Model Releases
Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
arXiv:2604.15714v1 Announce Type: cross Abstract: Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work se
arXiv:2604.15714v1 Announce Type: cross 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}270imes 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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Source: arXiv cs.LG | 2026-04-20