A Neural Networks Based Approach for the Real-Time Scheduling of Reconfigurable Embedded Systems with Minimization of Power Consumption

被引:0
|
作者
Rehaiem, Ghofrane [1 ]
Gharsellaoui, Hamza [2 ]
Ben Ahmed, Samir [1 ]
机构
[1] Tunis El Manar Univ, Fac Math Phys & Nat Sci Tunis FST, Tunis, Tunisia
[2] Carthage Univ, Natl Engn Sch Carthage, Tunis, Tunisia
关键词
Optimization; Neural Networks; Real-Time Scheduling; Low-Power Consumption;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
While most embedded systems are designed for real-time applications, they suffer from resource constraints. Many techniques have been proposed for real-time task scheduling to reduce energy consumption. A combination of Dynamic Voltage Scaling (DVS) and feedback scheduling can be used to scale dynamically the frequency by adjusting the operating voltage, and to improve the run-time reliability of embedded systems. We present in this paper a novel hybrid contribution that handles real-time scheduling of embedded systems and low power consumption based on the combination of DVS and Neural Feedback Scheduling NFS with the priority-energy earliest-deadline-first (PEDF) algorithm.
引用
收藏
页码:313 / 318
页数:6
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