## 4 Experiment and Results
### Generalization Experiments of SIT-Conversion
#### 4.2.2 Comparison of Different Transformer Models  
In this Section, the ANN-to-SNN conversion effects of applying SIT-conversion to the EdgeNeXt, Next-ViT, and UniFormer models were longitudinally compared on the CIFAR10, CIFAR100, and ImageNet-1K datasets, as shown in Table 6.  
On the ImageNet-1K dataset, the SIT-conversion accuracies of the EdgeNeXt, Next-ViT, and UniFormer models are 79.18%, 81.71%, and 77.11% respectively. The maximum accuracy loss of the SNN generated by the SIT-conversion of the EdgeNeXt model reaches 0.24%, while the accuracies after conversion for the Next-ViT and UniFormer models are improved by 0.05% and 0% respectively. On the CIFAR100 dataset, the SIT-conversion accuracies of the EdgeNeXt, Next-ViT, and UniFormer models are 84.37%, 86.55%, and 85.77% respectively. The maximum accuracy loss of the SNN generated by the SIT-conversion of the EdgeNeXt model reaches 0.03%, while the accuracies after conversion for the Next-ViT \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **ANN** & **Accuracy by** & **Conversion** & **Transformer** & \multirow{2}{*}{**Parameters**} & **FLOPs** & **Energy (mJ)** \\  & **Accuracy** & **SIT-Conversion** & **Loss** & & & & \\ \hline \multicolumn{8}{c}{ImageNet-1K} \\ EdgeNeXt & 79.42\% & 79.18\% & \(-\)0.24\% & 3 & 5.59 M & 1.26 G & 3.19 (\(-\)**44.96\%**\({}^{1}\)) \\ \hline Next-ViT & 81.71\% & **81.71\%**\({}^{1}\) & **0.00\%**\({}^{1}\) & 4 & 31.76 M & 5.8 G & 21.83 (\(-\)18.18\%) \\ \hline UniFormer & 77.16\% & 77.11\% & \(-\)0.05\% & 10 & 10.21 M & 1.3 G & 4.49 (\(-\)**24.92\%**) \\ \hline \multicolumn{8}{c}{CIFAR100} \\ EdgeNeXt & 85.40\% & 85.37\% & \(-\)0.03\% & 3 & 5.59 M & 1.26 G & 3.22 (\(-\)**44.44\%**\({}^{1}\)) \\ \hline Next-ViT & 86.54\% & **86.55\%**\({}^{1}\) & +0.01\% & 4 & 31.76 M & 5.8 G & 21.32 (\(-\)20.09\%) \\ \hline UniFormer & 85.70\% & 85.77\% & **+0.07\%**\({}^{1}\) & 10 & 10.21 M & 1.3 G & 4.48 (\(-\)25.08\%) \\ \hline \multicolumn{8}{c}{CIFAR10} \\ EdgeNeXt & 97.69\% & **97.69\%**\({}^{1}\) & 0.00\% & 3 & 5.59 M & 1.26 G & 3.18 (\(-\)**45.13\%**\({}^{1}\)) \\ \hline Next-ViT & 97.62\% & 97.61\% & \(-\)0.01\% & 4 & 31.76 M & 5.8 G & 21.56 (\(-\)19.19\%) \\ \hline UniFormer & 97.46\% & 97.59\% & **+0.13\%**\({}^{1}\) & 10 & 10.21 M & 1.3 G & 4.50 (\(-\)24.75\%) \\ \hline \hline \end{tabular} \({}^{1}\) Bold indicates the best results.  
\end{table} 
Table 6: The accuracy, conversion loss, and theoretical energy consumption per sample during inference (parentheses indicate energy reduction compared to ANN) of SNNs generated by SIT-conversion from three state-of-the-art Transformer models on CIFAR10, CIFAR100, and ImageNet-1K datasets are presented. SNNs generated by Transformer’s SIT-conversion achieve nearly the same accuracy as their ANN counterparts. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Dataset**} & **1r** & **Weight-Decay** & **Epoch** \\ \hline \multicolumn{4}{c}{EdgeNeXt} \\ CIFAR10 & \(6\times 10^{-3}\) & 0.05 & 100 \\ CIFAR100 & \(6\times 10^{-3}\) & 0.05 & 100 \\ ImageNet-1K & / & / & / \\ \hline \multicolumn{4}{c}{Next-ViT} \\ CIFAR10 & \(5\times 10^{-4}\) & 0.1 & 100 \\ CIFAR100 & \(5\times 10^{-4}\) & 0.1 & 100 \\ ImageNet-1K & \(5\times 10^{-6}\) & \(1\times 10^{-8}\) & 20 \\ \hline \multicolumn{4}{c}{UniFormer} \\ CIFAR10 & \(5\times 10^{-4}\) & 0.05 & 100 \\ CIFAR100 & \(5\times 10^{-4}\) & 0.05 & 100 \\ ImageNet-1K & \(4\times 10^{-5}\) & 0.05 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameter settings of EdgeNeXt, Next-ViT, and UniFormer models trained on CIFAR10, CIFAR100, and ImageNet-1K datasets. and UniFormer models are improved by 0.01% and 0.07%, respectively. On the CIFAR10 dataset, the SIT-conversion accuracies of the EdgeNeXt, Next-ViT, and UniFormer models are 97.69%, 97.61%, and 97.59% respectively. The maximum accuracy loss of the SNN generated by the SIT-conversion of the Next-ViT model is 0.01%, while the accuracy improvement after conversion for the UniFormer model is 0.13%.