Deep neural networks (DNNs) are state-of-the-art models adopted in many machine learning based systems and algorithms. However, a notable issue of DNNs is their considerable energy consumption for training and inference. At the hardware level, one current energy-saving solution at the inference phase is to reduce the voltage supplied to the DNN hardware accelerator. However, operating in the low-voltage regime would induce random bit errors saved in the memory and thereby degrade the model perfo…