Modeling Relational Logic Circuits for And-Inverter Graph Convolutional Network

• URL: http://arxiv.org/abs/2508.11991v3
• Date: Wed, 20 Aug 2025 01:16:52 GMT
• Title: Modeling Relational Logic Circuits for And-Inverter Graph Convolutional Network
• Authors: Weihao Sun, Shikai Guo, Siwen Wang, Qian Ma, Hui Li,
• Abstract summary: And-Inverter Graphs (AIGs) efficiently represent, optimize, and verify the functional characteristics of digital circuits.<n>AIGer consists of two components: 1) Node logic feature embedding embedding component and 2) AIGs feature learning network component.
• Score: 7.148148583507452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automation of logic circuit design enhances chip performance, energy efficiency, and reliability, and is widely applied in the field of Electronic Design Automation (EDA).And-Inverter Graphs (AIGs) efficiently represent, optimize, and verify the functional characteristics of digital circuits, enhancing the efficiency of EDA development.Due to the complex structure and large scale of nodes in real-world AIGs, accurate modeling is challenging, leading to existing work lacking the ability to jointly model functional and structural characteristics, as well as insufficient dynamic information propagation capability.To address the aforementioned challenges, we propose AIGer.Specifically, AIGer consists of two components: 1) Node logic feature initialization embedding component and 2) AIGs feature learning network component.The node logic feature initialization embedding component projects logic nodes, such as AND and NOT, into independent semantic spaces, to enable effective node embedding for subsequent processing.Building upon this, the AIGs feature learning network component employs a heterogeneous graph convolutional network, designing dynamic relationship weight matrices and differentiated information aggregation approaches to better represent the original structure and information of AIGs.The combination of these two components enhances AIGer's ability to jointly model functional and structural characteristics and improves its message passing capability. Experimental results indicate that AIGer outperforms the current best models in the Signal Probability Prediction (SSP) task, improving MAE and MSE by 18.95\% and 44.44\%, respectively. In the Truth Table Distance Prediction (TTDP) task, AIGer achieves improvements of 33.57\% and 14.79\% in MAE and MSE, respectively, compared to the best-performing models.

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