The spread of the Internet of Things (IoT) involving an uncountable number of applications,
combined with the rise of Machine Learning (ML), has enabled the rapid growth of pervasive …

With many devices deployed at the extreme edge in dynamic environments the ability to
learn continually on the device is a fast-emerging trend for ultra-low-power Microcontrollers …

Fine-tuning is the most effective way of adapting pre-trained large language models (LLMs)
to downstream applications. With the fast growth of LLM-enabled AI applications and …

Continual Learning (CL) allows applications such as user personalization and household
robots to learn on the fly and adapt to context. This is an important feature when context …

We propose MicroT, a low-energy, multi-task adaptive model framework for resource-
constrained MCUs. We divide the original model into a feature extractor and a classifier. The …

In the realm of efficient on-device learning under extreme memory and computation
constraints, a significant gap in successful approaches persists. Although a considerable …

The personalization of machine learning (ML) models to address data drift is a significant
challenge in the context of Internet of Things (IoT) applications. Presently, most approaches …

Human Activity Recognition (HAR) is an attractive topic to perceive human behavior and
supplying assistive services. Besides the classical inertial unit and vision-based HAR …

Sub-\SI {50}{\gram} nano-drones are gaining momentum in both academia and industry.
Their most compelling applications rely on onboard deep learning models for perception …

Miniaturized cyber-physical systems (CPSes) powered by tiny machine learning (TinyML),
such as nano-drones, are becoming an increasingly attractive technology. Their small form …