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

Machine learning (ML) sensors offer a new paradigm for sensing that enables intelligence at
the edge while empowering end-users with greater control of their data. As these ML …

The ability to forecast mobile traffic patterns is key to resource management for mobile
network operators and planning for local authorities. Several Deep Neural Networks (DNN) …

ML models are increasingly being pushed to mobile devices, for low-latency inference and
offline operation. However, once the models are deployed, it is hard for ML operators to track …

Results from the TinyML community demonstrate that, it is possible to execute machine
learning models directly on the terminals themselves, even if these are small microcontroller …

TinyML models often operate in remote, dynamic environments without cloud connectivity,
making them prone to failures. Ensuring reliability in such scenarios requires not only …

Practitioners in the field of TinyML lack so far a comprehensive,“batteries-included” toolkit to
streamline continuous integration, continuous deployment and performance assessments of …

Network delays, throughput bottlenecks and privacy issues push Artificial Intelligence of
Things (AIoT) designers towards evaluating the feasibility of moving model training and …

The unique requirements, complexities, and limits of industrial contexts can make it difficult
to deploy software solutions. When implementing new software solutions, industrial …

NEPHELE is a Research and Innovation Action (RIA) project funded by the Horizon Europe
programme under the topic" Future European platforms for the Edge: Meta Operating …