A Machine Learning-Oriented Survey on Tiny Machine Learning
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10433185/ |
| _version_ | 1827342134411264000 |
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| author | Luigi Capogrosso Federico Cunico Dong Seon Cheng Franco Fummi Marco Cristani |
| author_facet | Luigi Capogrosso Federico Cunico Dong Seon Cheng Franco Fummi Marco Cristani |
| author_sort | Luigi Capogrosso |
| collection | DOAJ |
| description | 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., ML-oriented, 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. Finally, we discuss the challenges and future directions. |
| first_indexed | 2024-03-07T22:03:18Z |
| format | Article |
| id | doaj.art-7ca7d27de73d4caf9ca89f9a8c3b7d8a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T22:03:18Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7ca7d27de73d4caf9ca89f9a8c3b7d8a2024-02-24T00:01:28ZengIEEEIEEE Access2169-35362024-01-0112234062342610.1109/ACCESS.2024.336534910433185A Machine Learning-Oriented Survey on Tiny Machine LearningLuigi Capogrosso0https://orcid.org/0000-0002-4941-2255Federico Cunico1https://orcid.org/0000-0001-9619-9656Dong Seon Cheng2https://orcid.org/0009-0004-4177-1749Franco Fummi3https://orcid.org/0000-0002-4404-5791Marco Cristani4https://orcid.org/0000-0002-0523-6042Department of Engineering for Innovation Medicine, University of Verona, Verona, ItalyDepartment of Engineering for Innovation Medicine, University of Verona, Verona, ItalyDepartment of Engineering for Innovation Medicine, University of Verona, Verona, ItalyDepartment of Engineering for Innovation Medicine, University of Verona, Verona, ItalyDepartment of Engineering for Innovation Medicine, University of Verona, Verona, ItalyThe 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., ML-oriented, 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. Finally, we discuss the challenges and future directions.https://ieeexplore.ieee.org/document/10433185/TinyMLedge intelligenceefficient deep learningembedded systems |
| spellingShingle | Luigi Capogrosso Federico Cunico Dong Seon Cheng Franco Fummi Marco Cristani A Machine Learning-Oriented Survey on Tiny Machine Learning IEEE Access TinyML edge intelligence efficient deep learning embedded systems |
| title | A Machine Learning-Oriented Survey on Tiny Machine Learning |
| title_full | A Machine Learning-Oriented Survey on Tiny Machine Learning |
| title_fullStr | A Machine Learning-Oriented Survey on Tiny Machine Learning |
| title_full_unstemmed | A Machine Learning-Oriented Survey on Tiny Machine Learning |
| title_short | A Machine Learning-Oriented Survey on Tiny Machine Learning |
| title_sort | machine learning oriented survey on tiny machine learning |
| topic | TinyML edge intelligence efficient deep learning embedded systems |
| url | https://ieeexplore.ieee.org/document/10433185/ |
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