A computer-implemented method of generating a fixed-point quantized neural network, the method comprising: analyzing a statistical distribution (900; 1000) for each channel of floating-point parameter values of feature maps and a kernel for each channel from data of a pre-trained floating-point neural network; determining a fixed-point expression (610, 620) of each of the parameters for each channel statistically covering a distribution range of the floating-point parameter values based on the statistical distribution (900; 1000) for each channel; determining fractional lengths of fixed-point expressions of a bias and a weight for each channel among the parameters for each channel based on a result of performing a convolution operation; and generating a fixed-point quantized neural network in which the bias and the weight for each channel have the determined fractional lengths, wherein the fixed-point quantized neural network is a convolutional neural network, wherein the convolution operation comprises a partial sum operation between a plurality of channels, the partial sum operation comprises a plurality of multiply-accumulate, MAC, operations and an Add operation, and the determining of the fractional lengths comprises determining the fractional length of the bias based on fractional lengths of input activations and fractional lengths of weights input to the MAC operations, wherein the determining of the fractional length of the bias comprises determining the fractional length of the bias based on a maximum fractional length among fractional lengths of fixed-point expressions corresponding to results of the MAC operations. analyzing a statistical distribution (900; 1000) for each channel of floating-point parameter values of feature maps and a kernel for each channel from data of a pre-trained floating-point neural network; determining a fixed-point expression (610, 620) of each of the parameters for each channel statistically covering a distribution range of the floating-point parameter values based on the statistical distribution (900; 1000) for each channel; determining fractional lengths of fixed-point expressions of a bias and a weight for each channel among the parameters for each channel based on a result of performing a convolution operation; and generating a fixed-point quantized neural network in which the bias and the weight for each channel have the determined fractional lengths, wherein the fixed-point quantized neural network is a convolutional neural network, wherein the convolution operation comprises a partial sum operation between a plurality of channels, the partial sum operation comprises a plurality of multiply-accumulate, MAC, operations and an Add operation, and the determining of the fractional lengths comprises determining the fractional length of the bias based on fractional lengths of input activations and fractional lengths of weights input to the MAC operations, wherein the determining of the fractional length of the bias comprises determining the fractional length of the bias based on a maximum fractional length among fractional lengths of fixed-point expressions corresponding to results of the MAC operations.