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用于神经形态计算的光子复用技术。

Photonic multiplexing techniques for neuromorphic computing.

作者信息

Bai Yunping, Xu Xingyuan, Tan Mengxi, Sun Yang, Li Yang, Wu Jiayang, Morandotti Roberto, Mitchell Arnan, Xu Kun, Moss David J

机构信息

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

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

出版信息

Nanophotonics. 2023 Jan 9;12(5):795-817. doi: 10.1515/nanoph-2022-0485. eCollection 2023 Mar.

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.

摘要

人工神经网络和光子集成技术的同步发展推动了光学计算和光学神经网络(ONN)的广泛研究。同时利用时间、波长和空间等多个物理维度的潜力使光学神经网络能够实现具有高并行性和大数据吞吐量的计算操作。基于这些多自由度的不同光子复用技术使光学神经网络具备大规模互连性和线性计算功能。在此,我们回顾基于不同光子复用方法的光学神经网络的最新进展,并对进一步推进光学神经网络的这些光子复用/混合复用技术所需的关键技术发表我们的看法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a553/11501529/f7fe372d3b8b/j_nanoph-2022-0485_fig_001.jpg

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