In-memory computing could dramatically speed up AI

Posted on Tuesday, May 01 2018 @ 10:13 CEST by Thomas De Maesschalck
Austin, Texas based startup Mythic is working on delivering huge performance boosts for machine learning by taking a new look at a decade-old processor architecture. EE Times writes the firm believes in-memory computing could speed up artificial intelligence workloads by up to 10,000x versus what's possible with today's GPUs. Mythic isn't the only one working on this, but could be the first to make it to market. The company's first production silicon is anticipated in late 2019:
Startup Mythic (Austin, Texas) aims to compute neural-network jobs inside a flash memory array, working in the analog domain to slash power consumption. It aims to have production silicon in late 2019, making it potentially one of the first to market of the new class of chips.

“Most of us in the academic community believe that emerging memories will become an enabling technology for processor-in-memory,” said Suman Datta, who chairs the department of electrical engineering at Notre Dame. “Adoption of the new non-volatile memories will mean creating new usage models, and in-memory processing is a key one.”

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Thomas De Maesschalck

Thomas has been messing with computer since early childhood and firmly believes the Internet is the best thing since sliced bread. Enjoys playing with new tech, is fascinated by science, and passionate about financial markets. When not behind a computer, he can be found with running shoes on or lifting heavy weights in the weight room.

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