An RRAM retention prediction framework using a convolutional neural network based on relaxation behavior. Issue 1 (1st March 2023)

• Record Type: Journal Article
• Authors: Zhang, Yibei; Zhang, Qingtian; Qin, Qi; Zhang, Wenbin; Xi, Yue; Jiang, Zhixing; Tang, Jianshi; Gao, Bin; Qian, He; Wu, Huaqiang
• Is Part Of: Neuromorphic computing and engineering. Volume 3:Issue 1(2023)
• Publication Date: 2023-03-01
• Subjects: RRAM -- retention -- in-memory computing -- convolutional neural network; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/acb965
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: The long-time retention issue of resistive random access memory (RRAM) brings a great challenge in the performance maintenance of large-scale RRAM-based computation-in-memory (CIM) systems. The periodic update is a feasible method to compensate for the accuracy loss caused by retention degradation, especially in demanding high-accuracy applications. In this paper, we propose a selective refresh strategy to reduce the updating cost by predicting the devices' retention behavior. A convolutional neural network-based retention prediction framework is developed. The framework can determine whether an RRAM device has poor retention that needs to be updated according to its short-time relaxation behavior. By reprogramming these few selected devices, the method can recover the accuracy of the RRAM-based CIM system effectively. This work provides a valuable retention coping strategy with low time and energy costs and new insights for analyzing the physical connection between the relaxation and retention behavior of the RRAM device.