In a preprint paper published a year ago, scientists at Google Research including Google AI lead Jeff Dean described an AI-based approach to chip desi

Google used reinforcement learning to design next-gen AI accelerator chips

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2021-06-09 16:30:03

In a preprint paper published a year ago, scientists at Google Research including Google AI lead Jeff Dean described an AI-based approach to chip design that could learn from past experience and improve over time, becoming better at generating architectures for unseen components. They claimed it completed designs in under six hours on average, which is significantly faster than the weeks it takes human experts in the loop.

While the work wasn’t entirely novel — it built upon a technique Google engineers proposed in a paper published in March 2020 — it advanced the state of the art in that it implied the placement of on-chip transistors can be largely automated. Now, in a paper published in the journal Nature, the original team of Google researchers claim they’ve fine-tuned the technique to design an upcoming, previously unannounced generation of Google’s tensor processing units (TPU), application-specific integrated circuits (ASICs) developed specifically to accelerate AI.

If made publicly available, the Google researchers’ technique could enable cash-strapped startups to develop their own chips for AI and other specialized purposes. Moreover, it could help to shorten the chip design cycle to allow hardware to better adapt to rapidly evolving research.