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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.02813 |
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| _version_ | 1866917884645605376 |
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| author | Nishioka, Daiki Kitano, Hina Namiki, Wataru Terabe, Kazuya Tsuchiya, Takashi |
| author_facet | Nishioka, Daiki Kitano, Hina Namiki, Wataru Terabe, Kazuya Tsuchiya, Takashi |
| contents | The rising energy demands of conventional AI systems underscore the need for efficient computing technologies like brain-inspired computing. Physical reservoir computing (PRC), leveraging the nonlinear dynamics of physical systems for information processing, has emerged as a promising approach for neuromorphic computing. However, current PRC systems are constrained by narrow operating timescales and limited performance. To address these challenges, an ion-gel/graphene electric double layer transistor-based ion-gating reservoir (IGR) was developed, offering adaptability across multi-time scales with an exceptionally wide operating range from 1 MHz to 20 Hz and high information processing capacity. The IGR achieved deep learning (DL)-level accuracy in chaotic time series prediction tasks while reducing computational resource requirements to 1/100 of those needed by DL. Principal component analysis reveals the IGR's superior performance stems from its high-dimensionality, driven by the ambipolar behavior of graphene and multiple relaxation processes. The proposed IGR represents a significant step forward in providing low-power, high-performance computing solutions, particularly for resource-constrained edge environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02813 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-time scale and high performance in-material reservoir computing using graphene-based ion-gating reservoir Nishioka, Daiki Kitano, Hina Namiki, Wataru Terabe, Kazuya Tsuchiya, Takashi Applied Physics The rising energy demands of conventional AI systems underscore the need for efficient computing technologies like brain-inspired computing. Physical reservoir computing (PRC), leveraging the nonlinear dynamics of physical systems for information processing, has emerged as a promising approach for neuromorphic computing. However, current PRC systems are constrained by narrow operating timescales and limited performance. To address these challenges, an ion-gel/graphene electric double layer transistor-based ion-gating reservoir (IGR) was developed, offering adaptability across multi-time scales with an exceptionally wide operating range from 1 MHz to 20 Hz and high information processing capacity. The IGR achieved deep learning (DL)-level accuracy in chaotic time series prediction tasks while reducing computational resource requirements to 1/100 of those needed by DL. Principal component analysis reveals the IGR's superior performance stems from its high-dimensionality, driven by the ambipolar behavior of graphene and multiple relaxation processes. The proposed IGR represents a significant step forward in providing low-power, high-performance computing solutions, particularly for resource-constrained edge environments. |
| title | Multi-time scale and high performance in-material reservoir computing using graphene-based ion-gating reservoir |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2501.02813 |