Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151455
Title: An optical neural chip for implementing complex-valued neural network
Authors: Zhang, Hui
Gu, Mile
Jiang, Xudong
Thompson, Jayne
Cai, Hong
Paesani‬‪, Stefano
Santagati, ‪Raffaele
Laing, ‪Anthony
Zhang, Yi
Yung, Man Hong
Shi, Yuzhi
Muhammad Faeyz Karim
Lo, Guo Qiang
Luo, Xian Shu
Dong, Bin
Kwong, Dim Lee
Kwek, Leong Chuan
Liu, Ai Qun
Keywords: Engineering
Issue Date: 2021
Source: Zhang, H., Gu, M., Jiang, X., Thompson, J., Cai, H., Paesani‬‪, S., Santagati, ‪., Laing, ‪., Zhang, Y., Yung, M. H., Shi, Y., Muhammad Faeyz Karim, Lo, G. Q., Luo, X. S., Dong, B., Kwong, D. L., Kwek, L. C. & Liu, A. Q. (2021). An optical neural chip for implementing complex-valued neural network. Nature Communications, 12(457). https://dx.doi.org/10.1038/s41467-020-20719-7
Journal: Nature Communications
Abstract: Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.
URI: https://hdl.handle.net/10356/151455
ISSN: 2041-1723
DOI: 10.1038/s41467-020-20719-7
Rights: © 2021 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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