An FPGA design framework for large-scale spiking neural networks

@article{Wang2014AnFD,
  title={An FPGA design framework for large-scale spiking neural networks},
  author={Runchun Wang and Tara Julia Hamilton and Jonathan Tapson and Andr{\'e} van Schaik},
  journal={2014 IEEE International Symposium on Circuits and Systems (ISCAS)},
  year={2014},
  pages={457-460}
}
We present an FPGA design framework for large-scale spiking neural networks, particularly the ones with a high-density of connections or all-to-all connections. The proposed FPGA design framework is based on a reconfigurable neural layer, which is implemented using a time-multiplexing approach to achieve up to 200,000 virtual neurons with one physical neuron using only a fraction of the hardware resources in commercial-off-the-shelf FPGAs (even entry level ones). Rather than using a… CONTINUE READING
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