An RRAM retention prediction framework using a convolutional neural network based on relaxation behavior

An RRAM retention prediction framework using a convolutional neural network based on relaxation behavior

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,...

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Bibliographic Details
Main Authors: Yibei Zhang, Qingtian Zhang, Qi Qin, Wenbin Zhang, Yue Xi, Zhixing Jiang, Jianshi Tang, Bin Gao, He Qian, Huaqiang Wu
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:Neuromorphic Computing and Engineering
Subjects:
RRAM
retention
in-memory computing
convolutional neural network
Online Access:https://doi.org/10.1088/2634-4386/acb965

Internet

https://doi.org/10.1088/2634-4386/acb965

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