Adiabatic Leaky Integrate-and-Fire Neurons with Tunable Refractory Period in 180nm CMOS Technology for Ultra-Low Energy Brain-Inspired Neuromorphic Computing

Adiabatic Leaky Integrate-and-Fire Neurons with Tunable Refractory Period in 180nm CMOS Technology for Ultra-Low Energy Brain-Inspired Neuromorphic Computing | 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 Adiabatic Leaky Integrate-and-Fire Neurons with Tunable Refractory Period in 180nm CMOS Technology for Ultra-Low Energy Brain-Inspired Neuromorphic Computing Marco Massarotto, Stefano Saggini, Mirko Loghi, David Esseni This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4349574/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract In recent years, the In-Memory-Computing in charge domain has gained significant interest as a promising solution to further enhance the energy efficiency of neuromorphic hardware. In this work, we explore the synergy between the brain-inspired computation and the adiabatic paradigm by presenting an adiabatic Leaky Integrate-and-Fire neuron in 180 nm CMOS technology, that is able to emulate the most important primitives for a valuable neuromorphic computation, such as the accumulation of the incoming input spikes, an exponential leakage of the membrane potential and a tunable refractory period. Differently from previous contributions in the literature, our design can exploit both the charging and recovery phases of the adiabatic operation to ensure a seamless and continuous computation, all the while exchanging energy with the power supply with an efficiency higher than 90% over a wide range of resonance frequencies, and even surpassing 99% for the lowest frequencies. Our simulations unveil a minimum energy per synaptic operation of 360 fJ at a 500 kHz resonance frequency, which yields a 12x energy saving with respect to a non-adiabatic operation. Adiabatic neuromorphic computing charge-domain in-memory-computing (IMC) resonant charge recovery spiking neural networks (SNN) leaky integrate-and-fire (LIF) ultra-low power (ULP) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Jul, 2024 Reviews received at journal 20 May, 2024 Reviews received at journal 14 May, 2024 Reviewers agreed at journal 11 May, 2024 Reviewers agreed at journal 04 May, 2024 Reviewers invited by journal 04 May, 2024 Editor assigned by journal 02 May, 2024 Submission checks completed at journal 01 May, 2024 First submitted to journal 30 Apr, 2024 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. 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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-4349574","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":299182985,"identity":"3464cd6d-71f9-4d80-9894-fce69fc7d1c5","order_by":0,"name":"Marco Massarotto","email":"data:image/png;base64,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","orcid":"","institution":"University of Udine","correspondingAuthor":true,"prefix":"","firstName":"Marco","middleName":"","lastName":"Massarotto","suffix":""},{"id":299182986,"identity":"c8662880-d5eb-4e7a-8df7-95b9ffdead04","order_by":1,"name":"Stefano Saggini","email":"","orcid":"","institution":"University of Udine","correspondingAuthor":false,"prefix":"","firstName":"Stefano","middleName":"","lastName":"Saggini","suffix":""},{"id":299182987,"identity":"bf8a9c37-06d7-49d6-814b-d4377620041a","order_by":2,"name":"Mirko Loghi","email":"","orcid":"","institution":"University of Udine","correspondingAuthor":false,"prefix":"","firstName":"Mirko","middleName":"","lastName":"Loghi","suffix":""},{"id":299182988,"identity":"21a1e37a-cf87-46b8-ba54-ff0745c11e42","order_by":3,"name":"David Esseni","email":"","orcid":"","institution":"University of Udine","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Esseni","suffix":""}],"badges":[],"createdAt":"2024-04-30 13:59:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4349574/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4349574/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56024289,"identity":"9de4fa29-d314-488d-ac3d-1e61f3925a14","added_by":"auto","created_at":"2024-05-07 16:47:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":819279,"visible":true,"origin":"","legend":"","description":"","filename":"Massarottomainpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349574/v1_covered_0233e608-6983-4032-a90d-fc4d88e3ede0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adiabatic Leaky Integrate-and-Fire Neurons with Tunable Refractory Period in 180nm CMOS Technology for Ultra-Low Energy Brain-Inspired Neuromorphic Computing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"npj-unconventional-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Unconventional Computing](https://www.nature.com/npjunconvcomput)","snPcode":"44335","submissionUrl":"https://submission.springernature.com/new-submission/44335/3","title":"npj Unconventional Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Adiabatic neuromorphic computing, charge-domain in-memory-computing (IMC), resonant charge recovery, spiking neural networks (SNN), leaky integrate-and-fire (LIF), ultra-low power (ULP)","lastPublishedDoi":"10.21203/rs.3.rs-4349574/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4349574/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In recent years, the In-Memory-Computing in charge domain has gained significant interest as a promising solution to further enhance the energy efficiency of neuromorphic hardware. 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