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Published in:
2012 | OriginalPaper | Chapter
A Large-Scale Spiking Neural Network Accelerator for FPGA Systems
Authors : Kit Cheung, Simon R. Schultz, Wayne Luk
Published in: Artificial Neural Networks and Machine Learning – ICANN 2012
Publisher: Springer Berlin Heidelberg
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Spiking neural networks (SNN) aim to mimic membrane potential dynamics of biological neurons. They have been used widely in neuromorphic applications and neuroscience modeling studies. We design a parallel SNN accelerator for producing large-scale cortical simulation targeting an off-the-shelf Field-Programmable Gate Array (FPGA)-based system. The accelerator parallelizes synaptic processing with run time proportional to the firing rate of the network. Using only one FPGA, this accelerator is estimated to support simulation of 64K neurons 2.5 times real-time, and achieves a spike delivery rate which is at least 1.4 times faster than a recent GPU accelerator with a benchmark toroidal network.