基于松弛行为的卷积神经网络的RRAM保留预测框架

Yibei Zhang, Qingtian Zhang, Qi Qin, Wenbin Zhang, Yue Xi, Zhixing Jiang, Jianshi Tang, B. Gao, H. Qian, Huaqiang Wu
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引用次数: 0

摘要

电阻式随机存取存储器(RRAM)的长时间保留问题给基于RRAM的大规模内存计算系统的性能维护带来了巨大的挑战。在精度要求较高的应用中,定期更新是一种补偿保留度下降造成的精度损失的可行方法。本文提出了一种选择性刷新策略,通过预测设备的保留行为来降低更新成本。提出了一种基于卷积神经网络的留存率预测框架。该框架可以根据RRAM器件的短时松弛行为判断其是否具有较差的保留性,是否需要更新。通过对所选器件的重新编程,该方法可以有效地恢复基于rram的CIM系统的精度。这项工作提供了一种有价值的低时间和能量成本的保留应对策略,并为分析RRAM器件的松弛和保留行为之间的物理联系提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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, 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.
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CiteScore
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