AlphaChip — это революционная система на базе обучения с подкреплением от Google DeepMind, созданная для автоматизации дизайна микросхем. ИИ способен проектировать оптимальные топологии чипов за считанные часы, на что у инженеров-людей уходили недели. Теперь веса модели открыты для сообщества, что дает мощный импульс всей индустрии полупроводников.
In 2020, we released a preprint introducing our novel reinforcement learning method for designing chip layouts, which we later published in Nature and open sourced. Today, we’re publishing a Nature addendum that describes more about our method and its impact on the field of chip design. We’re also releasing a pre-trained checkpoint, sharing the model weights and announcing its name: AlphaChip. Computer chips have fueled remarkable progress in artificial intelligence (AI), and AlphaChip returns the favor by using AI to accelerate and optimize chip design. The method has been used to design superhuman chip layouts in the last three generations of Google’s custom AI accelerator, the Tensor Processing Unit (TPU). AlphaChip was one of the first reinforcement learning approaches used to solve a real-world engineering problem. It generates superhuman or comparable chip layouts in hours, rather than taking weeks or months of human effort, and its layouts are used in chips all over the world, from data centers to mobile phones.