Pubblicazioni

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:

Sì

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. 12 , 2024 , pp. 23406-23426

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