DocumentCode :
2535027
Title :
Massively parallel processing implementation of the toroidal neural networks
Author :
Palazzari, P. ; Coli, M. ; Rughi, R.
Author_Institution :
HPCN Project, ENEA, Rome, Italy
fYear :
2000
fDate :
2000
Firstpage :
295
Lastpage :
300
Abstract :
The toroidal neural networks (TNN), recently introduced, are derived from discrete time cellular neural network (DT-CNN) and are characterized by an appealing mathematical description which allows the development of an exact learning algorithm. In this work, after reviewing the underlying theory, we describe the implementation of TNN on the APE100/Quadrics massively parallel system and, through an efficiency figure, we show that such type of synchronous SIMD systems are very well suited to support the TNN (and DT-CNN) computational paradigm
Keywords :
cellular neural nets; learning (artificial intelligence); parallel processing; SIMD; cellular neural network; learning algorithm; massively parallel processing; toroidal neural networks; Cellular neural networks; Cloning; Concurrent computing; Image processing; Joining processes; Network topology; Neural networks; Neurons; Parallel processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cellular Neural Networks and Their Applications, 2000. (CNNA 2000). Proceedings of the 2000 6th IEEE International Workshop on
Conference_Location :
Catania
Print_ISBN :
0-7803-6344-2
Type :
conf
DOI :
10.1109/CNNA.2000.876861
Filename :
876861
Link To Document :
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