A Mapping Method Tolerating SAF and Variation for Memristor Crossbar Array Based Neural Network Inference on Edge Devices

There is an increasing demand for running neural network inference on edge devices. Memristor crossbar array (MCA) based accelerators can be used to accelerate neural networks on edge devices. However, reliability issues in memristors, such as stuck-at faults (SAF) and variations, lead to weight deviation of neural networks and therefore have a severe influence on inference accuracy. In this work, we focus on the reliability issues in memristors for edge devices. We formulate the reliability problem as a 0-1 programming problem, based on the analysis of sumweight variation (SWV). In order to solve the problem, we simplify the problem with an approximation - different columns have the same weights, based on our observation of the weight distribution. Then we propose an effective mapping method to solve the simplified problem. We evaluate our proposed method with two neural network applications on two datasets. The experimental results on the classification application show that our proposed method can recover 95% accuracy considering SAF defects and can increase by up to 60% accuracy with variation s = 0.4. The results of the neural rendering application show that our proposed method can prevent render quality reduction.