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gerrit-public.fairphone.software / platform / external / XNNPACK / ba7c3bb0af12162ca0285e127f5beff3806f19da
commitba7c3bb0af12162ca0285e127f5beff3806f19da[log] [tgz]
authorMarat Dukhan <maratek@google.com>Sun Oct 27 19:53:54 2019 -0700
committerXNNPACK Team <xnnpack-github-robot@google.com>Sun Oct 27 19:54:20 2019 -0700
tree516ef610ced21fdd7cf5feadd5fc92db5634cbc0
parent918a4a66f30b5a062974bf7b7c868cd4ce3af822 [diff]
Merge generate-f32-gemminc.sh script into generate-f32-gemm.sh

GEMM and GEMMINC micro-kernels are generated from the same templates, and
commonly need to be re-generated together. Using two generator scripts have
sometimes caused the generated GEMM/GEMMINC micro-kernels get out of sync.

PiperOrigin-RevId: 276986833
3 files changed
tree: 516ef610ced21fdd7cf5feadd5fc92db5634cbc0
  1. bench/
  2. cmake/
  3. eval/
  4. include/
  5. models/
  6. scripts/
  7. src/
  8. test/
  9. third_party/
  10. tools/
  11. .bazelrc
  12. .gitignore
  13. BUILD.bazel
  14. build_defs.bzl
  15. CMakeLists.txt
  16. CONTRIBUTING.md
  17. emscripten.bzl
  18. LICENSE
  19. preamble.js.lds
  20. README.md
  21. WORKSPACE
README.md

XNNPACK

XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 (SSE2 level) platforms. XNNPACK is not intended for direct use by deep learning practitioners researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as MediaPipe, TensorFlow Lite, and TensorFlow.js.

Supported Architectures

  • ARM64 on Android and Linux
  • ARM on Android
  • WebAssembly MVP
  • WebAssembly SIMD (experimental)
  • x86 and x86-64 (up to SSE2 only) on Android, Linux, and macOS

Operator Coverage

XNNPACK implements the following neural network operators:

  • 2D Convolution (including grouped and depthwise)
  • 2D Deconvolution (AKA Transposed Convolution)
  • 2D Average Pooling
  • 2D Max Pooling
  • 2D ArgMax Pooling (Max Pooling + indices)
  • 2D Unpooling
  • Add (tensors of same shape)
  • Global Average Pooling
  • Channel Shuffle
  • Fully Connected
  • Clamp (includes ReLU and ReLU6)
  • HardSwish
  • PReLU

All operators in XNNPACK support NHWC layout, but additionally allow custom stride along the Channel dimension. Thus, operators can consume a subset of channels in the input tensor, and produce a subset of channels in the output tensor, providing a zero-cost Channel Split and Channel Concatenation operations.

Performance

The table below presents single-threaded performance of XNNPACK library on two generations of MobileNet models and three generations of Pixel phones.

ModelPixel, msPixel 2, msPixel 3a, ms
MobileNet v1 1.0X819388
MobileNet v2 1.0X485854

Benchmarked on October 9, 2019 with end2end_bench --benchmark_min_time=5 on an Android/ARM64 build (bazel build -c opt --config android_arm64 :end2end_bench) and neural network models with randomized weights and inputs.

Publications

Acknowledgements

XNNPACK is a based on QNNPACK library. However, unlike QNNPACK, XNNPACK focuses entirely on floating-point operators, and its API is no longer compatible with QNNPACK.