Noise-Resistant Optical Implementation of Analogue Neural Networks

Abstract

Analogue artificial neural networks are widely considered as promising computational models that more closely imitate the information processing capabilities of the human brain compared to digital neural networks. The significant computation power and the much reduced power consumption per operation make the analogue implementation of neural networks very attractive. There is an active research on artificial neural networks (ANNs) implementation using both analogue photonic and electronic hardware [1] – [4] . However, compared to digital realisations the conventional analogue systems are more sensitive to the noise that is inevitably present in practical implementations [2] , [3] . Noise properties in ANNs have been studied both in the electronic and photonic domains. However, photonic ANNs are much less investigated compared to the electronic implementations, for which some training techniques have been proposed to enhance ANNs robustness against noise [1] , [4] .

Publication DOI: https://doi.org/10.1109/CLEO/Europe-EQEC52157.2021.9541571
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
College of Engineering & Physical Sciences
Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Event Title: 2021 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)
Event Type: Other
Event Dates: 2021-06-21 - 2021-06-25
Uncontrolled Keywords: Training,Power demand,Artificial neural networks,Optical computing,Optical fiber networks,Robustness,Optical sensors,Electronic, Optical and Magnetic Materials,Atomic and Molecular Physics, and Optics
ISBN: 978-1-6654-4804-8, 978-1-6654-1876-8
Full Text Link:
Related URLs: https://ieeexpl ... ocument/9541571 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2021-09-30
Accepted Date: 2021-05-12
Authors: Arguello Ron, Diego (ORCID Profile 0000-0002-6004-385X)
Kamalian Kopae, Morteza (ORCID Profile 0000-0002-6278-976X)
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)

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