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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&#x0025; (VGG-16) and 70.37&#x0025; (ResNet-34) for ImageNet with the moderate latency of 250 cycles in SNNs.

TCL: an ANN-to-SNN Conversion with Trainable Clipping Layers