用轻量神经网络替代传统求解器，实现实时边缘推断
Neural Substitute Solver for Efficient Edge Inference of Power Electronic Hybrid Dynamics
- 用轻量神经网络替代矩阵运算和高阶积分步骤
- 在多级直流变换器上实现23倍加速与60%资源节省
- 适合电力电子系统实时控制与边缘部署场景
将电力电子系统（PES）动态推理推向实时边缘端具有变革性潜力，但受限于边缘硬件资源，在资源受限设备上高效推断其固有的混合连续-离散动力学仍具挑战。本文提出神经替代求解器（NSS），一种基于神经网络的框架，可实现快速、准确的推理并显著降低计算成本。具体而言，NSS利用轻量神经网络替代传统求解器中的耗时矩阵运算和高阶数值积分步骤，将串行瓶颈转化为适用于边缘硬件的高并行操作。在多级直流-直流变换器上的实验验证表明，与传统求解器相比，NSS实现了23倍加速和60%的硬件资源减少，为高保真度PES动态边缘推理部署铺平了道路。
原文摘要 · Abstract (English)
Advancing the dynamics inference of power electronic systems (PES) to the real-time edge-side holds transform-ative potential for testing, control, and monitoring. How-ever, efficiently inferring the inherent hybrid continu-ous-discrete dynamics on resource-constrained edge hardware remains a significant challenge. This letter pro-poses a neural substitute solver (NSS) approach, which is a neural-network-based framework aimed at rapid accurate inference with significantly reduced computational costs. Specifically, NSS leverages lightweight neural networks to substitute time-consuming matrix operation and high-order numerical integration steps in traditional solvers, which transforms sequential bottlenecks into highly parallel operation suitable for edge hardware. Experimental vali-dation on a multi-stage DC-DC converter demonstrates that NSS achieves 23x speedup and 60% hardware resource reduction compared to traditional solvers, paving the way for deploying edge inference of high-fidelity PES dynamics.
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