  As spiking neural networks (SNNs) are event-driven, energy efficiency is
higher than conventional artificial neural networks (ANNs). Since SNN delivers
data through discrete spikes, it is difficult to use gradient methods for
training, limiting its accuracy. To keep the accuracy of SNNs similar to ANN
counterparts, pre-trained ANNs are converted to SNNs (ANN-to-SNN conversion).
During the conversion, encoding activations of ANNs to a set of spikes in SNNs
is crucial for minimizing the conversion loss. In this work, we propose a
single-spike phase coding as an encoding scheme that minimizes the number of
spikes to transfer data between SNN layers. To minimize the encoding error due
to single-spike approximation in phase coding, threshold shift and base
manipulation are proposed. Without any additional retraining or architectural
constraints on ANNs, the proposed conversion method does not lose inference
accuracy (0.58% on average) verified on three convolutional neural networks
(CNNs) with CIFAR and ImageNet datasets.In addition, graph convolutional
networks (GCNs) are converted to SNNs successfully with an average accuracy
loss of 0.90%.Most importantly, the energy efficiency of our SNN improves by
4.6~17.3 X compared to the ANN baseline.
