Publication:
Data collection and utilization framework for edge AI applications

Simple item page
dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorRexha, Hergys
dc.contributor.authorLafond, Sébastien
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2021-11-16T11:37:46Z
dc.date.available2021-11-16T11:37:46Z
dc.date.issued2021-07-08
dc.descriptionProceeding of: 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN), Virtual conference (originally Madrid, Spain), 30-31 May 2021en
dc.description.abstractAs data being produced by IoT applications continues to explode, there is a growing need to bring computing power closer to the source of the data to meet the response-time, power dissipation and cost goals of performance-critical applications in various domains like Industrial Internet of Things (IIoT), Automated Driving, Medical Imaging or Surveillance among others. This paper proposes a data collection and utilization framework that allows runtime platform and application data to be sent to an edge and cloud system via data collection agents running close to the platform. Agents are connected to a cloud system able to train AI models to improve overall energy efficiency of an AI application executed on a edge platform. In the implementation part we show the benefits of FPGA-based platform for the task of object detection. Furthermore we show that it is feasible to collect relevant data from an FPGA platform, transmit the data to a cloud system for processing and receiving feedback actions to execute an edge AI application energy efficiently. As future work we foresee the possibility to train, deploy and continuously improve a base model able to efficiently adapt the execution of edge applications.es
dc.description.sponsorshipThis work has been partly funded by the European Commission through the projects EU-TW 5G-DIVE (Grant Agreement no. 859881).en
dc.format.extent3es
dc.identifier.bibliographicCitation2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN), Virtual conference (originally Madrid, Spain). IEEE, 2021, Pp. 105-108en
dc.identifier.doihttps://doi.org/10.1109/WAIN52551.2021.00023
dc.identifier.isbn978-1-6654-4470-5
dc.identifier.publicationfirstpage105es
dc.identifier.publicationlastpage108es
dc.identifier.publicationtitle2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN)en
dc.identifier.urihttps://hdl.handle.net/10016/33618
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate21-06-30es
dc.relation.eventplaceVirtual conference (originally Madrid, Spain)en
dc.relation.eventtitle2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN)en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/859881/5G-DIVEen
dc.rights© 2021 IEEE.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherCloud computingen
dc.subject.otherRuntimeen
dc.subject.otherImage edge detectionen
dc.subject.otherSurveillanceen
dc.subject.otherData collectionen
dc.subject.otherEnergy efficiencyen
dc.subject.otherPower dissipationen
dc.titleData collection and utilization framework for edge AI applicationsen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Data_WAIN_2021_ps.pdf
Size:
667.93 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
DIT - RYSC - Comunicaciones en Conferencias Internacionales / International Conferences
DIT - RYSC - Capítulos de monografías
OpenAIRE: Open Access Infrastructure for Research in Europe