Noise-Resistant Optical Implementation of Analogue Neural Networks


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] .

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Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
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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
Last Modified: 09 Apr 2024 07:30
Date Deposited: 12 Oct 2021 10:03
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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)



Version: Accepted Version

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