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A Comparative Study of Different Strategies using adaptive Differential Evolution for Best Scheduling in Architectural Level Synthesis

Affiliations

  • Department of Electrical Engineering Science, BMSCE, Visvesvaraya Technological University, Bangalore - 560019, Karnataka, India
  • Manuro Tech Research Pvt. Ltd, Bangalore - 560097, Karnataka, India

Abstract


This paper is a comparative study for optimal scheduling in architectural level synthesis using five different strategies in Differential Evolution. In this paper the comparison is performed using Hardware Abstraction Layer (HAL) benchmark scheduling problem using Integer Linear Programming method. The paper implements adaptive scaling factor for mutation operation and variable cross over operation in differential evolution. The experiment results evaluate the performance parameters optimal resource schedule, convergence time among the five strategies are presented.

Keywords

Architectural Level Synthesis, Differential Evolution, Evolutionary Computation, Hardware Abstraction Layer, Integer Linear Programming, Very Large Scale Integration.

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References


  • De Micheli G. Synthesis and optimization of digital circuits. USA: McGraw-Hill; 1994.
  • Shilpa KC, Lakshmi Narayana. Natural computation for optimal scheduling with ILP modeling in high level synthesis. Science Direct Procedia Engineering ELSEVIER Journal Publication. 2015; 46:167–75.
  • Storn R, Price K. Differential evolution: A simple and efficient adaptive scheme of global optimization over continuous spaces. Journal of Global Optimization. 1997; 11(4):341–59.
  • Price K. Differential Evolution: A fast and simple numerical optimizer. NAFIPS; 1996. p. 842–4.
  • Price KV. An introduction to Differential Evolution. New Ideas in Optimization. D. Corne, M. Dorigo and F. Glover, editors. UK: McGraw-Hill; 1999. p. 79–108.
  • Price K, Storn RM, Lampinen JA. Differential Evolution: A practical approach to global optimization. Springer-Verlag; 2005. ISBN: 3-540-20950-6.
  • Lee J, Hsu Y, Lin Y. A new Integer Linear Programming formulation for the scheduling problem in data path synthesis. Proceedings of the International Conference on Computer Aided Design; 1989. p. 20–3.
  • Brest J, Greiner S, Boskovic B, Mernik M, Zumer V. Self-adapting control parameters in Differential Evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation. 2006; 10(6):646–57.
  • Teo J. Exploring dynamic self-adaptive populations in Differential Evolution. Soft Computing - A Fusion of Foundations, Methodologies and Applications. 2006; 10(8):673–86.
  • Brest J, Boskovic B, Greiner VZS, Maucec M. Performance comparison of self-adaptive and adaptive Differential Evolution algorithms. Soft Computing - A Fusion of Foundations, Methodologies and Applications. 2007; 11(7):617–29.
  • Brest J, Maucec M. Self-adaptive Differential Evolution algorithm using population size reduction and three strategies. Soft Computing - A Fusion of Foundations, Methodologies and Applications. 2011; 15:2157–74.
  • Price K, Storn R, Lampinen J. Differential Evolution: A practical approach to global optimization. Springer-Verlag; 2005.

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