Abstract: Disclosed are a spiking neural network training method based the conversion aware training and a system thereof. The spiking neural network training method includes an ANN generation operation of generating an analog artificial neural network (ANN) model and inputting variable data, a conversion aware training operation of simulating a spiking neural network (SNN) model by using one or more activation functions with respect to the analog ANN model, and an SNN generation operation of generating the SNN model by correcting parameters and weights of layers based on a result of the simulation.

Abstract: Disclosed are a method of training a spiking neural network computation based on a temporal coding and a system thereof. The method includes a kernel generation operation of generating a kernel computation for a log computation, a conversion recognition learning operation of converting a spike timestep using the kernel computation and one or more activation functions, a PSP computation operation of computing a sum of postsynaptic potentials (PSPs) using the converted spike timestep, and an SNN learning operation, by a spiking neural network (SNN) model, of training data using a membrane potential value depending on the sum.

Abstract: Disclosed are a method for accelerating early determination training. The method for accelerating early determination training includes a timestep splitting operation of splitting a timestep, a membrane potential measuring operation of measuring first and second membrane potentials for each splitted timestep during a current training process, a threshold value calculation operation of calculating a threshold value to be used in a subsequent training process based on the first and second membrane potentials, and when a difference between the first and second membrane potentials in the splitted timestep is greater than the threshold value, an early training termination operation of determining that the image does not have the training contribution and terminating training at the splitted timestep.

Abstract: Disclosed are a method for accelerating supervised training of a spiking neural network. The method includes measuring first and second membrane potentials for each time step during a training process, extracting distribution data of the first and second membrane potentials based on the first and second membrane potentials for the each time step, calculating a threshold value to be used in a subsequent training process based on the distribution data of the first and second membrane potentials, classifying images having no training contribution based on the threshold value calculated in a previous training process, and terminating the training at the time step based on determining that the image does not have the training contribution when a difference between the first and second membrane potentials in the time step is greater than the threshold value.