Behavioral Model of Dot-Product Engine Implemented with 1T1R Memristor Crossbar Including Assessment

Abstract

Memristor is an emerging electrical device that enables non-volatile storage and in-memory computing. The memristive crossbar with high memory density and low energy consumption has drawn much attention for the implementation of dot-product engines, which can be deployed in power-hungry applications with intensive multiply-accumulate operations. However, simulating the crossbar containing a group of memristors based on the device-level modeling is time consuming. In this paper, we propose a model to simulate the memristive crossbar with high flexibility and automation at the behavioral level to perform the vector-matrix multiplication. This system-level model captures the non-linearity of memristors aiming for fast and accurate simulation. With the significantly reduced simulation time, this model enables simulating the systems containing memristive crossbar with large scale like neural networks in a more practical way. Moreover, this model can be exploited to analyze the effects of variations, which provides a condition and contributes to revealing potential computational errors. A multilayer perceptron detecting breast cancer is simulated based on this model to assess the classification accuracy with the presence of variabilities.