中心博士后白博文在硅基光子神经网络构架相关工作发表在了SCIENCE CHINA Information Sciences期刊的AI-Photonics专题中。该工作得到了国家重点研发计划(2018YFB2201704)、国家自然科学基金(61635001, 61822508)，北京市科委项目(Z19110004819006)的部分资助。

光子神经网络具有光子高带宽、高速度、低能耗的特点以及神经网络高效并行处理能力，有望实现超高速和高能效的神经网络计算。硅基光电子技术为光电子器件的大规模集成提供了理想平台，因此硅基光子神经网络集成度更高，能够完成更加复杂的信息处理任务。本文综述了近年来硅基光子神经网络的重要研究进展，并提出了一种用于超高速神经网络计算的硅基光电子智能处理器原型。该原型由高速I/O接口，光子神经网络，控制单元等部分构成，利用深度学习算法完成训练后，能够实现矩阵相乘、卷积计算等核心计算功能。

摘要: Brain-inspired photonic neural networks for artificial intelligence have attracted renewed interest. For many computational tasks, such as image recognition, speech processing and deep learning, photonic neural networks have the potential to increase the computing speed and energy efficiency on the orders of magnitude compared with digital electronics. Silicon Photonics, which combines the advantages of electronics and photonics, brings hope for the large-scale photonic neural network integration. This paper walks through the basic concept of artificial neural networks and focuses on the key devices which construct the silicon photonic neuromorphic systems. We review some recent important progress in silicon photonic neural networks, which include multilayer artificial neural networks and brain-like neuromorphic systems, for artificial intelligence. A prototype of silicon photonic artificial intelligence processor for ultra-fast neural network computing is also proposed. We hope this paper gives a detailed overview and a deeper understanding of this emerging field.

论文链接：

Bowen Bai, Haowen Shu, Xingjun Wang and Weiwen Zou, "Towards silicon photonic neural networks for artificial intelligence." SCIENCE CHINA Information Sciences. 63, 160403 (2020)