| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Machine Learning for Discovery of Structure-Process-Property Relations in Electronic Materials
|
| Presentation Title |
Fast and Generalizable Detailed Router Using Attention-based Reinforcement Learning |
| Author(s) |
Haiguang Liao, Levent Burak Kara, Qingyi Dong, Xuliang Dong, Wentai Zhang, Wangyang Zhang, Weiyi Qi, Elias Fallon |
| On-Site Speaker (Planned) |
Haiguang Liao |
| Abstract Scope |
In the physical design of integrated circuits, global and detailed routing are critical stages involving the determination of the interconnected paths of each net on a circuit while satisfying the design constraints. Existing actual routers as well as routability predictors either have to resort to expensive approaches that lead to high computational times, or use heuristics that do not generalize well. In this work, we propose a new router — attention router, which is the first attempt to solve the detailed routing problem using reinforcement learning. Complex design rule constraints are encoded into the routing algorithm and an attention-model-based REINFORCE algorithm is applied to solve the most critical step in routing. The attention router is applied to solve different commercial advanced technologies analog circuits problem sets. It demonstrates generalization ability to unseen problems and achieves more than 100× acceleration over the genetic router without significantly compromising the routing solution quality. |