A new mixed-signal feed-forward neural network with on-chip learning

Author

Mirhassani, Mitra ; Ahmadi, M. ; Miller, W.C.

Author_Institution

Dept. of Electr. & Comput. Eng., Windsor Univ., Ont., Canada

Volume

3

fYear

2004

fDate

25-29 July 2004

Firstpage

1729

Abstract

A new mixed-signal feed-forward neural network for pattern/shape recognition problems is proposed. The network has a mixed-signal structure, operations are performed in analog and weights are stored in digital. To increase the network robustness, on-chip training with Madaline Rule III is used. The proposed architecture uses time-multiplexing to increase the network density and resistive-type neurons for their self-scaling property. The results of an XOR network are presented to test the network.

Keywords

feedforward neural nets; learning (artificial intelligence); mixed analogue-digital integrated circuits; neural chips; neural net architecture; pattern recognition; system-on-chip; Madaline Rule III; XOR network; digital storage; mixed signal feedforward neural network; mixed signal structure; neural network density; neural network robustness; onchip learning; onchip training; pattern recognition; resistive type neurons; self scaling property; shape recognition; time multiplexing; Analog circuits; Complexity theory; Computer networks; Feedforward neural networks; Feedforward systems; Integrated circuit interconnections; Network-on-a-chip; Neural networks; Neurons; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-8359-1

Type

conf

DOI

10.1109/IJCNN.2004.1380864

Filename

1380864