Parasitic-Aware Modelling for Neural Networks Implemented with Memristor Crossbar Array

This paper presents a parasitic-aware modelling approach called αβ-matrix model for the simulation of neural network (NN) implemented with memristor crossbar array. The line resistance, which is the key parasitic in a memristor crossbar array is analyzed and incorporated into the model. The proposed method estimates the line resistance IR drop with computation complexity of O(mn), in contrast to O(m2n2) required by the classical matrix based Kirchhoff's Current Law (KCL) equations solver. The impact of the crossbar array parasitics to the vector-matrix multiplication (VMM) computation and multi-layer NN classification accuracy are also analyzed. The advantages of the proposed parasitic-aware model are demonstrated through an example of 2-layer perceptron implemented with resistive random access memory (RRAM) crossbar array for MNIST written digits classification. 97.3% classification accuracy is achieved on 64×64 6-bit RRAM crossbar arrays. Compared to the KCL solver, the classification accuracy degradation is less than 0.4% with line resistance up to 4.5Ω.