First Demonstration of Ferroelectric Digital In-Memory Computing for Scalable, Reliable and Ultra-Efficient Similarity Computation

First Demonstration of Ferroelectric Digital In-Memory Computing for Scalable, Reliable and Ultra-Efficient Similarity Computation | 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 First Demonstration of Ferroelectric Digital In-Memory Computing for Scalable, Reliable and Ultra-Efficient Similarity Computation Hussam Amrouch, Anirban Kar, Albi Mema, Thorgund Nemec, Stefan Duenkel, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5175674/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 Classification-based learning has become a cornerstone of deep neural networks, particularly in few-shot learning, where accurate similarity metrics, such as Hamming distance, are critical. However, conventional architectures require retrieving class vectors from a physically separated memory for Hamming distance calculations, incurring significant energy penalties due to data movement. This inefficiency poses a challenge to scalability and overall system performance. In-memory computing, which eliminates data transfers between processing and memory units, is increasingly recognized as a promising solution to this von Neumann bottleneck. Analog content-addressable memory (CAM)-based systems address this issue by embedding class vectors directly within CAM cells. However, their reliance on sensing circuits, particularly analog-to-digital converters (ADCs), introduces scalability and reliability challenges. The limited sense margin of ADCs, combined with device variability, further constrains array size and performance. These issues are exacerbated with emerging non-volatile memory devices like ferroelectric field-effect transistors (FeFETs). In this work, we present an innovative FeFET-based digital Logic-in-Memory (LiM) XOR cell, fabricated using GlobalFoundries’ 28 nm SLPe technology, eliminating the need for ADCs. Our 2T FeFET-based XOR cell offers a fully digital, compact, and energy-efficient solution that is robust to device variability and scalable for large systems. Applied to Hamming distance calculations for 4096-bit class vectors, our design achieves a 23-fold reduction in energy consumption, a 3-fold decrease in latency, and a 14-fold reduction in silicon footprint compared to state-of-the-art solutions. Crucially, our FeFET-based architecture demonstrates an unprecedented efficiency of 2337 Gsamples/(s·W·mm2 ), a 300-fold improvement over conventional designs, offering a unique competitive advantage where energy efficiency, reliability, and performance trade-offs have long been a concern. Our efficiency gains, while maintaining the maturity of digital computing, align with the industry’s demand for energy-efficient, scalable, and reliable in-memory computing. Furthermore, digital LiM supports the broader goal of energy-efficient AI hardware without sacrificing reliability, making it highly appealing to researchers and industries focused on sustainable computing. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Nanoscience and technology/Nanoscale devices/Electronic devices Full Text Additional Declarations There is NO Competing Interest. 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. 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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-5175674","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":451506534,"identity":"2f5a1bdb-4556-49b1-ada5-4b089f736d1c","order_by":0,"name":"Hussam Amrouch","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnElEQVRIiWNgGAWjYPCCA3JgiocULcY8JGtJ7CFaC/+0sw8f89TcSd8vkZ3A8KaCCC0St9ONjXmOPcvtkcjdwDjnDDHW3E5jk+ZtOAzWwszbRoQOeaiWdB6wln9EaDGAakmAaGkgQovh7TRmwznHDhv2nHm74eCcY0RokbudxvjgTc1hefb23I1ABhFaUMABUjWMglEwCkbBKMABAC8vNSe2iOaCAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5649-3102","institution":"Technical University Munich (TUM)","correspondingAuthor":true,"prefix":"","firstName":"Hussam","middleName":"","lastName":"Amrouch","suffix":""},{"id":451506535,"identity":"a946463c-7ddd-4dfe-8610-98f2c30d74c6","order_by":1,"name":"Anirban Kar","email":"","orcid":"https://orcid.org/0000-0003-0727-6192","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Anirban","middleName":"","lastName":"Kar","suffix":""},{"id":451506536,"identity":"2ea02720-e319-4382-ae49-6a40616b681d","order_by":2,"name":"Albi Mema","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Albi","middleName":"","lastName":"Mema","suffix":""},{"id":451506537,"identity":"69b97a75-2598-4251-81f9-f41752b61598","order_by":3,"name":"Thorgund Nemec","email":"","orcid":"https://orcid.org/0009-0009-1579-7570","institution":"GlobalFoundries Dresden Module One LLC \u0026 Co. 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However, conventional architectures require retrieving class vectors from a physically separated memory for Hamming distance calculations, incurring significant energy penalties due to data movement. This inefficiency poses a challenge to scalability and overall system performance. In-memory computing, which eliminates data transfers between processing and memory units, is increasingly recognized as a promising solution to this von Neumann bottleneck. Analog content-addressable memory (CAM)-based systems address this issue by embedding class vectors directly within CAM cells. However, their reliance on sensing circuits, particularly analog-to-digital converters (ADCs), introduces scalability and reliability challenges. The limited sense margin of ADCs, combined with device variability, further constrains array size and performance. These issues are exacerbated with emerging non-volatile memory devices like ferroelectric field-effect transistors (FeFETs). In this work, we present an innovative FeFET-based digital Logic-in-Memory (LiM) XOR cell, fabricated using GlobalFoundries’ 28 nm SLPe technology, eliminating the need for ADCs. Our 2T FeFET-based XOR cell offers a fully digital, compact, and energy-efficient solution that is robust to device variability and scalable for large systems. Applied to Hamming distance calculations for 4096-bit class vectors, our design achieves a 23-fold reduction in energy consumption, a 3-fold decrease in latency, and a 14-fold reduction in silicon footprint compared to state-of-the-art solutions. Crucially, our FeFET-based architecture demonstrates an unprecedented efficiency of 2337 Gsamples/(s·W·mm2 ), a 300-fold improvement over conventional designs, offering a unique competitive advantage where energy efficiency, reliability, and performance trade-offs have long been a concern. Our efficiency gains, while maintaining the maturity of digital computing, align with the industry’s demand for energy-efficient, scalable, and reliable in-memory computing. Furthermore, digital LiM supports the broader goal of energy-efficient AI hardware without sacrificing reliability, making it highly appealing to researchers and industries focused on sustainable computing.\u003c/p\u003e","manuscriptTitle":"First Demonstration of Ferroelectric Digital In-Memory Computing for Scalable, Reliable and Ultra-Efficient Similarity Computation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 12:06:55","doi":"10.21203/rs.3.rs-5175674/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":"f69ee94f-64f3-454c-9cfc-3b93114be2a3","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48030894,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":48030895,"name":"Physical sciences/Nanoscience and technology/Nanoscale devices/Electronic devices"}],"tags":[],"updatedAt":"2025-05-06T12:06:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 12:06:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5175674","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5175674","identity":"rs-5175674","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}