Interactive presentation: PowerQuest: trace driven data mining for power optimization
Interactive presentation: PowerQuest: trace driven data mining for power optimization
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交互式演示：PowerQuest：跟踪驱动的数据挖掘以实现功耗优化
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Moshe Y. Vardi
中科院分区：
文献类型：
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作者：
P. Babighian;Gila Kamhi;Moshe Y. Vardi
我们介绍了一个通用的框架，称为PowerQuest，从给定的模拟跟踪数据库中提取“有趣的”动态不变量的主要目标，并将其应用到功率降低问题，通过检测门控条件。PowerQuest采用机器学习技术进行数据挖掘。PowerQuest与其他最先进的动态电源管理（DynamicPowerManagement，简称DPM）技术相比的优势在于：1）选通的ODC条件的质量2）最小化为选通添加的额外逻辑。我们证明了我们的方法在降低功耗的有效性，通过使用ITC99基准和现实生活中的微处理器测试用例的实验结果。我们提出了高达22.7%的功耗降低与其他的MEMS技术相比。
We introduce a general framework, called PowerQuest, with the primary goal of extracting "interesting" dynamic invariants from a given simulation-trace database, and applying it to the power-reduction problem through detection of gating conditions. PowerQuest adopts machine-learning techniques for data mining. The advantages of PowerQuest in comparison with other state-of-the-art Dynamic Power Management (DPM) techniques are: 1) Quality of ODC conditions for gating 2) Minimization of extra logic added for gating. We demonstrate the validity of our approach in reducing power through experimental results using ITC99 benchmarks and real-life microprocessor test cases. We present up to 22.7 % power reduction in comparison with other DPM techniques.