commit | ef4416e0ca4c7f9e259572dae9fa993fc6cc8687 | [log] [tgz] |
---|---|---|
author | Marat Dukhan <maratek@google.com> | Thu Oct 31 13:44:40 2019 -0700 |
committer | XNNPACK Team <xnnpack-github-robot@google.com> | Thu Oct 31 13:45:11 2019 -0700 |
tree | 17f6e9d6057d8446ed0400658591ee0b011313aa | |
parent | e72e2872fa638fc84d8e82a062ee3341e99fbdd0 [diff] |
End-to-end benchmarks for DWCONV microkernels Evaluate performance of MobileNet v1/v2 models depending on DWCONV microkernels used. PiperOrigin-RevId: 277792619
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. Unlike QNNPACK, XNNPACK focuses entirely on floating-point operators, and its API is no longer compatible with QNNPACK.