Joint DNN partitioning and Resource Allocation for Completion Rate Maximization of Delay-Aware DNN inference Tasks in Wireless Powered Mobile Edge Computing

Joint DNN partitioning and Resource Allocation for Completion Rate Maximization of Delay-Aware DNN inference Tasks in Wireless Powered Mobile Edge Computing | 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 Joint DNN partitioning and Resource Allocation for Completion Rate Maximization of Delay-Aware DNN inference Tasks in Wireless Powered Mobile Edge Computing Xianzhong Tian, Pengcheng Xu, Yifan Shen, Yuheng Shao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3018311/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Sep, 2023 Read the published version in Peer-to-Peer Networking and Applications → Version 1 posted 7 You are reading this latest preprint version Abstract With the development of smart Internet of Things (IoT), it has seen a surge in wireless devices deploying Deep Neural Network (DNN) models for real-time computing tasks. However, the inherent resource and energy constraints of wireless devices make local completion of real-time inference tasks impractical. DNN model partitioning can partition the DNN model and use edge servers to assist in completing DNN model inference tasks, but offloading also requires a lot of transmission energy consumption. Additionally, the complex structure of DNN models means partitioning and offloading across different network layers impacts overall energy consumption significantly, complicating the development of an optimal partitioning strategy. Furthermore, in certain application contexts, regular battery charging or replacement for smart IoT devices is impractical and environmentally harmful. The development of wireless energy transfer technology enables devices to obtain RF energy through wireless transmission to achieve sustainable power supply. Motivated by this, We propose a problem of joint DNN model partition and resource allocation in Wireless Powered Edge Computing (WPMEC). However, time-varying channel state in the WPMEC have a significant impact on resource allocation decisions. How to jointly optimize DNN model partition and resource allocation decisions is also a significant challenge. We propose an online algorithm based on Deep Reinforcement Learning (DRL) to solve the time allocation decision, simplifying a Mixed Integer Nonlinear Problem (MINLP) into a convex optimization problem. Our approach seeks to maximize the completion rate of DNN inference tasks within the constraints of time-varying wireless channel states and delay constraints. Simulation results show the exceptional performance of this algorithm in enhancing task completion rates. Edge computing DNN inference Wireless power transfer Resource allocation Deep reinforcement learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Sep, 2023 Read the published version in Peer-to-Peer Networking and Applications → Version 1 posted Editorial decision: Major revision 27 Jul, 2023 Reviews received at journal 03 Jul, 2023 Reviewers agreed at journal 21 Jun, 2023 Reviewers invited by journal 21 Jun, 2023 Editor assigned by journal 19 Jun, 2023 Submission checks completed at journal 08 Jun, 2023 First submitted to journal 03 Jun, 2023 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-3018311","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":208036831,"identity":"c02543c3-f44c-495a-8cb9-d415c61ada37","order_by":0,"name":"Xianzhong Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYHACxgcMPGCGAXHqgYqZDYCkBEla2EDKSdBiL5H+rPKHzOE6BvbmbRIMNXeIsIXnQNoNCZ7DEgw8x8okGI49I0ILe8OxGwYgLRI5ZhKMDYeJ0MLM2FaQANIi/4ZYLezNbAwHwLbwEKvlzDFmyQaedMk2nrRii4RjRGhhn5H+8OPPHmt+fvbDG298qCFCCxgw9gBjB8RIIFIDEPwgXukoGAWjYBSMQAAAk6YvHLYHr64AAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xianzhong","middleName":"","lastName":"Tian","suffix":""},{"id":208036832,"identity":"aeb8cd4a-02ae-4409-8ff8-0d707852e506","order_by":1,"name":"Pengcheng Xu","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pengcheng","middleName":"","lastName":"Xu","suffix":""},{"id":208036835,"identity":"9b7cb0ee-77e8-4e6a-8950-537276a2c270","order_by":2,"name":"Yifan Shen","email":"","orcid":"","institution":"University of Illinois Urbana-Champaign","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Shen","suffix":""},{"id":208036837,"identity":"939409c1-0fab-494a-8e27-161d93e24891","order_by":3,"name":"Yuheng Shao","email":"","orcid":"","institution":"Zhejiang University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuheng","middleName":"","lastName":"Shao","suffix":""}],"badges":[],"createdAt":"2023-06-03 14:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3018311/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3018311/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12083-023-01564-z","type":"published","date":"2023-09-20T15:01:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":43640503,"identity":"e0931bef-0a25-491e-b7e6-248634bfdf52","added_by":"auto","created_at":"2023-09-25 15:07:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":610851,"visible":true,"origin":"","legend":"","description":"","filename":"JointDNN67.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3018311/v1_covered_d5df3e1f-42fc-4e63-8c47-51fcb59b987c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint DNN partitioning and Resource Allocation for Completion Rate Maximization of Delay-Aware DNN inference Tasks in Wireless Powered Mobile Edge Computing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"peer-to-peer-networking-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ppna","sideBox":"Learn more about [Peer-to-Peer Networking and Applications](http://link.springer.com/journal/12083)","snPcode":"12083","submissionUrl":"https://submission.nature.com/new-submission/12083/3","title":"Peer-to-Peer Networking and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Edge computing, DNN inference, Wireless power transfer, Resource allocation, Deep reinforcement learning","lastPublishedDoi":"10.21203/rs.3.rs-3018311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3018311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the development of smart Internet of Things (IoT), it has seen a surge in wireless devices deploying Deep Neural Network (DNN) models for real-time computing tasks. 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