Single Crossbar Array of Memristors with Bipolar Inputs for Neuromorphic Image Recognition

Abstract

In this paper, we propose a new crossbar architecture of memristors with bipolar inputs for an image recognition application. The performance of the proposed crossbar array with bipolar inputs is based on the simplified Exclusive-NOR function to measure the similarity between the input pattern and the stored patterns. In the proposed architecture, only one crossbar array is used instead of two crossbar arrays. The number of memristors in the proposed architecture is reduced by 50%, when compared to the complementary and the twin architectures. The proposed crossbar architecture with bipolar inputs consumes 16.7% and 7.2% less power than do the complementary and twin architectures. In addition, using only one crossbar array in the proposed architecture can improve the fault tolerance of the crossbar circuit. When 10% of memristors are defective, the proposed crossbar architecture shows a recognition rate improved by 5%, 7% and 4% over that of the memristor binarized neural network, the complementary architecture of the memristor crossbar and the twin architecture of the memristor crossbar when recognizing 10 images.