Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems
| dc.contributor.author | Matana Luza, Lucas | |
| dc.contributor.author | Söderström, Daniel | |
| dc.contributor.author | Tsiligiannis, Georgios | |
| dc.contributor.author | Puchner, Helmut | |
| dc.contributor.author | Cazzaniga, Carlo | |
| dc.contributor.author | Sanchez, Ernesto | |
| dc.contributor.author | Bosio, Alberto | |
| dc.contributor.author | Dilillo, Luigi | |
| dc.contributor.editor | Dilillo, Luigi | |
| dc.contributor.editor | Psarakis, Mihalis | |
| dc.contributor.editor | Siddiqua, Taniya | |
| dc.date.accessioned | 2021-05-04T06:21:59Z | |
| dc.date.available | 2021-05-04T06:21:59Z | |
| dc.date.issued | 2020 | |
| dc.identifier.citation | Matana Luza, L., Söderström, D., Tsiligiannis, G., Puchner, H., Cazzaniga, C., Sanchez, E., Bosio, A., & Dilillo, L. (2020). Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems. In L. Dilillo, M. Psarakis, & T. Siddiqua (Eds.), <i>DFT 2020 : 33rd IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems</i>. IEEE. Proceedings : IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems, 2020. <a href="https://doi.org/10.1109/DFT50435.2020.9250865" target="_blank">https://doi.org/10.1109/DFT50435.2020.9250865</a> | |
| dc.identifier.other | CONVID_68030575 | |
| dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/75284 | |
| dc.description.abstract | Approximate Computing (AxC) is a well-known paradigm able to reduce the computational and power overheads of a multitude of applications, at the cost of a decreased accuracy. Convolutional Neural Networks (CNNs) have proven to be particularly suited for AxC because of their inherent resilience to errors. However, the implementation of AxC techniques may affect the intrinsic resilience of the application to errors induced by Single Events in a harsh environment. This work introduces an experimental study of the impact of neutron irradiation on approximate computing techniques applied on the data representation of a CNN. | en |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | eng | |
| dc.publisher | IEEE | |
| dc.relation.ispartof | DFT 2020 : 33rd IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems | |
| dc.relation.ispartofseries | Proceedings : IEEE International Symposium on Defect and Fault Tolerance in VLSI Systems | |
| dc.rights | In Copyright | |
| dc.title | Investigating the Impact of Radiation-Induced Soft Errors on the Reliability of Approximate Computing Systems | |
| dc.type | conference paper | |
| dc.identifier.urn | URN:NBN:fi:jyu-202105042598 | |
| dc.contributor.laitos | Fysiikan laitos | fi |
| dc.contributor.laitos | Department of Physics | en |
| dc.contributor.oppiaine | Kiihdytinlaboratorio | fi |
| dc.contributor.oppiaine | Accelerator Laboratory | en |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
| dc.relation.isbn | 978-1-7281-9458-5 | |
| dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
| dc.description.reviewstatus | nonPeerReviewed | |
| dc.relation.issn | 1550-5774 | |
| dc.type.version | acceptedVersion | |
| dc.rights.copyright | © 2020 IEEE | |
| dc.rights.accesslevel | openAccess | fi |
| dc.type.publication | conferenceObject | |
| dc.relation.conference | IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems | |
| dc.relation.grantnumber | 721624 | |
| dc.relation.grantnumber | 721624 | |
| dc.relation.projectid | info:eu-repo/grantAgreement/EC/H2020/721624/EU//RADSAGA | |
| dc.subject.yso | neuroverkot | |
| dc.subject.yso | mikroprosessorit | |
| dc.subject.yso | säteilyfysiikka | |
| dc.format.content | fulltext | |
| jyx.subject.uri | http://www.yso.fi/onto/yso/p7292 | |
| jyx.subject.uri | http://www.yso.fi/onto/yso/p13435 | |
| jyx.subject.uri | http://www.yso.fi/onto/yso/p11069 | |
| dc.rights.url | http://rightsstatements.org/page/InC/1.0/?language=en | |
| dc.relation.doi | 10.1109/DFT50435.2020.9250865 | |
| dc.relation.funder | European Commission | en |
| dc.relation.funder | Euroopan komissio | fi |
| jyx.fundingprogram | MSCA Innovative Training Networks (ITN) | en |
| jyx.fundingprogram | MSCA Innovative Training Networks (ITN) | fi |
| dc.type.okm | B3 |
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