一种基于分块主成分分析的存储器容错方法研究

A Fault-Tolerant Method Based on Modular Principal Component Analysis for Memory

随着集成电路工艺水平提升,半导体器件尺寸越来越小,存储器更易受到周围环境的影响而导致数据存储故障。针对这一问题,该文提出了一种基于分块主成分分析的存储器容错方法。该方法应用分块主成分分析算法提取数据的主要特征,并对求取的特征数据作均值化处理,得到原始数据的最佳近似估计。该最佳近似估计可对数据中的任意故障做容错替换,使容错替换后的数据和原始数据的误差最小。实验结果表明,该方法可以使图片数据在0.003 5错误率的情况下仍保持峰值信噪比大于30 dB;与传统纠错码相比,执行时间缩短了约40%,内存消耗减少了约12%,获得了较好的容错效果。

With the improvement of integrated circuit manufacturing technology,the size of electronic components is shrinking accordingly.And that makes the memory components more susceptible to working environment.To solve this problem,this paper presents a memory fault tolerance method based on modular principal component analysis (PCA).Main features of the data were obtained via modular PCA firstly.Then, the feature data is averaged to obtain the best available estimate of the original data.This best available estimate can be used to make fault-tolerant replacements for any faults in the data,minimizing the sum of the squared errors of the fault-tolerant replaced data and the original data.Finally,using the reconstructed block data,fault-tolerant replacement of the erroneous data in the original data block can be performed.The experimental results show that the picture data can keep a peak signal to noise ratio of more than 30dB under 0.0035error rate.In comparison with conventional error correcting code approach,the execution time can be reduced about 40%,and the memory occupancy can be reduced about 12%.