A training strategy for improving the robustness of memristor-based binarized convolutional neural networks

Abstract

Abstract Memristor-based convolutional neural networks (CNNs) have been extensively studied in the field of edge computing, owing to the efficient characteristics of memristors, such as high integration density and powerful processing capability. However, constrained by the low yield of memristor array and the memristance variation, memristor-based CNNs have failed to be widely applied. Consequently, a training strategy is proposed to improve the robustness of memristor-based binarized neural networks for prompting embedded application. Simulation results on the MNIST dataset reveal that this strategy is able to improve the performance of a memristor-based two-layer CNN with device defects. Specifically, when the yield rate of the memristor array is 60%, the recognition rate of a two-layer memristor-based binarized convolutional neural network achieves around 91.19%, and when the characteristic of device variation is 28%, it reaches about 91.53%.