KCLNet: Electrically Equivalence-Oriented Graph Representation Learning for Analog Circuits
Peng Xu, Yapeng Li, Tinghuan Chen, Tsung-Yi Ho, Bei Yu
Abstract
Digital circuit representation learning has made remarkable progress in electronic design automation, effectively supporting critical tasks such as testability analysis and logic reasoning. However, representation learning for analog circuits remains challenging due to their continuous electrical characteristics compared to the discrete states of digital circuits. This paper presents a direct current (DC) electrically equivalent-oriented analog representation learning framework, named KCLNet. We will open-source the dataset and code upon publication. It comprises an asynchronous graph neural network structure with electrically-simulated message passing and a representation learning method inspired by Kirchhoff
BibTeX
@inproceedings{aaai2026_kclnetelectrical,
title = {KCLNet: Electrically Equivalence-Oriented Graph Representation Learning for Analog Circuits},
author = {Peng Xu and Yapeng Li and Tinghuan Chen and Tsung-Yi Ho and Bei Yu},
booktitle = {AAAI 2026},
year = {2026}
}