v0.20.1

This is a patch release containing following changes to Intel MKL-DNN v0.20.0:

• Addressed static initialization order issue in bf16 converters (aef88b7)
• Fixed out of bound memory access in LRN implementation for Intel AVX2 (1a5eca7)

v1.0-rc

This is a release candidate for Intel MKL-DNN v1.0. Please provide feedback and report bugs in Github issues.

v0.20

Performance optimizations

• Improved GEMM-based convolutions performance.
• Improved softmax performance.
• Added arbitrary eltwise fusion support in GEMM-based convolutions and inner product.

New functionality

• Introduced bfloat16 data type support in reorders, (de-)convolution, pooling, batch normalization, local response normalization, eltwise, inner product, shuffle, sum, and concat. The implementation relies on new instructions targeting future Intel Xeon Scalable processor (codename Cooper Lake). On the processors with Intel AVX512 support bfloat16 arithmetic is emulated.

Thanks to the contributors

This release contains contributions from many Intel Performance Libraries developers. We would also like to thank everyone who asked questions and reported issues.

v0.20-rc

This is a release candidate for Intel MKL-DNN v0.20. Please provide feedback and report bugs in Github issues.

v0.19

Performance optimizations

• Improved int8 convolutions performance for small batch cases.
• Improved performance of grouped convolutions with the number of channels in a group being multiple of 4.
• Improved GEMM-based convolutions performance.
• Improved performance of RNN cells.
• Improved SGEMM performance for Intel® AVX2 and Intel® AVX512 instruction sets.

New functionality

• Introduced int8 support in 1D convolution, deconvolution, inner product, and batch normalization

Usability improvements

• Added CMake package configuration file

Thanks to the contributors

This release contains contributions from many Intel Performance Libraries developers as well as Haitao Feng @fenghaitao, Klein Guillaume @guillaumekln, Alexander Grund @Flamefire, Rui Xia @harrysummer, and Shigeo Mitsunari @herumi. We would also like to thank everyone who asked questions and reported issues.

v0.19-rc

This is a release candidate for MKL-DNN v0.19. Please provide feedback and report bugs in Github issues.

v1.0-pc2

This is preview candidate 2 for Intel MKL-DNN v1.0.

It introduces support for Intel(R) Processor Graphics and implements changes announced in v1.0 RFC. Please provide feedback and report bugs in Github issues.

v0.18.1

This is a patch release containing following changes to Intel MKL-DNN v0.18.0:

• Fix bug in build system to do not break transitive linking when being used as a subproject (245b331)
• Fix fix bias conversion in int8 gemm-based convolution (9670998)

v1.0-pc

This is preview candidate for MKL-DNN v1.0.

The preview candidate implements changes announced in v1.0 RFC. Please provide feedback and report bugs in Github issues.

v0.18

Performance optimizations

• Improved RNN functionality performance.
• Improved performance of GEMM-based convolutions
• Improved performance of backpropagation for stided convolutions on processors with Intel® AVX2 support.
• Improved performance of the gemm_s8u8s32 and gemm_s8s8s32 functions on processors with Intel® AVX512 and Intel® AVX512-DL Boost instruction sets.
• Improved inner product performance on processors with Intel AVX512 and Intel AVX512-DL Boost instruction sets.
• Improved performance of int8 convolutions and deconvolutions on processors with Intel AVX512 and Intel AVX512-DL Boost instruction sets.

New functionality

• Convolutions support arbitrary elementwise operations in postops.
• Introduced support of signed int8 data for the inner product primitive.
• Introduced int8 LSTM cell support.
• Introduced automatic dispatching between the direct and Winograd convolution algorithms.

API deprecations and breaking changes

• Previously deprecated APIs were removed:
  • relu function
  • convolution_relu function
  • double precision scales support in sum
  • negative_slope parameter in eltwise
  • omit_stats flag in batch normalization

Usability improvements

• Added library version information to verbose output and to headers.
• Added information about detected instruction set to verbose output.
• Introduced mkldnn_version function.
• Added APIs to override behaviors controlled via environment variables, including verbose mode and JIT dump.

Thanks to the contributors

This release contains contributions from many Intel Performance Libraries developers as well as Ruslan Baratov @ruslo, Konstantin Basargin @basargin, Jacek Czaja @jczaja, Eugene Zhulenev @ezhulenev, Haitao Feng @fenghaitao, Yinghai Liu @yinghai, Masahiro Sakai @msakai, and Alexander Grund @Flamefire. We would also like to thank everyone who asked questions and reported issues.