tag | 3d196ef6df35145f9d5624fd33d77c01081b6638 | |
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tagger | The Android Open Source Project <initial-contribution@android.com> | Wed Mar 18 12:32:06 2020 -0700 |
object | 5261c034e1187ca8a1c36b2dfbc8d0fbe9c13a6b |
Android R Preview 2 (RPP2.200227.009)
commit | 5261c034e1187ca8a1c36b2dfbc8d0fbe9c13a6b | [log] [tgz] |
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author | Miao Wang <miaowang@google.com> | Tue Mar 17 00:15:02 2020 +0000 |
committer | Gerrit Code Review <noreply-gerritcodereview@google.com> | Tue Mar 17 00:15:02 2020 +0000 |
tree | a0cc24b66cd3da51d5550321cc3b6160b806e7ba | |
parent | 25a9750999bd53c266ea2e0ef337c00bd431b2a4 [diff] | |
parent | 2534c2f5a9fde7b2ed405233dd18b2bc68913710 [diff] |
Merge "Update Android.bp after XNNPACK rebase"
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.