Neural Substitute Solver for Efficient Edge Inference of Power Electronic Hybrid Dynamics

TL;DR

This paper introduces a neural substitute solver that enables fast, resource-efficient inference of power electronic system dynamics on edge hardware, facilitating real-time testing and control.

Contribution

The paper presents a neural-network-based framework that replaces traditional computationally intensive steps, significantly reducing computation time and resource usage for edge deployment.

Findings

Achieves 23x speedup over traditional solvers

Reduces hardware resource usage by 60%

Demonstrates effective real-time inference on power electronic systems

Abstract

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.