Universal photonic artificial intelligence acceleration

Abstract

Over the past decade, photonics research has explored accelerated tensor operations, foundational to artificial intelligence (AI) and deep learning^1-4, as a path towards enhanced energy efficiency and performance^5-14. The field is centrally motivated by finding alternative technologies to extend computational progress in a post-Moore's law and Dennard scaling era^15-19. Despite these advances, no photonic chip has achieved the precision necessary for practical AI applications, and demonstrations have been limited to simplified benchmark tasks. Here we introduce a photonic AI processor that executes advanced AI models, including ResNet³ and BERT^20,21, along with the Atari deep reinforcement learning algorithm originally demonstrated by DeepMind²². This processor achieves near-electronic precision for many workloads, marking a notable entry for photonic computing into competition with established electronic AI accelerators²³ and an essential step towards developing post-transistor computing technologies.