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.
Recommended Citation
N. Zhang and D. C. Wunsch, "A Switched-Resistor Approach to Hardware Implementation of Neural Networks," Proceedings of the 14th IEEE International Conference on Fuzzy Systems (2005, Reno, NV), pp. 336 - 340, Institute of Electrical and Electronics Engineers (IEEE), May 2005.
The definitive version is available at https://doi.org/10.1109/FUZZY.2005.1452416
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.
Publication Date
25 May 2005
