A Linear Implementation of an Analog Resonate-and-Fire Neuron
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915620569743360 |
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| author | Liu, Angqi Moro, Filippo Billaudelle, Sebastian Payvand, Melika |
| author_facet | Liu, Angqi Moro, Filippo Billaudelle, Sebastian Payvand, Melika |
| contents | Oscillatory dynamics have recently proven highly effective in machine learning (ML), particularly through State-Space-Models (SSM) that leverage structured linear recurrences for long-range temporal processing. Resonate-and-Fire neurons capture such oscillatory behavior in a spiking framework, offering strong expressivity with sparse event-based communication. While early analog RAF circuits employed nonlinear coupling and suffered from process sensitivity, modern ML practice favors linear recurrence. In this work, we introduce a resonate-and-fire (RAF) neuron, built in 22nm Fully-Depleted Silicon-on-Insulator technology, that aligns with SSM principles while retaining the efficiency of spike-based communication. We analyze its dynamics, linearity, and resilience to Process, Voltage, and Temperature variations, and evaluate its power, performance, and area trade-offs. We map the characteristics of our circuit into a system-level simulation where our RAF neuron is utilized in a keyword-spotting task, showing that its non-idealities do not hinder performance. Our results establish RAF neurons as robust, energy-efficient computational primitives for neuromorphic hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12297 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Linear Implementation of an Analog Resonate-and-Fire Neuron Liu, Angqi Moro, Filippo Billaudelle, Sebastian Payvand, Melika Signal Processing Oscillatory dynamics have recently proven highly effective in machine learning (ML), particularly through State-Space-Models (SSM) that leverage structured linear recurrences for long-range temporal processing. Resonate-and-Fire neurons capture such oscillatory behavior in a spiking framework, offering strong expressivity with sparse event-based communication. While early analog RAF circuits employed nonlinear coupling and suffered from process sensitivity, modern ML practice favors linear recurrence. In this work, we introduce a resonate-and-fire (RAF) neuron, built in 22nm Fully-Depleted Silicon-on-Insulator technology, that aligns with SSM principles while retaining the efficiency of spike-based communication. We analyze its dynamics, linearity, and resilience to Process, Voltage, and Temperature variations, and evaluate its power, performance, and area trade-offs. We map the characteristics of our circuit into a system-level simulation where our RAF neuron is utilized in a keyword-spotting task, showing that its non-idealities do not hinder performance. Our results establish RAF neurons as robust, energy-efficient computational primitives for neuromorphic hardware. |
| title | A Linear Implementation of an Analog Resonate-and-Fire Neuron |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.12297 |