uSense: Unary-Computing-based Stochastic Edge Neuromorphic Sensing

uSense: Unary-Computing-based Stochastic Edge Neuromorphic Sensing | 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 uSense: Unary-Computing-based Stochastic Edge Neuromorphic Sensing Mohsen Rakhshan, Zubaidah Al-Mashhadani, Di Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8961215/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 Tactile sensing on the edge is a new frontier of embodied artificial intelligence, advancing novel applications like robotics, prostheses, and immersive virtual reality. However, the high-dimensional data generated from tactile arrays requires heavy computation and communication for real-time responses. To address this problem, we propose unary-computing-based stochastic edge neuromorphic sensing (uSense), a fast Fourier transform (FFT) framework based on unary computing for near-sensor tactile sensing. uSense encodes raw signals from the tactile sensors into rate-coded probabilistic bitstreams and performs stochastic FFT with extremely simple digital logic, alleviating memory, energy, bandwidth, and latency bottlenecks. Such benefits result from three key techniques: dual-domain (i.e., unary and binary) dynamic scaling, stage-wise bitwidth optimization, and computational graph pruning. First, dual-domain dynamic scaling ensures that our unary computing approach minimizes the numerical accuracy loss throughout the FFT computation. Second, stage-wise bitwidth optimization progressively tunes the data precision at each FFT stage, achieving one order of magnitude data compression with minimized accuracy loss. Third, computational graph pruning considers the task sensitivity of frequency components and removes redundant computation and memory access by 50%, achieving another order of magnitude data compression with minimized accuracy loss. Our experiments demonstrate that the uSense framework achieves 99.18% data compression, preserves 97.64% texture recognition accuracy across 21 textures, only 1.35% accuracy loss compared to floating-point baselines. Our analysis reveals an overlooked fact: high numerical inaccuracy does not necessarily translate to low decoding inaccuracy, as long as the structure of the feature latent space is preserved. Further analytical modeling on FFT hardware shows uSense can reduce the computation and memory footprint of FFT by more than 50%, and enable real-time responses of FFT in less than 100 nanoseconds. Physical sciences/Mathematics and computing/Computational science Physical sciences/Engineering/Biomedical engineering 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. 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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-8961215","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":604091922,"identity":"8884f4fb-4e41-4497-a1eb-afaadc9e67db","order_by":0,"name":"Mohsen Rakhshan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYFACHhjNw8AMpOQYGBgfMDCwkaDFmIGB2YA0LYkNhLTotvce/vBxB0M+f8/Zw58Lau6kbzh+mIHhQ9lhnFrMzpxLk5x5hsFyxtm+NOkZx57lbjiTzMA44xweLTdyzJh52xgMGM7zmDHzsB3O3XCD/wBQBK8W489/gVrkz/MYf+b5dzjd4AYzA/Nf/FoMpBmBWgzO9hhIAw1PAGthxKflzBkzyd42CQNDIEOat++w4UygXw72nEvHreV4j/GHn202BnJngC7k+XZYnu/4YcYHP8qscWqBAglU7gFC6kfBKBgFo2AU4AcARQFVCZ5a2qEAAAAASUVORK5CYII=","orcid":"","institution":"University of Central Florida","correspondingAuthor":true,"prefix":"","firstName":"Mohsen","middleName":"","lastName":"Rakhshan","suffix":""},{"id":604091923,"identity":"13ada6c1-34f1-4477-845e-2b19b73aeb0c","order_by":1,"name":"Zubaidah Al-Mashhadani","email":"","orcid":"","institution":"University of Central florida","correspondingAuthor":false,"prefix":"","firstName":"Zubaidah","middleName":"","lastName":"Al-Mashhadani","suffix":""},{"id":604091924,"identity":"c21009e3-ede4-4092-b48d-19ed400a926e","order_by":2,"name":"Di Wu","email":"","orcid":"https://orcid.org/0000-0001-9775-8026","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-02-24 21:45:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8961215/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8961215/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104387446,"identity":"f79201d2-ae1b-4dea-854b-ad7c901d36f5","added_by":"auto","created_at":"2026-03-11 08:58:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2513143,"visible":true,"origin":"","legend":"Article File","description":"","filename":"uSenseAlMashhadani20260224.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8961215/v1_covered_0fa14deb-e4c4-4091-89ae-f777a902c0da.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"uSense: Unary-Computing-based Stochastic Edge Neuromorphic Sensing","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-8961215/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8961215/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Tactile sensing on the edge is a new frontier of embodied artificial intelligence, advancing novel applications like robotics, prostheses, and immersive virtual reality. However, the high-dimensional data generated from tactile arrays requires heavy computation and communication for real-time responses. To address this problem, we propose unary-computing-based stochastic edge neuromorphic sensing (uSense), a fast Fourier transform (FFT) framework based on unary computing for near-sensor tactile sensing. uSense encodes raw signals from the tactile sensors into rate-coded probabilistic bitstreams and performs stochastic FFT with extremely simple digital logic, alleviating memory, energy, bandwidth, and latency bottlenecks. Such benefits result from three key techniques: dual-domain (i.e., unary and binary) dynamic scaling, stage-wise bitwidth optimization, and computational graph pruning. First, dual-domain dynamic scaling\r\nensures that our unary computing approach minimizes the numerical accuracy loss throughout the FFT computation. 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