Dynamic Voltage-Frequency Optimization using Simultaneous Perturbation Stochastic Approximation

Bogdanov, Evgenii; Bozhnyuk, Alexander; Bykov, D. N.; Sartasov, Stanislav; Sergeenko, Anna; Granichin, Oleg

doi:10.1109/cdc45484.2021.9683064

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The exception is the Bluetooth module in bluetooth.on mode, as on the device under test the ADB always gave a zero power consumption value. Note that mentioned ADB commands provide only the data for Wi-Fi and Bluetooth components, so, for example, CPU power readings are deliberately excluded, as their contribution to total energy consumption might be considerable [Bogdanov et al, 2021].…”

Section: Example Of Calculationmentioning

confidence: 99%

Frequency-independent smartphone peripherals energy consumption estimation

Kuznetsov

Miroshnikov

Sartasov

2023

CAP

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The power consumption of mobile devices is a hot topic these days, and it is important to address it when developing applications. One of the most popular ways to measure it is accessing internal sensors using Android Debug Bridge (ADB). We discovered that measurement frequency may skew the power readings. Based on this approach we propose our own algorithm for calculating smartphone energy consumption constants — the power in milliamperes at nominal voltage for different peripherals states. Our algorithm takes measurement frequency bias into account, and its results are compared with the method previously published in literature as well as the baseline data from power profile. We conclude that the developed approach provides better estimation.

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Section: Example Of Calculationmentioning

confidence: 99%

Frequency-independent smartphone peripherals energy consumption estimation

Kuznetsov

Miroshnikov

Sartasov

2023

CAP

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On Stochastic Optimization for Smartphone CPU Energy Consumption Decrease

Pelogeiko,

Sartasov,

Granichin

2023

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Extending smartphone working time is an ongoing endeavour becoming more and more important with each passing year. It could be achieved by more advanced hardware or by introducing energy-aware practices to software, and the latter is a more accessible approach. As the CPU is one of the most power-hungry smartphone devices, Dynamic Voltage Frequency Scaling (DVFS) is a technique to adjust CPU frequency to the current computational needs, and different algorithms were already developed, both energy-aware and energy-agnostic kinds. Following our previous work on the subject, we propose a novel DVFS approach to use simultaneous perturbation stochastic approximation (SPSA) with two noisy observations for tracking the optimal frequency and implementing several algorithms based on it. Moreover, we also address an issue of hardware lag between a signal for the CPU to change frequency and its actual update. As Android OS could use a default task scheduler or an energy-aware one, which is capable of taking advantage of heterogeneous mobile CPU architectures such as ARM big.LITTLE, we also explore an integration scheme between the proposed algorithms and OS schedulers. A model-based testing methodology to compare the developed algorithms against existing ones is presented, and a test suite reflecting real-world use case scenarios is outlined. Our experiments show that the SPSA-based algorithm works well with EAS with a simplified integration scheme, showing CPU performance comparable to other energy-aware DVFS algorithms and a decreased energy consumption.

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Dynamic Voltage-Frequency Optimization using Simultaneous Perturbation Stochastic Approximation

Cited by 2 publications

References 11 publications

Frequency-independent smartphone peripherals energy consumption estimation

Frequency-independent smartphone peripherals energy consumption estimation

On Stochastic Optimization for Smartphone CPU Energy Consumption Decrease

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