Exploring Spiking Neural Network on Coarse-Grain Reconfigurable Architectures
Sergei Dytckov; Masoud Daneshtalab; Syed M. A. H. Jafri; Ahmed Hemani; Hassan Anwar; Masoumeh Ebrahimi
Exploring Spiking Neural Network on Coarse-Grain Reconfigurable Architectures
Sergei Dytckov
Masoud Daneshtalab
Syed M. A. H. Jafri
Ahmed Hemani
Hassan Anwar
Masoumeh Ebrahimi
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2021042714223
https://urn.fi/URN:NBN:fi-fe2021042714223
Tiivistelmä
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.
Kokoelmat
- Rinnakkaistallenteet [19207]