tag | 6b87e83b630a9e96327ab6cd61a0ea1a93a90512 | |
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tagger | The Android Open Source Project <initial-contribution@android.com> | Mon Apr 29 09:53:43 2024 -0700 |
object | 60c1d5b69d4a625eefc68c34c0bc6f33958e42e9 |
aml_art_341514450 (11720836,com.google.android.art,com.google.android.go.art)
commit | 60c1d5b69d4a625eefc68c34c0bc6f33958e42e9 | [log] [tgz] |
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author | Android Build Coastguard Worker <android-build-coastguard-worker@google.com> | Fri Jul 07 04:46:40 2023 +0000 |
committer | Android Build Coastguard Worker <android-build-coastguard-worker@google.com> | Fri Jul 07 04:46:40 2023 +0000 |
tree | cae5d9e85bae203795be4e415b574724d8b1262a | |
parent | 80f0be1d1faae31c69d695fd22dde3a5b6d285e9 [diff] | |
parent | ec808b18debf1b5c7a22dd088f64dc6f37ee8549 [diff] |
Snap for 10453563 from ec808b18debf1b5c7a22dd088f64dc6f37ee8549 to mainline-art-release Change-Id: I4aa1c8ae0c0c15b265c8f66e90d50e51f002ddc9
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 TensorFlow Lite, TensorFlow.js, PyTorch, and MediaPipe.
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 |
---|---|---|---|
FP32 MobileNet v1 1.0X | 82 | 86 | 88 |
FP32 MobileNet v2 1.0X | 49 | 53 | 55 |
FP32 MobileNet v3 Large | 39 | 42 | 44 |
FP32 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 |
---|---|---|---|
FP32 MobileNet v1 1.0X | 43 | 27 | 46 |
FP32 MobileNet v2 1.0X | 26 | 18 | 28 |
FP32 MobileNet v3 Large | 22 | 16 | 24 |
FP32 MobileNet v3 Small | 7 | 6 | 8 |
Benchmarked on March 27, 2020 with end2end_bench --benchmark_min_time=5
on an Android/ARM64 build with Android NDK r21 (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 Zero W (BCM2835), ms | RPi 2 (BCM2836), ms | RPi 3+ (BCM2837B0), ms | RPi 4 (BCM2711), ms | RPi 4 (BCM2711, ARM64), ms |
---|---|---|---|---|---|
FP32 MobileNet v1 1.0X | 3919 | 302 | 114 | 72 | 77 |
FP32 MobileNet v2 1.0X | 1987 | 191 | 79 | 41 | 46 |
FP32 MobileNet v3 Large | 1658 | 161 | 67 | 38 | 40 |
FP32 MobileNet v3 Small | 474 | 50 | 22 | 13 | 15 |
INT8 MobileNet v1 1.0X | 2589 | 128 | 46 | 29 | 24 |
INT8 MobileNet v2 1.0X | 1495 | 82 | 30 | 20 | 17 |
Benchmarked on Feb 8, 2022 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. INT8 inference was evaluated on per-channel quantization schema.
XNNPACK is a based on QNNPACK library. Over time its codebase diverged a lot, and XNNPACK API is no longer compatible with QNNPACK.