Method and device for verifying a neuron function in a neural network

1. A method for verifying a calculation of a neuron value of multiple neurons of a neural network, the method comprising:

performing or triggering a calculation of neuron functions of each of the multiple neurons, to obtain neuron values, the neuron functions being determined by individual weightings for each neuron input;

calculating a first comparison value as a sum of the neuron values of the multiple neurons;

performing or triggering a control calculation with one or multiple control neuron functions and with all neuron inputs of the multiple neurons, to obtain a second comparison value as a function of the neuron inputs of the multiple neurons and of a sum of the weightings of the multiple neurons assigned to the respective neuron input; and

recognizing an error as a function of the first comparison value and of the second comparison value.

2. The method of claim 1 , wherein the one or the multiple control neuron functions are performed by one or multiple additional provided control neurons.

3. The method of claim 1 , wherein the neuron functions are each determined as a function of a bias value, the neuron values of the multiple neurons being calculated with the aid of the neuron functions as a function of the bias values, the control calculation of the second comparison value with the one or the multiple control neuron functions being performed as a function of a sum of all bias values of the multiple neuron functions.

4. The method of claim 1 , wherein the multiple neurons are parts of multiple kernels for calculating a convolutional neural network based on a multi-dimensional data matrix having multiple channels, the control calculation being performed based on a control kernel, the weightings of the neuron functions of the control kernel being determined by a sum of the weightings of the multiple kernels assigned to a neuron input.

5. The method of claim 4 , wherein the sum of all correlating data points within the multi-dimensional data matrix across all channels is formed as first comparison value, the second comparison value being ascertained by applying the control calculation with the control kernel for a matrix position of the respectively correlating data points.

6. The method of claim 1 , wherein the multiple neurons are parts of a kernel for calculating a convolutional neural network based on a multi-dimensional data matrix, the control calculation being performed based on a number of sums of data values of the data matrix in a verification dimension, the second comparison value being determined as the sum of the products of the weightings of the kernel, in each case with one of the sums of the data values.

7. A verification system for verifying a calculation of neuron values of multiple neurons to be verified of a neural network, comprising:

a summing circuit for calculating a first comparison value as a sum of the neuron values of the multiple neurons, wherein the multiple neurons perform calculations of neuron functions for each of the multiple neurons, to obtain the neuron values, the neuron functions being determined by individual weightings for each neuron input, and wherein at least one control neuron, which is provided to perform a control calculation with one or multiple control neuron functions and with all neuron inputs of the multiple neurons, to obtain a second comparison value as a function of the neuron inputs of the multiple neurons and of a sum of the weightings of the multiple neurons assigned to the respective neuron input; and

a comparison circuit to recognize an error as a function of the first comparison value and of the second comparison value.

8. A non-transitory computer readable medium having a computer program, which is executable by a processor, comprising:

a program code arrangement having program code for verifying a calculation of a neuron value of multiple neurons of a neural network, by performing the following:

performing or triggering a calculation of neuron functions of each of the multiple neurons, to obtain neuron values, the neuron functions being determined by individual weightings for each neuron input;

calculating a first comparison value as a sum of the neuron values of the multiple neurons;

performing or triggering a control calculation with one or multiple control neuron functions and with all neuron inputs of the multiple neurons, to obtain a second comparison value as a function of the neuron inputs of the multiple neurons and of a sum of the weightings of the multiple neurons assigned to the respective neuron input; and

recognizing an error as a function of the first comparison value and of the second comparison value.

9. The computer readable medium of claim 8 , wherein the one or the multiple control neuron functions are performed by one or multiple additional provided control neurons.

10. The computer readable medium of claim 8 , wherein the neuron functions are each determined as a function of a bias value, the neuron values of the multiple neurons being calculated with the aid of the neuron functions as a function of the bias values, the control calculation of the second comparison value with the one or the multiple control neuron functions being performed as a function of a sum of all bias values of the multiple neuron functions.

11. The computer readable medium of claim 8 , wherein the multiple neurons are parts of multiple kernels for calculating a convolutional neural network based on a multi-dimensional data matrix having multiple channels, the control calculation being performed based on a control kernel, the weightings of the neuron functions of the control kernel being determined by a sum of the weightings of the multiple kernels assigned to a neuron input.

12. The computer readable medium of claim 11 , wherein the sum of all correlating data points within the multi-dimensional data matrix across all channels is formed as first comparison value, the second comparison value being ascertained by applying the control calculation with the control kernel for a matrix position of the respectively correlating data points.

13. The computer readable medium of claim 8 , wherein the multiple neurons are parts of a kernel for calculating a convolutional neural network based on a multi-dimensional data matrix, the control calculation being performed based on a number of sums of data values of the data matrix in a verification dimension, the second comparison value being determined as the sum of the products of the weightings of the kernel, in each case with one of the sums of the data values.