Presentation 2004/11/20
A Convolutional Neural Network VLSI Architecture Using Algorithms of Projection-field model and Weight Decomposition
Osamu NOMURA, Takashi MORIE, Keisuke KOREKADO, Masakazu MATSUGU, Atsushi IWATA,
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Abstract(in English) Hierarchical convolutional neural networks are a well-known robust image-recognition model. In order to apply this model to robot vision or various intelligent real-time vision systems, its VLSI implementation is essential. This paper proposes a new algorithm for reducing multiply-and-accumulation operation by thresholding in a projection field and by performing weight decomposition in a 2-D neuron array. We also propose a VLSI architecture based on the proposed algorithm, and estimate its operation performance.
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Keyword(in English) Convolutional Neural Network / VLSI / Pojection-field / Weight Decomposition
Paper # NLP2004-80,NC2004-96
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Committee NC
Conference Date 2004/11/20(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Convolutional Neural Network VLSI Architecture Using Algorithms of Projection-field model and Weight Decomposition
Sub Title (in English)
Keyword(1) Convolutional Neural Network
Keyword(2) VLSI
Keyword(3) Pojection-field
Keyword(4) Weight Decomposition
1st Author's Name Osamu NOMURA
1st Author's Affiliation Canon Inc. Leading Edge Technology Development Headquarters Intelligent I/F Project:Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology()
2nd Author's Name Takashi MORIE
2nd Author's Affiliation Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
3rd Author's Name Keisuke KOREKADO
3rd Author's Affiliation Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology
4th Author's Name Masakazu MATSUGU
4th Author's Affiliation Canon Inc. Leading Edge Technology Development Headquarters Intelligent I/F Project
5th Author's Name Atsushi IWATA
5th Author's Affiliation Graduate School of Advanced Sciences of Matter, Hiroshima University
Date 2004/11/20
Paper # NLP2004-80,NC2004-96
Volume (vol) vol.104
Number (no) 474
Page pp.pp.-
#Pages 5
Date of Issue