片上集成光学神经网络综述(特邀)  

Review of On-Chip Integrated Optical Neural Networks (Invited)

作　　者：符庭钊 孙润 黄禹尧 张检发 杨四刚[2,3] 朱志宏 陈宏伟[2,3] Fu Tingzhao;Sun Run;Huang Yuyao;Zhang Jianfa;Yang Sigang;Zhu Zhihong;Chen Hongwei(College of Advanced Interdisciplinary Studies,National University of Defense Technology,Changsha 410073,Hunan,China;Department of Electronic Engineering,Tsinghua University,Beijing 100084,China;Beijing National Research Center for Information Science and Technology,Beijing 100084,China;Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Devices,NationalUniversity of Defense Technology,Changsha 410073,Hunan,China;Nanhu Laser Laboratory,National University of Defense Technology,Changsha 410073,Hunan,China)

机构地区：[1]国防科技大学前沿交叉学科学院,湖南长沙410073 [2]清华大学电子工程系,北京100084 [3]北京信息科学与技术国家研究中心,北京100084 [4]国防科技大学新型纳米光电信息材料与器件湖南省重点实验室,湖南长沙410073 [5]国防科技大学南湖之光实验室,湖南长沙410073

出　　处：《中国激光》2024年第1期403-418,共16页Chinese Journal of Lasers

基　　金：国家自然科学基金(62135009)。

摘　　要：光学神经网络是区别于冯·诺依曼计算架构的一种高性能新型计算范式,具有低延时、低功耗、大带宽以及并行信号处理等优势。片上集成是光学神经网络微型化发展的一种典型方式,近年来片上集成光学神经网络获得了学术界及工业界的广泛关注。对基于不同计算单元结构的片上集成光学神经网络的相关研究工作进行了梳理,并分析了其设计原理、实现方法及系统架构特征。同时结合国内外最新研究进展,进一步分析了片上集成光学神经网络在计算单元大规模拓展、可重构、非线性运算和实用化等方面面临的挑战及其未来发展趋势。Significance With the advent of the era of artificial intelligence,advanced algorithms represented by deep learning algorithms are rapidly developing,driven by big-data resources.This is promoting the extensive application of neural networks in various fields of social development,including computer vision,natural language processing,speech recognition,automatic driving,and biomedicine.In the past two decades,advanced semiconductor technology has led to the creation of various types of computer hardware with excellent performances,which meet the computing capacity resource requirements of neural networks in various fields.However,with the continuous elevation of social intelligence in the future,neural networks will require even greater computing resources when processing complex tasks.Simultaneously,the machining accuracy of semiconductor process technology has approached the physical limit,and ultra-small on-chip devices are susceptible to quantum tunneling and thermal effects,which may prevent the proper operation of chips manufactured with this machining accuracy.Hence,it will be difficult to continue to increase computing capacity resources by further improving the processing accuracy of semiconductor processes.Consequently,it is imperative to find a new computing paradigm to replace the existing computing architecture to break through this computing-capacity bottleneck.An optical neural network(ONN)is a high-performance novel computing paradigm that differs from von Neumann computing schemes.It has advantages such as low latency,low power consumption,large bandwidth,and parallel signal processing.Its inference process relies on the diffraction and interference of light,and no additional energy supply is required for the entire calculation process.Compared with traditional electronic hardware,it has natural advantages in performing large-scale linear matrix operations.Progress This study comprehensively reviews the research progress and challenges related to on-chip integrated ONNs.These are typically design

关 键 词：集成光学 光计算 光学神经网络 芯片 人工智能 

分 类 号：O436[机械工程—光学工程]