Title :
A stochastic neural network approach for circuit partitioning
Author :
Ball, Carsten F. ; Mlynski, Dieter A.
Author_Institution :
Inst. fur Theoret. Elektrotech. und Messtech., Karlsruhe Univ., Germany
Abstract :
We propose a stochastic neural network for solving combinatorial optimization problems, that can find global minima in theory. The network dynamic is based on integration of Langevin equation of neurons motion. The continuous network is applied to bipartitioning circuits represented by their hypergraphs. For this a new fuzzy net-cut model is used treating hypergraphs without splitting multi-pin-nets into two-pin-nets. The neural network has been tested with an industrial example and results will be given
Keywords :
circuit optimisation; graph theory; neural nets; stochastic processes; Langevin equation; circuit partitioning; combinatorial optimization; dynamics; fuzzy net-cut model; global minima; hypergraph; multi-pin-net; stochastic neural network; Circuit testing; Diffusion processes; Equations; Hopfield neural networks; Integrated circuit interconnections; Jacobian matrices; Neural networks; Neurons; Probability distribution; Stochastic processes;
Conference_Titel :
Circuits and Systems, 1996. ISCAS '96., Connecting the World., 1996 IEEE International Symposium on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3073-0
DOI :
10.1109/ISCAS.1996.542117
