Lorrentz, Pierre, Howells, Gareth, McDonald-Maier, Klaus D. (2008) An Analysis of Hardware Configurations for an Adaptive Weightless Neural Network. In: Ao, S.I. and Gelman, Len and Hukins, David W.L. and Hunter, Andrew and Korsunsky, A.M., eds. World Congress On Engineering 2008, VOLS I-II. Lecture Notes in Engineering and Computer Science , I-II. pp. 66-71. IAE, INT ASSOC ENGINEERS-IAENG, UNIT1, 1-F, 37-39 HUNG TO ROAD, KWUN TONG, HONG KONG, 00000, PEOPLES R CHINA ISBN 978-988-98671-9-5. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:14765)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. |
Abstract
This paper examines the potential offered by adaptive hardware configurations of a class of weightless neural architecture called the Enhanced Probabilistic Convergent Network targeted on a Virtex-II pro FPGA which is re configurable.
The reconfiguration and adaptive capability of the Enhanced Probabilistic Convergent Network is a highly adaptive architecture offering a very fast, automated uninterrupted responses in potentially electronically harsh and isolated conditions. The hardware architecture is tested on a benchmark of unconstrained handwritten numerals from the Centre of Excellence for Document Analysis and Recognition.
Item Type: | Conference or workshop item (Paper) |
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Additional information: | World Congress on Engineering 2008 Imperial Coll London, London, ENGLAND, JUL 02-04, 2008 |
Uncontrolled keywords: | adaptive neural network; FPGA; handwritten characters; reconfiguration |
Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | J. Harries |
Date Deposited: | 18 Apr 2009 11:39 UTC |
Last Modified: | 16 Nov 2021 09:53 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/14765 (The current URI for this page, for reference purposes) |
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