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.

中文翻译：

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探索随机电报噪声引起的精度损失对基于电阻性 RAM 的深度神经网络的影响

对于基于电阻 RAM (RRAM) 的深度神经网络 (DNN)，随机电报噪声 (RTN) 在推理过程中会导致精度损失。在本文中，我们系统地研究了 RTN 对具有不同数据集的复杂 DNN 的影响。通过使用八个主流 DNN 和四个数据集，我们探索了导致 RTN 引起的精度损失的根源。基于上述理解，我们首次提出了一种新的估计精度损失的方法。该方法用其他十个未用于建立该方法的 DNN/数据集组合进行了验证。最后，我们讨论了它在 DNN 架构和 RRAM 技术协同优化中的潜在采用，为未来神经形态硬件系统缓解 RTN 引起的精度损失铺平了道路。