  Spiking neural networks (SNNs) have shown advantages in computation and
energy efficiency over traditional artificial neural networks (ANNs) thanks to
their event-driven representations. SNNs also replace weight multiplications in
ANNs with additions, which are more energy-efficient and less computationally
intensive. However, it remains a challenge to train deep SNNs due to the
discrete spike function. A popular approach to circumvent this challenge is
ANN-to-SNN conversion. However, due to the quantization error and accumulating
error, it often requires lots of time steps (high inference latency) to achieve
high performance, which negates SNN's advantages. To this end, this paper
proposes Fast-SNN that achieves high performance with low latency. We
demonstrate the equivalent mapping between temporal quantization in SNNs and
spatial quantization in ANNs, based on which the minimization of the
quantization error is transferred to quantized ANN training. With the
minimization of the quantization error, we show that the sequential error is
the primary cause of the accumulating error, which is addressed by introducing
a signed IF neuron model and a layer-wise fine-tuning mechanism. Our method
achieves state-of-the-art performance and low latency on various computer
vision tasks, including image classification, object detection, and semantic
segmentation. Codes are available at: https://github.com/yangfan-hu/Fast-SNN.
