A genetic algorithm based approach to pipelined memory-aware scheduling on an MPSoC

Salamy, Hassan; Aslan, Semih

doi:10.1109/dcas.2015.7356603

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…Ref. [10] a multiprocessor pipelined memory sensing scheduling method based on genetic algorithm, which uses the efficiency of heuristic method to improve the throughput without reducing the application running time. The task reconfiguration on the reconfiguration region is a time-consuming process.…”

Section: Related Workmentioning

confidence: 99%

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Resource Partitioning and Application Scheduling with Module Merging on Dynamically and Partially Reconfigurable FPGAs

et al. 2020

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Dynamically partially reconfigurable (DPR) technology based on FPGA is applied extensively in the field of high-performance computing (HPC) because of its advantages in processing efficiency and power consumption. To make full use of the advantages of DPR in execution efficiency, we build a DPR system model that meets to the actual application requirements and the objective constraints. According to the consistency of reconfiguration order and dependencies, we propose two algorithms based on simulated annealing (SA). The algorithms partition FPGA resource to several regions and schedule tasks to the regions. In order to improve the performance of the algorithms, we exploit the module merging technology to improve the parallelism of task execution and design a new solution generation method to speed up the convergence speed. Experimental results show that the proposed algorithms have a lower time complexity than mixed-integer linear programming (MILP), iterative scheduler (IS) and Ant Colony Optimization (ACO). For applications with more tasks, the proposed algorithms show performance advantages in producing better partitioning and scheduling results in a shorter time.

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Section: Related Workmentioning

confidence: 99%

“…The second part is task number ∈ (10,30]. In this part, the average value of RDNC-SA PIR of all DAGs was −10%, −12%, −38% corresponding to MILP, IS-k and ACO.…”

Section: Performance Analysis Of Different Algorithmsmentioning

confidence: 99%