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Arm and Nvidia collaborate on internet of things intelligence

A collaboration between Arm and Nvidia looks to provide deep learning and artificial intelligence on internet-connected devices

Arm and Nvidia have announced a collaboration which will see Nvdia’s Deep Learning Accelerator (Nvdla) architecture integrated into Arm’s Project Trillium platform for machine learning.

The pair have said the collaboration will make it simple for internet of things (IoT) chip companies to integrate artificial intelligence (AI) into their designs and help put intelligent, affordable products into the hands of billions of consumers worldwide.

The two companies said the new architecture will help IoT companies build inference into their devices.

Speaking at the GPU technology conference in San Jose, Deepu Talla, vice-president and general manager of autonomous machines at Nvidia, said: “Our partnership with Arm will help drive this wave of adoption by making it easy for hundreds of chip companies to incorporate deep learning technology.”

“Accelerating AI at the edge is critical in enabling Arm’s vision of connecting a trillion IoT devices,” said Rene Haas, executive vice-president, and president of the IP group, at Arm.

“Today we are one step closer to that vision by incorporating Nvdla into the Arm Project Trillium platform, as our entire ecosystem will immediately benefit from the expertise and capabilities our two companies bring in AI and IoT.”

Nvdla is based on Nvidia’s Xavier autonomous machine system on a chip. Nvida said Nvdla is supported by the company’s developer tools, including upcoming versions of TensorRT, a programmable deep learning accelerator.

The open-source design in Nvdla allows for cutting-edge features to be added regularly, including contributions from the research community, according to Nvidia.

While the public cloud give IoT devices access to vast AI and machine learning capabilities, the fragility of network connectivity, security and the need to process vast amounts of sensor data in real time, means that IoT device manufacturers are increasingly looking at edge computing. This is when some of the processing is conducted on the IoT device itself.

Read more about AI for IoT

The Arm/Nvidia collaboration has the potential to lead to deep learning being built into Arm-powered IoT devices. Arm and Nvida said the integration of Nvdla with Project Trillium will give deep learning developers the highest levels of performance as they leverage Arm’s flexibility and scalability across the wide range of IoT devices.

“This is a win/win for IoT, mobile and embedded chip companies looking to design accelerated AI inferencing solutions,” said Karl Freund, lead analyst for deep learning at Moor Insights and Strategy.

Read more on Chips and processor hardware

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