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Adaptive Particle Swarm Optimization Based Wire-length Minimization for Placement in FPGA

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • P. Sudhanya

    (MIT Campus, Anna University, Department of Electronics Engineering)

  • S. P. Joy Vasantha Rani

    (MIT Campus, Anna University, Department of Electronics Engineering)

Abstract

Placement is a critical step in FPGA physical design. Placement determines the locations of the logic and I/O blocks on the FPGA Proper placement reduces the wire-length and routing time and in turn, increases the overall efficiency of the FPGA. Here a modified adaptive inertia weight Particle Swarm Optimization (PSO) algorithm is applied for placement problem in FPGA. The convergence behavior of the modified adaptive inertia weight PSO algorithm is analyzed and fast convergence is observed. The algorithm is implemented in the VPR tool and the performance is evaluated based on the wire-length and compared with that of VPR placement algorithm using the MCNC benchmark circuits. The modified adaptive inertia weight PSO algorithm gives more optimized results for FPGA placement problem.

Suggested Citation

  • P. Sudhanya & S. P. Joy Vasantha Rani, 2020. "Adaptive Particle Swarm Optimization Based Wire-length Minimization for Placement in FPGA," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 793-801, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_79
    DOI: 10.1007/978-3-030-41862-5_79
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