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Exploring Spiking Neural Network on Coarse-Grain Reconfigurable Architectures
KTH, School of Information and Communication Technology (ICT), Electronic Systems.
KTH, School of Information and Communication Technology (ICT), Electronic Systems.ORCID iD: 0000-0001-6289-1521
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2014 (English)In: ACM International Conference Proceeding Series, 2014, 64-67 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. 64-67 p.
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:kth:diva-160504DOI: 10.1145/2613908.2613916Scopus ID: 2-s2.0-84904490569ISBN: 978-145032822-7 (print)OAI: oai:DiVA.org:kth-160504DiVA: diva2:789967
Conference
Proceedings of International Workshop on Manycore Embedded Systems
Note

QC 20150223

Available from: 2015-02-21 Created: 2015-02-21 Last updated: 2016-12-05Bibliographically approved

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Daneshtalab, MasoudHemani, AhmedTenhunen, Hannu

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Jafri, Syed Mohammad Asad HassanDaneshtalab, MasoudHemani, AhmedTenhunen, Hannu
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