A scheme for implementation of neural networks with replicated receptive fields

Author

Chuang, Michael

Author_Institution

MIT, Cambridge, MA, USA

fYear

1991

fDate

22-24 May 1991

Firstpage

69

Lastpage

73

Abstract

The authors shows how neural networks with local receptive fields and replicated weights can be mapped efficiently onto a CCD parallel processing architecture. Implementation of the neocognitron, a neural network for feature extraction and classification, on the CCD architecture was simulated. A modified training procedure for the neocognitron that improves its ability to extract features when using the CCD architecture is presented

Keywords

charge-coupled device circuits; feature extraction; image recognition; learning (artificial intelligence); neural chips; parallel algorithms; parallel architectures; self-organising feature maps; CCD parallel processing architecture; RWNN algorithm; feature classification; feature extraction; implementation; local receptive fields; modified training procedure; neocognitron; neural networks; replicated receptive fields; replicated weights; Charge coupled devices; Computer architecture; Delay lines; Feature extraction; Finite impulse response filter; Image processing; Multi-layer neural network; Neural networks; Parallel processing; Pixel;

fLanguage

English

Publisher

ieee

Conference_Titel

VLSI Technology, Systems, and Applications, 1991. Proceedings of Technical Papers, 1991 International Symposium on

Conference_Location

Taipei

ISSN

1524-766X

Print_ISBN

0-7803-0036-X

Type

conf

DOI

10.1109/VTSA.1991.246709

Filename

246709