Exploring Spiking Neural Network on Coarse-Grain Reconfigurable Architectures

Abstract

Today, reconfigurable architectures are becoming increasingly popular as the candidate platforms for neural networks. Existing works, that map neural networks on reconfigurable architectures, only address either FPGAs or Networks-on-chip, without any reference to the Coarse-Grain Reconfigurable Architectures (CGRAs). In this paper we investigate the overheads imposed by implementing spiking neural networks on a Coarse Grained Reconfigurable Architecture (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.