Approximate Fault-Tolerant Neural Network Systems - École Centrale de Lyon
Communication Dans Un Congrès Année : 2024

Approximate Fault-Tolerant Neural Network Systems

Résumé

This paper aims to comprehensively explore challenges and opportunities to design highly efficient Neural Network (NN) systems through Approximate Computing (AxC) techniques while ensuring fault tolerance properties. By highlighting the intrinsic conflicting goals of AxC and fault tolerance principles, the study aims to stimulate and contribute to a deeper understanding of how important it is to consider fault tolerance requirements while designing approximate-computing-based systems. This is key to developing highly efficient fault-tolerant architectures for Neural Networks
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hal-04674818 , version 1 (21-08-2024)

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Marcello Traiola, Salvatore Pappalardo, Ali Piri, Annachiara Ruospo, Bastien Deveautour, et al.. Approximate Fault-Tolerant Neural Network Systems. ETS 2024 - 29th IEEE European Test Symposium, May 2024, La Haye, Netherlands. pp.1-10, ⟨10.1109/ETS61313.2024.10567290⟩. ⟨hal-04674818⟩
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