{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:09:42Z","timestamp":1773248982639,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":20,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,1,16]]},"DOI":"10.1145\/3566097.3567844","type":"proceedings-article","created":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T18:40:49Z","timestamp":1675190449000},"page":"302-307","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Microarchitecture Power Modeling via Artificial Neural Network and Transfer Learning"],"prefix":"10.1145","author":[{"given":"Jianwang","family":"Zhai","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Yici","family":"Cai","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Bei","family":"Yu","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, China"}]}],"member":"320","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"HPCA","author":"Lee B. C.","year":"2007","unstructured":"B. C. Lee and D. M. Brooks, \"Illustrative design space studies with microarchitectural regression models,\" in Proc. HPCA, 2007."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1669112.1669172"},{"key":"e_1_3_2_1_3_1","volume-title":"ISLPED","author":"Lee W.","year":"2015","unstructured":"W. Lee, Y. Kim, J. H. Ryoo et al., \"PowerTrain: A learning-based calibration of McPAT power models,\" in Proc. ISLPED, 2015."},{"key":"e_1_3_2_1_4_1","volume-title":"Diestelhorst et al., \"Accurate and stable run-time power modeling for mobile and embedded CPUs,\" IEEE TCAD","author":"Walker M. J.","year":"2017","unstructured":"M. J. Walker, S. Diestelhorst et al., \"Accurate and stable run-time power modeling for mobile and embedded CPUs,\" IEEE TCAD, 2017."},{"key":"e_1_3_2_1_5_1","volume-title":"ICCAD","author":"Zhai J.","year":"2021","unstructured":"J. Zhai, C. Bai, B. Zhu et al., \"McPAT-Calib: A microarchitecture power modeling framework for modern CPUs,\" in Proc. ICCAD, 2021."},{"key":"e_1_3_2_1_6_1","volume-title":"ICCAD","author":"Bai C.","year":"2021","unstructured":"C. Bai, Q. Sun et al., \"BOOM-Explorer: RISC-V BOOM microarchitecture design space exploration framework,\" in Proc. ICCAD, 2021."},{"key":"e_1_3_2_1_7_1","volume-title":"SonicBOOM: The 3rd generation berkeley out-of-order machine,\" in Fourth Workshop on Computer Architecture Research with RISC-V","author":"Zhao J.","year":"2020","unstructured":"J. Zhao et al., \"SonicBOOM: The 3rd generation berkeley out-of-order machine,\" in Fourth Workshop on Computer Architecture Research with RISC-V, 2020."},{"key":"e_1_3_2_1_8_1","volume-title":"Jantan et al., \"State-of-the-art in artificial neural network applications: A survey,\" Heliyon","author":"Abiodun O. I.","year":"2018","unstructured":"O. I. Abiodun, A. Jantan et al., \"State-of-the-art in artificial neural network applications: A survey,\" Heliyon, 2018."},{"key":"e_1_3_2_1_9_1","volume-title":"NeurIPS","author":"Yosinski J.","year":"2014","unstructured":"J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, \"How transferable are features in deep neural networks?\" in Proc. NeurIPS, 2014."},{"key":"e_1_3_2_1_10_1","volume-title":"DAC","author":"Ji W.","year":"2020","unstructured":"W. Ji, T.-Y. Ho, and H. Yao, \"Transfer learning-based microfluidic design system for concentration generation,\" in Proc. DAC, 2020."},{"key":"e_1_3_2_1_11_1","volume-title":"Duan et al., \"A comprehensive survey on transfer learning,\" Proceedings of the IEEE","author":"Zhuang F.","year":"2020","unstructured":"F. Zhuang, Z. Qi, K. Duan et al., \"A comprehensive survey on transfer learning,\" Proceedings of the IEEE, 2020."},{"key":"e_1_3_2_1_12_1","volume-title":"ICLR","author":"Zhang H.","year":"2018","unstructured":"H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, \"mixup: Beyond empirical risk minimization,\" in Proc. ICLR, 2018."},{"key":"e_1_3_2_1_13_1","volume":"201","author":"Ganin Y.","unstructured":"Y. Ganin, E. Ustinova, H. Ajakan et al., \"Domain-adversarial training of neural networks,\" Journal of Machine Learning Research, 2016.","journal-title":"\"Domain-adversarial training of neural networks,\" Journal of Machine Learning Research"},{"key":"e_1_3_2_1_14_1","volume":"201","author":"Clark L. T.","unstructured":"L. T. Clark, V. Vashishtha et al., \"ASAP7: A 7-nm finFET predictive process design kit,\" Microelectronics Journal, 2016.","journal-title":"\"ASAP7: A 7-nm finFET predictive process design kit,\" Microelectronics Journal"},{"key":"e_1_3_2_1_15_1","volume-title":"Zhu et al., \"McPAT-Calib: A RISC-VBOOM microarchitecture power modeling framework,\" IEEE TCAD","author":"Zhai J.","year":"2022","unstructured":"J. Zhai, C. Bai, B. Zhu et al., \"McPAT-Calib: A RISC-VBOOM microarchitecture power modeling framework,\" IEEE TCAD, 2022."},{"key":"e_1_3_2_1_16_1","volume-title":"ICML","author":"Zhang Y.","year":"2019","unstructured":"Y. Zhang, T. Liu, M. Long, and M. Jordan, \"Bridging theory and algorithm for domain adaptation,\" in Proc. ICML, 2019."},{"key":"e_1_3_2_1_17_1","volume-title":"ICML","author":"Pardoe D.","year":"2010","unstructured":"D. Pardoe and P. Stone, \"Boosting for regression transfer,\" in Proc. ICML, 2010."},{"key":"e_1_3_2_1_18_1","volume-title":"NeurIPS","author":"Sugiyama M.","year":"2007","unstructured":"M. Sugiyama, S. Nakajima et al., \"Direct importance estimation with model selection and its application to covariate shift adaptation,\" in Proc. NeurIPS, 2007."},{"key":"e_1_3_2_1_19_1","volume-title":"NeurIPS","author":"Huang J.","year":"2006","unstructured":"J. Huang, A. Gretton, K. Borgwardt et al., \"Correcting sample selection bias by unlabeled data,\" in Proc. NeurIPS, 2006."},{"key":"e_1_3_2_1_20_1","volume-title":"ICTAI","author":"de Mathelin A.","year":"2021","unstructured":"A. de Mathelin, G. Richard, F. Deheeger et al., \"Adversarial weighting for domain adaptation in regression,\" in Proc. ICTAI, 2021."}],"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\/3566097.3567844","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3566097.3567844","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T17:32:44Z","timestamp":1767807164000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3566097.3567844"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,16]]},"references-count":20,"alternative-id":["10.1145\/3566097.3567844","10.1145\/3566097"],"URL":"https:\/\/doi.org\/10.1145\/3566097.3567844","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"}}]}}