A binary self-organizing map and its FPGA implementation

Authors:
Kofi Appiah;Andrew Hunter;Hongying Meng;Shigang Yue;Mervyn Hobden;Nigel Priestley;Peter Hobden;Cy Pettit
Affiliations:
Department of Computing and Informatics, University of Lincoln, UK;Department of Computing and Informatics, University of Lincoln, UK;Department of Computing and Informatics, University of Lincoln, UK;Department of Computing and Informatics, University of Lincoln, UK;e2v Technologies, Lincoln, UK;e2v Technologies, Lincoln, UK;e2v Technologies, Lincoln, UK;e2v Technologies, Lincoln, UK
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

A binary Self Organizing Map (SOM) has been designed and implemented on a Field Programmable Gate Array (FPGA) chip. A novel learning algorithm which takes binary inputs and maintains tri-state weights is presented. The binary SOM has the capability of recognizing binary input sequences after training. A novel tri-state rule is used in updating the network weights during the training phase. The rule implementation is highly suited to the FPGA architecture, and allows extremely rapid training. This architecture may be used in real-time for fast pattern clustering and classification of binary features.