{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:10:59Z","timestamp":1775067059498,"version":"3.50.1"},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T00:00:00Z","timestamp":1714694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province under Grant","award":["2022B0701180001"],"award-info":[{"award-number":["2022B0701180001"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U23A20361"],"award-info":[{"award-number":["U23A20361"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Des. Autom. Electron. Syst."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n            To speed up the design closure and improve the QoR of FPGA, supervised single-task machine learning techniques have been used to predict individual design metric based on placement results. However, the design objective is to achieve optimal performance while considering multiple conflicting metrics. The single-task approaches predict each metric in isolation and neglect the potential correlations or dependencies among them. To address the limitations, this article proposes a multi-task learning approach to jointly predict wirelength, congestion and power. By sharing the common feature representations and adopting the joint optimization strategy, the novel WCPNet models (including WCPNet-HS and WCPNet-SS) cannot only predict the three metrics of different scales simultaneously, but also outperform the majority of single-task models in terms of both prediction performance and time cost, which are demonstrated by the results of the cross design experiment. By adopting the cross-stitch structure in the encoder, WCPNet-SS outperforms WCPNet-HS in prediction performance, but WCPNet-HS is faster because of the simpler parameters sharing structure. The significance of the feature\n            <jats:italic>image<\/jats:italic>\n            <jats:sub>pinUtilization<\/jats:sub>\n            on predicting power and wirelength are demonstrated by the ablation experiment.\n          <\/jats:p>","DOI":"10.1145\/3656170","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T12:10:26Z","timestamp":1712578226000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["WCPNet: Jointly Predicting Wirelength, Congestion and Power for FPGA Using Multi-Task Learning"],"prefix":"10.1145","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8306-2341","authenticated-orcid":false,"given":"Juming","family":"Xian","sequence":"first","affiliation":[{"name":"School of Integrated Circuits, School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7651-5287","authenticated-orcid":false,"given":"Yan","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Guangdong University of Technology, Guangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2842-6439","authenticated-orcid":false,"given":"Shuting","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Guangdong University of Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1227-0791","authenticated-orcid":false,"given":"Weijun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Guangdong University of Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2421-7621","authenticated-orcid":false,"given":"Xiaoming","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Guangdong University of Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4643-8476","authenticated-orcid":false,"given":"Zhengfa","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASP-DAC47756.2020.9045178"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2023.3272582"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2023.3287970"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2022.3168259"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD.2017.8203878"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00159"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2017.54"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3451179"},{"issue":"4","key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3587817","article-title":"Routability-driven power\/ground network optimization based on machine learning","volume":"28","author":"Huang Ping-Wei","year":"2023","unstructured":"Ping-Wei Huang and Yao-Wen Chang. 2023. Routability-driven power\/ground network optimization based on machine learning. ACM Transactions on Design Automation of Electronic Systems 28, 4 (2023), 1\u201327.","journal-title":"ACM Transactions on Design Automation of Electronic Systems"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3439706.3446884"},{"issue":"209","key":"e_1_3_2_12_2","first-page":"1","article-title":"LibMTL: A python library for multi-task learning","volume":"24","author":"Lin Baijiong","year":"2023","unstructured":"Baijiong Lin and Yu Zhang. 2023. LibMTL: A python library for multi-task learning. Journal of Machine Learning Research 24, 209 (2023), 1\u20137.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/FPL.2018.00079"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/FPL50879.2020.00033"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/SBCCI55532.2022.9893251"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3388617"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11416-023-00465-2"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD.2017.8203880"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12020337"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2021.3124762"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12040935"},{"issue":"1","key":"e_1_3_2_22_2","first-page":"1","article-title":"A survey on FPGA electronic design automation technology based on machine learning","volume":"45","author":"Tian Chunsheng","year":"2023","unstructured":"Chunsheng Tian, Lei Chen, Yuan Wnag, Shuo Wang, Jing Zhou, Yongjiang Pang, and Zhong Du. 2023. A survey on FPGA electronic design automation technology based on machine learning. Journal of Electronics & Information Technology 45, 1 (2023), 1\u201313.","journal-title":"Journal of Electronics & Information Technology"},{"issue":"7","key":"e_1_3_2_23_2","first-page":"3614","article-title":"Multi-task learning for dense prediction tasks: A survey","volume":"44","author":"Vandenhende Simon","year":"2022","unstructured":"Simon Vandenhende, Stamatios Georgoulis, Wouter Van Gansbeke, Marc Proesmans, Dengxin Dai, and Luc Van Gool. 2022. Multi-task learning for dense prediction tasks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 44, 7 (2022), 3614\u20133633.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/FPL.2008.4629937"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240765.3240843"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317876"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3070203"}],"container-title":["ACM Transactions on Design Automation of Electronic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3656170","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3656170","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:09:33Z","timestamp":1750295373000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3656170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,3]]},"references-count":26,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5,31]]}},"alternative-id":["10.1145\/3656170"],"URL":"https:\/\/doi.org\/10.1145\/3656170","relation":{},"ISSN":["1084-4309","1557-7309"],"issn-type":[{"value":"1084-4309","type":"print"},{"value":"1557-7309","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,3]]},"assertion":[{"value":"2023-08-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-27","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}