{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T02:28:20Z","timestamp":1780367300178,"version":"3.54.1"},"reference-count":22,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T00:00:00Z","timestamp":1702857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000185","name":"DARPA","doi-asserted-by":"crossref","award":["HR0011-18-2-0032"],"award-info":[{"award-number":["HR0011-18-2-0032"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"crossref"}]},{"name":"NSF","award":["CCF-2112665"],"award-info":[{"award-number":["CCF-2112665"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Des. Autom. Electron. Syst."],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>\n            Due to the unavailability of routing information in design stages prior to detailed routing (DR), the tasks of timing prediction and optimization pose major challenges. Inaccurate timing prediction wastes design effort, hurts circuit performance, and may lead to design failure. This work focuses on timing prediction after clock tree synthesis and placement legalization, which is the earliest opportunity to time and optimize a \u201ccomplete\u201d netlist. The article first documents that having \u201coracle knowledge\u201d of the final post-DR parasitics enables post-global routing (GR) optimization to produce improved final timing outcomes. To bridge the gap between GR-based parasitic and timing estimation and post-DR results\n            <jats:italic>during post-GR optimization<\/jats:italic>\n            , machine learning (ML)-based models are proposed, including the use of features for macro blockages for accurate predictions for designs with macros. Based on a set of experimental evaluations, it is demonstrated that these models show higher accuracy than GR-based timing estimation. When used during post-GR optimization, the ML-based models show demonstrable improvements in post-DR circuit performance. The methodology is applied to two different tool flows\u2014OpenROAD and a commercial tool flow\u2014and results on an open-source 45nm bulk and a commercial 12nm FinFET enablement show improvements in post-DR timing slack metrics without increasing congestion. The models are demonstrated to be generalizable to designs generated under different clock period constraints and are robust to training data with small levels of noise.\n          <\/jats:p>","DOI":"10.1145\/3626959","type":"journal-article","created":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T11:30:07Z","timestamp":1696937407000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["A Machine Learning Approach to Improving Timing Consistency between Global Route and Detailed Route"],"prefix":"10.1145","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3273-0724","authenticated-orcid":false,"given":"Vidya A.","family":"Chhabria","sequence":"first","affiliation":[{"name":"Arizona State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0956-9602","authenticated-orcid":false,"given":"Wenjing","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of Minnesota, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4490-5018","authenticated-orcid":false,"given":"Andrew B.","family":"Kahng","sequence":"additional","affiliation":[{"name":"University of California\u2014San Diego, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5353-2364","authenticated-orcid":false,"given":"Sachin S.","family":"Sapatnekar","sequence":"additional","affiliation":[{"name":"University of Minnesota, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,18]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"T. Ajayi V. A. Chhabria M. Foga\u00e7a S. Hashemi A. Hosny A. B. Kahng M. Kim Jeongsup Lee U. Mallappa M. Neseem G. Pradipta S. Reda M. Saligane S. S. Sapatnekar C. Sechen M. Shalan W. Swartz L. Wang Z. Wang M. Woo and B. Xu. 2019. Toward an open-source digital flow: First learnings from the OpenROAD project. In Proceedings of the ACM\/IEEE Design Automation Conference . Association for Computing Machinery New York NY 1\u20134.","DOI":"10.1145\/3316781.3326334"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317857"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3414622.3431907"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASPDAC.2016.7428008"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18072.2020.9218712"},{"key":"e_1_3_1_8_2","first-page":"7","volume-title":"Proceedings of the IEEE\/ACM International Workshop on Machine Learning for CAD","author":"Chhabria V. A.","year":"2022","unstructured":"V. A. Chhabria, W. Jiang, A. B. Kahng, and S. S. Sapatnekar. 2022. From global route to detailed route: ML for fast and accurate wire parasitics and timing prediction. In Proceedings of the IEEE\/ACM International Workshop on Machine Learning for CAD. 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