TCL: an ANN-to-SNN Conversion with Trainable Clipping Layers
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Spiking Neural Networks (SNNs) provide significantly lower power dissipationthan deep neural networks (DNNs), called as analog neural networks (ANNs) inthis work. Conventionally, SNNs have failed to arrive at the training accuraciesof ANNs. However, several recent researches have shown that this challenge canbe addressed by converting ANN to SNN instead of the direct training of SNNs.Nonetheless, the large latency of SNNs still limits their application, more prob-lematic for large size datasets such as Imagenet. It is challenging to overcome thisproblem since in SNNs, there is the trade-off relation between their accuracy and la-tency. In this work, we elegantly alleviate the problem by using a trainable clippinglayers, so called TCL. By combining the TCL with traditional data-normalizationtechniques, we respectively obtain 71.12 after the ANN to SNN conversion with the latency constraint of250 cycles.
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