ReRAM SSD系统13倍续航力的机器学习预测

T. Iwasaki, S. Ning, Hiroki Yamazawa, Chao Sun, S. Tanakamaru, K. Takeuchi
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引用次数: 8

摘要

利用机器学习对ReRAM存储单元的可变行为进行建模。研究了两种类型的预测,在下一个周期中重置和细胞长期失效。一项新的提议,主动位冗余,在SSD控制器中引入了一个ml训练的预测引擎,通过冗余来预测故障单元,并在实际故障之前主动替换它们。使用无效屏蔽技术,预测的单元格在页面中被就地标记,因此不需要额外的地址表。因此,在最小的开销下,基于50nm AlxOy测试芯片获得了2.85倍的误码率降低或13倍的耐用性提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Prediction for 13X Endurance Enhancement in ReRAM SSD System
The variable behavior of ReRAM memory cells is modeled with machine learning. Two types of prediction are investigated, reset in the next-cycle and cell fail in the long term. A new proposal, Proactive Bit Redundancy, introduces a ML-trained Prediction Engine into the SSD controller, to predict fail cells and replace them proactively - before actual failure- by redundancy. With the Invalid Masking technique, predicted cells are marked in-place within the page, so that no extra address table is needed. Thus, with ninimal overhead, 2.85x bit error rate reduction or 13x endurance improvement is obtained based on a 50nm AlxOy testchip.
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