Tabu search with intensification strategy for functional partitioning in hardware-software codesign

Wiangtong, Theerayod; Cheung, Peter Y. K.; Luk, Wayne

doi:10.1109/fpga.2002.1106691

Cited by 32 publications

(78 citation statements)

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“…The framework of our SA implementation for both TCS and RCS is similar to the one reported in [44]. The acceptance of a more costly neighboring solution is determined by applying the Boltzmann probability criteria [45], which depends on the cost difference and the annealing temperature.…”

Section: Algorithmmentioning

confidence: 98%

“…The acceptance of a more costly neighboring solution is determined by applying the Boltzmann probability criteria [45], which depends on the cost difference and the annealing temperature. In our experiments, the most commonly known and used geometric cooling schedule [44] is applied, and the temperature decrement factor is set to 0.9. When it reaches the predefined maximum iteration number or the stop temperature, the best solution found by SA is reported.…”

Section: Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Ant Colony Optimizations for Resource- and Timing-Constrained Operation Scheduling

Wang

Gong

DeRenzi

et al. 2007

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-Operation scheduling (OS) is a fundamental problem in mapping an application to a computational device. It takes a behavioral application specification and produces a schedule to minimize either the completion time or the computing resources required to meet a given deadline. The OS problem is N P-hard; thus, effective heuristic methods are necessary to provide qualitative solutions. We present novel OS algorithms using the ant colony optimization approach for both timing-constrained scheduling (TCS) and resource-constrained scheduling (RCS) problems. The algorithms use a unique hybrid approach by combining the MAX-MIN ant system metaheuristic with traditional scheduling heuristics. We compiled a comprehensive testing benchmark set from real-world applications in order to verify the effectiveness and efficiency of our proposed algorithms. For TCS, our algorithm achieves better results compared with force-directed scheduling on almost all the testing cases with a maximum 19.5% reduction of the number of resources. For RCS, our algorithm outperforms a number of different list-scheduling heuristics with better stability and generates better results with up to 14.7% improvement. Our algorithms outperform the simulated annealing method for both scheduling problems in terms of quality, computing time, and stability.Index Terms-Force-directed scheduling (FDS), list scheduling, operation scheduling (OS), MAX-MIN ant system (MMAS).

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Section: Algorithmmentioning

confidence: 98%

Section: Algorithmmentioning

confidence: 99%

Ant Colony Optimizations for Resource- and Timing-Constrained Operation Scheduling

Wang

Gong

DeRenzi

et al. 2007

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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show abstract

“…The basic operations of GA include initialization, eva luation, se lection, reproduct ion, and tem1ination [4]. Starting from an initiul popu latio n (list scheduling is used), a population is randomly ini tiali zed with tasks assignment using the available hardware resources or sort processors.…”

Section: The Fpga Ne$uurces Assignment Algoritlllll~c Cncticmentioning

confidence: 99%

“…In this example, the Power Qual ity Monitor System (PQMS) [4] is placed and routed into Xilinx XC3S1000 FPGA in )JCA, Vol. 17, No.…”

Section: Fpga Resource Utilization Analysis Case Studi Esmentioning

confidence: 99%

Case study of finite resource optimization in FPGA using genetic algorithm

Wang

Loo

2009

Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation

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Modem Field-Programmable Gate Arrays (FPGAs) are becoming very popular in embedded systems and high perfonnance appl ications.FPGA has benefited from the shrinking of transistor featurc size, which allows more on-chip reconfigurable (e.g., memories and look-up tab les) and routing resourccs available.Unf011unately, the amount of reconfigurable resources in a FPGA is fixed and limited. This paper investigates the mapping scheme of the applications in a FPGA by utilizing sequent ial processing (e.g., Altera Nios II or Xilinx Microblaze, Ilsing C programming language) and task specific hardware (using hardware description language). Genetic Algorithm is used in this study. We found thaI plac ing sequential processor cores into FPGA can improve the resource utilization efficiency and achieve acceptable system perfonnancc.ln this paper, three cases were studied to detennine the trade-off between resource optimization and system performance.

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“…Most of the initial work, [5], [7], focused on the problem of meeting timing constraints with a secondary goal of minimizing the amount of hardware. Subsequently there has been a significant amount of work on optimizing performance under area constraints, [8], [9], [10].…”

Section: Related Workmentioning

confidence: 99%