commit | 1f8a2b89e2eec9d7c65c02edbe5337e1679e35ee | [log] [tgz] |
---|---|---|
author | Ashkan Aliabadi <ashkanaliabadi@fb.com> | Wed Nov 20 11:27:00 2019 -0800 |
committer | Ashkan Aliabadi <ashkanaliabadi@fb.com> | Wed Nov 20 11:27:00 2019 -0800 |
tree | 7d86e01804748fef7e9092de8cdc430aebdfdb38 | |
parent | f866a45bd4b448c1c679a940db64f76bf9829b2e [diff] |
Add memory.c to CMake.
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.