Journal of Electronic Testing

, Volume 33, Issue 1, pp 53–64 | Cite as

A High-Level Approach to Analyze the Effects of Soft Errors on Lossless Compression Algorithms

  • Serhiy Avramenko
  • Matteo Sonza Reorda
  • Massimo Violante
  • Görschwin Fey


In space applications, the data logging sub-system often requires compression to cope with large amounts of data as well as with limited storage and communication capabilities. The usage of Commercial off-the-Shelf (COTS) hardware components is becoming more common, since they are particularly suitable to meet high performance requirements and also to cut the cost with respect to space qualified ones. On the other side, given the characteristics of the space environment, the usage of COTS components makes radiation-induced soft errors highly probable. The purpose of this work is to analyze a set of lossless compression algorithms in order to compare their robustness against soft errors. The proposed approach works on the unhardened version of the programs, aiming to estimate their intrinsic robustness. The main contribution of the work lies in the investigation of the possibility of performing an early comparison between different compression algorithms at a high level, by only considering their data structures (corresponding to program variables). This approach is virtually agnostic of the downstream implementation details. This means that the proposed approach aims to perform a comparison (in terms of robustness against soft errors) between the considered programs before the final computing platform is defined. The results of the high-level analysis can also be used to collect useful information to optimize the hardening phase. Experimental results based on the OpenRISC processor are reported. They suggest that when properly adopted, the proposed approach makes it possible to perform a comparison between a set of compression algorithms, even with a very limited knowledge of the target computing system.


Soft errors Lossless compression Reliability High-level fault injection 



This work has been supported by the European Commission through the Horizon 2020 Project No. 637616 (MaMMoTH-UP).


  1. 1.
    (1997) Lossless data compression, recommendation for space data system standards. CCSDS 121.0-B-1. Blue Book, Issue 1. CCSDS, Washington, D.C.Google Scholar
  2. 2.
    Avramenko S, Sonza Reorda M, Violante M, Fey G (2016) Analysis of the effects of soft errors on compression algorithms through fault injection inside program variables. In: Proc. 17th Latin-American Test Symposium (LATS), 2016Google Scholar
  3. 3.
    Baumann RC (2005) Radiation-induced soft errors in advanced semiconductor technologies. IEEE Trans Device Mater ReliabGoogle Scholar
  4. 4.
    Benso A, Carlo SD, Di Natale G, Tagliaferri L, Prinetto P (2001) Validation of a software dependability tool via fault injection experiments. In: Proc. 7th International On-Line Testing Workshop (IOLTW), 2001Google Scholar
  5. 5.
    Cho H, Mirkhani S, Cher C-Y, Abraham JA, Mitra S (2013) Quantitative evaluation of soft error injection techniques for robust system design. In: Proc. 50th ACM/IEEE Design Automation Conference (DAC), 2013Google Scholar
  6. 6.
    Esposito S, Albanese C, Alderighi M, Casini F, Giganti L, Esposti ML, Monteleone C, Violante M (2015) COTS-based high-performance computing for space applications. IEEE Trans Nucl Sci 62(6)Google Scholar
  7. 7.
    Goloubeva O, Rebaudengo M, Sonza Reorda M, Violante M (2006) Software-implemented hardware fault tolerance. SpringerGoogle Scholar
  8. 8.
    Hakobyan H, Rech P, Sonza Reorda M, Violante M (2014) Early reliability evaluation of a biomedical system. In: Proc. 9th IEEE International Design & Test Symposium, 2014Google Scholar
  9. 9.
    Kooli M, Di Natale G (2014) A survey on simulation-based fault injection tools for complex systems. In: Proc. 9th International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS), 2014Google Scholar
  10. 10.
    Lattner C, Adve V (2004) LLVM: a compilation framework for lifelong program analysis & transformation. In: Proc. International Symposium on Code Generation and Optimization: Feedback-directed and Runtime Optimization, 2004Google Scholar
  11. 11.
    Leveugle R, Calvez A, Maistri P, Vanhauwaert P (2009) Statistical fault injection: Quantified error and confidence. In: Proc. Conference on Design, Automation and Test in Europe, 2009Google Scholar
  12. 12.
    MacKay DJC (2003) Information theory, inference and learning algorithms. Cambridge University Press, CambridgeMATHGoogle Scholar
  13. 13.
    Meß J-G, Schmidt R, Fey G (2017) Adaptive compression schemes for housekeeping data. In: Proc. IEEE Aerospace Conference, 2017Google Scholar
  14. 14.
    Pignol M (2006) DMT and DT2: two fault-tolerant architectures developed by CNES for COTS-based spacecraft supercomputers. In: Proc. 12th IEEE International On-Line Testing Symposium (IOLTS), 2006Google Scholar
  15. 15.
    Reis GA, Chang J, Vachharajani N, Mukherjee SS, Rangan R, August DI (2005) Design and evaluation of hybrid fault-detection systems. In: Proc. 32nd International Symposium on Computer Architecture, 2005Google Scholar
  16. 16.
    Salomon D (2007) Data compression: the complete reference. Springer-VerlagGoogle Scholar
  17. 17.
    Salomon D, Motta G (2010) Handbook of Data Compression, edn 5. Springer-Verlag, LondonGoogle Scholar
  18. 18.
    Sharma VC, Haran A, Rakamaric Z, Gopalakrishnan G (2013) Towards formal approaches to system resilience. In: Proc. 19th Pacific Rim International Symposium on Dependable Computing (PRDC), 2013Google Scholar
  19. 19.
    Velazco R, Foucard G, Pancher F, Mansour W, Marques-Costa G, Sohier D, Bui A (2011) Robustness with respect to radiation-induced soft errors of a self-converging algorithm. In Proc. 12th IEEE Latin American Test Workshop (LATW), 2011Google Scholar
  20. 20.
    Welch TA (1984) A technique for high-performance data compression. Computer 17(6):8–19CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Politecnico di TorinoTorinoItaly
  2. 2.German Aerospace Center (DLR)BremenGermany

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