Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
Hyeongmeen Baik, Hamed Poursiami, Maryam Parsa, Jinia Roy
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Why It Matters
First spiking neural network for sub-mW power converter health monitoring that decouples physics enforcement from temporal processing, enabling real-time edge inference without GPUs—critical for industrial IoT systems needing ultra-low-power reliability.
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.