5 papers
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…
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…
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…
Reliable edge machine learning hardware for scientific applications
Tommaso Baldi, Javier Campos, Ben Hawks +15
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementatio…
Stop-loss and Leverage in optimal Statistical Arbitrage with an application to Energy market
Roberto Baviera, Tommaso Santagostino Baldi
In this paper we develop a statistical arbitrage trading strategy with two key elements in hi-frequency trading: stop-loss and leverage. We consider, as in Bertram (2009), a mean-r…