[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118093-en":3,"doc-seo-118093-105":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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"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},118093,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Analyses and Optimizations of Timing-Constrained Embedded Systems - Considering Resource Synchronization and Machine Learning Approaches","Embedded systems have become pervasive, supporting applications from consumer electronics to industrial automation, while machine learning and statistical algorithms deliver advanced analytics and decision-making in domains including medical diagnosis, autonomous driving, and environmental analysis. The need for intelligent, time-sensitive services and rising data-privacy concerns makes on-device deployment essential. This dissertation presents optimization and deployment methods for timing-constrained embedded systems with limited computation, power budgets, and communication resources, addressing shared-resource access latency and distributed privacy restrictions, and includes a formal verification aspect plus practical system use cases.","Analyses and Optimizations of Timing-Constrained Embedded Systems Considering Resource Synchronization and Machine Learning Approaches  \nDissertation  \nzur Erlangung des Grades eines D o k t o r s d e r I n g e n i e u r w i s s e n s c h a f t e n  \nder Technischen Universität Dortmund an der Fakultät für Informatik  \nvon  \nJunjie Shi  \nDortmund  \n2023  \nTag der mündlichen Prüfung: 20 . November 2023  \nDekan / Dekanin: Prof. Dr. Gernot Fink  \nGutachter / Gutachterinnen: Prof. Dr. Jian-Jia Chen  \nProf. Dr. Alessandro Biondi (Scuola Superiore Sant’Anna)  \nAcknowledgments  \nEmbarking on this academic journey was both a challenge and a transformative experience. Along the way, I have been fortunate to have had the support, guidance, and camaraderie of many extraordinary individuals, and I wish to take a moment to express my heartfelt gratitude to all of them.  \nFirst and foremost, I extend my deepest appreciation to my supervisor, Prof. Dr. Jian-Jia Chen. Our shared journey from the days of my Master’s research to this culmination in my PhD has been nothing short of enriching. Your unwavering guidance, encouragement, and patience have been the driving force behind my research. Your expertise, critical insights, and belief in my capabilities have continually propelled me forward, even during the most challenging times.  \nI am sincerely grateful to the members of my defence committee, Prof. Dr. Alessandro Biondi, Prof. Dr.-Ing. Peter Ulbrich, and Prof. Dr. Falk Howar. Your thorough reviews, constructive feedback, and discussions have enriched my work and broadened my academic horizons.  \nTo my colleagues at DAES group, namely Georg von der Brüggen, Ching-Chi Lin, Mario Günzel, Christian Hakert, Nils Hölscher, Daniel Kuhse, Vahidreza Moghaddas, Noura Sleibi, Tristan Seidl, Harun Teper, Niklas Ueter, Zahra Valipour, Mikail Yayla, Claudia Graute, and Lars Dröge, thank you for creating a stimulating and supportive environment. Sharing ideas, coffee breaks, and moments of camaraderie with you all has been an integral part of this journey. A special mention to Kuan-Hsun Chen, who has semi-supervised me since my Master thesis, providing invaluable help and suggestions throughout my PhD. I extend my gratitude to Georg von der Brüggen and Niklas Ueter for their unwavering support and collaboration on our papers, as well as their invaluable feedback for my dissertation. I thank Mario Günzel and Nils Hölscher for the joy they brought to our shared spaces, and Christian Hakert for being an ever-helpful guide.  \nI cannot express enough gratitude to my family. To my parents, grandparents, uncles ,and aunts, your unwavering love, belief, and support have been the bedrock upon which I have built my aspirations. Your sacrifices, patience, and encouragement have been my guiding stars, ensuring I never lost sight of my goals, no matter how distant they seemed.  \nLastly, to my dear friends, especially Feifei Zheng, thank you for being my sounding board and my confidants. Your optimism, timely pep talks, and unending faith have been my source of energy.  \nIn essence, this dissertation is not just a reflection of my academic endeavor, but a testament to the wonderful people who believed in me, pushed me, and stood by me. I am eternally grateful.  \nAbstract  \nNowadays, embedded systems have become ubiquitous, powering a vast array of applications from consumer electronics to industrial automation. Concurrently, statistical and machine learning algorithms are being increasingly adopted across various application domains, such as medical diagnosis, autonomous driving, and environmental analysis, offering sophisticated data analysis and decision-making capabilities. As the demand for intelligent and time-sensitive applications continues to surge, accompanied by growing concerns regarding data privacy, the deployment of machine learning models on embedded devices has emerged as an indispensable requirement. However, this integration introduces both significant opp","cbCaikDgniA2T0rJ","https://ap.wps.com/l/cbCaikDgniA2T0rJ","pdf",4839830,1,256,"English","en",105,"# Abstract\n## Timing-sensitive embedded systems and on-device ML deployment\n## Challenges: limited resources, synchronization, and privacy constraints\n## Dissertation contributions\n## Resource synchronization and bounded worst-case response time\n## Deployment optimization under distributed privacy restrictions","[{\"question\":\"Why is deploying machine learning on embedded devices challenging in timing-constrained systems?\",\"answer\":\"Embedded devices have limited computation and power and must satisfy strict timing requirements. Integrating ML therefore requires additional adjustments to achieve both performance and timing correctness.\"},{\"question\":\"What is the dissertation’s approach to handling long access times for shared resources?\",\"answer\":\"It designs a resource synchronization protocol that bounds worst-case response time, supporting implementations on two RTOSes and proposing a formal verification framework under the assumption of a correct RTOS.\"},{\"question\":\"How does the dissertation address privacy constraints in distributed embedded systems?\",\"answer\":\"It optimizes deployment of a machine learning model using model-based optimization strategies that avoid raw data sharing by leveraging constrained communication and privacy-aware deployment.\"}]","Analyses and Optimizations of Timing-Constrained Embedded Systems - Considering Resource Synchronization and Machine Learning Approaches | PDF",1785681493,645,{"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},"analyses-and-optimizations-of-timing-constrained-embedded-systems-considering-resource-synchronization-and-machine-learning-approaches","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/analyses-and-optimizations-of-timing-constrained-embedded-systems-considering-resource-synchronization-and-machine-learning-approaches/118093/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is deploying machine learning on embedded devices challenging in timing-constrained systems?","Question",{"text":75,"@type":76},"Embedded devices have limited computation and power and must satisfy strict timing requirements. Integrating ML therefore requires additional adjustments to achieve both performance and timing correctness.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the dissertation’s approach to handling long access times for shared resources?",{"text":80,"@type":76},"It designs a resource synchronization protocol that bounds worst-case response time, supporting implementations on two RTOSes and proposing a formal verification framework under the assumption of a correct RTOS.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the dissertation address privacy constraints in distributed embedded systems?",{"text":84,"@type":76},"It optimizes deployment of a machine learning model using model-based optimization strategies that avoid raw data sharing by leveraging constrained communication and privacy-aware deployment.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]