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H. Tamukoh, T. Aso, K. Horio and T. Yamakawa, “Self-organizing Map Hardware Accelerator System and Its Application to Real Time Image Enlargement,” Proceedings of 2004 IEEE International Joint Conference on Neural Networks, Budapest, 25-29 July 2004, pp. 2686-2687. doi:10.1109/IJCNN.2004.1381073

has been cited by the following article:

• TITLE: Visualization of Pareto Solutions by Spherical Self-Organizing Map and It’s acceleration on a GPU

  AUTHORS: Masato Yoshimi, Takuya Kuhara, Kaname Nishimoto, Mitsunori Miki, Tomoyuki Hiroyasu

  KEYWORDS: Self-Organizing Map; SOM; Spherical; GPU; Pareto-Optimal Solutions; GPU Acceleration

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.3, March 29, 2012

  ABSTRACT: In this study, we visualize Pareto-optimum solutions derived from multiple-objective optimization using spherical self-organizing maps (SOMs) that lay out SOM data in three dimensions. There have been a wide range of studies involving plane SOMs where Pareto-optimal solutions are mapped to a plane. However, plane SOMs have an issue that similar data differing in a few specific variables are often placed at far ends of the map, compromising intuitiveness of the visualization. We show in this study that spherical SOMs allow us to find similarities in data otherwise undetectable with plane SOMs. We also implement and evaluate the performance using parallel sphere processing with several GPU environments.