1. An RRAM retention prediction framework using a convolutional neural network based on relaxation behavior
- Author
-
Yibei Zhang, Qingtian Zhang, Qi Qin, Wenbin Zhang, Yue Xi, Zhixing Jiang, Jianshi Tang, Bin Gao, He Qian, and Huaqiang Wu
- Subjects
RRAM ,retention ,in-memory computing ,convolutional neural network ,Electronic computers. Computer science ,QA75.5-76.95 - 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.
- Published
- 2023
- Full Text
- View/download PDF