Transferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits

TL;DR

This paper introduces CircuitGCL, a graph contrastive learning framework that improves transferability and robustness in AMS circuit parasitic estimation by combining topology-invariant embeddings with label rebalancing techniques.

Contribution

The paper presents a novel self-supervised graph contrastive learning method with label rebalancing for transferable AMS circuit representations, addressing data scarcity and label imbalance.

Findings

CircuitGCL outperforms SOTA methods in parasitic capacitance estimation.

Achieves 33.64% to 44.20% R^2 improvement in edge regression.

F1-score increases by 0.9 to 2.1 times in node classification.

Abstract

Graph representation learning on Analog-Mixed Signal (AMS) circuits is crucial for various downstream tasks, e.g., parasitic estimation. However, the scarcity of design data, the unbalanced distribution of labels, and the inherent diversity of circuit implementations pose significant challenges to learning robust and transferable circuit representations. To address these limitations, we propose CircuitGCL, a novel graph contrastive learning framework that integrates representation scattering and label rebalancing to enhance transferability across heterogeneous circuit graphs. CircuitGCL employs a self-supervised strategy to learn topology-invariant node embeddings through hyperspherical representation scattering, eliminating dependency on large-scale data. Simultaneously, balanced mean squared error (BMSE) and balanced softmax cross-entropy (BSCE) losses are introduced to mitigate label distribution disparities between circuits, enabling robust and transferable parasitic estimation. Evaluated on parasitic capacitance estimation (edge-level task) and ground capacitance classification (node-level task) across TSMC 28nm AMS designs, CircuitGCL outperforms all state-of-the-art (SOTA) methods, with the $R^2$ improvement of $33.64\% \sim 44.20\%$ for edge regression and F1-score gain of $0.9\times \sim 2.1\times$ for node classification. Our code is available at https://github.com/ShenShan123/CircuitGCL.