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An FPGA Based Adaptive Weightless Neural Network Hardware

Lorrentz, Pierre and Howells, Gareth and McDonald-Maier, Klaus D. (2008) An FPGA Based Adaptive Weightless Neural Network Hardware. In: Keymeulen, Didier and Arslan, Tughrul and Seuss, Martin and Stoica, Adrian and Erdogan, Ahmet T. and Merodio, David, eds. 2008 NASA/ESA Conference on Adaptive Hardware and Systems. IEEE, pp. 220-227. ISBN 978-0-7695-3166-3. (doi:10.1109/AHS.2008.19)

Abstract

This paper explores the significant practical difficulties inherent in mapping large artificial neural structures onto digital hardware. Specifically, a class of weightless neural architecture called the Enhanced Probabilistic Convergent Network is examined due to the inherent simplicity of the control algorithms associated with the architecture. The advantages for such an approach follow from the observation that, for many situations for which an intelligent machine requires very fast, unmanned, and uninterrupted responses, a PC-based system is unsuitable especially in electronically harsh and isolated conditions, The target architecture for the design is an FPGA, the Virtex-II pro which is statically and dynamically reconfigurable, enhancing its suitability for an adaptive weightless neural networks. This hardware is tested on a benchmark of unconstrained handwritten numbers from the National Institute of Standards and Technology (NIST), USA.

Item Type: Book section
DOI/Identification number: 10.1109/AHS.2008.19
Uncontrolled keywords: artificial neural networks; neurons; training; hardware; random access memory; field programmable gate arrays; computer architecture
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: J. Harries
Date Deposited: 18 Apr 2009 11:40 UTC
Last Modified: 17 Sep 2019 14:51 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14766 (The current URI for this page, for reference purposes)
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