Presentation
Arbitrary-size Multi-layer OARSMT RL Router Trained with Combinatorial Monte-Carlo Tree Search
DescriptionThis paper presents a novel reinforcement-learning-trained router for building a multi-layer obstacle-avoiding rectilinear Steiner minimum tree (OARSMT). The router is trained by our proposed combinatorial Monte-Carlo tree search to select a proper set of Steiner points for OARSMT with only one inference. By using a Hanna-grid graph as the input and a 3D UNet as the network architecture, the router can handle layouts with any dimensions and any routing costs between grids. The experiments on both random cases and public benchmarks demonstrate that our router can significantly outperform previous algorithmic routers and other RL routers using Alpha-Go-like or PPO-based training.
Event Type
Research Manuscript
TimeWednesday, June 264:15pm - 4:30pm PDT
Location3010, 3rd Floor
EDA
Physical Design and Verification