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Rethinking the role of normalization and residual blocks for spiking neural networks
Biologically inspired spiking neural networks (SNNs) are widely used to realize ultralow-power energy consumption.
S$^3$NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Spiking Neural Networks
To solve these problems, we propose a single-step spiking neural network (S$^3$NN), an energy-efficient neural network with low computational cost and high precision.
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