2020年6月26日，本课题组景凌林老师及杜意德同学发表IEEE Transactions on Electron Devices文章一篇。纪志罡教授为通讯作者。这篇文章探索了RTN噪声对基于RRAM的复杂神经网络的影响，提出了一种估计精度损失的新方法并进行了验证，最后讨论了该方法在优化基于RRAM的DNN技术方面的潜在应用，为未来降低由RTN导致的精度损失提供了方向。

Exploring the Impact of Random Telegraph Noise-Induced Accuracy Loss on Resistive RAM-Based Deep Neural Network

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.