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PowerProbe: Run-time power modeling through automatic RTL instrumentation

机译:PowerProbe:通过自动RTL仪器进行运行时功率建模

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摘要

Online power monitoring represents a de-facto solution to enable energyand power-aware run-time optimizations for current and future computing architectures. Traditionally, the performance counters of the target architecture are used to feed a software-based, power model that is continuously updated to deliver the required run-time power estimates. The solution introduces a non-negligible performance and energy overhead. Moreover, itis limited to the availability of such performance counters that, however, are not primarily intended for online power monitoring. This paper introduces PowerProbe, a run-time power monitoring methodology that automatically extracts and implements a power model from the RTL description of the target architecture. The solution does not leverage any performance counter to ensure wide applicability. Moreover, the use of ad-hoc hardware that continuously updates the power estimate minimizes both the performance and the power overheads. We employ a fully compliant OpenRisc 1000 implementation to validate PowerProbe. The results highlight an average prediction error within 9% (standard deviation less than 2%), with a power and area overheads limited to 6.89% and 4.71%, respectively.
机译:在线电源监控代表一种事实上的解决方案,可为当前和未来的计算架构实现能源和功耗感知的运行时优化。传统上,目标体系结构的性能计数器用于提供基于软件的功耗模型,该模型会不断更新以提供所需的运行时功耗估算。该解决方案引入了不可忽略的性能和能源开销。而且,仅限于这种性能计数器的可用性,然而,这些性能计数器并非主要用于在线功率监视。本文介绍PowerProbe,这是一种运行时电源监视方法,可从目标体系结构的RTL描述中自动提取并实现电源模型。该解决方案不利用任何性能计数器来确保广泛的适用性。此外,使用可连续更新功率估计值的临时硬件可最大程度地降低性能和功率开销。我们采用完全兼容的OpenRisc 1000实施方案来验证PowerProbe。结果表明,平均预测误差在9%以内(标准偏差小于2%),功率和面积开销分别限制为6.89%和4.71%。

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