An optical neural chip for implementing complex-valued neural network

H. Zhang, M. Gu (), X. D. Jiang (), J. Thompson, H. Cai, S. Paesani, R. Santagati, A. Laing, Y. Zhang, M. H. Yung, Y. Z. Shi, F. K. Muhammad, G. Q. Lo, X. S. Luo, B. Dong, D. L. Kwong, L. C. Kwek () and A. Q. Liu ()
Additional contact information
H. Zhang: Nanyang Technological University
M. Gu: Nanyang Technological University
X. D. Jiang: Nanyang Technological University
J. Thompson: National University of Singapore
H. Cai: A*STAR (Agency for Science, Technology and Research)
S. Paesani: University of Bristol
R. Santagati: University of Bristol
A. Laing: University of Bristol
Y. Zhang: Nanyang Technological University
M. H. Yung: Southern University of Science and Technology
Y. Z. Shi: Nanyang Technological University
F. K. Muhammad: Nanyang Technological University
G. Q. Lo: Advanced Micro Foundry
X. S. Luo: Advanced Micro Foundry
B. Dong: Advanced Micro Foundry
D. L. Kwong: A*STAR (Agency for Science, Technology and Research)
L. C. Kwek: Nanyang Technological University
A. Q. Liu: Nanyang Technological University

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: 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.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
https://www.nature.com/articles/s41467-020-20719-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20719-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-20719-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().