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

Source: [TCL: an ANN-to-SNN Conversion with Trainable Clipping Layers](<https://www.sota2.com/research/paper/68fa6b611e209131cea2de02>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:47:07.195Z

Authors: Nguyen-Dong Ho, Ik-Joon Chang

Published: 2020-08-11T04:20:27.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2008.04509>)

[PDF](<https://arxiv.org/pdf/2008.04509.pdf>)