Title: New optimal solutions for real-time scheduling of reconfigurable embedded systems based on neural networks with minimisation of power consumption
Authors: Rehaiem Ghofrane; Gharsellaoui Hamza; Ben Ahmed Samir
Addresses: LISI-INSAT Laboratory, INSAT Institute, Carthage University, Tunisia ' University College – Khurma, Taif University, Saudi Arabia ' Faculty of Mathematical, Physical and Natural Sciences of Tunis (FST), Tunis El Manar University, Tunisia
Abstract: Due to increasing energy requirements and associated environmental impacts, nowadays most embedded systems suffer from resource constraints as they are designed for applications that run in real-time. Many techniques have been proposed for both the planning of tasks and reducing energy consumption. In fact, a combination of dynamic voltage scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage. Indeed, we present in this paper a new hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and neural feedback planning (NFP) with the energy priority earlier deadline first (PEDF) algorithm. The preliminary experiments to compare the reconfigurable resulting from conventional methods are presented. The results are then discussed.
Keywords: optimisation; neural networks; real-time scheduling; low-power consumption; embedded systems; reconfigurable systems; power minimisation; intelligent engineering.
International Journal of Intelligent Engineering Informatics, 2018 Vol.6 No.6, pp.569 - 585
Received: 26 Apr 2018
Accepted: 05 Sep 2018
Published online: 28 Nov 2018 *