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dc.contributor.authorExenberger Becker, Pedro Henrique
dc.contributor.authorArnau Montañés, José María
dc.contributor.authorGonzález Colás, Antonio María
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2021-11-15T11:21:33Z
dc.date.available2021-11-15T11:21:33Z
dc.date.issued2021-09-15
dc.identifier.citationExenberger, P.; Arnau, J.; González, A. Characterizing self-driving tasks in general-purpose architectures. A: "ACACES 2021 poster abstracts: September 15, 2021 Fiuggi, Italy". European Network of Excellence on High Performance and Embedded Architecture and Compilation (HiPEAC), 2021, p. 117-120.
dc.identifier.isbn978-88-905806-8-0
dc.identifier.urihttp://hdl.handle.net/2117/356419
dc.description.abstractAutonomous Vehicles (AVs) have the potential to radically change the automotive industry. How- ever, computing solutions for AVs have to meet severe performance constraints to guarantee a safe driving experience. Current solutions either exhibit high cost or fail to meet the stringent latency constraints. Therefore, the popularization of AVs requires a low-cost yet effective computing sys- tem. Understanding the sources of latency is key in order to improve autonomous driving systems. Here, we present a detailed characterization of Autoware, a modern self-driving car system. We analyze the performance of the different components and leverage hardware counters to identify the main bottlenecks.
dc.description.sponsorshipThis work has been supported by the the CoCoUnit ERC Advanced Grant of the EU’s Horizon 2020 program (grant No 833057), the Spanish State Research Agency under grant PID2020-113172RB-I00 (AEI/FEDER, EU), the ICREA Academia program, and the grant 2020 FPI-UPC_033.
dc.format.extent4 p.
dc.language.isoeng
dc.publisherEuropean Network of Excellence on High Performance and Embedded Architecture and Compilation (HiPEAC)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subject.lcshAutonomous vehicles
dc.subject.otherAutonomous driving
dc.subject.otherCharacterization
dc.titleCharacterizing self-driving tasks in general-purpose architectures
dc.typePart of book or chapter of book
dc.subject.lemacVehicles autònoms
dc.contributor.groupUniversitat Politècnica de Catalunya. ARCO - Microarquitectura i Compiladors
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
local.identifier.drac32048669
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/833057/EU/CoCoUnit: An Energy-Efficient Processing Unit for Cognitive Computing/CoCoUnit
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113172RB-I00/ES/ARQUITECTURAS DE DOMINIO ESPECIFICO PARA SISTEMAS DE COMPUTACION ENERGETICAMENTE EFICIENTES/
local.citation.authorExenberger, P.; Arnau, J.; González, A.
local.citation.publicationNameACACES 2021 poster abstracts: September 15, 2021 Fiuggi, Italy
local.citation.startingPage117
local.citation.endingPage120


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