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International Journal of Reconfigurable Computing
Volume 2009, Article ID 908740, 13 pages
http://dx.doi.org/10.1155/2009/908740
Research Article

A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

1School of Computing and Intelligent Systems, University of Ulster, Derry BT48 7JL, Northern Ireland
2Bio-Inspired Electronics & Reconfigurable Computing Group, NUI Galway, Galway, Ireland
3Department of Electrical Engineering & Electronics, University of Liverpool, Liverpool L69 3GJ, UK

Received 1 December 2008; Accepted 13 April 2009

Academic Editor: Michael Huebner

Copyright © 2009 Jim Harkin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs) applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE), incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of the architecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerant computing.