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Scalable and Energy-Efficient Deep Learning for Distributed AIoT Applications Using Modular Cognitive IoT Hardware

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dc.contributor.authorAbbasi, Maryam
dc.contributor.authorCardoso, Filipe
dc.contributor.authorSilva, José
dc.contributor.authorMartins, Pedro
dc.date.accessioned2024-01-11T08:48:57Z
dc.date.available2024-01-11T08:48:57Z
dc.date.issued2023
dc.description.abstractIn this scientific paper, we present a novel approach to develop energy-efficient Deep Learning models for distributed AIoT applications. Our approach considers the optimization of algorithms, whilealso addressing safety and security challenges that arise in such systems. We propose a modular and scalable cognitive IoT hardware platform that leverages microserver technology, allowing users to customize hardware configurations to suit a broad range of applications. We provide a comprehensive design flow for developing Next-Generation IoT devices that can collaboratively solve complex Deep Learning applications across dis- tributed systems. Our methods have been thoroughly tested on diverse use-cases, ranging from Smart Home to Automotive and Industrial IoT appliances. Our results demonstrate the effectiveness of our approach in significantly reducing energy consumption while maintaining high performance in Deep Learning applications. Overall, this work contributes to advancing the development of energy-efficient and scalable Deep Learning for distributed AIoT applications, providing an important step towards enabling the deployment of intelligent systems in diverse real-world scenarios.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAbbasi, M., Cardoso, F., Silva, J. & Martins, P. (2023). Scalable and energy-efficient deep learning for distributed AIoT Applications using Modular Cognitive IoT Hardware. In D. H. de la Iglesia, J. F. de Paz Santana & A. J. López Rivero (Eds.), New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence: Vol. 1452. DiTTEt 2023. Advances in Intelligent Systems and Computing (pp. 85–96). Springer. https://doi.org/10.1007/978-3-031-38344-1_9pt_PT
dc.identifier.doihttps://doi.org/10.1007/978-3-031-38344-1_9pt_PT
dc.identifier.isbn978-3-031-38344-1
dc.identifier.urihttp://hdl.handle.net/10400.15/4693
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-031-38344-1_9pt_PT
dc.subjectEnergy-efficient Deep Learningpt_PT
dc.subjectDistributed AIoT applicationspt_PT
dc.subjectModular cognitive IoT hardwarept_PT
dc.subjectMicroserver technologypt_PT
dc.subjectNext-Generation IoT devicept_PT
dc.titleScalable and Energy-Efficient Deep Learning for Distributed AIoT Applications Using Modular Cognitive IoT Hardwarept_PT
dc.typebook part
dspace.entity.typePublication
oaire.citation.conferencePlaceUniversity of Salamanca, Spainpt_PT
oaire.citation.endPage96pt_PT
oaire.citation.startPage85pt_PT
oaire.citation.titleDiTTEt 2023 : International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligencept_PT
oaire.citation.volume1452pt_PT
person.familyNameGonçalves Cardoso
person.givenNameFilipe
person.identifier19-AQJEAAAAJ
person.identifier.ciencia-id8219-19E8-C070
person.identifier.orcid0000-0002-3916-5182
person.identifier.scopus-author-id57486516500
rcaap.rightsrestrictedAccesspt_PT
rcaap.typebookPartpt_PT
relation.isAuthorOfPublicationf108cbea-41e4-459d-86e3-3225c293e4a6
relation.isAuthorOfPublication.latestForDiscoveryf108cbea-41e4-459d-86e3-3225c293e4a6

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