Cascaded Neural Network for Memristor based Neuromorphic Computing

Shengyang Sun, Hui Xu, Jiwei Li, Haijun Liu, Qingjiang Li

Abstract: Recent years, several memristor-based neuromorphic processing chips have been proposed. However, there is few architectures to consider the cascading problems, and the scalability is not strong while dealing some tasks. To address this issue, we present a memristor-based cascaded method with some basic computation unit, several neural network processing chips can be cascaded by this means to improve the processing capability of the dataset. Compared with VGGNet and GoogLeNet, the proposed cascaded framework can achieve 93.54% Fashion-MNIST accuracy under the 4.15M parameters. Extensive experiments are conducted show that the circuit simulation results can still provide a high recognition accuracy, the recognition accuracy loss after circuit simulation can be controlled at around 0.26%.