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 …

Severe constraints on memory and computation characterizing the Internet-of-Things (IoT)
units may prevent the execution of Deep Learning (DL)-based solutions, which typically …

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 (TinyML) is a novel research area aiming at designing machine and
deep learning models and algorithms able to be executed on tiny devices such as Internet-of …

The computing everywhere paradigm is paving the way for the pervasive diffusion of tiny
devices (such as Internet-of-Things or edge computing devices) endowed with intelligent …

Early Exit Neural Networks (EENNs) endow neural network architectures with auxiliary
classifiers to progressively process the input and make decisions at intermediate points of …

Early Exit Neural Networks (EENNs) endow astandard Deep Neural Network (DNN) with
Early Exit Classifiers (EECs), to provide predictions at intermediate points of the processing …

TinyML is a novel area of machine learning that gained huge momentum in the last few
years thanks to the ability to execute machine learning algorithms on tiny devices (such as …

Early exit neural networks (EENs) reduce the processing times of deep convolutional neural
networks by means of internal classifiers (ICs) that allow jobs, being the input of the EEN, to …

Tiny Machine Learning (TinyML) is a novel research area aiming at designing machine and
deep learning models and algorithms able to be executed on tiny devices such as Internet-of …