Google is using AI to design its next generation of AI chips more quickly than humans can

Google is using AI to design its next generation of AI chips more quickly than humans can

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Reported today on The Verge

For the full article visit: https://www.theverge.com/2021/6/10/22527476/google-machine-learning-chip-design-tpu-floorplanning

Reported today in The Verge.

Google is using AI to design its next generation of AI chips more quickly than humans can

Google is using machine learning to help design its next generation of machine learning chips. The algorithm's designs are "comparable or superior" to those created by humans, say Google's engineers, but can be generated much, much faster. According to the tech giant, work that takes months for humans can be accomplished by AI in under six hours.

Google has been working on how to use machine learning to create chips for years, but this recent effort - described this week in a paper in the journal Nature - seems to be the first time its research has been applied to a commercial product: an upcoming version of Google's own TPU (tensor processing unit) chips, which are optimized for AI computation.

"Our method has been used in production to design the next generation of Google TPU," write the authors of the paper, led by Google's head of ML for Systems, Azalia Mirhoseini.

AI, in other words, is helping accelerate the future of AI development.

In the paper, Google's engineers note that this work has "major implications" for the chip industry. It should allow companies to more quickly explore the possible architecture space for upcoming designs and more easily customize chips for specific workloads.

An editorial in Nature calls the research an "important achievement," and notes that such work could help offset the forecasted end of Moore's Law - an axiom of chip design from the 1970s that states that the number of transistors on a chip doubles every two years. AI won't necessarily solve the physical challenges of squeezing more and more transistors onto chips, but it could help find other paths to increasing performance at the same rate.

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