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A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-core CPUs

dc.contributor.authorLi, Dalin
dc.contributor.authorHuang, Lan
dc.contributor.authorWang, Kangping
dc.contributor.authorPang, Wei
dc.contributor.authorZhou, You
dc.contributor.authorZhang, Rui
dc.date.accessioned2018-11-22T10:00:06Z
dc.date.available2018-11-22T10:00:06Z
dc.date.issued2018
dc.identifier140789103
dc.identifieree0ef171-7084-4d28-a2dd-f561c612ffc2
dc.identifier85057162869
dc.identifier.citationLi , D , Huang , L , Wang , K , Pang , W , Zhou , Y & Zhang , R 2018 , ' A General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-core CPUs ' , IEEE Access , vol. 6 , pp. 72327 - 72344 . https://doi.org/10.1109/ACCESS.2018.2882455en
dc.identifier.issn2169-3536
dc.identifier.otherRIS: urn:266A55ED686786754667D3619DEBF416
dc.identifier.otherORCID: /0000-0002-1761-6659/work/59923336
dc.identifier.urihttp://hdl.handle.net/2164/11489
dc.descriptionThis work is supported by the National Natural Science Foundation of China (Grant Nos.61472159, 61572227, 61772227), Development Project of Jilin Province of China (Nos. 20160204022GX, 20170101006JC, 20170203002GX, 2017C030-1, 2017C033, 20180414012GH). This work is also supported in part by Premier-Discipline Enhancement Scheme supported by Zhuhai Government and Premier Key-Discipline Enhancement Scheme supported Guangdong Government Funds, and Jilin Provincial Key Laboratory of Big Data Intelligent Computing (20180622002JC).en
dc.format.extent19
dc.format.extent1952656
dc.language.isoeng
dc.relation.ispartofIEEE Accessen
dc.subjectField programmable gate arraysen
dc.subjectMulticore processingen
dc.subjectParallel programmingen
dc.subjectParticle swarm optimizationen
dc.subjectPipeline processingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subject.lccQA75en
dc.titleA General Framework for Accelerating Swarm Intelligence Algorithms on FPGAs, GPUs and Multi-core CPUsen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.description.statusPeer revieweden
dc.identifier.doi10.1109/ACCESS.2018.2882455


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