Multicore thermal management with model predictive control

Zanini, Francesco; Atienza, David; Benini, Luca; Micheli, Giovanni De

doi:10.1109/ecctd.2009.5275073

Cited by 64 publications

(71 citation statements)

References 16 publications

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“…Researchers have even tried to implement different policies in conjunction with each other as proposed in [33]. Model predictive and optimal control theory have been recently employed for thermal management to achieve smooth control with minimal performance loss [45,50]. In these papers, authors input the workload requirement for each core and then regulate frequencies to meet these requirements while satisfying the thermal constraints, but they use feedback control methods to obtain the frequency values instead of a heuristic approach.…”

Section: Related Workmentioning

confidence: 99%

Predictive Dynamic Thermal and Power Management for Heterogeneous Mobile Platforms

Singla

Kaur

Unver

et al. 2015

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

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Heterogeneous multiprocessor systems-on-chip (MPSoCs) powering mobile platforms integrate multiple asymmetric CPU cores, a GPU, and many specialized processors. When the MPSoC operates close to its peak performance, power dissipation easily increases the temperature, hence adversely impacts reliability. Since using a fan is not a viable solution for hand-held devices, there is a strong need for dynamic thermal and power management (DTPM) algorithms that can regulate temperature with minimal performance impact. This abstract presents a DTPM algorithm based on a practical temperature prediction methodology using system identification. The DTPM algorithm dynamically computes a power budget using the predicted temperature, and controls the types and number of active processors as well as their frequencies. Experiments on an octa-core big.LITTLE processor and common Android apps demonstrate that the proposed technique predicts temperature within 3% accuracy, while the DTPM algorithm provides around 6× reduction in temperature variance, and as large as 16% reduction in total platform power compared to using a fan. Ogras, who not only taught and motivated me to pursue research, but also helped me achieve certain level of confidence and maturity. Without his valuable time, support and guidance, I could not have finished this work. I would also like to thank Dr. Bertan Bakkaloglu and Dr. Ali Unver for taking out time and agreeing to be a part of my thesis defense committee.

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Section: Related Workmentioning

confidence: 99%

Predictive Dynamic Thermal and Power Management for Heterogeneous Mobile Platforms

Singla

Kaur

Unver

et al. 2015

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

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“…In particular, the thermal policy techniques we consider are the linear quadratic regulator [Kang et al 2011;Coskun et al 2008a;Zanini et al 2009b] (i.e., unconstrained MPC with horizon equal to infinity), the explicit/implicit model predictive control-based approach [Wang et al 2009;Zanini et al 2009a] (i.e., traditional MPC), the approximated explicit model predictive control policy [Zanini et al 2010a] (i.e., approximated MPC), and finally, the convex optimization-based approaches [Hanumaiah and Vrudhula 2012;Zanini et al 2010b] (i.e., joint workload and thermal profile prediction). This last technique is solved with a convex solver; however, it is an MPC as well with a linear objective function.…”

Section: Theoretical Analysis Of Thermal Management Policiesmentioning

confidence: 99%

“…Several families of policies have been proposed in the literature for thermal management in MPSoC designs [Zanini et al 2009b[Zanini et al , 2009a[Zanini et al , 2010a[Zanini et al , 2010b. All these policies have been developed and analyzed independently by developing specific problem formulations.…”

Section: Introductionmentioning

confidence: 99%

Online thermal control methods for multiprocessor systems

Zanini

Atienza

Jones

et al. 2013

ACM Trans. Des. Autom. Electron. Syst.

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With technological advances, the number of cores integrated on a chip is increasing. This in turn is leading to thermal constraints and thermal design challenges. Temperature gradients and hotspots not only affect the performance of the system but also lead to unreliable circuit operation and affect the lifetime of the chip. Meeting temperature constraints and reducing hotspots are critical for achieving reliable and efficient operation of complex multi-core systems.In this article, we analyze the use of four of the most promising families of online control techniques for thermal management of multiprocessors system-on-chip (MPSoC). In particular, in our exploration, we aim at achieving an online smooth thermal control action that minimizes the performance loss as well as the computational and hardware overhead of embedding a thermal management system inside the MPSoC. The definition of the optimization problem to tackle in this work considers the thermal profile of the system, its evolution over time, and current time-varying workload requirements. Thus, this problem is formulated as a finite-horizon optimal control problem, and we analyze the control features of different online thermal control approaches. In addition, we implemented the policies on an MPSoC hardware simulation platform and performed experiments on a cycle-accurate model of the eight-core Niagara multi-core architecture using benchmarks ranging from Web-accessing to playing multimedia. Results show different trade-offs among the analyzed techniques regarding the thermal profile, the frequency setting, the power consumption, and the implementation complexity.

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“…Being completely linear, Tempo can be easily integrated in either model predictive control based techniques such as in [32] or multiparametric programming framework as we did in this work with no need for non-linear optimization. Our thermal management technique, TempoMP, considers performance, power and temperature characteristics of each core, which makes it applicable to heterogeneous (and homogeneous) MPSoCs.…”

Section: Introductionmentioning

confidence: 99%

“…In [33], a linear quadratic regulator is used to solve the frequency assignment problem for thermal balancing. To achieve a smooth control and to minimize performance loss and thermal fluctuations in an MPSoC, [32] proposes a technique based on model predictive control. In [10], the problem of scheduling a task graph on a homogeneous MPSoC to minimize the hotspots and balance the temperature distribution is formulated as Integer Linear Programming (ILP) and solved offline.…”

Section: Introductionmentioning

confidence: 99%

TempoMP: Integrated prediction and management of temperature in heterogeneous MPSoCs

Sharifi¹,

Ayoub²,

Rosing³

2012

2012 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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Abstract-Heterogeneous Multi-Processor Systems on a Chip (MPSoCs) are more complex from a thermal perspective compared to the homogeneous MPSoCs because of their inherent imbalance in power density. In this work we develop TempoMP, a new technique for thermal management of heterogeneous MPSoCs which leverages multi-parametric optimization along with our novel thermal predictor, Tempo. TempoMP is able to deliver locally optimal dynamic thermal management decisions to meet thermal constraints while minimizing power and maximizing performance. It leverages our Tempo predictor which, unlike the previous techniques, can estimate the impact of future power state changes at negligible overhead. Our experiments show that compared to the state of the art, Tempo can reduce the maximum prediction error by up to an order of magnitude. Our experiments with heterogeneous MPSoCs also show that TempoMP meets thermal constraints while reducing the average task lateness by 2.5X and energy-lateness product by 5X compared to the state of the art techniques. I. IntroductionHeterogeneous MPSoCs integrate cores of various performance and power characteristics to provide a better tradeoff with respect to performance, power and temperature by allowing customization of performance and power of the chip to match the requirements of the workload. uses ARMA model which has to be updated at run time to avoid inaccuracies due to workload changes, thus leading to overhead. A few techniques have been proposed that do not require online adaptation, such as [19] and [5], but both assume that previous thermal history is a good estimate of future thermal behavior, and thus neglect the impact of future power state changes on temperature. Proactive thermal management algorithms that leverage these predictors are heuristic in nature.A number of thermal management techniques have been proposed that leverage control theory and optimization. In [25], convex optimization is used to control the frequency of the cores on a homogeneous MPSoC to guaranty that thermal constraints are met. In [33], a linear quadratic regulator is used to solve the frequency assignment problem

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Multicore thermal management with model predictive control

Cited by 64 publications

References 16 publications

Predictive Dynamic Thermal and Power Management for Heterogeneous Mobile Platforms

Predictive Dynamic Thermal and Power Management for Heterogeneous Mobile Platforms

Online thermal control methods for multiprocessor systems

TempoMP: Integrated prediction and management of temperature in heterogeneous MPSoCs

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