Characterizing Deep Neural Networks Neutrons-Induced Error Model - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2022

Characterizing Deep Neural Networks Neutrons-Induced Error Model

Abstract

We characterize the fault models for Deep Neural Networks (DNNs) in GPUs exposed to neutron. We observe tolerable and critical errors, and show that ECC is not effective in reducing critical errors.
Fichier principal
Vignette du fichier
main_hal.pdf (384.17 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03652138 , version 1 (26-04-2022)

Licence

Identifiers

  • HAL Id : hal-03652138 , version 1

Cite

Fernando Fernandes dos Santos, Angeliki Kritikakou, Olivier Sentieys, Paolo Rech. Characterizing Deep Neural Networks Neutrons-Induced Error Model. NSREC 2022 - IEEE Nuclear & Space Radiation Effects Conference, Jul 2022, Provo, United States. pp.1-5. ⟨hal-03652138⟩
109 View
110 Download

Share

Gmail Mastodon Facebook X LinkedIn More