IS-SNN removes activation normalization from deep SNNs via topology-aware weight standardization folded into static weights, matching dynamic BN accuracy on ImageNet (68.05%) while cutting FPGA neuron LUT usage by 96.4%.
Advances in Neural Information Processing Systems35, 34377–34390 (2022)
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Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization
IS-SNN removes activation normalization from deep SNNs via topology-aware weight standardization folded into static weights, matching dynamic BN accuracy on ImageNet (68.05%) while cutting FPGA neuron LUT usage by 96.4%.