Circuit Routing Using Monte Carlo Tree Search and Deep Reinforcement Learning

Youbiao He, Hebi Li, Jin Tian, Forrest Sheng Bao

Abstract: We propose a new approach to circuit routing by modeling it as a sequential decision problem and solving it in MCTS with DRL-guided rollout. Compared with conventional routing algorithms that are either manually designed with domain knowledge or tailored to specific design rules, our approach can be reconfigured for nearly any routing constraints and goals without changing the algorithm itself because the AI agent explores solutions in a general search strategy. Experimental results on both randomly generated circuits and popular open-source hardware projects show that our method achieves 33.3% higher success rate than traditional A *-based approach.