[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122241-en":3,"doc-seo-122241-105":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},122241,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","EXPLORING MACHINE LEARNING TECHNIQUES FOR EMBEDDED HARDWARE - Master’s Thesis","This thesis investigates the integration of machine learning (ML) methods with embedded hardware systems, targeting improved efficiency and real-time performance across application domains. It addresses the challenge of running advanced ML algorithms on resource-limited embedded platforms while simultaneously optimizing computational performance and reducing energy consumption. The work develops streamlined execution strategies for embedded deployment and validates results using multiple real-world application settings, including wearable signal compression and embedded dronetracking models.","University of Texas at Arlington  \nMavMatrix  \n\n| Computer Science and Engineering Theses | Computer Science and Engineering Department |\n| --- | --- |\n| Spring 2024\u003Cbr>EXPLORING MACHINE LEARNING TECHNIQUES FOR EMBEDDED HARDWARE\u003Cbr>Neel R. Vora\u003Cbr>University of Texas at Arlington\u003Cbr>Follow this and additional works at: [https://mavmatrix.uta.edu/cse_theses](https://mavmatrix.uta.edu/cse_theses)\u003Cbr> Part of the Hardware Systems Commons, Other Computer Engineering Commons, and the VLSI and Circuits, Embedded and Hardware Systems Commons |  |\n\nRecommended Citation  \nVora, Neel R., \"EXPLORING MACHINE LEARNING TECHNIQUES FOR EMBEDDED HARDWARE\" (2024) . Computer Science and Engineering Theses. 2.  \n[https://mavmatrix.uta.edu/cse_theses/2](https://mavmatrix.uta.edu/cse_theses/2)  \nThis Thesis is brought to you for free and open access by the Computer Science and Engineering Department at MavMatrix. It has been accepted for inclusion in Computer Science and Engineering Theses by an authorized administrator of MavMatrix. For more information, please contact [leah.mccurdy@uta.edu](leah.mccurdy@uta.edu), [erica.rousseau@uta.edu](erica.rousseau@uta.edu),  \n[vanessa.garrett@uta.edu](vanessa.garrett@uta.edu).  \nEXPLORING MACHINE LEARNING TECHNIQUES FOR EMBEDDED  \nHARDWARE  \nby  \nNEEL VORA  \nPresented to the Faculty of the Graduate School of The University of Texas at Arlington in Partial Ful􀀌llment of the Requirements  \nfor the Degree of  \nMASTER OF SCIENCE  \nTHE UNIVERSITY OF TEXAS AT ARLINGTON  \nMay 2024  \nCopyright © by NEEL VORA 2024 All Rights Reserved  \nACKNOWLEDGEMENTS  \nI extend my deepest gratitude to my supervising professor, Dr. VP Nguyen, for his continuous guidance and encouragement throughout my graduate studies. His profound expertise in various sub-domains of computer science and broad knowledge of the 􀀌eld has been invaluable to my research journey. I am also thankful to my committee members, Dr. Jacob Luber and Dr. Yilun Xu, for their keen interest in my work and for dedicating their time to serve on my committee.  \nI am especially appreciative of the stress-free research environment that Dr. Nguyen fostered, allowing me to explore a wide array of topics that piqued my interest. My thanks also go to all the professors who have taught and inspired me during my time as a graduate student.  \nI would like to express my heartfelt appreciation to my lab partners and colleagues for their collaboration and the many ways in which they have enriched my research experience. Their camaraderie and support have made my journey through graduate school not only productive but also enjoyable.  \nTo my friends, who have provided a network of support and relief from the stresses of academic life, thank you for all the moments of fun, creativity, and relaxation. Your friendship has been a treasured part of my life during these years.  \nLastly, I would like to express my heartfelt thanks to my parents for their unwavering support and encouragement, and for 􀀌nancially supporting my graduate education. Their belief in my abilities has been a constant source of motivation.  \nApril 22nd ; 2024  \nABSTRACT  \nEXPLORING MACHINE LEARNING TECHNIQUES FOR EMBEDDED  \nHARDWARE  \nNEEL VORA, MS  \nThe University of Texas at Arlington, 2024  \nSupervising Professor: Dr. Phuc VP Nguyen  \nThis thesis delves into the intricate symbiosis between machine learning (ML) methodologies and embedded hardware systems, with a primary focus on augmenting e􀀎ciency and real-time processing capabilities across diverse application domains. It confronts the formidable challenge of deploying sophisticated ML algorithms on resource-constrained embedded hardware, aiming not only to optimize performance but also to minimize energy consumption. Innovative strategies are explored to tailor ML models for streamlined execution on embedded platforms, with validation conducted across various real-world application domains. Notable contributions include the development of a deep-learning fra","cbCaisFTvq1HaHpY","https://ap.wps.com/l/cbCaisFTvq1HaHpY","pdf",4836564,1,47,"English","en",105,"# ACKNOWLEDGEMENTS\n# ABSTRACT\n# TABLE OF CONTENTS\n## Chapter 1. INTRODUCTION\n## 1.1 Background\n## 2 VAE-BASED COMPRESSION FOR WEARABLE HEALTH MONITORING\n## 3 ON-CHIP CROSS-MODAL SYSTEM FOR CONTINUOUS SURVEILLANCE","[{\"question\":\"What is the main focus of the thesis?\",\"answer\":\"The thesis focuses on combining machine learning techniques with embedded hardware systems to improve efficiency and real-time processing while meeting embedded resource constraints.\"},{\"question\":\"How does the thesis handle embedded deployment constraints?\",\"answer\":\"It explores strategies to tailor ML models for streamlined execution on embedded platforms, including on-chip deployment approaches and model optimization such as compression.\"},{\"question\":\"What key ML approaches and hardware platforms are covered?\",\"answer\":\"The thesis highlights a VAE-based deep-learning framework for wearable signal compression, investigates transfer and cross-modal learning for embedded dronetracking, and proposes an FPGA-based ML solution for high-speed control systems.\"}]","EXPLORING MACHINE LEARNING TECHNIQUES FOR EMBEDDED HARDWARE - 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