Reconfigurable low-voltage low-power neuron cell for self-organizing maps

Self-Organizing Map (SOM), one type of Artificial Neural Networks (ANN), has wide application in pattern recognition, data clustering, and image processing. The cells in the network respond to various input patterns through competitive learning. The learning process involves complex non-linear mathe...

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Main Author: Li, Ren Shi.
Other Authors: Chang Chip Hong
Format: Final Year Project
Language:English
Published: 2009
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Online Access:http://hdl.handle.net/10356/18425
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-184252023-07-07T17:08:57Z Reconfigurable low-voltage low-power neuron cell for self-organizing maps Li, Ren Shi. Chang Chip Hong School of Electrical and Electronic Engineering Centre for Integrated Circuits and Systems DRNTU::Engineering::Electrical and electronic engineering::Integrated circuits Self-Organizing Map (SOM), one type of Artificial Neural Networks (ANN), has wide application in pattern recognition, data clustering, and image processing. The cells in the network respond to various input patterns through competitive learning. The learning process involves complex non-linear mathematical calculations, time sharing and parallel processing techniques for solving the real application. A new SOM neuron architecture is presented in this project which is a low-voltage low-power design with promising learning accuracy and convergence stability of the SOM in analog domain. This report presents a review of SOM algorithm with emphasis on the mathematical operations involved in the algorithm. After that, the proposed architectures and the key components of the network are illustrated. The prototype SOM network consists of four neurons which are used to evaluate and verify the functionality and learning quality with an accurate Gaussian tapering function. A low power consumption current multiplier operating in subthreshold region in analog domain is presented in this project. The current multiplier in subthreshold region promises low power consumption and simplicity without sacrificing its accuracy. The complexity of Gaussian neighborhood function and weight adaption are reduced by reusing the analog current multiplier for multiplication and squaring operations. Simulated results show that with the same input patterns, better learning quality and much lower power consumption can be achieved with sacrifice on the cycle time. Bachelor of Engineering 2009-06-29T01:50:09Z 2009-06-29T01:50:09Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/18425 en Nanyang Technological University 83 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Integrated circuits
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Integrated circuits
Li, Ren Shi.
Reconfigurable low-voltage low-power neuron cell for self-organizing maps
description Self-Organizing Map (SOM), one type of Artificial Neural Networks (ANN), has wide application in pattern recognition, data clustering, and image processing. The cells in the network respond to various input patterns through competitive learning. The learning process involves complex non-linear mathematical calculations, time sharing and parallel processing techniques for solving the real application. A new SOM neuron architecture is presented in this project which is a low-voltage low-power design with promising learning accuracy and convergence stability of the SOM in analog domain. This report presents a review of SOM algorithm with emphasis on the mathematical operations involved in the algorithm. After that, the proposed architectures and the key components of the network are illustrated. The prototype SOM network consists of four neurons which are used to evaluate and verify the functionality and learning quality with an accurate Gaussian tapering function. A low power consumption current multiplier operating in subthreshold region in analog domain is presented in this project. The current multiplier in subthreshold region promises low power consumption and simplicity without sacrificing its accuracy. The complexity of Gaussian neighborhood function and weight adaption are reduced by reusing the analog current multiplier for multiplication and squaring operations. Simulated results show that with the same input patterns, better learning quality and much lower power consumption can be achieved with sacrifice on the cycle time.
author2 Chang Chip Hong
author_facet Chang Chip Hong
Li, Ren Shi.
format Final Year Project
author Li, Ren Shi.
author_sort Li, Ren Shi.
title Reconfigurable low-voltage low-power neuron cell for self-organizing maps
title_short Reconfigurable low-voltage low-power neuron cell for self-organizing maps
title_full Reconfigurable low-voltage low-power neuron cell for self-organizing maps
title_fullStr Reconfigurable low-voltage low-power neuron cell for self-organizing maps
title_full_unstemmed Reconfigurable low-voltage low-power neuron cell for self-organizing maps
title_sort reconfigurable low-voltage low-power neuron cell for self-organizing maps
publishDate 2009
url http://hdl.handle.net/10356/18425
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