Zixuan Jiang, Ebrahim M. Songhori, Shen Wang, Anna Goldie, Azalia Mirhoseini, J. Jiang, Young-Joon Lee, David Z. Pan
{"title":"用强化学习深入研究宏观布局","authors":"Zixuan Jiang, Ebrahim M. Songhori, Shen Wang, Anna Goldie, Azalia Mirhoseini, J. Jiang, Young-Joon Lee, David Z. Pan","doi":"10.1109/MLCAD52597.2021.9531313","DOIUrl":null,"url":null,"abstract":"In physical design, human designers typically place macros via trial and error, which is a Markov decision process. Reinforcement learning (RL) methods have demonstrated superhuman performance on the macro placement. In this paper, we propose an extension to this prior work [1]. We first describe the details of the policy and value network architecture. We replace the force-directed method with DREAMPlace for placing standard cells in the RL environment. We also compare our improved method with other academic placers on public benchmarks.","PeriodicalId":210763,"journal":{"name":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Delving into Macro Placement with Reinforcement Learning\",\"authors\":\"Zixuan Jiang, Ebrahim M. Songhori, Shen Wang, Anna Goldie, Azalia Mirhoseini, J. Jiang, Young-Joon Lee, David Z. Pan\",\"doi\":\"10.1109/MLCAD52597.2021.9531313\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In physical design, human designers typically place macros via trial and error, which is a Markov decision process. Reinforcement learning (RL) methods have demonstrated superhuman performance on the macro placement. In this paper, we propose an extension to this prior work [1]. We first describe the details of the policy and value network architecture. We replace the force-directed method with DREAMPlace for placing standard cells in the RL environment. We also compare our improved method with other academic placers on public benchmarks.\",\"PeriodicalId\":210763,\"journal\":{\"name\":\"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MLCAD52597.2021.9531313\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MLCAD52597.2021.9531313","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Delving into Macro Placement with Reinforcement Learning
In physical design, human designers typically place macros via trial and error, which is a Markov decision process. Reinforcement learning (RL) methods have demonstrated superhuman performance on the macro placement. In this paper, we propose an extension to this prior work [1]. We first describe the details of the policy and value network architecture. We replace the force-directed method with DREAMPlace for placing standard cells in the RL environment. We also compare our improved method with other academic placers on public benchmarks.