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This paper presents the SEPELYNS architecture that permits to in-
terconnect multiple spiking neurons focused on hardware implementations.
SEPELYNS can connect millions of neur
ons with thousands of synapses per
neuron in a layered fabric that provides some capabilities such as connectivity,
expansion, flexibility, bio-plausibility and
reusing of resources that allows si-
mulation of very large networks. We presen
t the three layers of this architecture
(neuronal, network adapters and networks on chip layers) and explain its per-
formance parameters such as throughput, latency and hardware resources. Some
application examples of large neural networks on SEPELYNS are studied;
these will show that use of
on-chip parallel networks could permit the hardware
simulation of populations of spiking neurons.
CitationBarrera, A.; Moreno, J. A communication infrastructure for emulating large-scale neural networks models. A: International Conference on Artificial Neural Networks. "Artificial Neural Networks and Machine Learning-ICANN 2012: 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, proceedings, part I". Lausanne: Springer, 2012, p. 129-136.
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