Architectural Exploration of Reinforcement Learning and Graph Neural Networks for Gate-Level Logic Synthesis in Modern EDA
This review-style study examines how reinforcement learning agents and graph neural networks are being integrated into gate-level logic synthesis for electronic design automation. It discusses how GNN-derived structural embeddings support pre-layout estimation of signal probability and switching activity, how RL agents...