Compensate or ignore? meeting control robustness requirements through adaptive soft-error handling

Chen, Kuan-Hsun; Bönninghoff, Björn; Chen, Jian-Jia; Marwedel, Peter

doi:10.1145/2907950.2907952

Cited by 7 publications

(17 citation statements)

References 22 publications

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“…However, it can also be devised through a non-linear model depending upon the design requirements of the target system. • The (m,k) robustness constraint model [4,35] quantifies the potential inherent safety margins of control tasks. In this work, several error-handling approaches guarantee the minimal frequency of correctness over a static number of instances while satisfying the hard real-time constraints in the worst-case scenario.…”

Section: Dependability Modeling and Estimationmentioning

confidence: 99%

“…For each task an individual (m, k) constraint is possible to be given by other means analytically or empirically [35]. Without skipping any instances so likely achieving higher control performance, a static pattern-based approach [4] can be used to comply the reliable executions on the marked instances by following an (m, k)-pattern repeatedly to satisfy the given minimal requirement. To validate the schedulability, the multi-frame task model can then be applied to provide a hard real-time guarantee offline.…”

Section: Adaptive Soft Error Handlingmentioning

confidence: 99%

“…To validate the schedulability, the multi-frame task model can then be applied to provide a hard real-time guarantee offline. A run-time adaptive approach [4] can further decide the executing version on the fly by enhancing the static pattern-based approach and monitoring the current tolerance status with sporadic replenishment counters. It is worth noting that the resulting distribution of execution jobs can still follow the (m, k) static patterns even in the worst case.…”

Section: Adaptive Soft Error Handlingmentioning

confidence: 99%

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Dependable Software Generation and Execution on Embedded Systems

Kriebel

Chen

Rehman

et al. 2020

Dependable Embedded Systems

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For generating and executing dependable software, the effects of hardware layer faults at the software layer have to be accurately analyzed and modeled. This requires relevant information from the hardware and software layers, as well as an in-depth analysis of how an application’s outputs are affected by errors, and quantifying the error masking and error propagation on the software layer. Based on this analysis, techniques for generating dependable software can be proposed, e.g., by different dependability-aware compiler-based software transformations or selective instruction protection. Beside functional aspects, timing also plays an important role, as oftentimes tasks have to be finished before a certain deadline to provide useful information, especially in real-time systems. Both aspects are jointly taken into account by the run-time system software which decides—with the help of offline and online-generated data—for multiple concurrently executing applications how to protect and when to execute which application task to optimize for dependability and timing correctness. This is achieved for example by selecting appropriate application versions and protection levels for single and multi-core systems—for example using redundant multithreading (RMT) in different modes—under tolerable performance overhead constraints.

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Section: Dependability Modeling and Estimationmentioning

confidence: 99%

Section: Adaptive Soft Error Handlingmentioning

confidence: 99%

Section: Adaptive Soft Error Handlingmentioning

confidence: 99%

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Dependable Software Generation and Execution on Embedded Systems

Kriebel

Chen

Rehman

et al. 2020

Dependable Embedded Systems

Self Cite

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“…In practice, it has been noticed, that some safety-critical applications can tolerate a limited number of errors at the cost of temporarily downgrading the quality of service (QoS), without catastrophic consequences if some constraints of error tolerance are guaranteed. For instance, robotic applications can still successfully finish their tasks under a limited number of errors [9], [45], where an (m, k) robustness constraint is considered. That is, a task must have at least m correct jobs out of any k consecutive jobs.…”

Section: Introductionmentioning

confidence: 99%

“…The most prevalent techniques that are currently used to guarantee (m, k)-constraints, rely on static decisions to instantiate jobs using suitable fault-tolerance techniques to assure a reliable job execution such as the deeply red pattern (Rpattern) [24] or the evenly distributed pattern (E-pattern) [37]. To improve the adaptivity of the static pattern based techniques, Chen et al [9] proposed to track the current resilience during runtime and to adapt the patterns accordingly. More precisely, the pattern-based scheduler defers the time-costly reliable executions to the possible last moment by tracking the number of upcoming jobs that can still be faulty without violating the constraints.…”

Section: Introductionmentioning

confidence: 99%

Average Task Execution Time Minimization under (m, k) Soft Error Constraint

Shi

Ueter

Chen

et al. 2023

2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS)

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Safety-critical systems are often subjected to transient faults. Since these transient faults may lead to soft errors that cause catastrophic consequences, error-handling must be addressed by design. Full-protection against faults is too costly in terms of resource usage. A common approach to relax the resource demands and limit the impact of errors is to consider (m, k)-constraints, which requires that at least m jobs out of any k consecutive jobs are error-free. To assure (m, k)-compliance, static patterns are widely used to select the job execution modes, i.e., either in an error-free mode at the cost of increased worst-case execution time or in an error-prone mode with the advantage of less execution time. Although static patterns have been shown to be effective in energy-aware designs, resource over-provision is inevitable due to the relatively low rate of error probability. In this work, we propose two dynamic (and adaptive) approaches that allow the scheduler to opportunistically select execution modes based on the error-history of the past jobs and the actual error probability. We firstly propose a Markov chain based solution if the error-probability is known and static and secondly a reinforcement learning-based approach that can handle unknown error probabilities. Experimental evaluations show that our approaches outperform the state-of-the-art in most of the evaluated cases in terms of average utilization for each task and the overall utilization for multitask systems.1 2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS)

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Soft Error Handling for Embedded Systems using Compiler-OS Interaction

Engel

Marwedel

2020

Dependable Embedded Systems

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Advancing semiconductor technologies increasingly fail to provide expected gains in cost and energy reductions due to reaching the physical limits of Moore’s Law and Dennard scaling. Instead, shrinking semiconductor feature sizes increase a circuit’s susceptibility to soft errors. In order to ensure reliable operation, a significant hardware overhead would be required.The FEHLER project (Flexible Error Handling for Embedded Real-Time Systems) introduces error semantics into the software development process which provide a system with information about the criticality of a given data object to soft errors. Using this information, the overhead required for error correction can be reduced significantly for many applications, since only errors affecting critical data have to be corrected.In this chapter, the fundamental components of FEHLER that cooperate at design and runtime of an embedded system are presented. These include static compiler analyses and transformations as well as a fault-aware microkernel. Using examples of typical embedded applications, the efficiency of the approach is demonstrated, along with an extension towards approximate computing systems.

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Compensate or ignore? meeting control robustness requirements through adaptive soft-error handling

Cited by 7 publications

References 22 publications

Dependable Software Generation and Execution on Embedded Systems

Dependable Software Generation and Execution on Embedded Systems

Average Task Execution Time Minimization under (m, k) Soft Error Constraint

Soft Error Handling for Embedded Systems using Compiler-OS Interaction

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