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Optimal floor planning in VLSI using improved adaptive particle swarm optimization

  • S. B. Vinay KumarEmail author
  • P. V. Rao
  • Manoj Kumar Singh
Special Issue
  • 24 Downloads

Abstract

Floor planning is necessary to design the VLSI circuit. The complete computational characteristics of the manufactured chip are evaluated by floor planning process. It is the multi-objective problem in which different objectives are fulfilled at a time. Here, a new Interactive Self-Improvement based Adaptive Particle Swarm Optimization (ISI-APSO) technique is proposed to enhance the exploration efficiency and accuracy than convolutional PSO. Within less computation time the proposed ISI-APSO technique attains best global search throughout the space. The simulation results show that the proposed ISI-APSO algorithm achieves better performance than other heuristic algorithms in exploring efficiency and speed of convergence. In order to place the whole modules and their internally connected wire lengths, the Multi-objective optimization method is utilized. Therefore the necessary layout area is minimized. Moreover, the implemented results demonstrate the importance of the proposed algorithm with respect to the robust performance.

Keywords

VLSI Floor planning ISI-APSO Area Wirelength Overlap Convergence speed 

Abbreviations

VLSI

Very-large-scale integration

IC

Integrated circuit

ISI-APSO

Interactive Self-Improvement based Adaptive Particle Optimization

IP

Intellectual property

IO

Input output

NP-hard

Non-deterministic polynomial-time hard

SA

Simulated annealing

ACO

Ant colony optimization

SA

Simulated annealing

DE

Differential evolution

PSOEM

PSO improved Cauchy inertia weight Particle Swarm Optimization

PSONIW

Nonlinear inertia weight variation in Particle Swarm Optimization

PSORIW

Random function inertia weight Particle Swarm Optimization

PSODIW

Dynamic inertia weight Particle Swarm Optimization

PSOEIW

Evolutionary based inertia weight Particle Swarm Optimization

CI

Confidence interval

IIR

Infinite impulse response

AR

Attractor–repeller

GLS

Guided local search

MBS

Moving block sequence

TSVs

Through-silicon vias

AWPSO

Adaptive weight PSO

GA

Genetic algorithm

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • S. B. Vinay Kumar
    • 1
    Email author
  • P. V. Rao
    • 2
  • Manoj Kumar Singh
    • 3
  1. 1.School of Engineering and TechnologyBangaloreIndia
  2. 2.Vignana Bharathi Institute of TechnologyGhatkesar, HyderabadIndia
  3. 3.School of Engineering TechnologyBangaloreIndia

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