Exploring Spiking Neural Network on Coarse-Grain Reconfigurable Architectures
: Hassan Anwar, Syed M. A. H. Jafri, Sergei Dytckov, Masoud Daneshtalab, Masoumeh Ebrahimi, Ahmed Hemani
: International workshop on many-core embedded systems
: 2014
: Proceedings of International Workshop on Manycore Embedded Systems
: 64
: 67
: 4
: 978-1-4503-2822-7
DOI: https://doi.org/10.1145/2613908.2613916
: 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.
coarse grained reconfigurable architectures (CGRA), Neural network
