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Code Optim."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>\n            Sampling-based methods, such as SimPoint, are widely used for efficient pre-silicon\n            <jats:italic toggle=\"yes\">\u03bc<\/jats:italic>\n            Arch evaluations, where the costs are the number of simulation points multiplied by the number of evaluated\n            <jats:italic toggle=\"yes\">\u03bc<\/jats:italic>\n            Arch designs. However, these costs keep growing with an increasing number of simulation points and expanding\n            <jats:italic toggle=\"yes\">\u03bc<\/jats:italic>\n            Arch design space. Although techniques have been developed to accelerate the\n            <jats:italic toggle=\"yes\">\u03bc<\/jats:italic>\n            Arch design space exploration, less attention has been given to further reducing the simulation budget of each\n            <jats:italic toggle=\"yes\">\u03bc<\/jats:italic>\n            Arch evaluation. Common strategies like reducing simulation coverage or sampling fewer simulation points typically compromise estimation accuracy. Therefore, further reducing the simulation budget without compromising estimation accuracy remains a critical research problem.\n          <\/jats:p>\n          <jats:p>\n            In this work, we propose SnsBooster to enhance sampling-based\n            <jats:italic toggle=\"yes\">\u03bc<\/jats:italic>\n            Arch evaluation efficiency, based on two insights: (a)\u00a0large portions of simulation points\u2019 performance changes are typically insensitive to the evaluated\n            <jats:italic toggle=\"yes\">\u03bc<\/jats:italic>\n            Arch changes, and (b) simulation points\u2019 performance sensitivities under specific\n            <jats:italic toggle=\"yes\">\u03bc<\/jats:italic>\n            Arch change correlate with their inherent characteristics. By online building a\n            <jats:italic toggle=\"yes\">\u03bc<\/jats:italic>\n            Arch-specific performance sensitivity classifier via progressive simulation and continuous validation, SnsBooster can identify and selectively evaluate only performance-sensitive points, thus reducing the simulation budget without compromising estimation accuracy. When applied across various\n            <jats:italic toggle=\"yes\">\u03bc<\/jats:italic>\n            Arch changes, SnsBooster achieves an average simulation budget reduction of 39.04% with an accuracy loss of only 0.14%, compared to simulating all the sampled points. Under the same accuracy loss, SnsBooster\u2019s simulation budgets are only 64.73% and 65.60% of those required by methods of reducing simulation coverage or sampling fewer points. Besides, under identical simulation budgets, the average accuracy losses of these methods are 1.41% and 1.23%, which is substantially higher than that of SnsBooster.\n          <\/jats:p>","DOI":"10.1145\/3727637","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T13:55:37Z","timestamp":1743515737000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SnsBooster: Enhancing Sampling-based\n            <i>\u03bc<\/i>\n            Arch Evaluation Efficiency through Online Performance Sensitivity Analysis"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1247-9644","authenticated-orcid":false,"given":"Chenji","family":"Han","sequence":"first","affiliation":[{"name":"Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]},{"name":"University of Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9698-0435","authenticated-orcid":false,"given":"Zifei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]},{"name":"University of Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3118-8477","authenticated-orcid":false,"given":"Feng","family":"Xue","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]},{"name":"University of Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2640-8173","authenticated-orcid":false,"given":"Xinyu","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]},{"name":"University of Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9346-8499","authenticated-orcid":false,"given":"Yuxuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology Chinese Academy of Sciences","place":["Beijing, China"]},{"name":"University of Chinese Academy of Sciences","place":["Beijing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1724-4904","authenticated-orcid":false,"given":"Tingting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Loongson Technology Co. 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