DMCA
Parallel Environments for Implementing Neural Networks (1997)
| Venue: | Neural Computing Survey |
| Citations: | 22 - 1 self |
BibTeX
@ARTICLE{Misra97parallelenvironments,
author = {Manavendra Misra},
title = {Parallel Environments for Implementing Neural Networks},
journal = {Neural Computing Survey},
year = {1997},
volume = {1},
pages = {48--60}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
As artificial neural networks (ANNs) gain popularity in a variety of application domains, it is critical that these models run fast and generate results in real time. Although a number of implementations of neural networks are available on sequential machines, most of these implementations require an inordinate amount of time to train or run ANNs, especially when the ANN models are large. One approach for speeding up the implementation of ANNs is to implement them on parallel machines. This paper surveys the area of parallel environments for the implementations of ANNs, and prescribes desired characteristics to look for in such implementations. 1 Introduction Although traditional von Neumann computing has been successful in many applications, it has not proved effective in solving a variety of important complex problems. At the same time, it has been observed that human beings solve these problems routinely in real time. Typical problems that fall into this class consist of perception...
Keyphrases
parallel environment neural network real time generate result class consist inordinate amount important complex problem many application application domain human being typical problem ann model sequential machine parallel machine traditional von neumann computing artificial neural network
