Exploring Spiking Neural Network on Coarse-Grain Reconfigurable Architectures

• Today, reconfigurable architectures are becoming increas- ingly popular as the candidate platforms for neural net- works. Existing works, that map neural networks on re- configurable 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 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Inbäddad systemteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Embedded Systems (hsv//eng)

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)