Vincent Gripon's Homepage

Robustesse des réseaux de neurones profonds aux défaillances mémoire

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.

@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},
}