Spiking Neural Networks (SNNs) have gained great attraction due to their
distinctive properties of low power consumption and fast inference on
neuromorphic hardware. As the most effective method to get deep SNNs, ANN-SNN
conversion has achieved comparable performance as ANNs on large-scale datasets.
Despite this, it requires long time-steps to match the firing rates of SNNs to
the activation of ANNs. As a result, the converted SNN suffers severe
performance degradation problems with short time-steps, which hamper the
practical application of SNNs. In this paper, we theoretically analyze ANN-SNN
conversion error and derive the estimated activation function of SNNs. Then we
propose the quantization clip-floor-shift activation function to replace the
ReLU activation function in source ANNs, which can better approximate the
activation function of SNNs. We prove that the expected conversion error
between SNNs and ANNs is zero, enabling us to achieve high-accuracy and
ultra-low-latency SNNs. We evaluate our method on CIFAR-10/100 and ImageNet
datasets, and show that it outperforms the state-of-the-art ANN-SNN and
directly trained SNNs in both accuracy and time-steps. To the best of our
knowledge, this is the first time to explore high-performance ANN-SNN
conversion with ultra-low latency (4 time-steps). Code is available at
this https URL

本研究提出量化裁剪 - 移位激活函数以更好地逼近 Spiking Neural Networks 的激活函数，能在超低延迟 (4 time-steps) 的情况下实现高精度和超低延迟的 SNNs，并在 CIFAR-10/100 和 ImageNet 数据集上提高了性能。