[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118822-en":3,"doc-seo-118822-105":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},118822,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","A Machine Learning-oriented Survey on Tiny Machine Learning","Tiny Machine Learning (TinyML) advances Artificial Intelligence by enabling the joint design of resource-constrained IoT hardware and learning-based software architectures. TinyML plays a key role in the fourth and fifth industrial revolutions, supporting AI-infused computing in contexts such as smart cities, automotive platforms, and medical robotics. Motivated by its multidisciplinary nature, the survey provides an up-to-date overview of learning algorithms in TinyML-based solutions, structured using PRISMA methodology and complemented by system workflows, a taxonomy of model optimization, hardware/software tool state of the art, and a discussion of challenges and future directions.","Date of current version September 26, 2023.  \nDigital Object Identifier XXXXXXX.XXXXXXX  \nA Machine Learning-oriented Survey on Tiny Machine Learning  \n2023  \nLUIGI CAPOGROSSO,(Student, IEEE), FEDERICO CUNICO, DONG SEON CHENG, FRANCO FUMMI,(Member, IEEE), MARCO CRISTANI,(Member, IEEE),  \nDepartment of Engineering for Innovation Medicine, University of Verona, Verona, Italy,(e-mail: [name.surname@univr.it](name.surname@univr.it))  \nCorresponding author: Luigi Capogrosso (e-mail: [luigi.capogrosso@univr.it](luigi.capogrosso@univr.it)).  \nThis study was carried out within the PNRR research activities of the consortium iNEST (Interconnected North-Est Innovation Ecosystem) funded by the European Union Next-GenerationEU (Piano Nazionale di Ripresa e Resilienza (PNRR)– Missione 4 Componente 2, Investimento 1.5 – D.D. 1058 23/06/2022, ECS_ 00000043) . This manuscript reflects only the Authors’ views and opinions, neither the  \n11932v2 [ cs .LG] 26  \nABSTRACT The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies, and individuals employ effective AI-infused computing technologies (e.g., smart cities, automotive, and medical robotics) . Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-date overview focused on all the learning algorithms within TinyML-based solutions. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. In particular, firstly we will examine the three different workflows for implementing a TinyML-based system, i.e., MLoriented, HW-oriented, and co-design. Secondly, we propose a taxonomy that covers the learning panorama under the TinyML lens, examining in detail the different families of model optimization and design, as well as the state-of-the-art learning techniques. Thirdly, this survey will present the distinct features of hardware devices and software tools that represent the current state-of-the-art for TinyML intelligent edge applications.  \narXiv :2309 .  \nFinally, we discuss the challenges and future directions.  \n INDEX TERMS TinyML, Efficient Deep Learning, Edge Intelligence, Embedded Systems  \nI. INTRODUCTION  \nA prodigious amount of research has been invested over the past decades in improving embedded technologies in order to enable the use of real-time solutions for many complex and safety-critical applications [1] . In this regard, hardware-specific (e.g., Edge TPUs) and Micro-Controller Unit (MCU)-based embedded systems have earned a lot of attention, primarily due to their low power requirements, high performance, and, secondarily, for their maintainability, adaptability, and reliability [2] . Their integration with sensors enables the perception of the external world, their connection with activators allows different kinds of interventions, and  \ntheir interconnection unlocks distributed intelligence.  \nEmbedded technologies are essentially the pillars of the Internet of Things (IoT) [3] and the associated smart-X applications: smart buildings [4] and cities [5], smart metering [6], agriculture [7] and environment [8], smart health [9], smart logistics [10], and smart retail [11] . More recent advances  \nin the Industrial Internet of Things (IIoT) [12] have facilitated the real-time intelligent processing of massive amounts of data, promoting fields such as autonomous driving [13], smart factories [14], anomaly detection [15], and predictive maintenance [16] .  \nWhen we talk about the intelligence of onboard embedded technologies, we mean the learning algorithms that allow d","cbCait17m7FtswOE","https://ap.wps.com/l/cbCait17m7FtswOE","pdf",1376291,1,20,"English","en",105,"# Abstract\n## Introduction\n## Tiny Machine Learning definition and challenges\n## System workflows and taxonomy (overview)\n## Hardware devices and software tools\n## Challenges and future directions","[{\"question\":\"What is the main focus of the survey on Tiny Machine Learning?\",\"answer\":\"The survey focuses on providing an up-to-date overview of learning algorithms within TinyML-based solutions, covering workflows, model optimization taxonomy, and state-of-the-art techniques.\"},{\"question\":\"How does the survey structure its literature review process?\",\"answer\":\"It uses the PRISMA methodological flow to enable a systematic and complete literature survey.\"},{\"question\":\"What implementation workflows does the survey discuss for TinyML systems?\",\"answer\":\"The survey examines three workflows for implementing a TinyML-based system: ML-oriented, HW-oriented, and co-design.\"}]","A Machine Learning-oriented Survey on Tiny Machine Learning | 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is the main focus of the survey on Tiny Machine Learning?","Question",{"text":75,"@type":76},"The survey focuses on providing an up-to-date overview of learning algorithms within TinyML-based solutions, covering workflows, model optimization taxonomy, and state-of-the-art techniques.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the survey structure its literature review process?",{"text":80,"@type":76},"It uses the PRISMA methodological flow to enable a systematic and complete literature survey.",{"name":82,"@type":73,"acceptedAnswer":83},"What implementation workflows does the survey discuss for TinyML systems?",{"text":84,"@type":76},"The survey examines three workflows for implementing a TinyML-based system: ML-oriented, HW-oriented, and 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