This repository contains the implementation of a Graph Neural Network (GNN) model for processing And-Inverter Graphs (AIGs) together with logic synthesis recipes to predict key circuit metrics such as delay and area
Search for linearizing sets to solve inversion problem
Pure Python AIG (And-Inverter Graph) optimizer with FRAIG sweeping, NPN-guided DAG-aware rewriting, resubstitution, and stochastic multi-restart optimization. Benchmarks against Yosys and ABC.
Heuristics based approach for Logic Synthesis with the goal to reduce the delay in the circuit and the execution time.
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