###### Abstract
The SNN training algorithms can be categorized into three major approaches: bio-inspired learning approach [12, 13, 14, 15, 16, 17, 18], spike-based backpropagation approximation approach [19, 20, 21, 22, 23], and ANN-SNN conversion approach [24, 25, 26, 27, 28]. The biologically based plausible learning approach attempts to train SNNs by adjusting weights based on local learning rules for synaptic strength in an unsupervised manner [12, 13, 14] or in a semi-supervised manner [15, 16, 17, 18]. It exhibits a trade-off between biological plausibility and performance. The spike-based backpropagation approximation approach [19, 20, 21, 22, 23] directly trains SNNs by approximating the error backpropagation algorithm, widely used for training traditional artificial neural networks (ANN), so as to be applicable for spikes. Compared to the biologically plausible learning approach, the SNN learning algorithms of this approach have generally shown better accuracy and require a higher computational budget, but are less biologically plausible. The ANN-SNN conversion approach [24, 25, 26, 27, 28] has proven to be promising to train deep SNNs. It first trains an ANN with some constraints for the given training dataset, and then, it converts the trained ANN model into an SNN model, which consists of spiking neurons with appropriate firing thresholds. CNN models have been widely used as ANNs for the image recognition tasks. The CNN-SNN conversion algorithms [24, 25, 26, 27, 28] require a rather long inference latency, while having a trade-off between inference latency and accuracy. Figure 1: Behavioral differences between ANN and SNN models. (**a**) a general architecture of 2-layer network; (**b**) a 2-layer ANN architecture; and (**c**) a 2-layer SNN architecture. From these observations, we propose a new CNN-SNN conversion algorithm, which reduces the conversion loss from the trained CNN to an SNN with low inference latency. The proposed CNN-SNN conversion algorithm uses a threshold balancing technique, which pays attention to inference latency. The experimental results on the MNIST dataset [29] have shown that the proposed method could produce an SNN model, of which the accuracy of 99.33% is even higher than the accuracy of 99.31% of its corresponding CNN model, with a low inference latency of 64 time steps. In addition, the experimental results on the Fashion-MNIST [30] and CIFAR-10 datasets [31] have shown that the converted SNNs experience less conversion loss than other CNN-SNN conversion methods with low latency. Specifically, with a latency of 64 time steps, the proposed threshold balancing method has reduced conversion losses of approximately 10% and 8%, respectively, compared to the methods in [25,26]. For the latency of 128 time steps, experiments have shown that those reduced losses were 45% and 30%, respectively. The rest of this paper is organized as follows: The foundations of the CNN-SNN conversion methodology and related works are provided in the next section. Section 3 presents a new CNN-SNN conversion method with the proposed threshold balancing technique. The experimental results and further discussion are described in Sections 4 and 5, respectively. The last section draws the conclusions.