Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910138770653184 |
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| author | Baik, Hyeongmeen Poursiami, Hamed Parsa, Maryam Roy, Jinia |
| author_facet | Baik, Hyeongmeen Poursiami, Hamed Parsa, Maryam Roy, Jinia |
| contents | Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work separates spiking temporal processing from physics enforcement: a three-layer leaky integrate-and-fire SNN estimates passive component parameters while a differentiable ODE solver provides physics-consistent training by decoupling the ODE physics loss from the unrolled spiking loop. On an EMI-corrupted synchronous buck converter benchmark, the SNN reduces lumped resistance error from $25.8\%$ to $10.2\%$ versus a feedforward baseline, within the $\pm 10\%$ manufacturing tolerance of passive components, at a projected ${\sim}270\times$ energy reduction on neuromorphic hardware. Persistent membrane states further enable degradation tracking and event-driven fault detection via a $+5.5$ percentage-point spike-rate jump at abrupt faults. With $93\%$ spike sparsity, the architecture is suited for always-on deployment on Intel Loihi 2 or BrainChip Akida. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_15714 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks Baik, Hyeongmeen Poursiami, Hamed Parsa, Maryam Roy, Jinia Neural and Evolutionary Computing Machine Learning Systems and Control I.2.6; I.5.1; C.3 Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work separates spiking temporal processing from physics enforcement: a three-layer leaky integrate-and-fire SNN estimates passive component parameters while a differentiable ODE solver provides physics-consistent training by decoupling the ODE physics loss from the unrolled spiking loop. On an EMI-corrupted synchronous buck converter benchmark, the SNN reduces lumped resistance error from $25.8\%$ to $10.2\%$ versus a feedforward baseline, within the $\pm 10\%$ manufacturing tolerance of passive components, at a projected ${\sim}270\times$ energy reduction on neuromorphic hardware. Persistent membrane states further enable degradation tracking and event-driven fault detection via a $+5.5$ percentage-point spike-rate jump at abrupt faults. With $93\%$ spike sparsity, the architecture is suited for always-on deployment on Intel Loihi 2 or BrainChip Akida. |
| title | Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks |
| topic | Neural and Evolutionary Computing Machine Learning Systems and Control I.2.6; I.5.1; C.3 |
| url | https://arxiv.org/abs/2604.15714 |