Extremely parallel memristor crossbar architecture for convolutional neural network implementation

Abstract

This paper presents a simulated memristor crossbar based Convolutional Neural Network (CNN). Deep networks implemented on GPU clusters have become the state of the art in providing excellent classification ability, at the cost of a more complex data manipulation process. In this work we show that once deep networks are trained, the analog crossbar circuits in this paper can parallelize the recognition phase of a CNN algorithm. With the massively parallel structure proposed in this work, we are able to generate multiple output feature maps in a single processing cycle. We show the proposed system is capable of operating with a negligible loss in classification accuracy if the memristors utilized are able to store at least 16 unique values (essentially acting as 4-bit devices). Furthermore, we quantify the amount of classification accuracy lost due to analog amplification in the signal path. To the best of our knowledge, this is the first paper that presents a memristor based circuit that is able to completely parallelize the CNN recognition process.