Supervised Learning Based Power Management for Multicore Processors

Jung, Hwisung; Pedram, Massoud

doi:10.1109/tcad.2010.2059270

Cited by 109 publications

(53 citation statements)

References 36 publications

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“…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]. The motivation for utilizing a Bayesian classifier is to reduce the overhead of the power management activities which are performed regularly to determine and assign DVFS settings for each processor core in the system.…”

Section: Task Mapping and Parallelismmentioning

confidence: 99%

See 1 more Smart Citation

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: Task Mapping and Parallelismmentioning

confidence: 99%

“…A Bayesian classification approach to DVFS setting is used by Jung et al [31,37] in the prediction of power and performance. The predicted state is used to look up the optimal power management action from a pre-computed policy lookup table.…”

Section: Dvfsmentioning

confidence: 99%

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

“…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%

Learning-Based Power Management for Multicore Processors via Idle Period Manipulation

2014

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…A workload characteristics aware thread scheduler is proposed in [6] based on dynamic workload characterization. In [7], a supervised learning in the form of a Bayesian classifier for energy management is proposed. This framework learns to predict the system performance from the occupancy state of the global service queue.…”

Section: Introductionmentioning

confidence: 99%

Workload Uncertainty Characterization and Adaptive Frequency Scaling for Energy Minimization of Embedded Systems

Das¹,

Kumar²,

Veeravalli³

et al. 2015

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2015

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Abstract-A primary design optimization objective for multicore embedded systems is to minimize the energy consumption of applications while satisfying their performance requirement. A system-level approach to this problem is to scale the frequency of the processing cores based on the readings obtained from the hardware performance monitors. However, performance monitor readings contain uncertainty, which becomes prominent when applications are executed in a multicore environment. This uncertainty can be attributed to factors such as cache contention and DRAM access time, that are very difficult to predict dynamically. We demonstrate that such uncertainty can be controlled to make better decision on the processor frequency in order to minimize energy consumption. To achieve this, we propose a multinomial logistic regression model, which combines probabilistic interpretation with maximum likelihood (ML) estimation to classify an incoming workload, at run-time, into a finite set of classes. Every workload class corresponds to a frequency pre-determined using an appropriate training set and results in minimum energy consumption. The classifier incorporates (1) uncertainty with arbitrary probability distribution to estimate the actual frame workload; and (2) the frequency switching overhead, neither of which are considered in any of the existing approaches. The classified frequency is applied on the processing cores to execute the workload. The proposed approach is engineered into an embedded multicore system and is validated with a set of standard multimedia applications. Results demonstrate that the proposed approach minimizes energy consumption by an average 20% as compared to the existing techniques.

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Supervised Learning Based Power Management for Multicore Processors

Cited by 109 publications

References 36 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

Learning-Based Power Management for Multicore Processors via Idle Period Manipulation

Workload Uncertainty Characterization and Adaptive Frequency Scaling for Energy Minimization of Embedded Systems

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