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 …

Tiny machine learning (TinyML) is a new frontier of machine learning. By squeezing deep
learning models into billions of IoT devices and microcontrollers (MCUs), we expand the …

The field of Tiny Machine Learning (TinyML) has gained significant attention due to its
potential to enable intelligent applications on resource-constrained devices. This review …

Tiny Machine Learning (TinyML), a rapidly evolving edge computing concept that links
embedded systems (hardware and software) and machine learning, with the purpose of …

We use 250 billion microcontrollers daily in electronic devices that are capable of running
machine learning models inside them. Unfortunately, most of these microcontrollers are …

Tiny Machine Learning (TinyML) is an emerging concept that concerns the execution of ML
tasks on very constrained IoT devices. Although TinyML has generated a strong R&D …

The rapid growth in miniaturization of low-power embedded devices and advancement in
the optimization of machine learning (ML) algorithms have opened up a new prospect of the …

Tiny machine learning (TinyML) has gained widespread popularity where machine learning
(ML) is democratized on ubiquitous microcontrollers, processing sensor data everywhere in …

Incremental on-device learning is one of the most relevant and interesting challenges in the
field of Tiny Machine Learning (TinyML). Indeed, differently from traditional TinyML solutions …

Tiny Machine Learning (TML) is a novel research area aiming at designing and developing
Machine Learning (ML) techniques meant to be executed on Embedded Systems and …