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A Benchmark Suite to Evaluate DNN’s Resilience

Year: 2025
Type: conference-paper
Abstract: Assessing AI systems reliability is essential before deploying them in safety-critical applications. While recent efforts have focused on improving model resilience to random hardware faults, meaningful comparison remains difficult due to the lack of standardized reference models. Different authors use different implementations, which makes comparisons unfair and biased: resilience is influenced by the training processes, the software framework, and data representations. To address these issues, this work introduces a benchmark suite of CNN models to test the resilience of DNNs. The benchmark is structured on different axes: software framework, hardware platform, data representation, task and dataset. It is aimed at providing a shared foundation for fair and reproducible resilience evaluation.
DOI: 10.1109/itc58126.2025.00090
Authors: Cristiana Bolchini, Alberto Bosio, Luca Cassano, Antonio Miele, Salvatore Pappalardo, Dario Passariello, Annachiara Ruospo, Ernesto Sanchez, Matteo Sonza Reorda, Vittorio Turco
Institutions: Politecnico di Milano, Lyon 1 Université, Centre National de la Recherche Scientifique, Institut des Nanotechnologies de Lyon, Institut National des Sciences Appliquées de Lyon, École d'Ingénieurs en Chimie et Sciences du Numérique, Politecnico di Torino
Language: English
Open Access status: green
OpenAlex ID: W4415821530
FWCI:
Cites: 5
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