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v0.10
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@vpirogov vpirogov released this 11 Aug 20:18
v0.10
fbd4d7b

Performance optimizations

  • Improved performance on processors with Intel(R) AVX512 instruction set support
  • Added optimizations for future Intel(R) Xeon Phi(TM) processors with AVX512_4FMAPS and AVX512_4VNNIW instruction groups support

New functionality

  • Added support of Winograd convolution algorithm. The implementation has initial optimizations for Intel Xeon Phi processors with Intel AVX512 instruction set support.
  • Introduced elementwise primitive with 3 types of activations: ReLU (rectified linear unit), ELU (parametric exponential linear unit) and TANH (hyperbolic tangent non-linearity).
  • Added dilation support to forward convolution. The implementation is optimized for processors with Intel(R) SSE 4.2 and Intel(R) AVX instruction sets support.
  • Feature preview: Added int16 support in convolution, ReLU, pooling and inner product for training. Added optimized s16s16s32 convolution flavor for future Intel Xeon Phi processors.
  • Feature preview: Added optimized pooling with int8 support.

Usability improvements

  • Added Windows* support.
  • Added benchdnn test suite for comprehensive functional and performance testing of convolutions. The suite supports int8, int16 and fp32 data types.
  • Primitive implementation information can be queried using impl_info_str.

Deprecated functionality

  • ReLU primitive is deprecated and will be removed in future releases. Activation functions including ReLU are implemented in elementwise primitive.

Thanks to the contributors

This release contains contributions from many Intel(R) Performance Libraries developers as well as Guenther Schmuelling @guschmue, Yong Wu, Dmitriy Gorokhov, Menon Jaikrishnan, Erik @kruus, Zhong Z Cao @4pao, Gleb Gladilov and @tensor-tang. We would also like to thank everyone who asked questions and reported issues.

* Other names and brands may be claimed as the property of others.

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