Dependable neural networks through redundancy, a comparison of redundant architectures

Doran, Hans Dermot; Ganz, David; Ielpo, Gianluca; Zapke, Michael

doi:10.21256/zhaw-23980

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23980

Full metadata record

DC Field	Value	Language
dc.contributor.author	Doran, Hans Dermot	-
dc.contributor.author	Ganz, David	-
dc.contributor.author	Ielpo, Gianluca	-
dc.contributor.author	Zapke, Michael	-
dc.date.accessioned	2022-01-21T16:04:48Z	-
dc.date.available	2022-01-21T16:04:48Z	-
dc.date.issued	2021-03	-
dc.identifier.other	arXiv:2108.02565	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/23980	-
dc.description.abstract	With edge-AI finding an increasing number of real-world applications, especially in industry, the question of functionally safe applications using AI has begun to be asked. In this body of work, we explore the issue of achieving dependable operation of neural networks. We discuss the issue of dependability in general implementation terms before examining lockstep solutions. We intuit that it is not necessarily a given that two similar neural networks generate results at precisely the same time and that synchronization between the platforms will be required. We perform some preliminary measurements that may support this intuition and introduce some work in implementing lockstep neural network engines.	de_CH
dc.language.iso	en	de_CH
dc.publisher	WEKA	de_CH
dc.rights	https://creativecommons.org/licenses/by-nc-nd/4.0/	de_CH
dc.subject	Functional safety	de_CH
dc.subject	Lockstep processor	de_CH
dc.subject	FPGA	de_CH
dc.subject	GPU	de_CH
dc.subject	Machine learning	de_CH
dc.subject	Neural network	de_CH
dc.subject	Computer architecture	de_CH
dc.subject.ddc	006: Spezielle Computerverfahren	de_CH
dc.title	Dependable neural networks through redundancy, a comparison of redundant architectures	de_CH
dc.type	Konferenz: Paper	de_CH
dcterms.type	Text	de_CH
zhaw.departement	School of Engineering	de_CH
zhaw.organisationalunit	Institute of Embedded Systems (InES)	de_CH
dc.identifier.doi	10.21256/zhaw-23980	-
zhaw.conference.details	Embedded World Conference 2021, online, 1.-5. März 2021	de_CH
zhaw.funding.eu	No	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.publication.status	publishedVersion	de_CH
zhaw.publication.review	Peer review (Abstract)	de_CH
zhaw.title.proceedings	Proceedings of the Embedded World Conference 2021	de_CH
zhaw.author.additional	No	de_CH
zhaw.display.portrait	Yes	de_CH
Appears in collections:	Publikationen School of Engineering

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Doran, H. D., Ganz, D., Ielpo, G., & Zapke, M. (2021, March). Dependable neural networks through redundancy, a comparison of redundant architectures. Proceedings of the Embedded World Conference 2021. https://doi.org/10.21256/zhaw-23980

Doran, H.D. et al. (2021) ‘Dependable neural networks through redundancy, a comparison of redundant architectures’, in Proceedings of the Embedded World Conference 2021. WEKA. Available at: https://doi.org/10.21256/zhaw-23980.

H. D. Doran, D. Ganz, G. Ielpo, and M. Zapke, “Dependable neural networks through redundancy, a comparison of redundant architectures,” in Proceedings of the Embedded World Conference 2021, Mar. 2021. doi: 10.21256/zhaw-23980.

DORAN, Hans Dermot, David GANZ, Gianluca IELPO und Michael ZAPKE, 2021. Dependable neural networks through redundancy, a comparison of redundant architectures. In: Proceedings of the Embedded World Conference 2021. Conference paper. WEKA. März 2021

Doran, Hans Dermot, David Ganz, Gianluca Ielpo, and Michael Zapke. 2021. “Dependable Neural Networks through Redundancy, a Comparison of Redundant Architectures.” Conference paper. In Proceedings of the Embedded World Conference 2021. WEKA. https://doi.org/10.21256/zhaw-23980.

Doran, Hans Dermot, et al. “Dependable Neural Networks through Redundancy, a Comparison of Redundant Architectures.” Proceedings of the Embedded World Conference 2021, WEKA, 2021, https://doi.org/10.21256/zhaw-23980.