A processor-implemented method of quantizing parameters of a neural network used for performing one of a voice recognition, a recognition of an object included in an image indicated by frame data included in a video stream, an image recognition, a biometric information recognition based on received input data, the method comprising: calculating, for each of the parameters, a bit shift value indicating a degree outside a bit range of a fixed-point format for quantizing the parameters, the fixed-point format comprising a sign bit, an integer part and a fractional part; updating the fixed-point format based on the calculated bit shift values of the parameters by determining a number of occurrences of overflow and a maximum bit shift value from the calculated bit shift values, and updating the fixed-point format based on the number of occurrences of overflow and the maximum bit shift value, wherein in a case in which the number of occurrences of overflow is greater than a predetermined value, the updating is performed by reducing a fraction length of the fixed-point format by the maximum bit shift value, the predetermined value being based on a number of parameters; and quantizing parameters updated in a t th learning or inference process according to the updated fixed-point format based on parameters updated in a (t-1) th learning or inference process, wherein t is a natural number greater than or equal to 2, wherein calculating the bit shift value comprises: for each of the parameters, searching for bits within a bit range with regard to a first most significant bit being the integer part's most significant bit, and detecting, within the searched bit range, a second most significant bit being a most significant bit having a value '1'; and determining, for each of the parameters, a difference in a number of bits between the second most significant bit and the first most significant bit as the bit shift value. calculating, for each of the parameters, a bit shift value indicating a degree outside a bit range of a fixed-point format for quantizing the parameters, the fixed-point format comprising a sign bit, an integer part and a fractional part; updating the fixed-point format based on the calculated bit shift values of the parameters by determining a number of occurrences of overflow and a maximum bit shift value from the calculated bit shift values, and updating the fixed-point format based on the number of occurrences of overflow and the maximum bit shift value, wherein in a case in which the number of occurrences of overflow is greater than a predetermined value, the updating is performed by reducing a fraction length of the fixed-point format by the maximum bit shift value, the predetermined value being based on a number of parameters; and quantizing parameters updated in a t th learning or inference process according to the updated fixed-point format based on parameters updated in a (t-1) th learning or inference process, wherein t is a natural number greater than or equal to 2, wherein calculating the bit shift value comprises: for each of the parameters, searching for bits within a bit range with regard to a first most significant bit being the integer part's most significant bit, and detecting, within the searched bit range, a second most significant bit being a most significant bit having a value '1'; and determining, for each of the parameters, a difference in a number of bits between the second most significant bit and the first most significant bit as the bit shift value. for each of the parameters, searching for bits within a bit range with regard to a first most significant bit being the integer part's most significant bit, and detecting, within the searched bit range, a second most significant bit being a most significant bit having a value '1'; and determining, for each of the parameters, a difference in a number of bits between the second most significant bit and the first most significant bit as the bit shift value.