Archivio Istituzionale della RicercaEnsuring reliable execution of Deep Neural Networks (DNNs) is crucial for safety-critical applications. Traditional software-based approaches fail to capture real-world fault scenarios, overlooking accelerator datapath effects. We propose a hardware-aware, software-based fault injection platform that emulates systolic array processing, enabling effective fault propagation analysis without the overhead of time-consuming methods.
Hardware-Aware Software-Based Fault Injection Platform for DNN Accelerators / Vacca, Eleonora; Buccellato, Federico. - ELETTRONICO. - (2025), pp. 220-221. (Intervento presentato al convegno 22st ACM International Conference on Computing Frontiers tenutosi a Cagliari (ITA) nel 28-30 May 2025) [10.1145/3719276.3727952].
Hardware-Aware Software-Based Fault Injection Platform for DNN Accelerators
Vacca, Eleonora;Buccellato, Federico
2025
Abstract
Ensuring reliable execution of Deep Neural Networks (DNNs) is crucial for safety-critical applications. Traditional software-based approaches fail to capture real-world fault scenarios, overlooking accelerator datapath effects. We propose a hardware-aware, software-based fault injection platform that emulates systolic array processing, enabling effective fault propagation analysis without the overhead of time-consuming methods.
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3719276.3727952.pdf
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https://hdl.handle.net/11583/2999707