基于几何规划的最小能量实时任务动态供电电压电平生成

H. Manohara, B. Harish
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引用次数: 2

摘要

通信和计算正在向移动平台转移,以满足新兴应用程序的需求。尽管工艺和电池技术的进步使处理器能够提供更大的单位能量计算和更长的电池寿命,但性能和电池寿命之间的基本权衡仍然至关重要。为了最大限度地提高移动电子处理器的能源效率,动态电压缩放(DVS)通常用于动态改变电源电压,从而在运行时改变速度。CPU速度和功耗之间的非线性关系可以通过降低电压来利用可用的空闲性,从而在时间域中扩展任务执行,而不是在短时间内全速运行CPU,然后切换到空闲状态。在单处理器环境下,以任务利用率为控制变量,为任务集中的每个任务产生最优的供电电压,使实时周期任务集的每个任务的能耗最小。通过在固定任务集和随机任务集实例上在一定范围内改变频率,从而产生电源电压水平,使用几何规划(GP)实现任务的能量最小化。结果表明,根据任务集的电源延迟特性,对于标准任务集,节能幅度在18%到34%之间,对于随机生成的任务集,节能幅度平均为77%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Supply Voltage Level Generation for Minimum Energy Real Time Tasks using Geometric Programming
Communication and computation are moving towards mobile platforms to address the demands of emerging applications. Despite advances in process and battery technologies that allow processors to provide much greater computation per unit of energy and longer life of battery, the fundamental tradeoff between performance and battery life continues to remain critical. To maximize energy efficiency of processors in mobile electronics, Dynamic Voltage Scaling (DVS) is conventionally deployed to dynamically vary supply voltage and hence speed, at run time. The nonlinear relationship between CPU speed and power consumption enables spread out of task execution in time domain by leveraging on the available slackness by reducing voltage, than to run the CPU at full speed for short bursts and then switch to idle state. The proposed work aims to minimize the energy consumption of each task of real time periodic task sets, in a uniprocessor environment, using task utilization factor as a control variable for generating the optimized supply voltage to every task of task sets. The energy minimization of a task is implemented using Geometric Programming (GP), by varying frequency over a range on fixed task sets and on randomly varying task set instances and hence generating supply voltage levels. Results demonstrate that energy savings vary between 18% to 34%, for standard task sets and an average of 77% for randomly generated task sets, depending on the power delay characteristics of task sets.
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