###### Abstract  
Due to energy efficiency, spiking neural networks (SNNs) have gradually been considered as an alternative to convolutional neural networks (CNNs) in various machine learning tasks. In image recognition tasks, leveraging the superior capability of CNNs, the CNN-SNN conversion is considered one of the most successful approaches to training SNNs. However, previous works assume a rather long inference time period called inference latency to be allowed, while having a trade-off between inference latency and accuracy. One of the main reasons for this phenomenon stems from the difficulty in determining proper a firing threshold for spiking neurons. The threshold determination procedure is called a threshold balancing technique in the CNN-SNN conversion approach. This paper proposes a CNN-SNN conversion method with a new threshold balancing technique that obtains converted SNN models with good accuracy even with low latency. The proposed method organizes the SNN models with soft-reset IF spiking neurons. The threshold balancing technique estimates the thresholds for spiking neurons based on the maximum input current in a layerwise and channelwise manner. The experiment results have shown that our converted SNN models attain even higher accuracy than the corresponding trained CNN model for the MNIST dataset with low latency. In addition, for the Fashion-MNIST and CIFAR-10 datasets, our converted SNNs have shown less conversion loss than other methods in low latencies. The proposed method can be beneficial in deploying efficient SNN models for recognition tasks on resource-limited systems because the inference latency is strongly associated with energy consumption. CNN-SNN conversion; spiking neural network; intelligent mobile applications; threshold balancing technique; image recognition task; machine learning; artificial intelligence +
Footnote †: journal: Medical  
keeping some constant voltage continuously over some period. This helps reduce operational power compared to the conventional neural networks. The hardware devices for executing SNNs are denoted as neuromorphic devices [9, 10, 11]. Once such a neuromorphic device prevails, SNNs are expected to be deployed in various resource-limited devices such as IoT devices, embedded systems, and portable devices. With such expectation, SNNs are even referred to as the third-generation neural networks. In ANNs, there is only one kind of neuron, of which the behavior is just the weighted sum of their input values with an activation function. There are several activation functions such as sigmoid, hyper-tangent, ReLU, GeLU, Swish, and so on. On the contrary, in SNNs, there are different kinds of spiking neurons such as the Hodgkin-Huxley model, the leaky integrate-and-fire (LIF) model, the integrate-and-fire (IF) model, the soft-reset IF model, the spike response model (SRM),zhikevich's model, the FitzHugh-Nagumo (FHN) model, and so on [6, 7]. Due to the diversity of spiking neurons and their behavioral dynamics, SNNs are more difficult to train than ANNs. There have been various training algorithms developed for SNNs [6, 8]. The primary differences between CNNs (or ANNs in general) and SNNs lie in the data representation and the number of required forward computation passes for inference. In ANNs, the input and output signals of neurons are real-valued, and only a single feed-forward pass is required for inference. On the contrary, input and output signals in SNNs are sparse spikes over a certain time period, and their inference requires multiple feed-forward passes over the time period, also known as inference latency. Figure 1 shows the behaviors of an ANN and an SNN, where the ANN processes real values and the SNN processes spikes. In image recognition tasks, compared to the resounding successes achieved by CNNs over the past decade, SNN training algorithms have shown limited performance, yet remain an active research field.