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Robustesse des réseaux de neurones profonds aux défaillances mémoire

G. B. Hacene, F. Leduc-Primeau, A. B. Soussia, V. Gripon and F. Gagnon, "Robustesse des réseaux de neurones profonds aux défaillances mémoire," in GRETSI, August 2019.

Because deep neural networks (DNNs) rely on a large number of parameters and computations, their implementation in energy-constrained systems is challenging. In this paper, we investigate the solution of reducing the supply voltage of the memories used in the system, which results in bit-cell faults. We explore the robustness of state-of-the-art DNN architectures towards such defects and propose a regularizer meant to mitigate their effects on accuracy. Our experiments clearly demonstrate the interest of operating the system in a faulty regime to save energy without reducing accuracy.

Download manuscript.

Bibtex
@inproceedings{HacLedSouGriGag201908,
  author = {Ghouthi Boukli Hacene and Francois
Leduc-Primeau and Amal Ben Soussia and Vincent Gripon
and Francois Gagnon},
  title = {Robustesse des réseaux de neurones
profonds aux défaillances mémoire},
  booktitle = {GRETSI},
  year = {2019},
  month = {August},
}




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