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dc.contributor.authorLai, Bo-Cheng Charlesen_US
dc.contributor.authorLi, Kun-Chunen_US
dc.contributor.authorLi, Guan-Ruen_US
dc.contributor.authorChiang, Chin-Hsuanen_US
dc.date.accessioned2015-07-21T08:28:43Z-
dc.date.available2015-07-21T08:28:43Z-
dc.date.issued2015-04-01en_US
dc.identifier.issn0743-7315en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.jpdc.2015.01.005en_US
dc.identifier.urihttp://hdl.handle.net/11536/124706-
dc.description.abstractLeveraging multithreading on embedded multicore platforms has been proven effective on handling the increasing resolutions of target stimuli of object detection. However, complex tradeoffs and correlated design impacts between a parallel application and the underlying multicore platform necessitate an effective and adaptable multithreaded design. This paper introduces a hybrid multithreaded object detection with high parallelism and extensive data reuse. A self adaptable flow is proposed to adjust the multithreaded object detection to fully exploit various embedded multicore architectures. The ARM-based cycle accurate simulations of multicore systems have shown the superior performance returned by the proposed design. (C) 2015 Elsevier Inc. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectMultiprocessor systemsen_US
dc.subjectCache memoriesen_US
dc.subjectFace and gesture recognitionen_US
dc.titleSelf adaptable multithreaded object detection on embedded multicore systemsen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.jpdc.2015.01.005en_US
dc.identifier.journalJOURNAL OF PARALLEL AND DISTRIBUTED COMPUTINGen_US
dc.citation.volume78en_US
dc.citation.spage25en_US
dc.citation.epage38en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000352660700003en_US
dc.citation.woscount0en_US
Appears in Collections:Articles