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Rotating neurons for all-analog implementation of cyclic reservoir computing

Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly focused on the reservoir layer, whereas an end-to-end reservoir...

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Autores principales: Liang, Xiangpeng, Zhong, Yanan, Tang, Jianshi, Liu, Zhengwu, Yao, Peng, Sun, Keyang, Zhang, Qingtian, Gao, Bin, Heidari, Hadi, Qian, He, Wu, Huaqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8943160/
https://www.ncbi.nlm.nih.gov/pubmed/35322037
http://dx.doi.org/10.1038/s41467-022-29260-1
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author Liang, Xiangpeng
Zhong, Yanan
Tang, Jianshi
Liu, Zhengwu
Yao, Peng
Sun, Keyang
Zhang, Qingtian
Gao, Bin
Heidari, Hadi
Qian, He
Wu, Huaqiang
author_facet Liang, Xiangpeng
Zhong, Yanan
Tang, Jianshi
Liu, Zhengwu
Yao, Peng
Sun, Keyang
Zhang, Qingtian
Gao, Bin
Heidari, Hadi
Qian, He
Wu, Huaqiang
author_sort Liang, Xiangpeng
collection PubMed
description Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly focused on the reservoir layer, whereas an end-to-end reservoir architecture has yet to be developed. Here, we propose a versatile method for implementing cyclic reservoirs using rotating elements integrated with signal-driven dynamic neurons, whose equivalence to standard cyclic reservoir algorithm is mathematically proven. Simulations show that the rotating neuron reservoir achieves record-low errors in a nonlinear system approximation benchmark. Furthermore, a hardware prototype was developed for near-sensor computing, chaotic time-series prediction and handwriting classification. By integrating a memristor array as a fully-connected output layer, the all-analog reservoir computing system achieves 94.0% accuracy, while simulation shows >1000× lower system-level power than prior works. Therefore, our work demonstrates an elegant rotation-based architecture that explores hardware physics as computational resources for high-performance reservoir computing.
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spelling pubmed-89431602022-04-08 Rotating neurons for all-analog implementation of cyclic reservoir computing Liang, Xiangpeng Zhong, Yanan Tang, Jianshi Liu, Zhengwu Yao, Peng Sun, Keyang Zhang, Qingtian Gao, Bin Heidari, Hadi Qian, He Wu, Huaqiang Nat Commun Article Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly focused on the reservoir layer, whereas an end-to-end reservoir architecture has yet to be developed. Here, we propose a versatile method for implementing cyclic reservoirs using rotating elements integrated with signal-driven dynamic neurons, whose equivalence to standard cyclic reservoir algorithm is mathematically proven. Simulations show that the rotating neuron reservoir achieves record-low errors in a nonlinear system approximation benchmark. Furthermore, a hardware prototype was developed for near-sensor computing, chaotic time-series prediction and handwriting classification. By integrating a memristor array as a fully-connected output layer, the all-analog reservoir computing system achieves 94.0% accuracy, while simulation shows >1000× lower system-level power than prior works. Therefore, our work demonstrates an elegant rotation-based architecture that explores hardware physics as computational resources for high-performance reservoir computing. Nature Publishing Group UK 2022-03-23 /pmc/articles/PMC8943160/ /pubmed/35322037 http://dx.doi.org/10.1038/s41467-022-29260-1 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
Liang, Xiangpeng
Zhong, Yanan
Tang, Jianshi
Liu, Zhengwu
Yao, Peng
Sun, Keyang
Zhang, Qingtian
Gao, Bin
Heidari, Hadi
Qian, He
Wu, Huaqiang
Rotating neurons for all-analog implementation of cyclic reservoir computing
title Rotating neurons for all-analog implementation of cyclic reservoir computing
title_full Rotating neurons for all-analog implementation of cyclic reservoir computing
title_fullStr Rotating neurons for all-analog implementation of cyclic reservoir computing
title_full_unstemmed Rotating neurons for all-analog implementation of cyclic reservoir computing
title_short Rotating neurons for all-analog implementation of cyclic reservoir computing
title_sort rotating neurons for all-analog implementation of cyclic reservoir computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8943160/
https://www.ncbi.nlm.nih.gov/pubmed/35322037
http://dx.doi.org/10.1038/s41467-022-29260-1
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