SAFformer uses brain-inspired active predictive filtering in a spiking transformer to reach new state-of-the-art accuracy on CIFAR-10/100 and CIFAR10-DVS plus 80.5% top-1 on ImageNet-1K at 26.58M parameters and 5.88 mJ energy.
The free-energy principle: a rough guide to the brain? Trends in cognitive sciences , 13(7):293–301
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SAFformer:Improving Spiking Transformer via Active Predictive Filtering
SAFformer uses brain-inspired active predictive filtering in a spiking transformer to reach new state-of-the-art accuracy on CIFAR-10/100 and CIFAR10-DVS plus 80.5% top-1 on ImageNet-1K at 26.58M parameters and 5.88 mJ energy.