  Spiking-neural-networks (SNNs) are promising at edge devices since the
event-driven operations of SNNs provides significantly lower power compared to
analog-neural-networks (ANNs). Although it is difficult to efficiently train
SNNs, many techniques to convert trained ANNs to SNNs have been developed.
However, after the conversion, a trade-off relation between accuracy and
latency exists in SNNs, causing considerable latency in large size datasets
such as ImageNet. We present a technique, named as TCL, to alleviate the
trade-off problem, enabling the accuracy of 73.87% (VGG-16) and 70.37%
(ResNet-34) for ImageNet with the moderate latency of 250 cycles in SNNs.

Computer Vision and Pattern Recognition