Hardware Self-Organizing Map With Dithered Step Neighborhood Function

In this paper, a hardware-based SOM architecture with a dithered step (DS) neighborhood function is introduced. In the DS function, dithering is added to a conventional step neighborhood function, and the dithering endows the step function with nonlinear characteristics. The nonlinear characteristics given by the dithering are found to lead to better quantization performance, while the simple structure of the step function, which remains the basis of the proposed function, maintains a low hardware cost. Here, the characteristics of the dithered function were theoretically analyzed before clustering experiments utilizing the proposed SOM implemented on a field-programmable gate array (FPGA) were conducted to evaluate the extent to which the proposed neighborhood function improves the performance of the resulting SOM. The experimental results show that the proposed DS neighborhood function decreases the circuit size and power consumption by 13.86% and 17.61%, respectively. In addition, the DS neighborhood function was used to create a hardware-based SOM with 4.33% greater throughput.

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Publication Details

Journal
IEEE Transactions on Circuits and Systems I Regular Papers
Published
2025-01-22
DOI
https://doi.org/10.1109/tcsi.2025.3530612
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Hardware Self-Organizing Map With Dithered Step Neighborhood Function

Hiroomi Hikawa
IEEE Transactions on Circuits and Systems I Regular Papers
Neural Networks and Applications
article

Hardware Self-Organizing Map With Dithered Step Neighborhood Function

Hiroomi Hikawa
article en

Abstract

In this paper, a hardware-based SOM architecture with a dithered step (DS) neighborhood function is introduced. In the DS function, dithering is added to a conventional step neighborhood function, and the dithering endows the step function with nonlinear characteristics. The nonlinear characteristics given by the dithering are found to lead to better quantization performance, while the simple structure of the step function, which remains the basis of the proposed function, maintains a low hardware cost. Here, the characteristics of the dithered function were theoretically analyzed before clustering experiments utilizing the proposed SOM implemented on a field-programmable gate array (FPGA) were conducted to evaluate the extent to which the proposed neighborhood function improves the performance of the resulting SOM. The experimental results show that the proposed DS neighborhood function decreases the circuit size and power consumption by 13.86% and 17.61%, respectively. In addition, the DS neighborhood function was used to create a hardware-based SOM with 4.33% greater throughput.

IEEE Transactions on Circuits and Systems I Regular PapersVol. 72(9)
Kansai University (JP)
Japan Society for the Promotion of Science London
Openalex Percentile: Top 100%
Neural Networks and Applications
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Hardware Self-Organizing Map With Dithered Step Neighborhood Function — Hiroomi Hikawa · IEEE Transactions on Circuits and Systems I Regular Papers (2025) | TGRS Research Map | TGRS