commit | 0126feb3f84bee0be67860f1826f156c8fc13b2c | [log] [tgz] |
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author | XNNPACK Team <xnnpack-github-robot@google.com> | Tue Dec 10 18:02:01 2019 -0800 |
committer | XNNPACK Team <xnnpack-github-robot@google.com> | Tue Dec 10 18:02:01 2019 -0800 |
tree | 9cfe9944e4bdabd956d20a3546317fff8f36ee59 | |
parent | 9a88efe2d84fef93eb2b8acb6f0ac8f3cacee8b5 [diff] | |
parent | d94b8566bca6b5502d74b563dfba0da4e2e1eb91 [diff] |
Merge pull request #267 from AshkanAliabadi:build PiperOrigin-RevId: 284889859
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 and researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as MediaPipe, TensorFlow Lite, and TensorFlow.js.
XNNPACK implements the following neural network operators:
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.
The table below presents single-threaded performance of XNNPACK library on two generations of MobileNet models and three generations of Pixel phones.
Model | Pixel, ms | Pixel 2, ms | Pixel 3a, ms |
---|---|---|---|
MobileNet v1 1.0X | 81 | 93 | 88 |
MobileNet v2 1.0X | 48 | 58 | 54 |
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.
XNNPACK is a based on QNNPACK library. Unlike QNNPACK, XNNPACK focuses entirely on floating-point operators, and its API is no longer compatible with QNNPACK.