A Logic Optimization Method Using Reinforcement Learning

With the increasing complexity of designs, it is hard to expect certain fixed optimization sequences to generate optimum results for all designs. Automating the space exploration of optimization sequences for a design attracted a good trend of research work using deep learning methods. In this paper, we introduce a framework designed to generate scripts for executing logic synthesis flows. The framework integrates a novel precisely quantified multi-objective reward function and leverages mapped netlist circuit features to enhance state representation within the reinforcement learning paradigm. Experimental results on the EPFL dataset show that the proposed method outperforms existing exploratory methods in terms of area reduction while meeting delay constraints.