dc.creator | Velasco Montero, Delia | es |
dc.creator | Fernández Berni, Jorge | es |
dc.creator | Carmona Galán, Ricardo | es |
dc.creator | Rodríguez Vázquez, Ángel Benito | es |
dc.date.accessioned | 2023-11-06T09:20:51Z | |
dc.date.available | 2023-11-06T09:20:51Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Velasco Montero, D., Fernández Berni, J., Carmona Galán, R. y Rodríguez Vázquez, Á.B. (2020). Performance assessment of deep learning frameworks through metrics of CPU hardware exploitation on an embedded platform. International Journal of Electrical and Computer Engineering Systems, 11 (1), 1-11. https://doi.org/10.32985/ijeces.11.1.1. | |
dc.identifier.issn | 1847-6996 | es |
dc.identifier.issn | 1847-7003 | es |
dc.identifier.uri | https://hdl.handle.net/11441/150158 | |
dc.description.abstract | In this paper, we analyze heterogeneous performance exhibited by some popular deep learning software frameworks for visual inference on a resource-constrained hardware platform. Benchmarking of Caffe, OpenCV, TensorFlow, and Caffe2 is performed on the same set of convolutional neural networks in terms of instantaneous throughput, power consumption, memory footprint, and CPU utilization. To understand the resulting dissimilar behavior, we thoroughly examine how the resources in the processor are differently exploited by these frameworks. We demonstrate that a strong correlation exists between hardware events occurring in the processor and inference performance. The proposedhardware-aware analysis aims to findlimitations andbottlenecks emerging from the jointinteraction offrameworks andnetworks on a particular CPU-based platform. This provides insight into introducing suitable modifications in bothtypes of components to enhance their global performance. It also facilitates the selection of frameworks and networks among a large diversity of these components available these days for visual understanding. © 2020 J.J. Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology. All rights reserved. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades (MICINN). España RTI2018-097088-B-C31 | es |
dc.description.sponsorship | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) RTI2018-097088-B-C31 | es |
dc.description.sponsorship | European Union (EU) H2020 MSCA 765866 | es |
dc.description.sponsorship | Gobierno de España FPU17/02804 | es |
dc.description.sponsorship | US Office of Naval Research N00014-19-1-2156 | es |
dc.format | application/pdf | es |
dc.format.extent | 11 p. | es |
dc.language.iso | eng | es |
dc.publisher | J.J. Strossmayer University of Osijek | es |
dc.relation.ispartof | International Journal of Electrical and Computer Engineering Systems, 11 (1), 1-11. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Convolutional neural networks | es |
dc.subject | Deep learning | es |
dc.subject | Edge inference | es |
dc.subject | Embedded vision | es |
dc.subject | Hardware performance | es |
dc.subject | Software frameworks | es |
dc.title | Performance assessment of deep learning frameworks through metrics of CPU hardware exploitation on an embedded platform | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo | es |
dc.relation.projectID | RTI2018-097088-B-C31 | es |
dc.relation.projectID | 765866 | es |
dc.relation.projectID | FPU17/02804 | es |
dc.relation.projectID | N00014-19-1-2156 | es |
dc.relation.publisherversion | https://doi.org/10.32985/ijeces.11.1.1 | es |
dc.identifier.doi | 10.32985/ijeces.11.1.1 | es |
dc.journaltitle | International Journal of Electrical and Computer Engineering Systems | es |
dc.publication.volumen | 11 | es |
dc.publication.issue | 1 | es |
dc.publication.initialPage | 1 | es |
dc.publication.endPage | 11 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | es |
dc.contributor.funder | European Union (EU) H2020 MSCA | es |
dc.contributor.funder | Gobierno de España | es |
dc.contributor.funder | US Office of Naval Research | es |