Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
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 r...
| Authors | Hyeongmeen Baik, Hamed Poursiami, Maryam Parsa et al. |
|---|---|
| Primary category | cs.NE |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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