  Spiking neural networks (SNNs) can utilize spatio-temporal information and
have a nature of energy efficiency which is a good alternative to deep neural
networks(DNNs). The event-driven information processing makes SNNs can reduce
the expensive computation of DNNs and save a lot of energy consumption.
However, high training and inference latency is a limitation of the development
of deeper SNNs. SNNs usually need tens or even hundreds of time steps during
the training and inference process which causes not only the increase of
latency but also the waste of energy consumption. To overcome this problem, we
proposed a novel training method based on backpropagation (BP) for ultra-low
latency(1-2 time steps) SNN with multi-threshold. In order to increase the
information capacity of each spike, we introduce the multi-threshold Leaky
Integrate and Fired (LIF) model. In our proposed training method, we proposed
three approximated derivative for spike activity to solve the problem of the
non-differentiable issue which cause difficulties for direct training SNNs
based on BP. The experimental results show that our proposed method achieves an
average accuracy of 99.56%, 93.08%, and 87.90% on MNIST, FashionMNIST, and
CIFAR10, respectively with only 2 time steps. For the CIFAR10 dataset, our
proposed method achieve 1.12% accuracy improvement over the previously reported
direct trained SNNs with fewer time steps.
