A Massively Parallel Implementation of a Modular Self-Organizing Map on FPGAs

2019 ◽  
Vol 28 (03) ◽  
pp. 1950054 ◽  
Author(s):  
Khaled Ben Khalifa ◽  
Mohamed Hédi Bedoui
Keyword(s):  
Neural Networks ◽  
Online Learning ◽  
Input Data ◽  
Parallel Implementation ◽  
Fpga Implementation ◽  
Massively Parallel ◽  
Self Organizing Map ◽  
Element Input ◽  
Working Frequency ◽  
Self Organizing

This paper describes the architecture design of novel massively parallel self-organizing map (SOM) neural networks. The proposed architecture, referred to as the planar SOM (PSOM), is described as a soft IP core synthesized in VHDL. The SOM neural network’s size and the input data vectors’ dimension are adjustable parameters. In this work, several SOM architectures are synthesized and their performance is evaluated for Xilinx Virtex-7 FPGAs. The presented hardware architecture allows online learning and can be easily adapted to a large variety of SOM topologies without a considerable design effort. A [Formula: see text] SOM hardware is validated through the FPGA implementation and its performances with an estimated working frequency of 297[Formula: see text]MHz for a 23-element input vector will reach 21,970 MCUPS in the learning phase and 35,902 MCPS in the recall one.

Using an Extended Self-Organizing Map Network to Forecast Market Segment Membership

2011 ◽  
pp. 142-157
Author(s):  
Melody Y. Kiang ◽  
Dorothy M. Fisher ◽  
Michael Y. Hu ◽  
Robert T. Chi
Keyword(s):  
Input Data ◽  
Self Organizing Map ◽  
Market Segment ◽  
Extended Version ◽  
Extended Self ◽  
Data Set ◽  
Step Procedure ◽  
Som Network ◽  
Residential Market ◽  
Self Organizing

This chapter presents an extended Self-Organizing Map (SOM) network and demonstrates how it can be used to forecast market segment membership. The Kohonen’s SOM network is an unsupervised learning neural network that maps n-dimensional input data to a lower dimensional (usually one- or two-dimensional) output map while maintaining the original topological relations. We apply an extended version of SOM networks that further groups the nodes on the output map into a user-specified number of clusters to a residential market data set from AT&T. Specifically, the extended SOM is used to group survey respondents using their attitudes towards modes of communication. We then compare the extended SOM network solutions with a two-step procedure that uses the factor scores from factor analysis as inputs to K-means cluster analysis. Results using AT&T data indicate that the extended SOM network performs better than the two-step procedure.

Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality

2008 ◽  
Vol 34 (6) ◽  
pp. 782-790 ◽  
Author(s):  
Manuel Alvarez-Guerra ◽  
Cristina González-Piñuela ◽  
Ana Andrés ◽  
Berta Galán ◽  
Javier R. Viguri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sediment Quality ◽  
Self Organizing Map ◽  
Artificial Neural ◽  
Self Organizing

scholarly journals A binary Self-Organizing Map and its FPGA implementation

2009 ◽  
Author(s):  
Kofi Appiah ◽  
Andrew Hunter ◽  
Hongying Meng ◽  
Shigang Yue ◽  
Mervyn Hobden ◽  
...  
Keyword(s):  
Fpga Implementation ◽  
Self Organizing Map ◽  
Self Organizing

scholarly journals Pengelompokan Berita Indonesia Berdasarkan Histogram Kata Menggunakan Self-Organizing Map

2014 ◽  
Vol 8 (1) ◽  
pp. 101
Author(s):  
Ambarwati Ambarwati ◽  
Edi Winarko
Keyword(s):  
Feature Extraction ◽  
Data Processing ◽  
System Application ◽  
Input Data ◽  
Information Resources ◽  
The Self ◽  
News Article ◽  
Self Organizing Map ◽  
Map Algorithm ◽  
Self Organizing

AbstrakBerita merupakan sumber informasi yang dinantikan oleh manusia setiap harinya. Manusia membaca berita dengan kategori yang diinginkan. Jika komputer mampu mengelompokkan berita secara otomatis maka tentunya manusia akan lebih mudah membaca berita sesuai dengan kategori yang diinginkan. Pengelompokan berita yang berupa artikel secara otomatis sangatlah menarik karena mengorganisir artikel berita secara manual membutuhkan waktu dan biaya yang tidak sedikit.Tujuan penelitian ini adalah membuat sistem aplikasi untuk pengelompokkan artikel berita dengan menggunakan algoritma Self Organizing Map. Artikel berita digunakan sebagai input data. Kemudian sistem melakukan pemrosesan data untuk dikelompokkan. Proses yang dilakukan sistem meliputi preprocessing, feature extraction, clustering dan visualize.Sistem yang dikembangkan mampu menampilkan hasil clustering dengan algoritma Self Organizing Map dan memberikan visualisasi dengan smoothed data histograms berupa island map dari artikel berita. Selain itu sistem dapat menampilkan koleksi dokumen dari lima kategori berita yang ada pada tiap tahunnya dan banyaknya kata (histogram kata) yang sering muncul pada tiap arikel berita. Pengujian dari sistem ini dengan memasukan artikel berita, kemudian sistem memprosesnya dan mampu memberikan hasil cluster dari artikel berita yang dimasukan. Kata kunci—Pengelompokkan berita Indonesia, pengelompokkan berdasar histogram kata, pengelompokan berita menggunakan SOM  Abstract News is awaited information resources by humans every day. Human reading the news with the desired category. If the computer able to news clustering with automatically, humans of course will be easier to read the news according to the desired category. News clustering in the form of news articles with automatically very interesting because it organizes news articles manually takes time and costs not a little bit.The purpose of this research is to create a system application for grouping news articles by using the Self Organizing Map algorithm. News article be used as input into the system. News articles used as input data. Then the system performs data processing until to be clustered. Processes performed by the system covers: preprocessing, feature extraction, clustering and visualize.The system developed is able to display the results clustering of the Self Organizing Map algorithm and gives visualization of the Smoothed Data Histograms in the form of island map from news articles. Additionally the system can display a word histogram and news articles from five categories news in each year. Testing of this system by entering the news articles, then the system performs data processing and gives results of a cluster from news articles that input. Keywords—Indonesia news clustering, clustering based on words histograms, news clustering using SOM

scholarly journals Fréchet Metric in Neural Network Theory

2019 ◽  
Vol 27 (45) ◽  
pp. 102-106
Author(s):  
Renáta Masárová
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Theory ◽  
Input Data ◽  
Minimal Distance ◽  
Neural Network Theory ◽  
Self Organizing

Abstract This paper deals with application of a modified Fréchet metric to self-organizing neural networks, called Kohenen maps. The methodology used allows us to put more emphasis on the selected parameters in the input data. It can simplify finding the minimal distance dFj, since dFj∈ 〈0,1〉

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