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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorDoran, Hans Dermot-
dc.contributor.authorIelpo, Gianluca-
dc.contributor.authorGanz, David-
dc.contributor.authorZapke, Michael-
dc.date.accessioned2022-01-21T16:05:18Z-
dc.date.available2022-01-21T16:05:18Z-
dc.date.issued2021-06-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23981-
dc.description.abstractWe compare two similar machine learning implementations in a 2oo2 redundant configuration on two platforms, FPGA and GPU, in both an architectural and a performance sense. We examine the real-time characteristics in a theoretical and experimental sense, presenting measurements. We also examine the coordination/synchronisation issues between the redundant components and clearly enumerate the dependability considerations that need to be taken into account in this area. From these considerations we derive the requirements of the synchronisation and voting mechanisms and present first suggestions for these. We critically evaluate the platforms before ending the paper, aimed at decision makers as well as R&D interested, with a review and suggestions for further work/consideration.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectFunctional safetyde_CH
dc.subjectRedundant systemde_CH
dc.subjectEmbedded systemde_CH
dc.subjectComputer architecturede_CH
dc.subjectMachine learningde_CH
dc.subjectNeural networkde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleRedundant machine learning architectures for functional safety relevant applications – an evaluationde_CH
dc.typeKonferenz: Posterde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitute of Embedded Systems (InES)de_CH
zhaw.conference.details14. Embedded Computing Conference, Winterthur (online), 1. Juni 2021de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Doran, H. D., Ielpo, G., Ganz, D., & Zapke, M. (2021, June). Redundant machine learning architectures for functional safety relevant applications – an evaluation. 14. Embedded Computing Conference, Winterthur (Online), 1. Juni 2021.
Doran, H.D. et al. (2021) ‘Redundant machine learning architectures for functional safety relevant applications – an evaluation’, in 14. Embedded Computing Conference, Winterthur (online), 1. Juni 2021.
H. D. Doran, G. Ielpo, D. Ganz, and M. Zapke, “Redundant machine learning architectures for functional safety relevant applications – an evaluation,” in 14. Embedded Computing Conference, Winterthur (online), 1. Juni 2021, Jun. 2021.
DORAN, Hans Dermot, Gianluca IELPO, David GANZ und Michael ZAPKE, 2021. Redundant machine learning architectures for functional safety relevant applications – an evaluation. In: 14. Embedded Computing Conference, Winterthur (online), 1. Juni 2021. Conference poster. Juni 2021
Doran, Hans Dermot, Gianluca Ielpo, David Ganz, and Michael Zapke. 2021. “Redundant Machine Learning Architectures for Functional Safety Relevant Applications – an Evaluation.” Conference poster. In 14. Embedded Computing Conference, Winterthur (Online), 1. Juni 2021.
Doran, Hans Dermot, et al. “Redundant Machine Learning Architectures for Functional Safety Relevant Applications – an Evaluation.” 14. Embedded Computing Conference, Winterthur (Online), 1. Juni 2021, 2021.


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