Heterogeneous Multiprocessor System-on-Chip (MPSoC) are progressively becoming predominant in most modern mobile devices. These devices are required to perform processing of applications within thermal, energy and performance constraints. However, most stock power and thermal management mechanisms either neglect some of these constraints or rely on frequency scaling to achieve energy-efficiency and temperature reduction on the device. Although this inefficient technique can reduce temporal thermal gradient, but at the same time hurts the performance of the executing task. In this paper, we propose a thermal and energy management mechanism which achieves reduction in thermal gradient as well as energy-efficiency through resource mapping and thread-partitioning of applications with online optimization in heterogeneous MPSoCs. The efficacy of the proposed approach is experimentally appraised using different applications from Polybench benchmark suite on Odroid-XU4 developmental platform. Results show 28% performance improvement, 28.32% energy saving and reduced thermal variance of over 76% when compared to the existing approaches. Additionally, the method is able to free more than 90% in memory storage on the MPSoC, which would have been previously utilized to store several task-to-thread mapping configurations.
Heterogeneous multi-processor system-on-chip (MPSoC) smartphones are required to offer increasing performance and user quality-of-experience (QoE), despite comparatively slow advances in battery technology. Approaches to balance instantaneous power consumption, performance and QoE have been reported, but little research has considered how to perform longer-term budgeting of resources across a complete battery discharge cycle. Approaches that have considered this are oblivious to the daily variability in the user’s desired charging time-of-day (plug-in time), resulting in a failure to meet the user’s battery life expectations, or else an unnecessarily over-constrained QoE. This paper proposes QUAREM, an adaptive resource management approach in mobile MPSoC platforms that maximises QoE while meeting battery life expectations. The proposed approach utilises a model that learns and then predicts the dynamics of the energy usage pattern and plug-in times. Unlike state-of-the-art approaches, we maximise the QoE through the adaptive balancing of the battery life and the quality of service (QoS) for the duration of the battery discharge. Our model achieves a good degree of accuracy with a mean absolute percentage error of 3.47 % and 2.48 % for the energy demand and plug-in times respectively. Experimental evaluation on an off-the-shelf commercial smartphone shows that QUAREM achieves the expected battery life of the user within \(20-25\%\) energy demand variation with little or no QoE degradation.
Most modern mobile cyber-physical systems such as smartphones come equipped with multi-processor systems-on-chip (MPSoCs) with variant computing capacity both to cater to performance requirements and reduce power consumption when executing an application. In this paper, we propose a novel approach to dynamic voltage and frequency scaling (DVFS) on CPU, GPU and RAM in a mobile MPSoC, which caters to the performance requirements of the executing application while consuming low power. We evaluate our methodology on a real hardware platform, Odroid XU4, and the experimental results prove the approach to be 26% more power-efficient and 21% more thermal-efficient compared to the state-of-the-art system.
For an improved user experience, the display subsystem is expected to provide superior resolution and optimal brightness despite its impact on battery life. Existing brightness scaling approaches set the display brightness statically or adaptively in response to predefined events such as low-battery or ambient light of the environment, which are independent of the displayed content. Approaches that consider the displayed content are either limited to video content or do not account for the user's expected battery life, thereby failing to maximise the user experience. This paper proposes Content-and ambient Lightingaware Adaptive Brightness Scaling in mobile devices that maximises user experience while meeting battery life expectations. The approach employs a content-and ambient lighting-aware profiler that learns and classifies each sample into predefined clusters at runtime by leveraging insights on user perceptions of content and ambient luminance variations. We maximise user experience through adaptive scaling of the display's brightness using an energy prediction model that determines appropriate brightness levels while meeting expected battery life. The evaluation of the proposed approach on a commercial smartphone improves Quality of Experience (QoE) by up to 24.5 % compared to state-of-art.
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