DRiLLS: Deep Reinforcement Learning for Logic Synthesis

TL;DR

This paper introduces DRiLLS, a reinforcement learning approach that automates logic synthesis optimization, achieving better design quality without human intervention by efficiently exploring complex optimization sequences.

Contribution

It presents a novel A2C reinforcement learning framework for logic synthesis optimization, outperforming existing methods in design quality improvement.

Findings

Average 13% improvement in QoR on EPFL benchmarks

Outperforms existing exploration methodologies

Automates optimization with no human intervention

Abstract

Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used. Efficient design space exploration is challenging due to the exponential number of possible optimization permutations. Therefore, automating the optimization process is necessary. In this work, we propose a novel reinforcement learning-based methodology that navigates the optimization space without human intervention. We demonstrate the training of an Advantage Actor Critic (A2C) agent that seeks to minimize area subject to a timing constraint. Using the proposed methodology, designs can be optimized autonomously with no-humans in-loop. Evaluation on the comprehensive EPFL benchmark suite shows that the agent outperforms existing exploration methodologies and improves QoRs by an average of 13%.