CircuitGate: Logic-Consistent Circuit-Level Functional Modeling for And-Inverter Graphs

Key points

• And-Inverter Graphs (AIGs) are fundamental representations for logic synthesis and verification in Electronic Design Automation (EDA).1
• As structured representations of complex digital systems, AIGs require models to capture functional dependencies beyond local structure and remain robust to functionality-preserving transformations.1
• In learning-based AIG representation, existing approaches are predominantly based on GNNs and rely on local gate-level message passing, limiting their ability to capture circuit-level functional context and making the learned representations sensitive to topology-specific patterns.1
• CircuitGate explicitly encodes global primary-input (PI) support and models support-overlap-aware reconvergence between fanins, while incorporating logic-inspired Boolean constraints to encourage functionally consistent representations.1

Sources (1)

• [1]CircuitGate: Logic-Consistent Circuit-Level Functional Modeling for And-Inverter Graphs
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 06:48 AM

  Therefore, we propose CircuitGate, a function-aware AIG representation learning framework that advances from gate-level semantics to circuit-level functional modeling.

  And-Inverter Graphs (AIGs) are fundamental representations for logic synthesis and verification in Electronic Design Automation (EDA).

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