Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks

• URL: http://arxiv.org/abs/2604.15714v1
• Date: Fri, 17 Apr 2026 05:34:53 GMT
• Title: Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
• Abstract summary: Always-on converter health monitoring demands sub-mW edge inference.<n>This work separates spiking temporal processing from physics enforcement.<n>With $93%$ spike sparsity, the architecture is suited for always-on deployment on Intel Loihi 2 or BrainChip Akida.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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