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Please use this identifier to cite or link to this item: https://hdl.handle.net/10923/18182
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dc.contributor.authorPengmiao Zhang-
dc.contributor.authorAjitesh Srivastava-
dc.contributor.authorTa-Yang Wang-
dc.contributor.authorCesar Augusto Fonticielha De Rose-
dc.contributor.authorRajgopal Kannan-
dc.contributor.authorViktor K. Prasanna-
dc.date.accessioned2021-09-01T13:26:54Z-
dc.date.available2021-09-01T13:26:54Z-
dc.date.issued2021-
dc.identifier.issn2364-415X-
dc.identifier.urihttps://hdl.handle.net/10923/18182-
dc.language.isoen-
dc.relation.ispartofInternational Journal of Data Science and Analytics-
dc.rightsopenAccess-
dc.subjectGerência de recursos em máquinas paralelas-
dc.subjectEscalonamento de Recursos-
dc.subjectInteligência Artificial (IA)-
dc.titleC-MemMAP: clustering-driven compact, adaptable, and generalizable meta-LSTM models for memory access prediction-
dc.typeArticle-
dc.date.updated2021-09-01T13:26:54Z-
dc.identifier.doiDOI:10.1007/s41060-021-00268-y-
dc.jtitleInternational Journal of Data Science and Analytics-
dc.spage1-
dc.epage10-
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