DRiLLS: Deep Reinforcement Learning for Logic Synthesis

Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used. The authors propose a novel reinforcement learning-based methodology that navigates the optimization space without human intervention.