Ending an Ugly Chapter in Chip Design @IEEESpectrum
Discussions at chip design conferences rarely get heated. But a year ago at the International Symposium on Physical Design (ISPD), things got out of hand. It was described by observers as a “trainwreck” and an “ambush.” The crux of the clash was whether Google’s AI solution to one of chip design’s thornier problems was really better than those of humans or state-of-the-art algorithms.
Using Google’s open-source version of its process, called Circuit Training, and reverse-engineering some parts that were not clear enough for IEEE Fellow Andrew Kahng team, they set reinforcement learning against a human designer, commercial software, and state-of-the-art academic algorithms.
In most cases, Circuit Training was not the winner, but it was competitive. That’s especially notable given that the experiments did not allow Circuit Training to use its signature ability—to improve its performance by learning from other chip designs.
“Our goal has been clarity of understanding that will allow the community to move on,” he told engineers.
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