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Lifetime Improvement through Runtime Wear-based Task Mapping

机译:通过运行时磨损的任务映射寿命改进

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As transistors continue to become smaller, they become exponentially susceptible to permanent wearout faults. Without mitigation, these types of faults will render systems useless within unacceptably short time periods. Our work presents the design for a runtime task mapping subsystem which mitigates these faults using a wear-based heuristic. We compare our wear-based heuristic to power- and temperature-based heuristics used within the same system framework. Using a wide range of synthetic and real-world benchmarks, we show that our wear-based heuristic is able to improve total system lifetime by an average of 7.1% over temperature-based heuristics. Additionally, we show that our wear-based heuristic can be used to drastically improve the time to the first component failure (TTFF) of a system. TTFF is a metric that is of interest to designers who wish to avoid the design and verification difficulties of systems which are expected to recover after a component failure. Our wear-based heuristic improves TTFF by an average of 14.6% over temperature-based heuristics across all of our benchmarks. Our observations lead us to conclude that runtime, wear-based task mapping must be incorporated into systems for which lifetime is a primary design goal.
机译:随着晶体管继续变小,它们对永久磨损故障呈指数脑敏感。无减轻,这些类型的故障将在不可接受的短时间内呈现无用的系统。我们的工作介绍了运行时任务映射子系统的设计,该子系统使用基于磨损的启发式来减轻这些故障。我们将佩戴的启发式与基于电源和温度的启发式进行比较,在同一系统框架内使用的基于电源和温度的启发式。使用广泛的合成和现实世界基准,我们表明我们的磨损启发式能够在基于温度的启发式中平均提高整个系统寿命,平均为7.1%。此外,我们表明,我们的耐磨启发式可用于大大改善系统的第一组件故障(TTFF)的时间。 TTFF是希望避免在组件故障后预期恢复的系统设计和验证困难的设计师感兴趣的指标。我们的耐磨启发式在我们所有基准中的温度为基础上,平均改善了TTFF的平均高度为14.6%。我们的观察导致我们得出结论,必须将磨损的任务映射结合到一个生命周期是主要设计目标的系统中。

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