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However, performance-driven wire sizing for analog ICs has received very little research attention. In order to fill this void, we develop several techniques to facilitate an end-to-end automatic wire sizing approach. They include a circuit performance model based on customized graph neural network (GNN) and two optimization techniques: one using Bayesian optimization accelerated by the GNN model, and the other based on TensorFlow training. Experimental results show that our technique can achieve 11% circuit performance improvement or 8.7\u00d7 speedup compared to a conventional Bayesian optimization method.<\/jats:p>","DOI":"10.1145\/3559542","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T11:23:27Z","timestamp":1661513007000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Performance-driven Wire Sizing for Analog Integrated Circuits"],"prefix":"10.1145","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9425-034X","authenticated-orcid":false,"given":"Yaguang","family":"Li","sequence":"first","affiliation":[{"name":"Texas A&amp;M University, College Station, TX, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1555-4623","authenticated-orcid":false,"given":"Yishuang","family":"Lin","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, TX, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5101-2421","authenticated-orcid":false,"given":"Meghna","family":"Madhusudan","sequence":"additional","affiliation":[{"name":"University of Minnesota, Minneapolis, MN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1188-4924","authenticated-orcid":false,"given":"Arvind","family":"Sharma","sequence":"additional","affiliation":[{"name":"University of Minnesota, Minneapolis, MN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5353-2364","authenticated-orcid":false,"given":"Sachin","family":"Sapatnekar","sequence":"additional","affiliation":[{"name":"University of Minnesota, Minneapolis, MN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7691-566X","authenticated-orcid":false,"given":"Ramesh","family":"Harjani","sequence":"additional","affiliation":[{"name":"University of Minnesota, Minneapolis, MN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1157-7799","authenticated-orcid":false,"given":"Jiang","family":"Hu","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, College Station, TX, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,12,24]]},"reference":[{"key":"e_1_3_3_2_2","article-title":"TensorFlow: Large-scale machine learning on heterogeneous distributed systems","volume":"1603","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. 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