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. By modeling netlists as directed graphs, we formulate node-level regression to learn this topology-derived score using graph neural networks (GNNs).
Experiments and Results
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.
Case Study
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.
Blogger's Review: This paper delves into the structural characteristics of gate-level netlists using graph neural networks, showcasing the potential of topological analysis in circuit design, particularly in security assessment and optimization. The findings lay an important theoretical and practical foundation for future circuit design and analysis.