Parallel Implementation of Self-Organizing Map on the Partial Tree Shape Neurocomputer

被引：0
|
作者：
Pasi Kolinummi
Pasi Pulkkinen
Timo Hämäläinen
Jukka Saarinen
机构：
[1] Tampere University of Technology,Signal Processing Laboratory
[2] Tampere University of Technology,Signal Processing Laboratory
[3] Tampere University of Technology,Signal Processing Laboratory
来源：
Neural Processing Letters | 2000年 / 12卷
关键词：
computer architecture; neurocomputer; parallel hardware implementation; parallel mapping; self-organizing map;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A parallel mapping of self-organizing map (SOM) algorithm is presented for a partial tree shape neurocomputer (PARNEU). PARNEU is a general purpose parallel neurocomputer that is designed for soft computing applications. Practical scalability and a reconfigurable partial tree network are the main architectural features. The presented neuron parallel mapping of SOM with on-line learning illustrates a parallel winner neuron search and a coordinate transfer that are performed in the partial tree network. Phase times are measured to analyse speedup and scalability of the mapping. The performance of the learning phase in SOM with a four processor PARNEU configuration is about 26 MCUPS and the recall phase performs 30 MCPS. Compared to other mappings done for general purpose neurocomputers, PARNEU's performance is very good.
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页码：171 / 182
页数：11
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