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.

翻译：全时运行的变换器健康监测需要亚毫瓦级边缘推理能力，这是基于GPU的物理信息神经网络无法实现的。本工作将脉冲时序处理与物理约束分离：采用三层泄露积分点火脉冲神经网络估计无源元件参数，同时通过可微ODE求解器将ODE物理损失与展开的脉冲循环解耦，实现物理一致性训练。在受电磁干扰的同步降压变换器基准测试中，与传统前馈网络相比，该SNN将集总电阻估计误差从25.8%降低至10.2%，达到无源元件±10%制造公差范围内，且预测在神经形态硬件上可实现约270倍的能耗降低。持久膜状态还使退化跟踪成为可能，并通过在突发故障时+5.5个百分点的脉冲率跃变实现事件驱动故障检测。凭借93%的脉冲稀疏性，该架构适用于在Intel Loihi 2或BrainChip Akida平台上全时部署。