Performance analysis of CNNs deployed on low power arquitectures

Part of the book series: Springer Proceedings in Materials ((SPM,volume 50)) Included in the following conference series: X Workshop in R&D+i & International Workshop on STEM of EPS

Detalles Bibliográficos
Autores: Pérez-Peña, Antonio Manuel, Linares Barranco, Alejandro, Casanueva-Morato, Daniel, Barranco, Francisco, Romero García, Samuel F.
Tipo de recurso: capítulo de libro
Estado:Versión aceptada para publicación
Fecha de publicación:2024
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/164143
Acceso en línea:https://hdl.handle.net/11441/164143
https://doi.org/10.1007/978-3-031-64106-0_63
Access Level:acceso abierto
Palabra clave:Convolutional neural networks
FPGA
Hardware accelerator
Computer vision
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spelling Performance analysis of CNNs deployed on low power arquitecturesPérez-Peña, Antonio ManuelLinares Barranco, AlejandroCasanueva-Morato, DanielBarranco, FranciscoRomero García, Samuel F.Convolutional neural networksFPGAHardware acceleratorComputer visionPart of the book series: Springer Proceedings in Materials ((SPM,volume 50)) Included in the following conference series: X Workshop in R&D+i & International Workshop on STEM of EPSArtificial intelligence is getting a more important role in our lives day by day. It is present in many daily used devices. In the majority of cases, the use of these algorithms implies having an internet connection so as to work due to their high computational demand. With the lately increase in the computing power of embedded systems, the execution of neural networks is focusing on the so-called edge computing. This favors the execution of neural network algorithms in environments where is not possible an internet connection. This article accomplishes an analysis of efficiency in the execution of a convolutional neural network (CNN) in a dedicated embedded device with graphical processing units (GPU) and inside two different multi-processors programmable system-on-chip with FPGA devices (MPSoC) from Xilinx. The first motivation for this work is the study of the viability of the deployment of an application to help visual functional diversity people with embedded systems not requiring an internet connection and oriented to Edge-AI, and the second is the search for a low-power device for longer battery life. At the end of the study, it is concluded that the deployment of these algorithms inside the selected low-power devices makes it possible to maintain a high frameper- second (FPS) rate in some architectures with an energy demand considerably inferior to the general-purpose devices.SpringerArquitectura y Tecnología de ComputadoresTEP108: Robótica y Tecnología de Computadores2024info:eu-repo/semantics/bookPartinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/164143https://doi.org/10.1007/978-3-031-64106-0_63reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésRecent Advances and Emerging Challenges in STEMhttps://link.springer.com/chapter/10.1007/978-3-031-64106-0_63Cham, Suizainfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1641432026-06-17T12:51:07Z
dc.title.none.fl_str_mv Performance analysis of CNNs deployed on low power arquitectures
title Performance analysis of CNNs deployed on low power arquitectures
spellingShingle Performance analysis of CNNs deployed on low power arquitectures
Pérez-Peña, Antonio Manuel
Convolutional neural networks
FPGA
Hardware accelerator
Computer vision
title_short Performance analysis of CNNs deployed on low power arquitectures
title_full Performance analysis of CNNs deployed on low power arquitectures
title_fullStr Performance analysis of CNNs deployed on low power arquitectures
title_full_unstemmed Performance analysis of CNNs deployed on low power arquitectures
title_sort Performance analysis of CNNs deployed on low power arquitectures
dc.creator.none.fl_str_mv Pérez-Peña, Antonio Manuel
Linares Barranco, Alejandro
Casanueva-Morato, Daniel
Barranco, Francisco
Romero García, Samuel F.
author Pérez-Peña, Antonio Manuel
author_facet Pérez-Peña, Antonio Manuel
Linares Barranco, Alejandro
Casanueva-Morato, Daniel
Barranco, Francisco
Romero García, Samuel F.
author_role author
author2 Linares Barranco, Alejandro
Casanueva-Morato, Daniel
Barranco, Francisco
Romero García, Samuel F.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
TEP108: Robótica y Tecnología de Computadores
dc.subject.none.fl_str_mv Convolutional neural networks
FPGA
Hardware accelerator
Computer vision
topic Convolutional neural networks
FPGA
Hardware accelerator
Computer vision
description Part of the book series: Springer Proceedings in Materials ((SPM,volume 50)) Included in the following conference series: X Workshop in R&D+i & International Workshop on STEM of EPS
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/bookPart
info:eu-repo/semantics/acceptedVersion
format bookPart
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/164143
https://doi.org/10.1007/978-3-031-64106-0_63
url https://hdl.handle.net/11441/164143
https://doi.org/10.1007/978-3-031-64106-0_63
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Recent Advances and Emerging Challenges in STEM
https://link.springer.com/chapter/10.1007/978-3-031-64106-0_63
Cham, Suiza
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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