  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.
