IEICE TRANSACTIONS on Information
Self-Reconfigurable Multi-Layer Neural Networks with Genetic Algorithms
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E87-D No.8 pp.2021-2028
- Publication Date
- 2004/08/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Reconfigurable Systems)
- Category
- Recornfigurable Systems
Authors
Keyword
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Eiko SUGAWARA, Masaru FUKUSHI, Susumu HORIGUCHI, "Self-Reconfigurable Multi-Layer Neural Networks with Genetic Algorithms" in IEICE TRANSACTIONS on Information,
vol. E87-D, no. 8, pp. 2021-2028, August 2004, doi: .
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_8_2021/_p
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@ARTICLE{e87-d_8_2021,
author={Eiko SUGAWARA, Masaru FUKUSHI, Susumu HORIGUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={Self-Reconfigurable Multi-Layer Neural Networks with Genetic Algorithms},
year={2004},
volume={E87-D},
number={8},
pages={2021-2028},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Self-Reconfigurable Multi-Layer Neural Networks with Genetic Algorithms
T2 - IEICE TRANSACTIONS on Information
SP - 2021
EP - 2028
AU - Eiko SUGAWARA
AU - Masaru FUKUSHI
AU - Susumu HORIGUCHI
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2004
AB - 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.
ER -
English
