An Asynchronous Mixed-Signal Resonate-and-Fire Neuron
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917132829196288 |
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| author | Leo, Giuseppe Gibertini, Paolo Ilter, Irem Covi, Erika Richter, Ole Chicca, Elisabetta |
| author_facet | Leo, Giuseppe Gibertini, Paolo Ilter, Irem Covi, Erika Richter, Ole Chicca, Elisabetta |
| contents | Analog computing at the edge is an emerging strategy to limit data storage and transmission requirements, as well as energy consumption, and its practical implementation is in its initial stages of development. Translating properties of biological neurons into hardware offers a pathway towards low-power, real-time edge processing. Specifically, resonator neurons offer selectivity to specific frequencies as a potential solution for temporal signal processing. Here, we show a fabricated Complementary Metal-Oxide-Semiconductor (CMOS) mixed-signal Resonate-and-Fire (R&F) neuron circuit implementation that emulates the behavior of these neural cells responsible for controlling oscillations within the central nervous system. We integrate the design with asynchronous handshake capabilities, perform comprehensive variability analyses, and characterize its frequency detection functionality. Our results demonstrate the feasibility of large-scale integration within neuromorphic systems, thereby advancing the exploitation of bio-inspired circuits for efficient edge temporal signal processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07361 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Asynchronous Mixed-Signal Resonate-and-Fire Neuron Leo, Giuseppe Gibertini, Paolo Ilter, Irem Covi, Erika Richter, Ole Chicca, Elisabetta Signal Processing Neural and Evolutionary Computing Analog computing at the edge is an emerging strategy to limit data storage and transmission requirements, as well as energy consumption, and its practical implementation is in its initial stages of development. Translating properties of biological neurons into hardware offers a pathway towards low-power, real-time edge processing. Specifically, resonator neurons offer selectivity to specific frequencies as a potential solution for temporal signal processing. Here, we show a fabricated Complementary Metal-Oxide-Semiconductor (CMOS) mixed-signal Resonate-and-Fire (R&F) neuron circuit implementation that emulates the behavior of these neural cells responsible for controlling oscillations within the central nervous system. We integrate the design with asynchronous handshake capabilities, perform comprehensive variability analyses, and characterize its frequency detection functionality. Our results demonstrate the feasibility of large-scale integration within neuromorphic systems, thereby advancing the exploitation of bio-inspired circuits for efficient edge temporal signal processing. |
| title | An Asynchronous Mixed-Signal Resonate-and-Fire Neuron |
| topic | Signal Processing Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2512.07361 |