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.