Implementation of an on-chip learning artificial neural network using pulse width modification technique

Title:

Implementation of an on-chip learning artificial neural network using pulse width modification technique

Creator:

Chen, Qiong, Zheng, Qilun, and Ling, Weixin

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:1866f2c6-da5d-431b-a64c-38eb9f058e31
uuid:1866f2c6-da5d-431b-a64c-38eb9f058e31

Subject:

artificial neural networks, weight, and on-chip learning

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

An on-chip learning Artificial Neural Network (ANN) implementation using the Pulse Width Modulatioii (PWM) technique is proposed in this paper. Synapse and neuron are analog circuits, while digital counters are utilized to store the weights. Through the PWM circuit, the digital weight is converted into a pulse signal as the input of the analog synapse circuit. The analog modified quantity of weight is transformed into a weight-update pulse signal whose width is proportion to the value of the weight modiřication quantity. The learning rule is bcised on the weight perturbation algorithrn. In this way, the weight can be long-terni-stored and ecisily modified, thereas the synapse and the neuron are of a small size in the silicon area and the learning Circuit is feasible for implementation. Taking the advantages of both the analog and the digital realizations of the ANN, this method is a meaningful way to the implementation of on-chip ANN and fuzzy processors.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2002 Volume:12 | Number:1

Harvested from:

CDK

Metadata only:

false