Digitizing artificial neural networks

Author

Badgero, Micky L.

Author_Institution

Commun. Syst. Center, United States Air Force, Tinker AFB, OK, USA

Volume

6

fYear

1994

fDate

27 Jun- 2 Jul 1994

Firstpage

3986

Abstract

Most artificial neural networks are designed using an analog model derived from the McCulloch-Pitts neuron model. This design is then implemented as a simulation on a digital computer, and few designs are implemented in dedicated analog hardware. Many simulations run at a small fraction of the speed that analog hardware would allow, but custom designed analog circuitry is usually too expensive. In this paper I will introduce a digital neuron model for artificial neural networks. The purpose of this model is to simplify the design of digital neural networks and allow the design of custom hardware using common digital parts

Keywords

application specific integrated circuits; content-addressable storage; learning (artificial intelligence); logic design; neural chips; ANN digitization; McCulloch-Pitts neuron model; artificial neural networks; digital neuron model; learning methods; simple memory model; Artificial neural networks; Circuit simulation; Clocks; Computational modeling; Computer simulation; Counting circuits; Hardware; Neurofeedback; Neurons; Output feedback;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374850

Filename

374850