论文信息 - Parallelizing the self-organizing feature map on multiprocessor systems

Parallelizing the self-organizing feature map on multiprocessor systems

The Self-Organizing Feature Map (SOFM) proposed by Kohonen is a widely used vector quantization algorithm. A drawback of SOFM is the increase of computation time with an increase in the number of neurons. However, the inherent parallelism of SOFM allows parallel processing to speed up the computation. Parallel algorithms for implementing the Kohonen SOFM are presented in this paper. We have implemented the algorithms on a linear chain and a two-dimensional mesh of transputers. Significant speedup has been achieved. In addition, models to describe the performance of the algorithms are also presented. The performance of massively parallel systems is predicted from the models.

Chia-Jiu Wang | Chwan-Hwa John Wu | Russel E. Hodges

[1] D. S. Touretzky,et al. Neural network simulation at Warp speed: how we got 17 million connections per second , 1988, IEEE 1988 International Conference on Neural Networks.

[2] Emile H. L. Aarts,et al. Computations in massively parallel networks based on the Boltzmann machine: a review , 1989, Parallel Comput..

[3] D. Roweth,et al. Implementing Neural Network Models on Parallel Computers , 1987, Comput. J..

[4] Teuvo Kohonen,et al. Self-Organization and Associative Memory, Second Edition , 1988, Springer Series in Information Sciences.

[5] Teuvo Kohonen,et al. Self-Organization and Associative Memory , 1988 .

[6] T. Kohonen. Self-organized formation of topographically correct feature maps , 1982 .