9th International Symposium on Quality Electronic Design (Isqed 2008) 2008
DOI: 10.1109/isqed.2008.4479722
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Improving the Efficiency of Power Management Techniques by Using Bayesian Classification

Abstract: Abstract1 . This paper presents a supervised learning based dynamic

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Cited by 13 publications
(14 citation statements)
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“…As a result, the classification task is essentially the assignment of the maximum a posteriori (MAP) class given the vector x i and the prior of class assignments to y i [31]. An advantage of naive Bayes is that it only requires a small number of training data to estimate the parameters necessary for classification.…”
Section: Naive Bayesmentioning
confidence: 99%
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“…As a result, the classification task is essentially the assignment of the maximum a posteriori (MAP) class given the vector x i and the prior of class assignments to y i [31]. An advantage of naive Bayes is that it only requires a small number of training data to estimate the parameters necessary for classification.…”
Section: Naive Bayesmentioning
confidence: 99%
“…At run-time, it determines which kernels are likely to best utilize a device from a performance model that predicts a kernel's speedup based on its static code structure. Naive Bayes has also been used in power management to build power-performance model and perform classification [31]. In the context of this work, the goal is to devise a power management policy for issuing DVFS commands on a CMP system that minimize the total energy dissipation based on the load conditions and workload characteristics [37].…”
Section: Task Mapping and Parallelismmentioning
confidence: 99%
“…There are also some recent works that consider DPM in multi-core processors, which can be categorized into per-core approach [5,10,11] and chip-wide approach [13,14]. In [10], Canturk et al proposed an approach to set the power mode of each core to meet a power budget.…”
Section: A Related Workmentioning
confidence: 99%
“…In [10], Canturk et al proposed an approach to set the power mode of each core to meet a power budget. Jung et al [5,11] presented a supervised learning-based DPM framework for multi-core processors. Their approach, however, determines power management actions for each core based on their individual workload prediction and hence is not a "true" multi-core power management scheme.…”
Section: A Related Workmentioning
confidence: 99%
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