Training neural networks for an efficient implementation on hardware

A method for training an artificial neural network (ANN), which has a multitude of neurons, the method comprising the following steps:

• ascertaining a measure of a quality that the ANN and/or a subregion of the ANN has achieved overall within a time period in the past;
• evaluating one or more of the neurons based on a measure of their respective quantitative contributions to the ascertained quality;
• and
• specifying measures by which the evaluated neurons are trained in a further course of the training and/or significance values of the evaluated neurons in the ANN, based on the evaluations of the neurons.

A method for implementing an artificial neural network (ANN) on a predefined arithmetic unit, the method comprising the following steps:

• training a model of the ANN in a training environment outside the arithmetic unit by:
• ascertaining a measure of a quality that the ANN and/or a subregion of the ANN has achieved overall within a time period in the past,
• evaluating one or more of the neurons based on a measure of their respective quantitative contributions to the ascertained quality, and
• specifying measures by which the evaluated neurons are trained in a further course of the training and/or significance values of the evaluated neurons in the ANN, based on the evaluations of the neurons;
• implementing on the arithmetic unit neurons and connections between neurons that are activated at a conclusion of the training.

A method, comprising:

• training an artificial neural network (ANN) by
• ascertaining a measure of a quality that the ANN and/or a subregion of the ANN has achieved overall within a time period in the past,
• evaluating one or more of the neurons based on a measure of their respective quantitative contributions to the ascertained quality, and
• specifying measures by which the evaluated neurons are trained in a further course of the training and/or significance values of the evaluated neurons in the ANN, based on the evaluations of the neurons;
• operating the ANN in that an input variable or a plurality of input variables is conveyed to the ANN;
• and
• depending on output variables supplied by the ANN, actuating a vehicle, and/or a robot, and/or a quality control system and/or a system for monitoring a region based on sensor data.

A non-transitory machine-readable data carrier on which is stored a computer program for training an artificial neural network (ANN), which has a multitude of neurons, the computer program, when executed by a computer and/or control unit and/or embedded system, causes the computer and/or control unit and/or embedded system. the perform the following steps:

• ascertaining a measure of a quality that the ANN and/or a subregion of the ANN has achieved overall within a time period in the past;
• evaluating one or more of the neurons based on a measure of their respective quantitative contributions to the ascertained quality;
• and
• specifying measures by which the evaluated neurons are trained in a further course of the training and/or significance values of the evaluated neurons in the ANN, based on the evaluations of the neurons.

A computer configured to train an artificial neural network (ANN), which has a multitude of neurons, the computer configured to:

• ascertain a measure of a quality that the ANN and/or a subregion of the ANN has achieved overall within a time period in the past;
• evaluate one or more of the neurons based on a measure of their respective quantitative contributions to the ascertained quality;
• and
• specify measures by which the evaluated neurons are trained in a further course of the training and/or significance values of the evaluated neurons in the ANN, based on the evaluations of the neurons.