Benchmarking Continual Learning on Netlists with Circuit-Targeted Graph Neural Networks

The rapid evolution of integrated circuits demands that machine learning (ML) for electronic design automation (EDA) adapts to new circuit semantics without catastrophic forgetting (CF) of prior knowledge-a challenge unaddressed by commonly established, static training paradigms. Continual learning (CL) offers a promising approach, but its application to evolving netlists remains unexplored. Here, we present the first benchmarking study of CL on netlists with circuit-targeted graph neural networks (GNNs). We evaluate six CL methods, including parameter regularization, replay-based, and hybrid approaches, all for a fixed GNN architecture for fair comparison. Our benchmarking covers two foundational GNN applications commonly found in EDA: gate-level node classification and netlistlevel link prediction. We find that replay-based CL techniques are particularly suitable for hindering CF in such circuit-targeted GNN applications. This work paves the way for future adaptive EDA tools for emerging design landscapes. All benchmarking materials are released at [1].