Paper Info
Title
Neural network implementations and speed-up on Massively Parallel machines
Abstract
This paper investigates large scale learning algorithms and their implementation on Massively Parallel machines. The system prototype described in this paper is part of an integrated environment for developing neural network applications, consisting of: i) a library of neural models and associated tools and ii) a mapping system responsible for providing generic and efficient implementations on a spectrum of parallel machines ranging from coarse grain MIMD to fine grain, Massively Parallel SIMD machines. We also describe the implementation of standard learning algorithms onto the Distributed Array of Processors (DAP) and show that a speedup of 50 is obtained for a typical pattern recognition application.
Year
DOI
Venue
1992
10.1016/0165-6074(92)90398-Q
MICROPROCESSING AND MICROPROGRAMMING
DocType
Volume
Issue
Journal
35
1-5
ISSN
Citations 
PageRank 
0165-6074
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Magali E. Azema-barac15920.75
Apostolos Nikolaos Refenes26222.94