Model-Free Reinforcement Learning and Bayesian Classification in System-Level Power Management

Wang, Yanzhi; Pedram, Massoud

doi:10.1109/tc.2016.2543219

Cited by 34 publications

(13 citation statements)

References 18 publications

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“…Statically optimized resource and power management are not likely to achieve the best performance when the input characteristics are changing. As a result reinforcement learning has been used for DPM [22][23][24][25][26], DVFS [18-21,52], or combination of DPM, DVFS and mapping [28,53,54] in embedded, desktop and datacenter domains. A detailed classification of existing RL based approaches for power/energy management is given Table 2.…”

Section: Reinforcement Learning For Run-time Managementmentioning

confidence: 99%

“…Learning-based DPM techniques have been proved to be effective in reducing power consumption [22][23][24][25][26]. An on-line learning algorithm in [25] dynamically selects the best DPM policies from a set of candidate policies called experts.…”

Section: Dpmmentioning

confidence: 99%

“…As a solution to the above problems, Wang et al [24] presented an online adaptive DPM technique based on the model-free RL method, which requires no prior knowledge of the state transition probability function and the reward function. Furthermore, this approach can perform policy learning and power management in a continuous-time and event-driven manner to learn a desirable timeout policy (optimal DPM policy when the service requests inter-arrival times are stationary but non-exponentially distributed [74], which derives optimal timeout value using MDP methods).…”

Section: Dpmmentioning

confidence: 99%

“…The optimization of energy during application execution concerns to system level efforts. These efforts have tried to optimize energy by employing three essential ingredients: mapping [15][16][17], dynamic voltage and frequency scaling (DVFS) [18][19][20][21], and dynamic power management (DPM) [22][23][24][25][26]. The mapping defines assignment and ordering of the tasks and their communications onto resources of multi/many-core system in view of some optimization criteria such as compute performance and energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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Learning-Based Run-Time Power and Energy Management of Multi/Many-Core Systems: Current and Future Trends

Singh¹,

Leech²,

Basireddy³

et al. 2017

Journal of Low Power Electronics

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Abstract-Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the ever-increasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges.

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Section: Reinforcement Learning For Run-time Managementmentioning

confidence: 99%

Section: Dpmmentioning

confidence: 99%

Section: Dpmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning-Based Run-Time Power and Energy Management of Multi/Many-Core Systems: Current and Future Trends

Singh¹,

Leech²,

Basireddy³

et al. 2017

Journal of Low Power Electronics

View full text Add to dashboard Cite

show abstract

“…14 Meanwhile, the machine learning classification algorithm has been used in process modeling, 15,16 text classification, 17 image classification, 18 medical diagnosis 19 and pattern recognition. 20 In addition, a variety of classification algorithms have been derived, such as model-free Bayesian classifier (MFBC), 21,22 AdaBoost 23,24 and support vector machine (SVM). 25,26 The MFBC is a new type of Bayesian classifier, which is formed by joint probability estimation and Bayesian theory.…”

Section: Introductionmentioning

confidence: 99%

A pattern recognition modeling approach based on the intelligent ensemble classifier: Application to identification and appraisal of water-flooded layers

Geng

Ding

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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Since the actual chromatogram data of the water-flooded layer have characteristics of multiple dimension, complexity and noise, it is difficult to accurately identify and appraise the water-flooded layer in the oil and gas reservoirs. Therefore, this article proposes a recognition modeling approach based on the intelligent ensemble classifier, integrated model-free Bayesian classifier, the AdaBoost algorithm and the support vector machine algorithm. The effective chromatogram characteristic information can be obtained using the curve fitting method. In order to transform the sparse classification problem into a general classification problem, the synthetic minority over-sampling technique algorithm is used to process an unbalanced training sample as a general training sample. Moreover, the model-free Bayesian classifier, AdaBoost and support vector machine algorithms are used as the base classifiers to train the ensemble classification model. Compared to the traditional single classification approach, the robustness and the effectiveness of the ensemble classifier model are validated through the standard data source from the UCI (University of California at Irvine) repository. Finally, the proposed model is applied in the identification and appraisal of the water-flooded layers in a complex oil and gas recognition system. The chromatogram characteristic information and the prediction results are obtained to provide more reliable water-flooded layer information, guide the process of reservoir exploration and improve the oil development efficiency.

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A fuzzy hierarchical reinforcement learning based scheduling method for semiconductor wafer manufacturing systems

Wang

Gao

Zheng

et al. 2021

Journal of Manufacturing Systems

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Model-Free Reinforcement Learning and Bayesian Classification in System-Level Power Management

Cited by 34 publications

References 18 publications

Learning-Based Run-Time Power and Energy Management of Multi/Many-Core Systems: Current and Future Trends

Learning-Based Run-Time Power and Energy Management of Multi/Many-Core Systems: Current and Future Trends

A pattern recognition modeling approach based on the intelligent ensemble classifier: Application to identification and appraisal of water-flooded layers

A fuzzy hierarchical reinforcement learning based scheduling method for semiconductor wafer manufacturing systems

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