无片上PMU嵌入式系统的鲁棒精确细粒度功耗模型
Robust and accurate fine-grain power models for embedded systems with no on-chip PMU
摘要：
本文提出了一种新的方法，基于事件的功耗建模的嵌入式平台，没有一个性能监控单元（PMU）。该方法涉及用另一个平台补充测量物理功率数据的目标硬件平台，在该另一个平台上可以收集模型生成所需的CPU性能数据。该方法是用来生成精确的细粒度功耗模型的Gaisler GR712RC双核LEON3容错处理器的板载功率传感器和PMU。使用Kintex UltraScale FPGA作为支持平台，通过在GR712RC上运行双核LEON 3的软核表示，但使用PMU实现，来获取所需的CPU性能数据。两个平台执行相同的基准测试集，并且使用每个样本时间戳同步数据汇集，以便来自GR 712 RC板的功率传感器数据可以与来自FPGA的PMU数据相匹配。同步后的样本将由鲁棒能量和功率预测器选择（REPPS）软件进行处理，以生成功率模型。在工业用例上验证时，这些模型的功耗估计误差小于2%，并且可以成功地跟踪程序阶段，这使得它们适合运行时功耗分析。
英文摘要：
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.
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