{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T18:39:19Z","timestamp":1767811159103,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":15,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T00:00:00Z","timestamp":1673827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFB2202702"],"award-info":[{"award-number":["2018YFB2202702"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62274034"],"award-info":[{"award-number":["62274034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,1,16]]},"DOI":"10.1145\/3566097.3567904","type":"proceedings-article","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T18:40:49Z","timestamp":1675190449000},"page":"547-552","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Graph-Learning-Driven Path-Based Timing Analysis Results Predictor from Graph-Based Timing Analysis"],"prefix":"10.1145","author":[{"given":"Yuyang","family":"Ye","sequence":"first","affiliation":[{"name":"Southeast University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tinghuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Southeast University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifei","family":"Gao","sequence":"additional","affiliation":[{"name":"Southeast University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Yan","sequence":"additional","affiliation":[{"name":"Southeast University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bei","family":"Yu","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longxing","family":"Shi","sequence":"additional","affiliation":[{"name":"Southeast University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Synopsys \"Primetime user guide \" http:\/\/www.synopsys.com\/Tools\/Implementation\/SignOff\/Documents\/primetime_ds.pdf 2015."},{"key":"e_1_3_2_1_2_1","unstructured":"Cadence \"Tempus user guide.\" https:\/\/www.cadence.com\/content\/cadencewww\/global\/enUS\/home\/tools\/digital-design-and-signoff\/silicon-signoff\/tempustiming-signoff-solution.html 2015."},{"key":"e_1_3_2_1_3_1","first-page":"945","author":"Zhang Z.","year":"2022","unstructured":"Z. Zhang, Z. Guo, Y. Lin, R. Wang, and R. Huang, \"Eventtimer: fast and accurate event-based dynamic timing analysis,\" in Proc. DATE, 2022, pp. 945--950.","journal-title":"DATE"},{"key":"e_1_3_2_1_4_1","unstructured":"R. Molina \"EDA vendors should improve the runtime performance of path-based timing analysis \" Electronic Design vol. 20136 2013."},{"key":"e_1_3_2_1_5_1","first-page":"68","volume-title":"ISPD","author":"Kahng A. B.","year":"2018","unstructured":"A. B. Kahng, \"Machine learning applications in physical design: Recent results and directions,\" in Proc. ISPD, 2018, pp. 68--73."},{"key":"e_1_3_2_1_6_1","first-page":"603","volume-title":"ICCD","author":"Kahng A. B.","year":"2018","unstructured":"A. B. Kahng, U. Mallappa, and L. Saul, \"Using machine learning to predict path-based slack from graph-based timing analysis,\" in Proc. ICCD, 2018, pp. 603--612."},{"key":"e_1_3_2_1_7_1","volume-title":"NIPS","volume":"30","author":"Hamilton W.","year":"2017","unstructured":"W. Hamilton, Z. Ying, and J. Leskovec, \"Inductive representation learning on large graphs,\" Proc. NIPS, vol. 30, 2017."},{"key":"e_1_3_2_1_8_1","first-page":"1","volume-title":"DAC","author":"Ma Y.","year":"2019","unstructured":"Y. Ma, H. Ren, B. Khailany, H. Sikka, L. Luo, K. Natarajan, and B. Yu, \"High performance graph convolutional networks with applications in testability analysis,\" in Proc. DAC, 2019, pp. 1--6."},{"key":"e_1_3_2_1_9_1","first-page":"1207","volume-title":"DAC","author":"Guo Z.","year":"2022","unstructured":"Z. Guo, M. Liu, J. Gu, S. Zhang, D. Z. Pan, and Y. Lin, \"A timing engine inspired graph neural network model for pre-routing slack prediction,\" in Proc. DAC, 2022, pp. 1207--1212."},{"key":"e_1_3_2_1_10_1","volume-title":"Deep H-GCN: Fast analog IC aging-induced degradation estimation,\" IEEE TCAD","author":"Chen T.","year":"2021","unstructured":"T. Chen, Q. Sun, C. Zhan, C. Liu, H. Yu, and B. Yu, \"Deep H-GCN: Fast analog IC aging-induced degradation estimation,\" IEEE TCAD, 2021."},{"key":"e_1_3_2_1_11_1","first-page":"1233","author":"Sun S.","year":"2022","unstructured":"S. Sun, Y. Jiang, F. Yang, B. Yu, and X. Zeng, \"Efficient hotspot detection via graph neural network,\" in Proc. DATE, 2022, p. 1233--1238.","journal-title":"DATE"},{"key":"e_1_3_2_1_12_1","first-page":"1725","volume-title":"ICML","author":"Chen M.","year":"2020","unstructured":"M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li, \"Simple and deep graph convolutional networks,\" in Proc. ICML, 2020, pp. 1725--1735."},{"key":"e_1_3_2_1_13_1","volume-title":"Adam: A method for stochastic optimization,\" arXiv preprint arXiv:1412.6980","author":"Kingma D. P.","year":"2014","unstructured":"D. P. Kingma and J. Ba, \"Adam: A method for stochastic optimization,\" arXiv preprint arXiv:1412.6980, 2014."},{"key":"e_1_3_2_1_14_1","volume-title":"Graph attention networks,\" arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 P.","year":"2017","unstructured":"P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, \"Graph attention networks,\" arXiv preprint arXiv:1710.10903, 2017."},{"key":"e_1_3_2_1_15_1","first-page":"9211","volume-title":"CVPR","author":"Gong L.","year":"2019","unstructured":"L. Gong and Q. Cheng, \"Exploiting edge features for graph neural networks,\" in Proc. CVPR, 2019, pp. 9211--9219."}],"event":{"name":"ASPDAC '23: 28th Asia and South Pacific Design Automation Conference","location":"Tokyo Japan","acronym":"ASPDAC '23","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CEDA","IEICE","IEEE CAS","IPSJ"]},"container-title":["Proceedings of the 28th Asia and South Pacific Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3566097.3567904","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3566097.3567904","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T17:33:11Z","timestamp":1767807191000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3566097.3567904"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,16]]},"references-count":15,"alternative-id":["10.1145\/3566097.3567904","10.1145\/3566097"],"URL":"https:\/\/doi.org\/10.1145\/3566097.3567904","relation":{},"subject":[],"published":{"date-parts":[[2023,1,16]]},"assertion":[{"value":"2023-01-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}