Cases study of inputs split based calibration method for RRAM crossbar

Abstract

Many mainstream applications require multiply-accumulate calculations, such as image processing and neuromorphic computing. Multiply-accumulate calculations using memristor crossbar arrays is a remarkable method for extremely high implementation efficiency, whereas the memristor array fabrication technology is still not mature and it is difficult to fabricate large-scale arrays with high-yield, which will seriously affect the performance of the application running on the RRAM crossbar. This paper proposes an inputs split based calibration method that improves the application accuracy in tolerating variations and stuck-at-fault of memristor devices. To demonstrate the performance of the calibration algorithm, the case of image sharpening and three neural networks architectures are applied for simulation experiments. And experimental results show that the calculation accuracy can be improved by up to 26.83% at 90% yield of crossbar arrays, and the success rate of the algorithm can be as high as 99.3% when there are several arrays cascaded. It is of great significance to the application of arrays in multiply-accumulate computation.