{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:02:26Z","timestamp":1768413746495,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,9,9]]},"DOI":"10.1145\/3670474.3685940","type":"proceedings-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T06:22:27Z","timestamp":1725344547000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning to Compare Hardware Designs for High-Level Synthesis"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1623-6184","authenticated-orcid":false,"given":"Yunsheng","family":"Bai","sequence":"first","affiliation":[{"name":"NVIDIA Corporation and University of California, Los Angeles"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1156-3306","authenticated-orcid":false,"given":"Atefeh","family":"Sohrabizadeh","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4555-2077","authenticated-orcid":false,"given":"Zijian","family":"Ding","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8626-2359","authenticated-orcid":false,"given":"Rongjian","family":"Liang","sequence":"additional","affiliation":[{"name":"NVIDIA Corporation"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5801-9500","authenticated-orcid":false,"given":"Weikai","family":"Li","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1166-7326","authenticated-orcid":false,"given":"Ding","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1028-3860","authenticated-orcid":false,"given":"Haoxing","family":"Ren","sequence":"additional","affiliation":[{"name":"NVIDIA Corporation"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1812-6843","authenticated-orcid":false,"given":"Yizhou","family":"Sun","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2887-6963","authenticated-orcid":false,"given":"Jason","family":"Cong","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles"}]}],"member":"320","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Accessed","year":"2024","unstructured":"2023. Ranked Choice Voting. https:\/\/www.fairvote.org\/rcv. Accessed: May 22, 2024."},{"key":"e_1_3_2_1_2_1","volume-title":"https:\/\/www.xilinx.com\/products\/design-tools\/vitis\/vitis-hls.html. Accessed","author":"Xilinx AMD.","year":"2024","unstructured":"AMD. 2024. AMD\/Xilinx. https:\/\/www.xilinx.com\/products\/design-tools\/vitis\/vitis-hls.html. Accessed: June 1, 2024."},{"key":"e_1_3_2_1_3_1","first-page":"45288","article-title":"Towards a Comprehensive Benchmark for High-Level Synthesis Targeted to FPGAs","volume":"36","author":"Bai Yunsheng","year":"2023","unstructured":"Yunsheng Bai, Atefeh Sohrabizadeh, Zongyue Qin, Ziniu Hu, Yizhou Sun, and Jason Cong. 2023. Towards a Comprehensive Benchmark for High-Level Synthesis Targeted to FPGAs. Advances in Neural Information Processing Systems 36 (2023), 45288--45299.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102363"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1950413.1950423"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3530775"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2011.2110592"},{"key":"e_1_3_2_1_9_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_10_1","volume-title":"Preference learning","author":"F\u00fcrnkranz Johannes","unstructured":"Johannes F\u00fcrnkranz and Eyke H\u00fcllermeier. 2010. Preference learning and ranking by pairwise comparison. In Preference learning. Springer, 65--82."},{"key":"e_1_3_2_1_11_1","volume-title":"Pairwise neural machine translation evaluation. arXiv preprint arXiv:1912.03135","author":"Guzm\u00e1n Francisco","year":"2019","unstructured":"Francisco Guzm\u00e1n, Shafiq Joty, Llu\u00eds M\u00e0rquez, and Preslav Nakov. 2019. Pairwise neural machine translation evaluation. arXiv preprint arXiv:1912.03135 (2019)."},{"key":"e_1_3_2_1_12_1","volume-title":"Learning to rank quantum circuits for hardware-optimized performance enhancement. arXiv preprint arXiv:2404.06535","author":"Hartnett Gavin S","year":"2024","unstructured":"Gavin S Hartnett, Aaron Barbosa, Pranav S Mundada, Michael Hush, Michael J Biercuk, and Yuval Baum. 2024. Learning to rank quantum circuits for hardware-optimized performance enhancement. arXiv preprint arXiv:2404.06535 (2024)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/30.1-2.81"},{"key":"e_1_3_2_1_15_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Tie-Yan Liu et al. 2009. Learning to rank for information retrieval. Foundations and Trends\u00ae in Information Retrieval 3 3 (2009) 225--331.","DOI":"10.1561\/1500000016"},{"key":"e_1_3_2_1_17_1","volume-title":"Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983","author":"Loshchilov Ilya","year":"2016","unstructured":"Ilya Loshchilov and Frank Hutter. 2016. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)."},{"key":"e_1_3_2_1_18_1","volume-title":"Metric learning for novelty and anomaly detection. arXiv preprint arXiv:1808.05492","author":"Masana Marc","year":"2018","unstructured":"Marc Masana, Idoia Ruiz, Joan Serrat, Joost van de Weijer, and Antonio M Lopez. 2018. Metric learning for novelty and anomaly detection. arXiv preprint arXiv:1808.05492 (2018)."},{"key":"e_1_3_2_1_19_1","volume-title":"Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 918--923","author":"Meng Pingfan","year":"2016","unstructured":"Pingfan Meng, Alric Althoff, Quentin Gautier, and Ryan Kastner. 2016. Adaptive threshold non-Pareto elimination: Re-thinking machine learning for system level design space exploration on FPGAs. In 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 918--923."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2015.2513673"},{"key":"e_1_3_2_1_21_1","volume-title":"Iterative ranking from pair-wise comparisons. Advances in neural information processing systems 25","author":"Negahban Sahand","year":"2012","unstructured":"Sahand Negahban, Sewoong Oh, and Devavrat Shah. 2012. Iterative ranking from pair-wise comparisons. Advances in neural information processing systems 25 (2012)."},{"key":"e_1_3_2_1_22_1","unstructured":"Long Ouyang Jeffrey Wu Xu Jiang Diogo Almeida Carroll Wainwright Pamela Mishkin Chong Zhang Sandhini Agarwal Katarina Slama Alex Ray et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems 35 (2022) 27730--27744."},{"key":"e_1_3_2_1_23_1","first-page":"1773","article-title":"The Legality of Ranked-Choice Voting","volume":"109","author":"Pildes Richard H","year":"2021","unstructured":"Richard H Pildes and Michael Parsons. 2021. The Legality of Ranked-Choice Voting. Cal. L. Rev. 109 (2021), 1773.","journal-title":"Cal. L. Rev."},{"key":"e_1_3_2_1_24_1","volume-title":"Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems 36","author":"Rafailov Rafael","year":"2023","unstructured":"Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems 36 (2023)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2012.2197679"},{"key":"e_1_3_2_1_26_1","first-page":"1","article-title":"Simple, robust and optimal ranking from pairwise comparisons","volume":"18","author":"Shah Nihar B","year":"2018","unstructured":"Nihar B Shah and Martin J Wainwright. 2018. Simple, robust and optimal ranking from pairwise comparisons. Journal of machine learning research 18, 199 (2018), 1--38.","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_27_1","volume-title":"Masked label prediction: Unified message passing model for semi-supervised classification. ICAI","author":"Shi Yunsheng","year":"2021","unstructured":"Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun. 2021. Masked label prediction: Unified message passing model for semi-supervised classification. ICAI (2021)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3489517.3530409"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD57390.2023.10323853"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494534"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i17.29865"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00477"},{"key":"e_1_3_2_1_33_1","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."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-short.49"},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Machine Learning. PMLR, 109--117","author":"Wauthier Fabian","year":"2013","unstructured":"Fabian Wauthier, Michael Jordan, and Nebojsa Jojic. 2013. Efficient ranking from pairwise comparisons. In International Conference on Machine Learning. PMLR, 109--117."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3453688.3461495"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2022.3185540"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3489517.3530408"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_40_1","volume-title":"How powerful are graph neural networks? ICLR","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? ICLR (2019)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2435264.2435271"}],"event":{"name":"MLCAD '24: 2024 ACM\/IEEE International Symposium on Machine Learning for CAD","location":"Salt Lake City UT USA","acronym":"MLCAD '24","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CEDA"]},"container-title":["Proceedings of the 2024 ACM\/IEEE International Symposium on Machine Learning for CAD"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3670474.3685940","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3670474.3685940","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T23:43:12Z","timestamp":1755906192000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3670474.3685940"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,9]]},"references-count":41,"alternative-id":["10.1145\/3670474.3685940","10.1145\/3670474"],"URL":"https:\/\/doi.org\/10.1145\/3670474.3685940","relation":{},"subject":[],"published":{"date-parts":[[2024,9,9]]},"assertion":[{"value":"2024-09-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}