Analysis and optimization of soft error tolerance strategies for real-time systems

Zheng, Bowen; Gao, Yue; Zhu, Qi; Gupta, Sandeep

doi:10.1109/codesisss.2015.7331368

Cited by 16 publications

(26 citation statements)

References 25 publications

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“…In our previous work [26], we formulate the impact of EOC and EED on system timing for dierent platform congurations. An MILP (mixed integer linear programming) model is then developed to explore task allocation and scheduling, together with the selections of error detection techniques for individual tasks.…”

Section: Related Workmentioning

confidence: 99%

“…EOC can achieve almost 100% error detection at the cost of 100% execution time overhead (temporal redundancy) or 100% resource overhead (spatial redundancy). In this work, we assume EOC detection rate is 100%, similarly as in [26].…”

Section: Soft Error Tolerance Modelmentioning

confidence: 99%

“…System error coverage: During the hyperperiod T h per of a task set T (i.e., the least common multiple of the task periods), a total number of K 0 errors may occur. System error coverage is then dened as the probability that all errors are either i) detected and recovered within hyperperiod while all timing constraints are satised or ii) happened during idle time [26].…”

Section: Soft Error Tolerance Modelmentioning

confidence: 99%

“…Fault tolerance techniques are greatly needed to detect and recover errors such as application crashes, illegal branches and silent data corruption. In this paper, similarly as in [8,26], we focus on two categories of error detection techniques: embedded error detection (EED) and explicit output comparison (EOC). More specically, EED includes a variety of error detection techniques such as instruction signature checking, control ow check (CFC) and watchdog timers [17].…”

Section: Introductionmentioning

confidence: 99%

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Addressing Extensibility and Fault Tolerance in CAN-based Automotive Systems

Liang

Wang

Zheng

et al. 2017

Proceedings of the Eleventh IEEE/ACM International Symposium on Networks-on-Chip

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The design of automotive electronic systems needs to address a variety of important objectives, including safety, performance, fault tolerance, reliability, security, extensibility, etc. To obtain a feasible design, timing constraints must be satised and latencies of certain functional paths should not exceed their deadlines. From functionality perspective, soft errors caused by transient or intermittent faults need to be detected and recovered with fault tolerance techniques. Moreover, during the lifetime of a vehicle design or even the same car, updates are often needed to add new features or x bugs in existing ones. It is therefore critical to improve the design extensibility for accommodating such updates without incurring major redesign and re-verication cost. In this work, we discuss the metrics for measuring latency, fault tolerance and extensibility, and present a simulated annealing based algorithm to search the design space with respect to them. Experimental results on industrial and synthetic examples demonstrate clear trade-os among these objectives, and hence the importance of quantitatively analyzing such trade-os and exploring the design space with automation tools. CCS CONCEPTS • Computer systems organization → Embedded systems; Embedded software;

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Section: Related Workmentioning

confidence: 99%

Section: Soft Error Tolerance Modelmentioning

confidence: 99%

Section: Soft Error Tolerance Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Addressing Extensibility and Fault Tolerance in CAN-based Automotive Systems

Liang

Wang

Zheng

et al. 2017

Proceedings of the Eleventh IEEE/ACM International Symposium on Networks-on-Chip

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“…Furthermore, there are a variety of objectives and metrics that need to be addressed during the design and operation of automotive systems, such as safety, performance, fault tolerance, reliability, extensibility and security. Many of these metrics are heavily influenced by system timing behavior [35,42,46], and often lead to conflicting requirements [12,13,18,48,47]. For instance, shorter sampling periods and endto-end latencies of control loops usually lead to better sensing and control performance [13], but may be detrimental to schedulability, extensibility and even security (as there is less timing slack for adding strong security techniques [47]).…”

Section: Introductionmentioning

confidence: 99%

Convince

Zheng

Lin

et al. 2016

Proceedings of the 35th International Conference on Computer-Aided Design

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Next-generation autonomous and semi-autonomous vehicles will not only precept the environment with their own sensors, but also communicate with other vehicles and surrounding infrastructures for vehicle safety and transportation e ciency. The design, analysis and validation of various vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) applications involve multiple layers, from V2V/V2I communication networks down to software and hardware of individual vehicles, and concern with stringent requirements on multiple metrics such as timing, security, reliability and fault tolerance. To cope with these challenges, we have been developing CONVINCE, a cross-layer modeling, exploration and validation framework for connected vehicles. The framework includes mathematical models, synthesis and validation algorithms, and a heterogeneous simulator for inter-vehicle communications and intra-vehicle software and hardware in a holistic environment. It explores various design options with respect to constraints and objectives on system safety, security, reliability, cost, etc. A V2V application is used in the case study to demonstrate the e↵ectiveness of the proposed framework.

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