457 citations · 547 across the 21 of their papers we have counts for
9 papers · 1 filter
Neuromorphic Heart Rate Monitors: Neural State Machines for Monotonic Change Detection
Alessio Carpegna, Chiara De Luca, Federico Emanuele Pozzi +4
Detecting monotonic changes in heart rate (HR) is crucial for early identification of cardiac conditions and health management. This is particularly important for dementia patients…
Can social media shape the security of next-generation connected vehicles?
Nicola Scarano, Luca Mannella, Alessandro Savino +1
The increasing adoption of connectivity and electronic components in vehicles makes these systems valuable targets for attackers. While automotive vendors prioritize safety, there…
Compressed Latent Replays for Lightweight Continual Learning on Spiking Neural Networks
Alberto Dequino, Alessio Carpegna, Davide Nadalini +4
Rehearsal-based Continual Learning (CL) has been intensely investigated in Deep Neural Networks (DNNs). However, its application in Spiking Neural Networks (SNNs) has not been expl…
Navigating the road to automotive cybersecurity compliance
Franco Oberti, Fabrizio Abrate, Alessandro Savino +2
The automotive industry has evolved significantly since the introduction of the Ford Model T in 1908. Today's vehicles are not merely mechanical constructs; they are integral compo…
Vitamin-V: Expanding Open-Source RISC-V Cloud Environments
Ramon Canal, Stefano Di Carlo, Dimitris Gizopoulos +12
Among the key contributions of Vitamin-V (2023-2025 Horizon Europe project), we develop a complete RISC-V open-source software stack for cloud services with comparable performance…
CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks Simulation
Sadek Misto Kirdi, Nicola Scarano, Franco Oberti +3
Modern vehicles are increasingly vulnerable to attacks that exploit network infrastructures, particularly the Controller Area Network (CAN) networks. To effectively counter such th…
R-CONV: An Analytical Approach for Efficient Data Reconstruction via Convolutional Gradients
Tamer Ahmed Eltaras, Qutaibah Malluhi, Alessandro Savino +3
In the effort to learn from extensive collections of distributed data, federated learning has emerged as a promising approach for preserving privacy by using a gradient-sharing mec…
SpikeExplorer: hardware-oriented Design Space Exploration for Spiking Neural Networks on FPGA
Dario Padovano, Alessio Carpegna, Alessandro Savino +1
One of today's main concerns is to bring Artificial Intelligence power to embedded systems for edge applications. The hardware resources and power consumption required by state-of-…
Spiker+: a framework for the generation of efficient Spiking Neural Networks FPGA accelerators for inference at the edge
Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
Including Artificial Neural Networks in embedded systems at the edge allows applications to exploit Artificial Intelligence capabilities directly within devices operating at the ne…