Back-propagation learning algorithm and parallel computers: The CLEPSYDRA mapping scheme
This paper deals with the parallel implementation of the back-propagation of errors learning algorithm. To obtain the partitioning of the neural network on the processor network the author describes a new mapping scheme that uses a mixture of synapse parallelism, neuron parallelism and training exam...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 31; H. 1; S. 67 - 85 |
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| 1. Verfasser: | |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.03.2000
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| Schlagworte: | |
| ISSN: | 0925-2312, 1872-8286 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | This paper deals with the parallel implementation of the back-propagation of errors learning algorithm. To obtain the partitioning of the neural network on the processor network the author describes a new mapping scheme that uses a mixture of synapse parallelism, neuron parallelism and training examples parallelism (if any). The proposed mapping scheme allows to describe the back-propagation algorithm as a collection of SIMD processes, so that both SIMD and MIMD machines can be used. The main feature of the obtained parallel algorithm is the absence of point-to-point communication; in fact, for each training pattern, an all-to-one broadcasting with an associative operator (combination) and an one-to-all broadcasting (that can be both realized in log
P time) are needed. A performance model is proposed and tested on a ring-connected MIMD parallel computer. Simulation results on MIMD and SIMD parallel machines are also shown and commented. |
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| Bibliographie: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| ISSN: | 0925-2312 1872-8286 |
| DOI: | 10.1016/S0925-2312(99)00151-4 |