## 5 Further Discussion  
Over the past several years, SNNs have attracted significant research interest due to their energy efficiency. Specifically, recent concerns about training SNNs lie in not only improving the accuracy, but also minimizing their power consumption. As mentioned in Section 1, the SNN training algorithms can be categorized into the bio-inspired learning approach [12; 13; 14; 15; 16; 17; 18], the spike-based backpropagation approximation approach [19; 20; 21; 22; 23], and the ANN-SNN conversion approach [24; 25; 26; 27]. The biologically based plausible learning approach generally uses local learning rules for shallow networks, which have some restrictions on their scalability and expressive power. The spike-based backpropagation approximation approach uses some variants of the error backpropagation algorithm, which  
Figure 11: Performance comparison of the proposed method and other CNN–SNN conversion methods on the CIFAR-10 dataset [25; 26]. approximates the derivatives of spike signals with surrogate functions. Compared to the biologically plausible learning approach, the approximation approach has generally shown better accuracy, requires a higher computational cost, and is difficult to apply to training deeper SNNs. The ANN-SNN conversion approach including the CNN-SNN conversion approach indirectly trains SNNs by using the weights of the trained SNNs having the same architecture. The conversion method does not care much about the number of layers as in the bio-inspired learning approach and the spike-based backpropagation approximation approach because the weights of the model are trained in its corresponding ANN or CNN model. Hence, the ANN-SNN conversion approach has the features of the scalability of the model architecture, yet usually requires a rather long inference latency while, having a trade-off between inference latency and accuracy. In the conversion approach, the determination of the threshold values for the spike neurons is one of the key factors that strongly affects the performance of the converted SNNs. The proposed threshold balancing method determines the threshold values for each channel at the convolutional layers. [PERSON] et al. 's method [25] takes a similar approach to the proposed method, but it does not take into account the channels in determining the threshold values. The proposed threshold method has shown good performance for low latency compared to the existing methods [24; 25; 26; 27; 28]. From the experiments for a specific SNN architecture on the MNIST and Fashion-MNIST datasets, we observed that the proposed conversion method could produce SNN models with better performance with low latency. The experiments with the deep SNN models on the CIFAR-10 dataset showed that the conversion method could generate comparable deep SNNs to other conversion techniques. Table 5 shows the performance of the SNN models on the MNIST dataset surveyed in the literature. It shows the accuracies along with the allowed inference latency for the SNN models, which might have different architecture from each other. It also describes the used neural encoding method, the training approach, and the learning type, such as supervised, unsupervised, and semi-supervised learning. As observed in Table 5, the bio-inspired learning approach usually produces SNNs with lower accuracy than the rest of the training approaches [12; 16]. Although [PERSON] et al. 's method [21] obtained an SNN model with better accuracy than our work, it requires a much higher training cost and higher inference latency than our work. One reason for this slightly inferior performance compared to their model lies in that the accuracy of our trained CNN model (99.31%) is lower than that (99.59%) of their trained SNN model. At a latency of 64 time steps, our method produced an SNN model with better performance than all other methods. Even at a latency of only four time steps, our method produced an SNN model with comparable performance.