This paper presents a novel approach to event-based power modelling for embedded platforms that do not have a Performance Monitoring Unit (PMU). The method involves complementing the target hardware platform, where the physical power data is measured, with another platform on which the CPU performance data, that is needed for model generation, can be collected. The methodology is used to generate accurate fine-grain power models for the the Gaisler GR712RC dual-core LEON3 fault-tolerant SPARC processor with on-board power sensors and no PMU. A Kintex UltraScale FPGA is used as the support platform to obtain the required CPU performance data, by running a soft-core representation of the dual-core LEON3 as on the GR712RC but with a PMU implementation. Both platforms execute the same benchmark set and data collection is synchronised using per-sample timestamps so that the power sensor data from the GR712RC board can be matched to the PMU data from the FPGA. The synchronised samples are then processed by the Robust Energy and Power Predictor Selection (REPPS) software in order to generate power models. The models achieve less than 2% power estimation error when validated on an industrial use-case and can successfully follow program phases, which makes them suitable for runtime power profiling.

翻译：本文介绍了对没有绩效监测股(PMU)的嵌入平台进行基于事件的权力建模的新做法。该方法包括补充目标硬件平台,对物理力数据进行测量,并辅之以另一个平台,据以收集模型生成所需的CPU性能数据。该方法用于为Gaisler GR712RC双芯LEON3 防故障 SPARC处理器生成精确的精细重力模型,该处理器带有机载动力传感器,没有PMU。Kintex UltraS UltraS UPGA是用作获取所需的CPU性能数据的支持平台,其方法是运行一个软核心代表GR712RC的双核心LON3,但有一个PMU。两个平台使用相同的基准集和数据收集同步,使用每模版LOPER712RC委员会的电感应感测器数据与PMU数据匹配。随后由Robust Ener和动力预测器预测器选择的同步样本进行软性电压模型,在运行2号周期模型时,可成功生成电压模型。