中国科学院高精度光计算研究取得进展

Core Insights - The rapid development of artificial intelligence neural networks has put significant pressure on traditional electronic processors due to large-scale matrix operations and frequent data iterations [1] - Optical-electrical hybrid computing shows remarkable computational performance, but practical applications are limited by issues such as the separation of training and inference stages, leading to information entropy degradation and reduced computational accuracy [1] Group 1: Optical Processing Unit (OPU) Development - The Chinese Academy of Sciences Semiconductor Research Institute has proposed a programmable optical processing unit (OPU) based on a phase pixel array, utilizing Lyapunov stability theory for flexible programming [2] - An end-to-end closed-loop optical-electrical hybrid computing architecture (ECA) has been constructed, achieving full-process closed-loop optimization of training and inference, effectively compensating for information entropy loss [2] - The architecture employs a noise self-learning mechanism for joint optimization of optical and electrical parameters, enabling adaptive compensation of computational accuracy [2] Group 2: Performance Metrics - The OPU supports an operational rate of 30.67 GBaud/s, achieving a computational capability of 981.3 GOPS and a computational density of 3.97 TOPS/mm² [3] - The theoretical analysis indicates that the structure can be further expanded to a 128×128 scale, with a potential computational capability of 1005 TOPS, a computational density of 4.09 TOPS/mm², and energy efficiency of 37.81 fJ/MAC [3] - Experimental results show that with a 4-bit OPU, the ECA achieves a 90.8% inference accuracy on the MNIST handwritten digit recognition task, nearing the theoretical limit of 90.9% for an 8-bit traditional computing architecture (TCA) [2]