FuncGNN: Learning Functional Semantics of Logic Circuits with Graph Neural Networks (2506.06787v1)

Abstract: As integrated circuit scale grows and design complexity rises, effective circuit representation helps support logic synthesis, formal verification, and other automated processes in electronic design automation. And-Inverter Graphs (AIGs), as a compact and canonical structure, are widely adopted for representing Boolean logic in these workflows. However, the increasing complexity and integration density of modern circuits introduce structural heterogeneity and global logic information loss in AIGs, posing significant challenges to accurate circuit modeling. To address these issues, we propose FuncGNN, which integrates hybrid feature aggregation to extract multi-granularity topological patterns, thereby mitigating structural heterogeneity and enhancing logic circuit representations. FuncGNN further introduces gate-aware normalization that adapts to circuit-specific gate distributions, improving robustness to structural heterogeneity. Finally, FuncGNN employs multi-layer integration to merge intermediate features across layers, effectively synthesizing local and global semantic information for comprehensive logic representations. Experimental results on two logic-level analysis tasks (i.e., signal probability prediction and truth-table distance prediction) demonstrate that FuncGNN outperforms existing state-of-the-art methods, achieving improvements of 2.06% and 18.71%, respectively, while reducing training time by approximately 50.6% and GPU memory usage by about 32.8%.

• The paper introduces FuncGNN, which integrates hybrid feature aggregation, global context normalization, and multi-layer integration to enhance the learning of functional semantics in logic circuits.
• The methodology effectively improves Signal Probability and Truth-Table Distance predictions by 2.06% and 18.71%, while significantly reducing training time and GPU memory usage.
• The approach offers promising applications in EDA by addressing structural heterogeneity and preserving multi-level logic information for optimized circuit design.

Analysis of FuncGNN: Learning Functional Semantics of Logic Circuits with Graph Neural Networks

The complexity and integration density of modern integrated circuits have escalated, necessitating sophisticated methods to accurately model circuit designs. The paper "FuncGNN: Learning Functional Semantics of Logic Circuits with Graph Neural Networks" introduces a novel framework intended to address these challenges, particularly within the Electronic Design Automation (EDA) domain using And-Inverter Graphs (AIGs) as the standard representation for Boolean logic.

Methodological Framework

FuncGNN, the proposed framework, integrates three distinct components to enhance the robustness and efficiency of AIG representation learning:

1. Hybrid Feature Aggregation Component: This component addresses structural heterogeneity in AIGs by combining GraphSAGE-based neighborhood aggregation with GINConv-based nonlinear enhancement. It efficiently extracts local and global structural information, adapting to variations in gate arrangements and topology found in AIGs.
2. Global Context Normalization Component: It incorporates gate-aware normalization utilizing global logic statistics such as the AND-to-NOT gate ratio across circuits. This component mitigates discrepancies due to structural diversity across AIGs, enhancing training stability by aligning feature distributions according to circuit-wide proportions.
3. Multi-Layer Integration Component: This component preserves and synthesizes logic information across multiple layers. It leverages dense concatenation and linear projection techniques to fuse intermediate outputs, maintaining computational efficiency while preventing information loss and over-squashing.

Experimental Evaluation

The effectiveness of FuncGNN was demonstrated through experiments on nearly 10,000 AIG samples from four benchmark circuit suites, focusing on two specific tasks: Signal Probability Prediction (SPP) and Truth-Table Distance Prediction (TTDP). The results highlight FuncGNN’s superior performance, showcasing improvements of 2.06% in SPP and 18.71% in TTDP tasks compared to existing methodologies, alongside notable reductions in training time (50.6%) and GPU memory usage (32.8%).

Implications and Future Potential

The architectural design of FuncGNN provides a substantial advancement in AIG representation learning for EDA-related applications. By effectively addressing challenges like structural heterogeneity and global logic information loss, FuncGNN paves the way for enhanced circuit optimization, synthesis processes, and verification workflows. This framework's ability to learn and abstract functional semantics from circuits suggests promising applicability in broader machine learning tasks.

Future research could explore the extension of FuncGNN to diverse circuit configurations and its integration within various EDA tasks. The theoretical exploration of incorporating global circuit statistics into normalization processes might further optimize learning stability and expand its use in modeling complex logic functions.

In conclusion, the paper provides a significant contribution to functional representation learning in logic circuits, presenting a scalable and efficient model that addresses critical challenges inherent in modern circuit design. The findings encourage further investigation into adaptive learning methodologies for electronic design automation, with potential advancements in constructing robust and context-aware representations.