Arm NN is an inference engine for CPUs, GPUs and NPUs. It 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-A CPUs, Arm Mali GPUs and Arm Ethos NPUs.
Arm NN is free of charge.
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 Cortex-A CPUs, Arm Mali GPUs and Arm Ethos NPUs.
The latest release supports Caffe, TensorFlow, TensorFlow Lite, and ONNX. Arm NN takes networks from these frameworks, translates them to the internal Arm NN format and then, through the Compute Library, deploys 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 NNAPI provides this HAL for Mali GPUs. A further release adds support for Arm Ethos-N NPUs.
Arm support for Android NNAPI gives >4x performance boost.
Download Arm NN for Android sources.
Arm NN performance relative to other NN frameworks
- Arm NN open-source collaboration enables optimal third-party implementations
- Deployed in multiple production devices (>250Mu)
Support for Cortex-M CPUs
Machine learning support for Cortex-M microcontrollers is provided by TensorFlow Lite Micro. Further optimization is available via CMSIS-NN, 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.Download CMSIS-NN
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.
Webinar - Project Trillium: Optimizing ML Performance for any Application
Project Trillium is a suite of Arm IP designed to deliver scalable ML and neural network functionality at any point on the performance curve, from sensors, to mobile, and beyond.
|Answered||hardfault error||0 votes||575 views||3 replies||Latest 14 hours ago by Indy||Answer this|
|Suggested answer||Reading ETB from software||0 votes||1554 views||1 replies||Latest 4 days ago by 42Bastian Schick||Answer this|
|Suggested answer||Cortex-M3 frequency||0 votes||721 views||2 replies||Latest 5 days ago by Joseph Yiu||Answer this|
|Suggested answer||Problem obtaining frame pointer in the call stack for STM32L4R9AI board||0 votes||1336 views||6 replies||Latest 6 days ago by Charlie97||Answer this|
|Not answered||Installing Xamarin.iOS and Xamarin.Android||0 votes||221 views||0 replies||Started 8 days ago by mounika123||Answer this|
|Suggested answer||Does Qualcomm Centriq 2400 support sve instruction set?||1 votes||2493 views||5 replies||Latest 9 days ago by 42Bastian Schick||Answer this|
|Answered||hardfault error Latest 14 hours ago by Indy||3 replies 575 views|
|Suggested answer||Reading ETB from software Latest 4 days ago by 42Bastian Schick||1 replies 1554 views|
|Suggested answer||Cortex-M3 frequency Latest 5 days ago by Joseph Yiu||2 replies 721 views|
|Suggested answer||Problem obtaining frame pointer in the call stack for STM32L4R9AI board Latest 6 days ago by Charlie97||6 replies 1336 views|
|Not answered||Installing Xamarin.iOS and Xamarin.Android Started 8 days ago by mounika123||0 replies 221 views|
|Suggested answer||Does Qualcomm Centriq 2400 support sve instruction set? Latest 9 days ago by 42Bastian Schick||5 replies 2493 views|