Photonic multiplexing techniques for neuromorphic computing-Reference-Cited by-同舟云学术

Photonic multiplexing techniques for neuromorphic computing

Published:2023-01-09 Issue:5 Volume:12 Page:795-817
ISSN:2192-8614
Container-title:Nanophotonics
language:en
Short-container-title:

Author:

Bai Yunping¹,Xu Xingyuan¹,Tan Mengxi²,Sun Yang³,Li Yang³,Wu Jiayang³,Morandotti Roberto⁴,Mitchell Arnan²,Xu Kun¹,Moss David J.³^ORCID

Affiliation:

1. State Key Laboratory of Information Photonics and Optical Communications , Beijing University of Posts and Telecommunications , Beijing 100876 , China

2. Faculty of Engineering , RMIT University , Melbourne , VIC 3001 , Australia

3. Optical Sciences Centre , Swinburne University of Technology , Hawthorn , VIC 3122 , Australia

4. INRS-Énergie, Matériaux et Télécommunications , 1650 Boulevard Lionel-Boulet , Varennes , QC J3X 1S2 , Canada

Abstract

Abstract 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.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials,Biotechnology

Link

https://www.degruyter.com/document/doi/10.1515/nanoph-2022-0485/pdf

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