Energy minimization for dynamic supply voltage scaling using data dependent voltage level selection

L. H. Chandrasena, M. Liebelt
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引用次数: 1

Abstract

In this paper we propose a workload distribution based quantized voltage level selection method called data-dependent level selection (DDLS) for minimizing energy in data-dependent computations using dynamic supply voltage scaling. In previous works, voltage levels have been selected by dividing maximum normalized workload by the number of discrete voltage levels, and translating workloads to voltage levels. The existing techniques place no emphasis on workload characteristics of data in voltage level selection. Our DDLS technique is workload distribution based and therefore selects minimum energy yielding voltage levels for a given data sequence. Our analysis of Akiyo video sequence shows energy savings of up to 55% when compared to existing methods of voltage level selection.
能量最小化的动态电源电压缩放使用数据相关的电压电平选择
在本文中,我们提出了一种基于工作负载分布的量化电压电平选择方法,称为数据相关电平选择(DDLS),用于在使用动态电源电压缩放的数据相关计算中最小化能量。在以前的工作中,电压级别是通过将最大规范化工作负载除以离散电压级别的数量,并将工作负载转换为电压级别来选择的。现有技术在电压电平选择中不重视数据的工作负载特性。我们的DDLS技术是基于工作负载分布的,因此为给定的数据序列选择最小能量产生电压水平。我们对Akiyo视频序列的分析表明,与现有的电压电平选择方法相比,节能高达55%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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