Fault-tolerant Deep Learning using Regularization

Resistive random-access memory has become one of the most popular choices of hardware implementation for machine learning application workloads. However, these devices exhibit non-ideal behavior, which presents a challenge towards widespread adoption. Training/inferencing on these faulty devices can lead to poor prediction accuracy. However, existing fault tolerant methods are associated with high implementation overheads. In this paper, we present some new directions for solving reliability issues using software solutions. These software-based methods are inherent in deep learning training/inferencing, and they can also be used to address hardware reliability issues as well. These methods prevent accuracy drop during training/inferencing due to unreliable ReRAMs and are associated with lower area and power overheads.