Dynamic and static task mapping in a network-on-chip using machine learning techniques

Detalhes bibliográficos
Autor(a) principal: Brondani, Juliana Rubenich
Data de Publicação: 2015
Idioma: eng
Título da fonte: Manancial - Repositório Digital da UFSM
dARK ID: ark:/26339/001300000r1p7
Texto Completo: http://repositorio.ufsm.br/handle/1/25250
Resumo: Trabalho de conclusão de curso (graduação) - Universidade Federal de Santa Maria, Centro de Tecnologia, Curso de Engenharia de Computação, RS, 2015.
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spelling Dynamic and static task mapping in a network-on-chip using machine learning techniquesNetwork on chipDynamicMappingMachine learningAlgorithmsSVMCNPQ::ENGENHARIASTrabalho de conclusão de curso (graduação) - Universidade Federal de Santa Maria, Centro de Tecnologia, Curso de Engenharia de Computação, RS, 2015.In the past few years, the number of cores encapsulated in a single die has been increasing dramatically. As the performance of bus infrastructure does not scale with the increasing number of cores, NoC (Network-on-Chip) has been largely used to connect them in multiprocessing systems. However, even using NoC, the overall system performance can be affected due to a poor task mapping. Considering such scenario, this work proposes a static and a dynamic task mapping flow to mitigate the communication overhead between tasks in a NoC. A workflow using machine learning algorithms is considered since: it can find an almost optimal configuration as a solution; and it can deal with the overhead generated by other algorithms, which makes they timely unfeasible to be used dynamically. At compile time, a genetic algorithm, which can find a static solution, combined with cluster techniques is used for generating a dataset which can be used for training of unsupervised algorithms. At execution time, a Support Vector Machine, due to its low overhead and robustness, use the training set generated at compile time for mapping tasks on the fly according to its communication behavior. Results show that with good classifications and training of supervised algorithms the proposed work flow can reduce the overhead and generate good mapping results, which makes a dynamic mapping possible.Universidade Federal de Santa MariaBrasilUFSMCentro de TecnologiaBeck Rutzig, MateusBrondani, Juliana Rubenich2022-07-06T19:53:53Z2022-07-06T19:53:53Z2015-12-162015Trabalho de Conclusão de Curso de Graduaçãoinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://repositorio.ufsm.br/handle/1/25250ark:/26339/001300000r1p7engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2022-09-23T12:48:11Zoai:repositorio.ufsm.br:1/25250Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2022-09-23T12:48:11Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false
dc.title.none.fl_str_mv Dynamic and static task mapping in a network-on-chip using machine learning techniques
title Dynamic and static task mapping in a network-on-chip using machine learning techniques
spellingShingle Dynamic and static task mapping in a network-on-chip using machine learning techniques
Brondani, Juliana Rubenich
Network on chip
Dynamic
Mapping
Machine learning
Algorithms
SVM
CNPQ::ENGENHARIAS
title_short Dynamic and static task mapping in a network-on-chip using machine learning techniques
title_full Dynamic and static task mapping in a network-on-chip using machine learning techniques
title_fullStr Dynamic and static task mapping in a network-on-chip using machine learning techniques
title_full_unstemmed Dynamic and static task mapping in a network-on-chip using machine learning techniques
title_sort Dynamic and static task mapping in a network-on-chip using machine learning techniques
author Brondani, Juliana Rubenich
author_facet Brondani, Juliana Rubenich
author_role author
dc.contributor.none.fl_str_mv Beck Rutzig, Mateus
dc.contributor.author.fl_str_mv Brondani, Juliana Rubenich
dc.subject.por.fl_str_mv Network on chip
Dynamic
Mapping
Machine learning
Algorithms
SVM
CNPQ::ENGENHARIAS
topic Network on chip
Dynamic
Mapping
Machine learning
Algorithms
SVM
CNPQ::ENGENHARIAS
description Trabalho de conclusão de curso (graduação) - Universidade Federal de Santa Maria, Centro de Tecnologia, Curso de Engenharia de Computação, RS, 2015.
publishDate 2015
dc.date.none.fl_str_mv 2015-12-16
2015
2022-07-06T19:53:53Z
2022-07-06T19:53:53Z
dc.type.driver.fl_str_mv Trabalho de Conclusão de Curso de Graduação
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.ufsm.br/handle/1/25250
dc.identifier.dark.fl_str_mv ark:/26339/001300000r1p7
url http://repositorio.ufsm.br/handle/1/25250
identifier_str_mv ark:/26339/001300000r1p7
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Santa Maria
Brasil
UFSM
Centro de Tecnologia
publisher.none.fl_str_mv Universidade Federal de Santa Maria
Brasil
UFSM
Centro de Tecnologia
dc.source.none.fl_str_mv reponame:Manancial - Repositório Digital da UFSM
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Manancial - Repositório Digital da UFSM
collection Manancial - Repositório Digital da UFSM
repository.name.fl_str_mv Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)
repository.mail.fl_str_mv atendimento.sib@ufsm.br||tedebc@gmail.com
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