A Novel Parallel Model for Self-Organizing Map and its Efficient Implementation on a Data-Driven Multiprocessor
Ruck Thawonmas*, Makoto Iwata**, and Satoshi Fukunaga**,***
*Department of Computer Science, Ritsumeikan University, Kusatsu, Shiga, 525-8577, Japan
**Course of Information Systems Engineering, Kochi University of Technology, Tosayamada-cho, Kami-gun, Kochi, 782-8502, Japan
***FUJISOFT ABC Inc.
The self-organizing map (SOM), with its related extensions, is one of the most widely used artificial neural algorithms in unsupervised learning and a wide variety of applications. Dealing with very large data sets, however, the training time on a single processor is too high to be acceptable for time-critical application domains. To cope with this problem, we present a scheme consisting of a novel parallel model and its implementation on a dynamic data-driven multiprocessor. The parallel model ensures that no load imbalance will occur, while the dynamic data-driven multiprocessor yields high scalability. We demonstrate the effectiveness of the scheme by comparing the parallel model with an existing parallel model, and the proposed implementation with an implementation on another multiprocessor.
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