Title :
Quadrisectioning based placement with a normalized mean field neural network
Author :
Unaltuna, M. Kemal ; Pitchumani, Vijay
Author_Institution :
Dept. of Electr. & Comput. Eng., Syracuse Univ., NY, USA
Abstract :
A quadrisectioning based neural network algorithm for the placement problem in VLSI layout synthesis is presented. The mean field theory neural network with graded neurons proposed by Peterson and Soderberg is used. It is renamed normalized mean field net. The problem is solved by recursive quadrisectioning where, at each step, all neurons in the network evolve simultaneously, maintaining a level of globality. In the authors´ simulations, the network is able to find optimal solutions to all hand constructed test problems with up to 256 modules
Keywords :
VLSI; circuit layout CAD; integrated circuit layout; network routing; neural nets; recursive functions; VLSI layout synthesis; globality; graded neurons; hand constructed test problems; normalized mean field neural network; placement problem; quadrisectioning based neural network algorithm; recursive quadrisectioning; Encoding; Equations; Hopfield neural networks; Network synthesis; Neural networks; Neurons; Optimization methods; Simulated annealing; Testing; Very large scale integration;
Conference_Titel :
Circuits and Systems, 1993., ISCAS '93, 1993 IEEE International Symposium on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-1281-3
DOI :
10.1109/ISCAS.1993.394158
