RL-Legalizer: Reinforcement Learning-based Cell Priority Optimization in Mixed-Height Standard Cell Legalization
- Title
- RL-Legalizer: Reinforcement Learning-based Cell Priority Optimization in Mixed-Height Standard Cell Legalization
- Authors
- LEE, SUNGYUN; Park, Seonghyeon; Kim, Daeyeon; 김민재; Le, Tuyen P.; KANG, SEOKHYEONG
- Date Issued
- 2023-04-17
- Publisher
- IEEE
- Abstract
- Cell legalization order has a substantial effect on the quality of modern VLSI designs, which use mixed-height standard cells. In this paper, we propose a deep reinforcement learning framework to optimize cell priority in the legalization phase of various designs. We extract the selected features of movable cells and their surroundings, then embed them into cell-wise deep neural networks. We then determine cell priority and legalize them in order using a pixel-wise search algorithm. The proposed framework uses a policy gradient algorithm and several training techniques, including grid-cell subepisode, data normalization, reduced-dimensional state, and network optimization. We aim to resolve the suboptimality of existing sequential legalization algorithms with respect to displacement and wirelength. On average, our proposed framework achieved 34% lower legalization costs in various benchmarks compared to that of the state-of-the-art legalization algorithm.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/118731
- ISSN
- 1530-1591
- Article Type
- Conference
- Citation
- 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2023-04-17
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.