Reinforcement Learning Driven Physical Synthesis : (Invited Paper)

Physical synthesis has emerged as a core component in a modern circuit design flow. Large-scale optimization problem is often involved in the process, which requires substantial efforts to solve and no optimality is guaranteed. Reinforcement learning provides one direction to deal with the above issue by automatically acquiring knowledge through experience, which has shown great success in various applications. In this paper, we introduce the foundation of and the progress in reinforcement learning, and review some recent approaches in applying reinforcement learning to physical synthesis. We hope to inspire more work and to see more talented ideas in this field.