Large-scale integrated optoelectronic chaos for machine learning acceleration

Large-scale integrated optoelectronic chaos for machine learning acceleration | 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 Large-scale integrated optoelectronic chaos for machine learning acceleration Jijun He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7848914/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Chaos finds widespread use in modern machine learning, yet its implementations in traditional nonlinear circuits have encountered speed bottlenecks due to ever-expanding computational needs. The optical chaotic source offers an attractive alternative, combining ultra-wideband capabilities, inherent nonlinearity, and massive parallelism. However, existing photonic schemes typically trade off between the single-channel throughput and multi-channel scalability, preventing effective learning acceleration of large-scale tasks. Here, an integrated microcomb-optoelectronic chaos engine (iMOCE) is demonstrated. By using a microcomb to optoelectronic nonlinear cavity, massively parallel channels with a 6-dB bandwidth of 25 GHz per channel are achieved, representing a two-order-of-magnitude improvement over previous approaches using microcombs. The proposed iMOCE achieves a total random-bit generation rate of 32.768 Tbps (1.024 Tbps per channel), unparalleled by existing optical chaotic sources. The chip is fabricated in a commercial foundry and is compatible with wafer-scale production, ensuring manufacturability and scalability. To showcase its learning acceleration capability, the iMOCE is applied to four learning accelerator tasks, including the multi-armed bandit problem, connect-3 game, traveling-salesman solving, and electrocardiogram trace recognition task. Compared with MCU/GPU baselines, iMOCE reduces per-inference time by about two orders of magnitude across tasks. By bridging wafer-scale integrated photonics with probabilistic computing, our iMOCE establishes a scalable, massively parallel chaos primitive for accelerating learning, decision-making, and combinatorial optimization. Physical sciences/Optics and photonics/Applied optics/Integrated optics Physical sciences/Optics and photonics/Applied optics/Microwave photonics Physical sciences/Optics and photonics/Optical physics/Nonlinear optics Physical sciences/Optics and photonics/Other photonics/Frequency combs Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryinformationforLargescaleintegratedoptoelectronicchaosformachinelearningacceleration.pdf Cite Share Download PDF Status: Under Review 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-7848914","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":530864120,"identity":"0bced32b-ee6d-4690-920f-62fdd507e8c9","order_by":0,"name":"Jijun He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACxmaGBCB1gIEfJtBAtBbJBmK1QMEBBoMDxGphbmd4Jl3AcEfO+HaP4YcfDDayGw4wP3tAwGFp0jMYnhmb3TljLNnDkGa84QCbuQFBLTwMhxO33cgxkAAxNhzgYZMgRkv95hk5xj//MPwnXkuCgUSOGZBxgCgtydY8BocNZ9xIK7OWMUg2nnmYzQyvFsP+M4m3eSoOy/PPSN58802FnWzf8eZn+LU08CQwMMBDCMRgxqceCOQZ2A8QUDIKRsEoGAUjHgAAhbVE11p9EgEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7011-4386","institution":"Nanjing University of Aeronautics and Astronautics","correspondingAuthor":true,"prefix":"","firstName":"Jijun","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-10-13 12:20:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7848914/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7848914/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94134772,"identity":"2d18c4e7-3ba8-4a81-b47e-d8ed62aa04b4","added_by":"auto","created_at":"2025-10-22 18:45:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35339231,"visible":true,"origin":"","legend":"","description":"","filename":"Largescaleintegratedoptoelectronicchaosformachinelearningacceleration.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7848914/v1/055b53d361b10f23e88c6530.pdf"},{"id":94134770,"identity":"1cfddea8-c5ba-4bc2-9928-65fa0e293aa6","added_by":"auto","created_at":"2025-10-22 18:45:38","extension":"json","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3865,"visible":true,"origin":"","legend":"","description":"","filename":"NCOMMS2581922.json","url":"https://assets-eu.researchsquare.com/files/rs-7848914/v1/1e887c62379edfa16faf97ee.json"},{"id":94135488,"identity":"6da13d29-5126-437b-aec1-8cfce2b3100c","added_by":"auto","created_at":"2025-10-22 18:54:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":28270914,"visible":true,"origin":"","legend":"Article File","description":"","filename":"Largescaleintegratedoptoelectronicchaosformachinelearningacceleration.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7848914/v1_covered_9dfd5657-cc90-411d-917d-e1f94b0e76d7.pdf"},{"id":94134771,"identity":"6f5104d4-d41b-40bb-a588-761e8219aa01","added_by":"auto","created_at":"2025-10-22 18:45:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9728642,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryinformationforLargescaleintegratedoptoelectronicchaosformachinelearningacceleration.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7848914/v1/19429bbc7c4c97f07406bb7a.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Large-scale integrated optoelectronic chaos for machine learning acceleration","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7848914/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7848914/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Chaos finds widespread use in modern machine learning, yet its implementations in traditional nonlinear circuits have encountered speed bottlenecks due to ever-expanding computational needs. 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