Google 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.
Specifically, Google’s new AI can draw up a chip’s “floorplan.” This essentially involves plotting where components like CPUs, GPUs, and memory are placed on the silicon die in relation to one another — their positioning on these miniscule boards is important as it affects the chip’s power consumption and processing speed.
It takes humans months to optimally design these floorplans but Google’s deep reinforcement learning system — an algorithm that’s trained to take certain actions in order to maximize its chance of earning a reward — can do it with relatively little effort.
“Our method has been used in production to design the next generation of Google TPU,” write the paper’s authors, co-led by Google research scientists Azalia Mirhoseini and Anna Goldie.
AI, in other words, is helping accelerate the future of AI development.
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