An information processor comprising: in computation in a plurality of layers of a neural network for performing deep learning, an acquisition unit configured to acquire first statistical information about distribution of most significant bit position that is not a sign and least significant bit position that is not zero for each of a plurality of first fixed-point number data, the data being a computation result of the computation in the first layer; an execution unit configured to execute computation on a plurality of output data of the first layer according to a predetermined rule, in the computation in the second layer, the predetermined rule being different according to a layer type of the second layer; and a control unit configured to acquire second statistical information based on the predetermined rule and the first statistical information, and determine a bit range which indicates a digit range identified without a saturation and an omission in limiting the bit width of a computation result data when a plurality of second fixed-point number data output as a result of a computation in the second layer, the data being the computation result data, are stored in a register, based on the second statistical information, wherein: when the layer type of the second layer is a first type layer: the computation according to the predetermined rule includes multiplying a predetermined ratio of data of the plurality of output data of the first layer by 0, and multiplying remaining output data of the first layer by a coefficient found from the predetermined ratio, and the control unit is configured to acquire the second statistical information by shifting the first statistical information based on the coefficient, and when the layer type of the second layer is a second type layer: the computation according to the predetermined rule includes the plurality of output data of the first layer and a plurality of output data from a third layer are concatenated, and the control unit is configured to acquire the second statistical information by summing the first statistical information and a third statistical information, the third statistical information being statistical information about distribution of most significant bit position and least significant bit position for each of a plurality of third fixed-point number data, the data being a computation result of a computation in the third layer. in computation in a plurality of layers of a neural network for performing deep learning, an acquisition unit configured to acquire first statistical information about distribution of most significant bit position that is not a sign and least significant bit position that is not zero for each of a plurality of first fixed-point number data, the data being a computation result of the computation in the first layer; an execution unit configured to execute computation on a plurality of output data of the first layer according to a predetermined rule, in the computation in the second layer, the predetermined rule being different according to a layer type of the second layer; and a control unit configured to acquire second statistical information based on the predetermined rule and the first statistical information, and determine a bit range which indicates a digit range identified without a saturation and an omission in limiting the bit width of a computation result data when a plurality of second fixed-point number data output as a result of a computation in the second layer, the data being the computation result data, are stored in a register, based on the second statistical information, wherein: when the layer type of the second layer is a first type layer: the computation according to the predetermined rule includes multiplying a predetermined ratio of data of the plurality of output data of the first layer by 0, and multiplying remaining output data of the first layer by a coefficient found from the predetermined ratio, and the control unit is configured to acquire the second statistical information by shifting the first statistical information based on the coefficient, and the computation according to the predetermined rule includes multiplying a predetermined ratio of data of the plurality of output data of the first layer by 0, and multiplying remaining output data of the first layer by a coefficient found from the predetermined ratio, and the control unit is configured to acquire the second statistical information by shifting the first statistical information based on the coefficient, and when the layer type of the second layer is a second type layer: the computation according to the predetermined rule includes the plurality of output data of the first layer and a plurality of output data from a third layer are concatenated, and the control unit is configured to acquire the second statistical information by summing the first statistical information and a third statistical information, the third statistical information being statistical information about distribution of most significant bit position and least significant bit position for each of a plurality of third fixed-point number data, the data being a computation result of a computation in the third layer.