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Logic synthesis requires extensive tuning of the synthesis optimization flowwhere the quality of results (QoR) depends on the sequence of optimizationsused. Efficient design space exploration is challenging due to the exponentialnumber of possible optimization permutations. Therefore, automating theoptimization process is necessary. In this work, we propose a novelreinforcement learning-based methodology that navigates the optimization spacewithout human intervention. We demonstrate the training of an Advantage ActorCritic (A2C) agent that seeks to minimize area subject to a timing constraint.Using the proposed methodology, designs can be optimized autonomously withno-humans in-loop. Evaluation on the comprehensive EPFL benchmark suite showsthat the agent outperforms existing exploration methodologies and improves QoRsby an average of 13%.

DRiLLS: Deep Reinforcement Learning for Logic Synthesis