SEDSP: A Salience-Driven 17-Moment Cognitive Engine for Ultra-Low Power Edge Intelligence

SEDSP: A Salience-Driven 17-Moment Cognitive Engine for Ultra-Low Power Edge Intelligence | 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 Article SEDSP: A Salience-Driven 17-Moment Cognitive Engine for Ultra-Low Power Edge Intelligence Qiuhua Zhou, Kaiyao WU, Jiaxu ZHOU, Yifan WU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9156868/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 We present the Sparse Event-Driven Sequence Processing (SEDSP) engine, a bio-inspired neuromorphic architecture that translates the 17-moment Vithi cycle from Abhidhamma philosophy into a computationally efficient framework for edge intelligence. In contrast to traditional Transformers, which incur constant computational overhead regardless of input salience, SEDSP leverages Temporal Elasticity and Proactive Salience-Driven Gating to achieve true energy proportionality. Empirical evaluations demonstrate a 138× reduction in total energy consumption in sparse, real-world environments and a 10.4× improvement in active-inference efficiency compared to dense baselines. By employing a fixed 7-step recursive “Javana” loop within the 15-moment Cognitive Enhancement path, SEDSP recovers up to 21.6 percentage points of absolute accuracy in high-noise conditions (SNR = 5 dB) relative to single-pass feed-forward baselines, while localized “Karmic Registration” in the 17th moment effectively mitigates catastrophic forgetting during continual learning. These results position SEDSP as a promising solution to the Efficiency-Robustness Trilemma in ultra-low-power edge deployment. Physical sciences/Mathematics and computing Humanities/Religion Social science/Psychology Full Text Additional Declarations There is NO Competing Interest. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9156868","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":608132802,"identity":"d17bebe9-217e-4de2-bde6-a632068131f2","order_by":0,"name":"Qiuhua Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYFAC5gYGxgYGBn5m5sMPiNTCCNEi2c6WZkCaFoPzPAoSRGnQbW9sfPhzh03i5sM8DAYMNTbRBLWYnTnYbCB5Ji1x22HeAw8YjqXlNhDUciOxTcKw7TBQC1+CAWPDYSK03H/YJpEI1LK5mcdAgjgtNxjbJA4CtWxgJlrLmcRmw8a2NOMZh4GBnECUX44fPvjwZ5uNbH//4cMPPtTYENaCChJIUz4KRsEoGAWjABcAALWMQ/9h3kBPAAAAAElFTkSuQmCC","orcid":"","institution":"Key Laboratory of Brain-Machine Intelligence for Information Behavior Ministry of Education","correspondingAuthor":true,"prefix":"","firstName":"Qiuhua","middleName":"","lastName":"Zhou","suffix":""},{"id":608132803,"identity":"b3396328-1078-4bbf-a0b5-2f94d29e1a32","order_by":1,"name":"Kaiyao WU","email":"","orcid":"","institution":"School of Statistics and Data Science, Shanghai University of International Business and Economics","correspondingAuthor":false,"prefix":"","firstName":"Kaiyao","middleName":"","lastName":"WU","suffix":""},{"id":608132804,"identity":"c9ea379a-e36f-4bbb-8401-ea1a411a819d","order_by":2,"name":"Jiaxu ZHOU","email":"","orcid":"","institution":"School of Statistics and Data Science, Shanghai University of International Business and Economics","correspondingAuthor":false,"prefix":"","firstName":"Jiaxu","middleName":"","lastName":"ZHOU","suffix":""},{"id":608132805,"identity":"fd18ed4b-339e-4a7a-b6cf-da6b9b32c03b","order_by":3,"name":"Yifan WU","email":"","orcid":"","institution":"College of Computing, Data Science and Society, University of California, Berkeley","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"WU","suffix":""}],"badges":[],"createdAt":"2026-03-18 08:51:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9156868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9156868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106994062,"identity":"aa6e52a2-bbe8-41aa-892d-1d5dd6309c9b","added_by":"auto","created_at":"2026-04-15 15:03:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":715213,"visible":true,"origin":"","legend":"","description":"","filename":"SEDSP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9156868/v1_covered_a30abb09-b044-4340-832d-c4ef73fa34d0.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"SEDSP: A Salience-Driven 17-Moment Cognitive Engine for Ultra-Low Power Edge Intelligence","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"","lastPublishedDoi":"10.21203/rs.3.rs-9156868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9156868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We present the Sparse Event-Driven Sequence Processing (SEDSP) engine, a bio-inspired neuromorphic architecture that translates the 17-moment Vithi cycle from Abhidhamma philosophy into a computationally efficient framework for edge intelligence. 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