A reconfigurable `ANN´ architecture

Author

Madraswala, T.H. ; Mohd, B.J. ; Ali, M. ; Premi, R. ; Bayoumi, M.A.

Author_Institution

Center for Adv. Comput. Studies, Univ. of Southwestern Louisiana, Lafayette, LA, USA

Volume

3

fYear

1992

fDate

10-13 May 1992

Firstpage

1569

Abstract

Proposes a design of a digital artificial neural network (ANN). The architecture is based on a single-instruction multiple-data (SIMD) processing configuration. Communication is done through broadcasting and also by systolic methods. With the help of a microprogrammed control unit, the design is mainly capable of implementing the following three models: (1) Hamming, (2) Hopfield, and (3) Carpenter/Grossberg algorithms. The architecture was also designed to achieve parallelism, modularity, adaptability, flexibility, speed, low cost, smaller silicon area, and expandability

Keywords

Hopfield neural nets; VLSI; neural chips; parallel architectures; Carpenter/Grossberg algorithms; Hamming algorithms; Hopfield algorithms; SIMD processing; adaptability; digital artificial neural network; expandability; flexibility; microprogrammed control unit; modularity; silicon area; speed; systolic methods; Artificial neural networks; Computer architecture; Computer networks; Costs; Integrated circuit interconnections; Machine learning; Neural networks; Neurons; Silicon; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0593-0

Type

conf

DOI

10.1109/ISCAS.1992.230198

Filename

230198