Simplified Spiking Recurrent Cells enable FPGA SNNs to reach 92-96% MNIST accuracy at 0.45-1.74 mJ per classification while retaining richer dynamics than basic LIF models.
Lapicque’s introduction of the integrate-and-fire model neuron (1907).Brain research bulletin, 50(5-6):303–304
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Energy-Efficient Implementation of Spiking Recurrent Cells on FPGA
Simplified Spiking Recurrent Cells enable FPGA SNNs to reach 92-96% MNIST accuracy at 0.45-1.74 mJ per classification while retaining richer dynamics than basic LIF models.