{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:06:38Z","timestamp":1776978398499,"version":"3.51.4"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["L233005"],"award-info":[{"award-number":["L233005"]}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["62125304"],"award-info":[{"award-number":["62125304"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["62192751"],"award-info":[{"award-number":["62192751"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["62073182"],"award-info":[{"award-number":["62073182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2022YFA1004600"],"award-info":[{"award-number":["2022YFA1004600"]}]},{"name":"Beijing National Research Center for Information Science and Technology (BNRist) Project","award":["BNR2024TD03003"],"award-info":[{"award-number":["BNR2024TD03003"]}]},{"name":"111 International Collaboration Project","award":["B25027"],"award-info":[{"award-number":["B25027"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Automat. Sci. Eng."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tase.2026.3682745","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T19:56:02Z","timestamp":1776369362000},"page":"8263-8275","source":"Crossref","is-referenced-by-count":0,"title":["A Bayesian Optimization Method for Design Space Exploration of Processors With Efficient Evaluation Budget Allocation"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1282-3805","authenticated-orcid":false,"given":"Yuhang","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Automation, BNRist, Center for Intelligent and Networked Systems, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyuan","family":"Hou","sequence":"additional","affiliation":[{"name":"Systems Engineering Institute, MOE KLINNS Laboratory, Xi’an Jiaotong University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6565-6701","authenticated-orcid":false,"given":"Xiaoliang","family":"Lv","sequence":"additional","affiliation":[{"name":"Systems Engineering Institute, MOE KLINNS Laboratory, Xi’an Jiaotong University, Xi’an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4683-7215","authenticated-orcid":false,"given":"Qing-Shan","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Automation, BNRist, Center for Intelligent and Networked Systems, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8826-0362","authenticated-orcid":false,"given":"Xiaohong","family":"Guan","sequence":"additional","affiliation":[{"name":"Department of Automation, BNRist, Center for Intelligent and Networked Systems, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1250662.1250712"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2016.2547387"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/1534909.1534910"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/1105734.1105747"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/2.982917"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3185768.3185771"},{"key":"ref7","volume-title":"Geekbench 6. Primate Labs Inc","author":"Labs","year":"2022"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.13643"},{"key":"ref9","first-page":"3","article-title":"Bayesian optimization is superior to random search for machine learning hyperparameter tuning: Analysis of the black-box optimization challenge 2020","volume-title":"Proc. NeurIPS Competition Demonstration Track","author":"Turner"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/DAC.2018.8465872"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2017.2768826"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD51958.2021.9643455"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2023.3311620"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-023-3963-7"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-68692-9"},{"key":"ref16","first-page":"3306","article-title":"Batch Bayesian optimization via multi-objective acquisition ensemble for automated analog circuit design","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lyu"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2021.3054811"},{"key":"ref18","first-page":"1237","article-title":"PPATuner: Pareto-driven tool parameter auto-tuning in physical design via Gaussian process transfer learning","volume-title":"Proc. 59th ACM\/IEEE Design Autom. Conf.","author":"Geng"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/DAC56929.2023.10247790"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CASE59546.2024.10711474"},{"key":"ref21","first-page":"4033","article-title":"FAIR: Fair collaborative active learning with individual rationality for scientific discovery","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Xu"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ceramint.2021.02.155"},{"key":"ref23","first-page":"9851","article-title":"Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"33","author":"Daulton"},{"key":"ref24","first-page":"1492","article-title":"Predictive entropy search for multi-objective Bayesian optimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hern\u00e1ndez-Lobato"},{"key":"ref25","first-page":"7825","article-title":"Max-value entropy search for multi-objective Bayesian optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"32","author":"Belakaria"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0721"},{"key":"ref27","first-page":"354","article-title":"Bayesian optimization of composite functions","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Astudillo"},{"key":"ref28","first-page":"19274","article-title":"Bayesian optimization with high-dimensional outputs","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"34","author":"Maddox"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-79621-7"},{"key":"ref30","first-page":"14463","article-title":"Bayesian optimization of function networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"34","author":"Astudillo"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/9.566675"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/9.739080"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2021.3078445"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2021.3137842"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2023.3240106"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2023.3303175"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3005125"},{"key":"ref38","first-page":"5190","article-title":"Variational inference for Gaussian process models with linear complexity","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"30","author":"Cheng"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107151"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2025.111353"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972757.38"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/BF01588971"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1162\/106365601750190398"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2990567"}],"container-title":["IEEE Transactions on Automation Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8856\/11323516\/11482194.pdf?arnumber=11482194","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:00:48Z","timestamp":1776974448000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11482194\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/tase.2026.3682745","relation":{},"ISSN":["1545-5955","1558-3783"],"issn-type":[{"value":"1545-5955","type":"print"},{"value":"1558-3783","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}