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Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch

Photonic neural network has been sought as an alternative solution to surpass the efficiency and speed bottlenecks of electronic neural network. Despite that the integrated Mach–Zehnder Interferometer (MZI) mesh can perform vector-matrix multiplication in photonic neural network, a programmable in-s...

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Autores principales: Xu, Zefeng, Tang, Baoshan, Zhang, Xiangyu, Leong, Jin Feng, Pan, Jieming, Hooda, Sonu, Zamburg, Evgeny, Thean, Aaron Voon-Yew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537414/
https://www.ncbi.nlm.nih.gov/pubmed/36202804
http://dx.doi.org/10.1038/s41377-022-00976-5
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author Xu, Zefeng
Tang, Baoshan
Zhang, Xiangyu
Leong, Jin Feng
Pan, Jieming
Hooda, Sonu
Zamburg, Evgeny
Thean, Aaron Voon-Yew
author_facet Xu, Zefeng
Tang, Baoshan
Zhang, Xiangyu
Leong, Jin Feng
Pan, Jieming
Hooda, Sonu
Zamburg, Evgeny
Thean, Aaron Voon-Yew
author_sort Xu, Zefeng
collection PubMed
description Photonic neural network has been sought as an alternative solution to surpass the efficiency and speed bottlenecks of electronic neural network. Despite that the integrated Mach–Zehnder Interferometer (MZI) mesh can perform vector-matrix multiplication in photonic neural network, a programmable in-situ nonlinear activation function has not been proposed to date, suppressing further advancement of photonic neural network. Here, we demonstrate an efficient in-situ nonlinear accelerator comprising a unique solution-processed two-dimensional (2D) MoS(2) Opto-Resistive RAM Switch (ORS), which exhibits tunable nonlinear resistance switching that allow us to introduce nonlinearity to the photonic neuron which overcomes the linear voltage-power relationship of typical photonic components. Our reconfigurable scheme enables implementation of a wide variety of nonlinear responses. Furthermore, we confirm its feasibility and capability for MNIST handwritten digit recognition, achieving a high accuracy of 91.6%. Our accelerator constitutes a major step towards the realization of in-situ photonic neural network and pave the way for the integration of photonic integrated circuits (PIC).
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spelling pubmed-95374142022-10-08 Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch Xu, Zefeng Tang, Baoshan Zhang, Xiangyu Leong, Jin Feng Pan, Jieming Hooda, Sonu Zamburg, Evgeny Thean, Aaron Voon-Yew Light Sci Appl Article Photonic neural network has been sought as an alternative solution to surpass the efficiency and speed bottlenecks of electronic neural network. Despite that the integrated Mach–Zehnder Interferometer (MZI) mesh can perform vector-matrix multiplication in photonic neural network, a programmable in-situ nonlinear activation function has not been proposed to date, suppressing further advancement of photonic neural network. Here, we demonstrate an efficient in-situ nonlinear accelerator comprising a unique solution-processed two-dimensional (2D) MoS(2) Opto-Resistive RAM Switch (ORS), which exhibits tunable nonlinear resistance switching that allow us to introduce nonlinearity to the photonic neuron which overcomes the linear voltage-power relationship of typical photonic components. Our reconfigurable scheme enables implementation of a wide variety of nonlinear responses. Furthermore, we confirm its feasibility and capability for MNIST handwritten digit recognition, achieving a high accuracy of 91.6%. Our accelerator constitutes a major step towards the realization of in-situ photonic neural network and pave the way for the integration of photonic integrated circuits (PIC). Nature Publishing Group UK 2022-10-06 /pmc/articles/PMC9537414/ /pubmed/36202804 http://dx.doi.org/10.1038/s41377-022-00976-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Xu, Zefeng
Tang, Baoshan
Zhang, Xiangyu
Leong, Jin Feng
Pan, Jieming
Hooda, Sonu
Zamburg, Evgeny
Thean, Aaron Voon-Yew
Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch
title Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch
title_full Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch
title_fullStr Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch
title_full_unstemmed Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch
title_short Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch
title_sort reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive ram switch
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537414/
https://www.ncbi.nlm.nih.gov/pubmed/36202804
http://dx.doi.org/10.1038/s41377-022-00976-5
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