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PowerProbe: Run-time power modeling through automatic RTL instrumentation

机译:PowerProbe:通过自动RTL仪器运行时间功率建模

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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:在线电源监控代表了一个事实上的解决方案,以便为当前和未来的计算架构启用能量和能量感知运行时优化。传统上,目标架构的性能计数器用于馈送基于软件的功率模型,该模型被连续更新,以提供所需的运行时间功率估计。该解决方案引入了不可忽略的性能和能量开销。此外,ITIS限于这种性能计数器的可用性,但是,这不是主要用于在线电力监控。本文介绍了PowerProbe,一种从目标架构的RTL描述中自动提取和实现电源模型的运行时功率监控方法。解决方案不利用任何性能计数器以确保广泛的适用性。此外,使用ad-hoc硬件连续更新功率估计最小化性能和电源开销。我们使用完全符合的OpenRisc 1000实现来验证PowerProbe。结果突出显示9 %(标准差小于2 %)内的平均预测误差,电源和面积开销分别限制为6.89 %和4.71 %。

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