An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable Systems
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
|
| Series: | International Journal of Reconfigurable Computing |
| Online Access: | http://dx.doi.org/10.1155/2016/9012909 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849684779904008192 |
|---|---|
| author | A. Al-Wattar S. Areibi G. Grewal |
| author_facet | A. Al-Wattar S. Areibi G. Grewal |
| author_sort | A. Al-Wattar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ba981d7ec00b4c96929f6e9040a6fd5b |
| institution | DOAJ |
| issn | 1687-7195 1687-7209 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Reconfigurable Computing |
| spelling | doaj-art-ba981d7ec00b4c96929f6e9040a6fd5b2025-08-20T03:23:22ZengWileyInternational Journal of Reconfigurable Computing1687-71951687-72092016-01-01201610.1155/2016/90129099012909An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable SystemsA. Al-Wattar0S. Areibi1G. Grewal2School of Engineering and Computer Science, University of Guelph, Guelph, ON, N1G 2W1, CanadaSchool of Engineering and Computer Science, University of Guelph, Guelph, ON, N1G 2W1, CanadaSchool of Engineering and Computer Science, University of Guelph, Guelph, ON, N1G 2W1, CanadaSeveral 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.http://dx.doi.org/10.1155/2016/9012909 |
| spellingShingle | A. Al-Wattar S. Areibi G. Grewal An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable Systems International Journal of Reconfigurable Computing |
| title | An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable Systems |
| title_full | An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable Systems |
| title_fullStr | An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable Systems |
| title_full_unstemmed | An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable Systems |
| title_short | An Efficient Evolutionary Task Scheduling/Binding Framework for Reconfigurable Systems |
| title_sort | efficient evolutionary task scheduling binding framework for reconfigurable systems |
| url | http://dx.doi.org/10.1155/2016/9012909 |
| work_keys_str_mv | AT aalwattar anefficientevolutionarytaskschedulingbindingframeworkforreconfigurablesystems AT sareibi anefficientevolutionarytaskschedulingbindingframeworkforreconfigurablesystems AT ggrewal anefficientevolutionarytaskschedulingbindingframeworkforreconfigurablesystems AT aalwattar efficientevolutionarytaskschedulingbindingframeworkforreconfigurablesystems AT sareibi efficientevolutionarytaskschedulingbindingframeworkforreconfigurablesystems AT ggrewal efficientevolutionarytaskschedulingbindingframeworkforreconfigurablesystems |