'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
For Resistive RAM (RRAM)-based deep neural network, Random telegraph noise (RTN) causes accuracy loss during inference. In this work, we systematically investigated the impact of RTN on the complex deep neural networks (DNNs) with different datasets. By using 8 mainstream DNNs and 4 datasets, 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 without going through time-consuming RTN simulation. The method was verified with other 10 DNN/dataset combinations that were not used for establishing the method. Finally, we discussed its potential adoption for the co-optimization of the DNN architecture and the RRAM technology, paving ways to RTN-induced accuracy loss mitigation for future neuromorphic hardware systems
