A Machine Learning-oriented Survey on Tiny Machine Learning

• URL: http://arxiv.org/abs/2309.11932v2
• Date: Tue, 26 Sep 2023 14:03:32 GMT
• Title: A Machine Learning-oriented Survey on Tiny Machine Learning
• Authors: Luigi Capogrosso, Federico Cunico, Dong Seon Cheng, Franco Fummi, Marco Cristani
• Abstract summary: The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies, and individuals employ effective AI-infused computing technologies.
• Score: 9.690117347832722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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