3 papers
cs.LG2025
Decentor-V: Lightweight ML Training on Low-Power RISC-V Edge Devices
Marcelo Ribeiro, Diogo Costa, Gonçalo Moreira +2
Modern IoT devices increasingly rely on machine learning solutions to process data locally. However, the lack of graphics processing units (GPUs) or dedicated accelerators on most…
cs.CR2024
CROSSCON: Cross-platform Open Security Stack for Connected Devices
Bruno Crispo, Marco Roveri, Sandro Pinto +8
The proliferation of Internet of Things (IoT) embedded devices is expected to reach 30 billion by 2030, creating a dynamic landscape where diverse devices must coexist. This presen…
cs.LG2024
David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge
Miguel Costa, Sandro Pinto
ML is shifting from the cloud to the edge. Edge computing reduces the surface exposing private data and enables reliable throughput guarantees in real-time applications. Of the pan…