An artificial neural network simulator on the loosely coupled parallel processors

Author

Oohashi, T. ; Ejima, Toru

Author_Institution

Dept. of Artificial Intelligence, Kyushu Inst. of Technol., Fukuoka

fYear

1991

fDate

8-14 Jul 1991

Abstract

Summary form only given, as follows. The authors examine the parallelism of a multilayered ANN (artificial neural network) and discuss a parallel algorithm suited to loosely coupled parallel processors. A mapping of a multilayered network to large-grain processors is proposed, and its performance is evaluated. For a two-layer backpropagation model which has N units in each layer, the highest speedup ratio is obtained with 8N processors but the parallel efficiency is less than 20%. With 2N processors and N/2 processors, the parallel efficiencies of the mapping are 50% and 80%, respectively. It is also shown that the proposed parallel algorithm is more efficient for a larger network

Keywords

learning systems; neural nets; parallel algorithms; virtual machines; artificial neural network simulator; large-grain processors; loosely coupled parallel processors; multilayered network; parallel algorithm; parallel efficiency; speedup ratio; two-layer backpropagation model; Acceleration; Artificial intelligence; Artificial neural networks; Parallel algorithms; Parallel processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7803-0164-1

Type

conf

DOI

10.1109/IJCNN.1991.155575

Filename

155575