Adaptive Hardware/Software Co-Design for Medical Image Processing: From Systematic Review to Adaptive Framework Development

Adaptive Hardware/Software Co-Design for Medical Image Processing: From Systematic Review to Adaptive Framework Development | 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 Hardware/Software Co-Design for Medical Image Processing: From Systematic Review to Adaptive Framework Development NOUR ABU JASSAR, Dheya Mustafa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8550340/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 In modern computing, hardware/software co-design represents a pivotal paradigm for advancing image processing systems. This paper presents a multi-faceted investigation that evolves from a systematic review to comprehensive framework development. First, we conduct a systematic review of 22 studies on hardware/software co-design methods for image processing, following PRISMA guidelines. Our analysis identifies key techniques including FPGA-based acceleration, high-level synthesis, and dynamic reconfiguration that address computational bottlenecks. Second, we validate these principles through experimental simulation using the MedMNIST dataset across CPU, FPGA, ARM, and GPU platforms. Results demonstrate FPGA’s optimal balance between latency (10.8 ms) and energy efficiency (51.1% reduction versus CPU), ARM’s superiority in energy-constrained scenarios (0.047 J per inference), and GPU’s maximum throughput (31,335 FPS) for high-volume applications. Third, building on these insights, we propose and develop the Adaptive Hybrid Co-Design Framework (AHCDF), evolving through five iterative versions that progressively address performance-reliability trade-offs. The Enhanced AHCDF version achieves 117,405 FPS throughput with 34.0% clinical constraint satisfaction through machine learning-driven hardware partitioning. Our comparative analysis reveals evolutionary trade- offs: while initial versions prioritize reliability (100% success rate), later versions achieve throughput improvements of up to 14.3× over baseline hardware. The framework demonstrates GPU dominance (50-76% allocation) in throughput-optimized configurations, while maintaining ARM’s energy efficiency for wearable applications. These findings provide quantitative evidence supporting co-design strategies and offer actionable deployment guidelines: ARM for portable diagnostics, FPGA for clinical workstations, and GPU for centralized screening. The paper concludes by highlighting directions for adaptive, secure, and scalable image processing systems that balance performance, energy efficiency, and clinical applicability. hardware/software co-design adaptive hybrid framework medical image processing FPGA edge computing GPU acceleration energy efficiency clinical deployment performance optimization hardware partitioning Full Text Additional Declarations No competing interests reported. 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-8550340","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581356961,"identity":"68ed275b-3249-4af2-a92b-8ed26f18d912","order_by":0,"name":"NOUR ABU JASSAR","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIie3RsUpDMRSA4VMOZIp1TZfmFRLuWvBVThFup8J1ETcrhUx9gHbqK8Q3OOVCXC527eBwS8G5q0sx2kVBUruJ5J8OgY+QE4Bc7g92Ces9092hH2eDn0ccpxTpPbBt24YLEL8lpubCbh0PJ19JusClIvEyWi6nHm8c9LsbgmqfEJ0ZB0XydeyDqHDhoOhFYucJgmrlFCkceyENXjgY+kgKmSBC74QigyPtjuT+JJEQ0BDVBOFIyJwiCppOS1xaH8qqls/KLprtJPmWK25g9XYYaD2tH3fydqC7T9fBpDb2Lf64NW7RJb/yx7A9m+Ryudx/7h3Vw1HJYBOvawAAAABJRU5ErkJggg==","orcid":"","institution":"Hashemite University","correspondingAuthor":true,"prefix":"","firstName":"NOUR","middleName":"ABU","lastName":"JASSAR","suffix":""},{"id":581356962,"identity":"3544ed5e-b766-49f0-ac4b-99c7484fe810","order_by":1,"name":"Dheya Mustafa","email":"","orcid":"","institution":"Hashemite University","correspondingAuthor":false,"prefix":"","firstName":"Dheya","middleName":"","lastName":"Mustafa","suffix":""}],"badges":[],"createdAt":"2026-01-08 10:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8550340/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8550340/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101398561,"identity":"05f51281-63ae-471e-ab56-784f72019975","added_by":"auto","created_at":"2026-01-29 09:42:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1296288,"visible":true,"origin":"","legend":"","description":"","filename":"Codesigning4on6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8550340/v1_covered_6453e884-ef20-4516-9681-865299d159c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Hardware/Software Co-Design for Medical Image Processing: From Systematic Review to Adaptive Framework Development","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":"hardware/software co-design, adaptive hybrid framework, medical image processing, FPGA, edge computing, GPU acceleration, energy efficiency, clinical deployment, performance optimization, hardware partitioning","lastPublishedDoi":"10.21203/rs.3.rs-8550340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8550340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn modern computing, hardware/software co-design represents a pivotal paradigm for advancing image processing systems. This paper presents a multi-faceted investigation that evolves from a systematic review to comprehensive framework development. First, we conduct a systematic review of 22 studies on hardware/software co-design methods for image processing, following PRISMA guidelines. Our analysis identifies key techniques including FPGA-based acceleration, high-level synthesis, and dynamic reconfiguration that address computational bottlenecks. Second, we validate these principles through experimental simulation using the MedMNIST dataset across CPU, FPGA, ARM, and GPU platforms. Results demonstrate FPGA’s optimal balance between latency (10.8 ms) and energy efficiency (51.1% reduction versus CPU), ARM’s superiority in energy-constrained scenarios (0.047 J per inference), and GPU’s maximum throughput (31,335 FPS) for high-volume applications. Third, building on these insights, we propose and develop the Adaptive Hybrid Co-Design Framework (AHCDF), evolving through five iterative versions that progressively address performance-reliability trade-offs. The Enhanced AHCDF version achieves 117,405 FPS throughput with 34.0% clinical constraint satisfaction through machine learning-driven hardware partitioning. Our comparative analysis reveals evolutionary trade- offs: while initial versions prioritize reliability (100% success rate), later versions achieve throughput improvements of up to 14.3× over baseline hardware. The framework demonstrates GPU dominance (50-76% allocation) in throughput-optimized configurations, while maintaining ARM’s energy efficiency for wearable applications. These findings provide quantitative evidence supporting co-design strategies and offer actionable deployment guidelines: ARM for portable diagnostics, FPGA for clinical workstations, and GPU for centralized screening. The paper concludes by highlighting directions for adaptive, secure, and scalable image processing systems that balance performance, energy efficiency, and clinical applicability.\u003c/p\u003e","manuscriptTitle":"Adaptive Hardware/Software Co-Design for Medical Image Processing: From Systematic Review to Adaptive Framework Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 06:44:35","doi":"10.21203/rs.3.rs-8550340/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":"70c94243-0b85-4b30-9177-c5bd885306c7","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T06:44:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 06:44:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8550340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8550340","identity":"rs-8550340","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}