VOTA: A 2.45TFLOPS/W Heterogeneous Multi-Core Visual Object Tracking Accelerator Based on Correlation Filters

dc.contributor.authorZhu, Junkang
dc.contributor.authorTang, Wei
dc.contributor.authorLee, Ching-En
dc.contributor.authorYe, Haolei
dc.contributor.authorMcCreath, Eric
dc.contributor.authorZhang, Zhengya
dc.coverage.spatialKyoto, Japan
dc.date.accessioned2024-01-29T04:41:34Z
dc.date.created13-19 June 2021
dc.date.issued2021
dc.date.updated2022-10-02T07:18:34Z
dc.description.abstractVOTA is a domain-specific accelerator for correlation filter (CF)-based visual object tracking (VOT). It encompasses a Winograd convolution core, a FFT core and a vector core in a high-bandwidth starring topology. VOTA's frame-based instructions and execution enable a 537GFLOPS performance and reduce the code size. An instruction-chaining mechanism permits inter-core pipelining to improve the utilization to 84.2%. A 10.2mm2 28nm FP16 VOTA prototype incorporating a RISC-V host CPU is measured to achieve 2.45TFLOPS/W at 0.72V. Running OPCF, a CF-based VOT enhanced by adaptive boosting and particle filters, the chip achieves 1157FPS on 640×480 input frames at 0.9V and 175MHz, consuming 296mW.en_AU
dc.description.sponsorshipThe authors would like to thank Ford-UM Research Alliance for funding this work and the TSMC University Shuttle Program for chip fabrication support.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.isbn978-4-86348-780-2en_AU
dc.identifier.urihttp://hdl.handle.net/1885/312083
dc.language.isoen_AUen_AU
dc.publisherIEEEen_AU
dc.relation.ispartofseries2021 Symposia on VLSI Technology and Circuitsen_AU
dc.rights© 2021 IEEEen_AU
dc.source2021 Symposium on VLSI Circuitsen_AU
dc.titleVOTA: A 2.45TFLOPS/W Heterogeneous Multi-Core Visual Object Tracking Accelerator Based on Correlation Filtersen_AU
dc.typeConference paperen_AU
local.bibliographicCitation.lastpage2en_AU
local.bibliographicCitation.startpage1en_AU
local.contributor.affiliationZhu, Junkang, University of Michiganen_AU
local.contributor.affiliationTang, Wei, University of Michiganen_AU
local.contributor.affiliationLee, Ching-En, University of Michiganen_AU
local.contributor.affiliationYe, Haolei, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationMcCreath, Eric, College of Engineering and Computer Science, ANUen_AU
local.contributor.affiliationZhang, Zhengya, University of Michiganen_AU
local.contributor.authoruidYe, Haolei, u5870415en_AU
local.contributor.authoruidMcCreath, Eric, u4033585en_AU
local.description.embargo2099-12-31
local.description.notesImported from ARIESen_AU
local.description.refereedYes
local.identifier.absfor460607 - High performance computingen_AU
local.identifier.ariespublicationa383154xPUB21754en_AU
local.identifier.doi10.23919/VLSICircuits52068.2021.9492379en_AU
local.identifier.scopusID2-s2.0-85111796678
local.publisher.urlhttps://www.ieee.org/en_AU
local.type.statusPublished Versionen_AU

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