A switched-resistor approach to hardware implementation of neural networks

被引：0

作者：

Zhang, N ^{[1
]}

Wunsch, DC ^{[1
]}

机构：

[1] S Dakota Sch Mines & Technol, Dept Elect & Comp Engn, Rapid City, SD 57701 USA

来源：

FUZZ-IEEE 2005: PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS: BIGGEST LITTLE CONFERENCE IN THE WORLD | 2005年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：336 / 340

页数：5

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