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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Learning-Directed Dynamic Voltage and Frequency Scaling Scheme with Adjustable Performance for Single-Core and Multi-Core Embedded and Mobile Systems
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Learning-Directed Dynamic Voltage and Frequency Scaling Scheme with Adjustable Performance for Single-Core and Multi-Core Embedded and Mobile Systems

机译：具有学习性能的学习型动态电压和频率缩放方案适用于单核和多核嵌入式和移动系统

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摘要

Dynamic voltage and frequency scaling (DVFS) is a well-known method for saving energy consumption. Several DVFS studies have applied learning-based methods to implement the DVFS prediction model instead of complicated mathematical models. This paper proposes a lightweight learning-directed DVFS method that involves using counter propagation networks to sense and classify the task behavior and predict the best voltage/frequency setting for the system. An intelligent adjustment mechanism for performance is also provided to users under various performance requirements. The comparative experimental results of the proposed algorithms and other competitive techniques are evaluated on the NVIDIA JETSON Tegra K1 multicore platform and Intel PXA270 embedded platforms. The results demonstrate that the learning-directed DVFS method can accurately predict the suitable central processing unit (CPU) frequency, given the runtime statistical information of a running program, and achieve an energy savings rate up to 42%. Through this method, users can easily achieve effective energy consumption and performance by specifying the factors of performance loss.
机译：动态电压和频率缩放（DVFS）是一种众所周知的节能方法。若干DVFS研究已应用基于学习的方法来实现DVFS预测模型，而不是复杂的数学模型。本文提出了一种轻量级的面向学习的DVFS方法，该方法涉及使用计数器传播网络来感测和分类任务行为，并预测系统的最佳电压/频率设置。还为用户提供了各种性能要求下的性能智能调整机制。在NVIDIA JETSON Tegra K1多核平台和Intel PXA270嵌入式平台上评估了所提出算法和其他竞争技术的对比实验结果。结果表明，基于学习的DVFS方法可以在给定正在运行的程序的运行时统计信息的情况下准确预测合适的中央处理器（CPU）的频率，并实现高达42％的节能率。通过这种方法，用户可以通过指定性能损失的因素轻松实现有效的能耗和性能。

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