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

arXiv: https://arxiv.org/abs/2604.15714 · HTML: https://mlsanity.com/paper/2604.15714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 17 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://mlsanity.com/paper/1801.06601/) (2018)
- [Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware](https://mlsanity.com/paper/1812.01739/) (2018)
- [Spiking neural networks for nonlinear regression](https://mlsanity.com/paper/2210.03515/) (2022)
- [Spiking Neural Networks for Low-Power Vibration-Based Predictive Maintenance](https://mlsanity.com/paper/2506.13416/) (2025)

## Abstract (arXiv)

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 ± 10% manufacturing tolerance of passive components, at a projected ~270× 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.