Asynchronous Reinforcement Learning Framework for Net Order Exploration in Detailed Routing
Tong Qu1,2, Yibo Lin3, Zongqing Lu3, Yajuan Su1,2,4 and Yayi Wei1,2,4
1Institute of Microelectronics of the Chinese Academy of Sciences, Beijing, China
2University of Chinese Academy of Sciences, Beijing, China
3CS Department, Peking University, Beijing, China
4Guangdong Greater Bay Area Applied Research Institute of Integrated Circuit and Systems, Guangdong, China
ABSTRACT
The net orders in detailed routing are crucial to routing closure, especially in most modern routers following the sequential routing manner with the rip-up and reroute scheme. In advanced technology nodes, detailed routing has to deal with complicated design rules and large problem sizes, making its performance more sensitive to the order of nets to be routed. In literature, the net orders are mostly determined by simple heuristic rules tuned for specific benchmarks. In this work, we propose an asynchronous reinforcement learning (RL) framework to search for optimal ordering strategies automatically. By asynchronous querying the router and training the RL agents, we can generate highperformance routing sequences to achieve better solution quality.
