A method for determining an FL value to quantize convolutional neural network, CNN, parameters and feature values from a floating point format to a fixed point format for hardware execution, wherein the FL value is an absolute value of an exponent of the number represented by the Least Significant Bit, LSB, comprising steps executed by a computing device of: (a) if an original vector including original values is acquired, determining a range of FL candidate values by referring to a BW value, the largest value and the smallest value among the original values, wherein the BW value corresponds to the number of bits used to represent each of the quantized values, and applying quantization operations to the original values by referring to (i) the BW value, and (ii) each of the FL candidate values, to thereby generate each of quantized vectors, including the quantized values, corresponding to each of the FL candidate values; (b) generating each of weighted quantization loss values, corresponding to each of the FL candidate values, by applying weighted quantization loss operations to information on each of differences between the original values included in the original vector and the quantized values included in each of the quantized vectors; and (c) determining the FL value among the FL candidate values by referring to the weighted quantization loss values, wherein, at the step of (b), the weighted quantization loss operation follows a formula below, L l = ‖ W → ⋅ Q l → − x → ‖ p wherein x is the original vector, Q l is an l-th quantized vector, W is a weight vector, wherein each component of the weight vector corresponds to an inverse value of a respective component of the original vector ■ is an operation that generates an integrated vector whose elements are determined as results of element-wise multiplication of the weight vector W and a difference vector ( Q l - x), and ∥ ∥ p is an L-P norm operation, wherein, at the step of (a), the quantization operations follow a formula below, a k − th element of Q l → = min 2 BW − FL − 1, max − 2 BW − FL − 1, x k × 2 FL + ε 2 FL wherein, Q l is an l-th quantized vector, x k is a k-th original value included in the original vector, ε is a constant for determining a type of the quantization operation, and [] is a Gaussian operation. (a) if an original vector including original values is acquired, determining a range of FL candidate values by referring to a BW value, the largest value and the smallest value among the original values, wherein the BW value corresponds to the number of bits used to represent each of the quantized values, and applying quantization operations to the original values by referring to (i) the BW value, and (ii) each of the FL candidate values, to thereby generate each of quantized vectors, including the quantized values, corresponding to each of the FL candidate values; (b) generating each of weighted quantization loss values, corresponding to each of the FL candidate values, by applying weighted quantization loss operations to information on each of differences between the original values included in the original vector and the quantized values included in each of the quantized vectors; and (c) determining the FL value among the FL candidate values by referring to the weighted quantization loss values, wherein, at the step of (b), the weighted quantization loss operation follows a formula below, L l = ‖ W → ⋅ Q l → − x → ‖ p wherein x is the original vector, Q l is an l-th quantized vector, W is a weight vector, wherein each component of the weight vector corresponds to an inverse value of a respective component of the original vector ■ is an operation that generates an integrated vector whose elements are determined as results of element-wise multiplication of the weight vector W and a difference vector ( Q l - x), and ∥ ∥ p is an L-P norm operation, wherein, at the step of (a), the quantization operations follow a formula below, a k − th element of Q l → = min 2 BW − FL − 1, max − 2 BW − FL − 1, x k × 2 FL + ε 2 FL wherein, Q l is an l-th quantized vector, x k is a k-th original value included in the original vector, ε is a constant for determining a type of the quantization operation, and [] is a Gaussian operation. wherein, at the step of (b), the weighted quantization loss operation follows a formula below, L l = ‖ W → ⋅ Q l → − x → ‖ p wherein x is the original vector, Q l is an l-th quantized vector, W is a weight vector, wherein each component of the weight vector corresponds to an inverse value of a respective component of the original vector ■ is an operation that generates an integrated vector whose elements are determined as results of element-wise multiplication of the weight vector W and a difference vector ( Q l - x), and ∥ ∥ p is an L-P norm operation, wherein, at the step of (a), the quantization operations follow a formula below, a k − th element of Q l → = min 2 BW − FL − 1, max − 2 BW − FL − 1, x k × 2 FL + ε 2 FL wherein, Q l is an l-th quantized vector, x k is a k-th original value included in the original vector, ε is a constant for determining a type of the quantization operation, and [] is a Gaussian operation.