    }
+};
+
+template <SIMPLE_REORDER_TEMPL_DECL>
+struct simple_reorder_impl<SIMPLE_REORDER_TEMPL_CALL,
+typename utils::enable_if<tag_i == any
+    && (tag_traits<tag_o>::block_dims == bd::_AB
+            || tag_traits<tag_o>::block_dims == bd::_BC)
+    && IMPLICATION(tag_traits<tag_o>::block_dims == bd::_AB,
+            tag_traits<tag_o>::ndims >= 3 && tag_traits<tag_o>::ndims <= 5)
+    && IMPLICATION(tag_traits<tag_o>::block_dims == bd::_BC,
+            tag_traits<tag_o>::ndims >= 4 && tag_traits<tag_o>::ndims <= 6)
+    >::type>
+{
+    PLAIN_TO_BLOCKED_IS_APPLICABLE();
+
+    static status_t execute(const cpu_reorder_pd_t *pd,
+        const data_t<type_i> *input, data_t<type_o> *output) {
+        DECLARE_COMMON_PARAMS();
+
+        const auto &flat_d = order_keep ? input_d : output_d;
+        const auto &dims = input_d.dims();
+        const auto &pdims = order_keep
+            ? output_d.padded_dims()
+            : input_d.padded_dims();
+
+        constexpr int ndims = tag_traits<tag_o>::ndims;
+
+        static constexpr bool with_g = tag_traits<tag_o>::block_dims == bd::_BC;
+        const dim_t G = with_g ? dims[0] : 1;
+
+        const dim_t H0 = dims[0 + with_g];
+        const dim_t H1 = dims[1 + with_g];
+
+        const dim_t M0 = ndims >= 5 + with_g ? dims[ndims - 3] : 1;
+        const dim_t M1 = ndims >= 4 + with_g ? dims[ndims - 2] : 1;
+        const dim_t M2 = ndims >= 3 + with_g ? dims[ndims - 1] : 1;
+
+        constexpr int blksize_0 = false ? 0
+            : utils::one_of(tag_traits<tag_o>::inner_blks,
+                    ib::_4b4a, ib::_4b4c, ib::_4c4b)
+            ? 4
+            : utils::one_of(tag_traits<tag_o>::inner_blks,
+                    ib::_8a8b, ib::_8b8a, ib::_8b8c, ib::_8c8b, ib::_2c8b4c)
+            ? 8
+            : utils::one_of(tag_traits<tag_o>::inner_blks,
+                    ib::_16a16b, ib::_16a4b, ib::_16b16a, ib::_16b4c,
+                    ib::_16b16c, ib::_16c16b, ib::_8a16b2a, ib::_4b16a4b,
+                    ib::_8b16a2b, ib::_8b16c2b, ib::_4c16b4c, ib::_8c16b2c)
+            ? 16 : INT_MIN;
+
+        constexpr int blksize_1 = utils::one_of(tag_traits<tag_o>::inner_blks,
+                    ib::_8a8b, ib::_8b8a, ib::_8b8c, ib::_8c8b, ib::_2c8b4c)
+            ? 8
+            : utils::one_of(tag_traits<tag_o>::inner_blks,
+                    ib::_16a16b, ib::_16b16a, ib::_16b16c, ib::_16c16b,
+                    ib::_8a16b2a, ib::_4b16a4b, ib::_8b16a2b, ib::_8b16c2b,
+                    ib::_4c16b4c, ib::_8c16b2c)
+            ? 16
+            : utils::one_of(tag_traits<tag_o>::inner_blks,
+                    ib::_4b4a, ib::_4b4c, ib::_4c4b,
+                    ib::_16a4b, ib::_16b4c)
+            ? 4
+            : INT_MIN;
+
+        const dim_t NB_H0 = pdims[0 + with_g] / blksize_0;
+        const dim_t NB_H1 = pdims[1 + with_g] / blksize_1;
+
+        auto ker = [&](const data_t<type_i> *i, data_t<type_o> *o,
+            const int block_h0, const int block_h1) {
+#           define blk_off AB_or_BC_blk_off<tag_traits<tag_o>::inner_blks>
+
+            if (alpha == 1.0 && beta == 0.0) {
+                for (int h0 = 0; h0 < block_h0; ++h0)
+                for (int h1 = 0; h1 < block_h1; ++h1) {
+                    const dim_t flat_off = 0
+                        + h0 * flat_d.blocking_desc().strides[with_g + 0]
+                        + h1 * flat_d.blocking_desc().strides[with_g + 1];
+                    if (order_keep) {
+                        o[blk_off(h0, h1)] = _qz_a1b0<type_i, type_o>()(
+                                i[flat_off]);
+                    } else {
+                        o[flat_off] = _qz_a1b0<type_i, type_o>()(
+                                i[blk_off(h0, h1)]);
+                    }
+                }
+            } else {
+                for (int h0 = 0; h0 < block_h0; ++h0)
+                for (int h1 = 0; h1 < block_h1; ++h1) {
+                    const dim_t flat_off = 0
+                        + h0 * flat_d.blocking_desc().strides[with_g + 0]
+                        + h1 * flat_d.blocking_desc().strides[with_g + 1];
+                    if (order_keep) {
+                        o[blk_off(h0, h1)] = _qz<type_i, type_o>()(i[flat_off],
+                                o[blk_off(h0, h1)], alpha, beta);
+                    } else {
+                        o[flat_off] = _qz<type_i, type_o>()(i[blk_off(h0, h1)],
+                                o[flat_off], alpha, beta);
+                    }
+                }
+            }
+
+#           undef blk_off
+        };
+
+        constexpr int i_mult_0 = order_keep ? blksize_0 : 1;
+        constexpr int o_mult_0 = order_keep ? 1 : blksize_0;
+
+        constexpr int i_mult_1 = order_keep ? blksize_1 : 1;
+        constexpr int o_mult_1 = order_keep ? 1 : blksize_1;
+
+#       define off(md, g, h0, h1, m0, m1, m2) \
+        (ndims >= 5 + with_g ? (md).blk_off<!with_g>(g, h0, h1, m0, m1, m2) \
+         : ndims >= 4 + with_g ? (md).blk_off<!with_g>(g, h0, h1, m1, m2) \
+         : /* ndims >= 3 + with_g ? */ (md).blk_off<!with_g>(g, h0, h1, m2))
+
+        parallel_nd(G, NB_H0, NB_H1, M0, M1, M2,
+            [&](dim_t g, dim_t nb_h0, dim_t nb_h1, dim_t m0, dim_t m1, dim_t m2) {
+            auto i = &input[off(input_d,
+                    g, i_mult_0 * nb_h0, i_mult_1 * nb_h1, m0, m1, m2)];
+            auto o = &output[off(output_d,
+                    g, o_mult_0 * nb_h0, o_mult_1 * nb_h1, m0, m1, m2)];
+            const int block_h0 = nstl::min<int>(blksize_0, H0 - nb_h0 * blksize_0);
+            const int block_h1 = nstl::min<int>(blksize_1, H1 - nb_h1 * blksize_1);
+            ker(i, o, block_h0, block_h1);
+        });
+
+#       undef off
+
+        return success;
+    }
+};
+
+/* generic and direct-copy reorders */
+
+template <SIMPLE_REORDER_TEMPL_DECL>
+struct simple_reorder_impl<SIMPLE_REORDER_TEMPL_CALL,
+    typename utils::enable_if<
+        tag_i == any && tag_o == any && order_keep == fmt_order::any,
+    spec::direct_copy>::type>
+{
+    static bool is_applicable(const memory_desc_wrapper &input_d,
+            const memory_desc_wrapper &output_d, const primitive_attr_t *attr) {
+        /* FIXME: is the formula correct? */
+        return input_d.similar_to(output_d, true, false, 0)
+            && input_d.is_dense() && output_d.is_dense()
+            && simple_attr_check(attr, false);
+    }
+
+    static status_t execute(const cpu_reorder_pd_t *pd,
+        const data_t<type_i> *input, data_t<type_o> *output) {
+        DECLARE_COMMON_PARAMS();
+
+        assert(input_d.is_dense());
+
+        input += input_d.blk_off(0);
+        output += output_d.blk_off(0);
+
+        const size_t nelems = input_d.nelems();
+
+        constexpr int block_size = 16;
+        const auto num_blocks = nelems / block_size;
+        const auto rem_elems = nelems % block_size;
+
+        parallel(0, [&](const int ithr, const int nthr) {
+            size_t start{0}, end{0};
+            balance211(num_blocks, nthr, ithr, start, end);
+            start = start * block_size;
+            end = end * block_size;
+
+            if (alpha == 1.0 && beta == 0.0) {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start; e < end; ++e) {
+                    output[e] = qz_a1b0<data_t<type_i>, data_t<type_o>>()
+                                (input[e]);
+                }
+            } else if (alpha == 1.0) {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start; e < end; ++e) {
+                    output[e] = qz_a1<data_t<type_i>, data_t<type_o>>()
+                                (input[e], output[e], beta);
+                }
+            } else if (beta == 0.0) {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start; e < end; ++e) {
+                    output[e] = qz_b0<data_t<type_i>, data_t<type_o>>()
+                                (input[e], alpha);
+                }
+            } else {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start; e < end; ++e) {
+                    output[e] = qz<data_t<type_i>, data_t<type_o>>()
+                                (input[e], output[e], alpha, beta);
+                }
+            }
+
+            if (rem_elems != 0 && ithr == nthr - 1){
+                if (alpha == 1.0 && beta == 0.0) {
+                    PRAGMA_OMP_SIMD()
+                    for (size_t e = nelems - rem_elems; e < nelems; ++e) {
+                        output[e] = qz_a1b0<data_t<type_i>,
+                            data_t<type_o>>()(input[e]);
+                    }
+                } else if (alpha == 1.0) {
+                    PRAGMA_OMP_SIMD()
+                    for (size_t e = nelems - rem_elems; e < nelems; ++e) {
+                        output[e] = qz_a1<data_t<type_i>,
+                            data_t<type_o>>()(input[e], output[e], beta);
+                    }
+                } else if (beta == 0.0) {
+                    PRAGMA_OMP_SIMD()
+                    for (size_t e = nelems - rem_elems; e < nelems; ++e) {
+                        output[e] = qz_b0<data_t<type_i>,
+                            data_t<type_o>>()(input[e], alpha);
+                    }
+                } else {
+                    PRAGMA_OMP_SIMD()
+                    for (size_t e = nelems - rem_elems; e < nelems; ++e) {
+                        output[e] = qz<data_t<type_i>, data_t<type_o>>()
+                                    (input[e], output[e], alpha, beta);
+                   }
+               }
+            }
+        });
+        return success;
+    }
+};
+
+template <SIMPLE_REORDER_TEMPL_DECL>
+struct simple_reorder_impl<SIMPLE_REORDER_TEMPL_CALL,
+    typename utils::enable_if<
+        tag_i == any && tag_o == any && order_keep == fmt_order::any,
+    spec::direct_copy_except_dim_0>::type>
+{
+    static bool is_applicable(const memory_desc_wrapper &input_d,
+            const memory_desc_wrapper &output_d, const primitive_attr_t *attr) {
+        auto is_dense_no_0 = [](const memory_desc_wrapper &data_d) {
+            return nelems_no_dim_0(data_d) == _size_no_dim_0(data_d);
+        };
+        /* FIXME: is the formula correct? */
+        return input_d.similar_to(output_d, true, false, 1)
+            && is_dense_no_0(input_d) && is_dense_no_0(output_d)
+            && simple_attr_check(attr, false);
+    }
+
+    static status_t execute(const cpu_reorder_pd_t *pd,
+        const data_t<type_i> *input, data_t<type_o> *output) {
+        DECLARE_COMMON_PARAMS();
+
+        input += input_d.blk_off(0);
+        output += output_d.blk_off(0);
+
+        const int N = input_d.dims()[0];
+        const dim_t is = input_d.blocking_desc().strides[0];
+        const dim_t os = output_d.blocking_desc().strides[0];
+        const dim_t nelems_no_d0 = nelems_no_dim_0(input_d);
+        const dim_t work_amount = N * nelems_no_d0;
+
+        if (alpha == 1.0 && beta == 0.0) {
+            parallel(0, [&](const int ithr, const int nthr) {
+                dim_t n{0}, dim1_s{0};
+                dim_t start{0}, end{0};
+                balance211(work_amount, nthr, ithr, start, end);
+                nd_iterator_init(start, n, N, dim1_s, nelems_no_d0);
+                while(start < end) {
+                    dim_t work_rem = end - start;
+                    dim_t dim1_e = dim1_s + work_rem > nelems_no_d0
+                        ? nelems_no_d0 : dim1_s + work_rem;
+                    PRAGMA_OMP_SIMD()
+                    for (dim_t e = dim1_s; e < dim1_e; ++e) {
+                        output[os * n + e] = _qz_a1b0<type_i, type_o>()(
+                                input[is * n + e]);
+                    }
+                    nd_iterator_jump(start, end, n, N, dim1_s, nelems_no_d0);
+                }
+            });
+        } else {
+            parallel(0, [&](const int ithr, const int nthr) {
+                dim_t n{0}, dim1_s{0};
+                dim_t start{0}, end{0};
+                balance211(work_amount, nthr, ithr, start, end);
+                nd_iterator_init(start, n, N, dim1_s, nelems_no_d0);
+                while(start < end) {
+                    dim_t work_rem = end - start;
+                    dim_t dim1_e =
+                        dim1_s + work_rem > nelems_no_d0 ? nelems_no_d0
+                        : dim1_s + work_rem;
+                    PRAGMA_OMP_SIMD()
+                    for (dim_t e = dim1_s; e < dim1_e; ++e){
+                        output[os * n + e] = _qz<type_i, type_o>()(
+                                input[is * n + e], output[os * n + e], alpha,
+                                beta);
+                    }
+                    nd_iterator_jump(start, end, n, N, dim1_s, nelems_no_d0);
+                }
+            });
+        }
+
+        return success;
+    }
+
+private:
+    static dim_t nelems_no_dim_0(const memory_desc_wrapper &data_d) {
+        const int ndims = data_d.ndims();
+        if (ndims <= 1) return 1;
+        return utils::array_product(data_d.dims() + 1, data_d.ndims() - 1);
+    }
+
+    static dim_t _size_no_dim_0(const memory_desc_wrapper &data_d) {
+        dims_t blocks;
+        data_d.compute_blocks(blocks);
+
+        const auto &blk = data_d.blocking_desc();
+
+        dim_t blk_size = 1;
+        for (int iblk = 0; iblk < blk.inner_nblks; ++iblk)
+            blk_size *= blk.inner_blks[iblk];
+
+        dim_t max_size = blk_size;
+        for (int d = 1; d < data_d.ndims(); ++d) {
+            max_size = nstl::max(max_size,
+                    data_d.padded_dims()[d] / blocks[d] * blk.strides[d]);
+        }
+
+        return max_size;
+    }
+};
+
+template <SIMPLE_REORDER_TEMPL_DECL>
+struct simple_reorder_impl<SIMPLE_REORDER_TEMPL_CALL,
+    typename utils::enable_if<
+        tag_i == any && tag_o == any && order_keep == fmt_order::any,
+    spec::reference>::type>
+{
+    static bool is_applicable(const memory_desc_wrapper &input_d,
+            const memory_desc_wrapper &output_d, const primitive_attr_t *attr) {
+        /* supported smask: 0x0...011..10...0,
+         * i.e. 1 should be contiguous */
+        int smask = attr ? attr->output_scales_.mask_ : 0;
+        for (; smask > 0 && !(smask & 0x1); smask >>= 1);
+        for (; smask > 0 && smask & 0x1; smask >>= 1);
+        return true
+            && input_d.is_blocking_desc()
+            && output_d.is_blocking_desc()
+            && !output_d.is_additional_buffer()
+            && !input_d.is_additional_buffer()
+            && smask == 0;
+    }
+
+    static status_t execute(const cpu_reorder_pd_t *pd,
+        const data_t<type_i> *input, data_t<type_o> *output) {
+        DECLARE_COMMON_PARAMS();
+
+        const size_t nelems = input_d.nelems();
+
+        int ndims_start = 0, ndims_mask = 0;
+        int smask = pd->attr()->output_scales_.mask_;
+        for (; smask > 0 && !(smask & 0x1); smask >>= 1) ++ndims_start;
+        for (; smask > 0 && smask & 0x1; smask >>= 1) ++ndims_mask;
+        assert(smask == 0);
+
+        const ptrdiff_t D_start
+            = utils::array_product(input_d.dims(), ndims_start);
+        const ptrdiff_t D_mask
+            = utils::array_product(input_d.dims() + ndims_start, ndims_mask);
+        const ptrdiff_t D_rest = nelems / D_start / D_mask;
+
+        const float *scales = pd->attr()->output_scales_.scales_;
+
+        parallel_nd(D_start, D_mask, D_rest,
+            [&](ptrdiff_t ds, ptrdiff_t dm, ptrdiff_t dr) {
+            const float scale = scales[dm];
+
+            const size_t e = (ds * D_mask + dm) * D_rest + dr;
+            const auto &i = input[input_d.off_l(e)];
+            auto &o = output[output_d.off_l(e)];
+
+            o = _qz<type_i, type_o>()(i, o, scale, beta);
+        });
+
+        return success;
+    }
+};
+
+
+/* high level class declaration */
+
+template <SIMPLE_REORDER_TEMPL_DECL, typename spec = void>
+struct simple_reorder_t: public cpu_primitive_t {
+    struct pd_t: public cpu_reorder_pd_t {
+        using cpu_reorder_pd_t::cpu_reorder_pd_t;
+
+        DECLARE_COMMON_PD_T("simple:any", simple_reorder_t);
+
+        static status_t create(reorder_pd_t **reorder_pd,
+                engine_t *engine, const primitive_attr_t *attr,
+                engine_t *src_engine, const memory_desc_t *src_md,
+                engine_t *dst_engine, const memory_desc_t *dst_md) {
+            bool args_ok = true
+                && src_md->data_type == type_i
+                && dst_md->data_type == type_o
+                && simple_reorder_impl<SIMPLE_REORDER_TEMPL_CALL, spec>::
+                is_applicable(src_md, dst_md, attr);
+            if (!args_ok)
+                return status::invalid_arguments;
+
+            auto _pd = new pd_t(engine, attr, src_engine, src_md, dst_engine,
+                    dst_md);
+            if (_pd == nullptr) return status::out_of_memory;
+            if (_pd->init() != status::success) {
+                delete _pd;
+                return status::unimplemented;
+            }
+            return safe_ptr_assign<reorder_pd_t>(*reorder_pd, _pd);
+        }
+    };
+
+    simple_reorder_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        auto input = CTX_IN_MEM(const data_t<type_i> *, MKLDNN_ARG_FROM);
+        auto output = CTX_OUT_MEM(data_t<type_o> *, MKLDNN_ARG_TO);
+        simple_reorder_impl<SIMPLE_REORDER_TEMPL_CALL, spec>::execute(
+                pd(), input, output);
+        return status::success;
+    }
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+#undef SIMPLE_REORDER_TEMPL_DECL
+#undef SIMPLE_REORDER_TEMPL_CALL
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/simple_sum.cpp b/thirdparty/oidn/mkl-dnn/src/cpu/simple_sum.cpp
new file mode 100644
index 0000000000..f0947573a9
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/simple_sum.cpp
@@ -0,0 +1,91 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "mkldnn_thread.hpp"
+
+#include "simple_sum.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <data_type_t data_type>
+status_t simple_sum_t<data_type>::execute(const exec_ctx_t &ctx) const {
+    auto output = CTX_OUT_MEM(data_t *, MKLDNN_ARG_DST);
+
+    const memory_desc_wrapper o_d(pd()->dst_md());
+    output += o_d.blk_off(0);
+
+    const int num_arrs = pd()->n_inputs();
+    const data_t *input_ptrs[max_num_arrs];
+    const size_t nelems = o_d.nelems();
+
+    for (int a = 0; a < num_arrs; ++a) {
+        const memory_desc_wrapper i_d(pd()->src_md(a));
+        input_ptrs[a] = CTX_IN_MEM(const data_t *, MKLDNN_ARG_MULTIPLE_SRC + a)
+            + i_d.blk_off(0);
+    }
+
+    const size_t block_size = 16 * 1024 / sizeof(data_type);
+    const size_t blocks_number = nelems / block_size;
+    const size_t tail = nelems % block_size;
+
+    const auto scales = pd()->scales();
+    parallel(0, [&](const int ithr, const int nthr) {
+        size_t start{0}, end{0};
+        balance211(blocks_number, nthr, ithr, start, end);
+
+        for (size_t nb = start; nb < end; ++nb) {
+            size_t start_e = nb * block_size;
+            size_t end_e = start_e + block_size;
+
+            PRAGMA_OMP_SIMD()
+            for (size_t e = start_e; e < end_e; e++) {
+                output[e] = data_t(scales[0] * input_ptrs[0][e]);
+            }
+            for (int a = 1; a < num_arrs; a++) {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start_e; e < end_e; e++) {
+                    output[e] += data_t(scales[a] * input_ptrs[a][e]);
+                }
+            }
+        }
+
+        if (tail != 0 && ithr == nthr - 1) {
+            size_t start_e = nelems - tail;
+            size_t end_e = nelems;
+
+            PRAGMA_OMP_SIMD()
+            for (size_t e = start_e; e < end_e; e++) {
+                output[e] = data_t(scales[0] * input_ptrs[0][e]);
+            }
+            for (int a = 1; a < num_arrs; a++) {
+                PRAGMA_OMP_SIMD()
+                for (size_t e = start_e; e < end_e; e++) {
+                    output[e] += data_t(scales[a] * input_ptrs[a][e]);
+                }
+            }
+        }
+    });
+
+    return status::success;
+}
+
+template struct simple_sum_t<data_type::f32>;
+
+}
+}
+}
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/simple_sum.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/simple_sum.hpp
new file mode 100644
index 0000000000..2a0187a184
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/simple_sum.hpp
@@ -0,0 +1,74 @@
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#ifndef SIMPLE_SUM_HPP
+#define SIMPLE_SUM_HPP
+
+#include "cpu_sum_pd.hpp"
+#include "cpu_primitive.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <data_type_t data_type>
+struct simple_sum_t: public cpu_primitive_t {
+    struct pd_t: public cpu_sum_pd_t {
+        using cpu_sum_pd_t::cpu_sum_pd_t;
+
+        DECLARE_SUM_PD_T("simple:any", simple_sum_t);
+
+        status_t init() {
+            const int n = n_inputs();
+
+            bool ok = true
+                && cpu_sum_pd_t::init() == status::success
+                && n <= max_num_arrs;
+            if (!ok) return status::unimplemented;
+
+            const memory_desc_wrapper o_d(dst_md());
+            ok = ok
+                && o_d.data_type() == data_type
+                && o_d.is_dense();
+            if (!ok) return status::unimplemented;
+
+            for (int i = 0; i < n; ++i) {
+                const memory_desc_wrapper i_d(src_md(i));
+                if (i_d != o_d) return status::unimplemented;
+            }
+
+            return status::success;
+        }
+    };
+
+    simple_sum_t(const pd_t *apd): cpu_primitive_t(apd) {}
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override;
+
+    enum {max_num_arrs = 16 };
+    typedef typename prec_traits<data_type>::type data_t;
+
+private:
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+};
+
+}
+}
+}
+
+#endif
+
+// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/wino_reorder.hpp b/thirdparty/oidn/mkl-dnn/src/cpu/wino_reorder.hpp
new file mode 100644
index 0000000000..c2082d7d62
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/wino_reorder.hpp
@@ -0,0 +1,376 @@
+/*******************************************************************************
+ * Copyright 2017-2018 Intel Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *******************************************************************************/
+
+#ifndef CPU_WINO_REORDER_HPP
+#define CPU_WINO_REORDER_HPP
+
+#include "mkldnn_thread.hpp"
+
+#include "simple_q10n.hpp"
+
+namespace mkldnn {
+namespace impl {
+namespace cpu {
+
+template <data_type_t type_i, data_type_t type_o>
+struct wino_reorder_t : public cpu_primitive_t {
+    struct pd_t : public cpu_reorder_pd_t {
+        using cpu_reorder_pd_t::cpu_reorder_pd_t;
+
+        DECLARE_COMMON_PD_T("wino_reorder", wino_reorder_t);
+
+        static status_t create(reorder_pd_t **reorder_pd,
+                engine_t *engine, const primitive_attr_t *attr,
+                engine_t *src_engine, const memory_desc_t *src_md,
+                engine_t *dst_engine, const memory_desc_t *dst_md) {
+            const memory_desc_wrapper id(src_md), od(dst_md);
+            bool args_ok = true
+                && id.data_type() == type_i
+                && od.data_type() == type_o
+                && id.matches_tag(utils::pick(id.ndims() - 4,
+                            format_tag::oihw, format_tag::goihw))
+                && od.format_kind() == format_kind::wino
+                && utils::one_of(od.wino_desc().wino_format,
+                        mkldnn_wino_wei_aaOIoi, mkldnn_wino_wei_aaOio,
+                        mkldnn_wino_wei_aaOBiOo, mkldnn_wino_wei_OBaaIBOIio);
+            if (!args_ok) return status::invalid_arguments;
+
+            auto _pd = new pd_t(engine, attr, src_engine, src_md, dst_engine,
+                    dst_md);
+            if (_pd == nullptr) return status::out_of_memory;
+            if (_pd->init() != status::success) {
+                delete _pd;
+                return status::unimplemented;
+            }
+            return safe_ptr_assign<reorder_pd_t>(*reorder_pd, _pd);
+        }
+
+        status_t init() {
+            status_t status = cpu_reorder_pd_t::init();
+            if (status != status::success) return status;
+
+            init_scratchpad();
+
+            return status::success;
+        }
+
+    private:
+        void init_scratchpad() {
+            auto &o = memory_desc_wrapper(dst_md()).wino_desc();
+            size_t transform_space_size = (size_t)o.r * o.alpha * o.oc_block;
+            size_t plain_size = (size_t)o.alpha * o.alpha * o.oc * o.ic;
+
+            using namespace memory_tracking::names;
+            auto scratchpad = scratchpad_registry().registrar();
+            scratchpad.book(key_reorder_wino_transform_space,
+                    sizeof(in_data_t) * transform_space_size);
+            scratchpad.book(key_reorder_wino_plain,
+                    sizeof(out_data_t) * plain_size);
+        }
+    };
+
+private:
+    typedef typename prec_traits<type_i>::type in_data_t;
+    typedef typename prec_traits<type_o>::type out_data_t;
+    const int unsign_val_in_wino_domain_ = 5;
+
+    wino_reorder_t(const pd_t *apd): cpu_primitive_t(apd) {
+        const memory_desc_wrapper src_d(pd()->src_md());
+        const memory_desc_wrapper dst_d(pd()->dst_md());
+
+        r_ = dst_d.wino_desc().r;
+        w_alpha_ = dst_d.wino_desc().alpha;
+        wino_format_ = dst_d.wino_desc().wino_format;
+
+        const auto &in_dims = src_d.dims();
+        int groups;
+        int groups_offset;
+        if (src_d.ndims() == 5) {
+            groups = in_dims[0];
+            groups_offset = 1;
+        } else {
+            groups = 1;
+            groups_offset = 0;
+        }
+        assert(groups == 1); // groups are not supported now
+        MAYBE_UNUSED(groups);
+
+        or_oc_ = in_dims[0 + groups_offset];
+        or_ic_ = in_dims[1 + groups_offset];
+        kh_ = in_dims[2 + groups_offset];
+        kw_ = in_dims[3 + groups_offset];
+
+        oc_ = dst_d.wino_desc().oc;
+        ic_ = dst_d.wino_desc().ic;
+        oc_block_ = dst_d.wino_desc().oc_block;
+        ic_block_ = dst_d.wino_desc().ic_block;
+        assert(oc_ % oc_block_ == 0 && ic_ % ic_block_ == 0);
+        nb_oc_ = oc_ / oc_block_;
+        nb_ic_ = ic_ / ic_block_;
+        ic2_block_ = 1;
+        if (wino_format_ == mkldnn_wino_wei_OBaaIBOIio)
+            ic2_block_ = dst_d.wino_desc().ic2_block;
+        oc2_block_ = dst_d.wino_desc().oc2_block;
+        assert(nb_ic_ % ic2_block_ == 0 && nb_oc_ % oc2_block_ == 0);
+
+        adj_scale_ = dst_d.wino_desc().adj_scale;
+
+        size_wino_wei_ = w_alpha_ * w_alpha_ * oc_ * ic_;
+        size_wspace_ = r_ * w_alpha_ * oc_block_;
+    }
+
+    void transform(out_data_t *__restrict tmp_wei,
+            const in_data_t *__restrict input,
+            in_data_t *__restrict wspace) const {
+        const memory_desc_wrapper src_d(pd()->src_md());
+
+        const int smask = pd()->attr()->output_scales_.mask_;
+        const int ndims_mask = math::ilog2q(smask + 1);
+        const size_t D_mask = utils::array_product(src_d.dims(), ndims_mask);
+        const float *__restrict scales = pd()->attr()->output_scales_.scales_;
+        assert(D_mask == 1 || D_mask == (size_t)oc_);
+
+        /* transform weights to winograd domain */
+        const float G_2x2_3x3[4][3] = { { 1.0, 0.0, 0.0 }, { 0.5, 0.5, 0.5 },
+            { 0.5, -0.5, 0.5 }, { 0.0, 0.0, 1.0 } };
+
+        const float G_4x4_3x3[6][3] = { { 1.13777777777778f, 0.f, 0.f },
+            { -0.688403361344538f, -0.430252100840336f, -0.26890756302521f },
+            { -0.688403361344538f, 0.430252100840336f, -0.26890756302521f },
+            { 0.119514472455649f, 0.179271708683473f, 0.26890756302521f },
+            { 0.119514472455649f, -0.179271708683473f, 0.26890756302521f },
+            { 0.f, 0.f, 1.f } };
+
+        float *__restrict g;
+        if (utils::one_of(wino_format_, mkldnn_wino_wei_aaOIoi,
+                    mkldnn_wino_wei_aaOio, mkldnn_wino_wei_aaOBiOo))
+            g = (float *)G_2x2_3x3;
+        else if (wino_format_ == mkldnn_wino_wei_OBaaIBOIio)
+            g = (float *)G_4x4_3x3;
+        else {
+            assert("Unknown winograd weights target layout");
+            return;
+        }
+
+        int Z = oc_ * ic_;
+        assert(r_ == kh_ && r_ == kw_);
+
+        for (int iic = 0; iic < ic_; iic++) {
+        for (int ob = 0; ob < nb_oc_; ob++) {
+            const in_data_t *__restrict _inp
+                    = input + (ob * oc_block_ * or_ic_ + iic) * kh_ * kw_;
+            out_data_t *__restrict _out
+                    = tmp_wei + (iic * nb_oc_ + ob) * oc_block_;
+
+            for_nd(0, 1, size_wspace_, [&](int i) { wspace[i] = 0.f; });
+
+            for_nd(0, 1, r_, w_alpha_, oc_block_,
+                [&](int ih, int j, int ioc) {
+                for (int iw = 0; iw < r_; ++iw) {
+                    int inp_oc = ob * oc_block_ + ioc;
+                    int inp_ic = iic;
+                    in_data_t inp_v = (inp_ic < or_ic_ && inp_oc < or_oc_)
+                        ? _inp[ioc * or_ic_ * kh_ * kw_ + ih * kw_ + iw]
+                        : 0.f;
+                    wspace[(ih * w_alpha_ + j) * oc_block_ + ioc]
+                            += inp_v * g[j * r_ + iw];
+                }
+            });
+
+            for_nd(0, 1, w_alpha_, w_alpha_, oc_block_,
+                [&](int i, int j, int ioc) {
+                float t = 0;
+                for (int k = 0; k < r_; ++k)
+                    t += g[i * r_ + k]
+                            * wspace[(k * w_alpha_ + j) * oc_block_ + ioc];
+                if (type_o == data_type::s8) {
+                    const float scale = (D_mask == 1)
+                        ? scales[0]
+                        : scales[ob * oc_block_ + ioc];
+                    _out[(i * w_alpha_ + j) * Z + ioc]
+                            = qz_b0<in_data_t, out_data_t>()(
+                                    (in_data_t)t, scale * adj_scale_);
+                } else {
+                    _out[(i * w_alpha_ + j) * Z + ioc] = (out_data_t)t;
+                }
+            });
+        }}
+    }
+
+    void reorder_to_aaOIoi(out_data_t *__restrict output,
+            const out_data_t *__restrict tmp_wei) const {
+        int32_t *__restrict dst_bias = nullptr;
+        if (type_o == data_type::s8) {
+            const auto bias_shift = sizeof(out_data_t) * size_wino_wei_;
+            const size_t bias_size = w_alpha_ * w_alpha_ * oc_;
+
+            dst_bias = (int32_t *)(output + bias_shift);
+            utils::array_set((int32_t *)dst_bias, 0, bias_size);
+        }
+        int index = 0;
+        for (int u_h = 0; u_h < w_alpha_; u_h++) {
+        for (int u_w = 0; u_w < w_alpha_; u_w++) {
+            for_nd(0, 1, nb_oc_, oc_block_, [&](int ob, int o) {
+                int u_h_shift = u_h * w_alpha_ * ic_ * oc_;
+                int u_w_shift = u_w * ic_ * oc_;
+                int u_h_shift_b = u_h * w_alpha_ * oc_;
+                int u_w_shift_b = u_w * oc_;
+                int oc_block_shift = ob * oc_block_ * ic_ + o * ic_block_;
+                for (int ib = 0; ib < nb_ic_; ib++) {
+                for (int i = 0; i < ic_block_; i++) {
+                    int _i = ib * ic_block_;
+                    int _o = ob * oc_block_;
+                    int ic_shift = (_i + i) * oc_;
+                    int oc_shift = (_o + o);
+                    int ic_block_shift = ib * oc_block_ * ic_block_ + i;
+                    int src_offset =
+                            u_h_shift + u_w_shift + ic_shift + oc_shift;
+                    int dst_offset = u_h_shift + u_w_shift + oc_block_shift
+                            + ic_block_shift;
+
+                    output[dst_offset] = tmp_wei[src_offset];
+                    if (type_o == data_type::s8) {
+                        int bias_offset = u_h_shift_b + u_w_shift_b + oc_shift;
+                        if (index != unsign_val_in_wino_domain_)
+                            dst_bias[bias_offset]
+                                    -= (128 * (int32_t)output[dst_offset]);
+                        else
+                            dst_bias[bias_offset] = 0;
+                    }
+                }}
+            });
+            index++;
+        }}
+    }
+
+    void reorder_to_aaOio(out_data_t *__restrict output,
+            const out_data_t *__restrict tmp_wei) const {
+        for_nd(0, 1, w_alpha_, w_alpha_, nb_oc_,
+            [&](int u_h, int u_w, int ob) {
+            for (int ib = 0; ib < nb_ic_; ib++) {
+            for (int i = 0; i < ic_block_; i++) {
+            for (int o = 0; o < oc_block_; o++) {
+                int src_offset = u_h * w_alpha_ * ic_ * oc_ + u_w * ic_ * oc_
+                    + (ib * ic_block_ + i) * oc_ + (ob * oc_block_ + o);
+
+                int dst_offset
+                    = u_h * w_alpha_ * nb_oc_ * nb_ic_ * ic_block_ * oc_block_
+                    + u_w * nb_oc_ * nb_ic_ * ic_block_ * oc_block_
+                    + ob * nb_ic_ * ic_block_ * oc_block_
+                    + ib * ic_block_ * oc_block_ + i * oc_block_ + o;
+                output[dst_offset] = tmp_wei[src_offset];
+            }}}
+        });
+    }
+
+    void reorder_to_aaOBiOo(out_data_t *__restrict output,
+            const out_data_t *__restrict tmp_wei) const {
+        int oc_chunks = nb_oc_ / oc2_block_;
+
+        for_nd(0, 1, w_alpha_, w_alpha_, oc_chunks,
+            [&](int u_h, int u_w, int occ) {
+            for (int ib = 0; ib < nb_ic_; ib++) {
+                out_data_t *__restrict wei_ptr = output
+                    + (((u_h * w_alpha_ + u_w) * oc_chunks + occ) * nb_ic_ + ib)
+                    * oc2_block_ * ic_block_ * oc_block_;
+                int wei_offset = 0;
+                for (int i = 0; i < ic_block_; i++) {
+                for (int ob2 = 0; ob2 < oc2_block_; ob2++) {
+                    for (int o = 0; o < oc_block_; o++) {
+                        int icp = ib * ic_block_ + i;
+                        int ocp =
+                            occ * oc2_block_ * oc_block_ + ob2 * oc_block_ + o;
+
+                        int src_offset = u_h * w_alpha_ * ic_ * oc_
+                            + u_w * ic_ * oc_ + icp * oc_ + ocp;
+                        wei_ptr[wei_offset + o] = tmp_wei[src_offset];
+                    }
+                    wei_offset += oc_block_;
+                }}
+            }
+        });
+    }
+
+    void reorder_to_OBaaIBOIio(out_data_t *__restrict output,
+            const out_data_t *__restrict tmp_wei) const {
+        int ic_chunks = nb_ic_ / ic2_block_;
+        int oc_chunks = nb_oc_ / oc2_block_;
+
+        for_nd(0, 1, oc_chunks, w_alpha_, w_alpha_,
+            [&](int occ, int u_h, int u_w) {
+            for (int icc = 0; icc < ic_chunks; icc++) {
+            for (int ob = 0; ob < oc2_block_; ob++) {
+                int ocp = (occ * oc2_block_ + ob) * oc_block_;
+                for (int ib = 0; ib < ic2_block_; ib++) {
+                for (int i = 0; i < ic_block_; i++) {
+                    int icp = (icc * ic2_block_ + ib) * ic_block_ + i;
+
+                    int src_offset = u_h * w_alpha_ * ic_ * oc_
+                        + u_w * ic_ * oc_ + icp * oc_ + ocp;
+                    int wei_offset
+                        = ((((((occ * w_alpha_ + u_h) * w_alpha_ + u_w)
+                            * ic_chunks + icc) * oc2_block_ + ob) * ic2_block_
+                            + ib) * ic_block_ + i) * oc_block_;
+                    for (int o = 0; o < oc_block_; o++)
+                        output[wei_offset + o] = tmp_wei[src_offset + o];
+                }}
+            }}
+        });
+    }
+
+    virtual status_t execute(const exec_ctx_t &ctx) const override {
+        auto input = CTX_IN_MEM(const in_data_t *, MKLDNN_ARG_FROM);
+        auto output = CTX_OUT_MEM(out_data_t *, MKLDNN_ARG_TO);
+
+        auto wspace = (in_data_t *__restrict)scratchpad(ctx).template get<void>(
+                memory_tracking::names::key_reorder_wino_transform_space);
+        auto tmp_wei = (out_data_t *__restrict)scratchpad(ctx).template get<void>(
+                memory_tracking::names::key_reorder_wino_plain);
+
+        transform(tmp_wei, input, wspace);
+
+        /* reorder to winograd domain */
+        switch (wino_format_) {
+        case mkldnn_wino_wei_aaOIoi:
+            reorder_to_aaOIoi(output, tmp_wei); break;
+        case mkldnn_wino_wei_aaOio:
+            reorder_to_aaOio(output, tmp_wei); break;
+        case mkldnn_wino_wei_aaOBiOo:
+            reorder_to_aaOBiOo(output, tmp_wei); break;
+        case mkldnn_wino_wei_OBaaIBOIio:
+            reorder_to_OBaaIBOIio(output, tmp_wei); break;
+        default: assert("Unknown wino format"); break;
+        }
+
+        return status::success;
+    }
+
+    const pd_t *pd() const { return (const pd_t *)primitive_t::pd(); }
+    int r_, w_alpha_;
+    int ic_, oc_, or_ic_, or_oc_, kh_, kw_;
+    int oc_block_, ic_block_, oc2_block_, ic2_block_;
+    float adj_scale_;
+    int nb_oc_, nb_ic_;
+    mkldnn_wino_memory_format_t wino_format_;
+    int size_wino_wei_;
+    int size_wspace_;
+};
+
+} // namespace cpu
+} // namespace impl
+} // namespace mkldnn
+
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/COPYRIGHT b/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/COPYRIGHT
new file mode 100644
index 0000000000..66b6ea55d0
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/COPYRIGHT
@@ -0,0 +1,47 @@
+
+Copyright (c) 2007 MITSUNARI Shigeo
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimer.
+Redistributions in binary form must reproduce the above copyright notice,
+this list of conditions and the following disclaimer in the documentation
+and/or other materials provided with the distribution.
+Neither the name of the copyright owner nor the names of its contributors may
+be used to endorse or promote products derived from this software without
+specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+THE POSSIBILITY OF SUCH DAMAGE.
+-----------------------------------------------------------------------------
+ソースコード形式かバイナリ形式か、変更するかしないかを問わず、以下の条件を満た
+す場合に限り、再頒布および使用が許可されます。
+
+ソースコードを再頒布する場合、上記の著作権表示、本条件一覧、および下記免責条項
+を含めること。
+バイナリ形式で再頒布する場合、頒布物に付属のドキュメント等の資料に、上記の著作
+権表示、本条件一覧、および下記免責条項を含めること。
+書面による特別の許可なしに、本ソフトウェアから派生した製品の宣伝または販売促進
+に、著作権者の名前またはコントリビューターの名前を使用してはならない。
+本ソフトウェアは、著作権者およびコントリビューターによって「現状のまま」提供さ
+れており、明示黙示を問わず、商業的な使用可能性、および特定の目的に対する適合性
+に関する暗黙の保証も含め、またそれに限定されない、いかなる保証もありません。
+著作権者もコントリビューターも、事由のいかんを問わず、 損害発生の原因いかんを
+問わず、かつ責任の根拠が契約であるか厳格責任であるか（過失その他の）不法行為で
+あるかを問わず、仮にそのような損害が発生する可能性を知らされていたとしても、
+本ソフトウェアの使用によって発生した（代替品または代用サービスの調達、使用の
+喪失、データの喪失、利益の喪失、業務の中断も含め、またそれに限定されない）直接
+損害、間接損害、偶発的な損害、特別損害、懲罰的損害、または結果損害について、
+一切責任を負わないものとします。
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak.h b/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak.h
new file mode 100644
index 0000000000..cf5771332f
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak.h
@@ -0,0 +1,2658 @@
+/*******************************************************************************
+* Copyright 2016-2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/*******************************************************************************
+* Copyright (c) 2007 MITSUNARI Shigeo
+* All rights reserved.
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions are met:
+*
+* Redistributions of source code must retain the above copyright notice, this
+* list of conditions and the following disclaimer.
+* Redistributions in binary form must reproduce the above copyright notice,
+* this list of conditions and the following disclaimer in the documentation
+* and/or other materials provided with the distribution.
+* Neither the name of the copyright owner nor the names of its contributors may
+* be used to endorse or promote products derived from this software without
+* specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+* THE POSSIBILITY OF SUCH DAMAGE.
+*******************************************************************************/
+
+#pragma once
+#ifndef XBYAK_XBYAK_H_
+#define XBYAK_XBYAK_H_
+/*!
+	@file xbyak.h
+	@brief Xbyak ; JIT assembler for x86(IA32)/x64 by C++
+	@author herumi
+	@url https://github.com/herumi/xbyak
+	@note modified new BSD license
+	http://opensource.org/licenses/BSD-3-Clause
+*/
+#ifndef XBYAK_NO_OP_NAMES
+	#if not +0 // trick to detect whether 'not' is operator or not
+		#error "use -fno-operator-names option if you want to use and(), or(), xor(), not() as function names, Or define XBYAK_NO_OP_NAMES and use and_(), or_(), xor_(), not_()."
+	#endif
+#endif
+
+#include <stdio.h> // for debug print
+#include <assert.h>
+#include <list>
+#include <string>
+#include <algorithm>
+#ifndef NDEBUG
+#include <iostream>
+#endif
+
+// #define XBYAK_DISABLE_AVX512
+
+//#define XBYAK_USE_MMAP_ALLOCATOR
+#if !defined(__GNUC__) || defined(__MINGW32__)
+	#undef XBYAK_USE_MMAP_ALLOCATOR
+#endif
+
+#ifdef __GNUC__
+	#define XBYAK_GNUC_PREREQ(major, minor) ((__GNUC__) * 100 + (__GNUC_MINOR__) >= (major) * 100 + (minor))
+#else
+	#define XBYAK_GNUC_PREREQ(major, minor) 0
+#endif
+
+// This covers -std=(gnu|c)++(0x|11|1y), -stdlib=libc++, and modern Microsoft.
+#if ((defined(_MSC_VER) && (_MSC_VER >= 1600)) || defined(_LIBCPP_VERSION) ||\
+				 ((__cplusplus >= 201103) || defined(__GXX_EXPERIMENTAL_CXX0X__)))
+	#include <unordered_set>
+	#define XBYAK_STD_UNORDERED_SET std::unordered_set
+	#include <unordered_map>
+	#define XBYAK_STD_UNORDERED_MAP std::unordered_map
+	#define XBYAK_STD_UNORDERED_MULTIMAP std::unordered_multimap
+
+/*
+	Clang/llvm-gcc and ICC-EDG in 'GCC-mode' always claim to be GCC 4.2, using
+	libstdcxx 20070719 (from GCC 4.2.1, the last GPL 2 version).
+*/
+#elif XBYAK_GNUC_PREREQ(4, 5) || (XBYAK_GNUC_PREREQ(4, 2) && __GLIBCXX__ >= 20070719) || defined(__INTEL_COMPILER) || defined(__llvm__)
+	#include <tr1/unordered_set>
+	#define XBYAK_STD_UNORDERED_SET std::tr1::unordered_set
+	#include <tr1/unordered_map>
+	#define XBYAK_STD_UNORDERED_MAP std::tr1::unordered_map
+	#define XBYAK_STD_UNORDERED_MULTIMAP std::tr1::unordered_multimap
+
+#elif defined(_MSC_VER) && (_MSC_VER >= 1500) && (_MSC_VER < 1600)
+	#include <unordered_set>
+	#define XBYAK_STD_UNORDERED_SET std::tr1::unordered_set
+	#include <unordered_map>
+	#define XBYAK_STD_UNORDERED_MAP std::tr1::unordered_map
+	#define XBYAK_STD_UNORDERED_MULTIMAP std::tr1::unordered_multimap
+
+#else
+	#include <set>
+	#define XBYAK_STD_UNORDERED_SET std::set
+	#include <map>
+	#define XBYAK_STD_UNORDERED_MAP std::map
+	#define XBYAK_STD_UNORDERED_MULTIMAP std::multimap
+#endif
+#ifdef _WIN32
+	#include <winsock2.h>
+	#include <windows.h>
+	#include <malloc.h>
+#elif defined(__GNUC__)
+	#include <unistd.h>
+	#include <sys/mman.h>
+	#include <stdlib.h>
+#endif
+#if !defined(_MSC_VER) || (_MSC_VER >= 1600)
+	#include <stdint.h>
+#endif
+
+#if defined(_WIN64) || defined(__MINGW64__) || (defined(__CYGWIN__) && defined(__x86_64__))
+	#define XBYAK64_WIN
+#elif defined(__x86_64__)
+	#define XBYAK64_GCC
+#endif
+#if !defined(XBYAK64) && !defined(XBYAK32)
+	#if defined(XBYAK64_GCC) || defined(XBYAK64_WIN)
+		#define XBYAK64
+	#else
+		#define XBYAK32
+	#endif
+#endif
+
+#if (__cplusplus >= 201103) || (_MSC_VER >= 1800)
+	#define XBYAK_VARIADIC_TEMPLATE
+#endif
+
+#ifdef _MSC_VER
+	#pragma warning(push)
+	#pragma warning(disable : 4514) /* remove inline function */
+	#pragma warning(disable : 4786) /* identifier is too long */
+	#pragma warning(disable : 4503) /* name is too long */
+	#pragma warning(disable : 4127) /* constant expresison */
+#endif
+
+namespace Xbyak {
+
+enum {
+	DEFAULT_MAX_CODE_SIZE = 4096,
+	VERSION = 0x5760 /* 0xABCD = A.BC(D) */
+};
+
+#ifndef MIE_INTEGER_TYPE_DEFINED
+#define MIE_INTEGER_TYPE_DEFINED
+#ifdef _MSC_VER
+	typedef unsigned __int64 uint64;
+	typedef __int64 sint64;
+#else
+	typedef uint64_t uint64;
+	typedef int64_t sint64;
+#endif
+typedef unsigned int uint32;
+typedef unsigned short uint16;
+typedef unsigned char uint8;
+#endif
+
+#ifndef MIE_ALIGN
+	#ifdef _MSC_VER
+		#define MIE_ALIGN(x) __declspec(align(x))
+	#else
+		#define MIE_ALIGN(x) __attribute__((aligned(x)))
+	#endif
+#endif
+#ifndef MIE_PACK // for shufps
+	#define MIE_PACK(x, y, z, w) ((x) * 64 + (y) * 16 + (z) * 4 + (w))
+#endif
+
+enum {
+	ERR_NONE = 0,
+	ERR_BAD_ADDRESSING,
+	ERR_CODE_IS_TOO_BIG,
+	ERR_BAD_SCALE,
+	ERR_ESP_CANT_BE_INDEX,
+	ERR_BAD_COMBINATION,
+	ERR_BAD_SIZE_OF_REGISTER,
+	ERR_IMM_IS_TOO_BIG,
+	ERR_BAD_ALIGN,
+	ERR_LABEL_IS_REDEFINED,
+	ERR_LABEL_IS_TOO_FAR,
+	ERR_LABEL_IS_NOT_FOUND,
+	ERR_CODE_ISNOT_COPYABLE,
+	ERR_BAD_PARAMETER,
+	ERR_CANT_PROTECT,
+	ERR_CANT_USE_64BIT_DISP,
+	ERR_OFFSET_IS_TOO_BIG,
+	ERR_MEM_SIZE_IS_NOT_SPECIFIED,
+	ERR_BAD_MEM_SIZE,
+	ERR_BAD_ST_COMBINATION,
+	ERR_OVER_LOCAL_LABEL, // not used
+	ERR_UNDER_LOCAL_LABEL,
+	ERR_CANT_ALLOC,
+	ERR_ONLY_T_NEAR_IS_SUPPORTED_IN_AUTO_GROW,
+	ERR_BAD_PROTECT_MODE,
+	ERR_BAD_PNUM,
+	ERR_BAD_TNUM,
+	ERR_BAD_VSIB_ADDRESSING,
+	ERR_CANT_CONVERT,
+	ERR_LABEL_ISNOT_SET_BY_L,
+	ERR_LABEL_IS_ALREADY_SET_BY_L,
+	ERR_BAD_LABEL_STR,
+	ERR_MUNMAP,
+	ERR_OPMASK_IS_ALREADY_SET,
+	ERR_ROUNDING_IS_ALREADY_SET,
+	ERR_K0_IS_INVALID,
+	ERR_EVEX_IS_INVALID,
+	ERR_SAE_IS_INVALID,
+	ERR_ER_IS_INVALID,
+	ERR_INVALID_BROADCAST,
+	ERR_INVALID_OPMASK_WITH_MEMORY,
+	ERR_INVALID_ZERO,
+	ERR_INVALID_RIP_IN_AUTO_GROW,
+	ERR_INVALID_MIB_ADDRESS,
+	ERR_INTERNAL,
+	ERR_X2APIC_IS_NOT_SUPPORTED
+};
+
+class Error : public std::exception {
+	int err_;
+public:
+	explicit Error(int err) : err_(err)
+	{
+		if (err_ < 0 || err_ > ERR_INTERNAL) {
+			fprintf(stderr, "bad err=%d in Xbyak::Error\n", err_);
+			//exit(1);
+		}
+	}
+	operator int() const { return err_; }
+	const char *what() const throw()
+	{
+		static const char *errTbl[] = {
+			"none",
+			"bad addressing",
+			"code is too big",
+			"bad scale",
+			"esp can't be index",
+			"bad combination",
+			"bad size of register",
+			"imm is too big",
+			"bad align",
+			"label is redefined",
+			"label is too far",
+			"label is not found",
+			"code is not copyable",
+			"bad parameter",
+			"can't protect",
+			"can't use 64bit disp(use (void*))",
+			"offset is too big",
+			"MEM size is not specified",
+			"bad mem size",
+			"bad st combination",
+			"over local label",
+			"under local label",
+			"can't alloc",
+			"T_SHORT is not supported in AutoGrow",
+			"bad protect mode",
+			"bad pNum",
+			"bad tNum",
+			"bad vsib addressing",
+			"can't convert",
+			"label is not set by L()",
+			"label is already set by L()",
+			"bad label string",
+			"err munmap",
+			"opmask is already set",
+			"rounding is already set",
+			"k0 is invalid",
+			"evex is invalid",
+			"sae(suppress all exceptions) is invalid",
+			"er(embedded rounding) is invalid",
+			"invalid broadcast",
+			"invalid opmask with memory",
+			"invalid zero",
+			"invalid rip in AutoGrow",
+			"invalid mib address",
+			"internal error",
+			"x2APIC is not supported"
+		};
+		assert((size_t)err_ < sizeof(errTbl) / sizeof(*errTbl));
+		return errTbl[err_];
+	}
+};
+
+inline const char *ConvertErrorToString(const Error& err)
+{
+	return err.what();
+}
+
+inline void *AlignedMalloc(size_t size, size_t alignment)
+{
+#ifdef __MINGW32__
+	return __mingw_aligned_malloc(size, alignment);
+#elif defined(_WIN32)
+	return _aligned_malloc(size, alignment);
+#else
+	void *p;
+	int ret = posix_memalign(&p, alignment, size);
+	return (ret == 0) ? p : 0;
+#endif
+}
+
+inline void AlignedFree(void *p)
+{
+#ifdef __MINGW32__
+	__mingw_aligned_free(p);
+#elif defined(_MSC_VER)
+	_aligned_free(p);
+#else
+	free(p);
+#endif
+}
+
+template<class To, class From>
+inline const To CastTo(From p) throw()
+{
+	return (const To)(size_t)(p);
+}
+namespace inner {
+
+static const size_t ALIGN_PAGE_SIZE = 4096;
+
+inline bool IsInDisp8(uint32 x) { return 0xFFFFFF80 <= x || x <= 0x7F; }
+inline bool IsInInt32(uint64 x) { return ~uint64(0x7fffffffu) <= x || x <= 0x7FFFFFFFU; }
+
+inline uint32 VerifyInInt32(uint64 x)
+{
+#ifdef XBYAK64
+	if (!IsInInt32(x)) throw Error(ERR_OFFSET_IS_TOO_BIG);
+#endif
+	return static_cast<uint32>(x);
+}
+
+enum LabelMode {
+	LasIs, // as is
+	Labs, // absolute
+	LaddTop // (addr + top) for mov(reg, label) with AutoGrow
+};
+
+} // inner
+
+/*
+	custom allocator
+*/
+struct Allocator {
+	virtual uint8 *alloc(size_t size) { return reinterpret_cast<uint8*>(AlignedMalloc(size, inner::ALIGN_PAGE_SIZE)); }
+	virtual void free(uint8 *p) { AlignedFree(p); }
+	virtual ~Allocator() {}
+	/* override to return false if you call protect() manually */
+	virtual bool useProtect() const { return true; }
+};
+
+#ifdef XBYAK_USE_MMAP_ALLOCATOR
+class MmapAllocator : Allocator {
+	typedef XBYAK_STD_UNORDERED_MAP<uintptr_t, size_t> SizeList;
+	SizeList sizeList_;
+public:
+	uint8 *alloc(size_t size)
+	{
+		const size_t alignedSizeM1 = inner::ALIGN_PAGE_SIZE - 1;
+		size = (size + alignedSizeM1) & ~alignedSizeM1;
+#ifdef MAP_ANONYMOUS
+		const int mode = MAP_PRIVATE | MAP_ANONYMOUS;
+#elif defined(MAP_ANON)
+		const int mode = MAP_PRIVATE | MAP_ANON;
+#else
+		#error "not supported"
+#endif
+		void *p = mmap(NULL, size, PROT_READ | PROT_WRITE, mode, -1, 0);
+		if (p == MAP_FAILED) throw Error(ERR_CANT_ALLOC);
+		assert(p);
+		sizeList_[(uintptr_t)p] = size;
+		return (uint8*)p;
+	}
+	void free(uint8 *p)
+	{
+		if (p == 0) return;
+		SizeList::iterator i = sizeList_.find((uintptr_t)p);
+		if (i == sizeList_.end()) throw Error(ERR_BAD_PARAMETER);
+		if (munmap((void*)i->first, i->second) < 0) throw Error(ERR_MUNMAP);
+		sizeList_.erase(i);
+	}
+};
+#endif
+
+class Address;
+class Reg;
+
+class Operand {
+	static const uint8 EXT8BIT = 0x20;
+	unsigned int idx_:6; // 0..31 + EXT8BIT = 1 if spl/bpl/sil/dil
+	unsigned int kind_:9;
+	unsigned int bit_:10;
+protected:
+	unsigned int zero_:1;
+	unsigned int mask_:3;
+	unsigned int rounding_:3;
+	void setIdx(int idx) { idx_ = idx; }
+public:
+	enum Kind {
+		NONE = 0,
+		MEM = 1 << 0,
+		REG = 1 << 1,
+		MMX = 1 << 2,
+		FPU = 1 << 3,
+		XMM = 1 << 4,
+		YMM = 1 << 5,
+		ZMM = 1 << 6,
+		OPMASK = 1 << 7,
+		BNDREG = 1 << 8
+	};
+	enum Code {
+#ifdef XBYAK64
+		RAX = 0, RCX, RDX, RBX, RSP, RBP, RSI, RDI, R8, R9, R10, R11, R12, R13, R14, R15,
+		R8D = 8, R9D, R10D, R11D, R12D, R13D, R14D, R15D,
+		R8W = 8, R9W, R10W, R11W, R12W, R13W, R14W, R15W,
+		R8B = 8, R9B, R10B, R11B, R12B, R13B, R14B, R15B,
+		SPL = 4, BPL, SIL, DIL,
+#endif
+		EAX = 0, ECX, EDX, EBX, ESP, EBP, ESI, EDI,
+		AX = 0, CX, DX, BX, SP, BP, SI, DI,
+		AL = 0, CL, DL, BL, AH, CH, DH, BH
+	};
+	Operand() : idx_(0), kind_(0), bit_(0), zero_(0), mask_(0), rounding_(0) { }
+	Operand(int idx, Kind kind, int bit, bool ext8bit = 0)
+		: idx_(static_cast<uint8>(idx | (ext8bit ? EXT8BIT : 0)))
+		, kind_(kind)
+		, bit_(bit)
+		, zero_(0), mask_(0), rounding_(0)
+	{
+		assert((bit_ & (bit_ - 1)) == 0); // bit must be power of two
+	}
+	Kind getKind() const { return static_cast<Kind>(kind_); }
+	int getIdx() const { return idx_ & (EXT8BIT - 1); }
+	bool isNone() const { return kind_ == 0; }
+	bool isMMX() const { return is(MMX); }
+	bool isXMM() const { return is(XMM); }
+	bool isYMM() const { return is(YMM); }
+	bool isZMM() const { return is(ZMM); }
+	bool isXMEM() const { return is(XMM | MEM); }
+	bool isYMEM() const { return is(YMM | MEM); }
+	bool isZMEM() const { return is(ZMM | MEM); }
+	bool isOPMASK() const { return is(OPMASK); }
+	bool isBNDREG() const { return is(BNDREG); }
+	bool isREG(int bit = 0) const { return is(REG, bit); }
+	bool isMEM(int bit = 0) const { return is(MEM, bit); }
+	bool isFPU() const { return is(FPU); }
+	bool isExt8bit() const { return (idx_ & EXT8BIT) != 0; }
+	bool isExtIdx() const { return (getIdx() & 8) != 0; }
+	bool isExtIdx2() const { return (getIdx() & 16) != 0; }
+	bool hasEvex() const { return isZMM() || isExtIdx2() || getOpmaskIdx() || getRounding(); }
+	bool hasRex() const { return isExt8bit() || isREG(64) || isExtIdx(); }
+	bool hasZero() const { return zero_; }
+	int getOpmaskIdx() const { return mask_; }
+	int getRounding() const { return rounding_; }
+	void setKind(Kind kind)
+	{
+		if ((kind & (XMM|YMM|ZMM)) == 0) return;
+		kind_ = kind;
+		bit_ = kind == XMM ? 128 : kind == YMM ? 256 : 512;
+	}
+	void setBit(int bit) { bit_ = bit; }
+	void setOpmaskIdx(int idx, bool ignore_idx0 = false)
+	{
+		if (!ignore_idx0 && idx == 0) throw Error(ERR_K0_IS_INVALID);
+		if (mask_) throw Error(ERR_OPMASK_IS_ALREADY_SET);
+		mask_ = idx;
+	}
+	void setRounding(int idx)
+	{
+		if (rounding_) throw Error(ERR_ROUNDING_IS_ALREADY_SET);
+		rounding_ = idx;
+	}
+	void setZero() { zero_ = true; }
+	// ah, ch, dh, bh?
+	bool isHigh8bit() const
+	{
+		if (!isBit(8)) return false;
+		if (isExt8bit()) return false;
+		const int idx = getIdx();
+		return AH <= idx && idx <= BH;
+	}
+	// any bit is accetable if bit == 0
+	bool is(int kind, uint32 bit = 0) const
+	{
+		return (kind == 0 || (kind_ & kind)) && (bit == 0 || (bit_ & bit)); // cf. you can set (8|16)
+	}
+	bool isBit(uint32 bit) const { return (bit_ & bit) != 0; }
+	uint32 getBit() const { return bit_; }
+	const char *toString() const
+	{
+		const int idx = getIdx();
+		if (kind_ == REG) {
+			if (isExt8bit()) {
+				static const char *tbl[4] = { "spl", "bpl", "sil", "dil" };
+				return tbl[idx - 4];
+			}
+			static const char *tbl[4][16] = {
+				{ "al", "cl", "dl", "bl", "ah", "ch", "dh", "bh", "r8b", "r9b", "r10b",  "r11b", "r12b", "r13b", "r14b", "r15b" },
+				{ "ax", "cx", "dx", "bx", "sp", "bp", "si", "di", "r8w", "r9w", "r10w",  "r11w", "r12w", "r13w", "r14w", "r15w" },
+				{ "eax", "ecx", "edx", "ebx", "esp", "ebp", "esi", "edi", "r8d", "r9d", "r10d",  "r11d", "r12d", "r13d", "r14d", "r15d" },
+				{ "rax", "rcx", "rdx", "rbx", "rsp", "rbp", "rsi", "rdi", "r8", "r9", "r10",  "r11", "r12", "r13", "r14", "r15" },
+			};
+			return tbl[bit_ == 8 ? 0 : bit_ == 16 ? 1 : bit_ == 32 ? 2 : 3][idx];
+		} else if (isOPMASK()) {
+			static const char *tbl[8] = { "k0", "k1", "k2", "k3", "k4", "k5", "k6", "k7" };
+			return tbl[idx];
+		} else if (isZMM()) {
+			static const char *tbl[32] = {
+				"zmm0", "zmm1", "zmm2", "zmm3", "zmm4", "zmm5", "zmm6", "zmm7", "zmm8", "zmm9", "zmm10", "zmm11", "zmm12", "zmm13", "zmm14", "zmm15",
+				"zmm16", "zmm17", "zmm18", "zmm19", "zmm20", "zmm21", "zmm22", "zmm23", "zmm24", "zmm25", "zmm26", "zmm27", "zmm28", "zmm29", "zmm30", "zmm31"
+			};
+			return tbl[idx];
+		} else if (isYMM()) {
+			static const char *tbl[32] = {
+				"ymm0", "ymm1", "ymm2", "ymm3", "ymm4", "ymm5", "ymm6", "ymm7", "ymm8", "ymm9", "ymm10", "ymm11", "ymm12", "ymm13", "ymm14", "ymm15",
+				"ymm16", "ymm17", "ymm18", "ymm19", "ymm20", "ymm21", "ymm22", "ymm23", "ymm24", "ymm25", "ymm26", "ymm27", "ymm28", "ymm29", "ymm30", "ymm31"
+			};
+			return tbl[idx];
+		} else if (isXMM()) {
+			static const char *tbl[32] = {
+				"xmm0", "xmm1", "xmm2", "xmm3", "xmm4", "xmm5", "xmm6", "xmm7", "xmm8", "xmm9", "xmm10", "xmm11", "xmm12", "xmm13", "xmm14", "xmm15",
+				"xmm16", "xmm17", "xmm18", "xmm19", "xmm20", "xmm21", "xmm22", "xmm23", "xmm24", "xmm25", "xmm26", "xmm27", "xmm28", "xmm29", "xmm30", "xmm31"
+			};
+			return tbl[idx];
+		} else if (isMMX()) {
+			static const char *tbl[8] = { "mm0", "mm1", "mm2", "mm3", "mm4", "mm5", "mm6", "mm7" };
+			return tbl[idx];
+		} else if (isFPU()) {
+			static const char *tbl[8] = { "st0", "st1", "st2", "st3", "st4", "st5", "st6", "st7" };
+			return tbl[idx];
+		} else if (isBNDREG()) {
+			static const char *tbl[4] = { "bnd0", "bnd1", "bnd2", "bnd3" };
+			return tbl[idx];
+		}
+		throw Error(ERR_INTERNAL);
+	}
+	bool isEqualIfNotInherited(const Operand& rhs) const { return idx_ == rhs.idx_ && kind_ == rhs.kind_ && bit_ == rhs.bit_ && zero_ == rhs.zero_ && mask_ == rhs.mask_ && rounding_ == rhs.rounding_; }
+	bool operator==(const Operand& rhs) const;
+	bool operator!=(const Operand& rhs) const { return !operator==(rhs); }
+	const Address& getAddress() const;
+	const Reg& getReg() const;
+};
+
+class Label;
+
+struct Reg8;
+struct Reg16;
+struct Reg32;
+#ifdef XBYAK64
+struct Reg64;
+#endif
+class Reg : public Operand {
+public:
+	Reg() { }
+	Reg(int idx, Kind kind, int bit = 0, bool ext8bit = false) : Operand(idx, kind, bit, ext8bit) { }
+	Reg changeBit(int bit) const { return Reg(getIdx(), getKind(), bit, isExt8bit()); }
+	uint8 getRexW() const { return isREG(64) ? 8 : 0; }
+	uint8 getRexR() const { return isExtIdx() ? 4 : 0; }
+	uint8 getRexX() const { return isExtIdx() ? 2 : 0; }
+	uint8 getRexB() const { return isExtIdx() ? 1 : 0; }
+	uint8 getRex(const Reg& base = Reg()) const
+	{
+		uint8 rex = getRexW() | getRexR() | base.getRexW() | base.getRexB();
+		if (rex || isExt8bit() || base.isExt8bit()) rex |= 0x40;
+		return rex;
+	}
+	Reg8 cvt8() const;
+	Reg16 cvt16() const;
+	Reg32 cvt32() const;
+#ifdef XBYAK64
+	Reg64 cvt64() const;
+#endif
+};
+
+inline const Reg& Operand::getReg() const
+{
+	assert(!isMEM());
+	return static_cast<const Reg&>(*this);
+}
+
+struct Reg8 : public Reg {
+	explicit Reg8(int idx = 0, bool ext8bit = false) : Reg(idx, Operand::REG, 8, ext8bit) { }
+};
+
+struct Reg16 : public Reg {
+	explicit Reg16(int idx = 0) : Reg(idx, Operand::REG, 16) { }
+};
+
+struct Mmx : public Reg {
+	explicit Mmx(int idx = 0, Kind kind = Operand::MMX, int bit = 64) : Reg(idx, kind, bit) { }
+};
+
+struct EvexModifierRounding {
+	enum {
+		T_RN_SAE = 1,
+		T_RD_SAE = 2,
+		T_RU_SAE = 3,
+		T_RZ_SAE = 4,
+		T_SAE = 5
+	};
+	explicit EvexModifierRounding(int rounding) : rounding(rounding) {}
+	int rounding;
+};
+struct EvexModifierZero{EvexModifierZero() {}};
+
+struct Xmm : public Mmx {
+	explicit Xmm(int idx = 0, Kind kind = Operand::XMM, int bit = 128) : Mmx(idx, kind, bit) { }
+	Xmm(Kind kind, int idx) : Mmx(idx, kind, kind == XMM ? 128 : kind == YMM ? 256 : 512) { }
+	Xmm operator|(const EvexModifierRounding& emr) const { Xmm r(*this); r.setRounding(emr.rounding); return r; }
+	Xmm copyAndSetIdx(int idx) const { Xmm ret(*this); ret.setIdx(idx); return ret; }
+	Xmm copyAndSetKind(Operand::Kind kind) const { Xmm ret(*this); ret.setKind(kind); return ret; }
+};
+
+struct Ymm : public Xmm {
+	explicit Ymm(int idx = 0, Kind kind = Operand::YMM, int bit = 256) : Xmm(idx, kind, bit) { }
+	Ymm operator|(const EvexModifierRounding& emr) const { Ymm r(*this); r.setRounding(emr.rounding); return r; }
+};
+
+struct Zmm : public Ymm {
+	explicit Zmm(int idx = 0) : Ymm(idx, Operand::ZMM, 512) { }
+	Zmm operator|(const EvexModifierRounding& emr) const { Zmm r(*this); r.setRounding(emr.rounding); return r; }
+};
+
+struct Opmask : public Reg {
+	explicit Opmask(int idx = 0) : Reg(idx, Operand::OPMASK, 64) {}
+};
+
+struct BoundsReg : public Reg {
+	explicit BoundsReg(int idx = 0) : Reg(idx, Operand::BNDREG, 128) {}
+};
+
+template<class T>T operator|(const T& x, const Opmask& k) { T r(x); r.setOpmaskIdx(k.getIdx()); return r; }
+template<class T>T operator|(const T& x, const EvexModifierZero&) { T r(x); r.setZero(); return r; }
+template<class T>T operator|(const T& x, const EvexModifierRounding& emr) { T r(x); r.setRounding(emr.rounding); return r; }
+
+struct Fpu : public Reg {
+	explicit Fpu(int idx = 0) : Reg(idx, Operand::FPU, 32) { }
+};
+
+struct Reg32e : public Reg {
+	explicit Reg32e(int idx, int bit) : Reg(idx, Operand::REG, bit) {}
+};
+struct Reg32 : public Reg32e {
+	explicit Reg32(int idx = 0) : Reg32e(idx, 32) {}
+};
+#ifdef XBYAK64
+struct Reg64 : public Reg32e {
+	explicit Reg64(int idx = 0) : Reg32e(idx, 64) {}
+};
+struct RegRip {
+	sint64 disp_;
+	const Label* label_;
+	bool isAddr_;
+	explicit RegRip(sint64 disp = 0, const Label* label = 0, bool isAddr = false) : disp_(disp), label_(label), isAddr_(isAddr) {}
+	friend const RegRip operator+(const RegRip& r, int disp) {
+		return RegRip(r.disp_ + disp, r.label_, r.isAddr_);
+	}
+	friend const RegRip operator-(const RegRip& r, int disp) {
+		return RegRip(r.disp_ - disp, r.label_, r.isAddr_);
+	}
+	friend const RegRip operator+(const RegRip& r, sint64 disp) {
+		return RegRip(r.disp_ + disp, r.label_, r.isAddr_);
+	}
+	friend const RegRip operator-(const RegRip& r, sint64 disp) {
+		return RegRip(r.disp_ - disp, r.label_, r.isAddr_);
+	}
+	friend const RegRip operator+(const RegRip& r, const Label& label) {
+		if (r.label_ || r.isAddr_) throw Error(ERR_BAD_ADDRESSING);
+		return RegRip(r.disp_, &label);
+	}
+	friend const RegRip operator+(const RegRip& r, const void *addr) {
+		if (r.label_ || r.isAddr_) throw Error(ERR_BAD_ADDRESSING);
+		return RegRip(r.disp_ + (sint64)addr, 0, true);
+	}
+};
+#endif
+
+inline Reg8 Reg::cvt8() const
+{
+	const int idx = getIdx();
+	if (isBit(8)) return Reg8(idx, isExt8bit());
+#ifdef XBYAK32
+	if (idx >= 4) throw Error(ERR_CANT_CONVERT);
+#endif
+	return Reg8(idx, 4 <= idx && idx < 8);
+}
+
+inline Reg16 Reg::cvt16() const
+{
+	const int idx = getIdx();
+	if (isBit(8) && (4 <= idx && idx < 8) && !isExt8bit()) throw Error(ERR_CANT_CONVERT);
+	return Reg16(idx);
+}
+
+inline Reg32 Reg::cvt32() const
+{
+	const int idx = getIdx();
+	if (isBit(8) && (4 <= idx && idx < 8) && !isExt8bit()) throw Error(ERR_CANT_CONVERT);
+	return Reg32(idx);
+}
+
+#ifdef XBYAK64
+inline Reg64 Reg::cvt64() const
+{
+	const int idx = getIdx();
+	if (isBit(8) && (4 <= idx && idx < 8) && !isExt8bit()) throw Error(ERR_CANT_CONVERT);
+	return Reg64(idx);
+}
+#endif
+
+#ifndef XBYAK_DISABLE_SEGMENT
+// not derived from Reg
+class Segment {
+	int idx_;
+public:
+	enum {
+		es, cs, ss, ds, fs, gs
+	};
+	explicit Segment(int idx) : idx_(idx) { assert(0 <= idx_ && idx_ < 6); }
+	int getIdx() const { return idx_; }
+	const char *toString() const
+	{
+		static const char tbl[][3] = {
+			"es", "cs", "ss", "ds", "fs", "gs"
+		};
+		return tbl[idx_];
+	}
+};
+#endif
+
+class RegExp {
+public:
+#ifdef XBYAK64
+	enum { i32e = 32 | 64 };
+#else
+	enum { i32e = 32 };
+#endif
+	RegExp(size_t disp = 0) : scale_(0), disp_(disp) { }
+	RegExp(const Reg& r, int scale = 1)
+		: scale_(scale)
+		, disp_(0)
+	{
+		if (!r.isREG(i32e) && !r.is(Reg::XMM|Reg::YMM|Reg::ZMM)) throw Error(ERR_BAD_SIZE_OF_REGISTER);
+		if (scale == 0) return;
+		if (scale != 1 && scale != 2 && scale != 4 && scale != 8) throw Error(ERR_BAD_SCALE);
+		if (r.getBit() >= 128 || scale != 1) { // xmm/ymm is always index
+			index_ = r;
+		} else {
+			base_ = r;
+		}
+	}
+	bool isVsib(int bit = 128 | 256 | 512) const { return index_.isBit(bit); }
+	RegExp optimize() const
+	{
+		RegExp exp = *this;
+		// [reg * 2] => [reg + reg]
+		if (index_.isBit(i32e) && !base_.getBit() && scale_ == 2) {
+			exp.base_ = index_;
+			exp.scale_ = 1;
+		}
+		return exp;
+	}
+	bool operator==(const RegExp& rhs) const
+	{
+		return base_ == rhs.base_ && index_ == rhs.index_ && disp_ == rhs.disp_ && scale_ == rhs.scale_;
+	}
+	const Reg& getBase() const { return base_; }
+	const Reg& getIndex() const { return index_; }
+	int getScale() const { return scale_; }
+	size_t getDisp() const { return disp_; }
+	void verify() const
+	{
+		if (base_.getBit() >= 128) throw Error(ERR_BAD_SIZE_OF_REGISTER);
+		if (index_.getBit() && index_.getBit() <= 64) {
+			if (index_.getIdx() == Operand::ESP) throw Error(ERR_ESP_CANT_BE_INDEX);
+			if (base_.getBit() && base_.getBit() != index_.getBit()) throw Error(ERR_BAD_SIZE_OF_REGISTER);
+		}
+	}
+	friend RegExp operator+(const RegExp& a, const RegExp& b);
+	friend RegExp operator-(const RegExp& e, size_t disp);
+	uint8 getRex() const
+	{
+		uint8 rex = index_.getRexX() | base_.getRexB();
+		return rex ? uint8(rex | 0x40) : 0;
+	}
+private:
+	/*
+		[base_ + index_ * scale_ + disp_]
+		base : Reg32e, index : Reg32e(w/o esp), Xmm, Ymm
+	*/
+	Reg base_;
+	Reg index_;
+	int scale_;
+	size_t disp_;
+};
+
+inline RegExp operator+(const RegExp& a, const RegExp& b)
+{
+	if (a.index_.getBit() && b.index_.getBit()) throw Error(ERR_BAD_ADDRESSING);
+	RegExp ret = a;
+	if (!ret.index_.getBit()) { ret.index_ = b.index_; ret.scale_ = b.scale_; }
+	if (b.base_.getBit()) {
+		if (ret.base_.getBit()) {
+			if (ret.index_.getBit()) throw Error(ERR_BAD_ADDRESSING);
+			// base + base => base + index * 1
+			ret.index_ = b.base_;
+			// [reg + esp] => [esp + reg]
+			if (ret.index_.getIdx() == Operand::ESP) std::swap(ret.base_, ret.index_);
+			ret.scale_ = 1;
+		} else {
+			ret.base_ = b.base_;
+		}
+	}
+	ret.disp_ += b.disp_;
+	return ret;
+}
+inline RegExp operator*(const Reg& r, int scale)
+{
+	return RegExp(r, scale);
+}
+inline RegExp operator-(const RegExp& e, size_t disp)
+{
+	RegExp ret = e;
+	ret.disp_ -= disp;
+	return ret;
+}
+
+// 2nd parameter for constructor of CodeArray(maxSize, userPtr, alloc)
+void *const AutoGrow = (void*)1; //-V566
+void *const DontSetProtectRWE = (void*)2; //-V566
+
+class CodeArray {
+	enum Type {
+		USER_BUF = 1, // use userPtr(non alignment, non protect)
+		ALLOC_BUF, // use new(alignment, protect)
+		AUTO_GROW // automatically move and grow memory if necessary
+	};
+	CodeArray(const CodeArray& rhs);
+	void operator=(const CodeArray&);
+	bool isAllocType() const { return type_ == ALLOC_BUF || type_ == AUTO_GROW; }
+	struct AddrInfo {
+		size_t codeOffset; // position to write
+		size_t jmpAddr; // value to write
+		int jmpSize; // size of jmpAddr
+		inner::LabelMode mode;
+		AddrInfo(size_t _codeOffset, size_t _jmpAddr, int _jmpSize, inner::LabelMode _mode)
+			: codeOffset(_codeOffset), jmpAddr(_jmpAddr), jmpSize(_jmpSize), mode(_mode) {}
+		uint64 getVal(const uint8 *top) const
+		{
+			uint64 disp = (mode == inner::LaddTop) ? jmpAddr + size_t(top) : (mode == inner::LasIs) ? jmpAddr : jmpAddr - size_t(top);
+			if (jmpSize == 4) disp = inner::VerifyInInt32(disp);
+			return disp;
+		}
+	};
+	typedef std::list<AddrInfo> AddrInfoList;
+	AddrInfoList addrInfoList_;
+	const Type type_;
+#ifdef XBYAK_USE_MMAP_ALLOCATOR
+	MmapAllocator defaultAllocator_;
+#else
+	Allocator defaultAllocator_;
+#endif
+	Allocator *alloc_;
+protected:
+	size_t maxSize_;
+	uint8 *top_;
+	size_t size_;
+	bool isCalledCalcJmpAddress_;
+
+	bool useProtect() const { return alloc_->useProtect(); }
+	/*
+		allocate new memory and copy old data to the new area
+	*/
+	void growMemory()
+	{
+		const size_t newSize = (std::max<size_t>)(DEFAULT_MAX_CODE_SIZE, maxSize_ * 2);
+		uint8 *newTop = alloc_->alloc(newSize);
+		if (newTop == 0) throw Error(ERR_CANT_ALLOC);
+		for (size_t i = 0; i < size_; i++) newTop[i] = top_[i];
+		alloc_->free(top_);
+		top_ = newTop;
+		maxSize_ = newSize;
+	}
+	/*
+		calc jmp address for AutoGrow mode
+	*/
+	void calcJmpAddress()
+	{
+		if (isCalledCalcJmpAddress_) return;
+		for (AddrInfoList::const_iterator i = addrInfoList_.begin(), ie = addrInfoList_.end(); i != ie; ++i) {
+			uint64 disp = i->getVal(top_);
+			rewrite(i->codeOffset, disp, i->jmpSize);
+		}
+		isCalledCalcJmpAddress_ = true;
+	}
+public:
+	enum ProtectMode {
+		PROTECT_RW = 0, // read/write
+		PROTECT_RWE = 1, // read/write/exec
+		PROTECT_RE = 2 // read/exec
+	};
+	explicit CodeArray(size_t maxSize, void *userPtr = 0, Allocator *allocator = 0)
+		: type_(userPtr == AutoGrow ? AUTO_GROW : (userPtr == 0 || userPtr == DontSetProtectRWE) ? ALLOC_BUF : USER_BUF)
+		, alloc_(allocator ? allocator : (Allocator*)&defaultAllocator_)
+		, maxSize_(maxSize)
+		, top_(type_ == USER_BUF ? reinterpret_cast<uint8*>(userPtr) : alloc_->alloc((std::max<size_t>)(maxSize, 1)))
+		, size_(0)
+		, isCalledCalcJmpAddress_(false)
+	{
+		if (maxSize_ > 0 && top_ == 0) throw Error(ERR_CANT_ALLOC);
+		if ((type_ == ALLOC_BUF && userPtr != DontSetProtectRWE && useProtect()) && !setProtectMode(PROTECT_RWE, false)) {
+			alloc_->free(top_);
+			throw Error(ERR_CANT_PROTECT);
+		}
+	}
+	virtual ~CodeArray()
+	{
+		if (isAllocType()) {
+			if (useProtect()) setProtectModeRW(false);
+			alloc_->free(top_);
+		}
+	}
+	bool setProtectMode(ProtectMode mode, bool throwException = true)
+	{
+		bool isOK = protect(top_, maxSize_, mode);
+		if (isOK) return true;
+		if (throwException) throw Error(ERR_CANT_PROTECT);
+		return false;
+	}
+	bool setProtectModeRE(bool throwException = true) { return setProtectMode(PROTECT_RE, throwException); }
+	bool setProtectModeRW(bool throwException = true) { return setProtectMode(PROTECT_RW, throwException); }
+	void resetSize()
+	{
+		size_ = 0;
+		addrInfoList_.clear();
+		isCalledCalcJmpAddress_ = false;
+	}
+	void db(int code)
+	{
+		if (size_ >= maxSize_) {
+			if (type_ == AUTO_GROW) {
+				growMemory();
+			} else {
+				throw Error(ERR_CODE_IS_TOO_BIG);
+			}
+		}
+		top_[size_++] = static_cast<uint8>(code);
+	}
+	void db(const uint8 *code, size_t codeSize)
+	{
+		for (size_t i = 0; i < codeSize; i++) db(code[i]);
+	}
+	void db(uint64 code, size_t codeSize)
+	{
+		if (codeSize > 8) throw Error(ERR_BAD_PARAMETER);
+		for (size_t i = 0; i < codeSize; i++) db(static_cast<uint8>(code >> (i * 8)));
+	}
+	void dw(uint32 code) { db(code, 2); }
+	void dd(uint32 code) { db(code, 4); }
+	void dq(uint64 code) { db(code, 8); }
+	const uint8 *getCode() const { return top_; }
+	template<class F>
+	const F getCode() const { return reinterpret_cast<F>(top_); }
+	const uint8 *getCurr() const { return &top_[size_]; }
+	template<class F>
+	const F getCurr() const { return reinterpret_cast<F>(&top_[size_]); }
+	size_t getSize() const { return size_; }
+	void setSize(size_t size)
+	{
+		if (size > maxSize_) throw Error(ERR_OFFSET_IS_TOO_BIG);
+		size_ = size;
+	}
+	void dump() const
+	{
+		const uint8 *p = getCode();
+		size_t bufSize = getSize();
+		size_t remain = bufSize;
+		for (int i = 0; i < 4; i++) {
+			size_t disp = 16;
+			if (remain < 16) {
+				disp = remain;
+			}
+			for (size_t j = 0; j < 16; j++) {
+				if (j < disp) {
+					printf("%02X", p[i * 16 + j]);
+				}
+			}
+			putchar('\n');
+			remain -= disp;
+			if (remain == 0) {
+				break;
+			}
+		}
+	}
+	/*
+		@param offset [in] offset from top
+		@param disp [in] offset from the next of jmp
+		@param size [in] write size(1, 2, 4, 8)
+	*/
+	void rewrite(size_t offset, uint64 disp, size_t size)
+	{
+		assert(offset < maxSize_);
+		if (size != 1 && size != 2 && size != 4 && size != 8) throw Error(ERR_BAD_PARAMETER);
+		uint8 *const data = top_ + offset;
+		for (size_t i = 0; i < size; i++) {
+			data[i] = static_cast<uint8>(disp >> (i * 8));
+		}
+	}
+	void save(size_t offset, size_t val, int size, inner::LabelMode mode)
+	{
+		addrInfoList_.push_back(AddrInfo(offset, val, size, mode));
+	}
+	bool isAutoGrow() const { return type_ == AUTO_GROW; }
+	bool isCalledCalcJmpAddress() const { return isCalledCalcJmpAddress_; }
+	/**
+		change exec permission of memory
+		@param addr [in] buffer address
+		@param size [in] buffer size
+		@param protectMode [in] mode(RW/RWE/RE)
+		@return true(success), false(failure)
+	*/
+	static inline bool protect(const void *addr, size_t size, int protectMode)
+	{
+#if defined(_WIN32)
+		const DWORD c_rw = PAGE_READWRITE;
+		const DWORD c_rwe = PAGE_EXECUTE_READWRITE;
+		const DWORD c_re = PAGE_EXECUTE_READ;
+		DWORD mode;
+#else
+		const int c_rw = PROT_READ | PROT_WRITE;
+		const int c_rwe = PROT_READ | PROT_WRITE | PROT_EXEC;
+		const int c_re = PROT_READ | PROT_EXEC;
+		int mode;
+#endif
+		switch (protectMode) {
+		case PROTECT_RW: mode = c_rw; break;
+		case PROTECT_RWE: mode = c_rwe; break;
+		case PROTECT_RE: mode = c_re; break;
+		default:
+			return false;
+		}
+#if defined(_WIN32)
+		DWORD oldProtect;
+		return VirtualProtect(const_cast<void*>(addr), size, mode, &oldProtect) != 0;
+#elif defined(__GNUC__)
+		size_t pageSize = sysconf(_SC_PAGESIZE);
+		size_t iaddr = reinterpret_cast<size_t>(addr);
+		size_t roundAddr = iaddr & ~(pageSize - static_cast<size_t>(1));
+#ifndef NDEBUG
+		if (pageSize != 4096) fprintf(stderr, "large page(%zd) is used. not tested enough.\n", pageSize);
+#endif
+		return mprotect(reinterpret_cast<void*>(roundAddr), size + (iaddr - roundAddr), mode) == 0;
+#else
+		return true;
+#endif
+	}
+	/**
+		get aligned memory pointer
+		@param addr [in] address
+		@param alignedSize [in] power of two
+		@return aligned addr by alingedSize
+	*/
+	static inline uint8 *getAlignedAddress(uint8 *addr, size_t alignedSize = 16)
+	{
+		return reinterpret_cast<uint8*>((reinterpret_cast<size_t>(addr) + alignedSize - 1) & ~(alignedSize - static_cast<size_t>(1)));
+	}
+};
+
+class Address : public Operand {
+public:
+	enum Mode {
+		M_ModRM,
+		M_64bitDisp,
+		M_rip,
+		M_ripAddr
+	};
+	Address(uint32 sizeBit, bool broadcast, const RegExp& e)
+		: Operand(0, MEM, sizeBit), e_(e), label_(0), mode_(M_ModRM), broadcast_(broadcast)
+	{
+		e_.verify();
+	}
+#ifdef XBYAK64
+	explicit Address(size_t disp)
+		: Operand(0, MEM, 64), e_(disp), label_(0), mode_(M_64bitDisp), broadcast_(false){ }
+	Address(uint32 sizeBit, bool broadcast, const RegRip& addr)
+		: Operand(0, MEM, sizeBit), e_(addr.disp_), label_(addr.label_), mode_(addr.isAddr_ ? M_ripAddr : M_rip), broadcast_(broadcast) { }
+#endif
+	RegExp getRegExp(bool optimize = true) const
+	{
+		return optimize ? e_.optimize() : e_;
+	}
+	Mode getMode() const { return mode_; }
+	bool is32bit() const { return e_.getBase().getBit() == 32 || e_.getIndex().getBit() == 32; }
+	bool isOnlyDisp() const { return !e_.getBase().getBit() && !e_.getIndex().getBit(); } // for mov eax
+	size_t getDisp() const { return e_.getDisp(); }
+	uint8 getRex() const
+	{
+		if (mode_ != M_ModRM) return 0;
+		return getRegExp().getRex();
+	}
+	bool is64bitDisp() const { return mode_ == M_64bitDisp; } // for moffset
+	bool isBroadcast() const { return broadcast_; }
+	const Label* getLabel() const { return label_; }
+	bool operator==(const Address& rhs) const
+	{
+		return getBit() == rhs.getBit() && e_ == rhs.e_ && label_ == rhs.label_ && mode_ == rhs.mode_ && broadcast_ == rhs.broadcast_;
+	}
+	bool operator!=(const Address& rhs) const { return !operator==(rhs); }
+	bool isVsib() const { return e_.isVsib(); }
+private:
+	RegExp e_;
+	const Label* label_;
+	Mode mode_;
+	bool broadcast_;
+};
+
+inline const Address& Operand::getAddress() const
+{
+	assert(isMEM());
+	return static_cast<const Address&>(*this);
+}
+
+inline bool Operand::operator==(const Operand& rhs) const
+{
+	if (isMEM() && rhs.isMEM()) return this->getAddress() == rhs.getAddress();
+	return isEqualIfNotInherited(rhs);
+}
+
+class AddressFrame {
+	void operator=(const AddressFrame&);
+	AddressFrame(const AddressFrame&);
+public:
+	const uint32 bit_;
+	const bool broadcast_;
+	explicit AddressFrame(uint32 bit, bool broadcast = false) : bit_(bit), broadcast_(broadcast) { }
+	Address operator[](const RegExp& e) const
+	{
+		return Address(bit_, broadcast_, e);
+	}
+	Address operator[](const void *disp) const
+	{
+		return Address(bit_, broadcast_, RegExp(reinterpret_cast<size_t>(disp)));
+	}
+#ifdef XBYAK64
+	Address operator[](uint64 disp) const { return Address(disp); }
+	Address operator[](const RegRip& addr) const { return Address(bit_, broadcast_, addr); }
+#endif
+};
+
+struct JmpLabel {
+	size_t endOfJmp; /* offset from top to the end address of jmp */
+	int jmpSize;
+	inner::LabelMode mode;
+	size_t disp; // disp for [rip + disp]
+	explicit JmpLabel(size_t endOfJmp = 0, int jmpSize = 0, inner::LabelMode mode = inner::LasIs, size_t disp = 0)
+		: endOfJmp(endOfJmp), jmpSize(jmpSize), mode(mode), disp(disp)
+	{
+	}
+};
+
+class LabelManager;
+
+class Label {
+	mutable LabelManager *mgr;
+	mutable int id;
+	friend class LabelManager;
+public:
+	Label() : mgr(0), id(0) {}
+	Label(const Label& rhs);
+	Label& operator=(const Label& rhs);
+	~Label();
+	void clear() { mgr = 0; id = 0; }
+	int getId() const { return id; }
+	const uint8 *getAddress() const;
+
+	// backward compatibility
+	static inline std::string toStr(int num)
+	{
+		char buf[16];
+#if defined(_MSC_VER) && (_MSC_VER < 1900)
+		_snprintf_s
+#else
+		snprintf
+#endif
+		(buf, sizeof(buf), ".%08x", num);
+		return buf;
+	}
+};
+
+class LabelManager {
+	// for string label
+	struct SlabelVal {
+		size_t offset;
+		SlabelVal(size_t offset) : offset(offset) {}
+	};
+	typedef XBYAK_STD_UNORDERED_MAP<std::string, SlabelVal> SlabelDefList;
+	typedef XBYAK_STD_UNORDERED_MULTIMAP<std::string, const JmpLabel> SlabelUndefList;
+	struct SlabelState {
+		SlabelDefList defList;
+		SlabelUndefList undefList;
+	};
+	typedef std::list<SlabelState> StateList;
+	// for Label class
+	struct ClabelVal {
+		ClabelVal(size_t offset = 0) : offset(offset), refCount(1) {}
+		size_t offset;
+		int refCount;
+	};
+	typedef XBYAK_STD_UNORDERED_MAP<int, ClabelVal> ClabelDefList;
+	typedef XBYAK_STD_UNORDERED_MULTIMAP<int, const JmpLabel> ClabelUndefList;
+	typedef XBYAK_STD_UNORDERED_SET<Label*> LabelPtrList;
+
+	CodeArray *base_;
+	// global : stateList_.front(), local : stateList_.back()
+	StateList stateList_;
+	mutable int labelId_;
+	ClabelDefList clabelDefList_;
+	ClabelUndefList clabelUndefList_;
+	LabelPtrList labelPtrList_;
+
+	int getId(const Label& label) const
+	{
+		if (label.id == 0) label.id = labelId_++;
+		return label.id;
+	}
+	template<class DefList, class UndefList, class T>
+	void define_inner(DefList& defList, UndefList& undefList, const T& labelId, size_t addrOffset)
+	{
+		// add label
+		typename DefList::value_type item(labelId, addrOffset);
+		std::pair<typename DefList::iterator, bool> ret = defList.insert(item);
+		if (!ret.second) throw Error(ERR_LABEL_IS_REDEFINED);
+		// search undefined label
+		for (;;) {
+			typename UndefList::iterator itr = undefList.find(labelId);
+			if (itr == undefList.end()) break;
+			const JmpLabel *jmp = &itr->second;
+			const size_t offset = jmp->endOfJmp - jmp->jmpSize;
+			size_t disp;
+			if (jmp->mode == inner::LaddTop) {
+				disp = addrOffset;
+			} else if (jmp->mode == inner::Labs) {
+				disp = size_t(base_->getCurr());
+			} else {
+				disp = addrOffset - jmp->endOfJmp + jmp->disp;
+#ifdef XBYAK64
+				if (jmp->jmpSize <= 4 && !inner::IsInInt32(disp)) throw Error(ERR_OFFSET_IS_TOO_BIG);
+#endif
+				if (jmp->jmpSize == 1 && !inner::IsInDisp8((uint32)disp)) throw Error(ERR_LABEL_IS_TOO_FAR);
+			}
+			if (base_->isAutoGrow()) {
+				base_->save(offset, disp, jmp->jmpSize, jmp->mode);
+			} else {
+				base_->rewrite(offset, disp, jmp->jmpSize);
+			}
+			undefList.erase(itr);
+		}
+	}
+	template<class DefList, class T>
+	bool getOffset_inner(const DefList& defList, size_t *offset, const T& label) const
+	{
+		typename DefList::const_iterator i = defList.find(label);
+		if (i == defList.end()) return false;
+		*offset = i->second.offset;
+		return true;
+	}
+	friend class Label;
+	void incRefCount(int id, Label *label)
+	{
+		clabelDefList_[id].refCount++;
+		labelPtrList_.insert(label);
+	}
+	void decRefCount(int id, Label *label)
+	{
+		labelPtrList_.erase(label);
+		ClabelDefList::iterator i = clabelDefList_.find(id);
+		if (i == clabelDefList_.end()) return;
+		if (i->second.refCount == 1) {
+			clabelDefList_.erase(id);
+		} else {
+			--i->second.refCount;
+		}
+	}
+	template<class T>
+	bool hasUndefinedLabel_inner(const T& list) const
+	{
+#ifndef NDEBUG
+		for (typename T::const_iterator i = list.begin(); i != list.end(); ++i) {
+			std::cerr << "undefined label:" << i->first << std::endl;
+		}
+#endif
+		return !list.empty();
+	}
+	// detach all labels linked to LabelManager
+	void resetLabelPtrList()
+	{
+		for (LabelPtrList::iterator i = labelPtrList_.begin(), ie = labelPtrList_.end(); i != ie; ++i) {
+			(*i)->clear();
+		}
+		labelPtrList_.clear();
+	}
+public:
+	LabelManager()
+	{
+		reset();
+	}
+	~LabelManager()
+	{
+		resetLabelPtrList();
+	}
+	void reset()
+	{
+		base_ = 0;
+		labelId_ = 1;
+		stateList_.clear();
+		stateList_.push_back(SlabelState());
+		stateList_.push_back(SlabelState());
+		clabelDefList_.clear();
+		clabelUndefList_.clear();
+		resetLabelPtrList();
+	}
+	void enterLocal()
+	{
+		stateList_.push_back(SlabelState());
+	}
+	void leaveLocal()
+	{
+		if (stateList_.size() <= 2) throw Error(ERR_UNDER_LOCAL_LABEL);
+		if (hasUndefinedLabel_inner(stateList_.back().undefList)) throw Error(ERR_LABEL_IS_NOT_FOUND);
+		stateList_.pop_back();
+	}
+	void set(CodeArray *base) { base_ = base; }
+	void defineSlabel(std::string label)
+	{
+		if (label == "@b" || label == "@f") throw Error(ERR_BAD_LABEL_STR);
+		if (label == "@@") {
+			SlabelDefList& defList = stateList_.front().defList;
+			SlabelDefList::iterator i = defList.find("@f");
+			if (i != defList.end()) {
+				defList.erase(i);
+				label = "@b";
+			} else {
+				i = defList.find("@b");
+				if (i != defList.end()) {
+					defList.erase(i);
+				}
+				label = "@f";
+			}
+		}
+		SlabelState& st = *label.c_str() == '.' ? stateList_.back() : stateList_.front();
+		define_inner(st.defList, st.undefList, label, base_->getSize());
+	}
+	void defineClabel(Label& label)
+	{
+		define_inner(clabelDefList_, clabelUndefList_, getId(label), base_->getSize());
+		label.mgr = this;
+		labelPtrList_.insert(&label);
+	}
+	void assign(Label& dst, const Label& src)
+	{
+		ClabelDefList::const_iterator i = clabelDefList_.find(src.id);
+		if (i == clabelDefList_.end()) throw Error(ERR_LABEL_ISNOT_SET_BY_L);
+		define_inner(clabelDefList_, clabelUndefList_, dst.id, i->second.offset);
+		dst.mgr = this;
+		labelPtrList_.insert(&dst);
+	}
+	bool getOffset(size_t *offset, std::string& label) const
+	{
+		const SlabelDefList& defList = stateList_.front().defList;
+		if (label == "@b") {
+			if (defList.find("@f") != defList.end()) {
+				label = "@f";
+			} else if (defList.find("@b") == defList.end()) {
+				throw Error(ERR_LABEL_IS_NOT_FOUND);
+			}
+		} else if (label == "@f") {
+			if (defList.find("@f") != defList.end()) {
+				label = "@b";
+			}
+		}
+		const SlabelState& st = *label.c_str() == '.' ? stateList_.back() : stateList_.front();
+		return getOffset_inner(st.defList, offset, label);
+	}
+	bool getOffset(size_t *offset, const Label& label) const
+	{
+		return getOffset_inner(clabelDefList_, offset, getId(label));
+	}
+	void addUndefinedLabel(const std::string& label, const JmpLabel& jmp)
+	{
+		SlabelState& st = *label.c_str() == '.' ? stateList_.back() : stateList_.front();
+		st.undefList.insert(SlabelUndefList::value_type(label, jmp));
+	}
+	void addUndefinedLabel(const Label& label, const JmpLabel& jmp)
+	{
+		clabelUndefList_.insert(ClabelUndefList::value_type(label.id, jmp));
+	}
+	bool hasUndefSlabel() const
+	{
+		for (StateList::const_iterator i = stateList_.begin(), ie = stateList_.end(); i != ie; ++i) {
+			if (hasUndefinedLabel_inner(i->undefList)) return true;
+		}
+		return false;
+	}
+	bool hasUndefClabel() const { return hasUndefinedLabel_inner(clabelUndefList_); }
+	const uint8 *getCode() const { return base_->getCode(); }
+	bool isReady() const { return !base_->isAutoGrow() || base_->isCalledCalcJmpAddress(); }
+};
+
+inline Label::Label(const Label& rhs)
+{
+	id = rhs.id;
+	mgr = rhs.mgr;
+	if (mgr) mgr->incRefCount(id, this);
+}
+inline Label& Label::operator=(const Label& rhs)
+{
+	if (id) throw Error(ERR_LABEL_IS_ALREADY_SET_BY_L);
+	id = rhs.id;
+	mgr = rhs.mgr;
+	if (mgr) mgr->incRefCount(id, this);
+	return *this;
+}
+inline Label::~Label()
+{
+	if (id && mgr) mgr->decRefCount(id, this);
+}
+inline const uint8* Label::getAddress() const
+{
+	if (mgr == 0 || !mgr->isReady()) return 0;
+	size_t offset;
+	if (!mgr->getOffset(&offset, *this)) return 0;
+	return mgr->getCode() + offset;
+}
+
+class CodeGenerator : public CodeArray {
+public:
+	enum LabelType {
+		T_SHORT,
+		T_NEAR,
+		T_AUTO // T_SHORT if possible
+	};
+private:
+	CodeGenerator operator=(const CodeGenerator&); // don't call
+#ifdef XBYAK64
+	enum { i32e = 32 | 64, BIT = 64 };
+	static const size_t dummyAddr = (size_t(0x11223344) << 32) | 55667788;
+	typedef Reg64 NativeReg;
+#else
+	enum { i32e = 32, BIT = 32 };
+	static const size_t dummyAddr = 0x12345678;
+	typedef Reg32 NativeReg;
+#endif
+	// (XMM, XMM|MEM)
+	static inline bool isXMM_XMMorMEM(const Operand& op1, const Operand& op2)
+	{
+		return op1.isXMM() && (op2.isXMM() || op2.isMEM());
+	}
+	// (MMX, MMX|MEM) or (XMM, XMM|MEM)
+	static inline bool isXMMorMMX_MEM(const Operand& op1, const Operand& op2)
+	{
+		return (op1.isMMX() && (op2.isMMX() || op2.isMEM())) || isXMM_XMMorMEM(op1, op2);
+	}
+	// (XMM, MMX|MEM)
+	static inline bool isXMM_MMXorMEM(const Operand& op1, const Operand& op2)
+	{
+		return op1.isXMM() && (op2.isMMX() || op2.isMEM());
+	}
+	// (MMX, XMM|MEM)
+	static inline bool isMMX_XMMorMEM(const Operand& op1, const Operand& op2)
+	{
+		return op1.isMMX() && (op2.isXMM() || op2.isMEM());
+	}
+	// (XMM, REG32|MEM)
+	static inline bool isXMM_REG32orMEM(const Operand& op1, const Operand& op2)
+	{
+		return op1.isXMM() && (op2.isREG(i32e) || op2.isMEM());
+	}
+	// (REG32, XMM|MEM)
+	static inline bool isREG32_XMMorMEM(const Operand& op1, const Operand& op2)
+	{
+		return op1.isREG(i32e) && (op2.isXMM() || op2.isMEM());
+	}
+	// (REG32, REG32|MEM)
+	static inline bool isREG32_REG32orMEM(const Operand& op1, const Operand& op2)
+	{
+		return op1.isREG(i32e) && ((op2.isREG(i32e) && op1.getBit() == op2.getBit()) || op2.isMEM());
+	}
+	void rex(const Operand& op1, const Operand& op2 = Operand())
+	{
+		uint8 rex = 0;
+		const Operand *p1 = &op1, *p2 = &op2;
+		if (p1->isMEM()) std::swap(p1, p2);
+		if (p1->isMEM()) throw Error(ERR_BAD_COMBINATION);
+		if (p2->isMEM()) {
+			const Address& addr = p2->getAddress();
+			if (BIT == 64 && addr.is32bit()) db(0x67);
+			rex = addr.getRex() | p1->getReg().getRex();
+		} else {
+			// ModRM(reg, base);
+			rex = op2.getReg().getRex(op1.getReg());
+		}
+		// except movsx(16bit, 32/64bit)
+		if ((op1.isBit(16) && !op2.isBit(i32e)) || (op2.isBit(16) && !op1.isBit(i32e))) db(0x66);
+		if (rex) db(rex);
+	}
+	enum AVXtype {
+		// low 3 bit
+		T_N1 = 1,
+		T_N2 = 2,
+		T_N4 = 3,
+		T_N8 = 4,
+		T_N16 = 5,
+		T_N32 = 6,
+		T_NX_MASK = 7,
+		//
+		T_N_VL = 1 << 3, // N * (1, 2, 4) for VL
+		T_DUP = 1 << 4, // N = (8, 32, 64)
+		T_66 = 1 << 5,
+		T_F3 = 1 << 6,
+		T_F2 = 1 << 7,
+		T_0F = 1 << 8,
+		T_0F38 = 1 << 9,
+		T_0F3A = 1 << 10,
+		T_L0 = 1 << 11,
+		T_L1 = 1 << 12,
+		T_W0 = 1 << 13,
+		T_W1 = 1 << 14,
+		T_EW0 = 1 << 15,
+		T_EW1 = 1 << 16,
+		T_YMM = 1 << 17, // support YMM, ZMM
+		T_EVEX = 1 << 18,
+		T_ER_X = 1 << 19, // xmm{er}
+		T_ER_Y = 1 << 20, // ymm{er}
+		T_ER_Z = 1 << 21, // zmm{er}
+		T_SAE_X = 1 << 22, // xmm{sae}
+		T_SAE_Y = 1 << 23, // ymm{sae}
+		T_SAE_Z = 1 << 24, // zmm{sae}
+		T_MUST_EVEX = 1 << 25, // contains T_EVEX
+		T_B32 = 1 << 26, // m32bcst
+		T_B64 = 1 << 27, // m64bcst
+		T_M_K = 1 << 28, // mem{k}
+		T_VSIB = 1 << 29,
+		T_MEM_EVEX = 1 << 30, // use evex if mem
+		T_XXX
+	};
+	void vex(const Reg& reg, const Reg& base, const Operand *v, int type, int code, bool x = false)
+	{
+		int w = (type & T_W1) ? 1 : 0;
+		bool is256 = (type & T_L1) ? true : (type & T_L0) ? false : reg.isYMM();
+		bool r = reg.isExtIdx();
+		bool b = base.isExtIdx();
+		int idx = v ? v->getIdx() : 0;
+		if ((idx | reg.getIdx() | base.getIdx()) >= 16) throw Error(ERR_BAD_COMBINATION);
+		uint32 pp = (type & T_66) ? 1 : (type & T_F3) ? 2 : (type & T_F2) ? 3 : 0;
+		uint32 vvvv = (((~idx) & 15) << 3) | (is256 ? 4 : 0) | pp;
+		if (!b && !x && !w && (type & T_0F)) {
+			db(0xC5); db((r ? 0 : 0x80) | vvvv);
+		} else {
+			uint32 mmmm = (type & T_0F) ? 1 : (type & T_0F38) ? 2 : (type & T_0F3A) ? 3 : 0;
+			db(0xC4); db((r ? 0 : 0x80) | (x ? 0 : 0x40) | (b ? 0 : 0x20) | mmmm); db((w << 7) | vvvv);
+		}
+		db(code);
+	}
+	void verifySAE(const Reg& r, int type) const
+	{
+		if (((type & T_SAE_X) && r.isXMM()) || ((type & T_SAE_Y) && r.isYMM()) || ((type & T_SAE_Z) && r.isZMM())) return;
+		throw Error(ERR_SAE_IS_INVALID);
+	}
+	void verifyER(const Reg& r, int type) const
+	{
+		if (((type & T_ER_X) && r.isXMM()) || ((type & T_ER_Y) && r.isYMM()) || ((type & T_ER_Z) && r.isZMM())) return;
+		throw Error(ERR_ER_IS_INVALID);
+	}
+	// (a, b, c) contains non zero two or three values then err
+	int verifyDuplicate(int a, int b, int c, int err)
+	{
+		int v = a | b | c;
+		if ((a > 0 && a != v) + (b > 0 && b != v) + (c > 0 && c != v) > 0) return Error(err);
+		return v;
+	}
+	int evex(const Reg& reg, const Reg& base, const Operand *v, int type, int code, bool x = false, bool b = false, int aaa = 0, uint32 VL = 0, bool Hi16Vidx = false)
+	{
+		if (!(type & (T_EVEX | T_MUST_EVEX))) throw Error(ERR_EVEX_IS_INVALID);
+		int w = (type & T_EW1) ? 1 : 0;
+		uint32 mm = (type & T_0F) ? 1 : (type & T_0F38) ? 2 : (type & T_0F3A) ? 3 : 0;
+		uint32 pp = (type & T_66) ? 1 : (type & T_F3) ? 2 : (type & T_F2) ? 3 : 0;
+
+		int idx = v ? v->getIdx() : 0;
+		uint32 vvvv = ~idx;
+
+		bool R = !reg.isExtIdx();
+		bool X = x ? false : !base.isExtIdx2();
+		bool B = !base.isExtIdx();
+		bool Rp = !reg.isExtIdx2();
+		int LL;
+		int rounding = verifyDuplicate(reg.getRounding(), base.getRounding(), v ? v->getRounding() : 0, ERR_ROUNDING_IS_ALREADY_SET);
+		int disp8N = 1;
+		if (rounding) {
+			if (rounding == EvexModifierRounding::T_SAE) {
+				verifySAE(base, type); LL = 0;
+			} else {
+				verifyER(base, type); LL = rounding - 1;
+			}
+			b = true;
+		} else {
+			if (v) VL = (std::max)(VL, v->getBit());
+			VL = (std::max)((std::max)(reg.getBit(), base.getBit()), VL);
+			LL = (VL == 512) ? 2 : (VL == 256) ? 1 : 0;
+			if (b) {
+				disp8N = (type & T_B32) ? 4 : 8;
+			} else if (type & T_DUP) {
+				disp8N = VL == 128 ? 8 : VL == 256 ? 32 : 64;
+			} else {
+				if ((type & (T_NX_MASK | T_N_VL)) == 0) {
+					type |= T_N16 | T_N_VL; // default
+				}
+				int low = type & T_NX_MASK;
+				if (low > 0) {
+					disp8N = 1 << (low - 1);
+					if (type & T_N_VL) disp8N *= (VL == 512 ? 4 : VL == 256 ? 2 : 1);
+				}
+			}
+		}
+		bool Vp = !((v ? v->isExtIdx2() : 0) | Hi16Vidx);
+		bool z = reg.hasZero() || base.hasZero() || (v ? v->hasZero() : false);
+		if (aaa == 0) aaa = verifyDuplicate(base.getOpmaskIdx(), reg.getOpmaskIdx(), (v ? v->getOpmaskIdx() : 0), ERR_OPMASK_IS_ALREADY_SET);
+		db(0x62);
+		db((R ? 0x80 : 0) | (X ? 0x40 : 0) | (B ? 0x20 : 0) | (Rp ? 0x10 : 0) | (mm & 3));
+		db((w == 1 ? 0x80 : 0) | ((vvvv & 15) << 3) | 4 | (pp & 3));
+		db((z ? 0x80 : 0) | ((LL & 3) << 5) | (b ? 0x10 : 0) | (Vp ? 8 : 0) | (aaa & 7));
+		db(code);
+		return disp8N;
+	}
+	void setModRM(int mod, int r1, int r2)
+	{
+		db(static_cast<uint8>((mod << 6) | ((r1 & 7) << 3) | (r2 & 7)));
+	}
+	void setSIB(const RegExp& e, int reg, int disp8N = 0)
+	{
+		size_t disp64 = e.getDisp();
+#ifdef XBYAK64
+		size_t high = disp64 >> 32;
+		if (high != 0 && high != 0xFFFFFFFF) throw Error(ERR_OFFSET_IS_TOO_BIG);
+#endif
+		uint32 disp = static_cast<uint32>(disp64);
+		const Reg& base = e.getBase();
+		const Reg& index = e.getIndex();
+		const int baseIdx = base.getIdx();
+		const int baseBit = base.getBit();
+		const int indexBit = index.getBit();
+		enum {
+			mod00 = 0, mod01 = 1, mod10 = 2
+		};
+		int mod = mod10; // disp32
+		if (!baseBit || ((baseIdx & 7) != Operand::EBP && disp == 0)) {
+			mod = mod00;
+		} else {
+			if (disp8N == 0) {
+				if (inner::IsInDisp8(disp)) {
+					mod = mod01;
+				}
+			} else {
+				// disp must be casted to signed
+				uint32 t = static_cast<uint32>(static_cast<int>(disp) / disp8N);
+				if ((disp % disp8N) == 0 && inner::IsInDisp8(t)) {
+					disp = t;
+					mod = mod01;
+				}
+			}
+		}
+		const int newBaseIdx = baseBit ? (baseIdx & 7) : Operand::EBP;
+		/* ModR/M = [2:3:3] = [Mod:reg/code:R/M] */
+		bool hasSIB = indexBit || (baseIdx & 7) == Operand::ESP;
+#ifdef XBYAK64
+		if (!baseBit && !indexBit) hasSIB = true;
+#endif
+		if (hasSIB) {
+			setModRM(mod, reg, Operand::ESP);
+			/* SIB = [2:3:3] = [SS:index:base(=rm)] */
+			const int idx = indexBit ? (index.getIdx() & 7) : Operand::ESP;
+			const int scale = e.getScale();
+			const int SS = (scale == 8) ? 3 : (scale == 4) ? 2 : (scale == 2) ? 1 : 0;
+			setModRM(SS, idx, newBaseIdx);
+		} else {
+			setModRM(mod, reg, newBaseIdx);
+		}
+		if (mod == mod01) {
+			db(disp);
+		} else if (mod == mod10 || (mod == mod00 && !baseBit)) {
+			dd(disp);
+		}
+	}
+	LabelManager labelMgr_;
+	bool isInDisp16(uint32 x) const { return 0xFFFF8000 <= x || x <= 0x7FFF; }
+	void opModR(const Reg& reg1, const Reg& reg2, int code0, int code1 = NONE, int code2 = NONE)
+	{
+		rex(reg2, reg1);
+		db(code0 | (reg1.isBit(8) ? 0 : 1)); if (code1 != NONE) db(code1); if (code2 != NONE) db(code2);
+		setModRM(3, reg1.getIdx(), reg2.getIdx());
+	}
+	void opModM(const Address& addr, const Reg& reg, int code0, int code1 = NONE, int code2 = NONE, int immSize = 0)
+	{
+		if (addr.is64bitDisp()) throw Error(ERR_CANT_USE_64BIT_DISP);
+		rex(addr, reg);
+		db(code0 | (reg.isBit(8) ? 0 : 1)); if (code1 != NONE) db(code1); if (code2 != NONE) db(code2);
+		opAddr(addr, reg.getIdx(), immSize);
+	}
+	void opMIB(const Address& addr, const Reg& reg, int code0, int code1)
+	{
+		if (addr.is64bitDisp()) throw Error(ERR_CANT_USE_64BIT_DISP);
+		if (addr.getMode() != Address::M_ModRM) throw Error(ERR_INVALID_MIB_ADDRESS);
+		if (BIT == 64 && addr.is32bit()) db(0x67);
+		const RegExp& regExp = addr.getRegExp(false);
+		uint8 rex = regExp.getRex();
+		if (rex) db(rex);
+		db(code0); db(code1);
+		setSIB(regExp, reg.getIdx());
+	}
+	void makeJmp(uint32 disp, LabelType type, uint8 shortCode, uint8 longCode, uint8 longPref)
+	{
+		const int shortJmpSize = 2;
+		const int longHeaderSize = longPref ? 2 : 1;
+		const int longJmpSize = longHeaderSize + 4;
+		if (type != T_NEAR && inner::IsInDisp8(disp - shortJmpSize)) {
+			db(shortCode); db(disp - shortJmpSize);
+		} else {
+			if (type == T_SHORT) throw Error(ERR_LABEL_IS_TOO_FAR);
+			if (longPref) db(longPref);
+			db(longCode); dd(disp - longJmpSize);
+		}
+	}
+	template<class T>
+	void opJmp(T& label, LabelType type, uint8 shortCode, uint8 longCode, uint8 longPref)
+	{
+		if (isAutoGrow() && size_ + 16 >= maxSize_) growMemory(); /* avoid splitting code of jmp */
+		size_t offset = 0;
+		if (labelMgr_.getOffset(&offset, label)) { /* label exists */
+			makeJmp(inner::VerifyInInt32(offset - size_), type, shortCode, longCode, longPref);
+		} else {
+			int jmpSize = 0;
+			if (type == T_NEAR) {
+				jmpSize = 4;
+				if (longPref) db(longPref);
+				db(longCode); dd(0);
+			} else {
+				jmpSize = 1;
+				db(shortCode); db(0);
+			}
+			JmpLabel jmp(size_, jmpSize, inner::LasIs);
+			labelMgr_.addUndefinedLabel(label, jmp);
+		}
+	}
+	void opJmpAbs(const void *addr, LabelType type, uint8 shortCode, uint8 longCode, uint8 longPref = 0)
+	{
+		if (isAutoGrow()) {
+			if (type != T_NEAR) throw Error(ERR_ONLY_T_NEAR_IS_SUPPORTED_IN_AUTO_GROW);
+			if (size_ + 16 >= maxSize_) growMemory();
+			if (longPref) db(longPref);
+			db(longCode);
+			dd(0);
+			save(size_ - 4, size_t(addr) - size_, 4, inner::Labs);
+		} else {
+			makeJmp(inner::VerifyInInt32(reinterpret_cast<const uint8*>(addr) - getCurr()), type, shortCode, longCode, longPref);
+		}
+
+	}
+	// reg is reg field of ModRM
+	// immSize is the size for immediate value
+	// disp8N = 0(normal), disp8N = 1(force disp32), disp8N = {2, 4, 8} ; compressed displacement
+	void opAddr(const Address &addr, int reg, int immSize = 0, int disp8N = 0, bool permitVisb = false)
+	{
+		if (!permitVisb && addr.isVsib()) throw Error(ERR_BAD_VSIB_ADDRESSING);
+		if (addr.getMode() == Address::M_ModRM) {
+			setSIB(addr.getRegExp(), reg, disp8N);
+		} else if (addr.getMode() == Address::M_rip || addr.getMode() == Address::M_ripAddr) {
+			setModRM(0, reg, 5);
+			if (addr.getLabel()) { // [rip + Label]
+				putL_inner(*addr.getLabel(), true, addr.getDisp() - immSize);
+			} else {
+				size_t disp = addr.getDisp();
+				if (addr.getMode() == Address::M_ripAddr) {
+					if (isAutoGrow()) throw Error(ERR_INVALID_RIP_IN_AUTO_GROW);
+					disp -= (size_t)getCurr() + 4 + immSize;
+				}
+				dd(inner::VerifyInInt32(disp));
+			}
+		}
+	}
+	/* preCode is for SSSE3/SSE4 */
+	void opGen(const Operand& reg, const Operand& op, int code, int pref, bool isValid(const Operand&, const Operand&), int imm8 = NONE, int preCode = NONE)
+	{
+		if (isValid && !isValid(reg, op)) throw Error(ERR_BAD_COMBINATION);
+		if (pref != NONE) db(pref);
+		if (op.isMEM()) {
+			opModM(op.getAddress(), reg.getReg(), 0x0F, preCode, code, (imm8 != NONE) ? 1 : 0);
+		} else {
+			opModR(reg.getReg(), op.getReg(), 0x0F, preCode, code);
+		}
+		if (imm8 != NONE) db(imm8);
+	}
+	void opMMX_IMM(const Mmx& mmx, int imm8, int code, int ext)
+	{
+		if (mmx.isXMM()) db(0x66);
+		opModR(Reg32(ext), mmx, 0x0F, code);
+		db(imm8);
+	}
+	void opMMX(const Mmx& mmx, const Operand& op, int code, int pref = 0x66, int imm8 = NONE, int preCode = NONE)
+	{
+		opGen(mmx, op, code, mmx.isXMM() ? pref : NONE, isXMMorMMX_MEM, imm8, preCode);
+	}
+	void opMovXMM(const Operand& op1, const Operand& op2, int code, int pref)
+	{
+		if (pref != NONE) db(pref);
+		if (op1.isXMM() && op2.isMEM()) {
+			opModM(op2.getAddress(), op1.getReg(), 0x0F, code);
+		} else if (op1.isMEM() && op2.isXMM()) {
+			opModM(op1.getAddress(), op2.getReg(), 0x0F, code | 1);
+		} else {
+			throw Error(ERR_BAD_COMBINATION);
+		}
+	}
+	void opExt(const Operand& op, const Mmx& mmx, int code, int imm, bool hasMMX2 = false)
+	{
+		if (hasMMX2 && op.isREG(i32e)) { /* pextrw is special */
+			if (mmx.isXMM()) db(0x66);
+			opModR(op.getReg(), mmx, 0x0F, 0xC5); db(imm);
+		} else {
+			opGen(mmx, op, code, 0x66, isXMM_REG32orMEM, imm, 0x3A);
+		}
+	}
+	void opR_ModM(const Operand& op, int bit, int ext, int code0, int code1 = NONE, int code2 = NONE, bool disableRex = false, int immSize = 0)
+	{
+		int opBit = op.getBit();
+		if (disableRex && opBit == 64) opBit = 32;
+		if (op.isREG(bit)) {
+			opModR(Reg(ext, Operand::REG, opBit), op.getReg().changeBit(opBit), code0, code1, code2);
+		} else if (op.isMEM()) {
+			opModM(op.getAddress(), Reg(ext, Operand::REG, opBit), code0, code1, code2, immSize);
+		} else {
+			throw Error(ERR_BAD_COMBINATION);
+		}
+	}
+	void opShift(const Operand& op, int imm, int ext)
+	{
+		verifyMemHasSize(op);
+		opR_ModM(op, 0, ext, (0xC0 | ((imm == 1 ? 1 : 0) << 4)), NONE, NONE, false, (imm != 1) ? 1 : 0);
+		if (imm != 1) db(imm);
+	}
+	void opShift(const Operand& op, const Reg8& _cl, int ext)
+	{
+		if (_cl.getIdx() != Operand::CL) throw Error(ERR_BAD_COMBINATION);
+		opR_ModM(op, 0, ext, 0xD2);
+	}
+	void opModRM(const Operand& op1, const Operand& op2, bool condR, bool condM, int code0, int code1 = NONE, int code2 = NONE, int immSize = 0)
+	{
+		if (condR) {
+			opModR(op1.getReg(), op2.getReg(), code0, code1, code2);
+		} else if (condM) {
+			opModM(op2.getAddress(), op1.getReg(), code0, code1, code2, immSize);
+		} else {
+			throw Error(ERR_BAD_COMBINATION);
+		}
+	}
+	void opShxd(const Operand& op, const Reg& reg, uint8 imm, int code, const Reg8 *_cl = 0)
+	{
+		if (_cl && _cl->getIdx() != Operand::CL) throw Error(ERR_BAD_COMBINATION);
+		opModRM(reg, op, (op.isREG(16 | i32e) && op.getBit() == reg.getBit()), op.isMEM() && (reg.isREG(16 | i32e)), 0x0F, code | (_cl ? 1 : 0), NONE, _cl ? 0 : 1);
+		if (!_cl) db(imm);
+	}
+	// (REG, REG|MEM), (MEM, REG)
+	void opRM_RM(const Operand& op1, const Operand& op2, int code)
+	{
+		if (op1.isREG() && op2.isMEM()) {
+			opModM(op2.getAddress(), op1.getReg(), code | 2);
+		} else {
+			opModRM(op2, op1, op1.isREG() && op1.getKind() == op2.getKind(), op1.isMEM() && op2.isREG(), code);
+		}
+	}
+	// (REG|MEM, IMM)
+	void opRM_I(const Operand& op, uint32 imm, int code, int ext)
+	{
+		verifyMemHasSize(op);
+		uint32 immBit = inner::IsInDisp8(imm) ? 8 : isInDisp16(imm) ? 16 : 32;
+		if (op.isBit(8)) immBit = 8;
+		if (op.getBit() < immBit) throw Error(ERR_IMM_IS_TOO_BIG);
+		if (op.isBit(32|64) && immBit == 16) immBit = 32; /* don't use MEM16 if 32/64bit mode */
+		if (op.isREG() && op.getIdx() == 0 && (op.getBit() == immBit || (op.isBit(64) && immBit == 32))) { // rax, eax, ax, al
+			rex(op);
+			db(code | 4 | (immBit == 8 ? 0 : 1));
+		} else {
+			int tmp = immBit < (std::min)(op.getBit(), 32U) ? 2 : 0;
+			opR_ModM(op, 0, ext, 0x80 | tmp, NONE, NONE, false, immBit / 8);
+		}
+		db(imm, immBit / 8);
+	}
+	void opIncDec(const Operand& op, int code, int ext)
+	{
+		verifyMemHasSize(op);
+#ifndef XBYAK64
+		if (op.isREG() && !op.isBit(8)) {
+			rex(op); db(code | op.getIdx());
+			return;
+		}
+#endif
+		code = 0xFE;
+		if (op.isREG()) {
+			opModR(Reg(ext, Operand::REG, op.getBit()), op.getReg(), code);
+		} else {
+			opModM(op.getAddress(), Reg(ext, Operand::REG, op.getBit()), code);
+		}
+	}
+	void opPushPop(const Operand& op, int code, int ext, int alt)
+	{
+		int bit = op.getBit();
+		if (bit == 16 || bit == BIT) {
+			if (bit == 16) db(0x66);
+			if (op.isREG()) {
+				if (op.getReg().getIdx() >= 8) db(0x41);
+				db(alt | (op.getIdx() & 7));
+				return;
+			}
+			if (op.isMEM()) {
+				opModM(op.getAddress(), Reg(ext, Operand::REG, 32), code);
+				return;
+			}
+		}
+		throw Error(ERR_BAD_COMBINATION);
+	}
+	void verifyMemHasSize(const Operand& op) const
+	{
+		if (op.isMEM() && op.getBit() == 0) throw Error(ERR_MEM_SIZE_IS_NOT_SPECIFIED);
+	}
+	/*
+		mov(r, imm) = db(imm, mov_imm(r, imm))
+	*/
+	int mov_imm(const Reg& reg, size_t imm)
+	{
+		int bit = reg.getBit();
+		const int idx = reg.getIdx();
+		int code = 0xB0 | ((bit == 8 ? 0 : 1) << 3);
+		if (bit == 64 && (imm & ~size_t(0xffffffffu)) == 0) {
+			rex(Reg32(idx));
+			bit = 32;
+		} else {
+			rex(reg);
+			if (bit == 64 && inner::IsInInt32(imm)) {
+				db(0xC7);
+				code = 0xC0;
+				bit = 32;
+			}
+		}
+		db(code | (idx & 7));
+		return bit / 8;
+	}
+	template<class T>
+	void putL_inner(T& label, bool relative = false, size_t disp = 0)
+	{
+		const int jmpSize = relative ? 4 : (int)sizeof(size_t);
+		if (isAutoGrow() && size_ + 16 >= maxSize_) growMemory();
+		size_t offset = 0;
+		if (labelMgr_.getOffset(&offset, label)) {
+			if (relative) {
+				db(inner::VerifyInInt32(offset + disp - size_ - jmpSize), jmpSize);
+			} else if (isAutoGrow()) {
+				db(uint64(0), jmpSize);
+				save(size_ - jmpSize, offset, jmpSize, inner::LaddTop);
+			} else {
+				db(size_t(top_) + offset, jmpSize);
+			}
+			return;
+		}
+		db(uint64(0), jmpSize);
+		JmpLabel jmp(size_, jmpSize, (relative ? inner::LasIs : isAutoGrow() ? inner::LaddTop : inner::Labs), disp);
+		labelMgr_.addUndefinedLabel(label, jmp);
+	}
+	void opMovxx(const Reg& reg, const Operand& op, uint8 code)
+	{
+		if (op.isBit(32)) throw Error(ERR_BAD_COMBINATION);
+		int w = op.isBit(16);
+#ifdef XBYAK64
+		if (op.isHigh8bit()) throw Error(ERR_BAD_COMBINATION);
+#endif
+		bool cond = reg.isREG() && (reg.getBit() > op.getBit());
+		opModRM(reg, op, cond && op.isREG(), cond && op.isMEM(), 0x0F, code | w);
+	}
+	void opFpuMem(const Address& addr, uint8 m16, uint8 m32, uint8 m64, uint8 ext, uint8 m64ext)
+	{
+		if (addr.is64bitDisp()) throw Error(ERR_CANT_USE_64BIT_DISP);
+		uint8 code = addr.isBit(16) ? m16 : addr.isBit(32) ? m32 : addr.isBit(64) ? m64 : 0;
+		if (!code) throw Error(ERR_BAD_MEM_SIZE);
+		if (m64ext && addr.isBit(64)) ext = m64ext;
+
+		rex(addr, st0);
+		db(code);
+		opAddr(addr, ext);
+	}
+	// use code1 if reg1 == st0
+	// use code2 if reg1 != st0 && reg2 == st0
+	void opFpuFpu(const Fpu& reg1, const Fpu& reg2, uint32 code1, uint32 code2)
+	{
+		uint32 code = reg1.getIdx() == 0 ? code1 : reg2.getIdx() == 0 ? code2 : 0;
+		if (!code) throw Error(ERR_BAD_ST_COMBINATION);
+		db(uint8(code >> 8));
+		db(uint8(code | (reg1.getIdx() | reg2.getIdx())));
+	}
+	void opFpu(const Fpu& reg, uint8 code1, uint8 code2)
+	{
+		db(code1); db(code2 | reg.getIdx());
+	}
+	void opVex(const Reg& r, const Operand *p1, const Operand& op2, int type, int code, int imm8 = NONE)
+	{
+		if (op2.isMEM()) {
+			const Address& addr = op2.getAddress();
+			const RegExp& regExp = addr.getRegExp();
+			const Reg& base = regExp.getBase();
+			const Reg& index = regExp.getIndex();
+			if (BIT == 64 && addr.is32bit()) db(0x67);
+			int disp8N = 0;
+			bool x = index.isExtIdx();
+			if ((type & (T_MUST_EVEX|T_MEM_EVEX)) || r.hasEvex() || (p1 && p1->hasEvex()) || addr.isBroadcast() || addr.getOpmaskIdx()) {
+				int aaa = addr.getOpmaskIdx();
+				if (aaa && !(type & T_M_K)) throw Error(ERR_INVALID_OPMASK_WITH_MEMORY);
+				bool b = false;
+				if (addr.isBroadcast()) {
+					if (!(type & (T_B32 | T_B64))) throw Error(ERR_INVALID_BROADCAST);
+					b = true;
+				}
+				int VL = regExp.isVsib() ? index.getBit() : 0;
+				disp8N = evex(r, base, p1, type, code, x, b, aaa, VL, index.isExtIdx2());
+			} else {
+				vex(r, base, p1, type, code, x);
+			}
+			opAddr(addr, r.getIdx(), (imm8 != NONE) ? 1 : 0, disp8N, (type & T_VSIB) != 0);
+		} else {
+			const Reg& base = op2.getReg();
+			if ((type & T_MUST_EVEX) || r.hasEvex() || (p1 && p1->hasEvex()) || base.hasEvex()) {
+				evex(r, base, p1, type, code);
+			} else {
+				vex(r, base, p1, type, code);
+			}
+			setModRM(3, r.getIdx(), base.getIdx());
+		}
+		if (imm8 != NONE) db(imm8);
+	}
+	// (r, r, r/m) if isR_R_RM
+	// (r, r/m, r)
+	void opGpr(const Reg32e& r, const Operand& op1, const Operand& op2, int type, uint8 code, bool isR_R_RM, int imm8 = NONE)
+	{
+		const Operand *p1 = &op1;
+		const Operand *p2 = &op2;
+		if (!isR_R_RM) std::swap(p1, p2);
+		const unsigned int bit = r.getBit();
+		if (p1->getBit() != bit || (p2->isREG() && p2->getBit() != bit)) throw Error(ERR_BAD_COMBINATION);
+		type |= (bit == 64) ? T_W1 : T_W0;
+		opVex(r, p1, *p2, type, code, imm8);
+	}
+	void opAVX_X_X_XM(const Xmm& x1, const Operand& op1, const Operand& op2, int type, int code0, int imm8 = NONE)
+	{
+		const Xmm *x2 = static_cast<const Xmm*>(&op1);
+		const Operand *op = &op2;
+		if (op2.isNone()) { // (x1, op1) -> (x1, x1, op1)
+			x2 = &x1;
+			op = &op1;
+		}
+		// (x1, x2, op)
+		if (!((x1.isXMM() && x2->isXMM()) || ((type & T_YMM) && ((x1.isYMM() && x2->isYMM()) || (x1.isZMM() && x2->isZMM()))))) throw Error(ERR_BAD_COMBINATION);
+		opVex(x1, x2, *op, type, code0, imm8);
+	}
+	void opAVX_K_X_XM(const Opmask& k, const Xmm& x2, const Operand& op3, int type, int code0, int imm8 = NONE)
+	{
+		if (!op3.isMEM() && (x2.getKind() != op3.getKind())) throw Error(ERR_BAD_COMBINATION);
+		opVex(k, &x2, op3, type, code0, imm8);
+	}
+	// (x, x/m), (y, x/m256), (z, y/m)
+	void checkCvt1(const Operand& x, const Operand& op) const
+	{
+		if (!op.isMEM() && !(x.is(Operand::XMM | Operand::YMM) && op.isXMM()) && !(x.isZMM() && op.isYMM())) throw Error(ERR_BAD_COMBINATION);
+	}
+	// (x, x/m), (x, y/m256), (y, z/m)
+	void checkCvt2(const Xmm& x, const Operand& op) const
+	{
+		if (!(x.isXMM() && op.is(Operand::XMM | Operand::YMM | Operand::MEM)) && !(x.isYMM() && op.is(Operand::ZMM | Operand::MEM))) throw Error(ERR_BAD_COMBINATION);
+	}
+	void opCvt2(const Xmm& x, const Operand& op, int type, int code)
+	{
+		checkCvt2(x, op);
+		Operand::Kind kind = x.isXMM() ? (op.isBit(256) ? Operand::YMM : Operand::XMM) : Operand::ZMM;
+		opVex(x.copyAndSetKind(kind), &xm0, op, type, code);
+	}
+	void opCvt3(const Xmm& x1, const Xmm& x2, const Operand& op, int type, int type64, int type32, uint8 code)
+	{
+		if (!(x1.isXMM() && x2.isXMM() && (op.isREG(i32e) || op.isMEM()))) throw Error(ERR_BAD_SIZE_OF_REGISTER);
+		Xmm x(op.getIdx());
+		const Operand *p = op.isREG() ? &x : &op;
+		opVex(x1, &x2, *p, type | (op.isBit(64) ? type64 : type32), code);
+	}
+	const Xmm& cvtIdx0(const Operand& x) const
+	{
+		return x.isZMM() ? zm0 : x.isYMM() ? ym0 : xm0;
+	}
+	// support (x, x/m, imm), (y, y/m, imm)
+	void opAVX_X_XM_IMM(const Xmm& x, const Operand& op, int type, int code, int imm8 = NONE)
+	{
+		opAVX_X_X_XM(x, cvtIdx0(x), op, type, code, imm8);
+	}
+	// QQQ:need to refactor
+	void opSp1(const Reg& reg, const Operand& op, uint8 pref, uint8 code0, uint8 code1)
+	{
+		if (reg.isBit(8)) throw Error(ERR_BAD_SIZE_OF_REGISTER);
+		bool is16bit = reg.isREG(16) && (op.isREG(16) || op.isMEM());
+		if (!is16bit && !(reg.isREG(i32e) && (op.isREG(reg.getBit()) || op.isMEM()))) throw Error(ERR_BAD_COMBINATION);
+		if (is16bit) db(0x66);
+		db(pref); opModRM(reg.changeBit(i32e == 32 ? 32 : reg.getBit()), op, op.isREG(), true, code0, code1);
+	}
+	void opGather(const Xmm& x1, const Address& addr, const Xmm& x2, int type, uint8 code, int mode)
+	{
+		const RegExp& regExp = addr.getRegExp();
+		if (!regExp.isVsib(128 | 256)) throw Error(ERR_BAD_VSIB_ADDRESSING);
+		const int y_vx_y = 0;
+		const int y_vy_y = 1;
+//		const int x_vy_x = 2;
+		const bool isAddrYMM = regExp.getIndex().getBit() == 256;
+		if (!x1.isXMM() || isAddrYMM || !x2.isXMM()) {
+			bool isOK = false;
+			if (mode == y_vx_y) {
+				isOK = x1.isYMM() && !isAddrYMM && x2.isYMM();
+			} else if (mode == y_vy_y) {
+				isOK = x1.isYMM() && isAddrYMM && x2.isYMM();
+			} else { // x_vy_x
+				isOK = !x1.isYMM() && isAddrYMM && !x2.isYMM();
+			}
+			if (!isOK) throw Error(ERR_BAD_VSIB_ADDRESSING);
+		}
+		opAVX_X_X_XM(isAddrYMM ? Ymm(x1.getIdx()) : x1, isAddrYMM ? Ymm(x2.getIdx()) : x2, addr, type, code);
+	}
+	enum {
+		xx_yy_zz = 0,
+		xx_yx_zy = 1,
+		xx_xy_yz = 2
+	};
+	void checkGather2(const Xmm& x1, const Reg& x2, int mode) const
+	{
+		if (x1.isXMM() && x2.isXMM()) return;
+		switch (mode) {
+		case xx_yy_zz: if ((x1.isYMM() && x2.isYMM()) || (x1.isZMM() && x2.isZMM())) return;
+			break;
+		case xx_yx_zy: if ((x1.isYMM() && x2.isXMM()) || (x1.isZMM() && x2.isYMM())) return;
+			break;
+		case xx_xy_yz: if ((x1.isXMM() && x2.isYMM()) || (x1.isYMM() && x2.isZMM())) return;
+			break;
+		}
+		throw Error(ERR_BAD_VSIB_ADDRESSING);
+	}
+	void opGather2(const Xmm& x, const Address& addr, int type, uint8 code, int mode)
+	{
+		if (x.hasZero()) throw Error(ERR_INVALID_ZERO);
+		checkGather2(x, addr.getRegExp().getIndex(), mode);
+		opVex(x, 0, addr, type, code);
+	}
+	/*
+		xx_xy_yz ; mode = true
+		xx_xy_xz ; mode = false
+	*/
+	void opVmov(const Operand& op, const Xmm& x, int type, uint8 code, bool mode)
+	{
+		if (mode) {
+			if (!op.isMEM() && !((op.isXMM() && x.isXMM()) || (op.isXMM() && x.isYMM()) || (op.isYMM() && x.isZMM())))  throw Error(ERR_BAD_COMBINATION);
+		} else {
+			if (!op.isMEM() && !op.isXMM()) throw Error(ERR_BAD_COMBINATION);
+		}
+		opVex(x, 0, op, type, code);
+	}
+	void opGatherFetch(const Address& addr, const Xmm& x, int type, uint8 code, Operand::Kind kind)
+	{
+		if (addr.hasZero()) throw Error(ERR_INVALID_ZERO);
+		if (addr.getRegExp().getIndex().getKind() != kind) throw Error(ERR_BAD_VSIB_ADDRESSING);
+		opVex(x, 0, addr, type, code);
+	}
+public:
+	unsigned int getVersion() const { return VERSION; }
+	using CodeArray::db;
+	const Mmx mm0, mm1, mm2, mm3, mm4, mm5, mm6, mm7;
+	const Xmm xmm0, xmm1, xmm2, xmm3, xmm4, xmm5, xmm6, xmm7;
+	const Ymm ymm0, ymm1, ymm2, ymm3, ymm4, ymm5, ymm6, ymm7;
+	const Zmm zmm0, zmm1, zmm2, zmm3, zmm4, zmm5, zmm6, zmm7;
+	const Xmm &xm0, &xm1, &xm2, &xm3, &xm4, &xm5, &xm6, &xm7;
+	const Ymm &ym0, &ym1, &ym2, &ym3, &ym4, &ym5, &ym6, &ym7;
+	const Ymm &zm0, &zm1, &zm2, &zm3, &zm4, &zm5, &zm6, &zm7;
+	const Reg32 eax, ecx, edx, ebx, esp, ebp, esi, edi;
+	const Reg16 ax, cx, dx, bx, sp, bp, si, di;
+	const Reg8 al, cl, dl, bl, ah, ch, dh, bh;
+	const AddressFrame ptr, byte, word, dword, qword, xword, yword, zword; // xword is same as oword of NASM
+	const AddressFrame ptr_b, xword_b, yword_b, zword_b; // broadcast such as {1to2}, {1to4}, {1to8}, {1to16}, {b}
+	const Fpu st0, st1, st2, st3, st4, st5, st6, st7;
+	const Opmask k0, k1, k2, k3, k4, k5, k6, k7;
+	const BoundsReg bnd0, bnd1, bnd2, bnd3;
+	const EvexModifierRounding T_sae, T_rn_sae, T_rd_sae, T_ru_sae, T_rz_sae; // {sae}, {rn-sae}, {rd-sae}, {ru-sae}, {rz-sae}
+	const EvexModifierZero T_z; // {z}
+#ifdef XBYAK64
+	const Reg64 rax, rcx, rdx, rbx, rsp, rbp, rsi, rdi, r8, r9, r10, r11, r12, r13, r14, r15;
+	const Reg32 r8d, r9d, r10d, r11d, r12d, r13d, r14d, r15d;
+	const Reg16 r8w, r9w, r10w, r11w, r12w, r13w, r14w, r15w;
+	const Reg8 r8b, r9b, r10b, r11b, r12b, r13b, r14b, r15b;
+	const Reg8 spl, bpl, sil, dil;
+	const Xmm xmm8, xmm9, xmm10, xmm11, xmm12, xmm13, xmm14, xmm15;
+	const Xmm xmm16, xmm17, xmm18, xmm19, xmm20, xmm21, xmm22, xmm23;
+	const Xmm xmm24, xmm25, xmm26, xmm27, xmm28, xmm29, xmm30, xmm31;
+	const Ymm ymm8, ymm9, ymm10, ymm11, ymm12, ymm13, ymm14, ymm15;
+	const Ymm ymm16, ymm17, ymm18, ymm19, ymm20, ymm21, ymm22, ymm23;
+	const Ymm ymm24, ymm25, ymm26, ymm27, ymm28, ymm29, ymm30, ymm31;
+	const Zmm zmm8, zmm9, zmm10, zmm11, zmm12, zmm13, zmm14, zmm15;
+	const Zmm zmm16, zmm17, zmm18, zmm19, zmm20, zmm21, zmm22, zmm23;
+	const Zmm zmm24, zmm25, zmm26, zmm27, zmm28, zmm29, zmm30, zmm31;
+	const Xmm &xm8, &xm9, &xm10, &xm11, &xm12, &xm13, &xm14, &xm15; // for my convenience
+	const Xmm &xm16, &xm17, &xm18, &xm19, &xm20, &xm21, &xm22, &xm23;
+	const Xmm &xm24, &xm25, &xm26, &xm27, &xm28, &xm29, &xm30, &xm31;
+	const Ymm &ym8, &ym9, &ym10, &ym11, &ym12, &ym13, &ym14, &ym15;
+	const Ymm &ym16, &ym17, &ym18, &ym19, &ym20, &ym21, &ym22, &ym23;
+	const Ymm &ym24, &ym25, &ym26, &ym27, &ym28, &ym29, &ym30, &ym31;
+	const Zmm &zm8, &zm9, &zm10, &zm11, &zm12, &zm13, &zm14, &zm15;
+	const Zmm &zm16, &zm17, &zm18, &zm19, &zm20, &zm21, &zm22, &zm23;
+	const Zmm &zm24, &zm25, &zm26, &zm27, &zm28, &zm29, &zm30, &zm31;
+	const RegRip rip;
+#endif
+#ifndef XBYAK_DISABLE_SEGMENT
+	const Segment es, cs, ss, ds, fs, gs;
+#endif
+	void L(const std::string& label) { labelMgr_.defineSlabel(label); }
+	void L(Label& label) { labelMgr_.defineClabel(label); }
+	Label L() { Label label; L(label); return label; }
+	void inLocalLabel() { labelMgr_.enterLocal(); }
+	void outLocalLabel() { labelMgr_.leaveLocal(); }
+	/*
+		assign src to dst
+		require
+		dst : does not used by L()
+		src : used by L()
+	*/
+	void assignL(Label& dst, const Label& src) { labelMgr_.assign(dst, src); }
+	/*
+		put address of label to buffer
+		@note the put size is 4(32-bit), 8(64-bit)
+	*/
+	void putL(std::string label) { putL_inner(label); }
+	void putL(const Label& label) { putL_inner(label); }
+
+	void jmp(const Operand& op) { opR_ModM(op, BIT, 4, 0xFF, NONE, NONE, true); }
+	void jmp(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0xEB, 0xE9, 0); }
+	void jmp(const char *label, LabelType type = T_AUTO) { jmp(std::string(label), type); }
+	void jmp(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0xEB, 0xE9, 0); }
+	void jmp(const void *addr, LabelType type = T_AUTO) { opJmpAbs(addr, type, 0xEB, 0xE9); }
+
+	void call(const Operand& op) { opR_ModM(op, 16 | i32e, 2, 0xFF, NONE, NONE, true); }
+	// call(string label), not const std::string&
+	void call(std::string label) { opJmp(label, T_NEAR, 0, 0xE8, 0); }
+	void call(const char *label) { call(std::string(label)); }
+	void call(const Label& label) { opJmp(label, T_NEAR, 0, 0xE8, 0); }
+	// call(function pointer)
+#ifdef XBYAK_VARIADIC_TEMPLATE
+	template<class Ret, class... Params>
+	void call(Ret(*func)(Params...)) { call(reinterpret_cast<const void*>(func)); }
+#endif
+	void call(const void *addr) { opJmpAbs(addr, T_NEAR, 0, 0xE8); }
+
+	void test(const Operand& op, const Reg& reg)
+	{
+		opModRM(reg, op, op.isREG() && (op.getKind() == reg.getKind()), op.isMEM(), 0x84);
+	}
+	void test(const Operand& op, uint32 imm)
+	{
+		verifyMemHasSize(op);
+        int immSize = (std::min)(op.getBit() / 8, 4U);
+		if (op.isREG() && op.getIdx() == 0) { // al, ax, eax
+			rex(op);
+			db(0xA8 | (op.isBit(8) ? 0 : 1));
+		} else {
+			opR_ModM(op, 0, 0, 0xF6, NONE, NONE, false, immSize);
+		}
+		db(imm, immSize);
+	}
+	void imul(const Reg& reg, const Operand& op)
+	{
+		opModRM(reg, op, op.isREG() && (reg.getKind() == op.getKind()), op.isMEM(), 0x0F, 0xAF);
+	}
+	void imul(const Reg& reg, const Operand& op, int imm)
+	{
+		int s = inner::IsInDisp8(imm) ? 1 : 0;
+        int immSize = s ? 1 : reg.isREG(16) ? 2 : 4;
+		opModRM(reg, op, op.isREG() && (reg.getKind() == op.getKind()), op.isMEM(), 0x69 | (s << 1), NONE, NONE, immSize);
+		db(imm, immSize);
+	}
+	void push(const Operand& op) { opPushPop(op, 0xFF, 6, 0x50); }
+	void pop(const Operand& op) { opPushPop(op, 0x8F, 0, 0x58); }
+	void push(const AddressFrame& af, uint32 imm)
+	{
+		if (af.bit_ == 8 && inner::IsInDisp8(imm)) {
+			db(0x6A); db(imm);
+		} else if (af.bit_ == 16 && isInDisp16(imm)) {
+			db(0x66); db(0x68); dw(imm);
+		} else {
+			db(0x68); dd(imm);
+		}
+	}
+	/* use "push(word, 4)" if you want "push word 4" */
+	void push(uint32 imm)
+	{
+		if (inner::IsInDisp8(imm)) {
+			push(byte, imm);
+		} else {
+			push(dword, imm);
+		}
+	}
+	void mov(const Operand& reg1, const Operand& reg2)
+	{
+		const Reg *reg = 0;
+		const Address *addr = 0;
+		uint8 code = 0;
+		if (reg1.isREG() && reg1.getIdx() == 0 && reg2.isMEM()) { // mov eax|ax|al, [disp]
+			reg = &reg1.getReg();
+			addr= &reg2.getAddress();
+			code = 0xA0;
+		} else
+		if (reg1.isMEM() && reg2.isREG() && reg2.getIdx() == 0) { // mov [disp], eax|ax|al
+			reg = &reg2.getReg();
+			addr= &reg1.getAddress();
+			code = 0xA2;
+		}
+#ifdef XBYAK64
+		if (addr && addr->is64bitDisp()) {
+			if (code) {
+				rex(*reg);
+				db(reg1.isREG(8) ? 0xA0 : reg1.isREG() ? 0xA1 : reg2.isREG(8) ? 0xA2 : 0xA3);
+				db(addr->getDisp(), 8);
+			} else {
+				throw Error(ERR_BAD_COMBINATION);
+			}
+		} else
+#else
+		if (code && addr->isOnlyDisp()) {
+			rex(*reg, *addr);
+			db(code | (reg->isBit(8) ? 0 : 1));
+			dd(static_cast<uint32>(addr->getDisp()));
+		} else
+#endif
+		{
+			opRM_RM(reg1, reg2, 0x88);
+		}
+	}
+	void mov(const Operand& op, size_t imm)
+	{
+		if (op.isREG()) {
+			const int size = mov_imm(op.getReg(), imm);
+			db(imm, size);
+		} else if (op.isMEM()) {
+			verifyMemHasSize(op);
+			int immSize = op.getBit() / 8;
+			if (immSize <= 4) {
+				sint64 s = sint64(imm) >> (immSize * 8);
+				if (s != 0 && s != -1) throw Error(ERR_IMM_IS_TOO_BIG);
+			} else {
+				if (!inner::IsInInt32(imm)) throw Error(ERR_IMM_IS_TOO_BIG);
+				immSize = 4;
+			}
+			opModM(op.getAddress(), Reg(0, Operand::REG, op.getBit()), 0xC6, NONE, NONE, immSize);
+			db(static_cast<uint32>(imm), immSize);
+		} else {
+			throw Error(ERR_BAD_COMBINATION);
+		}
+	}
+	void mov(const NativeReg& reg, const char *label) // can't use std::string
+	{
+		if (label == 0) {
+			mov(static_cast<const Operand&>(reg), 0); // call imm
+			return;
+		}
+		mov_imm(reg, dummyAddr);
+		putL(label);
+	}
+	void mov(const NativeReg& reg, const Label& label)
+	{
+		mov_imm(reg, dummyAddr);
+		putL(label);
+	}
+	void xchg(const Operand& op1, const Operand& op2)
+	{
+		const Operand *p1 = &op1, *p2 = &op2;
+		if (p1->isMEM() || (p2->isREG(16 | i32e) && p2->getIdx() == 0)) {
+			p1 = &op2; p2 = &op1;
+		}
+		if (p1->isMEM()) throw Error(ERR_BAD_COMBINATION);
+		if (p2->isREG() && (p1->isREG(16 | i32e) && p1->getIdx() == 0)
+#ifdef XBYAK64
+			&& (p2->getIdx() != 0 || !p1->isREG(32))
+#endif
+		) {
+			rex(*p2, *p1); db(0x90 | (p2->getIdx() & 7));
+			return;
+		}
+		opModRM(*p1, *p2, (p1->isREG() && p2->isREG() && (p1->getBit() == p2->getBit())), p2->isMEM(), 0x86 | (p1->isBit(8) ? 0 : 1));
+	}
+
+#ifndef XBYAK_DISABLE_SEGMENT
+	void push(const Segment& seg)
+	{
+		switch (seg.getIdx()) {
+		case Segment::es: db(0x06); break;
+		case Segment::cs: db(0x0E); break;
+		case Segment::ss: db(0x16); break;
+		case Segment::ds: db(0x1E); break;
+		case Segment::fs: db(0x0F); db(0xA0); break;
+		case Segment::gs: db(0x0F); db(0xA8); break;
+		default:
+			assert(0);
+		}
+	}
+	void pop(const Segment& seg)
+	{
+		switch (seg.getIdx()) {
+		case Segment::es: db(0x07); break;
+		case Segment::cs: throw Error(ERR_BAD_COMBINATION);
+		case Segment::ss: db(0x17); break;
+		case Segment::ds: db(0x1F); break;
+		case Segment::fs: db(0x0F); db(0xA1); break;
+		case Segment::gs: db(0x0F); db(0xA9); break;
+		default:
+			assert(0);
+		}
+	}
+	void putSeg(const Segment& seg)
+	{
+		switch (seg.getIdx()) {
+		case Segment::es: db(0x2E); break;
+		case Segment::cs: db(0x36); break;
+		case Segment::ss: db(0x3E); break;
+		case Segment::ds: db(0x26); break;
+		case Segment::fs: db(0x64); break;
+		case Segment::gs: db(0x65); break;
+		default:
+			assert(0);
+		}
+	}
+	void mov(const Operand& op, const Segment& seg)
+	{
+		opModRM(Reg8(seg.getIdx()), op, op.isREG(16|i32e), op.isMEM(), 0x8C);
+	}
+	void mov(const Segment& seg, const Operand& op)
+	{
+		opModRM(Reg8(seg.getIdx()), op.isREG(16|i32e) ? static_cast<const Operand&>(op.getReg().cvt32()) : op, op.isREG(16|i32e), op.isMEM(), 0x8E);
+	}
+#endif
+
+	enum { NONE = 256 };
+	// constructor
+	CodeGenerator(size_t maxSize = DEFAULT_MAX_CODE_SIZE, void *userPtr = 0, Allocator *allocator = 0)
+		: CodeArray(maxSize, userPtr, allocator)
+		, mm0(0), mm1(1), mm2(2), mm3(3), mm4(4), mm5(5), mm6(6), mm7(7)
+		, xmm0(0), xmm1(1), xmm2(2), xmm3(3), xmm4(4), xmm5(5), xmm6(6), xmm7(7)
+		, ymm0(0), ymm1(1), ymm2(2), ymm3(3), ymm4(4), ymm5(5), ymm6(6), ymm7(7)
+		, zmm0(0), zmm1(1), zmm2(2), zmm3(3), zmm4(4), zmm5(5), zmm6(6), zmm7(7)
+		// for my convenience
+		, xm0(xmm0), xm1(xmm1), xm2(xmm2), xm3(xmm3), xm4(xmm4), xm5(xmm5), xm6(xmm6), xm7(xmm7)
+		, ym0(ymm0), ym1(ymm1), ym2(ymm2), ym3(ymm3), ym4(ymm4), ym5(ymm5), ym6(ymm6), ym7(ymm7)
+		, zm0(zmm0), zm1(zmm1), zm2(zmm2), zm3(zmm3), zm4(zmm4), zm5(zmm5), zm6(zmm6), zm7(zmm7)
+
+		, eax(Operand::EAX), ecx(Operand::ECX), edx(Operand::EDX), ebx(Operand::EBX), esp(Operand::ESP), ebp(Operand::EBP), esi(Operand::ESI), edi(Operand::EDI)
+		, ax(Operand::AX), cx(Operand::CX), dx(Operand::DX), bx(Operand::BX), sp(Operand::SP), bp(Operand::BP), si(Operand::SI), di(Operand::DI)
+		, al(Operand::AL), cl(Operand::CL), dl(Operand::DL), bl(Operand::BL), ah(Operand::AH), ch(Operand::CH), dh(Operand::DH), bh(Operand::BH)
+		, ptr(0), byte(8), word(16), dword(32), qword(64), xword(128), yword(256), zword(512)
+		, ptr_b(0, true), xword_b(128, true), yword_b(256, true), zword_b(512, true)
+		, st0(0), st1(1), st2(2), st3(3), st4(4), st5(5), st6(6), st7(7)
+		, k0(0), k1(1), k2(2), k3(3), k4(4), k5(5), k6(6), k7(7)
+		, bnd0(0), bnd1(1), bnd2(2), bnd3(3)
+		, T_sae(EvexModifierRounding::T_SAE), T_rn_sae(EvexModifierRounding::T_RN_SAE), T_rd_sae(EvexModifierRounding::T_RD_SAE), T_ru_sae(EvexModifierRounding::T_RU_SAE), T_rz_sae(EvexModifierRounding::T_RZ_SAE)
+		, T_z()
+#ifdef XBYAK64
+		, rax(Operand::RAX), rcx(Operand::RCX), rdx(Operand::RDX), rbx(Operand::RBX), rsp(Operand::RSP), rbp(Operand::RBP), rsi(Operand::RSI), rdi(Operand::RDI), r8(Operand::R8), r9(Operand::R9), r10(Operand::R10), r11(Operand::R11), r12(Operand::R12), r13(Operand::R13), r14(Operand::R14), r15(Operand::R15)
+		, r8d(8), r9d(9), r10d(10), r11d(11), r12d(12), r13d(13), r14d(14), r15d(15)
+		, r8w(8), r9w(9), r10w(10), r11w(11), r12w(12), r13w(13), r14w(14), r15w(15)
+		, r8b(8), r9b(9), r10b(10), r11b(11), r12b(12), r13b(13), r14b(14), r15b(15)
+		, spl(Operand::SPL, true), bpl(Operand::BPL, true), sil(Operand::SIL, true), dil(Operand::DIL, true)
+		, xmm8(8), xmm9(9), xmm10(10), xmm11(11), xmm12(12), xmm13(13), xmm14(14), xmm15(15)
+		, xmm16(16), xmm17(17), xmm18(18), xmm19(19), xmm20(20), xmm21(21), xmm22(22), xmm23(23)
+		, xmm24(24), xmm25(25), xmm26(26), xmm27(27), xmm28(28), xmm29(29), xmm30(30), xmm31(31)
+		, ymm8(8), ymm9(9), ymm10(10), ymm11(11), ymm12(12), ymm13(13), ymm14(14), ymm15(15)
+		, ymm16(16), ymm17(17), ymm18(18), ymm19(19), ymm20(20), ymm21(21), ymm22(22), ymm23(23)
+		, ymm24(24), ymm25(25), ymm26(26), ymm27(27), ymm28(28), ymm29(29), ymm30(30), ymm31(31)
+		, zmm8(8), zmm9(9), zmm10(10), zmm11(11), zmm12(12), zmm13(13), zmm14(14), zmm15(15)
+		, zmm16(16), zmm17(17), zmm18(18), zmm19(19), zmm20(20), zmm21(21), zmm22(22), zmm23(23)
+		, zmm24(24), zmm25(25), zmm26(26), zmm27(27), zmm28(28), zmm29(29), zmm30(30), zmm31(31)
+		// for my convenience
+		, xm8(xmm8), xm9(xmm9), xm10(xmm10), xm11(xmm11), xm12(xmm12), xm13(xmm13), xm14(xmm14), xm15(xmm15)
+		, xm16(xmm16), xm17(xmm17), xm18(xmm18), xm19(xmm19), xm20(xmm20), xm21(xmm21), xm22(xmm22), xm23(xmm23)
+		, xm24(xmm24), xm25(xmm25), xm26(xmm26), xm27(xmm27), xm28(xmm28), xm29(xmm29), xm30(xmm30), xm31(xmm31)
+		, ym8(ymm8), ym9(ymm9), ym10(ymm10), ym11(ymm11), ym12(ymm12), ym13(ymm13), ym14(ymm14), ym15(ymm15)
+		, ym16(ymm16), ym17(ymm17), ym18(ymm18), ym19(ymm19), ym20(ymm20), ym21(ymm21), ym22(ymm22), ym23(ymm23)
+		, ym24(ymm24), ym25(ymm25), ym26(ymm26), ym27(ymm27), ym28(ymm28), ym29(ymm29), ym30(ymm30), ym31(ymm31)
+		, zm8(zmm8), zm9(zmm9), zm10(zmm10), zm11(zmm11), zm12(zmm12), zm13(zmm13), zm14(zmm14), zm15(zmm15)
+		, zm16(zmm16), zm17(zmm17), zm18(zmm18), zm19(zmm19), zm20(zmm20), zm21(zmm21), zm22(zmm22), zm23(zmm23)
+		, zm24(zmm24), zm25(zmm25), zm26(zmm26), zm27(zmm27), zm28(zmm28), zm29(zmm29), zm30(zmm30), zm31(zmm31)
+		, rip()
+#endif
+#ifndef XBYAK_DISABLE_SEGMENT
+		, es(Segment::es), cs(Segment::cs), ss(Segment::ss), ds(Segment::ds), fs(Segment::fs), gs(Segment::gs)
+#endif
+	{
+		labelMgr_.set(this);
+	}
+	void reset()
+	{
+		resetSize();
+		labelMgr_.reset();
+		labelMgr_.set(this);
+	}
+	bool hasUndefinedLabel() const { return labelMgr_.hasUndefSlabel() || labelMgr_.hasUndefClabel(); }
+	/*
+		MUST call ready() to complete generating code if you use AutoGrow mode.
+		It is not necessary for the other mode if hasUndefinedLabel() is true.
+	*/
+	void ready(ProtectMode mode = PROTECT_RWE)
+	{
+		if (hasUndefinedLabel()) throw Error(ERR_LABEL_IS_NOT_FOUND);
+		if (isAutoGrow()) {
+			calcJmpAddress();
+			if (useProtect()) setProtectMode(mode);
+		}
+	}
+	// set read/exec
+	void readyRE() { return ready(PROTECT_RE); }
+#ifdef XBYAK_TEST
+	void dump(bool doClear = true)
+	{
+		CodeArray::dump();
+		if (doClear) size_ = 0;
+	}
+#endif
+
+#ifdef XBYAK_UNDEF_JNL
+	#undef jnl
+#endif
+
+	/*
+		use single byte nop if useMultiByteNop = false
+	*/
+	void nop(size_t size = 1, bool useMultiByteNop = true)
+	{
+		if (!useMultiByteNop) {
+			for (size_t i = 0; i < size; i++) {
+				db(0x90);
+			}
+			return;
+		}
+		/*
+			Intel Architectures Software Developer's Manual Volume 2
+			recommended multi-byte sequence of NOP instruction
+			AMD and Intel seem to agree on the same sequences for up to 9 bytes:
+			https://support.amd.com/TechDocs/55723_SOG_Fam_17h_Processors_3.00.pdf
+		*/
+		static const uint8 nopTbl[9][9] = {
+			{0x90},
+			{0x66, 0x90},
+			{0x0F, 0x1F, 0x00},
+			{0x0F, 0x1F, 0x40, 0x00},
+			{0x0F, 0x1F, 0x44, 0x00, 0x00},
+			{0x66, 0x0F, 0x1F, 0x44, 0x00, 0x00},
+			{0x0F, 0x1F, 0x80, 0x00, 0x00, 0x00, 0x00},
+			{0x0F, 0x1F, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00},
+			{0x66, 0x0F, 0x1F, 0x84, 0x00, 0x00, 0x00, 0x00, 0x00},
+		};
+		const size_t n = sizeof(nopTbl) / sizeof(nopTbl[0]);
+		while (size > 0) {
+			size_t len = (std::min)(n, size);
+			const uint8 *seq = nopTbl[len - 1];
+			db(seq, len);
+			size -= len;
+		}
+	}
+
+#ifndef XBYAK_DONT_READ_LIST
+#include "xbyak_mnemonic.h"
+	/*
+		use single byte nop if useMultiByteNop = false
+	*/
+	void align(size_t x = 16, bool useMultiByteNop = true)
+	{
+		if (x == 1) return;
+		if (x < 1 || (x & (x - 1))) throw Error(ERR_BAD_ALIGN);
+		if (isAutoGrow() && x > inner::ALIGN_PAGE_SIZE) fprintf(stderr, "warning:autoGrow mode does not support %d align\n", (int)x);
+		size_t remain = size_t(getCurr()) % x;
+		if (remain) {
+			nop(x - remain, useMultiByteNop);
+		}
+	}
+#endif
+};
+
+namespace util {
+static const Mmx mm0(0), mm1(1), mm2(2), mm3(3), mm4(4), mm5(5), mm6(6), mm7(7);
+static const Xmm xmm0(0), xmm1(1), xmm2(2), xmm3(3), xmm4(4), xmm5(5), xmm6(6), xmm7(7);
+static const Ymm ymm0(0), ymm1(1), ymm2(2), ymm3(3), ymm4(4), ymm5(5), ymm6(6), ymm7(7);
+static const Zmm zmm0(0), zmm1(1), zmm2(2), zmm3(3), zmm4(4), zmm5(5), zmm6(6), zmm7(7);
+static const Reg32 eax(Operand::EAX), ecx(Operand::ECX), edx(Operand::EDX), ebx(Operand::EBX), esp(Operand::ESP), ebp(Operand::EBP), esi(Operand::ESI), edi(Operand::EDI);
+static const Reg16 ax(Operand::AX), cx(Operand::CX), dx(Operand::DX), bx(Operand::BX), sp(Operand::SP), bp(Operand::BP), si(Operand::SI), di(Operand::DI);
+static const Reg8 al(Operand::AL), cl(Operand::CL), dl(Operand::DL), bl(Operand::BL), ah(Operand::AH), ch(Operand::CH), dh(Operand::DH), bh(Operand::BH);
+static const AddressFrame ptr(0), byte(8), word(16), dword(32), qword(64), xword(128), yword(256), zword(512);
+static const AddressFrame ptr_b(0, true), xword_b(128, true), yword_b(256, true), zword_b(512, true);
+static const Fpu st0(0), st1(1), st2(2), st3(3), st4(4), st5(5), st6(6), st7(7);
+static const Opmask k0(0), k1(1), k2(2), k3(3), k4(4), k5(5), k6(6), k7(7);
+static const BoundsReg bnd0(0), bnd1(1), bnd2(2), bnd3(3);
+static const EvexModifierRounding T_sae(EvexModifierRounding::T_SAE), T_rn_sae(EvexModifierRounding::T_RN_SAE), T_rd_sae(EvexModifierRounding::T_RD_SAE), T_ru_sae(EvexModifierRounding::T_RU_SAE), T_rz_sae(EvexModifierRounding::T_RZ_SAE);
+static const EvexModifierZero T_z;
+#ifdef XBYAK64
+static const Reg64 rax(Operand::RAX), rcx(Operand::RCX), rdx(Operand::RDX), rbx(Operand::RBX), rsp(Operand::RSP), rbp(Operand::RBP), rsi(Operand::RSI), rdi(Operand::RDI), r8(Operand::R8), r9(Operand::R9), r10(Operand::R10), r11(Operand::R11), r12(Operand::R12), r13(Operand::R13), r14(Operand::R14), r15(Operand::R15);
+static const Reg32 r8d(8), r9d(9), r10d(10), r11d(11), r12d(12), r13d(13), r14d(14), r15d(15);
+static const Reg16 r8w(8), r9w(9), r10w(10), r11w(11), r12w(12), r13w(13), r14w(14), r15w(15);
+static const Reg8 r8b(8), r9b(9), r10b(10), r11b(11), r12b(12), r13b(13), r14b(14), r15b(15), spl(Operand::SPL, true), bpl(Operand::BPL, true), sil(Operand::SIL, true), dil(Operand::DIL, true);
+static const Xmm xmm8(8), xmm9(9), xmm10(10), xmm11(11), xmm12(12), xmm13(13), xmm14(14), xmm15(15);
+static const Xmm xmm16(16), xmm17(17), xmm18(18), xmm19(19), xmm20(20), xmm21(21), xmm22(22), xmm23(23);
+static const Xmm xmm24(24), xmm25(25), xmm26(26), xmm27(27), xmm28(28), xmm29(29), xmm30(30), xmm31(31);
+static const Ymm ymm8(8), ymm9(9), ymm10(10), ymm11(11), ymm12(12), ymm13(13), ymm14(14), ymm15(15);
+static const Ymm ymm16(16), ymm17(17), ymm18(18), ymm19(19), ymm20(20), ymm21(21), ymm22(22), ymm23(23);
+static const Ymm ymm24(24), ymm25(25), ymm26(26), ymm27(27), ymm28(28), ymm29(29), ymm30(30), ymm31(31);
+static const Zmm zmm8(8), zmm9(9), zmm10(10), zmm11(11), zmm12(12), zmm13(13), zmm14(14), zmm15(15);
+static const Zmm zmm16(16), zmm17(17), zmm18(18), zmm19(19), zmm20(20), zmm21(21), zmm22(22), zmm23(23);
+static const Zmm zmm24(24), zmm25(25), zmm26(26), zmm27(27), zmm28(28), zmm29(29), zmm30(30), zmm31(31);
+static const RegRip rip;
+#endif
+#ifndef XBYAK_DISABLE_SEGMENT
+static const Segment es(Segment::es), cs(Segment::cs), ss(Segment::ss), ds(Segment::ds), fs(Segment::fs), gs(Segment::gs);
+#endif
+} // util
+
+#ifdef _MSC_VER
+	#pragma warning(pop)
+#endif
+
+} // end of namespace
+
+#endif // XBYAK_XBYAK_H_
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_bin2hex.h b/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_bin2hex.h
new file mode 100644
index 0000000000..a22e5224c3
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_bin2hex.h
@@ -0,0 +1,303 @@
+/*******************************************************************************
+* Copyright 2016-2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/*******************************************************************************
+* Copyright (c) 2007 MITSUNARI Shigeo
+* All rights reserved.
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions are met:
+*
+* Redistributions of source code must retain the above copyright notice, this
+* list of conditions and the following disclaimer.
+* Redistributions in binary form must reproduce the above copyright notice,
+* this list of conditions and the following disclaimer in the documentation
+* and/or other materials provided with the distribution.
+* Neither the name of the copyright owner nor the names of its contributors may
+* be used to endorse or promote products derived from this software without
+* specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+* THE POSSIBILITY OF SUCH DAMAGE.
+*******************************************************************************/
+
+enum {
+	B00000000= 0,
+	B00000001= 1,
+	B00000010= 2,
+	B00000011= 3,
+	B00000100= 4,
+	B00000101= 5,
+	B00000110= 6,
+	B00000111= 7,
+	B00001000= 8,
+	B00001001= 9,
+	B00001010= 10,
+	B00001011= 11,
+	B00001100= 12,
+	B00001101= 13,
+	B00001110= 14,
+	B00001111= 15,
+	B00010000= 16,
+	B00010001= 17,
+	B00010010= 18,
+	B00010011= 19,
+	B00010100= 20,
+	B00010101= 21,
+	B00010110= 22,
+	B00010111= 23,
+	B00011000= 24,
+	B00011001= 25,
+	B00011010= 26,
+	B00011011= 27,
+	B00011100= 28,
+	B00011101= 29,
+	B00011110= 30,
+	B00011111= 31,
+	B00100000= 32,
+	B00100001= 33,
+	B00100010= 34,
+	B00100011= 35,
+	B00100100= 36,
+	B00100101= 37,
+	B00100110= 38,
+	B00100111= 39,
+	B00101000= 40,
+	B00101001= 41,
+	B00101010= 42,
+	B00101011= 43,
+	B00101100= 44,
+	B00101101= 45,
+	B00101110= 46,
+	B00101111= 47,
+	B00110000= 48,
+	B00110001= 49,
+	B00110010= 50,
+	B00110011= 51,
+	B00110100= 52,
+	B00110101= 53,
+	B00110110= 54,
+	B00110111= 55,
+	B00111000= 56,
+	B00111001= 57,
+	B00111010= 58,
+	B00111011= 59,
+	B00111100= 60,
+	B00111101= 61,
+	B00111110= 62,
+	B00111111= 63,
+	B01000000= 64,
+	B01000001= 65,
+	B01000010= 66,
+	B01000011= 67,
+	B01000100= 68,
+	B01000101= 69,
+	B01000110= 70,
+	B01000111= 71,
+	B01001000= 72,
+	B01001001= 73,
+	B01001010= 74,
+	B01001011= 75,
+	B01001100= 76,
+	B01001101= 77,
+	B01001110= 78,
+	B01001111= 79,
+	B01010000= 80,
+	B01010001= 81,
+	B01010010= 82,
+	B01010011= 83,
+	B01010100= 84,
+	B01010101= 85,
+	B01010110= 86,
+	B01010111= 87,
+	B01011000= 88,
+	B01011001= 89,
+	B01011010= 90,
+	B01011011= 91,
+	B01011100= 92,
+	B01011101= 93,
+	B01011110= 94,
+	B01011111= 95,
+	B01100000= 96,
+	B01100001= 97,
+	B01100010= 98,
+	B01100011= 99,
+	B01100100= 100,
+	B01100101= 101,
+	B01100110= 102,
+	B01100111= 103,
+	B01101000= 104,
+	B01101001= 105,
+	B01101010= 106,
+	B01101011= 107,
+	B01101100= 108,
+	B01101101= 109,
+	B01101110= 110,
+	B01101111= 111,
+	B01110000= 112,
+	B01110001= 113,
+	B01110010= 114,
+	B01110011= 115,
+	B01110100= 116,
+	B01110101= 117,
+	B01110110= 118,
+	B01110111= 119,
+	B01111000= 120,
+	B01111001= 121,
+	B01111010= 122,
+	B01111011= 123,
+	B01111100= 124,
+	B01111101= 125,
+	B01111110= 126,
+	B01111111= 127,
+	B10000000= 128,
+	B10000001= 129,
+	B10000010= 130,
+	B10000011= 131,
+	B10000100= 132,
+	B10000101= 133,
+	B10000110= 134,
+	B10000111= 135,
+	B10001000= 136,
+	B10001001= 137,
+	B10001010= 138,
+	B10001011= 139,
+	B10001100= 140,
+	B10001101= 141,
+	B10001110= 142,
+	B10001111= 143,
+	B10010000= 144,
+	B10010001= 145,
+	B10010010= 146,
+	B10010011= 147,
+	B10010100= 148,
+	B10010101= 149,
+	B10010110= 150,
+	B10010111= 151,
+	B10011000= 152,
+	B10011001= 153,
+	B10011010= 154,
+	B10011011= 155,
+	B10011100= 156,
+	B10011101= 157,
+	B10011110= 158,
+	B10011111= 159,
+	B10100000= 160,
+	B10100001= 161,
+	B10100010= 162,
+	B10100011= 163,
+	B10100100= 164,
+	B10100101= 165,
+	B10100110= 166,
+	B10100111= 167,
+	B10101000= 168,
+	B10101001= 169,
+	B10101010= 170,
+	B10101011= 171,
+	B10101100= 172,
+	B10101101= 173,
+	B10101110= 174,
+	B10101111= 175,
+	B10110000= 176,
+	B10110001= 177,
+	B10110010= 178,
+	B10110011= 179,
+	B10110100= 180,
+	B10110101= 181,
+	B10110110= 182,
+	B10110111= 183,
+	B10111000= 184,
+	B10111001= 185,
+	B10111010= 186,
+	B10111011= 187,
+	B10111100= 188,
+	B10111101= 189,
+	B10111110= 190,
+	B10111111= 191,
+	B11000000= 192,
+	B11000001= 193,
+	B11000010= 194,
+	B11000011= 195,
+	B11000100= 196,
+	B11000101= 197,
+	B11000110= 198,
+	B11000111= 199,
+	B11001000= 200,
+	B11001001= 201,
+	B11001010= 202,
+	B11001011= 203,
+	B11001100= 204,
+	B11001101= 205,
+	B11001110= 206,
+	B11001111= 207,
+	B11010000= 208,
+	B11010001= 209,
+	B11010010= 210,
+	B11010011= 211,
+	B11010100= 212,
+	B11010101= 213,
+	B11010110= 214,
+	B11010111= 215,
+	B11011000= 216,
+	B11011001= 217,
+	B11011010= 218,
+	B11011011= 219,
+	B11011100= 220,
+	B11011101= 221,
+	B11011110= 222,
+	B11011111= 223,
+	B11100000= 224,
+	B11100001= 225,
+	B11100010= 226,
+	B11100011= 227,
+	B11100100= 228,
+	B11100101= 229,
+	B11100110= 230,
+	B11100111= 231,
+	B11101000= 232,
+	B11101001= 233,
+	B11101010= 234,
+	B11101011= 235,
+	B11101100= 236,
+	B11101101= 237,
+	B11101110= 238,
+	B11101111= 239,
+	B11110000= 240,
+	B11110001= 241,
+	B11110010= 242,
+	B11110011= 243,
+	B11110100= 244,
+	B11110101= 245,
+	B11110110= 246,
+	B11110111= 247,
+	B11111000= 248,
+	B11111001= 249,
+	B11111010= 250,
+	B11111011= 251,
+	B11111100= 252,
+	B11111101= 253,
+	B11111110= 254,
+	B11111111= 255
+};
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_mnemonic.h b/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_mnemonic.h
new file mode 100644
index 0000000000..28d2d222f9
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_mnemonic.h
@@ -0,0 +1,2017 @@
+/*******************************************************************************
+* Copyright 2016-2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/*******************************************************************************
+* Copyright (c) 2007 MITSUNARI Shigeo
+* All rights reserved.
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions are met:
+*
+* Redistributions of source code must retain the above copyright notice, this
+* list of conditions and the following disclaimer.
+* Redistributions in binary form must reproduce the above copyright notice,
+* this list of conditions and the following disclaimer in the documentation
+* and/or other materials provided with the distribution.
+* Neither the name of the copyright owner nor the names of its contributors may
+* be used to endorse or promote products derived from this software without
+* specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+* THE POSSIBILITY OF SUCH DAMAGE.
+*******************************************************************************/
+
+const char *getVersionString() const { return "5.76"; }
+void adc(const Operand& op, uint32 imm) { opRM_I(op, imm, 0x10, 2); }
+void adc(const Operand& op1, const Operand& op2) { opRM_RM(op1, op2, 0x10); }
+void adcx(const Reg32e& reg, const Operand& op) { opGen(reg, op, 0xF6, 0x66, isREG32_REG32orMEM, NONE, 0x38); }
+void add(const Operand& op, uint32 imm) { opRM_I(op, imm, 0x00, 0); }
+void add(const Operand& op1, const Operand& op2) { opRM_RM(op1, op2, 0x00); }
+void addpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x58, 0x66, isXMM_XMMorMEM); }
+void addps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x58, 0x100, isXMM_XMMorMEM); }
+void addsd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x58, 0xF2, isXMM_XMMorMEM); }
+void addss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x58, 0xF3, isXMM_XMMorMEM); }
+void addsubpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xD0, 0x66, isXMM_XMMorMEM); }
+void addsubps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xD0, 0xF2, isXMM_XMMorMEM); }
+void adox(const Reg32e& reg, const Operand& op) { opGen(reg, op, 0xF6, 0xF3, isREG32_REG32orMEM, NONE, 0x38); }
+void aesdec(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xDE, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void aesdeclast(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xDF, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void aesenc(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xDC, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void aesenclast(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xDD, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void aesimc(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xDB, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void aeskeygenassist(const Xmm& xmm, const Operand& op, uint8 imm) { opGen(xmm, op, 0xDF, 0x66, isXMM_XMMorMEM, imm, 0x3A); }
+void and_(const Operand& op, uint32 imm) { opRM_I(op, imm, 0x20, 4); }
+void and_(const Operand& op1, const Operand& op2) { opRM_RM(op1, op2, 0x20); }
+void andn(const Reg32e& r1, const Reg32e& r2, const Operand& op) { opGpr(r1, r2, op, T_0F38, 0xf2, true); }
+void andnpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x55, 0x66, isXMM_XMMorMEM); }
+void andnps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x55, 0x100, isXMM_XMMorMEM); }
+void andpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x54, 0x66, isXMM_XMMorMEM); }
+void andps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x54, 0x100, isXMM_XMMorMEM); }
+void bextr(const Reg32e& r1, const Operand& op, const Reg32e& r2) { opGpr(r1, op, r2, T_0F38, 0xf7, false); }
+void blendpd(const Xmm& xmm, const Operand& op, int imm) { opGen(xmm, op, 0x0D, 0x66, isXMM_XMMorMEM, static_cast<uint8>(imm), 0x3A); }
+void blendps(const Xmm& xmm, const Operand& op, int imm) { opGen(xmm, op, 0x0C, 0x66, isXMM_XMMorMEM, static_cast<uint8>(imm), 0x3A); }
+void blendvpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x15, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void blendvps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x14, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void blsi(const Reg32e& r, const Operand& op) { opGpr(Reg32e(3, r.getBit()), op, r, T_0F38, 0xf3, false); }
+void blsmsk(const Reg32e& r, const Operand& op) { opGpr(Reg32e(2, r.getBit()), op, r, T_0F38, 0xf3, false); }
+void blsr(const Reg32e& r, const Operand& op) { opGpr(Reg32e(1, r.getBit()), op, r, T_0F38, 0xf3, false); }
+void bnd() { db(0xF2); }
+void bndcl(const BoundsReg& bnd, const Operand& op) { db(0xF3); opR_ModM(op, i32e, bnd.getIdx(), 0x0F, 0x1A, NONE, !op.isMEM()); }
+void bndcn(const BoundsReg& bnd, const Operand& op) { db(0xF2); opR_ModM(op, i32e, bnd.getIdx(), 0x0F, 0x1B, NONE, !op.isMEM()); }
+void bndcu(const BoundsReg& bnd, const Operand& op) { db(0xF2); opR_ModM(op, i32e, bnd.getIdx(), 0x0F, 0x1A, NONE, !op.isMEM()); }
+void bndldx(const BoundsReg& bnd, const Address& addr) { opMIB(addr, bnd, 0x0F, 0x1A); }
+void bndmk(const BoundsReg& bnd, const Address& addr) { db(0xF3); opModM(addr, bnd, 0x0F, 0x1B); }
+void bndmov(const Address& addr, const BoundsReg& bnd) { db(0x66); opModM(addr, bnd, 0x0F, 0x1B); }
+void bndmov(const BoundsReg& bnd, const Operand& op) { db(0x66); opModRM(bnd, op, op.isBNDREG(), op.isMEM(), 0x0F, 0x1A); }
+void bndstx(const Address& addr, const BoundsReg& bnd) { opMIB(addr, bnd, 0x0F, 0x1B); }
+void bsf(const Reg&reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0xBC); }
+void bsr(const Reg&reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0xBD); }
+void bswap(const Reg32e& reg) { opModR(Reg32(1), reg, 0x0F); }
+void bt(const Operand& op, const Reg& reg) { opModRM(reg, op, op.isREG(16|32|64) && op.getBit() == reg.getBit(), op.isMEM(), 0x0f, 0xA3); }
+void bt(const Operand& op, uint8 imm) { opR_ModM(op, 16|32|64, 4, 0x0f, 0xba, NONE, false, 1); db(imm); }
+void btc(const Operand& op, const Reg& reg) { opModRM(reg, op, op.isREG(16|32|64) && op.getBit() == reg.getBit(), op.isMEM(), 0x0f, 0xBB); }
+void btc(const Operand& op, uint8 imm) { opR_ModM(op, 16|32|64, 7, 0x0f, 0xba, NONE, false, 1); db(imm); }
+void btr(const Operand& op, const Reg& reg) { opModRM(reg, op, op.isREG(16|32|64) && op.getBit() == reg.getBit(), op.isMEM(), 0x0f, 0xB3); }
+void btr(const Operand& op, uint8 imm) { opR_ModM(op, 16|32|64, 6, 0x0f, 0xba, NONE, false, 1); db(imm); }
+void bts(const Operand& op, const Reg& reg) { opModRM(reg, op, op.isREG(16|32|64) && op.getBit() == reg.getBit(), op.isMEM(), 0x0f, 0xAB); }
+void bts(const Operand& op, uint8 imm) { opR_ModM(op, 16|32|64, 5, 0x0f, 0xba, NONE, false, 1); db(imm); }
+void bzhi(const Reg32e& r1, const Operand& op, const Reg32e& r2) { opGpr(r1, op, r2, T_0F38, 0xf5, false); }
+void cbw() { db(0x66); db(0x98); }
+void cdq() { db(0x99); }
+void clc() { db(0xF8); }
+void cld() { db(0xFC); }
+void clflush(const Address& addr) { opModM(addr, Reg32(7), 0x0F, 0xAE); }
+void cli() { db(0xFA); }
+void cmc() { db(0xF5); }
+void cmova(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 7); }//-V524
+void cmovae(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 3); }//-V524
+void cmovb(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 2); }//-V524
+void cmovbe(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 6); }//-V524
+void cmovc(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 2); }//-V524
+void cmove(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 4); }//-V524
+void cmovg(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 15); }//-V524
+void cmovge(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 13); }//-V524
+void cmovl(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 12); }//-V524
+void cmovle(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 14); }//-V524
+void cmovna(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 6); }//-V524
+void cmovnae(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 2); }//-V524
+void cmovnb(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 3); }//-V524
+void cmovnbe(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 7); }//-V524
+void cmovnc(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 3); }//-V524
+void cmovne(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 5); }//-V524
+void cmovng(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 14); }//-V524
+void cmovnge(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 12); }//-V524
+void cmovnl(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 13); }//-V524
+void cmovnle(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 15); }//-V524
+void cmovno(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 1); }//-V524
+void cmovnp(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 11); }//-V524
+void cmovns(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 9); }//-V524
+void cmovnz(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 5); }//-V524
+void cmovo(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 0); }//-V524
+void cmovp(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 10); }//-V524
+void cmovpe(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 10); }//-V524
+void cmovpo(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 11); }//-V524
+void cmovs(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 8); }//-V524
+void cmovz(const Reg& reg, const Operand& op) { opModRM(reg, op, op.isREG(16 | i32e), op.isMEM(), 0x0F, 0x40 | 4); }//-V524
+void cmp(const Operand& op, uint32 imm) { opRM_I(op, imm, 0x38, 7); }
+void cmp(const Operand& op1, const Operand& op2) { opRM_RM(op1, op2, 0x38); }
+void cmpeqpd(const Xmm& x, const Operand& op) { cmppd(x, op, 0); }
+void cmpeqps(const Xmm& x, const Operand& op) { cmpps(x, op, 0); }
+void cmpeqsd(const Xmm& x, const Operand& op) { cmpsd(x, op, 0); }
+void cmpeqss(const Xmm& x, const Operand& op) { cmpss(x, op, 0); }
+void cmplepd(const Xmm& x, const Operand& op) { cmppd(x, op, 2); }
+void cmpleps(const Xmm& x, const Operand& op) { cmpps(x, op, 2); }
+void cmplesd(const Xmm& x, const Operand& op) { cmpsd(x, op, 2); }
+void cmpless(const Xmm& x, const Operand& op) { cmpss(x, op, 2); }
+void cmpltpd(const Xmm& x, const Operand& op) { cmppd(x, op, 1); }
+void cmpltps(const Xmm& x, const Operand& op) { cmpps(x, op, 1); }
+void cmpltsd(const Xmm& x, const Operand& op) { cmpsd(x, op, 1); }
+void cmpltss(const Xmm& x, const Operand& op) { cmpss(x, op, 1); }
+void cmpneqpd(const Xmm& x, const Operand& op) { cmppd(x, op, 4); }
+void cmpneqps(const Xmm& x, const Operand& op) { cmpps(x, op, 4); }
+void cmpneqsd(const Xmm& x, const Operand& op) { cmpsd(x, op, 4); }
+void cmpneqss(const Xmm& x, const Operand& op) { cmpss(x, op, 4); }
+void cmpnlepd(const Xmm& x, const Operand& op) { cmppd(x, op, 6); }
+void cmpnleps(const Xmm& x, const Operand& op) { cmpps(x, op, 6); }
+void cmpnlesd(const Xmm& x, const Operand& op) { cmpsd(x, op, 6); }
+void cmpnless(const Xmm& x, const Operand& op) { cmpss(x, op, 6); }
+void cmpnltpd(const Xmm& x, const Operand& op) { cmppd(x, op, 5); }
+void cmpnltps(const Xmm& x, const Operand& op) { cmpps(x, op, 5); }
+void cmpnltsd(const Xmm& x, const Operand& op) { cmpsd(x, op, 5); }
+void cmpnltss(const Xmm& x, const Operand& op) { cmpss(x, op, 5); }
+void cmpordpd(const Xmm& x, const Operand& op) { cmppd(x, op, 7); }
+void cmpordps(const Xmm& x, const Operand& op) { cmpps(x, op, 7); }
+void cmpordsd(const Xmm& x, const Operand& op) { cmpsd(x, op, 7); }
+void cmpordss(const Xmm& x, const Operand& op) { cmpss(x, op, 7); }
+void cmppd(const Xmm& xmm, const Operand& op, uint8 imm8) { opGen(xmm, op, 0xC2, 0x66, isXMM_XMMorMEM, imm8); }
+void cmpps(const Xmm& xmm, const Operand& op, uint8 imm8) { opGen(xmm, op, 0xC2, 0x100, isXMM_XMMorMEM, imm8); }
+void cmpsb() { db(0xA6); }
+void cmpsd() { db(0xA7); }
+void cmpsd(const Xmm& xmm, const Operand& op, uint8 imm8) { opGen(xmm, op, 0xC2, 0xF2, isXMM_XMMorMEM, imm8); }
+void cmpss(const Xmm& xmm, const Operand& op, uint8 imm8) { opGen(xmm, op, 0xC2, 0xF3, isXMM_XMMorMEM, imm8); }
+void cmpsw() { db(0x66); db(0xA7); }
+void cmpunordpd(const Xmm& x, const Operand& op) { cmppd(x, op, 3); }
+void cmpunordps(const Xmm& x, const Operand& op) { cmpps(x, op, 3); }
+void cmpunordsd(const Xmm& x, const Operand& op) { cmpsd(x, op, 3); }
+void cmpunordss(const Xmm& x, const Operand& op) { cmpss(x, op, 3); }
+void cmpxchg(const Operand& op, const Reg& reg) { opModRM(reg, op, (op.isREG() && reg.isREG() && op.getBit() == reg.getBit()), op.isMEM(), 0x0F, 0xB0 | (reg.isBit(8) ? 0 : 1)); }
+void cmpxchg8b(const Address& addr) { opModM(addr, Reg32(1), 0x0F, 0xC7); }
+void comisd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x2F, 0x66, isXMM_XMMorMEM); }
+void comiss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x2F, 0x100, isXMM_XMMorMEM); }
+void cpuid() { db(0x0F); db(0xA2); }
+void crc32(const Reg32e& reg, const Operand& op) { if (reg.isBit(32) && op.isBit(16)) db(0x66); db(0xF2); opModRM(reg, op, op.isREG(), op.isMEM(), 0x0F, 0x38, 0xF0 | (op.isBit(8) ? 0 : 1)); }
+void cvtdq2pd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xE6, 0xF3, isXMM_XMMorMEM); }
+void cvtdq2ps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5B, 0x100, isXMM_XMMorMEM); }
+void cvtpd2dq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xE6, 0xF2, isXMM_XMMorMEM); }
+void cvtpd2pi(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2D, 0x66, isMMX_XMMorMEM); }
+void cvtpd2ps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5A, 0x66, isXMM_XMMorMEM); }
+void cvtpi2pd(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2A, 0x66, isXMM_MMXorMEM); }
+void cvtpi2ps(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2A, 0x100, isXMM_MMXorMEM); }
+void cvtps2dq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5B, 0x66, isXMM_XMMorMEM); }
+void cvtps2pd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5A, 0x100, isXMM_XMMorMEM); }
+void cvtps2pi(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2D, 0x100, isMMX_XMMorMEM); }
+void cvtsd2si(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2D, 0xF2, isREG32_XMMorMEM); }
+void cvtsd2ss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5A, 0xF2, isXMM_XMMorMEM); }
+void cvtsi2sd(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2A, 0xF2, isXMM_REG32orMEM); }
+void cvtsi2ss(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2A, 0xF3, isXMM_REG32orMEM); }
+void cvtss2sd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5A, 0xF3, isXMM_XMMorMEM); }
+void cvtss2si(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2D, 0xF3, isREG32_XMMorMEM); }
+void cvttpd2dq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xE6, 0x66, isXMM_XMMorMEM); }
+void cvttpd2pi(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2C, 0x66, isMMX_XMMorMEM); }
+void cvttps2dq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5B, 0xF3, isXMM_XMMorMEM); }
+void cvttps2pi(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2C, 0x100, isMMX_XMMorMEM); }
+void cvttsd2si(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2C, 0xF2, isREG32_XMMorMEM); }
+void cvttss2si(const Operand& reg, const Operand& op) { opGen(reg, op, 0x2C, 0xF3, isREG32_XMMorMEM); }
+void cwd() { db(0x66); db(0x99); }
+void cwde() { db(0x98); }
+void dec(const Operand& op) { opIncDec(op, 0x48, 1); }
+void div(const Operand& op) { opR_ModM(op, 0, 6, 0xF6); }
+void divpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5E, 0x66, isXMM_XMMorMEM); }
+void divps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5E, 0x100, isXMM_XMMorMEM); }
+void divsd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5E, 0xF2, isXMM_XMMorMEM); }
+void divss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5E, 0xF3, isXMM_XMMorMEM); }
+void dppd(const Xmm& xmm, const Operand& op, int imm) { opGen(xmm, op, 0x41, 0x66, isXMM_XMMorMEM, static_cast<uint8>(imm), 0x3A); }
+void dpps(const Xmm& xmm, const Operand& op, int imm) { opGen(xmm, op, 0x40, 0x66, isXMM_XMMorMEM, static_cast<uint8>(imm), 0x3A); }
+void emms() { db(0x0F); db(0x77); }
+void extractps(const Operand& op, const Xmm& xmm, uint8 imm) { opExt(op, xmm, 0x17, imm); }
+void f2xm1() { db(0xD9); db(0xF0); }
+void fabs() { db(0xD9); db(0xE1); }
+void fadd(const Address& addr) { opFpuMem(addr, 0x00, 0xD8, 0xDC, 0, 0); }
+void fadd(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xD8C0, 0xDCC0); }
+void fadd(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xD8C0, 0xDCC0); }
+void faddp() { db(0xDE); db(0xC1); }
+void faddp(const Fpu& reg1) { opFpuFpu(reg1, st0, 0x0000, 0xDEC0); }
+void faddp(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0x0000, 0xDEC0); }
+void fchs() { db(0xD9); db(0xE0); }
+void fcmovb(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDAC0, 0x00C0); }
+void fcmovb(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDAC0, 0x00C0); }
+void fcmovbe(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDAD0, 0x00D0); }
+void fcmovbe(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDAD0, 0x00D0); }
+void fcmove(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDAC8, 0x00C8); }
+void fcmove(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDAC8, 0x00C8); }
+void fcmovnb(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDBC0, 0x00C0); }
+void fcmovnb(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDBC0, 0x00C0); }
+void fcmovnbe(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDBD0, 0x00D0); }
+void fcmovnbe(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDBD0, 0x00D0); }
+void fcmovne(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDBC8, 0x00C8); }
+void fcmovne(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDBC8, 0x00C8); }
+void fcmovnu(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDBD8, 0x00D8); }
+void fcmovnu(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDBD8, 0x00D8); }
+void fcmovu(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDAD8, 0x00D8); }
+void fcmovu(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDAD8, 0x00D8); }
+void fcom() { db(0xD8); db(0xD1); }
+void fcom(const Address& addr) { opFpuMem(addr, 0x00, 0xD8, 0xDC, 2, 0); }
+void fcom(const Fpu& reg) { opFpu(reg, 0xD8, 0xD0); }
+void fcomi(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDBF0, 0x00F0); }
+void fcomi(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDBF0, 0x00F0); }
+void fcomip(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDFF0, 0x00F0); }
+void fcomip(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDFF0, 0x00F0); }
+void fcomp() { db(0xD8); db(0xD9); }
+void fcomp(const Address& addr) { opFpuMem(addr, 0x00, 0xD8, 0xDC, 3, 0); }
+void fcomp(const Fpu& reg) { opFpu(reg, 0xD8, 0xD8); }
+void fcompp() { db(0xDE); db(0xD9); }
+void fcos() { db(0xD9); db(0xFF); }
+void fdecstp() { db(0xD9); db(0xF6); }
+void fdiv(const Address& addr) { opFpuMem(addr, 0x00, 0xD8, 0xDC, 6, 0); }
+void fdiv(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xD8F0, 0xDCF8); }
+void fdiv(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xD8F0, 0xDCF8); }
+void fdivp() { db(0xDE); db(0xF9); }
+void fdivp(const Fpu& reg1) { opFpuFpu(reg1, st0, 0x0000, 0xDEF8); }
+void fdivp(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0x0000, 0xDEF8); }
+void fdivr(const Address& addr) { opFpuMem(addr, 0x00, 0xD8, 0xDC, 7, 0); }
+void fdivr(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xD8F8, 0xDCF0); }
+void fdivr(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xD8F8, 0xDCF0); }
+void fdivrp() { db(0xDE); db(0xF1); }
+void fdivrp(const Fpu& reg1) { opFpuFpu(reg1, st0, 0x0000, 0xDEF0); }
+void fdivrp(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0x0000, 0xDEF0); }
+void ffree(const Fpu& reg) { opFpu(reg, 0xDD, 0xC0); }
+void fiadd(const Address& addr) { opFpuMem(addr, 0xDE, 0xDA, 0x00, 0, 0); }
+void ficom(const Address& addr) { opFpuMem(addr, 0xDE, 0xDA, 0x00, 2, 0); }
+void ficomp(const Address& addr) { opFpuMem(addr, 0xDE, 0xDA, 0x00, 3, 0); }
+void fidiv(const Address& addr) { opFpuMem(addr, 0xDE, 0xDA, 0x00, 6, 0); }
+void fidivr(const Address& addr) { opFpuMem(addr, 0xDE, 0xDA, 0x00, 7, 0); }
+void fild(const Address& addr) { opFpuMem(addr, 0xDF, 0xDB, 0xDF, 0, 5); }
+void fimul(const Address& addr) { opFpuMem(addr, 0xDE, 0xDA, 0x00, 1, 0); }
+void fincstp() { db(0xD9); db(0xF7); }
+void finit() { db(0x9B); db(0xDB); db(0xE3); }
+void fist(const Address& addr) { opFpuMem(addr, 0xDF, 0xDB, 0x00, 2, 0); }
+void fistp(const Address& addr) { opFpuMem(addr, 0xDF, 0xDB, 0xDF, 3, 7); }
+void fisttp(const Address& addr) { opFpuMem(addr, 0xDF, 0xDB, 0xDD, 1, 0); }
+void fisub(const Address& addr) { opFpuMem(addr, 0xDE, 0xDA, 0x00, 4, 0); }
+void fisubr(const Address& addr) { opFpuMem(addr, 0xDE, 0xDA, 0x00, 5, 0); }
+void fld(const Address& addr) { opFpuMem(addr, 0x00, 0xD9, 0xDD, 0, 0); }
+void fld(const Fpu& reg) { opFpu(reg, 0xD9, 0xC0); }
+void fld1() { db(0xD9); db(0xE8); }
+void fldcw(const Address& addr) { opModM(addr, Reg32(5), 0xD9, 0x100); }
+void fldl2e() { db(0xD9); db(0xEA); }
+void fldl2t() { db(0xD9); db(0xE9); }
+void fldlg2() { db(0xD9); db(0xEC); }
+void fldln2() { db(0xD9); db(0xED); }
+void fldpi() { db(0xD9); db(0xEB); }
+void fldz() { db(0xD9); db(0xEE); }
+void fmul(const Address& addr) { opFpuMem(addr, 0x00, 0xD8, 0xDC, 1, 0); }
+void fmul(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xD8C8, 0xDCC8); }
+void fmul(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xD8C8, 0xDCC8); }
+void fmulp() { db(0xDE); db(0xC9); }
+void fmulp(const Fpu& reg1) { opFpuFpu(reg1, st0, 0x0000, 0xDEC8); }
+void fmulp(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0x0000, 0xDEC8); }
+void fninit() { db(0xDB); db(0xE3); }
+void fnop() { db(0xD9); db(0xD0); }
+void fpatan() { db(0xD9); db(0xF3); }
+void fprem() { db(0xD9); db(0xF8); }
+void fprem1() { db(0xD9); db(0xF5); }
+void fptan() { db(0xD9); db(0xF2); }
+void frndint() { db(0xD9); db(0xFC); }
+void fscale() { db(0xD9); db(0xFD); }
+void fsin() { db(0xD9); db(0xFE); }
+void fsincos() { db(0xD9); db(0xFB); }
+void fsqrt() { db(0xD9); db(0xFA); }
+void fst(const Address& addr) { opFpuMem(addr, 0x00, 0xD9, 0xDD, 2, 0); }
+void fst(const Fpu& reg) { opFpu(reg, 0xDD, 0xD0); }
+void fstcw(const Address& addr) { db(0x9B); opModM(addr, Reg32(7), 0xD9, NONE); }
+void fstp(const Address& addr) { opFpuMem(addr, 0x00, 0xD9, 0xDD, 3, 0); }
+void fstp(const Fpu& reg) { opFpu(reg, 0xDD, 0xD8); }
+void fsub(const Address& addr) { opFpuMem(addr, 0x00, 0xD8, 0xDC, 4, 0); }
+void fsub(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xD8E0, 0xDCE8); }
+void fsub(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xD8E0, 0xDCE8); }
+void fsubp() { db(0xDE); db(0xE9); }
+void fsubp(const Fpu& reg1) { opFpuFpu(reg1, st0, 0x0000, 0xDEE8); }
+void fsubp(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0x0000, 0xDEE8); }
+void fsubr(const Address& addr) { opFpuMem(addr, 0x00, 0xD8, 0xDC, 5, 0); }
+void fsubr(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xD8E8, 0xDCE0); }
+void fsubr(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xD8E8, 0xDCE0); }
+void fsubrp() { db(0xDE); db(0xE1); }
+void fsubrp(const Fpu& reg1) { opFpuFpu(reg1, st0, 0x0000, 0xDEE0); }
+void fsubrp(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0x0000, 0xDEE0); }
+void ftst() { db(0xD9); db(0xE4); }
+void fucom() { db(0xDD); db(0xE1); }
+void fucom(const Fpu& reg) { opFpu(reg, 0xDD, 0xE0); }
+void fucomi(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDBE8, 0x00E8); }
+void fucomi(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDBE8, 0x00E8); }
+void fucomip(const Fpu& reg1) { opFpuFpu(st0, reg1, 0xDFE8, 0x00E8); }
+void fucomip(const Fpu& reg1, const Fpu& reg2) { opFpuFpu(reg1, reg2, 0xDFE8, 0x00E8); }
+void fucomp() { db(0xDD); db(0xE9); }
+void fucomp(const Fpu& reg) { opFpu(reg, 0xDD, 0xE8); }
+void fucompp() { db(0xDA); db(0xE9); }
+void fwait() { db(0x9B); }
+void fxam() { db(0xD9); db(0xE5); }
+void fxch() { db(0xD9); db(0xC9); }
+void fxch(const Fpu& reg) { opFpu(reg, 0xD9, 0xC8); }
+void fxtract() { db(0xD9); db(0xF4); }
+void fyl2x() { db(0xD9); db(0xF1); }
+void fyl2xp1() { db(0xD9); db(0xF9); }
+void gf2p8affineinvqb(const Xmm& xmm, const Operand& op, int imm) { opGen(xmm, op, 0xCF, 0x66, isXMM_XMMorMEM, static_cast<uint8>(imm), 0x3A); }
+void gf2p8affineqb(const Xmm& xmm, const Operand& op, int imm) { opGen(xmm, op, 0xCE, 0x66, isXMM_XMMorMEM, static_cast<uint8>(imm), 0x3A); }
+void gf2p8mulb(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xCF, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void haddpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x7C, 0x66, isXMM_XMMorMEM); }
+void haddps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x7C, 0xF2, isXMM_XMMorMEM); }
+void hsubpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x7D, 0x66, isXMM_XMMorMEM); }
+void hsubps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x7D, 0xF2, isXMM_XMMorMEM); }
+void idiv(const Operand& op) { opR_ModM(op, 0, 7, 0xF6); }
+void imul(const Operand& op) { opR_ModM(op, 0, 5, 0xF6); }
+void inc(const Operand& op) { opIncDec(op, 0x40, 0); }
+void insertps(const Xmm& xmm, const Operand& op, uint8 imm) { opGen(xmm, op, 0x21, 0x66, isXMM_XMMorMEM, imm, 0x3A); }
+void ja(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x77, 0x87, 0x0F); }//-V524
+void ja(const char *label, LabelType type = T_AUTO) { ja(std::string(label), type); }//-V524
+void ja(const void *addr) { opJmpAbs(addr, T_NEAR, 0x77, 0x87, 0x0F); }//-V524
+void ja(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x77, 0x87, 0x0F); }//-V524
+void jae(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x73, 0x83, 0x0F); }//-V524
+void jae(const char *label, LabelType type = T_AUTO) { jae(std::string(label), type); }//-V524
+void jae(const void *addr) { opJmpAbs(addr, T_NEAR, 0x73, 0x83, 0x0F); }//-V524
+void jae(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x73, 0x83, 0x0F); }//-V524
+void jb(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x72, 0x82, 0x0F); }//-V524
+void jb(const char *label, LabelType type = T_AUTO) { jb(std::string(label), type); }//-V524
+void jb(const void *addr) { opJmpAbs(addr, T_NEAR, 0x72, 0x82, 0x0F); }//-V524
+void jb(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x72, 0x82, 0x0F); }//-V524
+void jbe(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x76, 0x86, 0x0F); }//-V524
+void jbe(const char *label, LabelType type = T_AUTO) { jbe(std::string(label), type); }//-V524
+void jbe(const void *addr) { opJmpAbs(addr, T_NEAR, 0x76, 0x86, 0x0F); }//-V524
+void jbe(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x76, 0x86, 0x0F); }//-V524
+void jc(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x72, 0x82, 0x0F); }//-V524
+void jc(const char *label, LabelType type = T_AUTO) { jc(std::string(label), type); }//-V524
+void jc(const void *addr) { opJmpAbs(addr, T_NEAR, 0x72, 0x82, 0x0F); }//-V524
+void jc(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x72, 0x82, 0x0F); }//-V524
+void je(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x74, 0x84, 0x0F); }//-V524
+void je(const char *label, LabelType type = T_AUTO) { je(std::string(label), type); }//-V524
+void je(const void *addr) { opJmpAbs(addr, T_NEAR, 0x74, 0x84, 0x0F); }//-V524
+void je(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x74, 0x84, 0x0F); }//-V524
+void jg(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7F, 0x8F, 0x0F); }//-V524
+void jg(const char *label, LabelType type = T_AUTO) { jg(std::string(label), type); }//-V524
+void jg(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7F, 0x8F, 0x0F); }//-V524
+void jg(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7F, 0x8F, 0x0F); }//-V524
+void jge(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7D, 0x8D, 0x0F); }//-V524
+void jge(const char *label, LabelType type = T_AUTO) { jge(std::string(label), type); }//-V524
+void jge(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7D, 0x8D, 0x0F); }//-V524
+void jge(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7D, 0x8D, 0x0F); }//-V524
+void jl(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7C, 0x8C, 0x0F); }//-V524
+void jl(const char *label, LabelType type = T_AUTO) { jl(std::string(label), type); }//-V524
+void jl(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7C, 0x8C, 0x0F); }//-V524
+void jl(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7C, 0x8C, 0x0F); }//-V524
+void jle(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7E, 0x8E, 0x0F); }//-V524
+void jle(const char *label, LabelType type = T_AUTO) { jle(std::string(label), type); }//-V524
+void jle(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7E, 0x8E, 0x0F); }//-V524
+void jle(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7E, 0x8E, 0x0F); }//-V524
+void jna(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x76, 0x86, 0x0F); }//-V524
+void jna(const char *label, LabelType type = T_AUTO) { jna(std::string(label), type); }//-V524
+void jna(const void *addr) { opJmpAbs(addr, T_NEAR, 0x76, 0x86, 0x0F); }//-V524
+void jna(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x76, 0x86, 0x0F); }//-V524
+void jnae(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x72, 0x82, 0x0F); }//-V524
+void jnae(const char *label, LabelType type = T_AUTO) { jnae(std::string(label), type); }//-V524
+void jnae(const void *addr) { opJmpAbs(addr, T_NEAR, 0x72, 0x82, 0x0F); }//-V524
+void jnae(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x72, 0x82, 0x0F); }//-V524
+void jnb(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x73, 0x83, 0x0F); }//-V524
+void jnb(const char *label, LabelType type = T_AUTO) { jnb(std::string(label), type); }//-V524
+void jnb(const void *addr) { opJmpAbs(addr, T_NEAR, 0x73, 0x83, 0x0F); }//-V524
+void jnb(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x73, 0x83, 0x0F); }//-V524
+void jnbe(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x77, 0x87, 0x0F); }//-V524
+void jnbe(const char *label, LabelType type = T_AUTO) { jnbe(std::string(label), type); }//-V524
+void jnbe(const void *addr) { opJmpAbs(addr, T_NEAR, 0x77, 0x87, 0x0F); }//-V524
+void jnbe(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x77, 0x87, 0x0F); }//-V524
+void jnc(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x73, 0x83, 0x0F); }//-V524
+void jnc(const char *label, LabelType type = T_AUTO) { jnc(std::string(label), type); }//-V524
+void jnc(const void *addr) { opJmpAbs(addr, T_NEAR, 0x73, 0x83, 0x0F); }//-V524
+void jnc(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x73, 0x83, 0x0F); }//-V524
+void jne(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x75, 0x85, 0x0F); }//-V524
+void jne(const char *label, LabelType type = T_AUTO) { jne(std::string(label), type); }//-V524
+void jne(const void *addr) { opJmpAbs(addr, T_NEAR, 0x75, 0x85, 0x0F); }//-V524
+void jne(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x75, 0x85, 0x0F); }//-V524
+void jng(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7E, 0x8E, 0x0F); }//-V524
+void jng(const char *label, LabelType type = T_AUTO) { jng(std::string(label), type); }//-V524
+void jng(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7E, 0x8E, 0x0F); }//-V524
+void jng(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7E, 0x8E, 0x0F); }//-V524
+void jnge(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7C, 0x8C, 0x0F); }//-V524
+void jnge(const char *label, LabelType type = T_AUTO) { jnge(std::string(label), type); }//-V524
+void jnge(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7C, 0x8C, 0x0F); }//-V524
+void jnge(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7C, 0x8C, 0x0F); }//-V524
+void jnl(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7D, 0x8D, 0x0F); }//-V524
+void jnl(const char *label, LabelType type = T_AUTO) { jnl(std::string(label), type); }//-V524
+void jnl(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7D, 0x8D, 0x0F); }//-V524
+void jnl(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7D, 0x8D, 0x0F); }//-V524
+void jnle(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7F, 0x8F, 0x0F); }//-V524
+void jnle(const char *label, LabelType type = T_AUTO) { jnle(std::string(label), type); }//-V524
+void jnle(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7F, 0x8F, 0x0F); }//-V524
+void jnle(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7F, 0x8F, 0x0F); }//-V524
+void jno(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x71, 0x81, 0x0F); }//-V524
+void jno(const char *label, LabelType type = T_AUTO) { jno(std::string(label), type); }//-V524
+void jno(const void *addr) { opJmpAbs(addr, T_NEAR, 0x71, 0x81, 0x0F); }//-V524
+void jno(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x71, 0x81, 0x0F); }//-V524
+void jnp(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7B, 0x8B, 0x0F); }//-V524
+void jnp(const char *label, LabelType type = T_AUTO) { jnp(std::string(label), type); }//-V524
+void jnp(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7B, 0x8B, 0x0F); }//-V524
+void jnp(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7B, 0x8B, 0x0F); }//-V524
+void jns(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x79, 0x89, 0x0F); }//-V524
+void jns(const char *label, LabelType type = T_AUTO) { jns(std::string(label), type); }//-V524
+void jns(const void *addr) { opJmpAbs(addr, T_NEAR, 0x79, 0x89, 0x0F); }//-V524
+void jns(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x79, 0x89, 0x0F); }//-V524
+void jnz(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x75, 0x85, 0x0F); }//-V524
+void jnz(const char *label, LabelType type = T_AUTO) { jnz(std::string(label), type); }//-V524
+void jnz(const void *addr) { opJmpAbs(addr, T_NEAR, 0x75, 0x85, 0x0F); }//-V524
+void jnz(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x75, 0x85, 0x0F); }//-V524
+void jo(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x70, 0x80, 0x0F); }//-V524
+void jo(const char *label, LabelType type = T_AUTO) { jo(std::string(label), type); }//-V524
+void jo(const void *addr) { opJmpAbs(addr, T_NEAR, 0x70, 0x80, 0x0F); }//-V524
+void jo(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x70, 0x80, 0x0F); }//-V524
+void jp(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7A, 0x8A, 0x0F); }//-V524
+void jp(const char *label, LabelType type = T_AUTO) { jp(std::string(label), type); }//-V524
+void jp(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7A, 0x8A, 0x0F); }//-V524
+void jp(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7A, 0x8A, 0x0F); }//-V524
+void jpe(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7A, 0x8A, 0x0F); }//-V524
+void jpe(const char *label, LabelType type = T_AUTO) { jpe(std::string(label), type); }//-V524
+void jpe(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7A, 0x8A, 0x0F); }//-V524
+void jpe(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7A, 0x8A, 0x0F); }//-V524
+void jpo(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x7B, 0x8B, 0x0F); }//-V524
+void jpo(const char *label, LabelType type = T_AUTO) { jpo(std::string(label), type); }//-V524
+void jpo(const void *addr) { opJmpAbs(addr, T_NEAR, 0x7B, 0x8B, 0x0F); }//-V524
+void jpo(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x7B, 0x8B, 0x0F); }//-V524
+void js(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x78, 0x88, 0x0F); }//-V524
+void js(const char *label, LabelType type = T_AUTO) { js(std::string(label), type); }//-V524
+void js(const void *addr) { opJmpAbs(addr, T_NEAR, 0x78, 0x88, 0x0F); }//-V524
+void js(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x78, 0x88, 0x0F); }//-V524
+void jz(const Label& label, LabelType type = T_AUTO) { opJmp(label, type, 0x74, 0x84, 0x0F); }//-V524
+void jz(const char *label, LabelType type = T_AUTO) { jz(std::string(label), type); }//-V524
+void jz(const void *addr) { opJmpAbs(addr, T_NEAR, 0x74, 0x84, 0x0F); }//-V524
+void jz(std::string label, LabelType type = T_AUTO) { opJmp(label, type, 0x74, 0x84, 0x0F); }//-V524
+void lahf() { db(0x9F); }
+void lddqu(const Xmm& xmm, const Address& addr) { db(0xF2); opModM(addr, xmm, 0x0F, 0xF0); }
+void ldmxcsr(const Address& addr) { opModM(addr, Reg32(2), 0x0F, 0xAE); }
+void lea(const Reg& reg, const Address& addr) { if (!reg.isBit(16 | i32e)) throw Error(ERR_BAD_SIZE_OF_REGISTER); opModM(addr, reg, 0x8D); }
+void lfence() { db(0x0F); db(0xAE); db(0xE8); }
+void lock() { db(0xF0); }
+void lzcnt(const Reg&reg, const Operand& op) { opSp1(reg, op, 0xF3, 0x0F, 0xBD); }
+void maskmovdqu(const Xmm& reg1, const Xmm& reg2) { db(0x66);  opModR(reg1, reg2, 0x0F, 0xF7); }
+void maskmovq(const Mmx& reg1, const Mmx& reg2) { if (!reg1.isMMX() || !reg2.isMMX()) throw Error(ERR_BAD_COMBINATION); opModR(reg1, reg2, 0x0F, 0xF7); }
+void maxpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5F, 0x66, isXMM_XMMorMEM); }
+void maxps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5F, 0x100, isXMM_XMMorMEM); }
+void maxsd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5F, 0xF2, isXMM_XMMorMEM); }
+void maxss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5F, 0xF3, isXMM_XMMorMEM); }
+void mfence() { db(0x0F); db(0xAE); db(0xF0); }
+void minpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5D, 0x66, isXMM_XMMorMEM); }
+void minps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5D, 0x100, isXMM_XMMorMEM); }
+void minsd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5D, 0xF2, isXMM_XMMorMEM); }
+void minss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5D, 0xF3, isXMM_XMMorMEM); }
+void monitor() { db(0x0F); db(0x01); db(0xC8); }
+void movapd(const Address& addr, const Xmm& xmm) { db(0x66); opModM(addr, xmm, 0x0F, 0x29); }
+void movapd(const Xmm& xmm, const Operand& op) { opMMX(xmm, op, 0x28, 0x66); }
+void movaps(const Address& addr, const Xmm& xmm) { opModM(addr, xmm, 0x0F, 0x29); }
+void movaps(const Xmm& xmm, const Operand& op) { opMMX(xmm, op, 0x28, 0x100); }
+void movbe(const Address& addr, const Reg& reg) { opModM(addr, reg, 0x0F, 0x38, 0xF1); }
+void movbe(const Reg& reg, const Address& addr) { opModM(addr, reg, 0x0F, 0x38, 0xF0); }
+void movd(const Address& addr, const Mmx& mmx) { if (mmx.isXMM()) db(0x66); opModM(addr, mmx, 0x0F, 0x7E); }
+void movd(const Mmx& mmx, const Address& addr) { if (mmx.isXMM()) db(0x66); opModM(addr, mmx, 0x0F, 0x6E); }
+void movd(const Mmx& mmx, const Reg32& reg) { if (mmx.isXMM()) db(0x66); opModR(mmx, reg, 0x0F, 0x6E); }
+void movd(const Reg32& reg, const Mmx& mmx) { if (mmx.isXMM()) db(0x66); opModR(mmx, reg, 0x0F, 0x7E); }
+void movddup(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x12, 0xF2, isXMM_XMMorMEM, NONE, NONE); }
+void movdq2q(const Mmx& mmx, const Xmm& xmm) { db(0xF2); opModR(mmx, xmm, 0x0F, 0xD6); }
+void movdqa(const Address& addr, const Xmm& xmm) { db(0x66); opModM(addr, xmm, 0x0F, 0x7F); }
+void movdqa(const Xmm& xmm, const Operand& op) { opMMX(xmm, op, 0x6F, 0x66); }
+void movdqu(const Address& addr, const Xmm& xmm) { db(0xF3); opModM(addr, xmm, 0x0F, 0x7F); }
+void movdqu(const Xmm& xmm, const Operand& op) { opMMX(xmm, op, 0x6F, 0xF3); }
+void movhlps(const Xmm& reg1, const Xmm& reg2) {  opModR(reg1, reg2, 0x0F, 0x12); }
+void movhpd(const Operand& op1, const Operand& op2) { opMovXMM(op1, op2, 0x16, 0x66); }
+void movhps(const Operand& op1, const Operand& op2) { opMovXMM(op1, op2, 0x16, 0x100); }
+void movlhps(const Xmm& reg1, const Xmm& reg2) {  opModR(reg1, reg2, 0x0F, 0x16); }
+void movlpd(const Operand& op1, const Operand& op2) { opMovXMM(op1, op2, 0x12, 0x66); }
+void movlps(const Operand& op1, const Operand& op2) { opMovXMM(op1, op2, 0x12, 0x100); }
+void movmskpd(const Reg32e& reg, const Xmm& xmm) { db(0x66); movmskps(reg, xmm); }
+void movmskps(const Reg32e& reg, const Xmm& xmm) { opModR(reg, xmm, 0x0F, 0x50); }
+void movntdq(const Address& addr, const Xmm& reg) { opModM(addr, Reg16(reg.getIdx()), 0x0F, 0xE7); }
+void movntdqa(const Xmm& xmm, const Address& addr) { db(0x66); opModM(addr, xmm, 0x0F, 0x38, 0x2A); }
+void movnti(const Address& addr, const Reg32e& reg) { opModM(addr, reg, 0x0F, 0xC3); }
+void movntpd(const Address& addr, const Xmm& reg) { opModM(addr, Reg16(reg.getIdx()), 0x0F, 0x2B); }
+void movntps(const Address& addr, const Xmm& xmm) { opModM(addr, Mmx(xmm.getIdx()), 0x0F, 0x2B); }
+void movntq(const Address& addr, const Mmx& mmx) { if (!mmx.isMMX()) throw Error(ERR_BAD_COMBINATION); opModM(addr, mmx, 0x0F, 0xE7); }
+void movq(const Address& addr, const Mmx& mmx) { if (mmx.isXMM()) db(0x66); opModM(addr, mmx, 0x0F, mmx.isXMM() ? 0xD6 : 0x7F); }
+void movq(const Mmx& mmx, const Operand& op) { if (mmx.isXMM()) db(0xF3); opModRM(mmx, op, (mmx.getKind() == op.getKind()), op.isMEM(), 0x0F, mmx.isXMM() ? 0x7E : 0x6F); }
+void movq2dq(const Xmm& xmm, const Mmx& mmx) { db(0xF3); opModR(xmm, mmx, 0x0F, 0xD6); }
+void movsb() { db(0xA4); }
+void movsd() { db(0xA5); }
+void movsd(const Address& addr, const Xmm& xmm) { db(0xF2); opModM(addr, xmm, 0x0F, 0x11); }
+void movsd(const Xmm& xmm, const Operand& op) { opMMX(xmm, op, 0x10, 0xF2); }
+void movshdup(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x16, 0xF3, isXMM_XMMorMEM, NONE, NONE); }
+void movsldup(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x12, 0xF3, isXMM_XMMorMEM, NONE, NONE); }
+void movss(const Address& addr, const Xmm& xmm) { db(0xF3); opModM(addr, xmm, 0x0F, 0x11); }
+void movss(const Xmm& xmm, const Operand& op) { opMMX(xmm, op, 0x10, 0xF3); }
+void movsw() { db(0x66); db(0xA5); }
+void movsx(const Reg& reg, const Operand& op) { opMovxx(reg, op, 0xBE); }
+void movupd(const Address& addr, const Xmm& xmm) { db(0x66); opModM(addr, xmm, 0x0F, 0x11); }
+void movupd(const Xmm& xmm, const Operand& op) { opMMX(xmm, op, 0x10, 0x66); }
+void movups(const Address& addr, const Xmm& xmm) { opModM(addr, xmm, 0x0F, 0x11); }
+void movups(const Xmm& xmm, const Operand& op) { opMMX(xmm, op, 0x10, 0x100); }
+void movzx(const Reg& reg, const Operand& op) { opMovxx(reg, op, 0xB6); }
+void mpsadbw(const Xmm& xmm, const Operand& op, int imm) { opGen(xmm, op, 0x42, 0x66, isXMM_XMMorMEM, static_cast<uint8>(imm), 0x3A); }
+void mul(const Operand& op) { opR_ModM(op, 0, 4, 0xF6); }
+void mulpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x59, 0x66, isXMM_XMMorMEM); }
+void mulps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x59, 0x100, isXMM_XMMorMEM); }
+void mulsd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x59, 0xF2, isXMM_XMMorMEM); }
+void mulss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x59, 0xF3, isXMM_XMMorMEM); }
+void mulx(const Reg32e& r1, const Reg32e& r2, const Operand& op) { opGpr(r1, r2, op, T_F2 | T_0F38, 0xf6, true); }
+void mwait() { db(0x0F); db(0x01); db(0xC9); }
+void neg(const Operand& op) { opR_ModM(op, 0, 3, 0xF6); }
+void not_(const Operand& op) { opR_ModM(op, 0, 2, 0xF6); }
+void or_(const Operand& op, uint32 imm) { opRM_I(op, imm, 0x08, 1); }
+void or_(const Operand& op1, const Operand& op2) { opRM_RM(op1, op2, 0x08); }
+void orpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x56, 0x66, isXMM_XMMorMEM); }
+void orps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x56, 0x100, isXMM_XMMorMEM); }
+void pabsb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x1C, 0x66, NONE, 0x38); }
+void pabsd(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x1E, 0x66, NONE, 0x38); }
+void pabsw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x1D, 0x66, NONE, 0x38); }
+void packssdw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x6B); }
+void packsswb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x63); }
+void packusdw(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x2B, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void packuswb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x67); }
+void paddb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xFC); }
+void paddd(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xFE); }
+void paddq(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xD4); }
+void paddsb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xEC); }
+void paddsw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xED); }
+void paddusb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xDC); }
+void paddusw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xDD); }
+void paddw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xFD); }
+void palignr(const Mmx& mmx, const Operand& op, int imm) { opMMX(mmx, op, 0x0f, 0x66, static_cast<uint8>(imm), 0x3a); }
+void pand(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xDB); }
+void pandn(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xDF); }
+void pause() { db(0xF3); db(0x90); }
+void pavgb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xE0); }
+void pavgw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xE3); }
+void pblendvb(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x10, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pblendw(const Xmm& xmm, const Operand& op, int imm) { opGen(xmm, op, 0x0E, 0x66, isXMM_XMMorMEM, static_cast<uint8>(imm), 0x3A); }
+void pclmulhqhdq(const Xmm& xmm, const Operand& op) { pclmulqdq(xmm, op, 0x11); }
+void pclmulhqlqdq(const Xmm& xmm, const Operand& op) { pclmulqdq(xmm, op, 0x01); }
+void pclmullqhdq(const Xmm& xmm, const Operand& op) { pclmulqdq(xmm, op, 0x10); }
+void pclmullqlqdq(const Xmm& xmm, const Operand& op) { pclmulqdq(xmm, op, 0x00); }
+void pclmulqdq(const Xmm& xmm, const Operand& op, int imm) { opGen(xmm, op, 0x44, 0x66, isXMM_XMMorMEM, static_cast<uint8>(imm), 0x3A); }
+void pcmpeqb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x74); }
+void pcmpeqd(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x76); }
+void pcmpeqq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x29, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pcmpeqw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x75); }
+void pcmpestri(const Xmm& xmm, const Operand& op, uint8 imm) { opGen(xmm, op, 0x61, 0x66, isXMM_XMMorMEM, imm, 0x3A); }
+void pcmpestrm(const Xmm& xmm, const Operand& op, uint8 imm) { opGen(xmm, op, 0x60, 0x66, isXMM_XMMorMEM, imm, 0x3A); }
+void pcmpgtb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x64); }
+void pcmpgtd(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x66); }
+void pcmpgtq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x37, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pcmpgtw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x65); }
+void pcmpistri(const Xmm& xmm, const Operand& op, uint8 imm) { opGen(xmm, op, 0x63, 0x66, isXMM_XMMorMEM, imm, 0x3A); }
+void pcmpistrm(const Xmm& xmm, const Operand& op, uint8 imm) { opGen(xmm, op, 0x62, 0x66, isXMM_XMMorMEM, imm, 0x3A); }
+void pdep(const Reg32e& r1, const Reg32e& r2, const Operand& op) { opGpr(r1, r2, op, T_F2 | T_0F38, 0xf5, true); }
+void pext(const Reg32e& r1, const Reg32e& r2, const Operand& op) { opGpr(r1, r2, op, T_F3 | T_0F38, 0xf5, true); }
+void pextrb(const Operand& op, const Xmm& xmm, uint8 imm) { opExt(op, xmm, 0x14, imm); }
+void pextrd(const Operand& op, const Xmm& xmm, uint8 imm) { opExt(op, xmm, 0x16, imm); }
+void pextrw(const Operand& op, const Mmx& xmm, uint8 imm) { opExt(op, xmm, 0x15, imm, true); }
+void phaddd(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x02, 0x66, NONE, 0x38); }
+void phaddsw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x03, 0x66, NONE, 0x38); }
+void phaddw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x01, 0x66, NONE, 0x38); }
+void phminposuw(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x41, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void phsubd(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x06, 0x66, NONE, 0x38); }
+void phsubsw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x07, 0x66, NONE, 0x38); }
+void phsubw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x05, 0x66, NONE, 0x38); }
+void pinsrb(const Xmm& xmm, const Operand& op, uint8 imm) { opGen(xmm, op, 0x20, 0x66, isXMM_REG32orMEM, imm, 0x3A); }
+void pinsrd(const Xmm& xmm, const Operand& op, uint8 imm) { opGen(xmm, op, 0x22, 0x66, isXMM_REG32orMEM, imm, 0x3A); }
+void pinsrw(const Mmx& mmx, const Operand& op, int imm) { if (!op.isREG(32) && !op.isMEM()) throw Error(ERR_BAD_COMBINATION); opGen(mmx, op, 0xC4, mmx.isXMM() ? 0x66 : NONE, 0, imm); }
+void pmaddubsw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x04, 0x66, NONE, 0x38); }
+void pmaddwd(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xF5); }
+void pmaxsb(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x3C, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmaxsd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x3D, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmaxsw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xEE); }
+void pmaxub(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xDE); }
+void pmaxud(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x3F, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmaxuw(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x3E, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pminsb(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x38, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pminsd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x39, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pminsw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xEA); }
+void pminub(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xDA); }
+void pminud(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x3B, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pminuw(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x3A, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovmskb(const Reg32e& reg, const Mmx& mmx) { if (mmx.isXMM()) db(0x66); opModR(reg, mmx, 0x0F, 0xD7); }
+void pmovsxbd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x21, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovsxbq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x22, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovsxbw(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x20, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovsxdq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x25, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovsxwd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x23, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovsxwq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x24, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovzxbd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x31, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovzxbq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x32, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovzxbw(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x30, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovzxdq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x35, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovzxwd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x33, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmovzxwq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x34, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmuldq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x28, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmulhrsw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x0B, 0x66, NONE, 0x38); }
+void pmulhuw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xE4); }
+void pmulhw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xE5); }
+void pmulld(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x40, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void pmullw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xD5); }
+void pmuludq(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xF4); }
+void popcnt(const Reg&reg, const Operand& op) { opSp1(reg, op, 0xF3, 0x0F, 0xB8); }
+void popf() { db(0x9D); }
+void por(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xEB); }
+void prefetchnta(const Address& addr) { opModM(addr, Reg32(0), 0x0F, 0x18); }
+void prefetcht0(const Address& addr) { opModM(addr, Reg32(1), 0x0F, 0x18); }
+void prefetcht1(const Address& addr) { opModM(addr, Reg32(2), 0x0F, 0x18); }
+void prefetcht2(const Address& addr) { opModM(addr, Reg32(3), 0x0F, 0x18); }
+void prefetchw(const Address& addr) { opModM(addr, Reg32(1), 0x0F, 0x0D); }
+void prefetchwt1(const Address& addr) { opModM(addr, Reg32(2), 0x0F, 0x0D); }
+void psadbw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xF6); }
+void pshufb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x00, 0x66, NONE, 0x38); }
+void pshufd(const Mmx& mmx, const Operand& op, uint8 imm8) { opMMX(mmx, op, 0x70, 0x66, imm8); }
+void pshufhw(const Mmx& mmx, const Operand& op, uint8 imm8) { opMMX(mmx, op, 0x70, 0xF3, imm8); }
+void pshuflw(const Mmx& mmx, const Operand& op, uint8 imm8) { opMMX(mmx, op, 0x70, 0xF2, imm8); }
+void pshufw(const Mmx& mmx, const Operand& op, uint8 imm8) { opMMX(mmx, op, 0x70, 0x00, imm8); }
+void psignb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x08, 0x66, NONE, 0x38); }
+void psignd(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x0A, 0x66, NONE, 0x38); }
+void psignw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x09, 0x66, NONE, 0x38); }
+void pslld(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xF2); }
+void pslld(const Mmx& mmx, int imm8) { opMMX_IMM(mmx, imm8, 0x72, 6); }
+void pslldq(const Xmm& xmm, int imm8) { opMMX_IMM(xmm, imm8, 0x73, 7); }
+void psllq(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xF3); }
+void psllq(const Mmx& mmx, int imm8) { opMMX_IMM(mmx, imm8, 0x73, 6); }
+void psllw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xF1); }
+void psllw(const Mmx& mmx, int imm8) { opMMX_IMM(mmx, imm8, 0x71, 6); }
+void psrad(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xE2); }
+void psrad(const Mmx& mmx, int imm8) { opMMX_IMM(mmx, imm8, 0x72, 4); }
+void psraw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xE1); }
+void psraw(const Mmx& mmx, int imm8) { opMMX_IMM(mmx, imm8, 0x71, 4); }
+void psrld(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xD2); }
+void psrld(const Mmx& mmx, int imm8) { opMMX_IMM(mmx, imm8, 0x72, 2); }
+void psrldq(const Xmm& xmm, int imm8) { opMMX_IMM(xmm, imm8, 0x73, 3); }
+void psrlq(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xD3); }
+void psrlq(const Mmx& mmx, int imm8) { opMMX_IMM(mmx, imm8, 0x73, 2); }
+void psrlw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xD1); }
+void psrlw(const Mmx& mmx, int imm8) { opMMX_IMM(mmx, imm8, 0x71, 2); }
+void psubb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xF8); }
+void psubd(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xFA); }
+void psubq(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xFB); }
+void psubsb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xE8); }
+void psubsw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xE9); }
+void psubusb(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xD8); }
+void psubusw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xD9); }
+void psubw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xF9); }
+void ptest(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x17, 0x66, isXMM_XMMorMEM, NONE, 0x38); }
+void punpckhbw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x68); }
+void punpckhdq(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x6A); }
+void punpckhqdq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x6D, 0x66, isXMM_XMMorMEM); }
+void punpckhwd(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x69); }
+void punpcklbw(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x60); }
+void punpckldq(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x62); }
+void punpcklqdq(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x6C, 0x66, isXMM_XMMorMEM); }
+void punpcklwd(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0x61); }
+void pushf() { db(0x9C); }
+void pxor(const Mmx& mmx, const Operand& op) { opMMX(mmx, op, 0xEF); }
+void rcl(const Operand& op, const Reg8& _cl) { opShift(op, _cl, 2); }
+void rcl(const Operand& op, int imm) { opShift(op, imm, 2); }
+void rcpps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x53, 0x100, isXMM_XMMorMEM); }
+void rcpss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x53, 0xF3, isXMM_XMMorMEM); }
+void rcr(const Operand& op, const Reg8& _cl) { opShift(op, _cl, 3); }
+void rcr(const Operand& op, int imm) { opShift(op, imm, 3); }
+void rdmsr() { db(0x0F); db(0x32); }
+void rdpmc() { db(0x0F); db(0x33); }
+void rdrand(const Reg& r) { if (r.isBit(8)) throw Error(ERR_BAD_SIZE_OF_REGISTER); opModR(Reg(6, Operand::REG, r.getBit()), r, 0x0F, 0xC7); }
+void rdseed(const Reg& r) { if (r.isBit(8)) throw Error(ERR_BAD_SIZE_OF_REGISTER); opModR(Reg(7, Operand::REG, r.getBit()), r, 0x0F, 0xC7); }
+void rdtsc() { db(0x0F); db(0x31); }
+void rdtscp() { db(0x0F); db(0x01); db(0xF9); }
+void rep() { db(0xF3); }
+void ret(int imm = 0) { if (imm) { db(0xC2); dw(imm); } else { db(0xC3); } }
+void rol(const Operand& op, const Reg8& _cl) { opShift(op, _cl, 0); }
+void rol(const Operand& op, int imm) { opShift(op, imm, 0); }
+void ror(const Operand& op, const Reg8& _cl) { opShift(op, _cl, 1); }
+void ror(const Operand& op, int imm) { opShift(op, imm, 1); }
+void rorx(const Reg32e& r, const Operand& op, uint8 imm) { opGpr(r, op, Reg32e(0, r.getBit()), T_0F3A | T_F2, 0xF0, false, imm); }
+void roundpd(const Xmm& xmm, const Operand& op, uint8 imm) { opGen(xmm, op, 0x09, 0x66, isXMM_XMMorMEM, imm, 0x3A); }
+void roundps(const Xmm& xmm, const Operand& op, uint8 imm) { opGen(xmm, op, 0x08, 0x66, isXMM_XMMorMEM, imm, 0x3A); }
+void roundsd(const Xmm& xmm, const Operand& op, int imm) { opGen(xmm, op, 0x0B, 0x66, isXMM_XMMorMEM, static_cast<uint8>(imm), 0x3A); }
+void roundss(const Xmm& xmm, const Operand& op, int imm) { opGen(xmm, op, 0x0A, 0x66, isXMM_XMMorMEM, static_cast<uint8>(imm), 0x3A); }
+void rsqrtps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x52, 0x100, isXMM_XMMorMEM); }
+void rsqrtss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x52, 0xF3, isXMM_XMMorMEM); }
+void sahf() { db(0x9E); }
+void sal(const Operand& op, const Reg8& _cl) { opShift(op, _cl, 4); }
+void sal(const Operand& op, int imm) { opShift(op, imm, 4); }
+void sar(const Operand& op, const Reg8& _cl) { opShift(op, _cl, 7); }
+void sar(const Operand& op, int imm) { opShift(op, imm, 7); }
+void sarx(const Reg32e& r1, const Operand& op, const Reg32e& r2) { opGpr(r1, op, r2, T_F3 | T_0F38, 0xf7, false); }
+void sbb(const Operand& op, uint32 imm) { opRM_I(op, imm, 0x18, 3); }
+void sbb(const Operand& op1, const Operand& op2) { opRM_RM(op1, op2, 0x18); }
+void scasb() { db(0xAE); }
+void scasd() { db(0xAF); }
+void scasw() { db(0x66); db(0xAF); }
+void seta(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 7); }//-V524
+void setae(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 3); }//-V524
+void setb(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 2); }//-V524
+void setbe(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 6); }//-V524
+void setc(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 2); }//-V524
+void sete(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 4); }//-V524
+void setg(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 15); }//-V524
+void setge(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 13); }//-V524
+void setl(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 12); }//-V524
+void setle(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 14); }//-V524
+void setna(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 6); }//-V524
+void setnae(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 2); }//-V524
+void setnb(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 3); }//-V524
+void setnbe(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 7); }//-V524
+void setnc(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 3); }//-V524
+void setne(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 5); }//-V524
+void setng(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 14); }//-V524
+void setnge(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 12); }//-V524
+void setnl(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 13); }//-V524
+void setnle(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 15); }//-V524
+void setno(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 1); }//-V524
+void setnp(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 11); }//-V524
+void setns(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 9); }//-V524
+void setnz(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 5); }//-V524
+void seto(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 0); }//-V524
+void setp(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 10); }//-V524
+void setpe(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 10); }//-V524
+void setpo(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 11); }//-V524
+void sets(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 8); }//-V524
+void setz(const Operand& op) { opR_ModM(op, 8, 0, 0x0F, 0x90 | 4); }//-V524
+void sfence() { db(0x0F); db(0xAE); db(0xF8); }
+void sha1msg1(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xC9, NONE, isXMM_XMMorMEM, NONE, 0x38); }
+void sha1msg2(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xCA, NONE, isXMM_XMMorMEM, NONE, 0x38); }
+void sha1nexte(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xC8, NONE, isXMM_XMMorMEM, NONE, 0x38); }
+void sha1rnds4(const Xmm& xmm, const Operand& op, uint8 imm) { opGen(xmm, op, 0xCC, NONE, isXMM_XMMorMEM, imm, 0x3A); }
+void sha256msg1(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xCC, NONE, isXMM_XMMorMEM, NONE, 0x38); }
+void sha256msg2(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xCD, NONE, isXMM_XMMorMEM, NONE, 0x38); }
+void sha256rnds2(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0xCB, NONE, isXMM_XMMorMEM, NONE, 0x38); }
+void shl(const Operand& op, const Reg8& _cl) { opShift(op, _cl, 4); }
+void shl(const Operand& op, int imm) { opShift(op, imm, 4); }
+void shld(const Operand& op, const Reg& reg, const Reg8& _cl) { opShxd(op, reg, 0, 0xA4, &_cl); }
+void shld(const Operand& op, const Reg& reg, uint8 imm) { opShxd(op, reg, imm, 0xA4); }
+void shlx(const Reg32e& r1, const Operand& op, const Reg32e& r2) { opGpr(r1, op, r2, T_66 | T_0F38, 0xf7, false); }
+void shr(const Operand& op, const Reg8& _cl) { opShift(op, _cl, 5); }
+void shr(const Operand& op, int imm) { opShift(op, imm, 5); }
+void shrd(const Operand& op, const Reg& reg, const Reg8& _cl) { opShxd(op, reg, 0, 0xAC, &_cl); }
+void shrd(const Operand& op, const Reg& reg, uint8 imm) { opShxd(op, reg, imm, 0xAC); }
+void shrx(const Reg32e& r1, const Operand& op, const Reg32e& r2) { opGpr(r1, op, r2, T_F2 | T_0F38, 0xf7, false); }
+void shufpd(const Xmm& xmm, const Operand& op, uint8 imm8) { opGen(xmm, op, 0xC6, 0x66, isXMM_XMMorMEM, imm8); }
+void shufps(const Xmm& xmm, const Operand& op, uint8 imm8) { opGen(xmm, op, 0xC6, 0x100, isXMM_XMMorMEM, imm8); }
+void sqrtpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x51, 0x66, isXMM_XMMorMEM); }
+void sqrtps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x51, 0x100, isXMM_XMMorMEM); }
+void sqrtsd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x51, 0xF2, isXMM_XMMorMEM); }
+void sqrtss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x51, 0xF3, isXMM_XMMorMEM); }
+void stac() { db(0x0F); db(0x01); db(0xCB); }
+void stc() { db(0xF9); }
+void std() { db(0xFD); }
+void sti() { db(0xFB); }
+void stmxcsr(const Address& addr) { opModM(addr, Reg32(3), 0x0F, 0xAE); }
+void stosb() { db(0xAA); }
+void stosd() { db(0xAB); }
+void stosw() { db(0x66); db(0xAB); }
+void sub(const Operand& op, uint32 imm) { opRM_I(op, imm, 0x28, 5); }
+void sub(const Operand& op1, const Operand& op2) { opRM_RM(op1, op2, 0x28); }
+void subpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5C, 0x66, isXMM_XMMorMEM); }
+void subps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5C, 0x100, isXMM_XMMorMEM); }
+void subsd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5C, 0xF2, isXMM_XMMorMEM); }
+void subss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x5C, 0xF3, isXMM_XMMorMEM); }
+void tzcnt(const Reg&reg, const Operand& op) { opSp1(reg, op, 0xF3, 0x0F, 0xBC); }
+void ucomisd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x2E, 0x66, isXMM_XMMorMEM); }
+void ucomiss(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x2E, 0x100, isXMM_XMMorMEM); }
+void ud2() { db(0x0F); db(0x0B); }
+void unpckhpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x15, 0x66, isXMM_XMMorMEM); }
+void unpckhps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x15, 0x100, isXMM_XMMorMEM); }
+void unpcklpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x14, 0x66, isXMM_XMMorMEM); }
+void unpcklps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x14, 0x100, isXMM_XMMorMEM); }
+void vaddpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_66 | T_EW1 | T_YMM | T_EVEX | T_ER_Z | T_B64, 0x58); }
+void vaddps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x58); }
+void vaddsd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F2 | T_EW1 | T_EVEX | T_ER_Z | T_N8, 0x58); }
+void vaddss(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F3 | T_EW0 | T_EVEX | T_ER_Z | T_N4, 0x58); }
+void vaddsubpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_66 | T_0F | T_YMM, 0xD0); }
+void vaddsubps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_F2 | T_0F | T_YMM, 0xD0); }
+void vaesdec(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_66 | T_0F38 | T_YMM | T_EVEX, 0xDE); }
+void vaesdeclast(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_66 | T_0F38 | T_YMM | T_EVEX, 0xDF); }
+void vaesenc(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_66 | T_0F38 | T_YMM | T_EVEX, 0xDC); }
+void vaesenclast(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_66 | T_0F38 | T_YMM | T_EVEX, 0xDD); }
+void vaesimc(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F38 | T_W0, 0xDB); }
+void vaeskeygenassist(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F3A, 0xDF, imm); }
+void vandnpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_66 | T_EW1 | T_YMM | T_EVEX | T_ER_Z | T_B64, 0x55); }
+void vandnps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x55); }
+void vandpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_66 | T_EW1 | T_YMM | T_EVEX | T_ER_Z | T_B64, 0x54); }
+void vandps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x54); }
+void vblendpd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W0 | T_YMM, 0x0D, imm); }
+void vblendps(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W0 | T_YMM, 0x0C, imm); }
+void vblendvpd(const Xmm& x1, const Xmm& x2, const Operand& op, const Xmm& x4) { opAVX_X_X_XM(x1, x2, op, T_0F3A | T_66 | T_YMM, 0x4B, x4.getIdx() << 4); }
+void vblendvps(const Xmm& x1, const Xmm& x2, const Operand& op, const Xmm& x4) { opAVX_X_X_XM(x1, x2, op, T_0F3A | T_66 | T_YMM, 0x4A, x4.getIdx() << 4); }
+void vbroadcastf128(const Ymm& y, const Address& addr) { opAVX_X_XM_IMM(y, addr, T_0F38 | T_66 | T_W0 | T_YMM, 0x1A); }
+void vbroadcasti128(const Ymm& y, const Address& addr) { opAVX_X_XM_IMM(y, addr, T_0F38 | T_66 | T_W0 | T_YMM, 0x5A); }
+void vbroadcastsd(const Ymm& y, const Operand& op) { if (!op.isMEM() && !(y.isYMM() && op.isXMM()) && !(y.isZMM() && op.isXMM())) throw Error(ERR_BAD_COMBINATION); opAVX_X_XM_IMM(y, op, T_0F38 | T_66 | T_W0 | T_YMM | T_EVEX | T_EW1 | T_N8, 0x19); }
+void vbroadcastss(const Xmm& x, const Operand& op) { if (!(op.isXMM() || op.isMEM())) throw Error(ERR_BAD_COMBINATION); opAVX_X_XM_IMM(x, op, T_N4 | T_66 | T_0F38 | T_W0 | T_YMM | T_EVEX, 0x18); }
+void vcmpeq_ospd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 16); }
+void vcmpeq_osps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 16); }
+void vcmpeq_ossd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 16); }
+void vcmpeq_osss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 16); }
+void vcmpeq_uqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 8); }
+void vcmpeq_uqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 8); }
+void vcmpeq_uqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 8); }
+void vcmpeq_uqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 8); }
+void vcmpeq_uspd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 24); }
+void vcmpeq_usps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 24); }
+void vcmpeq_ussd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 24); }
+void vcmpeq_usss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 24); }
+void vcmpeqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 0); }
+void vcmpeqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 0); }
+void vcmpeqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 0); }
+void vcmpeqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 0); }
+void vcmpfalse_ospd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 27); }
+void vcmpfalse_osps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 27); }
+void vcmpfalse_ossd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 27); }
+void vcmpfalse_osss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 27); }
+void vcmpfalsepd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 11); }
+void vcmpfalseps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 11); }
+void vcmpfalsesd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 11); }
+void vcmpfalsess(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 11); }
+void vcmpge_oqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 29); }
+void vcmpge_oqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 29); }
+void vcmpge_oqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 29); }
+void vcmpge_oqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 29); }
+void vcmpgepd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 13); }
+void vcmpgeps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 13); }
+void vcmpgesd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 13); }
+void vcmpgess(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 13); }
+void vcmpgt_oqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 30); }
+void vcmpgt_oqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 30); }
+void vcmpgt_oqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 30); }
+void vcmpgt_oqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 30); }
+void vcmpgtpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 14); }
+void vcmpgtps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 14); }
+void vcmpgtsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 14); }
+void vcmpgtss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 14); }
+void vcmple_oqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 18); }
+void vcmple_oqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 18); }
+void vcmple_oqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 18); }
+void vcmple_oqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 18); }
+void vcmplepd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 2); }
+void vcmpleps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 2); }
+void vcmplesd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 2); }
+void vcmpless(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 2); }
+void vcmplt_oqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 17); }
+void vcmplt_oqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 17); }
+void vcmplt_oqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 17); }
+void vcmplt_oqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 17); }
+void vcmpltpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 1); }
+void vcmpltps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 1); }
+void vcmpltsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 1); }
+void vcmpltss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 1); }
+void vcmpneq_oqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 12); }
+void vcmpneq_oqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 12); }
+void vcmpneq_oqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 12); }
+void vcmpneq_oqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 12); }
+void vcmpneq_ospd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 28); }
+void vcmpneq_osps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 28); }
+void vcmpneq_ossd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 28); }
+void vcmpneq_osss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 28); }
+void vcmpneq_uspd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 20); }
+void vcmpneq_usps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 20); }
+void vcmpneq_ussd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 20); }
+void vcmpneq_usss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 20); }
+void vcmpneqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 4); }
+void vcmpneqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 4); }
+void vcmpneqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 4); }
+void vcmpneqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 4); }
+void vcmpnge_uqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 25); }
+void vcmpnge_uqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 25); }
+void vcmpnge_uqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 25); }
+void vcmpnge_uqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 25); }
+void vcmpngepd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 9); }
+void vcmpngeps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 9); }
+void vcmpngesd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 9); }
+void vcmpngess(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 9); }
+void vcmpngt_uqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 26); }
+void vcmpngt_uqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 26); }
+void vcmpngt_uqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 26); }
+void vcmpngt_uqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 26); }
+void vcmpngtpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 10); }
+void vcmpngtps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 10); }
+void vcmpngtsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 10); }
+void vcmpngtss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 10); }
+void vcmpnle_uqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 22); }
+void vcmpnle_uqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 22); }
+void vcmpnle_uqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 22); }
+void vcmpnle_uqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 22); }
+void vcmpnlepd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 6); }
+void vcmpnleps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 6); }
+void vcmpnlesd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 6); }
+void vcmpnless(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 6); }
+void vcmpnlt_uqpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 21); }
+void vcmpnlt_uqps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 21); }
+void vcmpnlt_uqsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 21); }
+void vcmpnlt_uqss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 21); }
+void vcmpnltpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 5); }
+void vcmpnltps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 5); }
+void vcmpnltsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 5); }
+void vcmpnltss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 5); }
+void vcmpord_spd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 23); }
+void vcmpord_sps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 23); }
+void vcmpord_ssd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 23); }
+void vcmpord_sss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 23); }
+void vcmpordpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 7); }
+void vcmpordps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 7); }
+void vcmpordsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 7); }
+void vcmpordss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 7); }
+void vcmppd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM, 0xC2, imm); }
+void vcmpps(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_0F | T_YMM, 0xC2, imm); }
+void vcmpsd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_F2 | T_0F, 0xC2, imm); }
+void vcmpss(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_F3 | T_0F, 0xC2, imm); }
+void vcmptrue_uspd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 31); }
+void vcmptrue_usps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 31); }
+void vcmptrue_ussd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 31); }
+void vcmptrue_usss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 31); }
+void vcmptruepd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 15); }
+void vcmptrueps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 15); }
+void vcmptruesd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 15); }
+void vcmptruess(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 15); }
+void vcmpunord_spd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 19); }
+void vcmpunord_sps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 19); }
+void vcmpunord_ssd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 19); }
+void vcmpunord_sss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 19); }
+void vcmpunordpd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmppd(x1, x2, op, 3); }
+void vcmpunordps(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpps(x1, x2, op, 3); }
+void vcmpunordsd(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpsd(x1, x2, op, 3); }
+void vcmpunordss(const Xmm& x1, const Xmm& x2, const Operand& op) { vcmpss(x1, x2, op, 3); }
+void vcomisd(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N8 | T_66 | T_0F | T_EW1 | T_EVEX | T_SAE_X, 0x2F); }
+void vcomiss(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N4 | T_0F | T_EW0 | T_EVEX | T_SAE_X, 0x2F); }
+void vcvtdq2pd(const Xmm& x, const Operand& op) { checkCvt1(x, op); opVex(x, 0, op, T_0F | T_F3 | T_YMM | T_EVEX | T_EW0 | T_B32 | T_N8 | T_N_VL, 0xE6); }
+void vcvtdq2ps(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x5B); }
+void vcvtpd2dq(const Xmm& x, const Operand& op) { opCvt2(x, op, T_0F | T_F2 | T_YMM | T_EVEX | T_EW1 | T_B64 | T_ER_Z, 0xE6); }
+void vcvtpd2ps(const Xmm& x, const Operand& op) { opCvt2(x, op, T_0F | T_66 | T_YMM | T_EVEX | T_EW1 | T_B64 | T_ER_Z, 0x5A); }
+void vcvtph2ps(const Xmm& x, const Operand& op) { checkCvt1(x, op); opVex(x, 0, op, T_0F38 | T_66 | T_W0 | T_EVEX | T_EW0 | T_N8 | T_N_VL | T_SAE_Y, 0x13); }
+void vcvtps2dq(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x5B); }
+void vcvtps2pd(const Xmm& x, const Operand& op) { checkCvt1(x, op); opVex(x, 0, op, T_0F | T_YMM | T_EVEX | T_EW0 | T_B32 | T_N8 | T_N_VL | T_SAE_Y, 0x5A); }
+void vcvtps2ph(const Operand& op, const Xmm& x, uint8 imm) { checkCvt1(x, op); opVex(x, 0, op, T_0F3A | T_66 | T_W0 | T_EVEX | T_EW0 | T_N8 | T_N_VL | T_SAE_Y, 0x1D, imm); }
+void vcvtsd2si(const Reg32& r, const Operand& op) { opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, T_0F | T_F2 | T_W0 | T_EVEX | T_EW0 | T_N4 | T_ER_X, 0x2D); }
+void vcvtsd2ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_F2 | T_0F | T_EW1 | T_EVEX | T_ER_X, 0x5A); }
+void vcvtsi2sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opCvt3(x1, x2, op, T_0F | T_F2 | T_EVEX, T_W1 | T_EW1 | T_ER_X | T_N8, T_W0 | T_EW0 | T_N4, 0x2A); }
+void vcvtsi2ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opCvt3(x1, x2, op, T_0F | T_F3 | T_EVEX | T_ER_X, T_W1 | T_EW1 | T_N8, T_W0 | T_EW0 | T_N4, 0x2A); }
+void vcvtss2sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_F3 | T_0F | T_EW0 | T_EVEX | T_SAE_X, 0x5A); }
+void vcvtss2si(const Reg32& r, const Operand& op) { opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, T_0F | T_F3 | T_W0 | T_EVEX | T_EW0 | T_ER_X | T_N8, 0x2D); }
+void vcvttpd2dq(const Xmm& x, const Operand& op) { opCvt2(x, op, T_66 | T_0F | T_YMM | T_EVEX |T_EW1 | T_B64 | T_ER_Z, 0xE6); }
+void vcvttps2dq(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_F3 | T_0F | T_EW0 | T_YMM | T_EVEX | T_SAE_Z | T_B32, 0x5B); }
+void vcvttsd2si(const Reg32& r, const Operand& op) { opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, T_0F | T_F2 | T_W0 | T_EVEX | T_EW0 | T_N4 | T_SAE_X, 0x2C); }
+void vcvttss2si(const Reg32& r, const Operand& op) { opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, T_0F | T_F3 | T_W0 | T_EVEX | T_EW0 | T_SAE_X | T_N8, 0x2C); }
+void vdivpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_66 | T_EW1 | T_YMM | T_EVEX | T_ER_Z | T_B64, 0x5E); }
+void vdivps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x5E); }
+void vdivsd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F2 | T_EW1 | T_EVEX | T_ER_Z | T_N8, 0x5E); }
+void vdivss(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F3 | T_EW0 | T_EVEX | T_ER_Z | T_N4, 0x5E); }
+void vdppd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W0, 0x41, imm); }
+void vdpps(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W0 | T_YMM, 0x40, imm); }
+void vextractf128(const Operand& op, const Ymm& y, uint8 imm) { if (!(op.isXMEM() && y.isYMM())) throw Error(ERR_BAD_COMBINATION); opVex(y, 0, op, T_0F3A | T_66 | T_W0 | T_YMM, 0x19, imm); }
+void vextracti128(const Operand& op, const Ymm& y, uint8 imm) { if (!(op.isXMEM() && y.isYMM())) throw Error(ERR_BAD_COMBINATION); opVex(y, 0, op, T_0F3A | T_66 | T_W0 | T_YMM, 0x39, imm); }
+void vextractps(const Operand& op, const Xmm& x, uint8 imm) { if (!((op.isREG(32) || op.isMEM()) && x.isXMM())) throw Error(ERR_BAD_COMBINATION); opVex(x, 0, op, T_0F3A | T_66 | T_W0 | T_EVEX | T_N4, 0x17, imm); }
+void vfmadd132pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x98); }
+void vfmadd132ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x98); }
+void vfmadd132sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0x99); }
+void vfmadd132ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0x99); }
+void vfmadd213pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xA8); }
+void vfmadd213ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xA8); }
+void vfmadd213sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0xA9); }
+void vfmadd213ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0xA9); }
+void vfmadd231pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xB8); }
+void vfmadd231ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xB8); }
+void vfmadd231sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0xB9); }
+void vfmadd231ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0xB9); }
+void vfmaddsub132pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x96); }
+void vfmaddsub132ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x96); }
+void vfmaddsub213pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xA6); }
+void vfmaddsub213ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xA6); }
+void vfmaddsub231pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xB6); }
+void vfmaddsub231ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xB6); }
+void vfmsub132pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x9A); }
+void vfmsub132ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x9A); }
+void vfmsub132sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0x9B); }
+void vfmsub132ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0x9B); }
+void vfmsub213pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xAA); }
+void vfmsub213ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xAA); }
+void vfmsub213sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0xAB); }
+void vfmsub213ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0xAB); }
+void vfmsub231pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xBA); }
+void vfmsub231ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xBA); }
+void vfmsub231sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0xBB); }
+void vfmsub231ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0xBB); }
+void vfmsubadd132pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x97); }
+void vfmsubadd132ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x97); }
+void vfmsubadd213pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xA7); }
+void vfmsubadd213ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xA7); }
+void vfmsubadd231pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xB7); }
+void vfmsubadd231ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xB7); }
+void vfnmadd132pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x9C); }
+void vfnmadd132ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x9C); }
+void vfnmadd132sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0x9D); }
+void vfnmadd132ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0x9D); }
+void vfnmadd213pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xAC); }
+void vfnmadd213ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xAC); }
+void vfnmadd213sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0xAD); }
+void vfnmadd213ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0xAD); }
+void vfnmadd231pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xBC); }
+void vfnmadd231ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xBC); }
+void vfnmadd231sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0xBD); }
+void vfnmadd231ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0xBD); }
+void vfnmsub132pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x9E); }
+void vfnmsub132ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x9E); }
+void vfnmsub132sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0x9F); }
+void vfnmsub132ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0x9F); }
+void vfnmsub213pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xAE); }
+void vfnmsub213ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xAE); }
+void vfnmsub213sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0xAF); }
+void vfnmsub213ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0xAF); }
+void vfnmsub231pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0xBE); }
+void vfnmsub231ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0xBE); }
+void vfnmsub231sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_W1 | T_EW1 | T_EVEX | T_ER_X, 0xBF); }
+void vfnmsub231ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_W0 | T_EW0 | T_EVEX | T_ER_X, 0xBF); }
+void vgatherdpd(const Xmm& x1, const Address& addr, const Xmm& x2) { opGather(x1, addr, x2, T_0F38 | T_66 | T_YMM | T_VSIB | T_W1, 0x92, 0); }
+void vgatherdps(const Xmm& x1, const Address& addr, const Xmm& x2) { opGather(x1, addr, x2, T_0F38 | T_66 | T_YMM | T_VSIB | T_W0, 0x92, 1); }
+void vgatherqpd(const Xmm& x1, const Address& addr, const Xmm& x2) { opGather(x1, addr, x2, T_0F38 | T_66 | T_YMM | T_VSIB | T_W1, 0x93, 1); }
+void vgatherqps(const Xmm& x1, const Address& addr, const Xmm& x2) { opGather(x1, addr, x2, T_0F38 | T_66 | T_YMM | T_VSIB | T_W0, 0x93, 2); }
+void vgf2p8affineinvqb(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W1 | T_EW1 | T_YMM | T_EVEX | T_SAE_Z | T_B64, 0xCF, imm); }
+void vgf2p8affineqb(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W1 | T_EW1 | T_YMM | T_EVEX | T_SAE_Z | T_B64, 0xCE, imm); }
+void vgf2p8mulb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_SAE_Z, 0xCF); }
+void vhaddpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_66 | T_0F | T_YMM, 0x7C); }
+void vhaddps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_F2 | T_0F | T_YMM, 0x7C); }
+void vhsubpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_66 | T_0F | T_YMM, 0x7D); }
+void vhsubps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_F2 | T_0F | T_YMM, 0x7D); }
+void vinsertf128(const Ymm& y1, const Ymm& y2, const Operand& op, uint8 imm) { if (!(y1.isYMM() && y2.isYMM() && op.isXMEM())) throw Error(ERR_BAD_COMBINATION); opVex(y1, &y2, op, T_0F3A | T_66 | T_W0 | T_YMM, 0x18, imm); }
+void vinserti128(const Ymm& y1, const Ymm& y2, const Operand& op, uint8 imm) { if (!(y1.isYMM() && y2.isYMM() && op.isXMEM())) throw Error(ERR_BAD_COMBINATION); opVex(y1, &y2, op, T_0F3A | T_66 | T_W0 | T_YMM, 0x38, imm); }
+void vinsertps(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F3A | T_W0 | T_EW0 | T_EVEX, 0x21, imm); }
+void vlddqu(const Xmm& x, const Address& addr) { opAVX_X_X_XM(x, cvtIdx0(x), addr, T_0F | T_F2 | T_W0 | T_YMM, 0xF0); }
+void vldmxcsr(const Address& addr) { opAVX_X_X_XM(xm2, xm0, addr, T_0F, 0xAE); }
+void vmaskmovdqu(const Xmm& x1, const Xmm& x2) { opAVX_X_X_XM(x1, xm0, x2, T_0F | T_66, 0xF7); }
+void vmaskmovpd(const Address& addr, const Xmm& x1, const Xmm& x2) { opAVX_X_X_XM(x2, x1, addr, T_0F38 | T_66 | T_W0 | T_YMM, 0x2F); }
+void vmaskmovpd(const Xmm& x1, const Xmm& x2, const Address& addr) { opAVX_X_X_XM(x1, x2, addr, T_0F38 | T_66 | T_W0 | T_YMM, 0x2D); }
+void vmaskmovps(const Address& addr, const Xmm& x1, const Xmm& x2) { opAVX_X_X_XM(x2, x1, addr, T_0F38 | T_66 | T_W0 | T_YMM, 0x2E); }
+void vmaskmovps(const Xmm& x1, const Xmm& x2, const Address& addr) { opAVX_X_X_XM(x1, x2, addr, T_0F38 | T_66 | T_W0 | T_YMM, 0x2C); }
+void vmaxpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_66 | T_EW1 | T_YMM | T_EVEX | T_ER_Z | T_B64, 0x5F); }
+void vmaxps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x5F); }
+void vmaxsd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F2 | T_EW1 | T_EVEX | T_ER_Z | T_N8, 0x5F); }
+void vmaxss(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F3 | T_EW0 | T_EVEX | T_ER_Z | T_N4, 0x5F); }
+void vminpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_66 | T_EW1 | T_YMM | T_EVEX | T_ER_Z | T_B64, 0x5D); }
+void vminps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x5D); }
+void vminsd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F2 | T_EW1 | T_EVEX | T_ER_Z | T_N8, 0x5D); }
+void vminss(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F3 | T_EW0 | T_EVEX | T_ER_Z | T_N4, 0x5D); }
+void vmovapd(const Address& addr, const Xmm& xmm) { opAVX_X_XM_IMM(xmm, addr, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_M_K, 0x29); }
+void vmovapd(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX, 0x28); }
+void vmovaps(const Address& addr, const Xmm& xmm) { opAVX_X_XM_IMM(xmm, addr, T_0F | T_EW0 | T_YMM | T_EVEX | T_M_K, 0x29); }
+void vmovaps(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_0F | T_EW0 | T_YMM | T_EVEX, 0x28); }
+void vmovd(const Operand& op, const Xmm& x) { if (!op.isREG(32) && !op.isMEM()) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x, xm0, op, T_0F | T_66 | T_W0 | T_EVEX | T_N4, 0x7E); }
+void vmovd(const Xmm& x, const Operand& op) { if (!op.isREG(32) && !op.isMEM()) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x, xm0, op, T_0F | T_66 | T_W0 | T_EVEX | T_N4, 0x6E); }
+void vmovddup(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_DUP | T_F2 | T_0F | T_EW1 | T_YMM | T_EVEX | T_ER_X | T_ER_Y | T_ER_Z, 0x12); }
+void vmovdqa(const Address& addr, const Xmm& xmm) { opAVX_X_XM_IMM(xmm, addr, T_66 | T_0F | T_YMM, 0x7F); }
+void vmovdqa(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F | T_YMM, 0x6F); }
+void vmovdqu(const Address& addr, const Xmm& xmm) { opAVX_X_XM_IMM(xmm, addr, T_F3 | T_0F | T_YMM, 0x7F); }
+void vmovdqu(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_F3 | T_0F | T_YMM, 0x6F); }
+void vmovhlps(const Xmm& x1, const Xmm& x2, const Operand& op = Operand()) { if (!op.isNone() && !op.isXMM()) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x1, x2, op, T_0F | T_EVEX | T_EW0, 0x12); }
+void vmovhpd(const Address& addr, const Xmm& x) { opAVX_X_X_XM(x, xm0, addr, T_0F | T_66 | T_EVEX | T_EW1 | T_N8, 0x17); }
+void vmovhpd(const Xmm& x, const Operand& op1, const Operand& op2 = Operand()) { if (!op2.isNone() && !op2.isMEM()) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x, op1, op2, T_0F | T_66 | T_EVEX | T_EW1 | T_N8, 0x16); }
+void vmovhps(const Address& addr, const Xmm& x) { opAVX_X_X_XM(x, xm0, addr, T_0F | T_EVEX | T_EW0 | T_N8, 0x17); }
+void vmovhps(const Xmm& x, const Operand& op1, const Operand& op2 = Operand()) { if (!op2.isNone() && !op2.isMEM()) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x, op1, op2, T_0F | T_EVEX | T_EW0 | T_N8, 0x16); }
+void vmovlhps(const Xmm& x1, const Xmm& x2, const Operand& op = Operand()) { if (!op.isNone() && !op.isXMM()) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x1, x2, op, T_0F | T_EVEX | T_EW0, 0x16); }
+void vmovlpd(const Address& addr, const Xmm& x) { opAVX_X_X_XM(x, xm0, addr, T_0F | T_66 | T_EVEX | T_EW1 | T_N8, 0x13); }
+void vmovlpd(const Xmm& x, const Operand& op1, const Operand& op2 = Operand()) { if (!op2.isNone() && !op2.isMEM()) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x, op1, op2, T_0F | T_66 | T_EVEX | T_EW1 | T_N8, 0x12); }
+void vmovlps(const Address& addr, const Xmm& x) { opAVX_X_X_XM(x, xm0, addr, T_0F | T_EVEX | T_EW0 | T_N8, 0x13); }
+void vmovlps(const Xmm& x, const Operand& op1, const Operand& op2 = Operand()) { if (!op2.isNone() && !op2.isMEM()) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x, op1, op2, T_0F | T_EVEX | T_EW0 | T_N8, 0x12); }
+void vmovmskpd(const Reg& r, const Xmm& x) { if (!r.isBit(i32e)) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x.isXMM() ? Xmm(r.getIdx()) : Ymm(r.getIdx()), cvtIdx0(x), x, T_0F | T_66 | T_W0 | T_YMM, 0x50); }
+void vmovmskps(const Reg& r, const Xmm& x) { if (!r.isBit(i32e)) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x.isXMM() ? Xmm(r.getIdx()) : Ymm(r.getIdx()), cvtIdx0(x), x, T_0F | T_W0 | T_YMM, 0x50); }
+void vmovntdq(const Address& addr, const Xmm& x) { opVex(x, 0, addr, T_0F | T_66 | T_YMM | T_EVEX | T_EW0, 0xE7); }
+void vmovntdqa(const Xmm& x, const Address& addr) { opVex(x, 0, addr, T_0F38 | T_66 | T_YMM | T_EVEX | T_EW0, 0x2A); }
+void vmovntpd(const Address& addr, const Xmm& x) { opVex(x, 0, addr, T_0F | T_66 | T_YMM | T_EVEX | T_EW1, 0x2B); }
+void vmovntps(const Address& addr, const Xmm& x) { opVex(x, 0, addr, T_0F | T_YMM | T_EVEX | T_EW0, 0x2B); }
+void vmovq(const Address& addr, const Xmm& x) { opAVX_X_X_XM(x, xm0, addr, T_0F | T_66 | T_EVEX | T_EW1 | T_N8, x.getIdx() < 16 ? 0xD6 : 0x7E); }
+void vmovq(const Xmm& x, const Address& addr) { int type, code; if (x.getIdx() < 16) { type = T_0F | T_F3; code = 0x7E; } else { type = T_0F | T_66 | T_EVEX | T_EW1 | T_N8; code = 0x6E; } opAVX_X_X_XM(x, xm0, addr, type, code); }
+void vmovq(const Xmm& x1, const Xmm& x2) { opAVX_X_X_XM(x1, xm0, x2, T_0F | T_F3 | T_EVEX | T_EW1 | T_N8, 0x7E); }
+void vmovsd(const Address& addr, const Xmm& x) { opAVX_X_X_XM(x, xm0, addr, T_N8 | T_F2 | T_0F | T_EW1 | T_EVEX | T_M_K, 0x11); }
+void vmovsd(const Xmm& x, const Address& addr) { opAVX_X_X_XM(x, xm0, addr, T_N8 | T_F2 | T_0F | T_EW1 | T_EVEX, 0x10); }
+void vmovsd(const Xmm& x1, const Xmm& x2, const Operand& op = Operand()) { if (!op.isNone() && !op.isXMM()) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x1, x2, op, T_N8 | T_F2 | T_0F | T_EW1 | T_EVEX, 0x10); }
+void vmovshdup(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_F3 | T_0F | T_EW0 | T_YMM | T_EVEX, 0x16); }
+void vmovsldup(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_F3 | T_0F | T_EW0 | T_YMM | T_EVEX, 0x12); }
+void vmovss(const Address& addr, const Xmm& x) { opAVX_X_X_XM(x, xm0, addr, T_N4 | T_F3 | T_0F | T_EW0 | T_EVEX | T_M_K, 0x11); }
+void vmovss(const Xmm& x, const Address& addr) { opAVX_X_X_XM(x, xm0, addr, T_N4 | T_F3 | T_0F | T_EW0 | T_EVEX, 0x10); }
+void vmovss(const Xmm& x1, const Xmm& x2, const Operand& op = Operand()) { if (!op.isNone() && !op.isXMM()) throw Error(ERR_BAD_COMBINATION); opAVX_X_X_XM(x1, x2, op, T_N4 | T_F3 | T_0F | T_EW0 | T_EVEX, 0x10); }
+void vmovupd(const Address& addr, const Xmm& xmm) { opAVX_X_XM_IMM(xmm, addr, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_M_K, 0x11); }
+void vmovupd(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX, 0x10); }
+void vmovups(const Address& addr, const Xmm& xmm) { opAVX_X_XM_IMM(xmm, addr, T_0F | T_EW0 | T_YMM | T_EVEX | T_M_K, 0x11); }
+void vmovups(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_0F | T_EW0 | T_YMM | T_EVEX, 0x10); }
+void vmpsadbw(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W0 | T_YMM, 0x42, imm); }
+void vmulpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_66 | T_EW1 | T_YMM | T_EVEX | T_ER_Z | T_B64, 0x59); }
+void vmulps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x59); }
+void vmulsd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F2 | T_EW1 | T_EVEX | T_ER_Z | T_N8, 0x59); }
+void vmulss(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F3 | T_EW0 | T_EVEX | T_ER_Z | T_N4, 0x59); }
+void vorpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_66 | T_EW1 | T_YMM | T_EVEX | T_ER_Z | T_B64, 0x56); }
+void vorps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x56); }
+void vpabsb(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F38 | T_YMM | T_EVEX, 0x1C); }
+void vpabsd(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x1E); }
+void vpabsw(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F38 | T_YMM | T_EVEX, 0x1D); }
+void vpackssdw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW0 | T_YMM | T_EVEX | T_B32, 0x6B); }
+void vpacksswb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0x63); }
+void vpackusdw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x2B); }
+void vpackuswb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0x67); }
+void vpaddb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xFC); }
+void vpaddd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW0 | T_YMM | T_EVEX | T_B32, 0xFE); }
+void vpaddq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_B64, 0xD4); }
+void vpaddsb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xEC); }
+void vpaddsw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xED); }
+void vpaddusb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xDC); }
+void vpaddusw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xDD); }
+void vpaddw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xFD); }
+void vpalignr(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_YMM | T_EVEX, 0x0F, imm); }
+void vpand(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM, 0xDB); }
+void vpandn(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM, 0xDF); }
+void vpavgb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xE0); }
+void vpavgw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xE3); }
+void vpblendd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W0 | T_YMM, 0x02, imm); }
+void vpblendvb(const Xmm& x1, const Xmm& x2, const Operand& op, const Xmm& x4) { opAVX_X_X_XM(x1, x2, op, T_0F3A | T_66 | T_YMM, 0x4C, x4.getIdx() << 4); }
+void vpblendw(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W0 | T_YMM, 0x0E, imm); }
+void vpbroadcastb(const Xmm& x, const Operand& op) { if (!(op.isXMM() || op.isMEM())) throw Error(ERR_BAD_COMBINATION); opAVX_X_XM_IMM(x, op, T_N1 | T_66 | T_0F38 | T_W0 | T_YMM | T_EVEX, 0x78); }
+void vpbroadcastd(const Xmm& x, const Operand& op) { if (!(op.isXMM() || op.isMEM())) throw Error(ERR_BAD_COMBINATION); opAVX_X_XM_IMM(x, op, T_N4 | T_66 | T_0F38 | T_W0 | T_YMM | T_EVEX, 0x58); }
+void vpbroadcastq(const Xmm& x, const Operand& op) { if (!(op.isXMM() || op.isMEM())) throw Error(ERR_BAD_COMBINATION); opAVX_X_XM_IMM(x, op, T_N8 | T_66 | T_0F38 | T_W0 | T_EW1 | T_YMM | T_EVEX, 0x59); }
+void vpbroadcastw(const Xmm& x, const Operand& op) { if (!(op.isXMM() || op.isMEM())) throw Error(ERR_BAD_COMBINATION); opAVX_X_XM_IMM(x, op, T_N2 | T_66 | T_0F38 | T_W0 | T_YMM | T_EVEX, 0x79); }
+void vpclmulqdq(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W0 | T_YMM | T_EVEX, 0x44, imm); }
+void vpcmpeqb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM, 0x74); }
+void vpcmpeqd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM, 0x76); }
+void vpcmpeqq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM, 0x29); }
+void vpcmpeqw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM, 0x75); }
+void vpcmpestri(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F3A, 0x61, imm); }
+void vpcmpestrm(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F3A, 0x60, imm); }
+void vpcmpgtb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM, 0x64); }
+void vpcmpgtd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM, 0x66); }
+void vpcmpgtq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM, 0x37); }
+void vpcmpgtw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM, 0x65); }
+void vpcmpistri(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F3A, 0x63, imm); }
+void vpcmpistrm(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F3A, 0x62, imm); }
+void vperm2f128(const Ymm& y1, const Ymm& y2, const Operand& op, uint8 imm) { if (!(y1.isYMM() && y2.isYMM() && op.isYMEM())) throw Error(ERR_BAD_COMBINATION); opVex(y1, &y2, op, T_0F3A | T_66 | T_W0 | T_YMM, 0x06, imm); }
+void vperm2i128(const Ymm& y1, const Ymm& y2, const Operand& op, uint8 imm) { if (!(y1.isYMM() && y2.isYMM() && op.isYMEM())) throw Error(ERR_BAD_COMBINATION); opVex(y1, &y2, op, T_0F3A | T_66 | T_W0 | T_YMM, 0x46, imm); }
+void vpermd(const Ymm& y1, const Ymm& y2, const Operand& op) { opAVX_X_X_XM(y1, y2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x36); }
+void vpermilpd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x0D); }
+void vpermilpd(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_EVEX | T_B64, 0x05, imm); }
+void vpermilps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x0C); }
+void vpermilps(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_EVEX | T_B32, 0x04, imm); }
+void vpermpd(const Ymm& y, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(y, op, T_66 | T_0F3A | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x01, imm); }
+void vpermpd(const Ymm& y1, const Ymm& y2, const Operand& op) { opAVX_X_X_XM(y1, y2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x16); }
+void vpermps(const Ymm& y1, const Ymm& y2, const Operand& op) { opAVX_X_X_XM(y1, y2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x16); }
+void vpermq(const Ymm& y, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(y, op, T_66 | T_0F3A | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x00, imm); }
+void vpermq(const Ymm& y1, const Ymm& y2, const Operand& op) { opAVX_X_X_XM(y1, y2, op, T_66 | T_0F38 | T_W0 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x36); }
+void vpextrb(const Operand& op, const Xmm& x, uint8 imm) { if (!((op.isREG(8|16|i32e) || op.isMEM()) && x.isXMM())) throw Error(ERR_BAD_COMBINATION); opVex(x, 0, op, T_0F3A | T_66 | T_EVEX | T_N1, 0x14, imm); }
+void vpextrd(const Operand& op, const Xmm& x, uint8 imm) { if (!((op.isREG(32) || op.isMEM()) && x.isXMM())) throw Error(ERR_BAD_COMBINATION); opVex(x, 0, op, T_0F3A | T_66 | T_W0 | T_EVEX | T_EW0 | T_N4, 0x16, imm); }
+void vpextrq(const Operand& op, const Xmm& x, uint8 imm) { if (!((op.isREG(64) || op.isMEM()) && x.isXMM())) throw Error(ERR_BAD_COMBINATION); opVex(x, 0, op, T_0F3A | T_66 | T_W1 | T_EVEX | T_EW1 | T_N8, 0x16, imm); }
+void vpextrw(const Operand& op, const Xmm& x, uint8 imm) { if (!((op.isREG(16|i32e) || op.isMEM()) && x.isXMM())) throw Error(ERR_BAD_COMBINATION); if (op.isREG() && x.getIdx() < 16) { opAVX_X_X_XM(Xmm(op.getIdx()), xm0, x, T_0F | T_66, 0xC5, imm); } else { opVex(x, 0, op, T_0F3A | T_66 | T_EVEX | T_N2, 0x15, imm); } }
+void vpgatherdd(const Xmm& x1, const Address& addr, const Xmm& x2) { opGather(x1, addr, x2, T_0F38 | T_66 | T_YMM | T_VSIB | T_W0, 0x90, 1); }
+void vpgatherdq(const Xmm& x1, const Address& addr, const Xmm& x2) { opGather(x1, addr, x2, T_0F38 | T_66 | T_YMM | T_VSIB | T_W1, 0x90, 0); }
+void vpgatherqd(const Xmm& x1, const Address& addr, const Xmm& x2) { opGather(x1, addr, x2, T_0F38 | T_66 | T_YMM | T_VSIB | T_W0, 0x91, 2); }
+void vpgatherqq(const Xmm& x1, const Address& addr, const Xmm& x2) { opGather(x1, addr, x2, T_0F38 | T_66 | T_YMM | T_VSIB | T_W1, 0x91, 1); }
+void vphaddd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM, 0x02); }
+void vphaddsw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM, 0x03); }
+void vphaddw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM, 0x01); }
+void vphminposuw(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F38, 0x41); }
+void vphsubd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM, 0x06); }
+void vphsubsw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM, 0x07); }
+void vphsubw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM, 0x05); }
+void vpinsrb(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { if (!(x1.isXMM() && x2.isXMM() && (op.isREG(32) || op.isMEM()))) throw Error(ERR_BAD_COMBINATION); opVex(x1, &x2, op, T_0F3A | T_66 | T_EVEX | T_N1, 0x20, imm); }
+void vpinsrd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { if (!(x1.isXMM() && x2.isXMM() && (op.isREG(32) || op.isMEM()))) throw Error(ERR_BAD_COMBINATION); opVex(x1, &x2, op, T_0F3A | T_66 | T_W0 | T_EVEX | T_EW0 | T_N4, 0x22, imm); }
+void vpinsrq(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { if (!(x1.isXMM() && x2.isXMM() && (op.isREG(64) || op.isMEM()))) throw Error(ERR_BAD_COMBINATION); opVex(x1, &x2, op, T_0F3A | T_66 | T_W1 | T_EVEX | T_EW1 | T_N8, 0x22, imm); }
+void vpinsrw(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { if (!(x1.isXMM() && x2.isXMM() && (op.isREG(32) || op.isMEM()))) throw Error(ERR_BAD_COMBINATION); opVex(x1, &x2, op, T_0F | T_66 | T_EVEX | T_N2, 0xC4, imm); }
+void vpmaddubsw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM | T_EVEX, 0x04); }
+void vpmaddwd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xF5); }
+void vpmaskmovd(const Address& addr, const Xmm& x1, const Xmm& x2) { opAVX_X_X_XM(x2, x1, addr, T_0F38 | T_66 | T_W0 | T_YMM, 0x8E); }
+void vpmaskmovd(const Xmm& x1, const Xmm& x2, const Address& addr) { opAVX_X_X_XM(x1, x2, addr, T_0F38 | T_66 | T_W0 | T_YMM, 0x8C); }
+void vpmaskmovq(const Address& addr, const Xmm& x1, const Xmm& x2) { opAVX_X_X_XM(x2, x1, addr, T_0F38 | T_66 | T_W1 | T_YMM, 0x8E); }
+void vpmaskmovq(const Xmm& x1, const Xmm& x2, const Address& addr) { opAVX_X_X_XM(x1, x2, addr, T_0F38 | T_66 | T_W1 | T_YMM, 0x8C); }
+void vpmaxsb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM | T_EVEX, 0x3C); }
+void vpmaxsd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x3D); }
+void vpmaxsw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xEE); }
+void vpmaxub(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xDE); }
+void vpmaxud(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x3F); }
+void vpmaxuw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM | T_EVEX, 0x3E); }
+void vpminsb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM | T_EVEX, 0x38); }
+void vpminsd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x39); }
+void vpminsw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xEA); }
+void vpminub(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xDA); }
+void vpminud(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x3B); }
+void vpminuw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM | T_EVEX, 0x3A); }
+void vpmovmskb(const Reg32e& r, const Xmm& x) { if (!x.is(Operand::XMM | Operand::YMM)) throw Error(ERR_BAD_COMBINATION); opVex(x.isYMM() ? Ymm(r.getIdx()) : Xmm(r.getIdx()), 0, x, T_0F | T_66 | T_YMM, 0xD7); }
+void vpmovsxbd(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N4 | T_N_VL | T_66 | T_0F38 | T_YMM | T_EVEX, 0x21); }
+void vpmovsxbq(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N2 | T_N_VL | T_66 | T_0F38 | T_YMM | T_EVEX, 0x22); }
+void vpmovsxbw(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N8 | T_N_VL | T_66 | T_0F38 | T_YMM | T_EVEX, 0x20); }
+void vpmovsxdq(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N8 | T_N_VL | T_66 | T_0F38 | T_EW0 | T_YMM | T_EVEX, 0x25); }
+void vpmovsxwd(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N8 | T_N_VL | T_66 | T_0F38 | T_YMM | T_EVEX, 0x23); }
+void vpmovsxwq(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N4 | T_N_VL | T_66 | T_0F38 | T_YMM | T_EVEX, 0x24); }
+void vpmovzxbd(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N4 | T_N_VL | T_66 | T_0F38 | T_YMM | T_EVEX, 0x31); }
+void vpmovzxbq(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N2 | T_N_VL | T_66 | T_0F38 | T_YMM | T_EVEX, 0x32); }
+void vpmovzxbw(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N8 | T_N_VL | T_66 | T_0F38 | T_YMM | T_EVEX, 0x30); }
+void vpmovzxdq(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N8 | T_N_VL | T_66 | T_0F38 | T_EW0 | T_YMM | T_EVEX, 0x35); }
+void vpmovzxwd(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N8 | T_N_VL | T_66 | T_0F38 | T_YMM | T_EVEX, 0x33); }
+void vpmovzxwq(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N4 | T_N_VL | T_66 | T_0F38 | T_YMM | T_EVEX, 0x34); }
+void vpmuldq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x28); }
+void vpmulhrsw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM | T_EVEX, 0x0B); }
+void vpmulhuw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xE4); }
+void vpmulhw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xE5); }
+void vpmulld(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x40); }
+void vpmullw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xD5); }
+void vpmuludq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_B64, 0xF4); }
+void vpor(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM, 0xEB); }
+void vpsadbw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xF6); }
+void vpshufb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM | T_EVEX, 0x00); }
+void vpshufd(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F | T_EW0 | T_YMM | T_EVEX | T_B32, 0x70, imm); }
+void vpshufhw(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_F3 | T_0F | T_YMM | T_EVEX, 0x70, imm); }
+void vpshuflw(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_F2 | T_0F | T_YMM | T_EVEX, 0x70, imm); }
+void vpsignb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM, 0x08); }
+void vpsignd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM, 0x0A); }
+void vpsignw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_YMM, 0x09); }
+void vpslld(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 6), x, op, T_66 | T_0F | T_EW0 | T_YMM | T_EVEX | T_B32 | T_MEM_EVEX, 0x72, imm); }
+void vpslld(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N16 | T_66 | T_0F | T_EW0 | T_YMM | T_EVEX, 0xF2); }
+void vpslldq(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 7), x, op, T_66 | T_0F | T_YMM | T_EVEX | T_MEM_EVEX, 0x73, imm); }
+void vpsllq(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 6), x, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_B64 | T_MEM_EVEX, 0x73, imm); }
+void vpsllq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N16 | T_66 | T_0F | T_EW1 | T_YMM | T_EVEX, 0xF3); }
+void vpsllvd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x47); }
+void vpsllvq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x47); }
+void vpsllw(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 6), x, op, T_66 | T_0F | T_YMM | T_EVEX | T_MEM_EVEX, 0x71, imm); }
+void vpsllw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N16 | T_66 | T_0F | T_YMM | T_EVEX, 0xF1); }
+void vpsrad(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 4), x, op, T_66 | T_0F | T_EW0 | T_YMM | T_EVEX | T_B32 | T_MEM_EVEX, 0x72, imm); }
+void vpsrad(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N16 | T_66 | T_0F | T_EW0 | T_YMM | T_EVEX, 0xE2); }
+void vpsravd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x46); }
+void vpsraw(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 4), x, op, T_66 | T_0F | T_YMM | T_EVEX | T_MEM_EVEX, 0x71, imm); }
+void vpsraw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N16 | T_66 | T_0F | T_YMM | T_EVEX, 0xE1); }
+void vpsrld(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 2), x, op, T_66 | T_0F | T_EW0 | T_YMM | T_EVEX | T_B32 | T_MEM_EVEX, 0x72, imm); }
+void vpsrld(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N16 | T_66 | T_0F | T_EW0 | T_YMM | T_EVEX, 0xD2); }
+void vpsrldq(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 3), x, op, T_66 | T_0F | T_YMM | T_EVEX | T_MEM_EVEX, 0x73, imm); }
+void vpsrlq(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 2), x, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_B64 | T_MEM_EVEX, 0x73, imm); }
+void vpsrlq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N16 | T_66 | T_0F | T_EW1 | T_YMM | T_EVEX, 0xD3); }
+void vpsrlvd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W0 | T_EW0 | T_YMM | T_EVEX | T_B32, 0x45); }
+void vpsrlvq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_W1 | T_EW1 | T_YMM | T_EVEX | T_B64, 0x45); }
+void vpsrlw(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 2), x, op, T_66 | T_0F | T_YMM | T_EVEX | T_MEM_EVEX, 0x71, imm); }
+void vpsrlw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N16 | T_66 | T_0F | T_YMM | T_EVEX, 0xD1); }
+void vpsubb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xF8); }
+void vpsubd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW0 | T_YMM | T_EVEX | T_B32, 0xFA); }
+void vpsubq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_B64, 0xFB); }
+void vpsubsb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xE8); }
+void vpsubsw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xE9); }
+void vpsubusb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xD8); }
+void vpsubusw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xD9); }
+void vpsubw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0xF9); }
+void vptest(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F38 | T_YMM, 0x17); }
+void vpunpckhbw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0x68); }
+void vpunpckhdq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW0 | T_YMM | T_EVEX | T_B32, 0x6A); }
+void vpunpckhqdq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_B64, 0x6D); }
+void vpunpckhwd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0x69); }
+void vpunpcklbw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0x60); }
+void vpunpckldq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW0 | T_YMM | T_EVEX | T_B32, 0x62); }
+void vpunpcklqdq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_B64, 0x6C); }
+void vpunpcklwd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM | T_EVEX, 0x61); }
+void vpxor(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_YMM, 0xEF); }
+void vrcpps(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_0F | T_YMM, 0x53); }
+void vrcpss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_F3 | T_0F, 0x53); }
+void vroundpd(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F3A | T_YMM, 0x09, imm); }
+void vroundps(const Xmm& xm, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F3A | T_YMM, 0x08, imm); }
+void vroundsd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W0, 0x0B, imm); }
+void vroundss(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_W0, 0x0A, imm); }
+void vrsqrtps(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_0F | T_YMM, 0x52); }
+void vrsqrtss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_F3 | T_0F, 0x52); }
+void vshufpd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_B64, 0xC6, imm); }
+void vshufps(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_0F | T_EW0 | T_YMM | T_EVEX | T_B32, 0xC6, imm); }
+void vsqrtpd(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_ER_Z | T_B64, 0x51); }
+void vsqrtps(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x51); }
+void vsqrtsd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_F2 | T_0F | T_EW1 | T_EVEX | T_ER_X, 0x51); }
+void vsqrtss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_F3 | T_0F | T_EW0 | T_EVEX | T_ER_X, 0x51); }
+void vstmxcsr(const Address& addr) { opAVX_X_X_XM(xm3, xm0, addr, T_0F, 0xAE); }
+void vsubpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_66 | T_EW1 | T_YMM | T_EVEX | T_ER_Z | T_B64, 0x5C); }
+void vsubps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x5C); }
+void vsubsd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F2 | T_EW1 | T_EVEX | T_ER_Z | T_N8, 0x5C); }
+void vsubss(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_F3 | T_EW0 | T_EVEX | T_ER_Z | T_N4, 0x5C); }
+void vtestpd(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F38 | T_YMM, 0x0F); }
+void vtestps(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_66 | T_0F38 | T_YMM, 0x0E); }
+void vucomisd(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N8 | T_66 | T_0F | T_EW1 | T_EVEX | T_SAE_X, 0x2E); }
+void vucomiss(const Xmm& xm, const Operand& op) { opAVX_X_XM_IMM(xm, op, T_N4 | T_0F | T_EW0 | T_EVEX | T_SAE_X, 0x2E); }
+void vunpckhpd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_B64, 0x15); }
+void vunpckhps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_0F | T_EW0 | T_YMM | T_EVEX | T_B32, 0x15); }
+void vunpcklpd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_EVEX | T_B64, 0x14); }
+void vunpcklps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_0F | T_EW0 | T_YMM | T_EVEX | T_B32, 0x14); }
+void vxorpd(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_66 | T_EW1 | T_YMM | T_EVEX | T_ER_Z | T_B64, 0x57); }
+void vxorps(const Xmm& xmm, const Operand& op1, const Operand& op2 = Operand()) { opAVX_X_X_XM(xmm, op1, op2, T_0F | T_EW0 | T_YMM | T_EVEX | T_ER_Z | T_B32, 0x57); }
+void vzeroall() { db(0xC5); db(0xFC); db(0x77); }
+void vzeroupper() { db(0xC5); db(0xF8); db(0x77); }
+void wait() { db(0x9B); }
+void wbinvd() { db(0x0F); db(0x09); }
+void wrmsr() { db(0x0F); db(0x30); }
+void xadd(const Operand& op, const Reg& reg) { opModRM(reg, op, (op.isREG() && reg.isREG() && op.getBit() == reg.getBit()), op.isMEM(), 0x0F, 0xC0 | (reg.isBit(8) ? 0 : 1)); }
+void xgetbv() { db(0x0F); db(0x01); db(0xD0); }
+void xlatb() { db(0xD7); }
+void xor_(const Operand& op, uint32 imm) { opRM_I(op, imm, 0x30, 6); }
+void xor_(const Operand& op1, const Operand& op2) { opRM_RM(op1, op2, 0x30); }
+void xorpd(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x57, 0x66, isXMM_XMMorMEM); }
+void xorps(const Xmm& xmm, const Operand& op) { opGen(xmm, op, 0x57, 0x100, isXMM_XMMorMEM); }
+#ifdef XBYAK_ENABLE_OMITTED_OPERAND
+void vblendpd(const Xmm& x, const Operand& op, uint8 imm) { vblendpd(x, x, op, imm); }
+void vblendps(const Xmm& x, const Operand& op, uint8 imm) { vblendps(x, x, op, imm); }
+void vblendvpd(const Xmm& x1, const Operand& op, const Xmm& x4) { vblendvpd(x1, x1, op, x4); }
+void vblendvps(const Xmm& x1, const Operand& op, const Xmm& x4) { vblendvps(x1, x1, op, x4); }
+void vcmpeq_ospd(const Xmm& x, const Operand& op) { vcmpeq_ospd(x, x, op); }
+void vcmpeq_osps(const Xmm& x, const Operand& op) { vcmpeq_osps(x, x, op); }
+void vcmpeq_ossd(const Xmm& x, const Operand& op) { vcmpeq_ossd(x, x, op); }
+void vcmpeq_osss(const Xmm& x, const Operand& op) { vcmpeq_osss(x, x, op); }
+void vcmpeq_uqpd(const Xmm& x, const Operand& op) { vcmpeq_uqpd(x, x, op); }
+void vcmpeq_uqps(const Xmm& x, const Operand& op) { vcmpeq_uqps(x, x, op); }
+void vcmpeq_uqsd(const Xmm& x, const Operand& op) { vcmpeq_uqsd(x, x, op); }
+void vcmpeq_uqss(const Xmm& x, const Operand& op) { vcmpeq_uqss(x, x, op); }
+void vcmpeq_uspd(const Xmm& x, const Operand& op) { vcmpeq_uspd(x, x, op); }
+void vcmpeq_usps(const Xmm& x, const Operand& op) { vcmpeq_usps(x, x, op); }
+void vcmpeq_ussd(const Xmm& x, const Operand& op) { vcmpeq_ussd(x, x, op); }
+void vcmpeq_usss(const Xmm& x, const Operand& op) { vcmpeq_usss(x, x, op); }
+void vcmpeqpd(const Xmm& x, const Operand& op) { vcmpeqpd(x, x, op); }
+void vcmpeqps(const Xmm& x, const Operand& op) { vcmpeqps(x, x, op); }
+void vcmpeqsd(const Xmm& x, const Operand& op) { vcmpeqsd(x, x, op); }
+void vcmpeqss(const Xmm& x, const Operand& op) { vcmpeqss(x, x, op); }
+void vcmpfalse_ospd(const Xmm& x, const Operand& op) { vcmpfalse_ospd(x, x, op); }
+void vcmpfalse_osps(const Xmm& x, const Operand& op) { vcmpfalse_osps(x, x, op); }
+void vcmpfalse_ossd(const Xmm& x, const Operand& op) { vcmpfalse_ossd(x, x, op); }
+void vcmpfalse_osss(const Xmm& x, const Operand& op) { vcmpfalse_osss(x, x, op); }
+void vcmpfalsepd(const Xmm& x, const Operand& op) { vcmpfalsepd(x, x, op); }
+void vcmpfalseps(const Xmm& x, const Operand& op) { vcmpfalseps(x, x, op); }
+void vcmpfalsesd(const Xmm& x, const Operand& op) { vcmpfalsesd(x, x, op); }
+void vcmpfalsess(const Xmm& x, const Operand& op) { vcmpfalsess(x, x, op); }
+void vcmpge_oqpd(const Xmm& x, const Operand& op) { vcmpge_oqpd(x, x, op); }
+void vcmpge_oqps(const Xmm& x, const Operand& op) { vcmpge_oqps(x, x, op); }
+void vcmpge_oqsd(const Xmm& x, const Operand& op) { vcmpge_oqsd(x, x, op); }
+void vcmpge_oqss(const Xmm& x, const Operand& op) { vcmpge_oqss(x, x, op); }
+void vcmpgepd(const Xmm& x, const Operand& op) { vcmpgepd(x, x, op); }
+void vcmpgeps(const Xmm& x, const Operand& op) { vcmpgeps(x, x, op); }
+void vcmpgesd(const Xmm& x, const Operand& op) { vcmpgesd(x, x, op); }
+void vcmpgess(const Xmm& x, const Operand& op) { vcmpgess(x, x, op); }
+void vcmpgt_oqpd(const Xmm& x, const Operand& op) { vcmpgt_oqpd(x, x, op); }
+void vcmpgt_oqps(const Xmm& x, const Operand& op) { vcmpgt_oqps(x, x, op); }
+void vcmpgt_oqsd(const Xmm& x, const Operand& op) { vcmpgt_oqsd(x, x, op); }
+void vcmpgt_oqss(const Xmm& x, const Operand& op) { vcmpgt_oqss(x, x, op); }
+void vcmpgtpd(const Xmm& x, const Operand& op) { vcmpgtpd(x, x, op); }
+void vcmpgtps(const Xmm& x, const Operand& op) { vcmpgtps(x, x, op); }
+void vcmpgtsd(const Xmm& x, const Operand& op) { vcmpgtsd(x, x, op); }
+void vcmpgtss(const Xmm& x, const Operand& op) { vcmpgtss(x, x, op); }
+void vcmple_oqpd(const Xmm& x, const Operand& op) { vcmple_oqpd(x, x, op); }
+void vcmple_oqps(const Xmm& x, const Operand& op) { vcmple_oqps(x, x, op); }
+void vcmple_oqsd(const Xmm& x, const Operand& op) { vcmple_oqsd(x, x, op); }
+void vcmple_oqss(const Xmm& x, const Operand& op) { vcmple_oqss(x, x, op); }
+void vcmplepd(const Xmm& x, const Operand& op) { vcmplepd(x, x, op); }
+void vcmpleps(const Xmm& x, const Operand& op) { vcmpleps(x, x, op); }
+void vcmplesd(const Xmm& x, const Operand& op) { vcmplesd(x, x, op); }
+void vcmpless(const Xmm& x, const Operand& op) { vcmpless(x, x, op); }
+void vcmplt_oqpd(const Xmm& x, const Operand& op) { vcmplt_oqpd(x, x, op); }
+void vcmplt_oqps(const Xmm& x, const Operand& op) { vcmplt_oqps(x, x, op); }
+void vcmplt_oqsd(const Xmm& x, const Operand& op) { vcmplt_oqsd(x, x, op); }
+void vcmplt_oqss(const Xmm& x, const Operand& op) { vcmplt_oqss(x, x, op); }
+void vcmpltpd(const Xmm& x, const Operand& op) { vcmpltpd(x, x, op); }
+void vcmpltps(const Xmm& x, const Operand& op) { vcmpltps(x, x, op); }
+void vcmpltsd(const Xmm& x, const Operand& op) { vcmpltsd(x, x, op); }
+void vcmpltss(const Xmm& x, const Operand& op) { vcmpltss(x, x, op); }
+void vcmpneq_oqpd(const Xmm& x, const Operand& op) { vcmpneq_oqpd(x, x, op); }
+void vcmpneq_oqps(const Xmm& x, const Operand& op) { vcmpneq_oqps(x, x, op); }
+void vcmpneq_oqsd(const Xmm& x, const Operand& op) { vcmpneq_oqsd(x, x, op); }
+void vcmpneq_oqss(const Xmm& x, const Operand& op) { vcmpneq_oqss(x, x, op); }
+void vcmpneq_ospd(const Xmm& x, const Operand& op) { vcmpneq_ospd(x, x, op); }
+void vcmpneq_osps(const Xmm& x, const Operand& op) { vcmpneq_osps(x, x, op); }
+void vcmpneq_ossd(const Xmm& x, const Operand& op) { vcmpneq_ossd(x, x, op); }
+void vcmpneq_osss(const Xmm& x, const Operand& op) { vcmpneq_osss(x, x, op); }
+void vcmpneq_uspd(const Xmm& x, const Operand& op) { vcmpneq_uspd(x, x, op); }
+void vcmpneq_usps(const Xmm& x, const Operand& op) { vcmpneq_usps(x, x, op); }
+void vcmpneq_ussd(const Xmm& x, const Operand& op) { vcmpneq_ussd(x, x, op); }
+void vcmpneq_usss(const Xmm& x, const Operand& op) { vcmpneq_usss(x, x, op); }
+void vcmpneqpd(const Xmm& x, const Operand& op) { vcmpneqpd(x, x, op); }
+void vcmpneqps(const Xmm& x, const Operand& op) { vcmpneqps(x, x, op); }
+void vcmpneqsd(const Xmm& x, const Operand& op) { vcmpneqsd(x, x, op); }
+void vcmpneqss(const Xmm& x, const Operand& op) { vcmpneqss(x, x, op); }
+void vcmpnge_uqpd(const Xmm& x, const Operand& op) { vcmpnge_uqpd(x, x, op); }
+void vcmpnge_uqps(const Xmm& x, const Operand& op) { vcmpnge_uqps(x, x, op); }
+void vcmpnge_uqsd(const Xmm& x, const Operand& op) { vcmpnge_uqsd(x, x, op); }
+void vcmpnge_uqss(const Xmm& x, const Operand& op) { vcmpnge_uqss(x, x, op); }
+void vcmpngepd(const Xmm& x, const Operand& op) { vcmpngepd(x, x, op); }
+void vcmpngeps(const Xmm& x, const Operand& op) { vcmpngeps(x, x, op); }
+void vcmpngesd(const Xmm& x, const Operand& op) { vcmpngesd(x, x, op); }
+void vcmpngess(const Xmm& x, const Operand& op) { vcmpngess(x, x, op); }
+void vcmpngt_uqpd(const Xmm& x, const Operand& op) { vcmpngt_uqpd(x, x, op); }
+void vcmpngt_uqps(const Xmm& x, const Operand& op) { vcmpngt_uqps(x, x, op); }
+void vcmpngt_uqsd(const Xmm& x, const Operand& op) { vcmpngt_uqsd(x, x, op); }
+void vcmpngt_uqss(const Xmm& x, const Operand& op) { vcmpngt_uqss(x, x, op); }
+void vcmpngtpd(const Xmm& x, const Operand& op) { vcmpngtpd(x, x, op); }
+void vcmpngtps(const Xmm& x, const Operand& op) { vcmpngtps(x, x, op); }
+void vcmpngtsd(const Xmm& x, const Operand& op) { vcmpngtsd(x, x, op); }
+void vcmpngtss(const Xmm& x, const Operand& op) { vcmpngtss(x, x, op); }
+void vcmpnle_uqpd(const Xmm& x, const Operand& op) { vcmpnle_uqpd(x, x, op); }
+void vcmpnle_uqps(const Xmm& x, const Operand& op) { vcmpnle_uqps(x, x, op); }
+void vcmpnle_uqsd(const Xmm& x, const Operand& op) { vcmpnle_uqsd(x, x, op); }
+void vcmpnle_uqss(const Xmm& x, const Operand& op) { vcmpnle_uqss(x, x, op); }
+void vcmpnlepd(const Xmm& x, const Operand& op) { vcmpnlepd(x, x, op); }
+void vcmpnleps(const Xmm& x, const Operand& op) { vcmpnleps(x, x, op); }
+void vcmpnlesd(const Xmm& x, const Operand& op) { vcmpnlesd(x, x, op); }
+void vcmpnless(const Xmm& x, const Operand& op) { vcmpnless(x, x, op); }
+void vcmpnlt_uqpd(const Xmm& x, const Operand& op) { vcmpnlt_uqpd(x, x, op); }
+void vcmpnlt_uqps(const Xmm& x, const Operand& op) { vcmpnlt_uqps(x, x, op); }
+void vcmpnlt_uqsd(const Xmm& x, const Operand& op) { vcmpnlt_uqsd(x, x, op); }
+void vcmpnlt_uqss(const Xmm& x, const Operand& op) { vcmpnlt_uqss(x, x, op); }
+void vcmpnltpd(const Xmm& x, const Operand& op) { vcmpnltpd(x, x, op); }
+void vcmpnltps(const Xmm& x, const Operand& op) { vcmpnltps(x, x, op); }
+void vcmpnltsd(const Xmm& x, const Operand& op) { vcmpnltsd(x, x, op); }
+void vcmpnltss(const Xmm& x, const Operand& op) { vcmpnltss(x, x, op); }
+void vcmpord_spd(const Xmm& x, const Operand& op) { vcmpord_spd(x, x, op); }
+void vcmpord_sps(const Xmm& x, const Operand& op) { vcmpord_sps(x, x, op); }
+void vcmpord_ssd(const Xmm& x, const Operand& op) { vcmpord_ssd(x, x, op); }
+void vcmpord_sss(const Xmm& x, const Operand& op) { vcmpord_sss(x, x, op); }
+void vcmpordpd(const Xmm& x, const Operand& op) { vcmpordpd(x, x, op); }
+void vcmpordps(const Xmm& x, const Operand& op) { vcmpordps(x, x, op); }
+void vcmpordsd(const Xmm& x, const Operand& op) { vcmpordsd(x, x, op); }
+void vcmpordss(const Xmm& x, const Operand& op) { vcmpordss(x, x, op); }
+void vcmppd(const Xmm& x, const Operand& op, uint8 imm) { vcmppd(x, x, op, imm); }
+void vcmpps(const Xmm& x, const Operand& op, uint8 imm) { vcmpps(x, x, op, imm); }
+void vcmpsd(const Xmm& x, const Operand& op, uint8 imm) { vcmpsd(x, x, op, imm); }
+void vcmpss(const Xmm& x, const Operand& op, uint8 imm) { vcmpss(x, x, op, imm); }
+void vcmptrue_uspd(const Xmm& x, const Operand& op) { vcmptrue_uspd(x, x, op); }
+void vcmptrue_usps(const Xmm& x, const Operand& op) { vcmptrue_usps(x, x, op); }
+void vcmptrue_ussd(const Xmm& x, const Operand& op) { vcmptrue_ussd(x, x, op); }
+void vcmptrue_usss(const Xmm& x, const Operand& op) { vcmptrue_usss(x, x, op); }
+void vcmptruepd(const Xmm& x, const Operand& op) { vcmptruepd(x, x, op); }
+void vcmptrueps(const Xmm& x, const Operand& op) { vcmptrueps(x, x, op); }
+void vcmptruesd(const Xmm& x, const Operand& op) { vcmptruesd(x, x, op); }
+void vcmptruess(const Xmm& x, const Operand& op) { vcmptruess(x, x, op); }
+void vcmpunord_spd(const Xmm& x, const Operand& op) { vcmpunord_spd(x, x, op); }
+void vcmpunord_sps(const Xmm& x, const Operand& op) { vcmpunord_sps(x, x, op); }
+void vcmpunord_ssd(const Xmm& x, const Operand& op) { vcmpunord_ssd(x, x, op); }
+void vcmpunord_sss(const Xmm& x, const Operand& op) { vcmpunord_sss(x, x, op); }
+void vcmpunordpd(const Xmm& x, const Operand& op) { vcmpunordpd(x, x, op); }
+void vcmpunordps(const Xmm& x, const Operand& op) { vcmpunordps(x, x, op); }
+void vcmpunordsd(const Xmm& x, const Operand& op) { vcmpunordsd(x, x, op); }
+void vcmpunordss(const Xmm& x, const Operand& op) { vcmpunordss(x, x, op); }
+void vcvtsd2ss(const Xmm& x, const Operand& op) { vcvtsd2ss(x, x, op); }
+void vcvtsi2sd(const Xmm& x, const Operand& op) { vcvtsi2sd(x, x, op); }
+void vcvtsi2ss(const Xmm& x, const Operand& op) { vcvtsi2ss(x, x, op); }
+void vcvtss2sd(const Xmm& x, const Operand& op) { vcvtss2sd(x, x, op); }
+void vdppd(const Xmm& x, const Operand& op, uint8 imm) { vdppd(x, x, op, imm); }
+void vdpps(const Xmm& x, const Operand& op, uint8 imm) { vdpps(x, x, op, imm); }
+void vinsertps(const Xmm& x, const Operand& op, uint8 imm) { vinsertps(x, x, op, imm); }
+void vmpsadbw(const Xmm& x, const Operand& op, uint8 imm) { vmpsadbw(x, x, op, imm); }
+void vpackssdw(const Xmm& x, const Operand& op) { vpackssdw(x, x, op); }
+void vpacksswb(const Xmm& x, const Operand& op) { vpacksswb(x, x, op); }
+void vpackusdw(const Xmm& x, const Operand& op) { vpackusdw(x, x, op); }
+void vpackuswb(const Xmm& x, const Operand& op) { vpackuswb(x, x, op); }
+void vpaddb(const Xmm& x, const Operand& op) { vpaddb(x, x, op); }
+void vpaddd(const Xmm& x, const Operand& op) { vpaddd(x, x, op); }
+void vpaddq(const Xmm& x, const Operand& op) { vpaddq(x, x, op); }
+void vpaddsb(const Xmm& x, const Operand& op) { vpaddsb(x, x, op); }
+void vpaddsw(const Xmm& x, const Operand& op) { vpaddsw(x, x, op); }
+void vpaddusb(const Xmm& x, const Operand& op) { vpaddusb(x, x, op); }
+void vpaddusw(const Xmm& x, const Operand& op) { vpaddusw(x, x, op); }
+void vpaddw(const Xmm& x, const Operand& op) { vpaddw(x, x, op); }
+void vpalignr(const Xmm& x, const Operand& op, uint8 imm) { vpalignr(x, x, op, imm); }
+void vpand(const Xmm& x, const Operand& op) { vpand(x, x, op); }
+void vpandn(const Xmm& x, const Operand& op) { vpandn(x, x, op); }
+void vpavgb(const Xmm& x, const Operand& op) { vpavgb(x, x, op); }
+void vpavgw(const Xmm& x, const Operand& op) { vpavgw(x, x, op); }
+void vpblendd(const Xmm& x, const Operand& op, uint8 imm) { vpblendd(x, x, op, imm); }
+void vpblendvb(const Xmm& x1, const Operand& op, const Xmm& x4) { vpblendvb(x1, x1, op, x4); }
+void vpblendw(const Xmm& x, const Operand& op, uint8 imm) { vpblendw(x, x, op, imm); }
+void vpclmulqdq(const Xmm& x, const Operand& op, uint8 imm) { vpclmulqdq(x, x, op, imm); }
+void vpcmpeqb(const Xmm& x, const Operand& op) { vpcmpeqb(x, x, op); }
+void vpcmpeqd(const Xmm& x, const Operand& op) { vpcmpeqd(x, x, op); }
+void vpcmpeqq(const Xmm& x, const Operand& op) { vpcmpeqq(x, x, op); }
+void vpcmpeqw(const Xmm& x, const Operand& op) { vpcmpeqw(x, x, op); }
+void vpcmpgtb(const Xmm& x, const Operand& op) { vpcmpgtb(x, x, op); }
+void vpcmpgtd(const Xmm& x, const Operand& op) { vpcmpgtd(x, x, op); }
+void vpcmpgtq(const Xmm& x, const Operand& op) { vpcmpgtq(x, x, op); }
+void vpcmpgtw(const Xmm& x, const Operand& op) { vpcmpgtw(x, x, op); }
+void vphaddd(const Xmm& x, const Operand& op) { vphaddd(x, x, op); }
+void vphaddsw(const Xmm& x, const Operand& op) { vphaddsw(x, x, op); }
+void vphaddw(const Xmm& x, const Operand& op) { vphaddw(x, x, op); }
+void vphsubd(const Xmm& x, const Operand& op) { vphsubd(x, x, op); }
+void vphsubsw(const Xmm& x, const Operand& op) { vphsubsw(x, x, op); }
+void vphsubw(const Xmm& x, const Operand& op) { vphsubw(x, x, op); }
+void vpinsrb(const Xmm& x, const Operand& op, uint8 imm) { vpinsrb(x, x, op, imm); }
+void vpinsrd(const Xmm& x, const Operand& op, uint8 imm) { vpinsrd(x, x, op, imm); }
+void vpinsrq(const Xmm& x, const Operand& op, uint8 imm) { vpinsrq(x, x, op, imm); }
+void vpinsrw(const Xmm& x, const Operand& op, uint8 imm) { vpinsrw(x, x, op, imm); }
+void vpmaddubsw(const Xmm& x, const Operand& op) { vpmaddubsw(x, x, op); }
+void vpmaddwd(const Xmm& x, const Operand& op) { vpmaddwd(x, x, op); }
+void vpmaxsb(const Xmm& x, const Operand& op) { vpmaxsb(x, x, op); }
+void vpmaxsd(const Xmm& x, const Operand& op) { vpmaxsd(x, x, op); }
+void vpmaxsw(const Xmm& x, const Operand& op) { vpmaxsw(x, x, op); }
+void vpmaxub(const Xmm& x, const Operand& op) { vpmaxub(x, x, op); }
+void vpmaxud(const Xmm& x, const Operand& op) { vpmaxud(x, x, op); }
+void vpmaxuw(const Xmm& x, const Operand& op) { vpmaxuw(x, x, op); }
+void vpminsb(const Xmm& x, const Operand& op) { vpminsb(x, x, op); }
+void vpminsd(const Xmm& x, const Operand& op) { vpminsd(x, x, op); }
+void vpminsw(const Xmm& x, const Operand& op) { vpminsw(x, x, op); }
+void vpminub(const Xmm& x, const Operand& op) { vpminub(x, x, op); }
+void vpminud(const Xmm& x, const Operand& op) { vpminud(x, x, op); }
+void vpminuw(const Xmm& x, const Operand& op) { vpminuw(x, x, op); }
+void vpmuldq(const Xmm& x, const Operand& op) { vpmuldq(x, x, op); }
+void vpmulhrsw(const Xmm& x, const Operand& op) { vpmulhrsw(x, x, op); }
+void vpmulhuw(const Xmm& x, const Operand& op) { vpmulhuw(x, x, op); }
+void vpmulhw(const Xmm& x, const Operand& op) { vpmulhw(x, x, op); }
+void vpmulld(const Xmm& x, const Operand& op) { vpmulld(x, x, op); }
+void vpmullw(const Xmm& x, const Operand& op) { vpmullw(x, x, op); }
+void vpmuludq(const Xmm& x, const Operand& op) { vpmuludq(x, x, op); }
+void vpor(const Xmm& x, const Operand& op) { vpor(x, x, op); }
+void vpsadbw(const Xmm& x, const Operand& op) { vpsadbw(x, x, op); }
+void vpsignb(const Xmm& x, const Operand& op) { vpsignb(x, x, op); }
+void vpsignd(const Xmm& x, const Operand& op) { vpsignd(x, x, op); }
+void vpsignw(const Xmm& x, const Operand& op) { vpsignw(x, x, op); }
+void vpslld(const Xmm& x, const Operand& op) { vpslld(x, x, op); }
+void vpslld(const Xmm& x, uint8 imm) { vpslld(x, x, imm); }
+void vpslldq(const Xmm& x, uint8 imm) { vpslldq(x, x, imm); }
+void vpsllq(const Xmm& x, const Operand& op) { vpsllq(x, x, op); }
+void vpsllq(const Xmm& x, uint8 imm) { vpsllq(x, x, imm); }
+void vpsllw(const Xmm& x, const Operand& op) { vpsllw(x, x, op); }
+void vpsllw(const Xmm& x, uint8 imm) { vpsllw(x, x, imm); }
+void vpsrad(const Xmm& x, const Operand& op) { vpsrad(x, x, op); }
+void vpsrad(const Xmm& x, uint8 imm) { vpsrad(x, x, imm); }
+void vpsraw(const Xmm& x, const Operand& op) { vpsraw(x, x, op); }
+void vpsraw(const Xmm& x, uint8 imm) { vpsraw(x, x, imm); }
+void vpsrld(const Xmm& x, const Operand& op) { vpsrld(x, x, op); }
+void vpsrld(const Xmm& x, uint8 imm) { vpsrld(x, x, imm); }
+void vpsrldq(const Xmm& x, uint8 imm) { vpsrldq(x, x, imm); }
+void vpsrlq(const Xmm& x, const Operand& op) { vpsrlq(x, x, op); }
+void vpsrlq(const Xmm& x, uint8 imm) { vpsrlq(x, x, imm); }
+void vpsrlw(const Xmm& x, const Operand& op) { vpsrlw(x, x, op); }
+void vpsrlw(const Xmm& x, uint8 imm) { vpsrlw(x, x, imm); }
+void vpsubb(const Xmm& x, const Operand& op) { vpsubb(x, x, op); }
+void vpsubd(const Xmm& x, const Operand& op) { vpsubd(x, x, op); }
+void vpsubq(const Xmm& x, const Operand& op) { vpsubq(x, x, op); }
+void vpsubsb(const Xmm& x, const Operand& op) { vpsubsb(x, x, op); }
+void vpsubsw(const Xmm& x, const Operand& op) { vpsubsw(x, x, op); }
+void vpsubusb(const Xmm& x, const Operand& op) { vpsubusb(x, x, op); }
+void vpsubusw(const Xmm& x, const Operand& op) { vpsubusw(x, x, op); }
+void vpsubw(const Xmm& x, const Operand& op) { vpsubw(x, x, op); }
+void vpunpckhbw(const Xmm& x, const Operand& op) { vpunpckhbw(x, x, op); }
+void vpunpckhdq(const Xmm& x, const Operand& op) { vpunpckhdq(x, x, op); }
+void vpunpckhqdq(const Xmm& x, const Operand& op) { vpunpckhqdq(x, x, op); }
+void vpunpckhwd(const Xmm& x, const Operand& op) { vpunpckhwd(x, x, op); }
+void vpunpcklbw(const Xmm& x, const Operand& op) { vpunpcklbw(x, x, op); }
+void vpunpckldq(const Xmm& x, const Operand& op) { vpunpckldq(x, x, op); }
+void vpunpcklqdq(const Xmm& x, const Operand& op) { vpunpcklqdq(x, x, op); }
+void vpunpcklwd(const Xmm& x, const Operand& op) { vpunpcklwd(x, x, op); }
+void vpxor(const Xmm& x, const Operand& op) { vpxor(x, x, op); }
+void vrcpss(const Xmm& x, const Operand& op) { vrcpss(x, x, op); }
+void vroundsd(const Xmm& x, const Operand& op, uint8 imm) { vroundsd(x, x, op, imm); }
+void vroundss(const Xmm& x, const Operand& op, uint8 imm) { vroundss(x, x, op, imm); }
+void vrsqrtss(const Xmm& x, const Operand& op) { vrsqrtss(x, x, op); }
+void vshufpd(const Xmm& x, const Operand& op, uint8 imm) { vshufpd(x, x, op, imm); }
+void vshufps(const Xmm& x, const Operand& op, uint8 imm) { vshufps(x, x, op, imm); }
+void vsqrtsd(const Xmm& x, const Operand& op) { vsqrtsd(x, x, op); }
+void vsqrtss(const Xmm& x, const Operand& op) { vsqrtss(x, x, op); }
+void vunpckhpd(const Xmm& x, const Operand& op) { vunpckhpd(x, x, op); }
+void vunpckhps(const Xmm& x, const Operand& op) { vunpckhps(x, x, op); }
+void vunpcklpd(const Xmm& x, const Operand& op) { vunpcklpd(x, x, op); }
+void vunpcklps(const Xmm& x, const Operand& op) { vunpcklps(x, x, op); }
+#endif
+#ifdef XBYAK64
+void jecxz(std::string label) { db(0x67); opJmp(label, T_SHORT, 0xe3, 0, 0); }
+void jecxz(const Label& label) { db(0x67); opJmp(label, T_SHORT, 0xe3, 0, 0); }
+void jrcxz(std::string label) { opJmp(label, T_SHORT, 0xe3, 0, 0); }
+void jrcxz(const Label& label) { opJmp(label, T_SHORT, 0xe3, 0, 0); }
+void cdqe() { db(0x48); db(0x98); }
+void cqo() { db(0x48); db(0x99); }
+void cmpsq() { db(0x48); db(0xA7); }
+void movsq() { db(0x48); db(0xA5); }
+void scasq() { db(0x48); db(0xAF); }
+void stosq() { db(0x48); db(0xAB); }
+void cmpxchg16b(const Address& addr) { opModM(addr, Reg64(1), 0x0F, 0xC7); }
+void movq(const Reg64& reg, const Mmx& mmx) { if (mmx.isXMM()) db(0x66); opModR(mmx, reg, 0x0F, 0x7E); }
+void movq(const Mmx& mmx, const Reg64& reg) { if (mmx.isXMM()) db(0x66); opModR(mmx, reg, 0x0F, 0x6E); }
+void movsxd(const Reg64& reg, const Operand& op) { if (!op.isBit(32)) throw Error(ERR_BAD_COMBINATION); opModRM(reg, op, op.isREG(), op.isMEM(), 0x63); }
+void pextrq(const Operand& op, const Xmm& xmm, uint8 imm) { if (!op.isREG(64) && !op.isMEM()) throw Error(ERR_BAD_COMBINATION); opGen(Reg64(xmm.getIdx()), op, 0x16, 0x66, 0, imm, 0x3A); }
+void pinsrq(const Xmm& xmm, const Operand& op, uint8 imm) { if (!op.isREG(64) && !op.isMEM()) throw Error(ERR_BAD_COMBINATION); opGen(Reg64(xmm.getIdx()), op, 0x22, 0x66, 0, imm, 0x3A); }
+void vcvtss2si(const Reg64& r, const Operand& op) { opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, T_0F | T_F3 | T_W1 | T_EVEX | T_EW1 | T_ER_X | T_N8, 0x2D); }
+void vcvttss2si(const Reg64& r, const Operand& op) { opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, T_0F | T_F3 | T_W1 | T_EVEX | T_EW1 | T_SAE_X | T_N8, 0x2C); }
+void vcvtsd2si(const Reg64& r, const Operand& op) { opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, T_0F | T_F2 | T_W1 | T_EVEX | T_EW1 | T_N4 | T_ER_X, 0x2D); }
+void vcvttsd2si(const Reg64& r, const Operand& op) { opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, T_0F | T_F2 | T_W1 | T_EVEX | T_EW1 | T_N4 | T_SAE_X, 0x2C); }
+void vmovq(const Xmm& x, const Reg64& r) { opAVX_X_X_XM(x, xm0, Xmm(r.getIdx()), T_66 | T_0F | T_W1 | T_EVEX | T_EW1, 0x6E); }
+void vmovq(const Reg64& r, const Xmm& x) { opAVX_X_X_XM(x, xm0, Xmm(r.getIdx()), T_66 | T_0F | T_W1 | T_EVEX | T_EW1, 0x7E); }
+#else
+void jcxz(std::string label) { db(0x67); opJmp(label, T_SHORT, 0xe3, 0, 0); }
+void jcxz(const Label& label) { db(0x67); opJmp(label, T_SHORT, 0xe3, 0, 0); }
+void jecxz(std::string label) { opJmp(label, T_SHORT, 0xe3, 0, 0); }
+void jecxz(const Label& label) { opJmp(label, T_SHORT, 0xe3, 0, 0); }
+void aaa() { db(0x37); }
+void aad() { db(0xD5); db(0x0A); }
+void aam() { db(0xD4); db(0x0A); }
+void aas() { db(0x3F); }
+void daa() { db(0x27); }
+void das() { db(0x2F); }
+void popad() { db(0x61); }
+void popfd() { db(0x9D); }
+void pusha() { db(0x60); }
+void pushad() { db(0x60); }
+void pushfd() { db(0x9C); }
+void popa() { db(0x61); }
+#endif
+#ifndef XBYAK_NO_OP_NAMES
+void and(const Operand& op1, const Operand& op2) { and_(op1, op2); }
+void and(const Operand& op, uint32 imm) { and_(op, imm); }
+void or(const Operand& op1, const Operand& op2) { or_(op1, op2); }
+void or(const Operand& op, uint32 imm) { or_(op, imm); }
+void xor(const Operand& op1, const Operand& op2) { xor_(op1, op2); }
+void xor(const Operand& op, uint32 imm) { xor_(op, imm); }
+void not(const Operand& op) { not_(op); }
+#endif
+#ifndef XBYAK_DISABLE_AVX512
+void kaddb(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W0, 0x4A); }
+void kaddd(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W1, 0x4A); }
+void kaddq(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W1, 0x4A); }
+void kaddw(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W0, 0x4A); }
+void kandb(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W0, 0x41); }
+void kandd(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W1, 0x41); }
+void kandnb(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W0, 0x42); }
+void kandnd(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W1, 0x42); }
+void kandnq(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W1, 0x42); }
+void kandnw(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W0, 0x42); }
+void kandq(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W1, 0x41); }
+void kandw(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W0, 0x41); }
+void kmovb(const Address& addr, const Opmask& k) { opVex(k, 0, addr, T_L0 | T_0F | T_66 | T_W0, 0x91); }
+void kmovb(const Opmask& k, const Operand& op) { opVex(k, 0, op, T_L0 | T_0F | T_66 | T_W0, 0x90); }
+void kmovb(const Opmask& k, const Reg32& r) { opVex(k, 0, r, T_L0 | T_0F | T_66 | T_W0, 0x92); }
+void kmovb(const Reg32& r, const Opmask& k) { opVex(r, 0, k, T_L0 | T_0F | T_66 | T_W0, 0x93); }
+void kmovd(const Address& addr, const Opmask& k) { opVex(k, 0, addr, T_L0 | T_0F | T_66 | T_W1, 0x91); }
+void kmovd(const Opmask& k, const Operand& op) { opVex(k, 0, op, T_L0 | T_0F | T_66 | T_W1, 0x90); }
+void kmovd(const Opmask& k, const Reg32& r) { opVex(k, 0, r, T_L0 | T_0F | T_F2 | T_W0, 0x92); }
+void kmovd(const Reg32& r, const Opmask& k) { opVex(r, 0, k, T_L0 | T_0F | T_F2 | T_W0, 0x93); }
+void kmovq(const Address& addr, const Opmask& k) { opVex(k, 0, addr, T_L0 | T_0F | T_W1, 0x91); }
+void kmovq(const Opmask& k, const Operand& op) { opVex(k, 0, op, T_L0 | T_0F | T_W1, 0x90); }
+void kmovw(const Address& addr, const Opmask& k) { opVex(k, 0, addr, T_L0 | T_0F | T_W0, 0x91); }
+void kmovw(const Opmask& k, const Operand& op) { opVex(k, 0, op, T_L0 | T_0F | T_W0, 0x90); }
+void kmovw(const Opmask& k, const Reg32& r) { opVex(k, 0, r, T_L0 | T_0F | T_W0, 0x92); }
+void kmovw(const Reg32& r, const Opmask& k) { opVex(r, 0, k, T_L0 | T_0F | T_W0, 0x93); }
+void knotb(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_66 | T_W0, 0x44); }
+void knotd(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_66 | T_W1, 0x44); }
+void knotq(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_W1, 0x44); }
+void knotw(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_W0, 0x44); }
+void korb(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W0, 0x45); }
+void kord(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W1, 0x45); }
+void korq(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W1, 0x45); }
+void kortestb(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_66 | T_W0, 0x98); }
+void kortestd(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_66 | T_W1, 0x98); }
+void kortestq(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_W1, 0x98); }
+void kortestw(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_W0, 0x98); }
+void korw(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W0, 0x45); }
+void kshiftlb(const Opmask& r1, const Opmask& r2, uint8 imm) { opVex(r1, 0, r2, T_66 | T_0F3A | T_W0, 0x32, imm); }
+void kshiftld(const Opmask& r1, const Opmask& r2, uint8 imm) { opVex(r1, 0, r2, T_66 | T_0F3A | T_W0, 0x33, imm); }
+void kshiftlq(const Opmask& r1, const Opmask& r2, uint8 imm) { opVex(r1, 0, r2, T_66 | T_0F3A | T_W1, 0x33, imm); }
+void kshiftlw(const Opmask& r1, const Opmask& r2, uint8 imm) { opVex(r1, 0, r2, T_66 | T_0F3A | T_W1, 0x32, imm); }
+void kshiftrb(const Opmask& r1, const Opmask& r2, uint8 imm) { opVex(r1, 0, r2, T_66 | T_0F3A | T_W0, 0x30, imm); }
+void kshiftrd(const Opmask& r1, const Opmask& r2, uint8 imm) { opVex(r1, 0, r2, T_66 | T_0F3A | T_W0, 0x31, imm); }
+void kshiftrq(const Opmask& r1, const Opmask& r2, uint8 imm) { opVex(r1, 0, r2, T_66 | T_0F3A | T_W1, 0x31, imm); }
+void kshiftrw(const Opmask& r1, const Opmask& r2, uint8 imm) { opVex(r1, 0, r2, T_66 | T_0F3A | T_W1, 0x30, imm); }
+void ktestb(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_66 | T_W0, 0x99); }
+void ktestd(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_66 | T_W1, 0x99); }
+void ktestq(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_W1, 0x99); }
+void ktestw(const Opmask& r1, const Opmask& r2) { opVex(r1, 0, r2, T_0F | T_W0, 0x99); }
+void kunpckbw(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W0, 0x4B); }
+void kunpckdq(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W1, 0x4B); }
+void kunpckwd(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W0, 0x4B); }
+void kxnorb(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W0, 0x46); }
+void kxnord(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W1, 0x46); }
+void kxnorq(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W1, 0x46); }
+void kxnorw(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W0, 0x46); }
+void kxorb(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W0, 0x47); }
+void kxord(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_66 | T_W1, 0x47); }
+void kxorq(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W1, 0x47); }
+void kxorw(const Opmask& r1, const Opmask& r2, const Opmask& r3) { opVex(r1, &r2, r3, T_L1 | T_0F | T_W0, 0x47); }
+void v4fmaddps(const Zmm& z1, const Zmm& z2, const Address& addr) { opAVX_X_X_XM(z1, z2, addr, T_0F38 | T_F2 | T_EW0 | T_YMM | T_MUST_EVEX | T_N16, 0x9A); }
+void v4fmaddss(const Xmm& x1, const Xmm& x2, const Address& addr) { opAVX_X_X_XM(x1, x2, addr, T_0F38 | T_F2 | T_EW0 | T_MUST_EVEX | T_N16, 0x9B); }
+void v4fnmaddps(const Zmm& z1, const Zmm& z2, const Address& addr) { opAVX_X_X_XM(z1, z2, addr, T_0F38 | T_F2 | T_EW0 | T_YMM | T_MUST_EVEX | T_N16, 0xAA); }
+void v4fnmaddss(const Xmm& x1, const Xmm& x2, const Address& addr) { opAVX_X_X_XM(x1, x2, addr, T_0F38 | T_F2 | T_EW0 | T_MUST_EVEX | T_N16, 0xAB); }
+void valignd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x03, imm); }
+void valignq(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX, 0x03, imm); }
+void vblendmpd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x65); }
+void vblendmps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x65); }
+void vbroadcastf32x2(const Ymm& y, const Operand& op) { opAVX_X_XM_IMM(y, op, T_66 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW0 | T_N8, 0x19); }
+void vbroadcastf32x4(const Ymm& y, const Address& addr) { opAVX_X_XM_IMM(y, addr, T_66 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW0 | T_N16, 0x1A); }
+void vbroadcastf32x8(const Zmm& y, const Address& addr) { opAVX_X_XM_IMM(y, addr, T_66 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW0 | T_N32, 0x1B); }
+void vbroadcastf64x2(const Ymm& y, const Address& addr) { opAVX_X_XM_IMM(y, addr, T_66 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW1 | T_N16, 0x1A); }
+void vbroadcastf64x4(const Zmm& y, const Address& addr) { opAVX_X_XM_IMM(y, addr, T_66 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW1 | T_N32, 0x1B); }
+void vbroadcasti32x2(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW0 | T_N8, 0x59); }
+void vbroadcasti32x4(const Ymm& y, const Operand& op) { opAVX_X_XM_IMM(y, op, T_66 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW0 | T_N16, 0x5A); }
+void vbroadcasti32x8(const Zmm& z, const Operand& op) { opAVX_X_XM_IMM(z, op, T_66 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW0 | T_N32, 0x5B); }
+void vbroadcasti64x2(const Ymm& y, const Operand& op) { opAVX_X_XM_IMM(y, op, T_66 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW1 | T_N16, 0x5A); }
+void vbroadcasti64x4(const Zmm& z, const Operand& op) { opAVX_X_XM_IMM(z, op, T_66 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW1 | T_N32, 0x5B); }
+void vcmppd(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_66 | T_0F | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX, 0xC2, imm); }
+void vcmpps(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_0F | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX, 0xC2, imm); }
+void vcmpsd(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_N8 | T_F2 | T_0F | T_EW1 | T_SAE_Z | T_MUST_EVEX, 0xC2, imm); }
+void vcmpss(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_N4 | T_F3 | T_0F | T_EW0 | T_SAE_Z | T_MUST_EVEX, 0xC2, imm); }
+void vcompressb(const Operand& op, const Xmm& x) { opAVX_X_XM_IMM(x, op, T_N1 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x63); }
+void vcompresspd(const Operand& op, const Xmm& x) { opAVX_X_XM_IMM(x, op, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x8A); }
+void vcompressps(const Operand& op, const Xmm& x) { opAVX_X_XM_IMM(x, op, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x8A); }
+void vcompressw(const Operand& op, const Xmm& x) { opAVX_X_XM_IMM(x, op, T_N2 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x63); }
+void vcvtpd2qq(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F | T_EW1 | T_YMM | T_ER_Z | T_MUST_EVEX | T_B64, 0x7B); }
+void vcvtpd2udq(const Xmm& x, const Operand& op) { opCvt2(x, op, T_0F | T_YMM | T_MUST_EVEX | T_EW1 | T_B64 | T_ER_Z, 0x79); }
+void vcvtpd2uqq(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F | T_EW1 | T_YMM | T_ER_Z | T_MUST_EVEX | T_B64, 0x79); }
+void vcvtps2qq(const Xmm& x, const Operand& op) { checkCvt1(x, op); opVex(x, 0, op, T_66 | T_0F | T_YMM | T_MUST_EVEX | T_EW0 | T_B32 | T_N8 | T_N_VL | T_ER_Y, 0x7B); }
+void vcvtps2udq(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_0F | T_EW0 | T_YMM | T_ER_Z | T_MUST_EVEX | T_B32, 0x79); }
+void vcvtps2uqq(const Xmm& x, const Operand& op) { checkCvt1(x, op); opVex(x, 0, op, T_66 | T_0F | T_YMM | T_MUST_EVEX | T_EW0 | T_B32 | T_N8 | T_N_VL | T_ER_Y, 0x79); }
+void vcvtqq2pd(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_F3 | T_0F | T_EW1 | T_YMM | T_ER_Z | T_MUST_EVEX | T_B64, 0xE6); }
+void vcvtqq2ps(const Xmm& x, const Operand& op) { opCvt2(x, op, T_0F | T_YMM | T_MUST_EVEX | T_EW1 | T_B64 | T_ER_Z, 0x5B); }
+void vcvtsd2usi(const Reg32e& r, const Operand& op) { int type = (T_F2 | T_0F | T_MUST_EVEX | T_N8 | T_ER_X) | (r.isREG(64) ? T_EW1 : T_EW0); opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, type, 0x79); }
+void vcvtss2usi(const Reg32e& r, const Operand& op) { int type = (T_F3 | T_0F | T_MUST_EVEX | T_N4 | T_ER_X) | (r.isREG(64) ? T_EW1 : T_EW0); opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, type, 0x79); }
+void vcvttpd2qq(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x7A); }
+void vcvttpd2udq(const Xmm& x, const Operand& op) { opCvt2(x, op, T_0F | T_YMM | T_MUST_EVEX | T_EW1 | T_B64 | T_SAE_Z, 0x78); }
+void vcvttpd2uqq(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x78); }
+void vcvttps2qq(const Xmm& x, const Operand& op) { checkCvt1(x, op); opVex(x, 0, op, T_66 | T_0F | T_YMM | T_MUST_EVEX | T_EW0 | T_B32 | T_N8 | T_N_VL | T_SAE_Y, 0x7A); }
+void vcvttps2udq(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_0F | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x78); }
+void vcvttps2uqq(const Xmm& x, const Operand& op) { checkCvt1(x, op); opVex(x, 0, op, T_66 | T_0F | T_YMM | T_MUST_EVEX | T_EW0 | T_B32 | T_N8 | T_N_VL | T_SAE_Y, 0x78); }
+void vcvttsd2usi(const Reg32e& r, const Operand& op) { int type = (T_F2 | T_0F | T_MUST_EVEX | T_N8 | T_SAE_X) | (r.isREG(64) ? T_EW1 : T_EW0); opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, type, 0x78); }
+void vcvttss2usi(const Reg32e& r, const Operand& op) { int type = (T_F3 | T_0F | T_MUST_EVEX | T_N4 | T_SAE_X) | (r.isREG(64) ? T_EW1 : T_EW0); opAVX_X_X_XM(Xmm(r.getIdx()), xm0, op, type, 0x78); }
+void vcvtudq2pd(const Xmm& x, const Operand& op) { checkCvt1(x, op); opVex(x, 0, op, T_F3 | T_0F | T_YMM | T_MUST_EVEX | T_EW0 | T_B32 | T_N8 | T_N_VL, 0x7A); }
+void vcvtudq2ps(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_F2 | T_0F | T_EW0 | T_YMM | T_ER_Z | T_MUST_EVEX | T_B32, 0x7A); }
+void vcvtuqq2pd(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_F3 | T_0F | T_EW1 | T_YMM | T_ER_Z | T_MUST_EVEX | T_B64, 0x7A); }
+void vcvtuqq2ps(const Xmm& x, const Operand& op) { opCvt2(x, op, T_F2 | T_0F | T_YMM | T_MUST_EVEX | T_EW1 | T_B64 | T_ER_Z, 0x7A); }
+void vcvtusi2sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opCvt3(x1, x2, op, T_F2 | T_0F | T_MUST_EVEX, T_W1 | T_EW1 | T_ER_X | T_N8, T_W0 | T_EW0 | T_N4, 0x7B); }
+void vcvtusi2ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opCvt3(x1, x2, op, T_F3 | T_0F | T_MUST_EVEX | T_ER_X, T_W1 | T_EW1 | T_N8, T_W0 | T_EW0 | T_N4, 0x7B); }
+void vdbpsadbw(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x42, imm); }
+void vexp2pd(const Zmm& z, const Operand& op) { opAVX_X_XM_IMM(z, op, T_66 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW1 | T_B64 | T_SAE_Z, 0xC8); }
+void vexp2ps(const Zmm& z, const Operand& op) { opAVX_X_XM_IMM(z, op, T_66 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW0 | T_B32 | T_SAE_Z, 0xC8); }
+void vexpandpd(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x88); }
+void vexpandps(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x88); }
+void vextractf32x4(const Operand& op, const Ymm& r, uint8 imm) { if (!op.is(Operand::MEM | Operand::XMM)) throw Error(ERR_BAD_COMBINATION); opVex(r, 0, op, T_N16 | T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x19, imm); }
+void vextractf32x8(const Operand& op, const Zmm& r, uint8 imm) { if (!op.is(Operand::MEM | Operand::YMM)) throw Error(ERR_BAD_COMBINATION); opVex(r, 0, op, T_N32 | T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x1B, imm); }
+void vextractf64x2(const Operand& op, const Ymm& r, uint8 imm) { if (!op.is(Operand::MEM | Operand::XMM)) throw Error(ERR_BAD_COMBINATION); opVex(r, 0, op, T_N16 | T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX, 0x19, imm); }
+void vextractf64x4(const Operand& op, const Zmm& r, uint8 imm) { if (!op.is(Operand::MEM | Operand::YMM)) throw Error(ERR_BAD_COMBINATION); opVex(r, 0, op, T_N32 | T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX, 0x1B, imm); }
+void vextracti32x4(const Operand& op, const Ymm& r, uint8 imm) { if (!op.is(Operand::MEM | Operand::XMM)) throw Error(ERR_BAD_COMBINATION); opVex(r, 0, op, T_N16 | T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x39, imm); }
+void vextracti32x8(const Operand& op, const Zmm& r, uint8 imm) { if (!op.is(Operand::MEM | Operand::YMM)) throw Error(ERR_BAD_COMBINATION); opVex(r, 0, op, T_N32 | T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x3B, imm); }
+void vextracti64x2(const Operand& op, const Ymm& r, uint8 imm) { if (!op.is(Operand::MEM | Operand::XMM)) throw Error(ERR_BAD_COMBINATION); opVex(r, 0, op, T_N16 | T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX, 0x39, imm); }
+void vextracti64x4(const Operand& op, const Zmm& r, uint8 imm) { if (!op.is(Operand::MEM | Operand::YMM)) throw Error(ERR_BAD_COMBINATION); opVex(r, 0, op, T_N32 | T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX, 0x3B, imm); }
+void vfixupimmpd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x54, imm); }
+void vfixupimmps(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x54, imm); }
+void vfixupimmsd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F3A | T_EW1 | T_SAE_Z | T_MUST_EVEX, 0x55, imm); }
+void vfixupimmss(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F3A | T_EW0 | T_SAE_Z | T_MUST_EVEX, 0x55, imm); }
+void vfpclasspd(const Opmask& k, const Operand& op, uint8 imm) { if (!op.isBit(128|256|512)) throw Error(ERR_BAD_MEM_SIZE); Reg x = k; x.setBit(op.getBit()); opVex(x, 0, op, T_66 | T_0F3A | T_MUST_EVEX | T_YMM | T_EW1 | T_B64, 0x66, imm); }
+void vfpclassps(const Opmask& k, const Operand& op, uint8 imm) { if (!op.isBit(128|256|512)) throw Error(ERR_BAD_MEM_SIZE); Reg x = k; x.setBit(op.getBit()); opVex(x, 0, op, T_66 | T_0F3A | T_MUST_EVEX | T_YMM | T_EW0 | T_B32, 0x66, imm); }
+void vfpclasssd(const Opmask& k, const Operand& op, uint8 imm) { if (!op.isXMEM()) throw Error(ERR_BAD_MEM_SIZE); opVex(k, 0, op, T_66 | T_0F3A | T_MUST_EVEX | T_EW1 | T_N8, 0x67, imm); }
+void vfpclassss(const Opmask& k, const Operand& op, uint8 imm) { if (!op.isXMEM()) throw Error(ERR_BAD_MEM_SIZE); opVex(k, 0, op, T_66 | T_0F3A | T_MUST_EVEX | T_EW0 | T_N4, 0x67, imm); }
+void vgatherdpd(const Xmm& x, const Address& addr) { opGather2(x, addr, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_VSIB, 0x92, 1); }
+void vgatherdps(const Xmm& x, const Address& addr) { opGather2(x, addr, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_VSIB, 0x92, 0); }
+void vgatherpf0dpd(const Address& addr) { opGatherFetch(addr, zm1, T_N8 | T_66 | T_0F38 | T_EW1 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC6, Operand::YMM); }
+void vgatherpf0dps(const Address& addr) { opGatherFetch(addr, zm1, T_N4 | T_66 | T_0F38 | T_EW0 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC6, Operand::ZMM); }
+void vgatherpf0qpd(const Address& addr) { opGatherFetch(addr, zm1, T_N8 | T_66 | T_0F38 | T_EW1 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC7, Operand::ZMM); }
+void vgatherpf0qps(const Address& addr) { opGatherFetch(addr, zm1, T_N4 | T_66 | T_0F38 | T_EW0 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC7, Operand::ZMM); }
+void vgatherpf1dpd(const Address& addr) { opGatherFetch(addr, zm2, T_N8 | T_66 | T_0F38 | T_EW1 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC6, Operand::YMM); }
+void vgatherpf1dps(const Address& addr) { opGatherFetch(addr, zm2, T_N4 | T_66 | T_0F38 | T_EW0 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC6, Operand::ZMM); }
+void vgatherpf1qpd(const Address& addr) { opGatherFetch(addr, zm2, T_N8 | T_66 | T_0F38 | T_EW1 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC7, Operand::ZMM); }
+void vgatherpf1qps(const Address& addr) { opGatherFetch(addr, zm2, T_N4 | T_66 | T_0F38 | T_EW0 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC7, Operand::ZMM); }
+void vgatherqpd(const Xmm& x, const Address& addr) { opGather2(x, addr, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_VSIB, 0x93, 0); }
+void vgatherqps(const Xmm& x, const Address& addr) { opGather2(x, addr, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_VSIB, 0x93, 2); }
+void vgetexppd(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x42); }
+void vgetexpps(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x42); }
+void vgetexpsd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_EW1 | T_SAE_X | T_MUST_EVEX, 0x43); }
+void vgetexpss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_EW0 | T_SAE_X | T_MUST_EVEX, 0x43); }
+void vgetmantpd(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(x, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x26, imm); }
+void vgetmantps(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(x, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x26, imm); }
+void vgetmantsd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F3A | T_EW1 | T_SAE_X | T_MUST_EVEX, 0x27, imm); }
+void vgetmantss(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F3A | T_EW0 | T_SAE_X | T_MUST_EVEX, 0x27, imm); }
+void vinsertf32x4(const Ymm& r1, const Ymm& r2, const Operand& op, uint8 imm) {if (!(r1.getKind() == r2.getKind() && op.is(Operand::MEM | Operand::XMM))) throw Error(ERR_BAD_COMBINATION); opVex(r1, &r2, op, T_N16 | T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x18, imm); }
+void vinsertf32x8(const Zmm& r1, const Zmm& r2, const Operand& op, uint8 imm) {if (!op.is(Operand::MEM | Operand::YMM)) throw Error(ERR_BAD_COMBINATION); opVex(r1, &r2, op, T_N32 | T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x1A, imm); }
+void vinsertf64x2(const Ymm& r1, const Ymm& r2, const Operand& op, uint8 imm) {if (!(r1.getKind() == r2.getKind() && op.is(Operand::MEM | Operand::XMM))) throw Error(ERR_BAD_COMBINATION); opVex(r1, &r2, op, T_N16 | T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX, 0x18, imm); }
+void vinsertf64x4(const Zmm& r1, const Zmm& r2, const Operand& op, uint8 imm) {if (!op.is(Operand::MEM | Operand::YMM)) throw Error(ERR_BAD_COMBINATION); opVex(r1, &r2, op, T_N32 | T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX, 0x1A, imm); }
+void vinserti32x4(const Ymm& r1, const Ymm& r2, const Operand& op, uint8 imm) {if (!(r1.getKind() == r2.getKind() && op.is(Operand::MEM | Operand::XMM))) throw Error(ERR_BAD_COMBINATION); opVex(r1, &r2, op, T_N16 | T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x38, imm); }
+void vinserti32x8(const Zmm& r1, const Zmm& r2, const Operand& op, uint8 imm) {if (!op.is(Operand::MEM | Operand::YMM)) throw Error(ERR_BAD_COMBINATION); opVex(r1, &r2, op, T_N32 | T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x3A, imm); }
+void vinserti64x2(const Ymm& r1, const Ymm& r2, const Operand& op, uint8 imm) {if (!(r1.getKind() == r2.getKind() && op.is(Operand::MEM | Operand::XMM))) throw Error(ERR_BAD_COMBINATION); opVex(r1, &r2, op, T_N16 | T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX, 0x38, imm); }
+void vinserti64x4(const Zmm& r1, const Zmm& r2, const Operand& op, uint8 imm) {if (!op.is(Operand::MEM | Operand::YMM)) throw Error(ERR_BAD_COMBINATION); opVex(r1, &r2, op, T_N32 | T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX, 0x3A, imm); }
+void vmovdqa32(const Address& addr, const Xmm& x) { opAVX_X_XM_IMM(x, addr, T_66 | T_0F | T_EW0 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX | T_M_K, 0x7F); }
+void vmovdqa32(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F | T_EW0 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX, 0x6F); }
+void vmovdqa64(const Address& addr, const Xmm& x) { opAVX_X_XM_IMM(x, addr, T_66 | T_0F | T_EW1 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX | T_M_K, 0x7F); }
+void vmovdqa64(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F | T_EW1 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX, 0x6F); }
+void vmovdqu16(const Address& addr, const Xmm& x) { opAVX_X_XM_IMM(x, addr, T_F2 | T_0F | T_EW1 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX | T_M_K, 0x7F); }
+void vmovdqu16(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_F2 | T_0F | T_EW1 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX, 0x6F); }
+void vmovdqu32(const Address& addr, const Xmm& x) { opAVX_X_XM_IMM(x, addr, T_F3 | T_0F | T_EW0 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX | T_M_K, 0x7F); }
+void vmovdqu32(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_F3 | T_0F | T_EW0 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX, 0x6F); }
+void vmovdqu64(const Address& addr, const Xmm& x) { opAVX_X_XM_IMM(x, addr, T_F3 | T_0F | T_EW1 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX | T_M_K, 0x7F); }
+void vmovdqu64(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_F3 | T_0F | T_EW1 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX, 0x6F); }
+void vmovdqu8(const Address& addr, const Xmm& x) { opAVX_X_XM_IMM(x, addr, T_F2 | T_0F | T_EW0 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX | T_M_K, 0x7F); }
+void vmovdqu8(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_F2 | T_0F | T_EW0 | T_YMM | T_ER_X | T_ER_Y | T_ER_Z | T_MUST_EVEX, 0x6F); }
+void vp4dpwssd(const Zmm& z1, const Zmm& z2, const Address& addr) { opAVX_X_X_XM(z1, z2, addr, T_0F38 | T_F2 | T_EW0 | T_YMM | T_MUST_EVEX | T_N16, 0x52); }
+void vp4dpwssds(const Zmm& z1, const Zmm& z2, const Address& addr) { opAVX_X_X_XM(z1, z2, addr, T_0F38 | T_F2 | T_EW0 | T_YMM | T_MUST_EVEX | T_N16, 0x53); }
+void vpabsq(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_MUST_EVEX | T_EW1 | T_B64 | T_YMM, 0x1F); }
+void vpandd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0xDB); }
+void vpandnd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0xDF); }
+void vpandnq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0xDF); }
+void vpandq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0xDB); }
+void vpblendmb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x66); }
+void vpblendmd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x64); }
+void vpblendmq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x64); }
+void vpblendmw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x66); }
+void vpbroadcastb(const Xmm& x, const Reg8& r) { opVex(x, 0, r, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x7A); }
+void vpbroadcastd(const Xmm& x, const Reg32& r) { opVex(x, 0, r, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x7C); }
+void vpbroadcastmb2q(const Xmm& x, const Opmask& k) { opVex(x, 0, k, T_F3 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW1, 0x2A); }
+void vpbroadcastmw2d(const Xmm& x, const Opmask& k) { opVex(x, 0, k, T_F3 | T_0F38 | T_YMM | T_MUST_EVEX | T_EW0, 0x3A); }
+void vpbroadcastw(const Xmm& x, const Reg16& r) { opVex(x, 0, r, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x7B); }
+void vpcmpb(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x3F, imm); }
+void vpcmpd(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x1F, imm); }
+void vpcmpeqb(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F | T_YMM | T_MUST_EVEX, 0x74); }
+void vpcmpeqd(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F | T_YMM | T_MUST_EVEX | T_B32, 0x76); }
+void vpcmpeqq(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x29); }
+void vpcmpeqw(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F | T_YMM | T_MUST_EVEX, 0x75); }
+void vpcmpgtb(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F | T_YMM | T_MUST_EVEX, 0x64); }
+void vpcmpgtd(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x66); }
+void vpcmpgtq(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x37); }
+void vpcmpgtw(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F | T_YMM | T_MUST_EVEX, 0x65); }
+void vpcmpq(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x1F, imm); }
+void vpcmpub(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX, 0x3E, imm); }
+void vpcmpud(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x1E, imm); }
+void vpcmpuq(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x1E, imm); }
+void vpcmpuw(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX, 0x3E, imm); }
+void vpcmpw(const Opmask& k, const Xmm& x, const Operand& op, uint8 imm) { opAVX_K_X_XM(k, x, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX, 0x3F, imm); }
+void vpcompressd(const Operand& op, const Xmm& x) { opAVX_X_XM_IMM(x, op, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x8B); }
+void vpcompressq(const Operand& op, const Xmm& x) { opAVX_X_XM_IMM(x, op, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x8B); }
+void vpconflictd(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0xC4); }
+void vpconflictq(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0xC4); }
+void vpdpbusd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x50); }
+void vpdpbusds(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x51); }
+void vpdpwssd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x52); }
+void vpdpwssds(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x53); }
+void vpermb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x8D); }
+void vpermi2b(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x75); }
+void vpermi2d(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x76); }
+void vpermi2pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x77); }
+void vpermi2ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x77); }
+void vpermi2q(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x76); }
+void vpermi2w(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x75); }
+void vpermt2b(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x7D); }
+void vpermt2d(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x7E); }
+void vpermt2pd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x7F); }
+void vpermt2ps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x7F); }
+void vpermt2q(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x7E); }
+void vpermt2w(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x7D); }
+void vpermw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x8D); }
+void vpexpandb(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_N1 | T_66 | T_0F38 | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX, 0x62); }
+void vpexpandd(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x89); }
+void vpexpandq(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x89); }
+void vpexpandw(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_N2 | T_66 | T_0F38 | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX, 0x62); }
+void vpgatherdd(const Xmm& x, const Address& addr) { opGather2(x, addr, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_VSIB, 0x90, 0); }
+void vpgatherdq(const Xmm& x, const Address& addr) { opGather2(x, addr, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_VSIB, 0x90, 1); }
+void vpgatherqd(const Xmm& x, const Address& addr) { opGather2(x, addr, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_VSIB, 0x91, 2); }
+void vpgatherqq(const Xmm& x, const Address& addr) { opGather2(x, addr, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_VSIB, 0x91, 0); }
+void vplzcntd(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x44); }
+void vplzcntq(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x44); }
+void vpmadd52huq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0xB5); }
+void vpmadd52luq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0xB4); }
+void vpmaxsq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x3D); }
+void vpmaxuq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x3F); }
+void vpminsq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x39); }
+void vpminuq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x3B); }
+void vpmovb2m(const Opmask& k, const Xmm& x) { opVex(k, 0, x, T_F3 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW0, 0x29); }
+void vpmovd2m(const Opmask& k, const Xmm& x) { opVex(k, 0, x, T_F3 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW0, 0x39); }
+void vpmovdb(const Operand& op, const Xmm& x) { opVmov(op, x, T_N4 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x31, false); }
+void vpmovdw(const Operand& op, const Xmm& x) { opVmov(op, x, T_N8 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x33, true); }
+void vpmovm2b(const Xmm& x, const Opmask& k) { opVex(x, 0, k, T_F3 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW0, 0x28); }
+void vpmovm2d(const Xmm& x, const Opmask& k) { opVex(x, 0, k, T_F3 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW0, 0x38); }
+void vpmovm2q(const Xmm& x, const Opmask& k) { opVex(x, 0, k, T_F3 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW1, 0x38); }
+void vpmovm2w(const Xmm& x, const Opmask& k) { opVex(x, 0, k, T_F3 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW1, 0x28); }
+void vpmovq2m(const Opmask& k, const Xmm& x) { opVex(k, 0, x, T_F3 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW1, 0x39); }
+void vpmovqb(const Operand& op, const Xmm& x) { opVmov(op, x, T_N2 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x32, false); }
+void vpmovqd(const Operand& op, const Xmm& x) { opVmov(op, x, T_N8 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x35, true); }
+void vpmovqw(const Operand& op, const Xmm& x) { opVmov(op, x, T_N4 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x34, false); }
+void vpmovsdb(const Operand& op, const Xmm& x) { opVmov(op, x, T_N4 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x21, false); }
+void vpmovsdw(const Operand& op, const Xmm& x) { opVmov(op, x, T_N8 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x23, true); }
+void vpmovsqb(const Operand& op, const Xmm& x) { opVmov(op, x, T_N2 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x22, false); }
+void vpmovsqd(const Operand& op, const Xmm& x) { opVmov(op, x, T_N8 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x25, true); }
+void vpmovsqw(const Operand& op, const Xmm& x) { opVmov(op, x, T_N4 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x24, false); }
+void vpmovswb(const Operand& op, const Xmm& x) { opVmov(op, x, T_N8 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x20, true); }
+void vpmovusdb(const Operand& op, const Xmm& x) { opVmov(op, x, T_N4 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x11, false); }
+void vpmovusdw(const Operand& op, const Xmm& x) { opVmov(op, x, T_N8 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x13, true); }
+void vpmovusqb(const Operand& op, const Xmm& x) { opVmov(op, x, T_N2 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x12, false); }
+void vpmovusqd(const Operand& op, const Xmm& x) { opVmov(op, x, T_N8 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x15, true); }
+void vpmovusqw(const Operand& op, const Xmm& x) { opVmov(op, x, T_N4 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x14, false); }
+void vpmovuswb(const Operand& op, const Xmm& x) { opVmov(op, x, T_N8 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x10, true); }
+void vpmovw2m(const Opmask& k, const Xmm& x) { opVex(k, 0, x, T_F3 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW1, 0x29); }
+void vpmovwb(const Operand& op, const Xmm& x) { opVmov(op, x, T_N8 | T_N_VL | T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x30, true); }
+void vpmullq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x40); }
+void vpmultishiftqb(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x83); }
+void vpopcntb(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX, 0x54); }
+void vpopcntd(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x55); }
+void vpopcntq(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x55); }
+void vpopcntw(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX, 0x54); }
+void vpord(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0xEB); }
+void vporq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0xEB); }
+void vprold(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 1), x, op, T_66 | T_0F | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x72, imm); }
+void vprolq(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 1), x, op, T_66 | T_0F | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x72, imm); }
+void vprolvd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x15); }
+void vprolvq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x15); }
+void vprord(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 0), x, op, T_66 | T_0F | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x72, imm); }
+void vprorq(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 0), x, op, T_66 | T_0F | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x72, imm); }
+void vprorvd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x14); }
+void vprorvq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x14); }
+void vpscatterdd(const Address& addr, const Xmm& x) { opGather2(x, addr, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_M_K | T_VSIB, 0xA0, 0); }
+void vpscatterdq(const Address& addr, const Xmm& x) { opGather2(x, addr, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_M_K | T_VSIB, 0xA0, 1); }
+void vpscatterqd(const Address& addr, const Xmm& x) { opGather2(x, addr, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_M_K | T_VSIB, 0xA1, 2); }
+void vpscatterqq(const Address& addr, const Xmm& x) { opGather2(x, addr, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_M_K | T_VSIB, 0xA1, 0); }
+void vpshldd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x71, imm); }
+void vpshldq(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x71, imm); }
+void vpshldvd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x71); }
+void vpshldvq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x71); }
+void vpshldvw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX, 0x70); }
+void vpshldw(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX, 0x70, imm); }
+void vpshrdd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x73, imm); }
+void vpshrdq(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x73, imm); }
+void vpshrdvd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x73); }
+void vpshrdvq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x73); }
+void vpshrdvw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX, 0x72); }
+void vpshrdw(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX, 0x72, imm); }
+void vpshufbitqmb(const Opmask& k, const Xmm& x, const Operand& op) { opVex(k, &x, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x8F); }
+void vpsllvw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x12); }
+void vpsraq(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_X_XM(Xmm(x.getKind(), 4), x, op, T_66 | T_0F | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x72, imm); }
+void vpsraq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N16 | T_66 | T_0F | T_EW1 | T_YMM | T_MUST_EVEX, 0xE2); }
+void vpsravq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x46); }
+void vpsravw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x11); }
+void vpsrlvw(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x10); }
+void vpternlogd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x25, imm); }
+void vpternlogq(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x25, imm); }
+void vptestmb(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x26); }
+void vptestmd(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x27); }
+void vptestmq(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x27); }
+void vptestmw(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x26); }
+void vptestnmb(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x26); }
+void vptestnmd(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_F3 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x27); }
+void vptestnmq(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_F3 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x27); }
+void vptestnmw(const Opmask& k, const Xmm& x, const Operand& op) { opAVX_K_X_XM(k, x, op, T_F3 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x26); }
+void vpxord(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0xEF); }
+void vpxorq(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0xEF); }
+void vrangepd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x50, imm); }
+void vrangeps(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x50, imm); }
+void vrangesd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F3A | T_EW1 | T_SAE_X | T_MUST_EVEX, 0x51, imm); }
+void vrangess(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F3A | T_EW0 | T_SAE_X | T_MUST_EVEX, 0x51, imm); }
+void vrcp14pd(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x4C); }
+void vrcp14ps(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x4C); }
+void vrcp14sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_EW1 | T_MUST_EVEX, 0x4D); }
+void vrcp14ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_EW0 | T_MUST_EVEX, 0x4D); }
+void vrcp28pd(const Zmm& z, const Operand& op) { opAVX_X_XM_IMM(z, op, T_66 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW1 | T_B64 | T_SAE_Z, 0xCA); }
+void vrcp28ps(const Zmm& z, const Operand& op) { opAVX_X_XM_IMM(z, op, T_66 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW0 | T_B32 | T_SAE_Z, 0xCA); }
+void vrcp28sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_EW1 | T_SAE_X | T_MUST_EVEX, 0xCB); }
+void vrcp28ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_EW0 | T_SAE_X | T_MUST_EVEX, 0xCB); }
+void vreducepd(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(x, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B64, 0x56, imm); }
+void vreduceps(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(x, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_SAE_Z | T_MUST_EVEX | T_B32, 0x56, imm); }
+void vreducesd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F3A | T_EW1 | T_SAE_X | T_MUST_EVEX, 0x57, imm); }
+void vreducess(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F3A | T_EW0 | T_SAE_X | T_MUST_EVEX, 0x57, imm); }
+void vrndscalepd(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(x, op, T_66 | T_0F3A | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x09, imm); }
+void vrndscaleps(const Xmm& x, const Operand& op, uint8 imm) { opAVX_X_XM_IMM(x, op, T_66 | T_0F3A | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x08, imm); }
+void vrndscalesd(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F3A | T_EW1 | T_MUST_EVEX, 0x0B, imm); }
+void vrndscaless(const Xmm& x1, const Xmm& x2, const Operand& op, uint8 imm) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F3A | T_EW0 | T_MUST_EVEX, 0x0A, imm); }
+void vrsqrt14pd(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_B64, 0x4E); }
+void vrsqrt14ps(const Xmm& x, const Operand& op) { opAVX_X_XM_IMM(x, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_B32, 0x4E); }
+void vrsqrt14sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x4F); }
+void vrsqrt14ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX, 0x4F); }
+void vrsqrt28pd(const Zmm& z, const Operand& op) { opAVX_X_XM_IMM(z, op, T_66 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW1 | T_B64 | T_SAE_Z, 0xCC); }
+void vrsqrt28ps(const Zmm& z, const Operand& op) { opAVX_X_XM_IMM(z, op, T_66 | T_0F38 | T_MUST_EVEX | T_YMM | T_EW0 | T_B32 | T_SAE_Z, 0xCC); }
+void vrsqrt28sd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_EW1 | T_SAE_X | T_MUST_EVEX, 0xCD); }
+void vrsqrt28ss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_EW0 | T_SAE_X | T_MUST_EVEX, 0xCD); }
+void vscalefpd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW1 | T_YMM | T_ER_Z | T_MUST_EVEX | T_B64, 0x2C); }
+void vscalefps(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_66 | T_0F38 | T_EW0 | T_YMM | T_ER_Z | T_MUST_EVEX | T_B32, 0x2C); }
+void vscalefsd(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N8 | T_66 | T_0F38 | T_EW1 | T_ER_X | T_MUST_EVEX, 0x2D); }
+void vscalefss(const Xmm& x1, const Xmm& x2, const Operand& op) { opAVX_X_X_XM(x1, x2, op, T_N4 | T_66 | T_0F38 | T_EW0 | T_ER_X | T_MUST_EVEX, 0x2D); }
+void vscatterdpd(const Address& addr, const Xmm& x) { opGather2(x, addr, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_M_K | T_VSIB, 0xA2, 1); }
+void vscatterdps(const Address& addr, const Xmm& x) { opGather2(x, addr, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_M_K | T_VSIB, 0xA2, 0); }
+void vscatterpf0dpd(const Address& addr) { opGatherFetch(addr, zm5, T_N8 | T_66 | T_0F38 | T_EW1 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC6, Operand::YMM); }
+void vscatterpf0dps(const Address& addr) { opGatherFetch(addr, zm5, T_N4 | T_66 | T_0F38 | T_EW0 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC6, Operand::ZMM); }
+void vscatterpf0qpd(const Address& addr) { opGatherFetch(addr, zm5, T_N8 | T_66 | T_0F38 | T_EW1 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC7, Operand::ZMM); }
+void vscatterpf0qps(const Address& addr) { opGatherFetch(addr, zm5, T_N4 | T_66 | T_0F38 | T_EW0 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC7, Operand::ZMM); }
+void vscatterpf1dpd(const Address& addr) { opGatherFetch(addr, zm6, T_N8 | T_66 | T_0F38 | T_EW1 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC6, Operand::YMM); }
+void vscatterpf1dps(const Address& addr) { opGatherFetch(addr, zm6, T_N4 | T_66 | T_0F38 | T_EW0 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC6, Operand::ZMM); }
+void vscatterpf1qpd(const Address& addr) { opGatherFetch(addr, zm6, T_N8 | T_66 | T_0F38 | T_EW1 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC7, Operand::ZMM); }
+void vscatterpf1qps(const Address& addr) { opGatherFetch(addr, zm6, T_N4 | T_66 | T_0F38 | T_EW0 | T_MUST_EVEX | T_M_K | T_VSIB, 0xC7, Operand::ZMM); }
+void vscatterqpd(const Address& addr, const Xmm& x) { opGather2(x, addr, T_N8 | T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX | T_M_K | T_VSIB, 0xA3, 0); }
+void vscatterqps(const Address& addr, const Xmm& x) { opGather2(x, addr, T_N4 | T_66 | T_0F38 | T_EW0 | T_YMM | T_MUST_EVEX | T_M_K | T_VSIB, 0xA3, 2); }
+void vshuff32x4(const Ymm& y1, const Ymm& y2, const Operand& op, uint8 imm) { opAVX_X_X_XM(y1, y2, op, T_66 | T_0F3A | T_YMM | T_MUST_EVEX | T_EW0 | T_B32, 0x23, imm); }
+void vshuff64x2(const Ymm& y1, const Ymm& y2, const Operand& op, uint8 imm) { opAVX_X_X_XM(y1, y2, op, T_66 | T_0F3A | T_YMM | T_MUST_EVEX | T_EW1 | T_B64, 0x23, imm); }
+void vshufi32x4(const Ymm& y1, const Ymm& y2, const Operand& op, uint8 imm) { opAVX_X_X_XM(y1, y2, op, T_66 | T_0F3A | T_YMM | T_MUST_EVEX | T_EW0 | T_B32, 0x43, imm); }
+void vshufi64x2(const Ymm& y1, const Ymm& y2, const Operand& op, uint8 imm) { opAVX_X_X_XM(y1, y2, op, T_66 | T_0F3A | T_YMM | T_MUST_EVEX | T_EW1 | T_B64, 0x43, imm); }
+#ifdef XBYAK64
+void kmovq(const Opmask& k, const Reg64& r) { opVex(k, 0, r, T_L0 | T_0F | T_F2 | T_W1, 0x92); }
+void kmovq(const Reg64& r, const Opmask& k) { opVex(r, 0, k, T_L0 | T_0F | T_F2 | T_W1, 0x93); }
+void vpbroadcastq(const Xmm& x, const Reg64& r) { opVex(x, 0, r, T_66 | T_0F38 | T_EW1 | T_YMM | T_MUST_EVEX, 0x7C); }
+#endif
+#endif
diff --git a/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_util.h b/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_util.h
new file mode 100644
index 0000000000..8ef076e680
--- /dev/null
+++ b/thirdparty/oidn/mkl-dnn/src/cpu/xbyak/xbyak_util.h
@@ -0,0 +1,772 @@
+/*******************************************************************************
+* Copyright 2016-2019 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+/*******************************************************************************
+* Copyright (c) 2007 MITSUNARI Shigeo
+* All rights reserved.
+*
+* Redistribution and use in source and binary forms, with or without
+* modification, are permitted provided that the following conditions are met:
+*
+* Redistributions of source code must retain the above copyright notice, this
+* list of conditions and the following disclaimer.
+* Redistributions in binary form must reproduce the above copyright notice,
+* this list of conditions and the following disclaimer in the documentation
+* and/or other materials provided with the distribution.
+* Neither the name of the copyright owner nor the names of its contributors may
+* be used to endorse or promote products derived from this software without
+* specific prior written permission.
+*
+* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+* ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+* LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+* THE POSSIBILITY OF SUCH DAMAGE.
+*******************************************************************************/
+
+#ifndef XBYAK_XBYAK_UTIL_H_
+#define XBYAK_XBYAK_UTIL_H_
+
+/**
+	utility class and functions for Xbyak
+	Xbyak::util::Clock ; rdtsc timer
+	Xbyak::util::Cpu ; detect CPU
+	@note this header is UNDER CONSTRUCTION!
+*/
+#include "xbyak.h"
+
+#if defined(__i386__) || defined(__x86_64__) || defined(_M_IX86) || defined(_M_X64)
+	#define XBYAK_INTEL_CPU_SPECIFIC
+#endif
+
+#ifdef XBYAK_INTEL_CPU_SPECIFIC
+#ifdef _MSC_VER
+	#if (_MSC_VER < 1400) && defined(XBYAK32)
+		static inline __declspec(naked) void __cpuid(int[4], int)
+		{
+			__asm {
+				push	ebx
+				push	esi
+				mov		eax, dword ptr [esp + 4 * 2 + 8] // eaxIn
+				cpuid
+				mov		esi, dword ptr [esp + 4 * 2 + 4] // data
+				mov		dword ptr [esi], eax
+				mov		dword ptr [esi + 4], ebx
+				mov		dword ptr [esi + 8], ecx
+				mov		dword ptr [esi + 12], edx
+				pop		esi
+				pop		ebx
+				ret
+			}
+		}
+	#else
+		#include <intrin.h> // for __cpuid
+	#endif
+#else
+	#ifndef __GNUC_PREREQ
+	#define __GNUC_PREREQ(major, minor) ((((__GNUC__) << 16) + (__GNUC_MINOR__)) >= (((major) << 16) + (minor)))
+	#endif
+	#if __GNUC_PREREQ(4, 3) && !defined(__APPLE__)
+		#include <cpuid.h>
+	#else
+		#if defined(__APPLE__) && defined(XBYAK32) // avoid err : can't find a register in class `BREG' while reloading `asm'
+			#define __cpuid(eaxIn, a, b, c, d) __asm__ __volatile__("pushl %%ebx\ncpuid\nmovl %%ebp, %%esi\npopl %%ebx" : "=a"(a), "=S"(b), "=c"(c), "=d"(d) : "0"(eaxIn))
+			#define __cpuid_count(eaxIn, ecxIn, a, b, c, d) __asm__ __volatile__("pushl %%ebx\ncpuid\nmovl %%ebp, %%esi\npopl %%ebx" : "=a"(a), "=S"(b), "=c"(c), "=d"(d) : "0"(eaxIn), "2"(ecxIn))
+		#else
+			#define __cpuid(eaxIn, a, b, c, d) __asm__ __volatile__("cpuid\n" : "=a"(a), "=b"(b), "=c"(c), "=d"(d) : "0"(eaxIn))
+			#define __cpuid_count(eaxIn, ecxIn, a, b, c, d) __asm__ __volatile__("cpuid\n" : "=a"(a), "=b"(b), "=c"(c), "=d"(d) : "0"(eaxIn), "2"(ecxIn))
+		#endif
+	#endif
+#endif
+#endif
+
+namespace Xbyak { namespace util {
+
+typedef enum {
+   SmtLevel = 1,
+   CoreLevel = 2
+} IntelCpuTopologyLevel;
+
+/**
+	CPU detection class
+*/
+class Cpu {
+	uint64 type_;
+	//system topology
+	bool x2APIC_supported_;
+	static const size_t maxTopologyLevels = 2;
+	unsigned int numCores_[maxTopologyLevels];
+
+	static const unsigned int maxNumberCacheLevels = 10;
+	unsigned int dataCacheSize_[maxNumberCacheLevels];
+	unsigned int coresSharignDataCache_[maxNumberCacheLevels];
+	unsigned int dataCacheLevels_;
+
+	unsigned int get32bitAsBE(const char *x) const
+	{
+		return x[0] | (x[1] << 8) | (x[2] << 16) | (x[3] << 24);
+	}
+	unsigned int mask(int n) const
+	{
+		return (1U << n) - 1;
+	}
+	void setFamily()
+	{
+		unsigned int data[4] = {};
+		getCpuid(1, data);
+		stepping = data[0] & mask(4);
+		model = (data[0] >> 4) & mask(4);
+		family = (data[0] >> 8) & mask(4);
+		// type = (data[0] >> 12) & mask(2);
+		extModel = (data[0] >> 16) & mask(4);
+		extFamily = (data[0] >> 20) & mask(8);
+		if (family == 0x0f) {
+			displayFamily = family + extFamily;
+		} else {
+			displayFamily = family;
+		}
+		if (family == 6 || family == 0x0f) {
+			displayModel = (extModel << 4) + model;
+		} else {
+			displayModel = model;
+		}
+	}
+	unsigned int extractBit(unsigned int val, unsigned int base, unsigned int end)
+	{
+		return (val >> base) & ((1u << (end - base)) - 1);
+	}
+	void setNumCores()
+	{
+		if ((type_ & tINTEL) == 0) return;
+
+		unsigned int data[4] = {};
+
+		 /* CAUTION: These numbers are configuration as shipped by Intel. */
+		getCpuidEx(0x0, 0, data);
+		if (data[0] >= 0xB) {
+			 /*
+				if leaf 11 exists(x2APIC is supported),
+				we use it to get the number of smt cores and cores on socket
+
+				leaf 0xB can be zeroed-out by a hypervisor
+			*/
+			x2APIC_supported_ = true;
+			for (unsigned int i = 0; i < maxTopologyLevels; i++) {
+				getCpuidEx(0xB, i, data);
+				IntelCpuTopologyLevel level = (IntelCpuTopologyLevel)extractBit(data[2], 8, 15);
+				if (level == SmtLevel || level == CoreLevel) {
+					numCores_[level - 1] = extractBit(data[1], 0, 15);
+				}
+			}
+		} else {
+			/*
+				Failed to deremine num of cores without x2APIC support.
+				TODO: USE initial APIC ID to determine ncores.
+			*/
+			numCores_[SmtLevel - 1] = 0;
+			numCores_[CoreLevel - 1] = 0;
+		}
+
+	}
+	void setCacheHierarchy()
+	{
+		if ((type_ & tINTEL) == 0) return;
+		const unsigned int NO_CACHE = 0;
+		const unsigned int DATA_CACHE = 1;
+//		const unsigned int INSTRUCTION_CACHE = 2;
+		const unsigned int UNIFIED_CACHE = 3;
+		unsigned int smt_width = 0;
+		unsigned int logical_cores = 0;
+		unsigned int data[4] = {};
+
+		if (x2APIC_supported_) {
+			smt_width = numCores_[0];
+			logical_cores = numCores_[1];
+		}
+
+		/*
+			Assumptions:
+			the first level of data cache is not shared (which is the
+			case for every existing architecture) and use this to
+			determine the SMT width for arch not supporting leaf 11.
+			when leaf 4 reports a number of core less than numCores_
+			on socket reported by leaf 11, then it is a correct number
+			of cores not an upperbound.
+		*/
+		for (int i = 0; dataCacheLevels_ < maxNumberCacheLevels; i++) {
+			getCpuidEx(0x4, i, data);
+			unsigned int cacheType = extractBit(data[0], 0, 4);
+			if (cacheType == NO_CACHE) break;
+			if (cacheType == DATA_CACHE || cacheType == UNIFIED_CACHE) {
+				unsigned int actual_logical_cores = extractBit(data[0], 14, 25) + 1;
+				if (logical_cores != 0) { // true only if leaf 0xB is supported and valid
+					actual_logical_cores = (std::min)(actual_logical_cores, logical_cores);
+				}
+				assert(actual_logical_cores != 0);
+				dataCacheSize_[dataCacheLevels_] =
+					(extractBit(data[1], 22, 31) + 1)
+					* (extractBit(data[1], 12, 21) + 1)
+					* (extractBit(data[1], 0, 11) + 1)
+					* (data[2] + 1);
+				if (cacheType == DATA_CACHE && smt_width == 0) smt_width = actual_logical_cores;
+				assert(smt_width != 0);
+				// FIXME: check and fix number of cores sharing L3 cache for different configurations
+				// (HT-, 2 sockets), (HT-, 1 socket), (HT+, 2 sockets), (HT+, 1 socket)
+				coresSharignDataCache_[dataCacheLevels_] = (std::max)(actual_logical_cores / smt_width, 1u);
+				dataCacheLevels_++;
+			}
+		}
+	}
+
+public:
+	int model;
+	int family;
+	int stepping;
+	int extModel;
+	int extFamily;
+	int displayFamily; // family + extFamily
+	int displayModel; // model + extModel
+
+	unsigned int getNumCores(IntelCpuTopologyLevel level) {
+		if (level != SmtLevel && level != CoreLevel) throw Error(ERR_BAD_PARAMETER);
+		if (!x2APIC_supported_) throw Error(ERR_X2APIC_IS_NOT_SUPPORTED);
+		return (level == CoreLevel)
+			? numCores_[level - 1] / numCores_[SmtLevel - 1]
+			: numCores_[level - 1];
+	}
+
+	unsigned int getDataCacheLevels() const { return dataCacheLevels_; }
+	unsigned int getCoresSharingDataCache(unsigned int i) const
+	{
+		if (i >= dataCacheLevels_) throw  Error(ERR_BAD_PARAMETER);
+		return coresSharignDataCache_[i];
+	}
+	unsigned int getDataCacheSize(unsigned int i) const
+	{
+		if (i >= dataCacheLevels_) throw  Error(ERR_BAD_PARAMETER);
+		return dataCacheSize_[i];
+	}
+
+	/*
+		data[] = { eax, ebx, ecx, edx }
+	*/
+	static inline void getCpuid(unsigned int eaxIn, unsigned int data[4])
+	{
+#ifdef XBYAK_INTEL_CPU_SPECIFIC
+	#ifdef _MSC_VER
+		__cpuid(reinterpret_cast<int*>(data), eaxIn);
+	#else
+		__cpuid(eaxIn, data[0], data[1], data[2], data[3]);
+	#endif
+#else
+		(void)eaxIn;
+		(void)data;
+#endif
+	}
+	static inline void getCpuidEx(unsigned int eaxIn, unsigned int ecxIn, unsigned int data[4])
+	{
+#ifdef XBYAK_INTEL_CPU_SPECIFIC
+	#ifdef _MSC_VER
+		__cpuidex(reinterpret_cast<int*>(data), eaxIn, ecxIn);
+	#else
+		__cpuid_count(eaxIn, ecxIn, data[0], data[1], data[2], data[3]);
+	#endif
+#else
+		(void)eaxIn;
+		(void)ecxIn;
+		(void)data;
+#endif
+	}
+	static inline uint64 getXfeature()
+	{
+#ifdef XBYAK_INTEL_CPU_SPECIFIC
+	#ifdef _MSC_VER
+		return _xgetbv(0);
+	#else
+		unsigned int eax, edx;
+		// xgetvb is not support on gcc 4.2
+//		__asm__ volatile("xgetbv" : "=a"(eax), "=d"(edx) : "c"(0));
+		__asm__ volatile(".byte 0x0f, 0x01, 0xd0" : "=a"(eax), "=d"(edx) : "c"(0));
+		return ((uint64)edx << 32) | eax;
+	#endif
+#else
+		return 0;
+#endif
+	}
+	typedef uint64 Type;
+
+	static const Type NONE = 0;
+	static const Type tMMX = 1 << 0;
+	static const Type tMMX2 = 1 << 1;
+	static const Type tCMOV = 1 << 2;
+	static const Type tSSE = 1 << 3;
+	static const Type tSSE2 = 1 << 4;
+	static const Type tSSE3 = 1 << 5;
+	static const Type tSSSE3 = 1 << 6;
+	static const Type tSSE41 = 1 << 7;
+	static const Type tSSE42 = 1 << 8;
+	static const Type tPOPCNT = 1 << 9;
+	static const Type tAESNI = 1 << 10;
+	static const Type tSSE5 = 1 << 11;
+	static const Type tOSXSAVE = 1 << 12;
+	static const Type tPCLMULQDQ = 1 << 13;
+	static const Type tAVX = 1 << 14;
+	static const Type tFMA = 1 << 15;
+
+	static const Type t3DN = 1 << 16;
+	static const Type tE3DN = 1 << 17;
+	static const Type tSSE4a = 1 << 18;
+	static const Type tRDTSCP = 1 << 19;
+	static const Type tAVX2 = 1 << 20;
+	static const Type tBMI1 = 1 << 21; // andn, bextr, blsi, blsmsk, blsr, tzcnt
+	static const Type tBMI2 = 1 << 22; // bzhi, mulx, pdep, pext, rorx, sarx, shlx, shrx
+	static const Type tLZCNT = 1 << 23;
+
+	static const Type tINTEL = 1 << 24;
+	static const Type tAMD = 1 << 25;
+
+	static const Type tENHANCED_REP = 1 << 26; // enhanced rep movsb/stosb
+	static const Type tRDRAND = 1 << 27;
+	static const Type tADX = 1 << 28; // adcx, adox
+	static const Type tRDSEED = 1 << 29; // rdseed
+	static const Type tSMAP = 1 << 30; // stac
+	static const Type tHLE = uint64(1) << 31; // xacquire, xrelease, xtest
+	static const Type tRTM = uint64(1) << 32; // xbegin, xend, xabort
+	static const Type tF16C = uint64(1) << 33; // vcvtph2ps, vcvtps2ph
+	static const Type tMOVBE = uint64(1) << 34; // mobve
+	static const Type tAVX512F = uint64(1) << 35;
+	static const Type tAVX512DQ = uint64(1) << 36;
+	static const Type tAVX512_IFMA = uint64(1) << 37;
+	static const Type tAVX512IFMA = tAVX512_IFMA;
+	static const Type tAVX512PF = uint64(1) << 38;
+	static const Type tAVX512ER = uint64(1) << 39;
+	static const Type tAVX512CD = uint64(1) << 40;
+	static const Type tAVX512BW = uint64(1) << 41;
+	static const Type tAVX512VL = uint64(1) << 42;
+	static const Type tAVX512_VBMI = uint64(1) << 43;
+	static const Type tAVX512VBMI = tAVX512_VBMI; // changed by Intel's manual
+	static const Type tAVX512_4VNNIW = uint64(1) << 44;
+	static const Type tAVX512_4FMAPS = uint64(1) << 45;
+	static const Type tPREFETCHWT1 = uint64(1) << 46;
+	static const Type tPREFETCHW = uint64(1) << 47;
+	static const Type tSHA = uint64(1) << 48;
+	static const Type tMPX = uint64(1) << 49;
+	static const Type tAVX512_VBMI2 = uint64(1) << 50;
+	static const Type tGFNI = uint64(1) << 51;
+	static const Type tVAES = uint64(1) << 52;
+	static const Type tVPCLMULQDQ = uint64(1) << 53;
+	static const Type tAVX512_VNNI = uint64(1) << 54;
+	static const Type tAVX512_BITALG = uint64(1) << 55;
+	static const Type tAVX512_VPOPCNTDQ = uint64(1) << 56;
+
+	Cpu()
+		: type_(NONE)
+		, x2APIC_supported_(false)
+		, numCores_()
+		, dataCacheSize_()
+		, coresSharignDataCache_()
+		, dataCacheLevels_(0)
+	{
+		unsigned int data[4] = {};
+		const unsigned int& EAX = data[0];
+		const unsigned int& EBX = data[1];
+		const unsigned int& ECX = data[2];
+		const unsigned int& EDX = data[3];
+		getCpuid(0, data);
+		const unsigned int maxNum = EAX;
+		static const char intel[] = "ntel";
+		static const char amd[] = "cAMD";
+		if (ECX == get32bitAsBE(amd)) {
+			type_ |= tAMD;
+			getCpuid(0x80000001, data);
+			if (EDX & (1U << 31)) type_ |= t3DN;
+			if (EDX & (1U << 15)) type_ |= tCMOV;
+			if (EDX & (1U << 30)) type_ |= tE3DN;
+			if (EDX & (1U << 22)) type_ |= tMMX2;
+			if (EDX & (1U << 27)) type_ |= tRDTSCP;
+		}
+		if (ECX == get32bitAsBE(intel)) {
+			type_ |= tINTEL;
+			getCpuid(0x80000001, data);
+			if (EDX & (1U << 27)) type_ |= tRDTSCP;
+			if (ECX & (1U << 5)) type_ |= tLZCNT;
+			if (ECX & (1U << 8)) type_ |= tPREFETCHW;
+		}
+		getCpuid(1, data);
+		if (ECX & (1U << 0)) type_ |= tSSE3;
+		if (ECX & (1U << 9)) type_ |= tSSSE3;
+		if (ECX & (1U << 19)) type_ |= tSSE41;
+		if (ECX & (1U << 20)) type_ |= tSSE42;
+		if (ECX & (1U << 22)) type_ |= tMOVBE;
+		if (ECX & (1U << 23)) type_ |= tPOPCNT;
+		if (ECX & (1U << 25)) type_ |= tAESNI;
+		if (ECX & (1U << 1)) type_ |= tPCLMULQDQ;
+		if (ECX & (1U << 27)) type_ |= tOSXSAVE;
+		if (ECX & (1U << 30)) type_ |= tRDRAND;
+		if (ECX & (1U << 29)) type_ |= tF16C;
+
+		if (EDX & (1U << 15)) type_ |= tCMOV;
+		if (EDX & (1U << 23)) type_ |= tMMX;
+		if (EDX & (1U << 25)) type_ |= tMMX2 | tSSE;
+		if (EDX & (1U << 26)) type_ |= tSSE2;
+
+		if (type_ & tOSXSAVE) {
+			// check XFEATURE_ENABLED_MASK[2:1] = '11b'
+			uint64 bv = getXfeature();
+			if ((bv & 6) == 6) {
+				if (ECX & (1U << 28)) type_ |= tAVX;
+				if (ECX & (1U << 12)) type_ |= tFMA;
+				if (((bv >> 5) & 7) == 7) {
+					getCpuidEx(7, 0, data);
+					if (EBX & (1U << 16)) type_ |= tAVX512F;
+					if (type_ & tAVX512F) {
+						if (EBX & (1U << 17)) type_ |= tAVX512DQ;
+						if (EBX & (1U << 21)) type_ |= tAVX512_IFMA;
+						if (EBX & (1U << 26)) type_ |= tAVX512PF;
+						if (EBX & (1U << 27)) type_ |= tAVX512ER;
+						if (EBX & (1U << 28)) type_ |= tAVX512CD;
+						if (EBX & (1U << 30)) type_ |= tAVX512BW;
+						if (EBX & (1U << 31)) type_ |= tAVX512VL;
+						if (ECX & (1U << 1)) type_ |= tAVX512_VBMI;
+						if (ECX & (1U << 6)) type_ |= tAVX512_VBMI2;
+						if (ECX & (1U << 8)) type_ |= tGFNI;
+						if (ECX & (1U << 9)) type_ |= tVAES;
+						if (ECX & (1U << 10)) type_ |= tVPCLMULQDQ;
+						if (ECX & (1U << 11)) type_ |= tAVX512_VNNI;
+						if (ECX & (1U << 12)) type_ |= tAVX512_BITALG;
+						if (ECX & (1U << 14)) type_ |= tAVX512_VPOPCNTDQ;
+						if (EDX & (1U << 2)) type_ |= tAVX512_4VNNIW;
+						if (EDX & (1U << 3)) type_ |= tAVX512_4FMAPS;
+					}
+				}
+			}
+		}
+		if (maxNum >= 7) {
+			getCpuidEx(7, 0, data);
+			if (type_ & tAVX && (EBX & (1U << 5))) type_ |= tAVX2;
+			if (EBX & (1U << 3)) type_ |= tBMI1;
+			if (EBX & (1U << 8)) type_ |= tBMI2;
+			if (EBX & (1U << 9)) type_ |= tENHANCED_REP;
+			if (EBX & (1U << 18)) type_ |= tRDSEED;
+			if (EBX & (1U << 19)) type_ |= tADX;
+			if (EBX & (1U << 20)) type_ |= tSMAP;
+			if (EBX & (1U << 4)) type_ |= tHLE;
+			if (EBX & (1U << 11)) type_ |= tRTM;
+			if (EBX & (1U << 14)) type_ |= tMPX;
+			if (EBX & (1U << 29)) type_ |= tSHA;
+			if (ECX & (1U << 0)) type_ |= tPREFETCHWT1;
+		}
+		setFamily();
+		setNumCores();
+		setCacheHierarchy();
+	}
+	void putFamily() const
+	{
+		printf("family=%d, model=%X, stepping=%d, extFamily=%d, extModel=%X\n",
+			family, model, stepping, extFamily, extModel);
+		printf("display:family=%X, model=%X\n", displayFamily, displayModel);
+	}
+	bool has(Type type) const
+	{
+		return (type & type_) != 0;
+	}
+};
+
+class Clock {
+public:
+	static inline uint64 getRdtsc()
+	{
+#ifdef XBYAK_INTEL_CPU_SPECIFIC
+	#ifdef _MSC_VER
+		return __rdtsc();
+	#else
+		unsigned int eax, edx;
+		__asm__ volatile("rdtsc" : "=a"(eax), "=d"(edx));
+		return ((uint64)edx << 32) | eax;
+	#endif
+#else
+		// TODO: Need another impl of Clock or rdtsc-equivalent for non-x86 cpu
+		return 0;
+#endif
+	}
+	Clock()
+		: clock_(0)
+		, count_(0)
+	{
+	}
+	void begin()
+	{
+		clock_ -= getRdtsc();
+	}
+	void end()
+	{
+		clock_ += getRdtsc();
+		count_++;
+	}
+	int getCount() const { return count_; }
+	uint64 getClock() const { return clock_; }
+	void clear() { count_ = 0; clock_ = 0; }
+private:
+	uint64 clock_;
+	int count_;
+};
+
+#ifdef XBYAK64
+const int UseRCX = 1 << 6;
+const int UseRDX = 1 << 7;
+
+class Pack {
+	static const size_t maxTblNum = 15;
+	const Xbyak::Reg64 *tbl_[maxTblNum];
+	size_t n_;
+public:
+	Pack() : tbl_(), n_(0) {}
+	Pack(const Xbyak::Reg64 *tbl, size_t n) { init(tbl, n); }
+	Pack(const Pack& rhs)
+		: n_(rhs.n_)
+	{
+		for (size_t i = 0; i < n_; i++) tbl_[i] = rhs.tbl_[i];
+	}
+	Pack& operator=(const Pack& rhs)
+	{
+		n_ = rhs.n_;
+		for (size_t i = 0; i < n_; i++) tbl_[i] = rhs.tbl_[i];
+		return *this;
+	}
+	Pack(const Xbyak::Reg64& t0)
+	{ n_ = 1; tbl_[0] = &t0; }
+	Pack(const Xbyak::Reg64& t1, const Xbyak::Reg64& t0)
+	{ n_ = 2; tbl_[0] = &t0; tbl_[1] = &t1; }
+	Pack(const Xbyak::Reg64& t2, const Xbyak::Reg64& t1, const Xbyak::Reg64& t0)
+	{ n_ = 3; tbl_[0] = &t0; tbl_[1] = &t1; tbl_[2] = &t2; }
+	Pack(const Xbyak::Reg64& t3, const Xbyak::Reg64& t2, const Xbyak::Reg64& t1, const Xbyak::Reg64& t0)
+	{ n_ = 4; tbl_[0] = &t0; tbl_[1] = &t1; tbl_[2] = &t2; tbl_[3] = &t3; }
+	Pack(const Xbyak::Reg64& t4, const Xbyak::Reg64& t3, const Xbyak::Reg64& t2, const Xbyak::Reg64& t1, const Xbyak::Reg64& t0)
+	{ n_ = 5; tbl_[0] = &t0; tbl_[1] = &t1; tbl_[2] = &t2; tbl_[3] = &t3; tbl_[4] = &t4; }
+	Pack(const Xbyak::Reg64& t5, const Xbyak::Reg64& t4, const Xbyak::Reg64& t3, const Xbyak::Reg64& t2, const Xbyak::Reg64& t1, const Xbyak::Reg64& t0)
+	{ n_ = 6; tbl_[0] = &t0; tbl_[1] = &t1; tbl_[2] = &t2; tbl_[3] = &t3; tbl_[4] = &t4; tbl_[5] = &t5; }
+	Pack(const Xbyak::Reg64& t6, const Xbyak::Reg64& t5, const Xbyak::Reg64& t4, const Xbyak::Reg64& t3, const Xbyak::Reg64& t2, const Xbyak::Reg64& t1, const Xbyak::Reg64& t0)
+	{ n_ = 7; tbl_[0] = &t0; tbl_[1] = &t1; tbl_[2] = &t2; tbl_[3] = &t3; tbl_[4] = &t4; tbl_[5] = &t5; tbl_[6] = &t6; }
+	Pack(const Xbyak::Reg64& t7, const Xbyak::Reg64& t6, const Xbyak::Reg64& t5, const Xbyak::Reg64& t4, const Xbyak::Reg64& t3, const Xbyak::Reg64& t2, const Xbyak::Reg64& t1, const Xbyak::Reg64& t0)
+	{ n_ = 8; tbl_[0] = &t0; tbl_[1] = &t1; tbl_[2] = &t2; tbl_[3] = &t3; tbl_[4] = &t4; tbl_[5] = &t5; tbl_[6] = &t6; tbl_[7] = &t7; }
+	Pack(const Xbyak::Reg64& t8, const Xbyak::Reg64& t7, const Xbyak::Reg64& t6, const Xbyak::Reg64& t5, const Xbyak::Reg64& t4, const Xbyak::Reg64& t3, const Xbyak::Reg64& t2, const Xbyak::Reg64& t1, const Xbyak::Reg64& t0)
+	{ n_ = 9; tbl_[0] = &t0; tbl_[1] = &t1; tbl_[2] = &t2; tbl_[3] = &t3; tbl_[4] = &t4; tbl_[5] = &t5; tbl_[6] = &t6; tbl_[7] = &t7; tbl_[8] = &t8; }
+	Pack(const Xbyak::Reg64& t9, const Xbyak::Reg64& t8, const Xbyak::Reg64& t7, const Xbyak::Reg64& t6, const Xbyak::Reg64& t5, const Xbyak::Reg64& t4, const Xbyak::Reg64& t3, const Xbyak::Reg64& t2, const Xbyak::Reg64& t1, const Xbyak::Reg64& t0)
+	{ n_ = 10; tbl_[0] = &t0; tbl_[1] = &t1; tbl_[2] = &t2; tbl_[3] = &t3; tbl_[4] = &t4; tbl_[5] = &t5; tbl_[6] = &t6; tbl_[7] = &t7; tbl_[8] = &t8; tbl_[9] = &t9; }
+	Pack& append(const Xbyak::Reg64& t)
+	{
+		if (n_ == maxTblNum) {
+			fprintf(stderr, "ERR Pack::can't append\n");
+			throw Error(ERR_BAD_PARAMETER);
+		}
+		tbl_[n_++] = &t;
+		return *this;
+	}
+	void init(const Xbyak::Reg64 *tbl, size_t n)
+	{
+		if (n > maxTblNum) {
+			fprintf(stderr, "ERR Pack::init bad n=%d\n", (int)n);
+			throw Error(ERR_BAD_PARAMETER);
+		}
+		n_ = n;
+		for (size_t i = 0; i < n; i++) {
+			tbl_[i] = &tbl[i];
+		}
+	}
+	const Xbyak::Reg64& operator[](size_t n) const
+	{
+		if (n >= n_) {
+			fprintf(stderr, "ERR Pack bad n=%d(%d)\n", (int)n, (int)n_);
+			throw Error(ERR_BAD_PARAMETER);
+		}
+		return *tbl_[n];
+	}
+	size_t size() const { return n_; }
+	/*
+		get tbl[pos, pos + num)
+	*/
+	Pack sub(size_t pos, size_t num = size_t(-1)) const
+	{
+		if (num == size_t(-1)) num = n_ - pos;
+		if (pos + num > n_) {
+			fprintf(stderr, "ERR Pack::sub bad pos=%d, num=%d\n", (int)pos, (int)num);
+			throw Error(ERR_BAD_PARAMETER);
+		}
+		Pack pack;
+		pack.n_ = num;
+		for (size_t i = 0; i < num; i++) {
+			pack.tbl_[i] = tbl_[pos + i];
+		}
+		return pack;
+	}
+	void put() const
+	{
+		for (size_t i = 0; i < n_; i++) {
+			printf("%s ", tbl_[i]->toString());
+		}
+		printf("\n");
+	}
+};
+
+class StackFrame {
+#ifdef XBYAK64_WIN
+	static const int noSaveNum = 6;
+	static const int rcxPos = 0;
+	static const int rdxPos = 1;
+#else
+	static const int noSaveNum = 8;
+	static const int rcxPos = 3;
+	static const int rdxPos = 2;
+#endif
+	static const int maxRegNum = 14; // maxRegNum = 16 - rsp - rax
+	Xbyak::CodeGenerator *code_;
+	int pNum_;
+	int tNum_;
+	bool useRcx_;
+	bool useRdx_;
+	int saveNum_;
+	int P_;
+	bool makeEpilog_;
+	Xbyak::Reg64 pTbl_[4];
+	Xbyak::Reg64 tTbl_[maxRegNum];
+	Pack p_;
+	Pack t_;
+	StackFrame(const StackFrame&);
+	void operator=(const StackFrame&);
+public:
+	const Pack& p;
+	const Pack& t;
+	/*
+		make stack frame
+		@param sf [in] this
+		@param pNum [in] num of function parameter(0 <= pNum <= 4)
+		@param tNum [in] num of temporary register(0 <= tNum, with UseRCX, UseRDX) #{pNum + tNum [+rcx] + [rdx]} <= 14
+		@param stackSizeByte [in] local stack size
+		@param makeEpilog [in] automatically call close() if true
+
+		you can use
+		rax
+		gp0, ..., gp(pNum - 1)
+		gt0, ..., gt(tNum-1)
+		rcx if tNum & UseRCX
+		rdx if tNum & UseRDX
+		rsp[0..stackSizeByte - 1]
+	*/
+	StackFrame(Xbyak::CodeGenerator *code, int pNum, int tNum = 0, int stackSizeByte = 0, bool makeEpilog = true)
+		: code_(code)
+		, pNum_(pNum)
+		, tNum_(tNum & ~(UseRCX | UseRDX))
+		, useRcx_((tNum & UseRCX) != 0)
+		, useRdx_((tNum & UseRDX) != 0)
+		, saveNum_(0)
+		, P_(0)
+		, makeEpilog_(makeEpilog)
+		, p(p_)
+		, t(t_)
+	{
+		using namespace Xbyak;
+		if (pNum < 0 || pNum > 4) throw Error(ERR_BAD_PNUM);
+		const int allRegNum = pNum + tNum_ + (useRcx_ ? 1 : 0) + (useRdx_ ? 1 : 0);
+		if (tNum_ < 0 || allRegNum > maxRegNum) throw Error(ERR_BAD_TNUM);
+		const Reg64& _rsp = code->rsp;
+		saveNum_ = (std::max)(0, allRegNum - noSaveNum);
+		const int *tbl = getOrderTbl() + noSaveNum;
+		for (int i = 0; i < saveNum_; i++) {
+			code->push(Reg64(tbl[i]));
+		}
+		P_ = (stackSizeByte + 7) / 8;
+		if (P_ > 0 && (P_ & 1) == (saveNum_ & 1)) P_++; // (rsp % 16) == 8, then increment P_ for 16 byte alignment
+		P_ *= 8;
+		if (P_ > 0) code->sub(_rsp, P_);
+		int pos = 0;
+		for (int i = 0; i < pNum; i++) {
+			pTbl_[i] = Xbyak::Reg64(getRegIdx(pos));
+		}
+		for (int i = 0; i < tNum_; i++) {
+			tTbl_[i] = Xbyak::Reg64(getRegIdx(pos));
+		}
+		if (useRcx_ && rcxPos < pNum) code_->mov(code_->r10, code_->rcx);
+		if (useRdx_ && rdxPos < pNum) code_->mov(code_->r11, code_->rdx);
+		p_.init(pTbl_, pNum);
+		t_.init(tTbl_, tNum_);
+	}
+	/*
+		make epilog manually
+		@param callRet [in] call ret() if true
+	*/
+	void close(bool callRet = true)
+	{
+		using namespace Xbyak;
+		const Reg64& _rsp = code_->rsp;
+		const int *tbl = getOrderTbl() + noSaveNum;
+		if (P_ > 0) code_->add(_rsp, P_);
+		for (int i = 0; i < saveNum_; i++) {
+			code_->pop(Reg64(tbl[saveNum_ - 1 - i]));
+		}
+
+		if (callRet) code_->ret();
+	}
+	~StackFrame()
+	{
+		if (!makeEpilog_) return;
+		try {
+			close();
+		} catch (std::exception& e) {
+			printf("ERR:StackFrame %s\n", e.what());
+			//exit(1);
+		}
+	}
+private:
+	const int *getOrderTbl() const
+	{
+		using namespace Xbyak;
+		static const int tbl[] = {
+#ifdef XBYAK64_WIN
+			Operand::RCX, Operand::RDX, Operand::R8, Operand::R9, Operand::R10, Operand::R11, Operand::RDI, Operand::RSI,
+#else
+			Operand::RDI, Operand::RSI, Operand::RDX, Operand::RCX, Operand::R8, Operand::R9, Operand::R10, Operand::R11,
+#endif
+			Operand::RBX, Operand::RBP, Operand::R12, Operand::R13, Operand::R14, Operand::R15
+		};
+		return &tbl[0];
+	}
+	int getRegIdx(int& pos) const
+	{
+		assert(pos < maxRegNum);
+		using namespace Xbyak;
+		const int *tbl = getOrderTbl();
+		int r = tbl[pos++];
+		if (useRcx_) {
+			if (r == Operand::RCX) { return Operand::R10; }
+			if (r == Operand::R10) { r = tbl[pos++]; }
+		}
+		if (useRdx_) {
+			if (r == Operand::RDX) { return Operand::R11; }
+			if (r == Operand::R11) { return tbl[pos++]; }
+		}
+		return r;
+	}
+};
+#endif
+
+} } // end of util
+#endif
diff --git a/thirdparty/oidn/weights/rtlightmap_hdr.tza b/thirdparty/oidn/weights/rtlightmap_hdr.tza
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