Important From release 22.05: 'master' branch has been replaced with'main' following our inclusive language update, more information here. Important From release 22.08: armv7a with Android build will no longer be tested or maintained. The Compute Library is a collection of low-level machine learning functions optimized for Arm Cortex -A, Arm Neoverse and Arm Mali GPUs architectures. The library provides superior performance to other open source alternatives and immediate support for new Arm technologies e.g. Note: The documentation includes the reference API, changelogs, build guide, contribution guide, errata, etc.

Automated tuning of compute kernels is a popular area of research, mainly focused on finding optimal kernel parameters for a problem with fixed input sizes. This approach is good for deploying machine learning models, where the network topology is constant, but machine learning research often involves changing network topologies and hyperparameters. Traditional kernel auto-tuning has limited impact in this case; a more general selection of kernels is required for libraries to accelerate machine learning research. In this paper we present initial results using machine learning to select kernels in a case study deploying high performance SYCL kernels in libraries that target a range of heterogeneous devices from desktop GPUs to embedded accelerators. The techniques investigated apply more generally and could similarly be integrated with other heterogeneous programming systems. By combining auto-tuning and machine learning these kernel selection processes can be deployed with little developer effort to achieve high performance on new hardware.

Compute Library for Deep Neural Networks (clDNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL inference on Intel HD Graphics Driver and Intel Iris graphics (also referred to as Intel Processor Graphics).