The article introduces SAF as a method to train Spiking Neural Networks (SNNs) efficiently. It addresses the challenge of training SNNs due to their non-differentiable neurons by proposing SAF, which propagates spike accumulation during training. The SAF method is compared with Online Training Through Time (OTTT) and Spike Representation in terms of accuracy, training time, memory usage, and firing rate. Experimental results on CIFAR-10 and CIFAR-100 datasets show that SAF-E is equivalent to OTTTO, while SAF-F is identical to Spike Representation. The study demonstrates that SAF can reduce training time and memory usage compared to traditional methods while maintaining accuracy.
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