{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T02:56:18Z","timestamp":1770346578383,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,1,20]]},"DOI":"10.1145\/3658617.3697742","type":"proceedings-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T14:23:57Z","timestamp":1741098237000},"page":"393-400","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Graph-Based Timing Prediction at Early-Stage RTL Using Large Language Model"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8433-0098","authenticated-orcid":false,"given":"Fahad Rahman","family":"Amik","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8352-2538","authenticated-orcid":false,"given":"Yousef","family":"Safari","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2364-9732","authenticated-orcid":false,"given":"Zhanguang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6176-5918","authenticated-orcid":false,"given":"Boris","family":"Vaisband","sequence":"additional","affiliation":[{"name":"Samueli School of Engineering, University of California, Irvine, Irvine, California, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"d.]. Cadence Tempus Timing Solution. Retrieved","year":"2024","unstructured":"[n. d.]. Cadence Tempus Timing Solution. Retrieved July 7, 2024 from https:\/\/www.cadence.com\/en_US\/home\/tools\/digital-design-and-signoff\/silicon-signoff\/tempus-timing-signoff-solution.html"},{"key":"e_1_3_2_1_2_1","volume-title":"IWLS 2005 benchmarks. In International Workshop for Logic Synthesis (IWLS)","volume":"9","author":"Albrecht Christoph","year":"2005","unstructured":"Christoph Albrecht. 2005. IWLS 2005 benchmarks. In International Workshop for Logic Synthesis (IWLS), Vol. 9."},{"key":"e_1_3_2_1_3_1","volume-title":"Llemma: An Open Language Model for Mathematics. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=4WnqRR915j","author":"Azerbayev Zhangir","year":"2024","unstructured":"Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos, Stephen Marcus McAleer, Albert Q. Jiang, Jia Deng, Stella Biderman, and Sean Welleck. 2024. Llemma: An Open Language Model for Mathematics. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=4WnqRR915j"},{"key":"e_1_3_2_1_4_1","volume-title":"International Conference on Machine Learning. PMLR, 599--608","author":"Balcilar Muhammet","year":"2021","unstructured":"Muhammet Balcilar, Pierre H\u00e9roux, Benoit Gauzere, Pascal Vasseur, S\u00e9bastien Adam, and Paul Honeine. 2021. Breaking the limits of message passing graph neural networks. In International Conference on Machine Learning. PMLR, 599--608."},{"key":"e_1_3_2_1_5_1","volume-title":"Llm2vec: Large language models are secretly powerful text encoders. arXiv preprint arXiv:2404.05961","author":"BehnamGhader Parishad","year":"2024","unstructured":"Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, Dzmitry Bahdanau, Nicolas Chapados, and Siva Reddy. 2024. Llm2vec: Large language models are secretly powerful text encoders. arXiv preprint arXiv:2404.05961 (2024)."},{"key":"e_1_3_2_1_6_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D. Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901."},{"key":"e_1_3_2_1_7_1","volume-title":"ISPD 2015 benchmarks with fence regions and routing blockages for detailed-routing-driven placement. In Proceedings of the 2015 Symposium on International Symposium on Physical Design. 157--164","author":"Bustany Ismail S","year":"2015","unstructured":"Ismail S Bustany, David Chinnery, Joseph R Shinnerl, and Vladimir Yutsis. 2015. ISPD 2015 benchmarks with fence regions and routing blockages for detailed-routing-driven placement. In Proceedings of the 2015 Symposium on International Symposium on Physical Design. 157--164."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3641289"},{"key":"e_1_3_2_1_9_1","volume-title":"Matteo Sonza Reorda, and Giovanni Squillero","author":"Corno Fulvio","year":"2000","unstructured":"Fulvio Corno, Matteo Sonza Reorda, and Giovanni Squillero. 2000. RT-level ITC'99 benchmarks and first ATPG results. IEEE Design & Test of computers 17, 3 (2000), 44--53."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2347736.2347755"},{"key":"e_1_3_2_1_11_1","unstructured":"EleutherAI. 2024. LLemma Model - EleutherAI\/llemma_7b. https:\/\/huggingface.co\/EleutherAI\/llemma_7b. Accessed on 3-May-2024."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3649329.3655671"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD57390.2023.10323951"},{"key":"e_1_3_2_1_14_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","unstructured":"Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_3_2_1_15_1","unstructured":"Gaisler Aviation. [n. d.]. Front Grade Gaisler. https:\/\/www.gaisler.com. Accessed on 3-May-2024."},{"key":"e_1_3_2_1_16_1","volume-title":"An Introduction to Statistical Learning: with Applications in R","author":"Gareth James","unstructured":"James Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: with Applications in R. Springer."},{"key":"e_1_3_2_1_18_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of the IEEE\/ACM International Conference On Computer Aided Design (ICCAD). 1--8.","author":"Lopera Daniela S\u00e1nchez","year":"2022","unstructured":"Daniela S\u00e1nchez Lopera and Wolfgang Ecker. 2022. Applying GNNs to Timing Estimation at RTL : (Invited Paper). In Proceedings of the IEEE\/ACM International Conference On Computer Aided Design (ICCAD). 1--8."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372780.3378173"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2018.06.001"},{"key":"e_1_3_2_1_22_1","volume-title":"d.]. OpenCores. https:\/\/opencores.org [Online","year":"2024","unstructured":"OpenCores. [n. d.]. OpenCores. https:\/\/opencores.org [Online; accessed 3-May-2024]."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLCAD58807.2023.10299840"},{"key":"e_1_3_2_1_24_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3508352.3561093"},{"key":"e_1_3_2_1_26_1","volume-title":"Proceedings of the IEEE\/ACM International Conference On Computer Aided Design (ICCAD). 1--9.","author":"Sengupta Prianka","year":"2022","unstructured":"Prianka Sengupta, Aakash Tyagi, Yiran Chen, and Jiang Hu. 2022. How Good Is Your Verilog RTL Code? A Quick Answer from Machine Learning. In Proceedings of the IEEE\/ACM International Conference On Computer Aided Design (ICCAD). 1--9."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLCAD58807.2023.10299879"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLCAD58807.2023.10299859"},{"key":"e_1_3_2_1_29_1","first-page":"10","article-title":"Graph Attention Networks","volume":"1050","author":"Velickovic Petar","year":"2017","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. 2017. Graph Attention Networks. Stat 1050, 20 (2017), 10--48550.","journal-title":"Stat"}],"event":{"name":"ASPDAC '25: 30th Asia and South Pacific Design Automation Conference","location":"Tokyo Japan","acronym":"ASPDAC '25","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEICE","IPSJ","IEEE CAS","IEEE CEDA"]},"container-title":["Proceedings of the 30th Asia and South Pacific Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3658617.3697742","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3658617.3697742","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:50Z","timestamp":1750295870000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3658617.3697742"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,20]]},"references-count":28,"alternative-id":["10.1145\/3658617.3697742","10.1145\/3658617"],"URL":"https:\/\/doi.org\/10.1145\/3658617.3697742","relation":{},"subject":[],"published":{"date-parts":[[2025,1,20]]},"assertion":[{"value":"2025-03-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}