Modeling and simulation of power consumption and execution times for real-time tasks on embedded heterogeneous architectures

Balsini, Alessio; Pannocchi, Luigi; Cucinotta, Tommaso

doi:10.1145/3373400.3373408

Cited by 10 publications

(16 citation statements)

References 22 publications

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“…Zhang et al [21] built a linear regression model based on data collected from a CPU, where they utilized the idle states and idle time of each core. In addition, Balsini et al [1] deploy a genetic algorithm to find the optimal parameters for a function that represents the theoretical relationship between power dissipation and quantities such as the core voltage and clock frequency.…”

Section: System Identification and Selection Of Methodsmentioning

confidence: 99%

“…This also makes these approaches less appealing when the objective of this work is to perform modeling based only on measurable processor state variables, like frequency and processor utilization. However, some previous works exploit the theoretical relationship between frequency, voltage and utilization to estimate the power dissipation of a processor [18] [1]. Therefore, combining such an approach with a linear model identification technique, such as the N4SID method suggested in [3], was selected as an approach to be evaluated in this work.…”

Section: System Identification and Selection Of Methodsmentioning

confidence: 99%

“…regressor set is shown in (1), where f is core frequency, u is core utilization and i indicates the number of cores.…”

Section: Polynomial N4sidmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs

Öhrling¹,

Lafond²,

Truşcan³

2021

Preprint

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In this paper we evaluate the use of system identification methods to build a thermal prediction model of heterogeneous SoC platforms that can be used to quickly predict the temperature of different configurations without the need of hardware. Specifically, we focus on modeling approaches that can predict the temperature based on the clock frequency and the utilization percentage of each core. We investigate three methods with respect to their prediction accuracy: a linear state-space identification approach using polynomial regressors, a NARX neural network approach and a recurrent neural network approach configured in an FIR model structure. We evaluate the methods on an Odroid-XU4 board featuring an Exynos 5422 SoC. The results show that the model based on polynomial regressors significantly outperformed the other two models when trained with 1 hour and 6 hours of data.

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Section: System Identification and Selection Of Methodsmentioning

confidence: 99%

Section: System Identification and Selection Of Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs

Öhrling¹,

Lafond²,

Truşcan³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Unlike, Walker et al, however, they utilize the idle states and idle time of each core. Another similar approach has been suggested by Balsini et al [21]. The latter approach deploys a genetic algorithm to find the optimal parameters for a function that represents the theoretical relationship between power dissipation and quantities such as the core voltage and clock frequency.…”

Section: Related Workmentioning

confidence: 99%

Enabling Fast Exploration and Validation of Thermal Dissipation Requirements for Heterogeneous SoCs

Öhrling¹,

Truşcan²,

Lafond³

2021

Preprint

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The management of the energy consumption and thermal dissipation of multi-core heterogeneous platforms is becoming increasingly important as it can have direct impact on the platform performance. This paper discusses an approach that enables fast exploration and validation of heterogeneous system on chips (SoCs) platform configurations with respect to their thermal dissipation. Such platforms can be configured to find the optimal trade-off between performance and power consumption. This directly reflects in the head dissipation of the platform, which when increases over a given threshold will actually decrease the performance of the platform. Therefore, it is important to be able to quickly probe and explore different configurations and identify the most suitable one. However, this task is hindered by the large space of possible configurations of such platforms and by the time required to benchmark each configurations. As such, we propose an approach in which we construct a model of the thermal dissipation of a given platform using a system identification methods and then we use this model to explore and validate different configurations. The approach allows us to decrease the exploration time with several orders of magnitude. We exemplify the approach on an Odroid-XU4 board featuring an Exynos 5422 SoC.

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“…Finally, a further extension idea might be the one to consider, as done in our recent preliminary investigation [55], that the power consumption of a modern complex CPU while processing depends in a non-negligible way on the workload type that is being run. So, this may add one further dimension and further complexity to the already challenging problem of scheduling real-time tasks (along with non-real time ones) on multi-core, DVFS-capable heterogeneous mobile platforms, in an energy-ecient way.…”

Section: Future Workmentioning

confidence: 99%

Energy-efficient low-latency audio on android

Balsini

Cucinotta

Abeni

et al. 2019

Journal of Systems and Software

Self Cite

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Counting more than two billion devices, Android is nowadays one of the most popular open-source general-purpose operating systems, based on Linux. Because of the diversity of applications that can be installed, it manages a number of dierent workloads, many of them requiring performance/QoS guarantees. When running audio processing applications, the user would like an uninterrupted, glitch-free, output stream that reacts to the user input, typically with a delay not bigger than 4 − 10 ms, while keeping the energy consumption of the mobile device as low as possible. This work focuses on improvements to the real-time audio processing performance on Android that preserves energy-eciency. Such improvements are achieved by using a deadline based scheduler and an adaptive scheduling strategy that dynamically and proactively modulates the allocated runtime. The proposed strategy is evaluated through an extensive experimentation, showing that 1) compared to the existing way to ensure low-latency audio processing, the proposed mechanism provides an energy saving of almost 40%, and 2) compared to the existing way to achieve a good balance between power consumption and latency in a glitch-free audio processing experience, the proposed solution reduces audio latency from 26.67 ms to 2.67 ms, at the expense of a limited

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Modeling and simulation of power consumption and execution times for real-time tasks on embedded heterogeneous architectures

Cited by 10 publications

References 22 publications

Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs

Evaluating System Identification Methods for Predicting Thermal Dissipation of Heterogeneous SoCs

Enabling Fast Exploration and Validation of Thermal Dissipation Requirements for Heterogeneous SoCs

Energy-efficient low-latency audio on android

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