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: Third NASA/ESA Conference on Adaptive Hardware and Systems (AHS-2008), June 22nd-25th 2008, Noordwijk, The Netherlands. (Full text available)

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http://dx.doi.org/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: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: J. Harries
Date Deposited: 18 Apr 2009 11:40
Last Modified: 17 Jul 2014 13:57
Resource URI: http://kar.kent.ac.uk/id/eprint/14766 (The current URI for this page, for reference purposes)
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