Exploring Logic Optimizations with Reinforcement Learning and Graph Convolutional Network
Logic synthesis for combinational circuits is to find the minimum equivalent representation for Boolean logic functions. A well-adopted logic synthesis paradigm represents the Boolean logic with standardized logic networks, such as and-inverter graphs (AIG), and performs logic minimization operations over the graph iteratively. Although the research for different logic representation and operations is fruitful, the sequence of using the operations are often determined by heuristics. We propose a Markov decision process (MDP) formulation of the logic synthesis problem and a reinforcement learning (RL) algorithm incorporating with graph convolutional network to explore the solution search space. The experimental results show that the proposed method outperforms the well-known logic synthesis heuristics with the same sequence length and action space.