Contents provider-assisted dynamic voltage scaling for low energy multimedia applications

2002 ◽  
Author(s):  
Eui-Young Chung ◽  
Giovanni De Micheli ◽  
Luca Benini
Keyword(s):  
Voltage Scaling ◽  
Dynamic Voltage Scaling ◽  
Multimedia Applications ◽  
Low Energy ◽  
Dynamic Voltage

Application-Support Particle Filter for Dynamic Voltage Scaling of Multimedia Applications

2012 ◽  
Vol 61 (9) ◽  
pp. 1256-1269 ◽  
Author(s):  
Jae-Beom Lee ◽  
Myoung-Jin Kim ◽  
Sungroh Yoon ◽  
Eui-Young Chung
Keyword(s):  
Particle Filter ◽  
Voltage Scaling ◽  
Dynamic Voltage Scaling ◽  
Multimedia Applications ◽  
Support Particle ◽  
Dynamic Voltage

Low-energy GALS NoC with FIFO—Monitoring dynamic voltage scaling

2011 ◽  
Vol 42 (6) ◽  
pp. 889-896 ◽  
Author(s):  
Abbas Rahimi ◽  
Mostafa E. Salehi ◽  
Siamak Mohammadi ◽  
Sied Mehdi Fakhraie
Keyword(s):  
Voltage Scaling ◽  
Dynamic Voltage Scaling ◽  
Low Energy ◽  
Dynamic Voltage

ENERGY-CONSTRAINED VDD HOPPING SCHEME WITH RUN-TIME POWER ESTIMATION FOR LOW-POWER REAL-TIME VLSI SYSTEMS

2002 ◽  
Vol 11 (06) ◽  
pp. 601-620 ◽  
Author(s):  
SEONGSOO LEE ◽  
SEUNGJUN LEE ◽  
TAKAYASU SAKURAI
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Supply Voltage ◽  
Voltage Scaling ◽  
Dynamic Voltage Scaling ◽  
Multimedia Applications ◽  
Variable Voltage ◽  
Dynamic Voltage ◽  
Energy Constrained ◽  
Variable Voltage Processor

In this paper, we propose a novel dynamic voltage scaling algorithm on a variable-voltage processor. It determines the supply voltage on timeslot-by-timeslot basis within the task boundary, and significantly reduces the power consumption by fully exploiting the slack time. Also, we modify this algorithm and propose an energy-constrained dynamic voltage scaling algorithm for low-power multimedia applications. In the multimedia applications, there are usually several alternative algorithms with different performance and power. Considering the trade-off between performance and power, the proposed algorithm adaptively determines the optimal alternative to achieve optimal performance under given energy constraint. Compared with the conventional algorithms, the power consumption is reduced to 1/14.4 ~ 1/5.6 without performance degradation.

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