Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
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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.
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References · 6
- 2018Neural Ordinary Differential Equationscited by 3,733
- 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
- 2018CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUscited by 116
- 2018Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardwarecited by 37
- 2022Spiking neural networks for nonlinear regressioncited by 10
- 2025Spiking Neural Networks for Low-Power Vibration-Based Predictive Maintenancecited by 1
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