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


Ethical statement:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials

    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 30 dB under 0.003 5 error 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%.

    Cited by
Get Citation

FANG Jiayan, SHAO Cuiping, LI Huiyun. A Fault-Tolerant Method Based on Modular Principal Component Analysis for Memory[J]. Journal of Integration Technology,2018,7(6):49-59

Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Received:
  • Revised:
  • Adopted:
  • Online: November 20,2018
  • Published: