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Photonic multiplexing techniques for neuromorphic computing

delete2023-01-09
delete44
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OA
AI
Y
Yunping Bai
X
Xingyuan Xu
M
Mengxi Tan
Y
Yang Sun
Y
Yang Li
J
Jiayang Wu
R
Roberto Morandotti
A
Arnan Mitchell
K
Kun Xu *
D
David Moss *
DOI:10.1515/nanoph-2022-0485delete
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Abstract

Abstract

En 中文
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.
Keywords:
integrated optics
optical computing operation
optical neural network
photonic multiplexing
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Journal

Nanophotonics cover
Nanophotonics
IF:
6.6
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2.9K
Citations:
1.6W

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B
beijing university of posts & telecommunications
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1.4W
Papers: 1.2W
Citations: 9
S
Swinburne University of Technology
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university of quebec
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