  Spiking neural network (SNN) has attracted great attention due to its
characteristic of high efficiency and accuracy. Currently, the ANN-to-SNN
conversion methods can obtain ANN on-par accuracy SNN with ultra-low latency (8
time-steps) in CNN structure on computer vision (CV) tasks. However, as
Transformer-based networks have achieved prevailing precision on both CV and
natural language processing (NLP), the Transformer-based SNNs are still
encounting the lower accuracy w.r.t the ANN counterparts. In this work, we
introduce a novel ANN-to-SNN conversion method called SpikeZIP-TF, where ANN
and SNN are exactly equivalent, thus incurring no accuracy degradation.
SpikeZIP-TF achieves 83.82% accuracy on CV dataset (ImageNet) and 93.79%
accuracy on NLP dataset (SST-2), which are higher than SOTA Transformer-based
SNNs. The code is available in GitHub:
https://github.com/Intelligent-Computing-Research-Group/SpikeZIP_transformer
