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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-8943160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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