Startup Finds Ways to Bring AI to the Edge

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eetasia.com, Apr. 08, 2020 – 

A silicon valley startup claims it has reinvented the mathematics of neural networks and has produced a complementary edge AI chip, already sampling, which does not use the usual large array of multiply-accumulate units. The chip can run the equivalent of 4 TOPS, with impressive power consumption of 55 TOPS/W, and according to the company, achieves data-center class inference in under 20mW (YOLOv3 at 30fps).

San Jose-based Percieve has been in super-stealth mode until now – as a spin-out from Xperi, it has been funded entirely by its parent since officially forming two years ago. The team is 41 people, with a similar number within Xperi working on apps for the chip. Founding CEO Steve Teig is also CTO of Xperi; he was previously founder and CTO of Tabula, the 3D programmable logic startup that closed its doors five years ago, and prior to that, CTO of Cadence.

Teig explained that the initial idea was to combine Xperi's classical knowledge of image and audio processing with machine learning. Xperi owns brands such as DTS, IMAX Enhanced and HD Radio – its technology portfolio includes image processing software for features like photo red-eye and image stabilization which are widely used in digital cameras, plus audio processing software for Blu-Ray disc players.

"We started with a clean sheet of paper, and used information theory to ask: what computations are neural networks actually doing? And is there a different way of approaching that computation that could change what is possible [at the edge]?" Teig said. "After a couple of years of doing this work, we discovered it was, and then decided... we should make a chip that embodies these ideas."

The idea Teig presented to the Xperi board was to spin out a company to make a chip that could do meaningful inference in edge devices with a power budget of 20mW. The result, a 7x7mm chip named Ergo, can run 4 TOPS without external RAM (in fact, it is running the equivalent of what a GPU rated at 4 TOPS can achieve, Teig explained). Ergo supports many styles of neural networks, including convolutional networks (CNNs) and recurrent networks (RNNs), in contrast with many solutions on the market which are tailored for CNNs. Ergo can even run several heterogeneous networks simultaneously.

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