  Spiking Neural Networks (SNNs) have been attached great importance due to the
distinctive properties of low power consumption, biological plausibility, and
adversarial robustness. The most effective way to train deep SNNs is through
ANN-to-SNN conversion, which have yielded the best performance in deep network
structure and large-scale datasets. However, there is a trade-off between
accuracy and latency. In order to achieve high precision as original ANNs, a
long simulation time is needed to match the firing rate of a spiking neuron
with the activation value of an analog neuron, which impedes the practical
application of SNN. In this paper, we aim to achieve high-performance converted
SNNs with extremely low latency (fewer than 32 time-steps). We start by
theoretically analyzing ANN-to-SNN conversion and show that scaling the
thresholds does play a similar role as weight normalization. Instead of
introducing constraints that facilitate ANN-to-SNN conversion at the cost of
model capacity, we applied a more direct way by optimizing the initial membrane
potential to reduce the conversion loss in each layer. Besides, we demonstrate
that optimal initialization of membrane potentials can implement expected
error-free ANN-to-SNN conversion. We evaluate our algorithm on the CIFAR-10,
CIFAR-100 and ImageNet datasets and achieve state-of-the-art accuracy, using
fewer time-steps. For example, we reach top-1 accuracy of 93.38\% on CIFAR-10
with 16 time-steps. Moreover, our method can be applied to other ANN-SNN
conversion methodologies and remarkably promote performance when the time-steps
is small.
