A reconfigurable and biologically inspired paradigm for computation using network-on-chip and spiking neural networks

Authors:
Jim Harkin;Fearghal Morgan;Liam McDaid;Steve Hall;Brian McGinley;Seamus Cawley
Affiliations:
School of Computing and Intelligent Systems, University of Ulster, Derry, Northern Ireland;Bio-Inspired Electronics & Reconfigurable Computing Group, NUI Galway, Galway, Ireland;School of Computing and Intelligent Systems, University of Ulster, Derry, Northern Ireland;Department of Electrical Engineering & Electronics, University of Liverpool, Liverpool, UK;Bio-Inspired Electronics & Reconfigurable Computing Group, NUI Galway, Galway, Ireland;Bio-Inspired Electronics & Reconfigurable Computing Group, NUI Galway, Galway, Ireland
Venue:
International Journal of Reconfigurable Computing - Selected papers from ReCoSoc08
Year:
2009

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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.