Analog feedforward neural networks with very low precision weights

Author

Alibeik, Shahram Abdollahi ; Nemati, Farid ; Sharif-Bakhtiar, Mehrdad

Author_Institution

Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran, Iran

Volume

1

fYear

1995

fDate

Nov/Dec 1995

Firstpage

90

Abstract

An off chip training algorithm for feedforward neural networks is presented. This algorithm has been successfully used to train networks with weight precision as low as 1 bit. The effect of reducing the weight precision on the generalization ability of the network is presented. The network performance, in the presence of hardware non-idealities, has also been investigated. It is shown that a network with low precision weights can well tolerate the effect of hardware non-idealities if the network is properly trained

Keywords

feedforward neural nets; generalisation (artificial intelligence); learning (artificial intelligence); neural chips; analog feedforward neural networks; generalization; low precision weights; off chip training; Analog circuits; Analog computers; Application software; Computer networks; Concurrent computing; Feedforward neural networks; High performance computing; Neural network hardware; Neural networks; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.487908

Filename

487908