Accelerated AI for Edge Computing

Simple item page
dc.contributor.authorRahnemoonfar, Maryam
dc.date.accessioned2020-04-13T18:07:49Z
dc.date.available2020-04-13T18:07:49Z
dc.date.issued2020-02-25
dc.description.sponsorshipNSF Workshop on Smart Cyberinfrastructure, Feb. 25-27, 2020, Hyatt Regency, Crystal City, VAen
dc.description.urihttp://smartci.sci.utah.edu/images/whitepapers/5e5042ab0eb95-Accelerated_AI.pdfen
dc.format.extent3 pagesen
dc.genreconference papers and proceedingsen
dc.identifierdoi:10.13016/m2dixz-3mko
dc.identifier.citationRahnemoonfar, Maryam; Accelerated AI for Edge Computing; NSF Workshop on Smart Cyberinfrastructure, Feb. 25-27, 2020, Hyatt Regency, Crystal City, VA; http://smartci.sci.utah.edu/images/whitepapers/5e5042ab0eb95-Accelerated_AI.pdfen
dc.identifier.urihttp://hdl.handle.net/11603/18030
dc.language.isoenen
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectAnalysis of the big datasets collected by various sensorsen
dc.subjectreal-time analysis of data on Edgeen
dc.subjectlearning algorithmsen
dc.subjectdeep learningen
dc.titleAccelerated AI for Edge Computingen
dc.typeTexten

Files

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Information Systems Department
UMBC Faculty Collection