A neuromorphic architecture dynamic selection method for modeling on the basis of SNN model parameters comprises the steps of: extracting information of an SNN model from an NPZ file received from an IoT application by means of a neuromorphic architecture abstraction (NAA) HOST; selecting at least one neuromorphic architecture capable of executing the SNN model, on the basis of the number of input dimensions, the number of output dimensions, and the maximum number of available neurons of the SNN model; calculating the time required for training of and inferences by the neuromorphic architecture if there are two or more selected neuromorphic architectures; and finally selecting a neuromorphic architecture having the shortest time required for training and making inferences. Accordingly, an optimal neuromorphic architecture capable of executing an SNN model can be dynamically selected.
NEUROMORPHIC ARCHITECTURE DYNAMIC SELECTION METHOD FOR MODELING ON BASIS OF SNN MODEL PARAMETER, AND RECORDING MEDIUM AND DEVICE FOR PERFORMING SAME