A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

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dc.contributor.authorGorospe, Joseba
dc.contributor.authorMulero, Rubén
dc.contributor.authorArbelaitz, Olatz
dc.contributor.authorMuguerza, Javier
dc.contributor.authorAntón, Miguel Ángel
dc.date.accessioned2021-02-09T11:02:14Z
dc.date.available2021-02-09T11:02:14Z
dc.date.issued2021-02-03
dc.description.abstractDeep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.en
dc.description.sponsorshipThis research was supported by Tecnalia, Basque Research, and the ERDF/Spanish Ministry of Science, Innovation and Universities–National Research Agency/PhysComp project under Grant Number TIN2017-85409-P, in collaboration with the University of the Basque Country.en
dc.divisionConstrucción Sostenibleen
dc.identifier.citationGorospe, Joseba, Rubén Mulero, Olatz Arbelaitz, Javier Muguerza, and Miguel Ángel Antón. “A Generalization Performance Study Using Deep Learning Networks in Embedded Systems.” Sensors 21, no. 4 (February 3, 2021): 1031. doi:10.3390/s21041031.en
dc.identifier.doi10.3390/s21041031en
dc.identifier.essn1424-8220en
dc.identifier.urihttp://hdl.handle.net/11556/1078
dc.issue.number4en
dc.journal.titleSensorsen
dc.language.isoengen
dc.page.initial1031en
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.keywordsDeep learningen
dc.subject.keywordsEdge computingen
dc.subject.keywordsComputer visionen
dc.subject.keywordsQuantisationen
dc.titleA Generalization Performance Study Using Deep Learning Networks in Embedded Systemsen
dc.typejournal articleen
dc.volume.number21en
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