  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 spiking neural network
(S$^3$NN), an energy-efficient neural network with low computational cost and
high precision. The proposed S$^3$NN processes the information between hidden
layers by spikes as SNNs. Nevertheless, it has no temporal dimension so that
there is no latency within training and inference phases as BNNs. Thus, the
proposed S$^3$NN has a lower computational cost than SNNs that require
time-series processing. However, S$^3$NN cannot adopt na\"{i}ve backpropagation
algorithms due to the non-differentiability nature of spikes. We deduce a
suitable neuron model by reducing the surrogate gradient for multi-time step
SNNs to a single-time step. We experimentally demonstrated that the obtained
surrogate gradient allows S$^3$NN to be trained appropriately. We also showed
that the proposed S$^3$NN could achieve comparable accuracy to full-precision
networks while being highly energy-efficient.
