Packing-based VLSI module placement using genetic algorithm with sequence-pair representation

2006 ◽  
Vol 153 (6) ◽  
pp. 545 ◽  
Author(s):  
A. Drakidis ◽  
R.J. Mack ◽  
R.E. Massara
Keyword(s):  
Genetic Algorithm ◽  
Sequence Pair ◽  
Pair Representation

Multi-objective voltage island floorplanning using sequence pair representation

2012 ◽  
Vol 2 (2) ◽  
pp. 58-70
Author(s):  
Dipanjan Sengupta ◽  
Andreas Veneris ◽  
Steve Wilton ◽  
Andre Ivanov
Keyword(s):  
Sequence Pair ◽  
Multi Objective ◽  
Pair Representation

A Binary Particle Swarm Optimization for IC Floorplanning

2020 ◽  
Vol 13 (1) ◽  
pp. 13-21
Author(s):  
Rajendra Bahadur Singh ◽  
Anurag Singh Baghel ◽  
Arun Solanki
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Second Phase ◽  
Wire Length ◽  
Sequence Pair ◽  
Swarm Optimization ◽  
Pair Representation

Background: In the field of IC physical design, there is a big problem in the IC floorplanning to find the early feedback to estimate the area, wire length, delay, etc. before IC fabrication. Objective: In this paper, minimization of the area and total wire length on the IC has been done using Binary Particle Swarm Optimization with sequence pair representation. Methods: Optimization of the IC floorplan works in two phases. In the first phase, the floorplan is constructed by sequence pair representation without any overlapping of the modules on IC floorplan. In the second phase, Binary Particle Swarm Optimization algorithm explores the packing of all modules in floorplan to find better optimal performances i.e. area and wire length. Results: The results obtained were compared with the solutions derived from other meta-heuristic algorithms, the area is improved maximum up to 10% and the wire length was improved maximum up to 28%. Conclusion: The Experimental results on Microelectronic Center of North Carolina benchmark circuits show that Binary Particle Swarm Optimization algorithm gives better convergence for the area and wire length optimization than other algorithms.

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