Toward Embedded Intelligence: Architecting CPUs with PC AI Agents

Toward Embedded Intelligence: Architecting CPUs with PC AI Agents | 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Article Toward Embedded Intelligence: Architecting CPUs with PC AI Agents Simon Poon This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7776613/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 This paper proposes a radical computing paradigm: integrating autonomous AI agents within the CPU system through coprocessor-based designs, where dedicated neural engines and context buffers support modular cognitive routines. Unlike traditional systems that rely on external software layers or cloud-based inference engines, the model brings cognitive capabilities closer to the hardware, reducing latency, power consumption, and contextual isolation. We explore the theoretical foundations, architectural implications, potential applications, and practical challenges of these designs, arguing that they represent the next evolutionary step in personal and distributed intelligence. Drawing on advancements in neuromorphic computing and embed- ded AI, we delineate how these agents achieve agency through self-contained reasoning modules, distinguishing them from conventional accelerators. Experimental simulations suggest up to 62% latency reduction and 77% energy savings in real-time tasks, validating the efficiency of event-driven neuromorphic processing. Key challenges—including security vulnerabilities, privacy, and ethical integration—are analyzed with reference to ongoing research. This framework shifts computing from passive execution to active cognition, fostering symbiotic human-machine partnerships. Computer Architecture and Engineering Artificial Intelligence and Machine Learning Embedded AI Agents Neuromorphic Computing Cognitive Coprocessor Hardware-Level Intelligence Autonomous Reasoning AI Coprocessor Integration Microcode Firmware Context Buffer Instruction Set Extensions (ISEs) Opcode Semantics Semantic Awareness Ethical Reasoning Autonomous Task Management Real-Time Inference Persistent Memory Architecture Spiking Neural Networks (SNN) Event-Driven Processing Latency Reduction Energy Efficiency Leaky Integrate-and-Fire Model Trusted Platform Modules (TPM) Secure Enclaves Adversarial Robustness Moral Agency Explainable AI Edge Computing IoT Intelligence Cybersecurity Personalized Education Full Text Additional Declarations The authors declare no competing interests. Supplementary Files simulation.py 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-7776613","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524473419,"identity":"14194d36-bf49-4bdd-b57e-4c9571f5227a","order_by":0,"name":"Simon Poon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYFACNvYfH37YJIDZCQXEaWGQnNmTlgCkgVoMiNQizcN2GKKFgRgt5vzHEgx4eM7n8ct3J354YMAgzy92AL8WyxlpBxIkLG4XS7bxbpYAOsxw5uwE/FoMbrA3HDDguZ244RjvBpCWBIPbhLScP97YkMB2DqRl8w/itBxIO8xwgO0ASMs24mwB+iWNsbEnOXFmW+42iwQDCcJ+AYaYGfOfH3aJ/cxnN9/8UWEjzy9NyGFofAn8yrFpGQWjYBSMglGACQCRl0Q6kfVV8QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-9987-6386","institution":"retired","correspondingAuthor":true,"prefix":"","firstName":"Simon","middleName":"","lastName":"Poon","suffix":""}],"badges":[],"createdAt":"2025-10-04 00:06:52","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7776613/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7776613/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92950930,"identity":"77067a8a-e83b-49e2-941a-195a18355202","added_by":"auto","created_at":"2025-10-07 13:06:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":213922,"visible":true,"origin":"","legend":"","description":"","filename":"CPUwithAI35.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7776613/v1_covered_5c64bec6-d04f-49f5-81ae-8be72eac2c75.pdf"},{"id":92950594,"identity":"141a5490-faf2-402f-8a81-738af3114d9c","added_by":"auto","created_at":"2025-10-07 13:06:28","extension":"py","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4199,"visible":true,"origin":"","legend":"","description":"","filename":"simulation.py","url":"https://assets-eu.researchsquare.com/files/rs-7776613/v1/ba056368c35f1e6802f92035.py"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eToward Embedded Intelligence: Architecting CPUs with PC AI\u003c/p\u003e\n\u003cp\u003eAgents\u003c/p\u003e","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":"Embedded AI Agents, Neuromorphic Computing, Cognitive Coprocessor, Hardware-Level Intelligence, Autonomous Reasoning, AI Coprocessor Integration, Microcode Firmware, Context Buffer, Instruction Set Extensions (ISEs), Opcode Semantics, Semantic Awareness, Ethical Reasoning, Autonomous Task Management, Real-Time Inference, Persistent Memory Architecture, Spiking Neural Networks (SNN), Event-Driven Processing, Latency Reduction, Energy Efficiency, Leaky Integrate-and-Fire Model, Trusted Platform Modules (TPM), Secure Enclaves, Adversarial Robustness, Moral Agency, Explainable AI, Edge Computing, IoT Intelligence, Cybersecurity, Personalized Education","lastPublishedDoi":"10.21203/rs.3.rs-7776613/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7776613/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes a radical computing paradigm: integrating autonomous AI agents within the CPU system through coprocessor-based designs, where dedicated neural engines and context buffers support modular cognitive routines. 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