A4 Refereed article in a conference publication

Exploring Spiking Neural Network on Coarse-Grain Reconfigurable Architectures




AuthorsHassan Anwar, Syed M. A. H. Jafri, Sergei Dytckov, Masoud Daneshtalab, Masoumeh Ebrahimi, Ahmed Hemani

Conference nameInternational workshop on many-core embedded systems

Publication year2014

Book title Proceedings of International Workshop on Manycore Embedded Systems

First page 64

Last page67

Number of pages4

ISBN978-1-4503-2822-7

DOIhttps://doi.org/10.1145/2613908.2613916(external)

Web address http://dl.acm.org/citation.cfm?id=2613916(external)


Abstract

Today, recongurable architectures are becoming increas-

ingly popular as the candidate platforms for neural net-

works. Existing works, that map neural networks on re-

congurable architectures, only address either FPGAs or

Networks-on-chip, without any reference to the Coarse-Grain

Recongurable Architectures (CGRAs). In this paper we

investigate the overheads imposed by implementing spiking

neural networks on a Coarse Grained Recongurable Ar-

chitecture (CGRAs). Experimental results (using point to

point connectivity) reveal that up to 1000 neurons can be

connected, with an average response time of 4.4 msec.



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