AI Silicon Preps for 2018 Debuts

A dozen startups chase deep learning

Rick Merritt, EETimes
1/22/2018 01:31 PM EST

SAN JOSE, Calif. — Deep neural networks are like a tsunami on the distant horizon.

Given their still-evolving algorithms and applications, it’s unclear what changes deep neural nets (DNNs) ultimately will bring. But their successes thus far in translating text and recognizing images and speech make it clear they will reshape computer design, and the changes are coming at a time of equally profound disruptions in how semiconductors are designed and manufactured.

The first merchant chips tailored for training DNNs will ship this year. As it can take weeks or months to train a new neural-net model, the chips likely will be some of the largest, and thus most expensive, chunks of commercial silicon made to date.

The industry this year may see a microprocessor ship from startup Graphcore that uses no DRAM and one from rival Cerebras Systems that pioneers wafer-level integration. The hefty 2.5-D Nervana chip acquired by Intel is already sampling, and a dozen other processors are in the works. Meanwhile, chip companies from ARM to Western Digital are working on cores to accelerate the inference part of deep neural nets.

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