A gem5-based framework for analyzing IoT microcontrollers with peripherals and firmware

A gem5-based framework for analyzing IoT microcontrollers with peripherals and firmware | 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 A gem5-based framework for analyzing IoT microcontrollers with peripherals and firmware Muhammad Awais, Maria Mushtaq, Lirida Naviner, Jawad Haj Yahya, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7754195/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 The rapid proliferation of Internet of Things (IoT) devices imposes stringent demands on energy efficiency, real-time responsiveness, and security, which traditional simulation approaches often fail to address due to their limited ability to capture microarchitectural interactions. This paper introduces Gem5-ML, a gem5-based framework extended with IoT-specific processor, memory, and peripheral models, coupled with machine learning (ML) to accelerate design-space exploration. Representative workloads including sensing, control, communication, and lightweight security kernels are executed in SE mode to collect fine-grained microarchitectural statistics such as cache activity, bus contention, interrupt latency, and execution cycles. These features are used to train surrogate ML models that enable fast and accurate prediction of performance and energy trade-offs, reducing exploration time by up to 10× while maintaining over 90% Pareto front coverage. The framework also incorporates anomaly detection mechanisms capable of identifying reliability issues such as interrupt storms, cache thrashing, and voltage droop with high accuracy. Experimental results demonstrate that Gem5-ML achieves efficient exploration, robust prediction, and actionable insights for energy-aware and secure IoT microcontroller design, highlighting its potential as a scalable methodology for next-generation low-power and resilient embedded systems. IoT Circuits Gem5 Simulation Machine Learning Design Space Exploration Anomaly Detection Energy Efficiency 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-7754195","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533286481,"identity":"da6c04fd-fa70-4ea7-91b8-6f8c6e09492d","order_by":0,"name":"Muhammad 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firmware","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"IoT Circuits, Gem5 Simulation, Machine Learning, Design Space Exploration, Anomaly Detection, Energy Efficiency","lastPublishedDoi":"10.21203/rs.3.rs-7754195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7754195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid proliferation of Internet of Things (IoT) devices imposes stringent demands on energy efficiency, real-time responsiveness, and security, which traditional simulation approaches often fail to address due to their limited ability to capture microarchitectural interactions. This paper introduces Gem5-ML, a gem5-based framework extended with IoT-specific processor, memory, and peripheral models, coupled with machine learning (ML) to accelerate design-space exploration. Representative workloads including sensing, control, communication, and lightweight security kernels are executed in SE mode to collect fine-grained microarchitectural statistics such as cache activity, bus contention, interrupt latency, and execution cycles. These features are used to train surrogate ML models that enable fast and accurate prediction of performance and energy trade-offs, reducing exploration time by up to 10\u0026times; while maintaining over 90% Pareto front coverage. The framework also incorporates anomaly detection mechanisms capable of identifying reliability issues such as interrupt storms, cache thrashing, and voltage droop with high accuracy. Experimental results demonstrate that Gem5-ML achieves efficient exploration, robust prediction, and actionable insights for energy-aware and secure IoT microcontroller design, highlighting its potential as a scalable methodology for next-generation low-power and resilient embedded systems.\u003c/p\u003e","manuscriptTitle":"A gem5-based framework for analyzing IoT\nmicrocontrollers with peripherals and firmware","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 05:33:16","doi":"10.21203/rs.3.rs-7754195/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":"f1e1c32e-3ca8-48ba-ad43-4abf5c7f7dcc","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-11T07:55:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 05:33:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7754195","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7754195","identity":"rs-7754195","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}