v0.9

Performance optimizations

• Improved performance on processors with Intel(R) AVX2 instruction set support
• Improved performance on processors with Intel(R) AVX512 instruction set support
• Added optimizations for Intel(R) Xeon processors with Intel AVX512 instruction set support
• Added inference optimizations for Intel(R) Atom processors with Intel(R) SSE4.2 support
• Added JIT implementation of SGEMM for Intel(R) Xeon Phi(TM) processors.

New functionality

• Average pooling supports 'exclude padding' mode
• LRN supports arbitrary local size
• Feature preview: Added int8 support in convolution, ReLU, pooling and inner product. Added optimized u8s8u8 convolution flavor for Intel Xeon processors with Intel AVX512 instruction set support.
• Feature preview: Added int16 support in convolution, ReLU, pooling and inner product. Added optimized s16s16s32 convolution flavor for future Intel Xeon Phi processors.

Usability improvements

• Improved build system to enable integration to other projects.
• Intel(R) OpenMP runtime is used when the library built with binary dependency
• Feature based dispatcher added to support wide range of Intel(R) processors and compatible

Thanks to the contributors

This release contains contributions from many Intel(R) Performance Libraries developers as well as Ismo Puustinen @ipuustin, Dmitry Gorokhov, Vladimir Dudnik @vladimir-dudnik, @pruthviIntel, and Chris Olivier @cjolivier01. We would also like to thank everyone who asked questions and reported issues.

v0.7

Changes:

• Improved performance on processors with Intel(R) AVX2 instruction set support
• Improved performance on processors with Intel(R) AVX512 instruction set support
• Extended backward propagation optimizations for Intel(R) AVX2 and Intel AVX512 instruction sets
• Added SGEMM-based reference convolution implementation significantly improving performance for cases not covered by JIT convolution
• Added JIT version of SGEMM function for Intel(R) AVX2 instruction set. This change allows to build optimized Intel(R) MKL-DNN without binary component.
• Added backward propagation examples

v0.5

Changes:

• Added runtime CPUID dispatching mechanism
• Added initial Intel(R) AVX512 optimizations
• Improved performance on processors with Intel(R) AVX2 instruction set support
• Added initial backward propagation optimizations
• Extended batch normalization primitive API with scale/shift and mean/variance parameters
• Updated XByak to version 5.40

v0.3

Changes:

• Added sum primitive
• Added backward propagation reference implementation

v0.2

Changes:

• Added batch normalization
• Added split and concat
• Added linear response normalization inside the channel
• Added average pooling

v0.1

This release is a technical preview with functionality limited to AlexNet and VGG topologies forward path.