QNNPACK

QNNPACK (Quantized Neural Networks PACKage) is a mobile-optimized library for low-precision high-performance neural network inference. QNNPACK provides implementation of convolutional, deconvolutional, and fully connected neural network operators on quantized 8-bit tensors.

QNNPACK is not intended to be directly used by machine learning researchers; instead it provides low-level performance primitives for high-level deep learning frameworks. As of today, QNNPACK is integrated in PyTorch 1.0 with Caffe2 graph representation.

Building

QNNPACK provides standard CMake-based build scripts.

Native compilation

Users are recommended to use scripts/build-local.sh script to build QNNPACK for the host machine.

Cross-compilation for Android

To cross-compile for Android, set $ANDROID_NDK environment variable (where $ANDROID_NDK is the path to Android NDK directorory, e.g. /opt/android-ndk-r15c) and use one of the scripts from the table below:

Notes:

• On armeabi-v7a qnnp_initialize will fail with qnnp_status_unsupported_hardware if the mobile CPU does not support ARM NEON. Don't set -DANDROID_ARM_NEON=1 for QNNPACK compilation as it can make qnnp_initialize crash on CPUs without ARM NEON.

Cross-compilation for iOS

To cross-compile for iOS, clone ios-cmake, and set $IOS_CMAKE_TOOLCHAIN_FILE environment variable (where $IOS_CMAKE_TOOLCHAIN_FILE is the path to ios.toolchain.cmake file in ios-cmake), and use one of the scripts from the table below:

Acknowledgements

QNNPACK is developed by Marat Dukhan, Yiming Wu, Hao Lu, and Bert Maher. We thank Andrew Tulloch and Yangqing Jia for advice during the development of QNNPACK.

License

QNNPACK is BSD licensed, as found in the LICENSE file.