Parallel implementation of backpropagation on transputers

Author

Foo, S.K. ; Saratchandran, P. ; Sundararajan, N.

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

Volume

3

fYear

1993

fDate

25-29 Oct. 1993

Firstpage

3058

Abstract

Backpropagation algorithm is one of the most popular training algorithms for multilayer feedforward neural networks. However training the network with this algorithm has proved to be computationally intensive for a sequential machine. In this paper, parallel implementation of the backpropagation algorithm is investigated using transputers hosted by a personal computer. Two methods of transputer implementations were considered. One method was the multi-tasking approach and the other the processor farming approach. Results showed that for all test cases, the training time for the neural network with the multi-tasking approach is shorter than the processor farming approach. Comparing with a serial 486-33 PC, it is found that as the problem size scales up, the improvement in training time from the parallel implementation becomes significant.

Keywords

backpropagation; feedforward neural nets; parallel processing; sequential machines; transputer systems; backpropagation; learning algorithms; multi-tasking approach; multilayer feedforward neural networks; parallel; processor farming approach; sequential machine; transputers; Backpropagation algorithms; Computer errors; Computer networks; Electronic mail; Feedforward neural networks; Hardware; Microcomputers; Multi-layer neural network; Neural networks; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on

Print_ISBN

0-7803-1421-2

Type

conf

DOI

10.1109/IJCNN.1993.714365

Filename

714365