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Acceleration package for neural networks on multi-core CPUs

Acceleration package for neural networks on multi-core CPUs

NNPACK

NNPACK is an acceleration package for neural network computations. NNPACK aims to provide high-performance implementations of convnet layers for multi-core CPUs.

NNPACK is not intended to be directly used by machine learning researchers; instead it provides low-level performance primitives to be leveraged by higher-level frameworks, such as Torch , Caffe , Tensorflow ,Theano, andMocha.jl.

Requirements

  • Linux or OS X system
    • Additionally, NNPACK supports cross-compilation for Native Client to run inside Chrome browser
  • x86-64 processor with AVX2 instruction set
    • NNPACK is optimized for Intel Skylake, but can run on Haswell & Broadwell processors too

Features

  • Fast convolution algorithms based on Fourier transform and Winograd transform.

    • Forward propagation performance on Intel Core i7 6700K vs BVLC Caffe master branch as of March 24, 2016 (protobufs fromconvnet-benchmarks, integration viacaffe-nnpack):

      Library Caffe NNPACK NNPACK NNPACK
      Algorithm im2col + sgemm FFT-8×8 FFT-16×16 Winograd F(6×6, 3×3)
      AlexNet:conv2 315 ms 129 ms 86 ms N/A
      AlexNet:conv3 182 ms 87 ms 44 ms 70 ms
      AlexNet:conv4 264 ms 109 ms 56 ms 89 ms
      AlexNet:conv5 177 ms 77 ms 40 ms 64 ms
      VGG-A:conv1 255 ms 303 ms 260 ms 404 ms
      VGG-A:conv2 902 ms 369 ms 267 ms 372 ms
      VGG-A:conv3.1 566 ms 308 ms 185 ms 279 ms
      VGG-A:conv3.2 1091 ms 517 ms 309 ms 463 ms
      VGG-A:conv4.1 432 ms 228 ms 149 ms 188 ms
      VGG-A:conv4.2 842 ms 402 ms 264 ms 329 ms
      VGG-A:conv5 292 ms 141 ms 83 ms 114 ms
      OverFeat:conv2 424 ms 158 ms 73 ms N/A
      OverFeat:conv3 250 ms 69 ms 74 ms 54 ms
      OverFeat:conv4 927 ms 256 ms 272 ms 173 ms
      OverFeat:conv5 1832 ms 466 ms 524 ms 315 ms
  • Built-in expert-tuned kernels with very high performance:

    • Fast Fourier transform
    • Winograd transform
    • Matrix-matrix multiplication (GEMM)
    • Matrix-vector multiplication (GEMV)
    • Max-pooling.
  • Multi-threaded SIMD-aware implementations of neural network layers.
  • Implemented in C99 and Python without external dependencies.
  • Extensive unit tests using C++ and Google Test.
  • Supports Native Client target and outperforms native Caffe/CPU when running inside Chrome.

Layers

  • Convolutional layer
    • Only convolutional layers without stride are currently supported
    • Training-optimized forward propagation ( nnp_convolution_output )
    • Training-optimized backward input gradient propagation ( nnp_convolution_input_gradient )
    • Training-optimized backward kernel gradient propagation ( nnp_convolution_kernel_gradient )
    • Inference-optimized forward propagation ( nnp_convolution_inference ) is a work-in-progress
  • Fully-connected layer
    • Training-optimized forward propagation ( nnp_fully_connected_output )
    • Inference-optimized forward propagation ( nnp_fully_connected_inference )
  • Max pooling layer
    • Only 2×2 pooling is currently supported
    • Forward propagation, both for training and inference, ( nnp_max_pooling_output )

Building

NNPACK can be build on OS X and Linux.

Download, build and install PeachPy

git clone https://github.com/Maratyszcza/PeachPy.git cd PeachPy [sudo] pip install --upgrade -r requirements.txt python setup.py generate [sudo] pip install --upgrade .

Install ninja build system and ninja-syntax Python module

sudo apt-get install ninja-build || brew install ninja [sudo] pip install ninja-syntax

Then clone and build NNPACK itself

git clone --recursive https://github.com/Maratyszcza/NNPACK.git cd NNPACK python ./configure.py ninja

Cross-compilation for Native Client

  • Download and setup Native Client SDK
  • Set NACL_SDK_ROOT variable to a versioned SDK directory (e.g. ~/nacl_sdk/pepper_49 ).
  • Configure NNPACK with --host=x86_64-nacl-glibc or --host=x86_64-nacl-newlib (recommended) option.

Testing

NNPACK contains extensive test suite for transformation and neural network layers.

After configuration type ninja -t targets and choose the unit test that matches your subsystem of interest.

Packaging

Binary packages need to distribute two files: include/nnpack.h and lib/libnnpack.a .

Acknowledgements

Acceleration package for neural networks on multi-core CPUs Acceleration package for neural networks on multi-core CPUs

The library is developed by Marat Dukhan of Georgia Tech with extensive advice from Nicolas Vasilache and Soumith Chintala of Facebook Artificial Intelligence Research. Andrew Tulloch of Facebook Artificial Intelligence Research contributed Caffe integration. We thankAndrew Lavin for fruitful discussions on Winograd transform-based implementations. NNPACK is a research project at Richard Vuduc ‘s HPC Garage lab in the Georgia Institute of Technology, College of Computing, School of Computational Science and Engineering.

This material is based upon work supported by the U.S. National Science Foundation (NSF) Award Number 1339745. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of NSF.

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