A4 Refereed article in a conference publication
Exploring Spiking Neural Network on Coarse-Grain Reconfigurable Architectures
Authors: Hassan Anwar, Syed M. A. H. Jafri, Sergei Dytckov, Masoud Daneshtalab, Masoumeh Ebrahimi, Ahmed Hemani
Conference name: International workshop on many-core embedded systems
Publication year: 2014
Book title : Proceedings of International Workshop on Manycore Embedded Systems
First page : 64
Last page: 67
Number of pages: 4
ISBN: 978-1-4503-2822-7
DOI: https://doi.org/10.1145/2613908.2613916
Web address : http://dl.acm.org/citation.cfm?id=2613916
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.
Keywords:
coarse grained reconfigurable architectures (CGRA), Neural network
Downloadable publication This is an electronic reprint of the original article. |  |
