VLSI technologies for artificial neural networks

Author

Goser, Karl ; Hilleringmann, Ulrich ; Rueckert, Ulrich ; Schumacher, Klaus

Author_Institution

Dept. of Electr. Eng., Dortmund Univ., West Germany

Volume

9

Issue

6

fYear

1989

Firstpage

28

Lastpage

44

Abstract

VLSI systems, basic integrated circuits, and silicon technologies are discussed. Novel circuit and design principles that provide a foundation for the implementation of a wide variety of neural network models in silicon are described. The key issues for a successful integration of neural systems are identified. The realization of algorithms in silicon is examined. Special-purpose hardware for carrying out the activation and transfer function and for the connection elements is discussed. A brief overview of the current silicon technologies is provided.<>

Keywords

CMOS integrated circuits; VLSI; analogue computer circuits; application specific integrated circuits; content-addressable storage; integrated circuit technology; linear integrated circuits; neural nets; parallel architectures; semiconductor technology; CMOSIC; VLSI systems; VLSI technologies; activation function; adaption function; artificial neural networks; associative memories; connection elements; floating gate transistor; integrated circuits; learning rule; neural network models; silicon technologies; special purpose hardware; threshold function; transfer function; Artificial neural networks; Biological neural networks; Computer displays; Integrated circuit technology; Microelectronics; Microprocessors; Neural networks; Neuroscience; Silicon; Very large scale integration;

fLanguage

English

Journal_Title

Micro, IEEE

Publisher

ieee

ISSN

0272-1732

Type

jour

DOI

10.1109/40.42985

Filename

42985