Arm NN bridges the gap between existing NN frameworks and the underlying IP. It enables efficient translation of existing neural network frameworks, such as TensorFlow and Caffe, allowing them to run efficiently – without modification – across Arm Cortex CPUs and Arm Mali GPUs.
Arm NN is free of charge. It will be available soon, under a permissive MIT open-source license.
Downloads, resources, and documentation
Available March 2018.
About Arm NN SDK
Arm NN SDK is a set of open-source Linux software and tools that enables machine learning workloads on power-efficient devices. It provides a bridge between existing neural network frameworks and power-efficient Arm Cortex CPUs, Arm Mali GPUs or the Arm Machine Learning processor.
Arm NN SDK utilizes the Compute Library to target programmable cores, such as Cortex-A CPUs and Mali GPUs, as efficiently as possible. It includes support for the Arm Machine Learning processor and, via
CMSIS-NN, support for Cortex-M CPUs.
The first release will support Caffe, with TensorFlow arriving soon after, and other neural network frameworks added subsequently. Arm NN will take networks from these frameworks, translate them to the internal Arm NN format and then, through the Compute Library, deploy them efficiently on Cortex-A CPUs – and, if present, Mali GPUs such as the Mali-G71 and Mali-G72.
Arm NN for Android
Also available is Arm NN for NNAPI, Google’s interface for accelerating neural networks on Android devices, made available in Android O. By default, NNAPI runs neural network workloads on the device’s CPU cores, but also provides a Hardware Abstraction Layer (HAL) that can target other processor types, such as GPUs. Arm NN for Android NAPI provides this HAL for Mali GPUs. A further release adds support for the Arm Machine Learning processor.
Arm support for Android NNAPI gives >4x performance boost.
Download Arm NN for Android sources.
Arm NN for CMSIS-NN
CMSIS-NN provides optimized low-level NN functions for Cortex-M CPUs: a collection of efficient neural network kernels developed to maximize the performance and minimize the memory footprint of neural networks on Cortex-M processor cores.
Arm NN future roadmap
Future releases of Arm NN will enable support for other machine learning frameworks as inputs, and other forms of processor cores as targets. This includes processor cores and accelerators from Arm’s partners, assuming availability of suitable extensions.