Title

A Switched-resistor Approach to Hardware Implementation of Neural Networks

Abstract

To overcome the shortcomings of fully analog and fully digital implementation of artificial neural networks (ANNs), we adopted mixed analog/digital technique. We proposed a switched-resistor (SR) element as a programmable synapse. The switched-resistor implementation of synapse captures both the advantages of analog implementation and the programmability of digital implementation. We also designed a CMOS analog neuron that performs a near-tanh nonlinearity function. We evaluated the performance of the neural networks using Pspice. The results showed that our approach can successfully implement the neural network, and exhibit a very high modularity.

Meeting Name

14th IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2005 (2005: May 22-25, Reno, NV)

Department(s)

Electrical and Computer Engineering

International Standard Serial Number (ISSN)

1098-7584

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


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