Space radiation interference especially the single event upset (SEU) effect can make a great impact on the normal and stable operation of the neural network chips, which can lead to the random bit flips of the weight parameters stored in the SRAM, and thus change the values of the neural weight parameters and the accuracy of the neural network chip outputs. In this paper, we analyze the commonly used radiation resistant methods, and try to overcome the problem of hardware consumption, recovery time, and processing speed. After the software simulation of the neural network accuracy with the different ratios of weight parameter errors, we adopt the dropout algorithm to construct the novel network framework to avoid the decline of accuracy. The algorithm can mask the neurons affected by SEU with some probability. The simulation experiment results show that this method can promote the accuracy of the neural network affected by SEU.
QIAN Huan
,
XIE Zhuochen
,
LIANG Xuwen
. Single-event upsets fault tolerance of convolutional neural networks based on dropout algorithm[J]. Journal of University of Chinese Academy of Sciences, 2021
, 38(5)
: 712
-719
.
DOI: 10.7523/j.issn.2095-6134.2021.05.016
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