Enhanced memristor-based MNNs performance on noisy dataset resulting from memristive stochasticity

摘要

Multilayer neural networks (MNNs) have achieved excellent performance in machine-learning domain. Memristors are a possible device for implementing MNNs in hardware with efficiency and limited area. In this work, a simple model of stochastic memristors was presented first. Then, an MNN architecture based on proposed memristor model was presented. The simulation processes on stochastic memristors were elaborated. The simulation demonstrates that the MNN classification accuracy based on stochastic memristors is usually higher than that based on deterministic memristors when the dataset noise is low. The results have significant meaning to develop analogue memristive devices or memristive chips for MNN applications.