Real-Time State Modulation and Acquisition Circuit in Neuromorphic Memristive Systems
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910605024165888 |
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| author | Wang, Shengbo Li, Cong Pu, Tongming Zhang, Jian Ma, Weihao Occhipinti, Luigi Nathan, Arokia Gao, Shuo |
| author_facet | Wang, Shengbo Li, Cong Pu, Tongming Zhang, Jian Ma, Weihao Occhipinti, Luigi Nathan, Arokia Gao, Shuo |
| contents | Memristive neuromorphic systems are designed to emulate human perception and cognition, where the memristor states represent essential historical information to perform both low-level and high-level tasks. However, current systems face challenges with the separation of state modulation and acquisition, leading to undesired time delays that impact real-time performance. To overcome this issue, we introduce a dual-function circuit that concurrently modulates and acquires memristor state information. This is achieved through two key features: 1) a feedback operational amplifier (op-amp) based circuit that ensures precise voltage application on the memristor while converting the passing current into a voltage signal; 2) a division calculation circuit that acquires state information from the modulation voltage and the converted voltage, improving stability by leveraging the intrinsic threshold characteristics of memristors. This circuit has been evaluated in a memristor-based nociceptor and a memristor crossbar, demonstrating exceptional performance. For instance, it achieves mean absolute acquisition errors below 1 Ω during the modulation process in the nociceptor application. These results demonstrate that the proposed circuit can operate at different scales, holding the potential to enhance a wide range of neuromorphic applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00378 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Real-Time State Modulation and Acquisition Circuit in Neuromorphic Memristive Systems Wang, Shengbo Li, Cong Pu, Tongming Zhang, Jian Ma, Weihao Occhipinti, Luigi Nathan, Arokia Gao, Shuo Applied Physics Neural and Evolutionary Computing Memristive neuromorphic systems are designed to emulate human perception and cognition, where the memristor states represent essential historical information to perform both low-level and high-level tasks. However, current systems face challenges with the separation of state modulation and acquisition, leading to undesired time delays that impact real-time performance. To overcome this issue, we introduce a dual-function circuit that concurrently modulates and acquires memristor state information. This is achieved through two key features: 1) a feedback operational amplifier (op-amp) based circuit that ensures precise voltage application on the memristor while converting the passing current into a voltage signal; 2) a division calculation circuit that acquires state information from the modulation voltage and the converted voltage, improving stability by leveraging the intrinsic threshold characteristics of memristors. This circuit has been evaluated in a memristor-based nociceptor and a memristor crossbar, demonstrating exceptional performance. For instance, it achieves mean absolute acquisition errors below 1 Ω during the modulation process in the nociceptor application. These results demonstrate that the proposed circuit can operate at different scales, holding the potential to enhance a wide range of neuromorphic applications. |
| title | Real-Time State Modulation and Acquisition Circuit in Neuromorphic Memristive Systems |
| topic | Applied Physics Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2406.00378 |