首页|模拟光计算的发展与应用

模拟光计算的发展与应用

扫码查看
随着人工智能的快速发展,人们对于数据处理的速度和能效的需求急剧上升,基于冯·诺依曼架构的传统电子计算机的局限性日渐凸显.相比之下,光计算是采用光信号作为信息处理的基本载体的新型计算体系,以光学神经网络和伊辛机为代表的模拟光计算系统在智能信息处理和计算任务中呈现出巨大的潜力.本文重点关注近几年光学模拟计算的关键技术应用与进展,主要回顾光学神经网络和空间光伊辛机计算的系统,并分析不同系统架构特征和设计原理.同时,进一步地概述当前模拟光计算的局限性和挑战,并讨论其潜在的发展与应用.
Development and applications of analog optical computing:A review
Optical computing is a novel computing system that regards light as the basic information processing carrier.Compared with classical electronic computers,optical computing systems have the advantages of low latency,low power consumption,and parallel processing,which have the potential to solve the problems of computing capacity and energy consumption.Recently,analog optical computing systems represented by optical neural networks(PNNs)and coherent Ising machines(CIMs)have shown great potential in intelligent computing.This review summarizes the relevant research progress based on different PNN structures.Concretely,PNN systems are mainly divided into the planar integrated architecture and free space interconnected architecture.First,the planar integrated PNN is mainly based on Mach-Zehnder interferometer(MZI),micro-ring resonator(MRR),phase change material(PCM)structure and other types of unit structures.For the integrated PNN system,the research work on MZI-PNN,MRR-PNN and PCM-PNN related architectures is mainly reviewed.On the other hand,we also summarize the research progress related to the free-space interconnected PNN.Such non-integrated architectures are typically based on spatial light modulators(SLMs),digital micromirror devices,single-mode optical fibers,and electro-optic modulators.In addition,the training method and physical noise of PNN systems are further analyzed.Currently,most PNN architectures adopt an offline solution of"electrical domain training and optical domain testing".Here,we present some improved PNN training methods.Meanwhile,several effective suppression schemes are summarized for the noise and error,for example,the MRR-PNN structure with convolution kernel pruning,the reduced rank PCM-PNN,and the binarized PNN,etc.In this review,we also summarize the related work on spatial optical CIM(SP-CIM)systems.Different from PNNs,the CIM system is used to solve combinatorial optimization problems.Especially,the SP-CIM system based on SLMs is one of the solutions to build large-scale CIMs,which has large computing scale,fast speed and low power consumption.Therefore,this review summarizes some research work on the improved SP-CIM structure.Finally,this review also analyzes several possible limitations of analog optical computing.For instance,the nonlinear transformation in PNNs is difficult to achieve via optical platforms.Meanwhile,the possible development trends of optical computing are summarized.This mainly includes:Further realizing the all-optical computing architecture and improving the noise robustness of systems,etc.In summary,by continuously improving the system architecture and algorithm performance,the optical computing platform can better exert its advantages in speed and energy consumption.

artificial intelligenceanalog optical computinghybrid optical-electrical systemoptical neural networkoptical Ising machine

毕岩峰、吴星宇、张璐矾、王铁军、杨大全、王川

展开 >

北京师范大学人工智能学院,北京 100875

北京邮电大学理学院,北京 100876

北京邮电大学信息与通信工程学院,北京 100876

人工智能 模拟光计算 混合光电系统 光学神经网络 光学伊辛机

2024

科学通报
中国科学院国家自然科学基金委员会

科学通报

CSTPCD北大核心
影响因子:1.269
ISSN:0023-074X
年,卷(期):2024.69(34)