RL-Sizer: VLSI Gate Sizing for Timing Optimization using Deep Reinforcement Learning

Gate sizing for timing optimization is performed extensively throughout electronic design automation (EDA) flows. However, increasing design sizes and time-to-market pressure force EDA tools to maintain pseudo-linear complexity, thereby limiting the global exploration done by the underlying sizing algorithms. Furthermore, high-performance low-power designs are pushing the envelope on power, performance and area (PPA), creating a need for last mile PPA closure using more powerful algorithms. Reinforcement learning (RL) is a disruptive paradigm that achieves high-quality optimization results beyond traditional algorithms. In this paper, we formulate gate sizing as an RL process, and propose RL-Sizer, an autonomous gate sizing agent, which performs timing optimization in an unsupervised manner. In the experiments, we demonstrate that RL-Sizer can improve the native sizing algorithms of an industry-leading EDA tool, Synopsys IC-Compiler II (ICC2), on 6 commercial designs in advanced process nodes (5 – 16nm). RL-Sizer delivers significantly better total negative slack (TNS) and number of violating endpoints (NVEs) on 4 designs with negligible power overhead, while achieving parity on athe others.