A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks
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...
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doaj-169baac68a8d47d09b2079fc7034fda42020-11-24T22:59:19ZengHindawi LimitedInternational Journal of Reconfigurable Computing1687-71951687-72092009-01-01200910.1155/2009/908740908740A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural NetworksJim Harkin0Fearghal Morgan1Liam McDaid2Steve Hall3Brian McGinley4Seamus Cawley5School of Computing and Intelligent Systems, University of Ulster, Derry BT48 7JL, Northern IrelandBio-Inspired Electronics & Reconfigurable Computing Group, NUI Galway, Galway, IrelandSchool of Computing and Intelligent Systems, University of Ulster, Derry BT48 7JL, Northern IrelandDepartment of Electrical Engineering & Electronics, University of Liverpool, Liverpool L69 3GJ, UKBio-Inspired Electronics & Reconfigurable Computing Group, NUI Galway, Galway, IrelandBio-Inspired Electronics & Reconfigurable Computing Group, NUI Galway, Galway, IrelandFPGA 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.http://dx.doi.org/10.1155/2009/908740 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jim Harkin Fearghal Morgan Liam McDaid Steve Hall Brian McGinley Seamus Cawley |
spellingShingle |
Jim Harkin Fearghal Morgan Liam McDaid Steve Hall Brian McGinley Seamus Cawley A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks International Journal of Reconfigurable Computing |
author_facet |
Jim Harkin Fearghal Morgan Liam McDaid Steve Hall Brian McGinley Seamus Cawley |
author_sort |
Jim Harkin |
title |
A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks |
title_short |
A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks |
title_full |
A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks |
title_fullStr |
A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks |
title_full_unstemmed |
A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks |
title_sort |
reconfigurable and biologically inspired paradigm for computation using network-on-chip and spiking neural networks |
publisher |
Hindawi Limited |
series |
International Journal of Reconfigurable Computing |
issn |
1687-7195 1687-7209 |
publishDate |
2009-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2009/908740 |
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