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

来源：

2016 IEEE/ACIS 15TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS) | 2016年

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

页码：313 / 318

页数：6

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