Large-scale and high-speed memristor crossbar array considering signal integrity (SI) in an analog-based neuromorphic system for deep neural network (DNN) inference

Abstract

In this paper, we first propose a large-scale high-speed memristor crossbar array considering signal integrity for deep neural network inference. This paper aims to design the interconnection of memristor crossbar array for the highest accuracy of inference. For this, we modeled and implemented the entire computational process for inference in addition to structural design and analysis. To get accurate inference results over a wide operating frequency band, we designed interconnections and an array that can cancel the ringing. Also, the signal integrity analysis in the designed structure was used to understand the phenomenon and to check the eye diagram. In order to verify the proposed design, DNN is implemented through hybrid-circuits model and inference accuracy was confirmed. Finally, it is confirmed that the accuracy is significantly improved compared to the existing array having the same network size.