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International Journal of Reconfigurable Computing
Volume 2016, Article ID 9012909, 24 pages
http://dx.doi.org/10.1155/2016/9012909
Research Article

An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable Systems

School of Engineering and Computer Science, University of Guelph, Guelph, ON, Canada N1G 2W1

Received 13 September 2015; Revised 26 November 2015; Accepted 16 December 2015

Academic Editor: Nadia Nedjah

Copyright © 2016 A. Al-Wattar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Several embedded application domains for reconfigurable systems tend to combine frequent changes with high performance demands of their workloads such as image processing, wearable computing, and network processors. Time multiplexing of reconfigurable hardware resources raises a number of new issues, ranging from run-time systems to complex programming models that usually form a reconfigurable operating system (ROS). In this paper, an efficient ROS framework that aids the designer from the early design stages all the way to the actual hardware implementation is proposed and implemented. An efficient reconfigurable platform is implemented along with novel placement/scheduling algorithms. The proposed algorithms tend to reuse hardware tasks to reduce reconfiguration overhead, migrate tasks between software and hardware to efficiently utilize resources, and reduce computation time. A supporting framework for efficient mapping of execution units to task graphs in a run-time reconfigurable system is also designed. The framework utilizes an Island Based Genetic Algorithm flow that optimizes several objectives including performance, area, and power consumption. The proposed Island Based GA framework achieves on average 55.2% improvement over a single-GA implementation and an 80.7% improvement over a baseline random allocation and binding approach.