Over the past decade, photonics research has explored accelerated tensor operations, foundational to artificial intelligence (AI) and deep learning1–4, as a path towards enhanced energy efficiency and performance5–14. The field is centrally motivated by finding alternative technologies to extend computational progress in a post-Moore’s law and Dennard scaling era15–19. 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 BERT20,21, 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.
