A Switched-Resistor Approach to Hardware Implementation of Neural Networks

Author

Zhang, Nian ; Wunsch, Donald C., II

Author_Institution

Dept. of Electr. & Comput. Eng., South Dakota Sch. of Mines & Technol., Rapid City, SD

fYear

2005

fDate

25-25 May 2005

Firstpage

336

Lastpage

340

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

Keywords

CMOS analogue integrated circuits; mixed analogue-digital integrated circuits; neural nets; programmable circuits; switched networks; CMOS analog neuron; CMOS analogue integrated circuits; artificial neural networks; hardware implementation; mixed analog-digital technique; mixed analogue-digital integrated circuits; near-tanh nonlinearity function; programmable synapse; switched networks; switched-resistor element; Artificial neural networks; Biomedical optical imaging; Computer networks; Neural network hardware; Neural networks; Neurons; Nonlinear optical devices; Nonlinear optics; Optical computing; Optical fiber networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on

Conference_Location

Reno, NV

Print_ISBN

0-7803-9159-4

Type

conf

DOI

10.1109/FUZZY.2005.1452416

Filename

1452416