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dc.contributor.authorLiang, Mingyu
dc.contributor.authorFu, Wenyin
dc.contributor.authorFeng, Louis
dc.contributor.authorLin, Zhongyi
dc.contributor.authorPanakanti, Pavani
dc.contributor.authorZheng, Shengbao
dc.contributor.authorSridharan, Srinivas
dc.contributor.authorDelimitrou, Christina
dc.date.accessioned2023-07-11T19:39:55Z
dc.date.available2023-07-11T19:39:55Z
dc.date.issued2023-06-17
dc.identifier.isbn979-8-4007-0095-8
dc.identifier.urihttps://hdl.handle.net/1721.1/151103
dc.publisherACM|Proceedings of the 50th Annual International Symposium on Computer Architectureen_US
dc.relation.isversionofhttps://doi.org/10.1145/3579371.3589072en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceAssociation for Computing Machineryen_US
dc.titleMystique: Enabling Accurate and Scalable Generation of Production AI Benchmarksen_US
dc.typeArticleen_US
dc.identifier.citationLiang, Mingyu, Fu, Wenyin, Feng, Louis, Lin, Zhongyi, Panakanti, Pavani et al. 2023. "Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks."
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.mitlicensePUBLISHER_POLICY
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2023-07-01T07:57:56Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2023-07-01T07:57:57Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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