S$^2$NN: Time Step Reduction of Spiking Surrogate Gradients for Training Energy Efficient Single-Step Neural Networks. (arXiv:2201.10879v1 [cs.LG])

As the scales of neural networks increase, techniques that enable them to run
with low computational cost and energy efficiency are required. From such
demands, various efficient neural network paradigms, such as spiking neural
networks (SNNs) or binary neural networks (BNNs), have been proposed. However,
they have sticky drawbacks, such as degraded inference accuracy and latency. To
solve these problems, we propose a single-step neural network (S$^2$NN), an
energy-efficient neural network with low computational cost and high precision.
The proposed …

arxiv energy networks neural networks time training