commit | 95bebc93a917b7c079a8125361c4d66be68e55bf | [log] [tgz] |
---|---|---|
author | Frank Barchard <fbarchard@google.com> | Fri Nov 15 18:18:28 2019 -0800 |
committer | XNNPACK Team <xnnpack-github-robot@google.com> | Fri Nov 15 18:19:01 2019 -0800 |
tree | 36a3e9dad547143d5dba43671cf3f07b21b1472f | |
parent | 179ac8589a4f6a05db262e42ac4544742ed32aac [diff] |
Benchmarks rename sgemm and sppmm to f32_gemm and f32_ppmm PiperOrigin-RevId: 280775266
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.