## 4 Experiment Results and Discussion
### Experiments on the MNIST and Fashion-MNIST Dataset
#### 4.1.2 Conversion to SNN and Performance Evaluation  
From the trained CNN models, we transferred the weights to the corresponding SNNs with the soft-reset IF neurons. The proposed threshold balancing technique was used to set the firing thresholds of the IF neurons for each channel of the convolutional layers and fully connected layers, sequentially, layer by layer. Figures 15 and 16 in the Appendix F shows the threshold values assigned to the channels for some layers at some time steps on the MNIST and Fashion-MNIST datasets, respectively. For the input layer, the inputs to the spike neurons are constant currents, which do not change over time. Hence, the assigned thresholds to the channels for the first layer do not change over the latency time duration, as shown in Figures 15 and 16. Figure 6 shows the performances of the proposed method and the other three SNN-CNN conversion methods such as [PERSON] et al. 's method [25], [PERSON] et al. 's method [26], and [PERSON] et al. 's method [27]. The converted model by the proposed method (green line in the figure) achieved a reasonable accuracy of 86.18% with a very low inference latency of four time steps. Furthermore, with a latency of only 64 time steps, the SNN model showed an accuracy of 99.33%, which is even higher than that of the corresponding CNN with an accuracy of 99.31%. To compare the effectiveness of the proposed method with other existing methods [25; 26; 27], we re-implemented those methods for the same network architecture. Some of them were proposed for different tasks such as object detection [27]. We made some modifications as described in Appendix C. Table 2 shows the classification accuracies for different latencies with respect to the four conversion methods on the MNIST dataset. In the table, \"/\" indicates the situations where the performance could not be measured due to low latency. As seen in Figure 6, the converted SNNs by the proposed method attained higher accuracies than other methods in the examined inference latencies. The major difference among the compared methods lies in the threshold balancing technique used in the SNN-CNN conversion. The experiment results imply that the proposed threshold balancing technique is more effective than others in transferring knowledge trained in CNN models to SNN models, even with low latency. Figure 7 shows the average firing rates at every channel of the first convolutional layer for some input sample with a latenc \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Training Configurations** & **Values** \\ \hline \multirow{4}{*}{MNIST} & [PERSON] & [PERSON] \\  & Learning rate & 0.001 \\  & Number of channels in Conv layers & 12, 64 \\  & Number of epochs & 100 \\  & Dropout & 50\% \\ \hline \multirow{4}{*}{Fashion-MNIST} & [PERSON] & [PERSON] \\  & Learning rate & \([10^{-3},10^{-4}]\) \\ \cline{1-1}  & Number of channels in Conv layers & 128, 256 \\ \cline{1-1}  & Number of epochs & 250 \\ \cline{1-1}  & Dropout & 50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Training configurations for both datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Latency (\(T_{infer}\))Methods** & [25] & [26] & [27] & **Our Method** \\ \hline
64 & 97.98\% & 97.87\% & 97.20\% & **99.33\%** \\
32 & 96.89\% & 96.89\% & 96.27\% & **99.30\%** \\
**16** & 79.05\% & 90.00\% & 93.06\% & **99.25\%** \\
8 & 17.89\% & 19.68\% & 77.64\% & **98.98\%** \\
**4** & / & / & 31.31\% & **86.18\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracies vs. latencies for the compared methods on the MNIST dataset. shown in Figure 7, our method made the converted SNN model preserve higher average firing rates in most channels compared with the other methods. The average firing rate \(R_{k}^{l}\) at the \(k\)-th channel in the \(l\)-th layer is calculated as follows:  
Figure 6: Performance comparison of the proposed method and other CNN-SNN conversion methods on the MNIST dataset [25; 26; 27].