Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
Designing efficient neuromorphic systems for complex temporal tasks remains a challenge. Zhong et al. develop a parallel memristor-based reservoir computing system capable of tuning critical parameters, achieving classification accuracy of 99.6% in spoken-digit recognition and time-series prediction...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-20692-1 |
| _version_ | 1818682746570014720 |
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| author | Yanan Zhong Jianshi Tang Xinyi Li Bin Gao He Qian Huaqiang Wu |
| author_facet | Yanan Zhong Jianshi Tang Xinyi Li Bin Gao He Qian Huaqiang Wu |
| author_sort | Yanan Zhong |
| collection | DOAJ |
| description | Designing efficient neuromorphic systems for complex temporal tasks remains a challenge. Zhong et al. develop a parallel memristor-based reservoir computing system capable of tuning critical parameters, achieving classification accuracy of 99.6% in spoken-digit recognition and time-series prediction error of 0.046 in the Hénon map. |
| first_indexed | 2024-12-17T10:23:45Z |
| format | Article |
| id | doaj.art-581e839a8b204dbd986773af5e0fb4bc |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-17T10:23:45Z |
| publishDate | 2021-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-581e839a8b204dbd986773af5e0fb4bc2022-12-21T21:52:44ZengNature PortfolioNature Communications2041-17232021-01-011211910.1038/s41467-020-20692-1Dynamic memristor-based reservoir computing for high-efficiency temporal signal processingYanan Zhong0Jianshi Tang1Xinyi Li2Bin Gao3He Qian4Huaqiang Wu5Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua UniversityInstitute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua UniversityInstitute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua UniversityInstitute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua UniversityInstitute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua UniversityInstitute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua UniversityDesigning efficient neuromorphic systems for complex temporal tasks remains a challenge. Zhong et al. develop a parallel memristor-based reservoir computing system capable of tuning critical parameters, achieving classification accuracy of 99.6% in spoken-digit recognition and time-series prediction error of 0.046 in the Hénon map.https://doi.org/10.1038/s41467-020-20692-1 |
| spellingShingle | Yanan Zhong Jianshi Tang Xinyi Li Bin Gao He Qian Huaqiang Wu Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing Nature Communications |
| title | Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing |
| title_full | Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing |
| title_fullStr | Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing |
| title_full_unstemmed | Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing |
| title_short | Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing |
| title_sort | dynamic memristor based reservoir computing for high efficiency temporal signal processing |
| url | https://doi.org/10.1038/s41467-020-20692-1 |
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