Reinforcement Learning for Electronic Design Automation: Case Studies and Perspectives: (Invited Paper)
Ahmet F. Budak, Zixuan Jiang, Keren Zhu, Azalia Mirhoseini, Anna Goldie, David Z. Pan
Abstract
Reinforcement learning (RL) algorithms have recently seen rapid advancement and adoption in the field of electronic design automation (EDA) in both academia and industry. In this paper, we first give an overview of RL and its applications in EDA. In particular, we discuss three case studies: chip macro placement, analog transistor sizing, and logic synthesis. In collaboration with Google Brain, we develop a hybrid RL and analytical mixed -size placer and achieve better results with less training time on public and proprietary benchmarks. Working with Intel, we develop an RL-inspired optimizer for analog circuit sizing, combining the strengths of deep neural networks and reinforcement learning to achieve state-of-the-art black-box optimization results. We also apply RL to the popular logic synthesis framework ABC and obtain promising results. Through these case studies, we discuss the advantages, disadvantages, opportunities, and challenges of RL in EDA.