ZOE: Zero Overhead ECC Techniques for Flash Memory Used in AI Accelerators

ZOE: Zero Overhead ECC Techniques for Flash Memory Used in AI 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 ZOE: Zero Overhead ECC Techniques for Flash Memory Used in AI Accelerators Shyue-Kung Lu, Yi-Zheng Wu This is a preprint; it has not been peer reviewed by a journal.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-8420084/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 One important role of flash memory is to store the trained weights for the state-of-the-art deep neural networks (DNNs). However, flash memory suffers from many reliability and endurance issues. Therefore, erroneous weights can degrade the classification accuracy, which is unacceptable for mission-critical applications. To address these challenges, a highly efficient zero-overhead Error Correction Code (ECC) technique named ZOE is proposed in this paper. By exploiting redundancies for weight representation, weights are partitioned into reducible weights (RWs) and irreducible weights (IRWs). Reducible weights can be represented with a shorter weight length and the saved bits can be used for storing check bits of the adopted ECC. For most DNN models, the weight values are mostly close to zero; therefore, the proportions of RWs are usually very high, allowing for many bits to be saved for ECC. Moreover, since a codeword of flash memory typically consists of many weights, the proportions of RW s in all codewords might vary. This variation can compromise the reduction efficiency of ZOE. Therefore, we also propose the weight leveling technique, which can evenly distribute RWs to all codewords. The algorithm for deriving control words to orchestrate the leveling is also proposed. The corresponding hardware architectures of ZOE are then developed. Experimental results demonstrate that the reliability and accuracy of typical DNN models can be significantly improved with negligible hardware overhead. Flash Memory Reliability Yield Accuracy AI Accelerator 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. 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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-8420084","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":563799333,"identity":"b5d56132-0578-4a09-9db4-c5993b774bfe","order_by":0,"name":"Shyue-Kung 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Accelerator","lastPublishedDoi":"10.21203/rs.3.rs-8420084/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8420084/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne important role of flash memory is to store the trained weights for the state-of-the-art deep neural networks (DNNs). However, flash memory suffers from many reliability and endurance issues. Therefore, erroneous weights can degrade the classification accuracy, which is unacceptable for mission-critical applications. To address these challenges, a highly efficient \u003cem\u003ezero-overhead Error Correction Code (ECC)\u003c/em\u003e technique named \u003cem\u003eZOE\u003c/em\u003e is proposed in this paper. By exploiting redundancies for weight representation, weights are partitioned into \u003cem\u003ereducible weights (RWs)\u003c/em\u003e and \u003cem\u003eirreducible weights (IRWs).\u003c/em\u003e Reducible weights can be represented with a shorter weight length and the saved bits can be used for storing check bits of the adopted ECC. For most DNN models, the weight values are mostly close to zero; therefore, the proportions of \u003cem\u003eRWs\u003c/em\u003e are usually very high, allowing for many bits to be saved for ECC. Moreover, since a codeword of flash memory typically consists of many weights, the proportions of \u003cem\u003eRW\u003c/em\u003es in all codewords might vary. This variation can compromise the reduction efficiency of ZOE. Therefore, we also propose the \u003cem\u003eweight leveling\u003c/em\u003e technique, which can evenly distribute \u003cem\u003eRWs\u003c/em\u003e to all codewords. The algorithm for deriving control words to orchestrate the leveling is also proposed. The corresponding hardware architectures of ZOE are then developed. Experimental results demonstrate that the reliability and accuracy of typical DNN models can be significantly improved with negligible hardware overhead.\u003c/p\u003e","manuscriptTitle":"ZOE: Zero Overhead ECC Techniques for Flash Memory Used in AI Accelerators","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-25 03:26:48","doi":"10.21203/rs.3.rs-8420084/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 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-25T03:26:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-25 03:26:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8420084","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8420084","identity":"rs-8420084","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}