11 citations · 41 across the 21 of their papers we have counts for
7 papers · 1 filter
LuxIA: A Lightweight Unitary matriX-based Framework Built on an Iterative Algorithm for Photonic Neural Network Training
Tzamn Melendez Carmona, Federico Marchesin, Marco P. Abrate +3
PNNs present promising opportunities for accelerating machine learning by leveraging the unique benefits of photonic circuits. However, current state of the art PNN simulation tool…
Power Side-Channel Analysis of the CVA6 RISC-V Core at the RTL Level Using VeriSide
Behnam Farnaghinejad, Antonio Porsia, Annachiara Ruospo +3
Security in modern RISC-V processors demands more than functional correctness: It requires resilience to side-channel attacks. This paper evaluates the vulnerability of the side ch…
Uncovering Privacy Vulnerabilities through Analytical Gradient Inversion Attacks
Tamer Ahmed Eltaras, Qutaibah Malluhi, Alessandro Savino +2
Federated learning has emerged as a prominent privacy-preserving technique for leveraging large-scale distributed datasets by sharing gradients instead of raw data. However, recent…
EHWGesture -- A dataset for multimodal understanding of clinical gestures
Gianluca Amprimo, Alberto Ancilotto, Alessandro Savino +5
Hand gesture understanding is essential for several applications in human-computer interaction, including automatic clinical assessment of hand dexterity. While deep learning has a…
CANDoSA: A Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN bus
Franco Oberti, Stefano Di Carlo, Alessandro Savino
The Controller Area Network (CAN) protocol, essential for automotive embedded systems, lacks inherent security features, making it vulnerable to cyber threats, especially with the…
SFATTI: Spiking FPGA Accelerator for Temporal Task-driven Inference -- A Case Study on MNIST
Alessio Caviglia, Filippo Marostica, Alessio Carpegna +2
Hardware accelerators are essential for achieving low-latency, energy-efficient inference in edge applications like image recognition. Spiking Neural Networks (SNNs) are particular…
Energy-Efficient Digital Design: A Comparative Study of Event-Driven and Clock-Driven Spiking Neurons
Filippo Marostica, Alessio Carpegna, Alessandro Savino +1
This paper presents a comprehensive evaluation of Spiking Neural Network (SNN) neuron models for hardware acceleration by comparing event driven and clock-driven implementations. W…