DocumentCode
1909683
Title
Problems of massive parallelism in neural network simulation
Author
Zell, Andreas ; Mache, N. ; Vogt, Michael ; Hüttel, Markus
Author_Institution
Inst. for Parallel & Distributed High Performance Syst., Stuttgart Univ., Germany
fYear
1993
fDate
1993
Firstpage
1890
Abstract
Different massively parallel implementations of multilayer feedforward neural networks are presented and compared on a MasPar MP-1216, a parallel single instruction, multiple data (SIMD) computer with 16384 processors. For multilayer feedforward networks, sustained rates of up to 348 M CPS and 129 M CUPS with backpropagation are obtained, a high mark for general purpose SIMD computers. Emphasis is placed on the problems of mapping neural networks to parallel hardware, on implementation problems in obtaining high propagation rates on a SIMD machine, and on problems with the resulting learning algorithms
Keywords
backpropagation; feedforward neural nets; parallel processing; virtual machines; MasPar MP-1216; SIMD; backpropagation; learning algorithms; mapping; massive parallelism; multilayer feedforward neural networks; neural network simulation; Backpropagation; Computational modeling; Computer aided instruction; Computer networks; Concurrent computing; Feedforward neural networks; Multi-layer neural network; Neural network hardware; Neural networks; Parallel processing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993., IEEE International Conference on
Conference_Location
San Francisco, CA
Print_ISBN
0-7803-0999-5
Type
conf
DOI
10.1109/ICNN.1993.298845
Filename
298845
Link To Document