Kompositionelle Zuverlässigkeitsanalyse auf Systemebene unter Berücksichtigung von Unsicherheiten

• Antragsteller: Professor Dr.-Ing. Michael Glaß; Professor Dr.-Ing. Jürgen Teich
• Fachliche Zuordnung: Rechnerarchitektur, eingebettete und massiv parallele Systeme
• Förderung: Förderung von 2010 bis 2018
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 186315300

• Erstellungsjahr: 2017

Zusammenfassung der Projektergebnisse

Continuous technology scaling results in higher vulnerability of today’s electronic devices to e. g., neutron-induced soft errors, negative bias temperature instability, short-channel effect, gate leakage, etc. These devices may be embedded in safety-critical systems, such as autonomous cars, where reliability plays an important role. Thus, it is vital to evaluate the reliability of such systems at design time to seek for an appropriate reliability improving technique if necessary. In this project, we first proposed a methodology for cross-level reliability analysis to tame the ever increasing analysis complexity of embedded systems. This methodology combines various reliability analysis techniques across different levels of abstraction. Later in this project, we developed and incorporated efficient reliability analysis technique using stochastic logic. Moreover, we proposed to analyze system reliability more accurately through the consideration of common-cause failures among components as well as imperfect fault coverage. On the other hand, due to manufacturing tolerances and environmental changes, the reliability of system’s components cannot be precisely measured at design time and is considered uncertain. In this regard, we proposed to explicitly model uncertainty in the reliability of each component and derive the uncertainty at system level. The developed methodology is integrated into an automatic Electronic System Level (ESL) tool to allow for cost evaluation of reliability-increasing techniques and for Design Space Exploration (DSE). This tool uses meta-heuristic optimization algorithms and enables the comparison of system implementation candidates with objectives represented by uncertainty distributions. Moreover, various novel Importance Measure (IM) analysis techniques have been proposed in this project to provide an insight into the contribution of each component regarding the reliability of the system. To cope with huge design spaces, these techniques have been employed to steer the optimization algorithms in order to enhance the efficiency of the DSE.

Projektbezogene Publikationen (Auswahl)

• (2017) On the Boolean extension of the Birnbaum importance to non-coherent systems. Reliability Engineering & System Safety 160 191–200
  Aliee, Hananeh; Borgonovo, Emanuele; Glaß, Michael; Teich, Jürgen
  
• “Cross-Level Compositional Reliability Analysis for Embedded Systems”. In: Proceedings of the International Conference on Computer Safety, Reliability and Security (SAFECOMP). 2012, pp. 111–124
  M. Glaß, H. Yu, F. Reimann, and J. Teich
  
• “Automatic Success Tree-Based Reliability Analysis for the Consideration of Transient and Permanent Faults”. In: Proceedings of Design, Automation, and Test in Europe (DATE). 2013, pp. 1621–1626
  H. Aliee, M. Glaß, F. Reimann, and J. Teich
  
• “An Efficient Technique for Computing Importance Measures in Automatic Design of Dependable Embedded Systems”. In: Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS). 2014, 34:1–34:10
  H. Aliee, M. Glaß, F. Khosravi, and J. Teich
  
• “Automatic Graph-based Success Tree Construction and Analysis”. In: Proceedings of the 60th Annual Reliability and Maintainability Symposium (RAMS). Best Paper Award. 2014, pp. 563–569
  H. Aliee, M. Glaß, R. Wanka, and J. Teich
  
• “Multi-Objective Local-Search Optimization using Reliability Importance Measuring”. In: Proceedings of the 51st Design Automation Conference (DAC). HiPEAC Paper Award. 2014, pp. 1–6
  F. Khosravi, F. Reimann, M. Glaß, and J. Teich
  
• “Uncertainty-Aware Reliability Analysis and Optimization”. In: Proceedings of the Design, Automation & Test in Europe (DATE). 2015, pp. 97–102
  F. Khosravi, M. Müller, M. Glaß, and J. Teich
  
• “A New Time-Independent Reliability Importance Measure”. In: European Journal of Operational Research 254.2 (2016), pp. 427–442. issn: 0377-2217
  E. Borgonovo, H. Aliee, M. Glaß, and J. Teich
  
• “Guiding Genetic Algorithms using importance measures for reliable design of embedded systems”. In: 2016 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT). 2016, pp. 53–56
  H. Aliee, S. Vitzethum, M. Glaß, J. Teich, and E. Borgonovo
  
• “Automatic Reliability Analysis in the Presence of Probabilistic Common Cause Failures”. In: IEEE Transactions on Reliability 66.2 (2017), pp. 319– 338
  F. Khosravi, M. Glaß, and J. Teich
  
• “Redundancy-aware Design Space Exploration for Memory Reliability in Many-cores”. In: Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV). 2017. S. 1-12
  H. Aliee, A. Banaiyianmofrad, M. Glaß, J. Teich, and N. Dutt
  
• “System-Level Reliability Analysis Considering Imperfect Fault Coverage”. In: 15th IEEE/ACM Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia). 2017, pp. 68–77
  F. Khosravi, H. Aliee, and J. Teich