Adaptive Fault Resilience Techniques for Flash Memory Used in DNN Accelerators

Adaptive Fault Resilience Techniques for Flash Memory Used in DNN Accelerators | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adaptive Fault Resilience Techniques for Flash Memory Used in DNN Accelerators Shyue-Kung Lu, Yi-Zheng Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8429480/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Deep neural networks (DNNs) are increasingly utilized in various applications, including smart appliances, facial recognition, and autonomous driving. The weight data generated during training are typically stored in flash memory, which is prone to reliability and endurance challenges. Given the inherent error tolerance of DNN applications, adaptive fault resilience techniques have been proposed to safeguard the weight data stored in flash memory. Initially, an analysis of bit significance is conducted to ascertain the priority of weight bits that require protection. Subsequently, a novel weight transposer and an address remapper are introduced to reallocate significant weight bits to more reliable or fault-free flash memory cells. A bipartite graph model is developed to facilitate the modeling of address remapping and the assessment of error scores. Additionally, corresponding hardware architectures for address remapping are proposed. The deep learning framework PyTorch is employed to evaluate inference accuracy across various DNN models. Experimental results indicate that, with an injected bit error rate (BER) of 0.01% in the weight data, the accuracy losses for commonly used DNN models remain below 1%, accompanied by negligible hardware overhead. Fault Resilience Flash Memory DNN Accelerator Reliability Accuracy Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8429480","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":564433200,"identity":"c063d40c-4b7d-44fc-bd6f-a1b6d999bfc9","order_by":0,"name":"Shyue-Kung 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Accelerators\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Science and Technology Council (Taiwan)","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fault Resilience, Flash Memory, DNN 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The weight data generated during training are typically stored in flash memory, which is prone to reliability and endurance challenges. Given the inherent error tolerance of DNN applications, \u003cem\u003eadaptive fault resilience\u003c/em\u003e techniques have been proposed to safeguard the weight data stored in flash memory. Initially, an analysis of bit significance is conducted to ascertain the priority of weight bits that require protection. Subsequently, a novel \u003cem\u003eweight transposer\u003c/em\u003e and an \u003cem\u003eaddress remapper\u003c/em\u003e are introduced to reallocate significant weight bits to more reliable or fault-free flash memory cells. A bipartite graph model is developed to facilitate the modeling of address remapping and the assessment of error scores. Additionally, corresponding hardware architectures for address remapping are proposed. The deep learning framework PyTorch is employed to evaluate inference accuracy across various DNN models. Experimental results indicate that, with an injected bit error rate (BER) of 0.01% in the weight data, the accuracy losses for commonly used DNN models remain below 1%, accompanied by negligible hardware overhead.\u003c/p\u003e","manuscriptTitle":"Adaptive Fault Resilience Techniques for Flash Memory Used in DNN Accelerators","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-24 16:00:10","doi":"10.21203/rs.3.rs-8429480/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90e26fec-ff1c-4a9f-bcc0-44ad384ecdfe","owner":[],"postedDate":"December 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T16:00:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-24 16:00:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8429480","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8429480","identity":"rs-8429480","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}