Abstract
With the continuous improvement of the integration of semiconductor chips, it has brought great challenges to the reliability and safety of the system. Among them, Silent Data Corruption (SDC), as one of the most harmful issues, is difficult to be detected due to its concealment. For the SDC error detection, literatures just focus on fault injection or program analysis, but ignore the relationship of instruction features on the SDC errors. To this end, we propose a SDC error detection model by analyzing the instruction feature importance to the SDC vulnerability (SDIFI) and design the vulnerability prediction method. For the SDC error detection, specifically, we first analyze the correlation between different instruction features and the SDC vulnerability, and characterize the importance of these features. Second, we propose a SDC error detection model based on the SDC vulnerability prediction by an improved LightGBM model, as well as detecting SDC errors by selective redundancy of high SDC vulnerability instructions. Experimental results on the Mibench benchmarks show that our method has better detection accuracy with low overhead.
Supported by the National Natural Science Foundation of China under Grant (No. 62072235).
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References
Chen, T., He, T., Benesty, et al.: Xgboost: extreme gradient boosting. R Package Version 0.4-2 1(4), 1–4 (2015)
Di, S., Cappello, F.: Adaptive impact-driven detection of silent data corruption for HPC applications. IEEE Trans. Parallel Distrib. Syst. 27(10), 2809–2823 (2016)
Earnshaw, A.M., Duggan, J.R., Creasy, T.J.: Code block reordering prior to forward error correction decoding based on predicted code block reliability. US Patent 8,327,234 (4 December 2012)
Gu, J., Zheng, W., Zhuang, Y., Zhang, Q.: Vulnerability analysis of instructions for SDC-causing error detection. IEEE Access 7, 168885–168898 (2019)
Janitza, S., Tutz, G., Boulesteix, A.L.: Random forest for ordinal responses: prediction and variable selection. Comput. Stat. Data Anal. 96, 57–73 (2016)
Jin, D., Lu, Y., Qin, J., Cheng, Z.: SwiftIDS: real-time intrusion detection system based on LightGBM and parallel intrusion detection mechanism. Comput. Secur. 97, 101984 (2020)
Laguna, I., Schulz, M., Richards, D.F., Calhoun, J., Olson, L.: IPAS: intelligent protection against silent output corruption in scientific applications. In: 2016 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), pp. 227–238. IEEE (2016)
Li, G., Pattabiraman, K., Hari, S.K.S., Sullivan, M., Tsai, T.: Modeling soft-error propagation in programs. In: 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). pp. 27–38. IEEE (2018)
Li, J., Tan, Q.: SmartInjector: exploiting intelligent fault injection for SDC rate analysis. In: 2013 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS), pp. 236–242. IEEE (2013)
Liu, C., Gu, J., Yan, Z., Zhuang, F., Wang, Y.: SDC-causing error detection based on lightweight vulnerability prediction. In: Asian Conference on Machine Learning, pp. 1049–1064. PMLR (2019)
Liu, L., Ci, L., Liu, W.: Identifying SDC-causing instructions based on random forests algorithm. KSII Trans. Internet Inf. Syst. 13(3), 1566–1582 (2019)
Liu, X., Jia, M., Zhang, X., Lu, W.: A novel multichannel Internet of Things based on dynamic spectrum sharing in 5G communication. IEEE Internet Things J. 6, 5962–5970 (2018)
Lu, Q., Li, G., Pattabiraman, K., Gupta, M.S., Rivers, J.A.: Configurable detection of SDC-causing errors in programs. ACM Trans. Embed. Comput. Syst. (TECS) 16, 1–25 (2017)
Ma, J., Wang, Y., Zhou, L., Hu, C., Wang, H.: SDCInfer: inference of silent data corruption causing instructions. In: 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), pp. 228–232. IEEE (2015)
Oh, N., Shirvani, P.P., McCluskey, E.J.: Control-flow checking by software signatures. IEEE Trans. Reliab. 51(1), 111–122 (2002)
Pattabiraman, K., Nakka, N., Kalbarczyk, Z., Iyer, R.: SymPLFIED: symbolic program-level fault injection and error detection framework. In: 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN), pp. 472–481. IEEE (2008)
Sridharan, V., Kaeli, D.R.: Eliminating microarchitectural dependency from architectural vulnerability. In: 2009 IEEE 15th International Symposium on High Performance Computer Architecture, pp. 117–128. IEEE (2009)
Tajary, A., Zarandi, H.R., Bagherzadeh, N.: IRHT: an SDC detection and recovery architecture based on value locality of instruction binary codes. Microprocess. Microsyst. 77, 103159 (2020)
Wang, C., Dryden, N., Cappello, F., Snir, M.: Neural network based silent error detector. In: 2018 IEEE International Conference on Cluster Computing (CLUSTER), pp. 168–178. IEEE (2018)
Wei, G., Zhao, J., Feng, Y., He, A., Yu, J.: A novel hybrid feature selection method based on dynamic feature importance. Appl. Soft Comput. 93, 106337 (2020)
Yu, H., Lu, J., Zhang, G.: Continuous support vector regression for nonstationary streaming data. IEEE Trans. Cybern. (2020)
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Fang, W., Gu, J., Yan, Z., Wang, Q. (2021). SDC Error Detection by Exploring the Importance of Instruction Features. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12937. Springer, Cham. https://doi.org/10.1007/978-3-030-85928-2_28
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DOI: https://doi.org/10.1007/978-3-030-85928-2_28
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