Exploring the Impact of Random Telegraph Noise-Induced Accuracy Loss on Resistive RAM-Based Deep Neural Network
Yide Du, Linglin Jing, Hui Fang, Hai‐Bao Chen, Yimao Cai, Runsheng Wang, Jianfu Zhang, Zhigang Ji
Abstract
For resistive RAM (RRAM)-based deep neural network (DNN), random telegraph noise (RTN) causes accuracy loss during inference. In this article, we systematically investigated the impact of RTN on the complex DNNs with different data sets. By using eight mainstream DNNs and four data sets, we explored the origin that caused the RTN-induced accuracy loss. Based on the understanding, for the first time, we proposed a new method to estimate the accuracy loss. The method was verified with other ten DNN/data set combinations that were not used for establishing the method. Finally, we discussed its potential adoption for the cooptimization of the DNN architecture and the RRAM technology, paving ways to RTN-induced accuracy loss mitigation for future neuromorphic hardware systems.