Abstract: A method is for training an artificial neural network, for classifying sensor data, as a function of a first loss function and a second loss function. The first loss function is calculated as a function of an output of the artificial neural network. The second loss function is configured such that the output of the artificial neural network is essentially normalized.

Abstract: A method for operating an artificial neural network (ANN) on a hardware platform. The ANN is designed to ascertain confidences with which input data are to be assigned to N discrete classes. The hardware platform includes a dedicated unit which forms from a list of M>N confidences expanded confidences by encoding into each confidence an identification number of its place in the list, and numerically sorts the expanded confidences. The unit is fed confidences 1, . . . , M?N, which have the minimal representable value, and confidences M?N+1, . . . , M, which correspond to the N discrete classes, and/or it is ensured that those confidences fed to the unit that correspond to one of the N discrete classes have a value higher than the minimal representable value. A ranking of the classes ordered according to confidences, to which the input data are to be assigned, is ascertained from the first N of the numerically sorted expanded confidences.

Abstract: A method for operating an artificial neural network (ANN) on a hardware platform. The ANN is designed to ascertain confidences with which input data are to be assigned to N discrete classes. The hardware platform includes a dedicated unit which forms from a list of M>N confidences expanded confidences by encoding into each confidence an identification number of its place in the list, and numerically sorts the expanded confidences. The unit is fed confidences 1, . . . , M?N, which have the minimal representable value, and confidences M?N+1, . . . , M, which correspond to the N discrete classes, and/or it is ensured that those confidences fed to the unit that correspond to one of the N discrete classes have a value higher than the minimal representable value. A ranking of the classes ordered according to confidences, to which the input data are to be assigned, is ascertained from the first N of the numerically sorted expanded confidences.

Abstract: A method for operating an artificial neural network is provided, including at least one convolution layer that is configured to convert an input matrix of the convolution layer into an output matrix, based on a convolution operation and a shift operation. The method includes ascertaining at least one first normalization value and one second normalization value based on inputs of the input matrix and/or based on a training data set, ascertaining a modified filter matrix based on an original filter matrix and based on at least one of the first normalization value and the second normalization value, and ascertaining a modified shift matrix based on an original shift matrix and based on at least one of the first normalization value and the second normalization value. The method further includes converting the input matrix into the output matrix by applying the modified filter matrix and the modified shift matrix.