# Equivalent-Gate Identification

The authors propose CircuitGate, a function-aware representation learning framework for And-Inverter Graphs that explicitly encodes polarity-aware global primary-input (PI) support, modulates fanin updates with support-overlap-aware reconvergence gating, and adds logic-inspired Boolean constraints; across ForgeEDA, EPFL and ITC'99 it outperforms the compared GNN and circuit-specific methods on equivalent-gate identification and signal-probability prediction, reducing MAE by 21.7% and 13.8% on ForgeEDA, and it attains the lowest equivalent-gate MAE under zero-shot ForgeEDA-to-OpenABC transfer.

The authors propose CircuitGate, a function-aware representation learning framework for And-Inverter Graphs that explicitly encodes polarity-aware global primary-input (PI) support, modulates fanin updates with support-overlap-aware reconvergence gating, and adds logic-inspired Boolean constraints; across ForgeEDA, EPFL and ITC'99 it outperforms the compared GNN and circuit-specific methods on equivalent-gate identification and signal-probability prediction, reducing MAE by 21.7% and 13.8% on ForgeEDA, and it attains the lowest equivalent-gate MAE under zero-shot ForgeEDA-to-OpenABC transfer.