Learning Structural Manipulability in Gate-Level Netlists Using Graph Neural Networks

Abstract:Gate-level netlists exhibit intrinsic structural properties that influence signal propagation independently of functional simulation. We define a topology-driven structural manipulability score that characterizes node-level structural flexibility using path participation, k-core embedding, symmetry, and centrality. Modeling netlists as directed graphs, we formulate node-level regression to learn this topology-derived score using graph neural networks (GNNs). Experiments on ISCAS85 and EPFL benchmarks evaluate how effectively different GNN architectures approximate this metric across held-out circuits, with hierarchical models yielding the most consistent rankings. Component-level and ablation analyses examine the contribution of individual factors. As an illustrative case study, analysis of Trojan-injected circuits using TrustHub templates reveals statistically distinguishable structural patterns, indicating that topology-based scoring provides complementary structural insight.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.16245 [cs.LG]

(or arXiv:2607.16245v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.16245 Focus to learn more arXiv-issued DOI via DataCite

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