Temperature-Aware DVFS for Hard Real-Time Applications on Multicore Processors

Hanumaiah, Vinay; Vrudhula, Sarma

doi:10.1109/tc.2011.156

Cited by 61 publications

(47 citation statements)

References 34 publications

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“…Moreover, the blocks are ordered in a way that all the processing components occupy the beginning part of the order list. To predict the temperature evolution, we take the advantage of the well-known electro-thermal analogy [7,10,15,16,17,18,19,20], i.e., the RC thermal network. Every thermal block is mapped onto a node of the thermal circuit.…”

Section: Thermal Modelmentioning

confidence: 99%

See 1 more Smart Citation

Minimizing Peak Temperature for Pipelined Hard Real-time Systems

Cheng

Huang

Chen

et al. 2016

Proceedings of the 2016 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Abstract-This paper addresses the problem of minimizing the peak temperature for pipelined multi-core systems under hard end-to-end deadline constraints by adversely using the PayBurst-Only-Once principle. The Periodic Thermal Management is adopted to control the temperature and every core is periodically switched between two power modes. With the peak temperature representation, we first formulate the problem of finding the thermal optimal periodic schemes which satisfies deadline constraints and then present a fast heuristic algorithm to solve it. Adopting real life processor platforms and applications, our simulation demonstrates that our approach reduces the peak temperature by up to 15 • C on the 4-stage ARM platform compared to sub-deadline partition approach. Moreover, our algorithm is shown to be scalable w.r.t. the number of pipelined stages and its effectiveness is validated by the brutally searching approach.

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Section: Thermal Modelmentioning

confidence: 99%

“…In 'active' and 'sleep' state P d is assumed to be constant, i.e., P a and P i , respectively. The dependency relationship between the leakage power and the temperature can be closely approximated by a linear function of the processor temperature [16,17,18,22,23]:…”

Section: Thermal Modelmentioning

confidence: 99%

Minimizing Peak Temperature for Pipelined Hard Real-time Systems

Cheng

Huang

Chen

et al. 2016

Proceedings of the 2016 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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show abstract

“…[2] proposes a DVFS approach under constraint of task deadline. [3] reduces the global energy dissipation by proposing the DVFS policy under the constraint of the end-to-end delay.…”

Section: Related Workmentioning

confidence: 99%

A Modeling Approach for Energy Saving Based on GA-BP Neural Network

Li¹,

Guo²,

Shen³

et al. 2016

Journal of Electrical Engineering and Technology

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-To cope with the increasing scale of scientific data and computational complexity of daily data, more and more cores have been integrated into GPU(Graphic Processing Units) and its working frequency is continually upgrading, which makes it being widely used in general computing for assisting CPU to accelerate program. While GPU offers powerful computing capability, the problem of the energy consumption becomes particularly prominently and it has become one of the important issues hindering development of GPU. For the purpose of solving this problem, DVFS (Dynamic Voltage Frequency Scaling) becomes an effective solution. Because the previous works only focus on single component and use linear relationship to do DVFS without considering energy saving of other units in system at software runtime, therefore we propose an energy saving model (CDVFS) of considering the characteristics of both GPU and memory at software runtime based on GA-BP (Genetic Algorithm-Back propagation) neural network to make better use of the relationship between components for energy saving. Firstly, the model assumes that functional relation between the software runtime characteristics of GPU and memory and the appropriate frequency which corresponds to the GPU and memory as nonlinear. Secondly, we extract five characteristics and use GA-BP neural network to fit the nonlinear functional relation. At last, experiments demonstrate the effectiveness of the approach and reasonableness of assumption, and also show that CDVFS can get average energy savings of 17.06% compared with previous works within acceptable performance loss.

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“…Recently, DVFS has been utilized to control the temperature of processing units, since the power consumption, performance and lifetime of embedded systems are all highly related to temperature. Hanumaiah et al in [27] presented a method that satisfies both hard real-time and temperature constraints on multi-core processors. Durand et al in [18] analyzed the nonlinearity between power and temperature, and implemented a chopped scheme to limit the temperature increase.…”

Section: Dynamic Voltage and Frequency Scaling In Real-time Embedded mentioning

confidence: 99%