A scalable, heterogeneous hardware platform for accelerated aiot based on microservers

DC ElementWertSprache
dc.contributor.authorGriessl, R.
dc.contributor.authorPorrmann, F.
dc.contributor.authorKucza, N.
dc.contributor.authorMika, K.
dc.contributor.authorHagemeyer, J.
dc.contributor.authorKaiser, M.
dc.contributor.authorPorrmann, M.
dc.contributor.authorTassemeier, M.
dc.contributor.authorFlottmann, M.
dc.contributor.authorQararyah, F.
dc.contributor.authorWaqar, M.
dc.contributor.authorTrancoso, P.
dc.contributor.authorÖdman, D.
dc.contributor.authorGugala, K.
dc.contributor.authorLatosinski, G.
dc.date.accessioned2024-01-04T10:28:33Z-
dc.date.available2024-01-04T10:28:33Z-
dc.date.issued2023
dc.identifier.isbn9788770040266
dc.identifier.isbn9788770040273
dc.identifier.urihttp://osnascholar.ub.uni-osnabrueck.de/handle/unios/72866-
dc.descriptionCited by: 0
dc.description.abstractPerformance and energy efficiency are key aspects of next-generation AIoT hardware. This chapter presents a scalable, heterogeneous hardware platform for accelerated AIoT based on microserver technology. It integrates several accelerator platforms based on technologies like CPUs, embedded GPUs, FPGAs, or specialized ASICs, supporting the full range of the cloud-edge-IoT continuum. The modular microserver approach enables the integration of different, heterogeneous accelerators into one platform. Benchmarking the various accelerators takes performance, energy efficiency, and accuracy into account. The results provide a solid overview of available accelerator solutions and guide hardware selection for AIoT applications from the far edge to the cloud. © The Editor(s) (if applicable) and The Author(s) 2023. All rights reserved.
dc.language.isoen
dc.publisherRiver Publishers
dc.relation.ispartofShaping the Future of IoT with Edge Intelligence: How Edge Computing Enables the Next Generation of IoT Applications
dc.subject(far) edge-computing
dc.subjectAccelerator
dc.subjectAIoT
dc.subjectDeep learning
dc.subjectEnergy-efficiency
dc.subjectFPGA
dc.subjectIoT
dc.subjectMachine learning
dc.subjectMicroserver
dc.subjectPerformance classification
dc.titleA scalable, heterogeneous hardware platform for accelerated aiot based on microservers
dc.typebook part
dc.identifier.scopus2-s2.0-85173023396
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85173023396&partnerID=40&md5=801665a7503d8e93263e7f1c77ce52e0
dc.description.startpage179 – 196
dcterms.isPartOf.abbreviationShap. the Future of IoT with Edge Intell.: How Edge Comput. Enables the Next Génér. of IoT Appl.
local.import.remainsaffiliations : Bielefeld University, Germany; Osnabrück University, Germany; Chalmers University of Technology, Sweden; EMBEDL AB, Sweden; Antmicro, Poland
local.import.remainscorrespondence_address : R. Griessl; Bielefeld University, Germany; email: rgriessl@techfak.uni-bielefeld.de
local.import.remainspublication_stage : Final
crisitem.author.deptFB 06 - Mathematik/Informatik-
crisitem.author.deptidfb06-
crisitem.author.orcid0000-0003-1005-5753-
crisitem.author.parentorgUniversität Osnabrück-
crisitem.author.netidPoMa309-
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