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.
Is the integrate-and-fire model good enough?—a review.Neural networks, 14 (6-7):955–975
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NE 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.