3 papers
cs.AR2026
ALADIN: Accuracy-Latency-Aware Design-space Inference Analysis for Embedded AI Accelerators
T. Baldi, D. Casini, A. Biondi
The inference of deep neural networks (DNNs) on resource-constrained embedded systems introduces non-trivial trade-offs among model accuracy, computational latency, and hardware li…
cs.CR2025
Edge-Only Universal Adversarial Attacks in Distributed Learning
Giulio Rossolini, Tommaso Baldi, Alessandro Biondi +1
Distributed learning frameworks, which partition neural network models across multiple computing nodes, enhance efficiency in collaborative edge-cloud systems, but may also introdu…
cs.LG2025
Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing
Tommaso Baldi, Javier Campos, Olivia Weng +4
In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensin…