Abstract
Electronic computing systems are integrating modern multicore processors and GPUs aiming to perform complex software stacks in different life-critical systems, including health devices and emerging self-driving cars. Such systems are expected to experience at least one soft error per day in the near future, which may lead to life-threatening failures. To prevent these failures, critical system must be tested and verified while under realistic workloads. This paper presents four novel non-intrusive fault injection techniques that enable full fault injection control and inspection of multicore systems behavior in the presence of faults. Proposed techniques were integrated into a fault injection framework and verified through a real automotive case study with up to 43 billions instructions. Results show that compared to traditional methods, the new techniques can increase the efficiency of fault injection campaigns during early development phase by 32.28%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A soft error campaign (and thus the evaluation of said campaign) in the context of this paper is considered to be relevant when the result can either identify the existence of vulnerabilities or their source.
- 2.
- 3.
All input images used in this work have six to ten objects detected in the reference execution.
References
Baumann, R.: Radiation-induced soft errors in advanced semiconductor technologies. IEEE Trans. Device Mater. Reliab. 5(3), 305–316 (2005)
Snir, M., et al.: Addressing failures in exascale computing. Int. J. High Perform. Comput. Appl. 28(2), 129–173 (2014)
Kaliorakis, M., Tselonis, S., Chatzidimitriou, A., Foutris, N., Gizopoulos, D.: Differential fault injection on microarchitectural simulators. In: International Symposium on Workload Characterization, Atlanta, GA, USA, pp. 172–182. IEEE (October 2015)
Didehban, M., Shrivastava, A.: nZDC: a compiler technique for near zero silent data corruption. In: Design Automation Conference, Austin, Texas. ACM Press (2016)
Tanikella, K., Koy, Y., Jeyapaul, R., Kyoungwoo, L., Shrivastava, A.: gemV: a validated toolset for the early exploration of system reliability. In: International Conference on Application-Specific Systems, Architectures and Processors, London, United Kingdom, pp. 159–163. IEEE (July 2016)
Bandeira, V., Rosa, F., Reis, R., Ost, L.: Non-intrusive fault injection techniques for efficient soft error vulnerability analysis. In: International Conference on Very Large Scale Integration (VLSI-SoC), Cuzco, Peru, pp. 123–128. IEEE (October 2019)
Hari, S.K.S., Adve, S.V., Naeimi, H., Ramachandran, P.: Relyzer: exploiting application-level fault equivalence to analyze application resiliency to transient faults. In: International Conference on Architectural Support for Programming Languages and Operating Systems, New York, NY, USA, Association for Computing Machinery, pp. 123–134 (2012)
Magnusson, P., et al.: Simics: a full system simulation platform. Computer 35(2), 50–58 (2002)
Martin, M.M.K., et al.: Multifacet’s general execution-driven multiprocessor simulator (GEMS) toolset. ACM SIGARCH Comput. Archit. News 33(4), 92 (2005)
de Aguiar Geissler, F., Kastensmidt, F.L., Souza, J.E.P.: Soft error injection methodology based on QEMU software platform. In: Latin American Test Workshop, Fortaleza, Brazil. IEEE (March 2014)
Guan, Q., et al.: Design, use and evaluation of P-FSEFI: a parallel soft error fault injection framework for emulating soft errors in parallel applications. In: International Conference on Simulation Tools and Techniques, Brussels, BEL, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 9–17 (2016)
Guan, Q., Debardeleben, N., Blanchard, S., Fu, S.: F-SEFI: a fine-grained soft error fault injection tool for profiling application vulnerability. In: International Parallel and Distributed Processing Symposium, Phoenix, AZ, USA, pp. 1245–1254. IEEE (May 2014)
Rosa, F., Kastensmidt, F., Reis, R., Ost, L.: A fast and scalable fault injection framework to evaluate multi/many-core soft error reliability. In: International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, Amherst, MA, USA, pp. 211–214. IEEE (October 2015)
Feng, S., Gupta, S., Ansari, A., Mahlke, S.: Shoestring: probabilistic soft error reliability on the cheap. In: International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS XV, New York, NY, USA, Association for Computing Machinery, pp. 385–396 (2010)
Cho, H., Mirkhani, S., Cher, C.Y., Abraham, J.A., Mitra, S.: Quantitative evaluation of soft error injection techniques for robust system design. In: Design Automation Conference, Austin, Texas. ACM Press (2013)
Bailey, D.H., et al.: The NAS parallel benchmarks-summary and preliminary results. In: Conference on Supercomputing, Albuquerque, New Mexico, United States. ACM Press (1991)
Redmon, J.: Darknet: open source neural networks in C (2016). https://pjreddie.com/darknet/
Dukhan, M.: Maratyszcza/NNPACK (February 2020)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv:1804.02767 (2018)
Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp. 3061–3070. IEEE (June 2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 IFIP International Federation for Information Processing
About this paper
Cite this paper
Bandeira, V., Rosa, F., Reis, R., Ost, L. (2020). Efficient Soft Error Vulnerability Analysis Using Non-intrusive Fault Injection Techniques. In: Metzler, C., Gaillardon, PE., De Micheli, G., Silva-Cardenas, C., Reis, R. (eds) VLSI-SoC: New Technology Enabler. VLSI-SoC 2019. IFIP Advances in Information and Communication Technology, vol 586. Springer, Cham. https://doi.org/10.1007/978-3-030-53273-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-53273-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-53272-7
Online ISBN: 978-3-030-53273-4
eBook Packages: Computer ScienceComputer Science (R0)
