Dynamic Voltage-Frequency and Workload Joint Scaling Power Management for Energy Harvesting Multi-Core WSN Node SoC

Li, Xiangyu; Xie, Nijie; Tian, Xinyue

doi:10.3390/s17020310

Cited by 12 publications

(13 citation statements)

References 33 publications

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“…Energy is the major concern in battery operated real time systems. Energy model can be expressed in three different ways such as energy production model and energy consumption model and energy storage model [5]. a.…”

Section: Energy Modelmentioning

confidence: 99%

“…Energy consumption can be reduced using DVS by scaling the voltage level on the basis of energy requirement of the system to execute a task. DVS gives the best performance when the energy requirement of a system is less than the available energy, the supply voltage can dynamically be reduced to the lowest possible requirement of energy to operate the system [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems

Kumar

Alam

2019

IJECE

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<span lang="EN-US">In this paper, a new approach for energy minimization in energy harvesting real time systems has been investigated. Lifetime of a real time systems is depend upon its battery life. Energy is a parameter by which the lifetime of system can be enhanced. To work continuously and successively, energy harvesting is used as a regular source of energy. EDF (Earliest Deadline First) is a traditional real time tasks scheduling algorithm and DVS (Dynamic Voltage Scaling) is used for reducing energy consumption. In this paper, we propose an Energy Harvesting Earliest Deadline First (EH-EDF) scheduling algorithm for increasing lifetime of real time systems using DVS for reducing energy consumption and EDF for tasks scheduling with energy harvesting as regular energy supply. Our experimental results show that the proposed approach perform better to reduce energy consumption and increases the system lifetime as compared with existing approaches. </span>

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Section: Energy Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems

Kumar

Alam

2019

IJECE

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“…Modern RTOS systems address this objective by only waking up the CPU when processing is required, and enter a low-power idle state when all tasks have completed or are waiting. While dynamic voltage frequency scaling (DVFS) and dynamic power management (DPM) are important and topical research issues [2][3][4], our focus in this paper is concerned with small embedded devices that typically can only be either running or idle, and only use one core voltage. It has been also shown that when the TS places a system into idle, the battery current is reduced, enabling certain battery chemistries to recover some charge [5], potentially up to 20%.…”

Section: Introductionmentioning

confidence: 99%

Task Scheduling to Constrain Peak Current Consumption in Wearable Healthcare Sensors

Sherratt

Janko

Hui³

et al. 2019

Electronics

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Small embedded systems, in our case wearable healthcare devices, have significant engineering challenges to reduce their power consumption for longer battery life, while at the same time supporting ever-increasing processing requirements for more intelligent applications. Research has primarily focused on achieving lower power operation through hardware designs and intelligent methods of scheduling software tasks, all with the objective of minimizing the overall consumed electrical power. However, such an approach inevitably creates points in time where software tasks and peripherals coincide to draw large peaks of electrical current, creating short-term electrical stress for the battery and power regulators, and adding to electromagnetic interference emissions. This position paper proposes that the power profile of an embedded device using a real-time operating system (RTOS) will significantly benefit if the task scheduler is modified to be informed of the electrical current profile required for each task. This enables the task scheduler to schedule tasks that require large amounts of current to be spread over time, thus constraining the peak current that the system will draw. We propose a solution to inform the task scheduler of a tasks' power profile, and we discuss our application scenario, which clearly benefited from the proposal.

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“…Due to performance limitations of single-core processors nowadays, the embedded systems generally run on heterogeneous multi-core hardware platforms that include different processing units such as CPU, DSP, FPGA and so on [3]. This structure greatly improves the performance of the embedded system while brings a lot of energy consumption [4], [5], [6], [7]. As high energy consumption is heavily restricting the development of embedded systems, how to reduce the energy consumption of embedded systems has become a non-negligible problem [5].…”

Section: Introductionmentioning

confidence: 99%

Energy-aware Task Scheduling Strategies for Multi-core Embeddded Systems

Liu¹,

Liu²

2018

EasyChair Preprints

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In this paper, we propose two energy-aware scheduling algorithms---(1) Reinforcement learning-based multiprocessor scheduling (RL) algorithm and (2) Mathematical morphology multiprocessor scheduling (MMS) algorithm---for scheduling time-constrained Directed Acyclic Graph (DAG) tasks in an embedded multiprocessor system with Dynamic Voltage And Frequency Scaling (DVFS) and Dynamic Power Management (DPM) technology. Unlike other heuristic scheduling algorithms, the proposed reinforcement learning (RL) is a machine learning algorithm, rarely considered for energy-aware scheduling in DAG tasks. The MMS, inspired by Mathematical morphology that is often used in image processing, continuously adjusts the coded scheduling through a probe matrix to optimize energy consumption. In this paper the genetic algorithm (GA) is compared with these two proposed algorithms by rigorous simulation. The results demonstrate that our algorithms are more energy efficient. Compared with the GA algorithm, the RL and the MMS algorithm significantly improve the energy consumption reduction rate by an average of 13.37% and 72.92% respectively. In addition, MMS algorithm shows better performance in high-density and high-complexity DAG tasks.

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Dynamic Voltage-Frequency and Workload Joint Scaling Power Management for Energy Harvesting Multi-Core WSN Node SoC

Cited by 12 publications

References 33 publications

Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems

Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems

Task Scheduling to Constrain Peak Current Consumption in Wearable Healthcare Sensors

Energy-aware Task Scheduling Strategies for Multi-core Embeddded Systems

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