Pubblicazioni

A Machine Learning-Oriented Survey on Tiny Machine Learning  (2024)

Autori:
Capogrosso, Luigi; Cunico, Federico; Cheng, Dong Seon; Fummi, Franco; Cristani, Marco
Titolo:
A Machine Learning-Oriented Survey on Tiny Machine Learning
Anno:
2024
Tipologia prodotto:
Articolo in Rivista
Tipologia ANVUR:
Articolo su rivista
Lingua:
Inglese
Formato:
Elettronico
Referee:
Nome rivista:
IEEE ACCESS
ISSN Rivista:
2169-3536
N° Volume:
12
Intervallo pagine:
23406-23426
Parole chiave:
TinyML; Edge Intelligence; Efficient Deep Learning; Embedded Systems
Breve descrizione dei contenuti:
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. Finally, we discuss the challenges and future directions.
Pagina Web:
https://ieeexplore.ieee.org/document/10433185
Id prodotto:
137830
Handle IRIS:
11562/1119486
ultima modifica:
28 gennaio 2025
Citazione bibliografica:
Capogrosso, Luigi; Cunico, Federico; Cheng, Dong Seon; Fummi, Franco; Cristani, Marco, A Machine Learning-Oriented Survey on Tiny Machine Learning «IEEE ACCESS» , vol. 122024pp. 23406-23426

Consulta la scheda completa presente nel repository istituzionale della Ricerca di Ateneo IRIS

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