Lifelong learning—an agent's ability to learn throughout its lifetime—is a hallmark of
biological learning systems and a central challenge for artificial intelligence (AI). The …

In the last few years, research and development on Deep Learning models & techniques for
ultra-low-power devices–in a word, TinyML–has mainly focused on a train-then-deploy …

Abstract Nowadays, Convolutional Neural Networks (CNNs) are widely used as prediction
models in different fields, with intensive use in real-time safety-critical systems. Recent …

The fast proliferation of extreme-edge applications using Deep Learning (DL) based
algorithms required dedicated hardware to satisfy extreme-edge applications' latency …

Human-machine interaction is showing promising results for robotic prosthesis control and
rehabilitation. In these fields, hand movement recognition via surface electromyographic …

The increasing interest in TinyML, ie, near-sensor machine learning on power budgets of a
few tens of mW, is currently pushing toward enabling TinyML-class training as opposed to …

Surface electromyography (sEMG) is a State-of-the-Art (SoA) sensing modality for non-
invasive human-machine interfaces for consumer, industrial, and rehabilitation use cases …

In recent years, we have witnessed a renewed interest in machine learning methodologies,
especially for deep representation learning, that could overcome basic iid assumptions and …

Neural networks excel at addressing real-world tasks, yet their computational demands often
confine them to cloud-based platforms. Recent literature has responded with compute …

Machine learning algorithms are widely used in many intelligent applications and cloud
services. Currently, the hottest topic in this field is Deep Learning represented often by …