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Self-Reconfigurable Multi-Layer Neural Networks with Genetic Algorithms
Eiko SUGAWARA Masaru FUKUSHI Susumu HORIGUCHI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2004/08/01
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Reconfigurable Systems)
Category: Recornfigurable Systems
self-reconfiguration, multi-layer neural network, weight training by genetic algorithm, FPGA,
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This paper addresses the issue of reconfiguring multi-layer neural networks implemented in single or multiple VLSI chips. The ability to adaptively reconfigure network configuration for a given application, considering the presence of faulty neurons, is a very valuable feature in a large scale neural network. In addition, it has become necessary to achieve systems that can automatically reconfigure a network and acquire optimal weights without any help from host computers. However, self-reconfigurable architectures for neural networks have not been studied sufficiently. In this paper, we propose an architecture for a self-reconfigurable multi-layer neural network employing both reconfiguration with spare neurons and weight training by GAs. This proposal offers the combined advantages of low hardware overhead for adding spare neurons and fast weight training time. To show the possibility of self-reconfigurable neural networks, the prototype system has been implemented on a field programmable gate array.