Good news! This could signify a breakthrough! In a new paper, Jeff Dean, Quoc V. Le and other researchers at Google use reinforcement learning to improve chip design beyond human capabilities!
"... AI-powered chip design could cut the cost of bespoke chips, leading to an explosion of special-purpose processing for all kinds of uses. ..."
"Chip floorplanning is the engineering task of designing the physical layout of a computer chip. Despite five decades of research1, chip floorplanning has defied automation, requiring months of intense effort by physical design engineers to produce manufacturable layouts. Here we present a deep reinforcement learning approach to chip floorplanning. In under six hours, our method automatically generates chip floorplans that are superior or comparable to those produced by humans in all key metrics, including power consumption, performance and chip area. ..."
"... Google engineers used a reinforcement learning system to arrange the billions of minuscule transistors in an upcoming version of its Tensor Processing Unit (TPU) chips optimized for computing neural networks. The system generated the design in six hours rather than the usual span of weeks, as detailed in Nature.
Key insight: Designing a chip is like playing a board game. A silicon wafer’s area resembles a board, parameters like macro counts and netlist topologies resemble pieces, and evaluation metrics resemble victory conditions. Reinforcement learning (RL) excels at meeting such challenges: Think of DeepMind’s AlphaGo — the RL model that, in 2015, became the first computer program to beat a Go master on a full-size board without a handicap. ..."
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