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:: A Resource Partitioning Approach for Task Allocation in Dynamically Reconfigurable Computing Systems ::
 | Post date: 2019/11/15 | 
 
A Resource Partitioning Approach for Task Allocation in
Dynamically Reconfigurable Computing Systems
  
Seyed Mehdi Mohtavipour
school of Electrical Engineering
Iran University of Science and Technology
Tehran, Iran
Hadi Shahriar Shahhoseini
School of Electrical Engineering
Iran University of Science and Technology
Tehran, Iran
Zahra Asgari
School of Electrical Engineering
Iran University of Science and Technology
Tehran, Iran

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Abstract:
Dynamic reconfiguration feature in recent embedded systems provides flexibility in the execution of applications. Using this feature, several tasks of an application can be mapped simultaneously into a limited area of resources in several Execution Cycles (ECs). The complexity of resource management and efficient scheduling of tasks in such systems require more consideration of the task’s attributes. In this paper, we proposed a new model for execution configuration which satisfies task constraints such as routing and resource wasting to derive multiple configurations for each task. In order to reduce the surface fragmentation and efficient resource utilization, one partition-based scheduling algorithm with a scheduled time metric has been proposed to properly select among possible configurations and partitions. Several experiments with different scenarios such as heavy and light workloads have been conducted and the results show that rejection ratio is reduced 42.10% and 24.14% in compared with two First and Best Fit algorithms, respectively. Some improvements in other evaluation metrics have been obtained, as well.

Keywords: Reconfigurable Systems, Resource Allocation; Resource Management; Partitioning Algorithms


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Cite this paper as:
S.M. phtavipour, H.S. Shahhoseini, and Z. Asgari "A Resource Partitioning Approach for Task Allocation in Dynamically Reconfigurable Computing Systems," 10th International Conference on Information and Knowledge Technology (IKT 2019), Tehran, Iran, 2019.
 
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