Impacts of State Instability and Retention Failure of Filamentary Analog RRAM on the Performance of Deep Neural Network

Abstract

In this work, an evaluation methodology is proposed to study the impacts of state instability and retention failure of filamentary analog resistive random access memory (FA-RRAM) on the performance of deep neural networks (DNNs). Based on the methodology, an analytic model for the statistical state instability and retention behaviors is applied to evaluate the impacts of the reliability of FA-RRAM on an 11-layer FA-RRAM-based DNN for CIFAR-10 recognition. Simulations indicate that the recognition accuracy of the 11-layer DNN decreases rapidly with the increase of the baking time (t = 104s, 16.3% accuracy loss at 125 °C) due to the overlapping among neighboring resistance levels. To mitigate the accuracy loss caused by state instability and retention failure, the optimization method including the optimized synapse cell and the refresh operation scheme is developed. With the optimization method, the robustness of the FA-RRAM-based DNN is enhanced significantly in which no accuracy loss is observed even after 107s (at 125 °C, ${5} \times {10}^{{3}}$ s/refresh).