[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126596-en":3,"doc-seo-126596-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126596,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning","Ageing detection and failure prediction are critical for IoT deployments that run large fleets of embedded devices unattended in the field for years. This paper delivers a large-scale empirical study of natural SRAM wear-out using 154 boards from a general-purpose testbed. By leveraging SRAM initialization bias collected at startup and applying feature extraction with common machine learning models, the work predicts each node’s operational age. Results show accurate usage-time estimation despite subtle ageing effects, with R2=0.77 and mean error 24% for regressors, and F1 above 0.6 for classifiers at six-month resolution.","If you cite this paper, please use the DSD reference: L. Lanzieri, P. Kietzmann, G. Fey, H. Schlarb, T. C. Schmidt. Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning. In Proc. of DSD, IEEE, 2023 .  \n1  \nAgeing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning  \nLeandro Lanzieri*, z, Peter Kietzmannz , Goerschwin Feyy , Holger Schlarb* , and Thomas C. Schmidtz  \n*Deutsches Elektronen-Synchrotron DESY, Germany 􀀁 fleandro.lanzieri, holger.schlarbg@desy.dezHamburg University of Applied Sciences, Germany 􀀁 fpeter.kietzmann, t.schmidtg@haw-hamburg.deyHamburg University of Technology, Germany 􀀁 [goerschwin.fey@tuhh.de](goerschwin.fey@tuhh.de)  \narXiv :2307 .06693v1 [ cs .AR] 13 Jul 2023  \nAbstract—Ageing detection and failure prediction are essential in many Internet of Things (IoT) deployments, which operate huge quantities of embedded devices unattended in the field for years. In this paper, we present a large-scale empirical analysis of natural SRAM wear-out using 154 boards from a generalpurpose testbed. Starting from SRAM initialization bias, which each node can easily collect at startup, we apply various metrics for feature extraction and experiment with common machine learning methods to predict the age of operation for this node. Our findings indicate that even though ageing impacts are subtle, our indicators can well estimate usage times with an R2 score of 0.77 and a mean error of 24% using regressors, and with an F1 score above 0.6 for classifiers applying a six-months resolution.  \nIndex Terms—Embedded hardware, predictive maintenance, machine learning, IoT  \nI. INTRODUCTION  \nAn increasing multitude of embedded devices serves highly dependable duties such as driver assistance in vehicles or beam control in particle accelerators. To reduce cost, Commercial Off-The-Shelf (COTS) devices are often deployed, even under harsh operating conditions. This leads to accelerated degradation and reduced lifetime of the hardware, which in turn may result in unexpected failures with critical consequences. It is thus imperative to monitor hardware degradation [1] for triggering early damage minimization or scheduled maintenance.  \nSRAM is ubiquitously present in microcontrollers and FPGAs. New applications for SRAM have emerged in recent years, including random number generation [2], device fingerprinting [3], [4], and timekeeping [5], which leverage physical behaviours of memories. Many of these applications rely on SRAM Physically Unclonable Functions (PUFs), which are a consequence of physical characteristics of the memory, such as cell imbalance or data retention time. Various ageing mechanisms affect SRAM transistors, potentially changing their behaviour and impacting PUF responses or other applications relying on physical SRAM characteristics. Monitoring hardware changes, particularly on secure and critical applications, can assist in preventing failures. Hardware usage and health estimation via Machine Learning (ML) models can advise on preventive maintenance actions on embedded devices deployments, based on physical characteristics modifications.  \nIn this paper, we target the question, whether SRAM ageing can be read from startup patterns. We analyse in detail the behaviour of SRAM cell initialization for a large collection of  \nFig. 1: We gather SRAM startup patterns of embedded systems from which we derive usage-correlated features to train ML classifiers and regressors for hardware usage time estimation.  \nchips that naturally aged over periods from two to 18 months of board utilization time. We contribute in detail:  \n1) a large-scale measurement study of SRAM initialization from 154 heterogeneously operated testbed nodes  \n2) a variety of subtle analyses and feature extractions from the data including blockwise averaging and spatial frequency analysis to capture age correlations  \n3) training a selection of ML models to explore their performance in estimating device usage.  ","cbCaiqRs14uxYn9I","https://ap.wps.com/l/cbCaiqRs14uxYn9I","pdf",2904326,2,1,"English","en",105,"# Abstract\n# Introduction\n## Motivation and problem statement\n## SRAM applications and ageing mechanisms\n# Background and Related Work\n## SRAM cells","[{\"question\":\"What data source does the paper use to infer SRAM ageing?\",\"answer\":\"The approach starts from SRAM initialization bias that each node can collect easily at startup, then derives usage-correlated features from startup patterns.\"},{\"question\":\"How do the authors estimate device ageing or usage time?\",\"answer\":\"The paper applies feature extraction metrics to the startup behavior and trains machine-learning regressors and classifiers to predict operational age from those features.\"},{\"question\":\"What performance do the machine-learning models achieve?\",\"answer\":\"Using regressors, the indicators estimate usage times with R2=0.77 and a mean error of 24%; classifiers reach an F1 score above 0.6 when using a six-month resolution.\"}]","Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning | PDF",1785933617,20,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"ageing-analysis-of-embedded-sram-on-a-large-scale-testbed-using-machine-learning","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/ageing-analysis-of-embedded-sram-on-a-large-scale-testbed-using-machine-learning/126596/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data source does the paper use to infer SRAM ageing?","Question",{"text":75,"@type":76},"The approach starts from SRAM initialization bias that each node can collect easily at startup, then derives usage-correlated features from startup patterns.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the authors estimate device ageing or usage time?",{"text":80,"@type":76},"The paper applies feature extraction metrics to the startup behavior and trains machine-learning regressors and classifiers to predict operational age from those features.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance do the machine-learning models achieve?",{"text":84,"@type":76},"Using regressors, the indicators estimate usage times with R2=0.77 and a mean error of 24%; 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