Empirical metal-oxide RRAM device endurance and retention model for deep learning simulations

Memristive devices including resistive random access memory (RRAM) cells are promising nanoscale low-power components projected to facilitate significant improvement in power and speed of Deep Learning (DL) accelerators, if structured in crossbar architectures. However, these devices possess non-ideal endurance and retention properties, which should be modeled efficiently. In this paper, we propose a novel generalized empirical metal-oxide RRAM endurance and retention model for use in large-scale DL simulations. To the best of our knowledge, the proposed model is the first to unify retention-endurance modeling while taking into account time, energy, SET-RESET cycles, device size, and temperature. We compare the model to state-of-the-art and demonstrate its versatility by applying it to experimental data from fabricated devices. Furthermore, we use the model for CIFAR-10 dataset classification using a large-scale deep memristive neural network (DMNN) implementing the MobileNetV2 architecture. Our results show that, even when ignoring other device non-idealities, retention and endurance losses significantly affect the performance of DL networks. Our proposed model and its DL simulations are made publicly available.