Title :
Local neurocomputing method for resolving VLSI relocation problems
Author :
Karimi, G.R. ; Verki, A. Azizi
Author_Institution :
Dept. of Electr. Eng., Fac. of Eng., Kermanshah, Iran
Abstract :
In this paper a fast migration method is proposed. Our method executes local relocation on a model placement where an additional module is added to it for modification with minimum number of engaging modules. This method is based on Mean Field Annealing (MFA) which produces a solution as liable as previously used method called simulated annealing (SA) in substantially less time and hardware. In addition, the runtime of solution is mostly independent of size and complexity of input model placement.
Keywords :
VLSI; simulated annealing; VLSI relocation problems; input model placement; local neurocomputing method; mean field annealing; model placement; simulated annealing; Algorithm design and analysis; Circuit simulation; Data mining; Design engineering; Hardware; Modular construction; Partitioning algorithms; Runtime; Simulated annealing; Very large scale integration; Circuit Modification; Mean Field Annealing; VLSI; component; formatting;
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on
Conference_Location :
Taichung
Print_ISBN :
978-1-4244-5045-9
Electronic_ISBN :
978-1-4244-5046-6
DOI :
10.1109/ICIEA.2010.5514997
