Dynamic Power Management and Dynamic Voltage Scaling in Real-time CMP Systems

Miao, Lei; Qi, Yong; Hou, Di; Wu, Chang-Li; Dai, Yuehua

doi:10.1109/nas.2007.23

Cited by 2 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various power management techniques for reducing the energy consumption have been investigated in the earlier works. One of them is Dynamic Voltage Frequency Scaling (DVFS) [6] and the other is Dynamic Power Management (DPM) [7]. DVFS is a popular and broadly used power management method in realtime systems.…”

Section: Introductionmentioning

confidence: 99%

Optimal Real-Time Scheduling Algorithm for Wireless Sensors with Regenerative Energy

Ghor

Chetto

2021

The Computer Journal

View full text Add to dashboard Cite

Dynamic voltage and frequency scaling (DVFS) is a promising and broadly used energy-efficient technique to overcome the main problems arising from using a finite energy reservoir capacity and uncertain energy source in real-time embedded systems. This work investigates an energy management scheme for real-time task scheduling in variable voltage processors located in sensor nodes and powered by ambient energy sources. We use DVFS technique to decrease the energy consumption of sensors at the time when the energy sources are limited. In particular, we develop and prove an optimal real-time scheduling framework with speed stretching, namely energy guarantee DVFS (EG-DVFS), that jointly accounts not only for the timing constraints, but also for the energy state incurred by the properties of the system components. EG-DVFS relies on the well-known earliest deadline-harvesting scheduling algorithm combined with DVFS technique where the sensor processing frequency is fine tuned to further minimize energy consumption and to achieve an energy autonomy of the system. Further, an exact feasibility test for a set of periodic, aperiodic or even sporadic tasks is presented.

show abstract

Section: Introductionmentioning

confidence: 99%