tag | 643e7fe3ca4d1d78661050198b3aef84193b0a5f | |
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tagger | The Android Open Source Project <initial-contribution@android.com> | Mon Apr 27 18:55:56 2020 -0700 |
object | 49649a9019f8f5d244052302bfe43e655c5f0f24 |
Platform Tools Release 30.0.0 (6405830)
commit | 49649a9019f8f5d244052302bfe43e655c5f0f24 | [log] [tgz] |
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author | android-build-prod (mdb) <android-build-team-robot@google.com> | Mon Mar 30 21:41:44 2020 +0000 |
committer | android-build-prod (mdb) <android-build-team-robot@google.com> | Mon Mar 30 21:41:44 2020 +0000 |
tree | c3bda7aa040ab8dd75411adb19eba16c4ad1704c | |
parent | df77828f26c730925bb71c58359f652fafc64858 [diff] | |
parent | 3518d4d79ba74e0d67b3784a8e3b8d1418c8a80b [diff] |
Snap for 6348162 from 3518d4d79ba74e0d67b3784a8e3b8d1418c8a80b to sdk-release Change-Id: Ia4f9895a3b04e45ebd20b6b2c0e433b1928f4946
XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 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 three generations of MobileNet models and three generations of Pixel phones.
Model | Pixel, ms | Pixel 2, ms | Pixel 3a, ms |
---|---|---|---|
MobileNet v1 1.0X | 81 | 89 | 88 |
MobileNet v2 1.0X | 48 | 55 | 54 |
MobileNet v3 Large | 40 | 44 | 44 |
MobileNet v3 Small | 12 | 14 | 14 |
The following table presents multi-threaded (using as many threads as there are big cores) performance of XNNPACK library on three generations of MobileNet models and three generations of Pixel phones.
Model | Pixel, ms | Pixel 2, ms | Pixel 3a, ms |
---|---|---|---|
MobileNet v1 1.0X | 45 | 27 | 46 |
MobileNet v2 1.0X | 28 | 18 | 28 |
MobileNet v3 Large | 23 | 16 | 24 |
MobileNet v3 Small | 7 | 6 | 8 |
Benchmarked on January 9, 2020 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.
The table below presents multi-threaded performance of XNNPACK library on three generations of MobileNet models and three generations of Raspberry Pi boards.
Model | RPi 2 (BCM2836), ms | RPi 3+ (BCM2837B0), ms | RPi 4 (BCM2711), ms |
---|---|---|---|
MobileNet v1 1.0X | 341 | 115 | 75 |
MobileNet v2 1.0X | 197 | 79 | 44 |
MobileNet v3 Large | 165 | 67 | 41 |
MobileNet v3 Small | 53 | 23 | 14 |
Benchmarked on February 12, 2020 with end2end-bench --benchmark_min_time=5
on a Raspbian Buster build with CMake (./scripts/build-local.sh
) 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.