A method for data path synthesis using neural networks

Author

Harmanani, Haidar

Author_Institution

Dept. of Comput. Eng. & Sci., Lebanese American Univ., Beirut, Lebanon

Volume

1

fYear

1999

fDate

9-12 May 1999

Firstpage

456

Abstract

Presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. The method is based on the modified Hopfield neural network model of computation and the McCulloch-Pitts binary neuron model. The proposed algorithm has a running time complexity of O(1) for a neural network with n vertices and c cliques. A sequential simulator was implemented for the proposed algorithm on a Linux Pentium PC under X Windows. Several circuits hare been attempted, all yielding sub-optimal solutions.

Keywords

Hopfield neural nets; computational complexity; high level synthesis; logic simulation; microcomputer applications; parallel algorithms; Linux Pentium PC; McCulloch-Pitts binary neuron model; X Windows; cliques; computational model; data path allocation problem; data path synthesis; deterministic parallel algorithm; high-level synthesis; modified Hopfield neural network; sequential simulator; sub-optimal solutions; time complexity; Circuit simulation; Computational modeling; Computer networks; High level synthesis; Hopfield neural networks; Linux; Network synthesis; Neural networks; Neurons; Parallel algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 1999 IEEE Canadian Conference on

Conference_Location

Edmonton, Alberta, Canada

ISSN

0840-7789

Print_ISBN

0-7803-5579-2

Type

conf

DOI

10.1109/CCECE.1999.807241

Filename

807241