Efficient nonlinear activation function in optical neural network

An efficient nonlinear activation function for optical neural network is proposed and demonstrated by executing complex-valued algorithms and intensity measurement. Great learning capability are achieved by the inherent properties of the complex-valued optical neural network.

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Main Authors: Zhang, Hui, Wan, Ling Xiao, Gu, Mile, Jiang, Xudong, Thompson, Jayne, Cai, Hong, Paesani‬‪, Stefano, Santagati, ‪Raffaele, Laing, ‪Anthony, Lo, Guo Qiang, Kwong, Dim Lee, Kwek, Leong Chuan, Liu, Ai Qun
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
Online Access:https://hdl.handle.net/10356/151460
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1514602021-06-25T04:31:14Z Efficient nonlinear activation function in optical neural network Zhang, Hui Wan, Ling Xiao Gu, Mile Jiang, Xudong Thompson, Jayne Cai, Hong Paesani‬‪, Stefano Santagati, ‪Raffaele Laing, ‪Anthony Lo, Guo Qiang Kwong, Dim Lee Kwek, Leong Chuan Liu, Ai Qun School of Electrical and Electronic Engineering 2020 Conference on Lasers and Electro-Optics Engineering Optical Neural Network Nonlinear Activation Function An efficient nonlinear activation function for optical neural network is proposed and demonstrated by executing complex-valued algorithms and intensity measurement. Great learning capability are achieved by the inherent properties of the complex-valued optical neural network. Ministry of Education (MOE) Accepted version 2021-06-25T02:53:05Z 2021-06-25T02:53:05Z 2020 Conference Paper Zhang, H., Wan, L. X., Gu, M., Jiang, X., Thompson, J., Cai, H., Paesani‬‪, S., Santagati, ‪., Laing, ‪., Lo, G. Q., Kwong, D. L., Kwek, L. C. & Liu, A. Q. (2020). Efficient nonlinear activation function in optical neural network. 2020 Conference on Lasers and Electro-Optics. https://dx.doi.org/10.1364/CLEO_AT.2020.JTh2B.6 https://hdl.handle.net/10356/151460 10.1364/CLEO_AT.2020.JTh2B.6 en © 2020 The Author(s). All rights reserved. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Optical Neural Network
Nonlinear Activation Function
spellingShingle Engineering
Optical Neural Network
Nonlinear Activation Function
Zhang, Hui
Wan, Ling Xiao
Gu, Mile
Jiang, Xudong
Thompson, Jayne
Cai, Hong
Paesani‬‪, Stefano
Santagati, ‪Raffaele
Laing, ‪Anthony
Lo, Guo Qiang
Kwong, Dim Lee
Kwek, Leong Chuan
Liu, Ai Qun
Efficient nonlinear activation function in optical neural network
description An efficient nonlinear activation function for optical neural network is proposed and demonstrated by executing complex-valued algorithms and intensity measurement. Great learning capability are achieved by the inherent properties of the complex-valued optical neural network.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Zhang, Hui
Wan, Ling Xiao
Gu, Mile
Jiang, Xudong
Thompson, Jayne
Cai, Hong
Paesani‬‪, Stefano
Santagati, ‪Raffaele
Laing, ‪Anthony
Lo, Guo Qiang
Kwong, Dim Lee
Kwek, Leong Chuan
Liu, Ai Qun
format Conference or Workshop Item
author Zhang, Hui
Wan, Ling Xiao
Gu, Mile
Jiang, Xudong
Thompson, Jayne
Cai, Hong
Paesani‬‪, Stefano
Santagati, ‪Raffaele
Laing, ‪Anthony
Lo, Guo Qiang
Kwong, Dim Lee
Kwek, Leong Chuan
Liu, Ai Qun
author_sort Zhang, Hui
title Efficient nonlinear activation function in optical neural network
title_short Efficient nonlinear activation function in optical neural network
title_full Efficient nonlinear activation function in optical neural network
title_fullStr Efficient nonlinear activation function in optical neural network
title_full_unstemmed Efficient nonlinear activation function in optical neural network
title_sort efficient nonlinear activation function in optical neural network
publishDate 2021
url https://hdl.handle.net/10356/151460
_version_ 1703971156562804736