{"title":"GAN-Place:利用生成式对抗网络和迁移学习将开源拼版器提升至商业质量","authors":"Yi-Chen Lu, Haoxing Ren, Hao-Hsiang Hsiao, Sung Kyu Lim","doi":"10.1145/3636461","DOIUrl":null,"url":null,"abstract":"<p>Recently, GPU-accelerated placers such as DREAMPlace and Xplace have demonstrated their superiority over traditional CPU-reliant placers by achieving orders of magnitude speed up in placement runtime. However, due to their limited focus in placement objectives (e.g., wirelength and density), the placement quality achieved by DREAMPlace or Xplace is not comparable to that of commercial tools. In this paper, to bridge the gap between open-source and commercial placers, we present a novel placement optimization framework named GAN-Place that employs generative adversarial learning to transfer the placement quality of the industry-leading commercial placer, Synopsys ICC2, to existing open-source GPU-accelerated placers (DREAMPlace and Xplace). Without the knowledge of the underlying proprietary algorithms or constraints used by the commercial tools, our framework facilitates transfer learning to directly enhance the open-source placers by optimizing the proposed differentiable loss that denotes the “similarity” between DREAMPlace- or Xplace-generated placements and those in commercial databases. Experimental results on 7 industrial designs not only show the our GAN-Place immediately improves the Power, Performance, and Area (PPA) metrics at the placement stage, but also demonstrate that these improvements last firmly to the post-route stage, where we observe improvements by up to 8.3% in wirelength, 7.4% in power, and 37.6% in Total Negative Slack (TNS) on a commercial CPU benchmark.</p>","PeriodicalId":50944,"journal":{"name":"ACM Transactions on Design Automation of Electronic Systems","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GAN-Place: Advancing Open-Source Placers to Commercial-Quality using Generative Adversarial Networks and Transfer Learning\",\"authors\":\"Yi-Chen Lu, Haoxing Ren, Hao-Hsiang Hsiao, Sung Kyu Lim\",\"doi\":\"10.1145/3636461\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Recently, GPU-accelerated placers such as DREAMPlace and Xplace have demonstrated their superiority over traditional CPU-reliant placers by achieving orders of magnitude speed up in placement runtime. However, due to their limited focus in placement objectives (e.g., wirelength and density), the placement quality achieved by DREAMPlace or Xplace is not comparable to that of commercial tools. In this paper, to bridge the gap between open-source and commercial placers, we present a novel placement optimization framework named GAN-Place that employs generative adversarial learning to transfer the placement quality of the industry-leading commercial placer, Synopsys ICC2, to existing open-source GPU-accelerated placers (DREAMPlace and Xplace). Without the knowledge of the underlying proprietary algorithms or constraints used by the commercial tools, our framework facilitates transfer learning to directly enhance the open-source placers by optimizing the proposed differentiable loss that denotes the “similarity” between DREAMPlace- or Xplace-generated placements and those in commercial databases. Experimental results on 7 industrial designs not only show the our GAN-Place immediately improves the Power, Performance, and Area (PPA) metrics at the placement stage, but also demonstrate that these improvements last firmly to the post-route stage, where we observe improvements by up to 8.3% in wirelength, 7.4% in power, and 37.6% in Total Negative Slack (TNS) on a commercial CPU benchmark.</p>\",\"PeriodicalId\":50944,\"journal\":{\"name\":\"ACM Transactions on Design Automation of Electronic Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2023-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Design Automation of Electronic Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3636461\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Design Automation of Electronic Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3636461","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
GAN-Place: Advancing Open-Source Placers to Commercial-Quality using Generative Adversarial Networks and Transfer Learning
Recently, GPU-accelerated placers such as DREAMPlace and Xplace have demonstrated their superiority over traditional CPU-reliant placers by achieving orders of magnitude speed up in placement runtime. However, due to their limited focus in placement objectives (e.g., wirelength and density), the placement quality achieved by DREAMPlace or Xplace is not comparable to that of commercial tools. In this paper, to bridge the gap between open-source and commercial placers, we present a novel placement optimization framework named GAN-Place that employs generative adversarial learning to transfer the placement quality of the industry-leading commercial placer, Synopsys ICC2, to existing open-source GPU-accelerated placers (DREAMPlace and Xplace). Without the knowledge of the underlying proprietary algorithms or constraints used by the commercial tools, our framework facilitates transfer learning to directly enhance the open-source placers by optimizing the proposed differentiable loss that denotes the “similarity” between DREAMPlace- or Xplace-generated placements and those in commercial databases. Experimental results on 7 industrial designs not only show the our GAN-Place immediately improves the Power, Performance, and Area (PPA) metrics at the placement stage, but also demonstrate that these improvements last firmly to the post-route stage, where we observe improvements by up to 8.3% in wirelength, 7.4% in power, and 37.6% in Total Negative Slack (TNS) on a commercial CPU benchmark.
期刊介绍:
TODAES is a premier ACM journal in design and automation of electronic systems. It publishes innovative work documenting significant research and development advances on the specification, design, analysis, simulation, testing, and evaluation of electronic systems, emphasizing a computer science/engineering orientation. Both theoretical analysis and practical solutions are welcome.