Selective Neuron Re-Computation (SNRC) for Error-Tolerant Neural Networks

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Show simple item record Liu, Shanshan Reviriego Vasallo, Pedro Lombardi, Fabrizio 2022-03-17T16:55:12Z 2022-03-17T16:55:12Z 2022-03-01
dc.identifier.bibliographicCitation IEEE Transactions on Computers (2022), 71(3), pp.: 684-695.
dc.identifier.issn 0018-9340
dc.description.abstract Artificial Neural networks (ANNs) are widely used to solve classification problems for many machine learning applications. When errors occur in the computational units of an ANN implementation due to for example radiation effects, the result of an arithmetic operation can be changed, and therefore, the predicted classification class may be erroneously affected. This is not acceptable when ANNs are used in many safety-critical applications, because the incorrect classification may result in a system failure. Existing error-tolerant techniques usually rely on physically replicating parts of the ANN implementation or incurring in a significant computation overhead. Therefore, efficient protection schemes are needed for ANNs that are run on a processor and used in resource-limited platforms. A technique referred to as Selective Neuron Re-Computation (SNRC), is proposed in this paper. As per the ANN structure and algorithmic properties, SNRC can identify the cases in which the errors have no impact on the outcome; therefore, errors only need to be handled by re-computation when the classification result is detected as unreliable. Compared with existing temporal redundancy-based protection schemes, SNRC saves more than 60 percent of the re-computation (more than 90 percent in many cases) overhead to achieve complete error protection as assessed over a wide range of datasets. Different activation functions are also evaluated.
dc.description.sponsorship This research was supported by the National Science Foundation Grants CCF-1953961 and 1812467, by the ACHILLES project PID2019-104207RB-I00 and the Go2Edge network RED2018-102585-T funded by the Spanish Ministry of Science and Innovation and by the Madrid Community research project TAPIR-CM P2018/TCS-4496.
dc.format.extent 11
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
dc.subject.other Neural networks
dc.subject.other Machine learning
dc.subject.other Sigmoid
dc.subject.other Error-tolerance
dc.title Selective Neuron Re-Computation (SNRC) for Error-Tolerant Neural Networks
dc.type article
dc.description.status Publicado
dc.subject.eciencia Telecomunicaciones
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. PID2019-104207RB-I00/ACHILLES
dc.relation.projectID Gobierno de España. RED2018-102585-T
dc.relation.projectID Comunidad de Madrid. TAPIR-CM P2018/TCS-4496
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 684
dc.identifier.publicationissue 3
dc.identifier.publicationlastpage 695
dc.identifier.publicationtitle IEEE TRANSACTIONS ON COMPUTERS
dc.identifier.publicationvolume 71
dc.identifier.uxxi AR/0000027529
dc.contributor.funder Ministerio de Ciencia e Innovación (España)
dc.contributor.funder Comunidad de Madrid
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