Photonic multiplexing techniques for neuromorphic computing

被引：54

作者：

Bai, Yunping ^{[1
]}

Xu, Xingyuan ^{[1
]}

Tan, Mengxi ^{[3
]}

Sun, Yang ^{[2
]}

Li, Yang ^{[2
]}

Wu, Jiayang ^{[2
]}

Morandotti, Roberto ^{[4
]}

Mitchell, Arnan ^{[3
]}

Xu, Kun ^{[1
]}

Moss, David J. ^{[2
]}

机构：

[1] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China

[2] Swinburne Univ Technol, Opt Sci Ctr, Hawthorn, Vic 3122, Australia

[3] RMIT Univ, Fac Engn, Melbourne, Vic 3001, Australia

[4] INRS Energie Mat & Telecommun, 1650 Blvd Lionel Boulet, Varennes, PQ J3X 1S2, Canada

来源：

NANOPHOTONICS | 2023年 / 12卷 / 05期

基金：

中国国家自然科学基金;

关键词：

integrated optics; optical computing operation; optical neural network; photonic multiplexing; OPTICAL NEURAL-NETWORK; EXPERIMENTAL REALIZATION; PERFORMANCE; DESIGN; DYNAMICS; IMPLEMENTATION; PARALLEL; LESS; RF;

D O I：

10.1515/nanoph-2022-0485

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

The simultaneous advances in artificial neural networks and photonic integration technologies have spurred extensive research in optical computing and optical neural networks (ONNs). The potential to simultaneously exploit multiple physical dimensions of time, wavelength and space give ONNs the ability to achieve computing operations with high parallelism and large-data throughput. Different photonic multiplexing techniques based on these multiple degrees of freedom have enabled ONNs with large-scale interconnectivity and linear computing functions. Here, we review the recent advances of ONNs based on different approaches to photonic multiplexing, and present our outlook on key technologies needed to further advance these photonic multiplexing/hybrid-multiplexing techniques of ONNs.

引用

页码：795 / 817

页数：23

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