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An FPGA based adaptive weightless Neural Network Hardware

Lorrentz, P and Howells, WGJ and McDonald-Maier, KD (2008) An FPGA based adaptive weightless Neural Network Hardware. In: UNSPECIFIED, ? - ?.

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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. © 2008 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings of the 2008 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2008
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Clare Chatfield
Date Deposited: 19 Sep 2013 09:11
Last Modified: 23 Jan 2019 02:15
URI: http://repository.essex.ac.uk/id/eprint/6892

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