commit | 6f8d4d36af389046f23d821b72b7a0c4f69dcdff | [log] [tgz] |
---|---|---|
author | Marat Dukhan <maratek@google.com> | Fri Oct 25 17:07:09 2019 -0700 |
committer | XNNPACK Team <xnnpack-github-robot@google.com> | Fri Oct 25 17:07:34 2019 -0700 |
tree | c762b4087966eb9bfa466a292365a410bf9611c5 | |
parent | b3c6c6ee5ca85470776ec77c0b9156f94f4e79aa [diff] |
RADDEXTEXP and VSCALEEXTEXP micro-kernels for AVX2 and AVX512F PiperOrigin-RevId: 276788915
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.
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. However, unlike QNNPACK, XNNPACK focuses entirely on floating-point operators, and its API is no longer compatible with QNNPACK.