Variation-Tolerant Dynamic Power Management at the System-Level
Variation-Tolerant Dynamic Power Management at the System-Level
复制标题
系统级的容变动态电源管理
DOI：
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复制
发表时间：
2009
影响因子：
2.8
通讯作者：
S. Dey
中科院分区：
文献类型：
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作者：
Saumya Chandra;K. Lahiri;A. Raghunathan;S. Dey
在纳米技术中，系统级芯片（soc）的功率特性受到制造工艺变化的显著影响，因此在系统级功率分析和优化过程中考虑这些影响非常重要。在本文中，我们确定并解决了在存在这种变化的情况下设计有效电源管理方案的问题。特别是，我们证明了传统的电源管理方案，其设计没有考虑变化的影响，可以导致大量的电力浪费。因此，我们提出了两种变化感知电源管理方法，即特定设计和特定芯片方法。在每一种方法中，目标都是在获得电源管理策略参数时考虑变化的影响，以便优化与变化相关的指标。我们激励和引入这些指标，并提出精确和启发式的方法来优化它们。这些方法是在两种电源管理框架的背景下设计和实现的，即基于理想oracle的框架和基于超时的框架。我们使用ARM946处理器内核模型对所提出的想法进行了实验评估。对于基于oracle的框架，与不考虑变化的传统电源管理方案相比，变化感知电源管理可以使mu+sigma的性能提高高达59%，能量分布的第95百分位数的性能提高高达55%。对于基于超时的框架，我们在mu+sigma中获得了高达43%的减少，在能量分布的第99百分位数中获得了高达55%的减少。
The power characteristics of system-on-chips (SoCs) in nanoscale technologies are significantly impacted by manufacturing process variations, making it important to consider these effects during system-level power analysis and optimization. In this paper, we identify and address the problem of designing effective power management schemes in the presence of such variations. In particular, we demonstrate that conventional power management schemes, which are designed without considering the impact of variations, can result in substantial power wastage. We therefore propose two approaches to variation-aware power management, namely, design-specific and chip-specific approaches. In each of these approaches, the goal is to consider the impact of variations while deriving power management policy parameters, in order to optimize metrics that are relevant under variations. We motivate and introduce these metrics, and present both exact and heuristic approaches to optimize them. The methods are designed and implemented in the context of two power management frameworks, namely an ideal oracle-based framework and a timeout-based framework. We experimentally evaluate the proposed ideas using an ARM946 processor core model. For the oracle-based framework, variation-aware power management can result in improvements of upto 59% for mu+sigma , and upto 55% for 95th percentile of the energy distribution, over conventional power management schemes that do not consider variations. For the timeout-based framework, we obtain reductions of upto 43% in mu+sigma and upto 55% in the 99th percentile of the energy distribution.