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Paper Abstract and Keywords
Presentation 2009-09-25 11:00
Associative-Memory-Based LSI Architecture with Automatic Learning Functionality and Application to Handwritten-Character Recognition
Wataru Imafuku, Tania Ansari, Akio Kawabata, Hans Juergen Mattausch, Tetsushi Koide (Hiroshima Univ.) CAS2009-40 NLP2009-76
Abstract (in Japanese) (See Japanese page) 
(in English) n the presented research on VLSI-system design for handwritten-character learning and recognition, an associative memory architecture for searching the most similar data among previously stored reference data is applied.
The chosen associative memory achieves high speed, low power consumption and small implementation area due to a mixed digital-analog fully-parallel nearest-match search circuitry.
To recognize new data, a learning capability based on the concept of a short/long-term memory which tries to mimic the function of the human brain is realized.
For improvement of the recognition rate, we propose a reference-data-optimization algorithm that averages the recognized input patterns for each stored reference object and updates the corresponding reference pattern periodically.
We evaluated the proposed intelligent VLSI-design method for the application of hand-written character learning and recognition.
Therefore, test-chip in ${0.18}$ ${{\mu}m}$ CMOS technology was designed to demonstrate the proposed algorithm and design method.
The simulated processing capability of this test-chip amounts to \mbox{0.3 million} input-character images per second.
Keyword (in Japanese) (See Japanese page) 
(in English) Associative memory / Automatic learning / Short/long-term memory / Handwritten-character recognition / Reference-data optimization / / /  
Reference Info. IEICE Tech. Rep., vol. 109, no. 199, CAS2009-40, pp. 91-96, Sept. 2009.
Paper # CAS2009-40 
Date of Issue 2009-09-17 (CAS, NLP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee CAS NLP  
Conference Date 2009-09-24 - 2009-09-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Hiroshima Univ. Higashi Senda Campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To CAS 
Conference Code 2009-09-CAS-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Associative-Memory-Based LSI Architecture with Automatic Learning Functionality and Application to Handwritten-Character Recognition 
Sub Title (in English)  
Keyword(1) Associative memory  
Keyword(2) Automatic learning  
Keyword(3) Short/long-term memory  
Keyword(4) Handwritten-character recognition  
Keyword(5) Reference-data optimization  
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1st Author's Name Wataru Imafuku  
1st Author's Affiliation Hiroshima University (Hiroshima Univ.)
2nd Author's Name Tania Ansari  
2nd Author's Affiliation Hiroshima University (Hiroshima Univ.)
3rd Author's Name Akio Kawabata  
3rd Author's Affiliation Hiroshima University (Hiroshima Univ.)
4th Author's Name Hans Juergen Mattausch  
4th Author's Affiliation Hiroshima University (Hiroshima Univ.)
5th Author's Name Tetsushi Koide  
5th Author's Affiliation Hiroshima University (Hiroshima Univ.)
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Speaker Author-1 
Date Time 2009-09-25 11:00:00 
Presentation Time 25 minutes 
Registration for CAS 
Paper # CAS2009-40, NLP2009-76 
Volume (vol) vol.109 
Number (no) no.199(CAS), no.200(NLP) 
Page pp.91-96 
#Pages
Date of Issue 2009-09-17 (CAS, NLP) 


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