A method of performing neural network training or neural network fine-tuning, which is implemented by an artificial intelligence processor, comprising: determining quantization parameter(s), which comprises obtaining an analyzing result of each type of the data to be quantized, wherein the data to be quantized includes at least one type of data among neurons, weights, gradients, and biases of the neural network; and determining a corresponding quantization parameter according to the analyzing result of each type of data to be quantized and a data bit width, using the quantization parameter to perform corresponding quantization on data which are to be used in a process of neural network operation in the neural network training or the neural network fine-tuning, whereby high-precision data are converted into low-precision fixed-point data, using the quantized fixed-point data to perform the neural network operation in the neural network training or the neural network fine-tuning, characterized in that the quantization parameter includes a first scaling coefficient f 1 and a offset O,wherein a following formula is used to quantize the data to be quantized to obtain the quantized data I x: I x = round F x − O ƒ 1 in the formulas, f 1 refers to the first scaling coefficient, and f 1 = Z / (2 n-1 -1), Z refers to the maximum absolute value of all numbers in the number field of the data to be quantized, O refers to the offset, and O = Z min + Z max /2, Z min refers to the minimum value of all numbers in the number field of the data to be quantized and Z max refers to the maximum value of all numbers in the number field of the data to be quantized; I x refers to a n-bit binary representation value of data x after quantization; F x refers to a floating-point value of data x before quantization; and round refers to rounding calculation, alternatively, the round calculation is replaced by ceiling calculation or flooring calculation. obtaining an analyzing result of each type of the data to be quantized, wherein the data to be quantized includes at least one type of data among neurons, weights, gradients, and biases of the neural network; and determining a corresponding quantization parameter according to the analyzing result of each type of data to be quantized and a data bit width, using the quantization parameter to perform corresponding quantization on data which are to be used in a process of neural network operation in the neural network training or the neural network fine-tuning, whereby high-precision data are converted into low-precision fixed-point data, using the quantized fixed-point data to perform the neural network operation in the neural network training or the neural network fine-tuning, characterized in that the quantization parameter includes a first scaling coefficient f 1 and a offset O,wherein a following formula is used to quantize the data to be quantized to obtain the quantized data I x: I x = round F x − O ƒ 1 in the formulas, f 1 refers to the first scaling coefficient, and f 1 = Z / (2 n-1 -1), Z refers to the maximum absolute value of all numbers in the number field of the data to be quantized, O refers to the offset, and O = Z min + Z max /2, Z min refers to the minimum value of all numbers in the number field of the data to be quantized and Z max refers to the maximum value of all numbers in the number field of the data to be quantized; I x refers to a n-bit binary representation value of data x after quantization; F x refers to a floating-point value of data x before quantization; and round refers to rounding calculation, alternatively, the round calculation is replaced by ceiling calculation or flooring calculation.