Training Modern Deep Neural Networks for Memory-Fault Robustness

Ghouthi Boukli Hacene; François Leduc-Primeau; Amal Ben Soussia; Vincent Gripon; François Gagnon

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arXiv · 1911.10287

Training Modern Deep Neural Networks for Memory-Fault Robustness

Ghouthi Boukli Hacene·François Leduc-Primeau·Amal Ben Soussia·Vincent Gripon·François Gagnon

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Abstract

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.

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Ghouthi Boukli Hacene, François Leduc-Primeau, Amal Ben Soussia, Vincent Gripon, François Gagnon. 2019-11-23. Training Modern Deep Neural Networks for Memory-Fault Robustness. https://arxiv.org/abs/1911.10287

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