Title :
A neural network model for circuit partitioning in floorplan design
Author_Institution :
Dept. of Electr. & Comput. Syst. Eng., Monash Univ., Caulfield Campus, Vic., Australia
Abstract :
A floorplan design approach that combines both a neural network model for graph bipartitioning procedure and a slicing tree representation in the physical design of VLSI systems is reported in this paper. The circuit to be floorplanned contains a set of functional modules each having a number of possible dimensions and a net-list containing the connectivity information. The slicing tree representation provides an efficient tree traversal operations using recursion for obtaining area-efficient floorplans. The slicing method also eliminates the cyclical conflicts in module placement and hence ensures better routability
Keywords :
VLSI; circuit layout; circuit layout CAD; neural nets; VLSI systems; area-efficient floorplans; circuit partitioning; connectivity information; floorplan design; graph bipartitioning; module placement; net-list; neural network model; physical design; routability; slicing tree representation; Application software; Artificial neural networks; Computer networks; Design engineering; Integrated circuit interconnections; Integrated circuit modeling; Intelligent networks; Neural networks; Tree graphs; Wires;
Conference_Titel :
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-2559-1
DOI :
10.1109/ICSMC.1995.538015