A method for transforming convolutional layers of a CNN including m convolutional blocks, comprising steps of: (a) a computing device , if an input image to be used for determining scaling parameters is acquired, generating one or more k-th quantization loss values by referring to (i) one or more k-th initial weights of a k-th initial convolutional layer (211_k) included in a k-th convolutional block, (ii) (ii-1) the input image in case k is 1, (ii-2) a (k-1)-th feature map, corresponding to the input image, outputted from the (k-1)-th convolutional block in case k is from 2 to m, and (iii) (iii-1) each of k-th scaling parameters corresponding to each of channels included in the input image, in case k is 1 and (iii-2) each of k-th scaling parameters corresponding to each of channels included in the (k-1)-th feature map, in case k is from 2 to m, wherein k is an integer from 1 to m; (b) the computing device determining each of k-th optimized scaling parameters, corresponding to each of the channels included in the (k-1)-th feature map, among the k-th scaling parameters by referring to the k-th quantization loss values; (c) the computing device generating a k-th scaling layer (214_k) and a k-th inverse scaling layer (215_k) by referring to the k-th optimized scaling parameters; and (d) the computing device , (i) in case k is 1, transforming the k-th initial convolutional layer (211_k) into a k-th integrated convolutional layer (216_k) by merging the k-th initial convolutional layer (211_k) and the k-th scaling layer (214_k), and (ii) in case k is an integer from 2 to m, transforming the k-th initial convolutional layer (211_k) into the k-th integrated convolutional layer (216_k) by merging the k-th initial convolutional layer (211_k), the k-th scaling layer (214_k) and the (k-1)-th inverse scaling layer (215_(k-1)). (a) a computing device , if an input image to be used for determining scaling parameters is acquired, generating one or more k-th quantization loss values by referring to (i) one or more k-th initial weights of a k-th initial convolutional layer (211_k) included in a k-th convolutional block, (ii) (ii-1) the input image in case k is 1, (ii-2) a (k-1)-th feature map, corresponding to the input image, outputted from the (k-1)-th convolutional block in case k is from 2 to m, and (iii) (iii-1) each of k-th scaling parameters corresponding to each of channels included in the input image, in case k is 1 and (iii-2) each of k-th scaling parameters corresponding to each of channels included in the (k-1)-th feature map, in case k is from 2 to m, wherein k is an integer from 1 to m; (b) the computing device determining each of k-th optimized scaling parameters, corresponding to each of the channels included in the (k-1)-th feature map, among the k-th scaling parameters by referring to the k-th quantization loss values; (c) the computing device generating a k-th scaling layer (214_k) and a k-th inverse scaling layer (215_k) by referring to the k-th optimized scaling parameters; and (d) the computing device , (i) in case k is 1, transforming the k-th initial convolutional layer (211_k) into a k-th integrated convolutional layer (216_k) by merging the k-th initial convolutional layer (211_k) and the k-th scaling layer (214_k), and (ii) in case k is an integer from 2 to m, transforming the k-th initial convolutional layer (211_k) into the k-th integrated convolutional layer (216_k) by merging the k-th initial convolutional layer (211_k), the k-th scaling layer (214_k) and the (k-1)-th inverse scaling layer (215_(k-1)). <EOS>