Abstract
This paper presents a strategy for the implementation of large scale spiking neural network topologies on FPGA devices based on the I&F conductance model. Analysis of the logic requirements demonstrate that large scale implementations are not viable if a fully parallel implementation strategy is utilised. Thus the paper presents an alternative approach where a trade off in terms of speed/area is made and time multiplexing of the neuron model implemented on the FPGA is used to generate large network topologies. FPGA implementation results demonstrate a performance increase over a PC based simulation.
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Glackin, B., McGinnity, T.M., Maguire, L.P., Wu, Q.X., Belatreche, A. (2005). A Novel Approach for the Implementation of Large Scale Spiking Neural Networks on FPGA Hardware. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_68
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DOI: https://doi.org/10.1007/11494669_68
Publisher Name: Springer, Berlin, Heidelberg
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