Instruction SDC Vulnerability Prediction Using Long Short-Term Memory Neural Network

  • Yunfei Liu
  • Jing LiEmail author
  • Yi Zhuang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)


Silent Data Corruption (SDC) is one of the serious issues in soft errors and it is difficult to detect because it can cause erroneous results without any indication. In order to solve this problem, a new SDC vulnerability prediction method based on deep learning model is proposed. Our method predicts the SDC vulnerability of each instruction in the program based on the inherent and dependent features of each instruction in the Lower Level Virtual Machine (LLVM) intermediate. Firstly, the features are extracted from benchmarks by LLVM passes and feature selection is performed. Then, LLVM Based Fault Injection Tool (LLFI) is used to get SDC vulnerability labels to obtain the SDC prediction data set. Long Short-Term Memory (LSTM) neural network is applied to classification of SDC vulnerability. Finally, compared with the model based on SVM and Decision Tree, the experiment results show that the average accuracy of LSTM in classification of SDC vulnerability is 11.73% higher than SVM, and 10.74% higher than Decision Tree.


LSTM Silent data corruption Fault injection Prediction 



This paper is supported by the Fundamental Research Funds for the Central Universities (NS 2015092).


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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