Abstract: A hardware architecture for an artificial neural network ANN. The ANN includes a consecutive series made up of an input layer, multiple processing layers, and an output layer. Each layer maps a set of input variables onto a set of output variables, and output variables of the input layer and of each processing layer are input variables of the particular layer that follows in the series. The hardware architecture includes a plurality of processing units. The implementation of each layer is split among at least two of the processing units, and at least one resettable switch-off device is provided via which at least one processing unit is selectively deactivatable, independently of the input variables supplied to it, in such a way that at least one further processing unit remains activated in all layers whose implementation is contributed to by this processing unit.

Abstract: A model calculation unit for calculating a multilayer perceptron model, the model calculation unit being designed in hardware and being hardwired, including: a process or core; a memory; a DMA unit, which is designed to successively instruct the processor core to calculate a neuron layer, in each case based on input variables of an assigned input variable vector and to store the respectively resulting output variables of an output variable vector in an assigned data memory section, the data memory section for the input variable vector assigned to at least one of the neuron layers at least partially including in each case the data memory sections of at least two of the output variable vectors of two different neuron layers.

Abstract: A model calculating unit for the selective calculation of an RBF model or of a neural layer of a multilayer perceptron model having a hardwired processor core designed in hardware for calculating a fixedly specified computing algorithm in coupled function blocks. The processor core is designed to calculate an output variable for an RBF model as a function of one or multiple input variables of an input variable vector, of supporting points, of length scales, of parameters specified for each supporting point, the processor core furthermore being designed to calculate an output variable for each neuron for the neural layer of the multilayer perceptron model having a number of neurons as a function of the one or the multiple input variables of the input variable vector, of a weighting matrix having weighting factors and an offset value specified for each neuron.

Abstract: A method for operating a calculation system including a neural network, in particular a convolutional neural network, the calculation system including a processing unit for the sequential calculation of the neural network and a memory external thereto for buffering intermediate results of the calculations in the processing unit, including: incrementally calculating data sections, which each represent a group of intermediate results, with the aid of a neural network; lossy compression of one or multiple of the data sections to obtain compressed intermediate results; and transmitting the compressed intermediate results to the external memory.

Abstract: A model calculation unit for calculating an RBF model is described, including a hard-wired processor core designed as hardware for calculating a fixedly predefined processing algorithm in coupled functional blocks, the processor core being designed to calculate an output variable for an RBF model as a function of one or multiple input variable(s) of nodes V[j,k], of length scales (L[j,k]), of weighting parameters p3[j,k] predefined for each node, the output variable being formed as a sum of a value calculated for each node V[j,k], the value resulting from a product of a weighting parameter p3[j,k] assigned to the particular node V[j,k], and a result of an exponential function of a value resulting from the input variable vector as a function of a square distance of the particular node (V[j,k]), weighted by the length scales (L[j,k]), the length scales (L[j,k]) being provided separately for each of the nodes as local length scales.

Abstract: A method for verifying a calculation of a neuron value of multiple neurons of a neural network, including: carrying out or triggering a calculation of neuron functions of the multiple neurons, in each case to obtain a neuron value, the neuron functions being determined by individual weightings for each neuron input; calculating a first comparison value as the sum of the neuron values of the multiple neurons; carrying out 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 the 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.

Abstract: A method for calculating a neuron layer of a multi-layer perceptron model that includes a permanently hardwired processor core configured in hardware for calculating a permanently predefined processing algorithm in coupled functional blocks, a neuron of a neuron layer of the perceptron model being calculated with the aid of an activation function, the activation function corresponding to a simplified sigmoid function and to a simplified tank function, the activation function being formed by zero-point mirroring of the negative definition range of the exponential function.

Abstract: A hardware-implemented multi-layer perceptron model calculation unit includes: a processor core to calculate output quantities of a neuron layer based on input quantities of an input vector; a memory that has, for each neuron layer, a respective configuration segment for storing configuration parameters and a respective data storage segment for storing the input quantities of the input vector and the one or more output quantities; and a DMA unit to successively instruct the processor core to: calculate respective neuron layers based on the configuration parameters of each configuration segment, calculate input quantities of the input vector defined thereby, and store respectively resulting output quantities in a data storage segment defined by the corresponding configuration parameters, the configuration parameters of configuration segments successively taken into account indicating a data storage region for the resulting output quantities corresponding to the data storage region for the input quantities for

Abstract: A model calculation unit for calculating a data-based function model in a control unit is provided, the model calculation unit having a processor core which includes: a multiplication unit for carrying out a multiplication on the hardware side; an addition unit for carrying out an addition on the hardware side; an exponential function unit for calculating an exponential function on the hardware side; a memory in the form of a configuration register for storing hyperparameters and node data of the data-based function model to be calculated; and a logic circuit for controlling, on the hardware side, the calculation sequence in the multiplication unit, the addition unit, the exponential function unit and the memory in order to ascertain the data-based function model.

Abstract: A method for carrying out a calculation of a data-based function model in a control unit having a computing unit and a separate model calculation unit having a computing core, including: loading a first part of the configuration data, which contain hyperparameters of the data-based function model and a first part of supporting point data having multiple supporting points, into the model calculation unit; starting a calculation in the computing core of the model calculation unit, to obtain a model value at a predefined test point; and transferring a second part of the configuration data, which contain a second part of the supporting point data having multiple supporting points, into the model calculation unit, prior to the completion of the calculation in the computing core of the model calculation unit.

Abstract: A method for carrying out a calculation of a data-based function model, in particular a Gaussian process model, the data-based function model being defined by predefined hyperparameters and node data, multiple input variables being assigned to one output variable and having a sum of terms, each of which depend on one of the input variables, including the following: determining at least one input variable to be varied, for which multiple output values of a corresponding output variable are to be determined; calculating the sum of the terms, which depend on the input variables not to be varied; providing multiple input values for each of the determined at least one input variable to be varied; and ascertaining multiple output values of the output variable for the provided multiple input values, each based on the calculated sum of the terms, which depend on the input variables not to be varied.

Abstract: A method for operating a control unit, the control unit including a software-controlled main processing unit, a strictly hardware-based model calculation unit for calculating an algorithm, for carrying out a Bayesian regression method, based on configuration data, and a memory unit, a model memory area being defined in the memory unit to which a configuration register block for providing the configuration data in the model calculation unit is assigned, a calculation start-configuration register being assigned the highest address in the configuration register block into which configuration data are written, the writing into of which starts the calculation in the model calculation unit, the configuration data being written in a memory area of the memory unit from the model memory area into the configuration register block with an incremental copying process, the addresses being copied in the incremental copying process in ascending order.

Abstract: A method for generating a data-based function model includes: providing a first data-based partial model ascertained from a first training data record; providing at least one additional training data record; and performing the following steps for the at least one additional training data record: ascertaining a difference training data record having training data which correspond to the differences between the output values of the relevant additional training data record and the function value of the sum of the partial function values (ffirst_partial_model(x) fsecond_partial_model(x)) of the first data-based partial model and previously ascertained data-based partial model(s) at each of the measuring points of the relevant training data record; ascertaining an additional data-based partial model from the difference training data record; and forming a sum (f(x)) from the first and the additional data-based partial models.

Abstract: An FMA unit, for carrying out an arithmetic operation in a model computation unit of a control unit, is configured to process input of two factors and one summand in the form of floating point values, and provide a computation result of such processing as an output variable in the form of a floating point value. The FMA unit is designed to carry out a multiplication and a subsequent addition, the bit resolutions of the inputs for the factors being lower than the bit resolution of the input for the summand and the bit resolution of the output variable.

Abstract: A method for operating a control unit, the control unit including a software-controlled main processing unit, a strictly hardware-based model calculation unit for calculating an algorithm, for carrying out a Bayesian regression method, based on configuration data, and a memory unit, a model memory area being defined in the memory unit to which a configuration register block for providing the configuration data in the model calculation unit is assigned, a calculation start-configuration register being assigned the highest address in the configuration register block into which configuration data are written, the writing into of which starts the calculation in the model calculation unit, the configuration data being written in a memory area of the memory unit from the model memory area into the configuration register block with an incremental copying process, the addresses being copied in the incremental copying process in ascending order.

Abstract: An FMA unit, for carrying out an arithmetic operation in a model computation unit of a control unit, is configured to process input of two factors and one summand in the form of floating point values, and provide a computation result of such processing as an output variable in the form of a floating point value. The FMA unit is designed to carry out a multiplication and a subsequent addition, the bit resolutions of the inputs for the factors being lower than the bit resolution of the input for the summand and the bit resolution of the output variable.

Abstract: A method for carrying out a calculation of a data-based function model, in particular a Gaussian process model, the data-based function model being defined by predefined hyperparameters and node data, multiple input variables being assigned to one output variable and having a sum of terms, each of which depend on one of the input variables, including the following: determining at least one input variable to be varied, for which multiple output values of a corresponding output variable are to be determined; calculating the sum of the terms, which depend on the input variables not to be varied; providing multiple input values for each of the determined at least one input variable to be varied; and ascertaining multiple output values of the output variable for the provided multiple input values, each based on the calculated sum of the terms, which depend on the input variables not to be varied.

Abstract: A method for generating a data-based function model includes: providing a first data-based partial model ascertained from a first training data record; providing at least one additional training data record; and performing the following steps for the at least one additional training data record: ascertaining a difference training data record having training data which correspond to the differences between the output values of the relevant additional training data record and the function value of the sum of the partial function values (ffirst—partial—model(x) fsecond—partial—model(x)) of the first data-based partial model and previously ascertained data-based partial model(s) at each of the measuring points of the relevant training data record; ascertaining an additional data-based partial model from the difference training data record; and forming a sum (f(x)) from the first and the additional data-based partial models.

Abstract: A method for carrying out a calculation of a data-based function model in a control unit having a computing unit and a separate model calculation unit having a computing core, including: loading a first part of the configuration data, which contain hyperparameters of the data-based function model and a first part of supporting point data having multiple supporting points, into the model calculation unit; starting a calculation in the computing core of the model calculation unit, to obtain a model value at a predefined test point; and transferring a second part of the configuration data, which contain a second part of the supporting point data having multiple supporting points, into the model calculation unit, prior to the completion of the calculation in the computing core of the model calculation unit.

Abstract: A model calculation unit for calculating a data-based function model in a control unit is provided, the model calculation unit having a processor core which includes: a multiplication unit for carrying out a multiplication on the hardware side; an addition unit for carrying out an addition on the hardware side; an exponential function unit for calculating an exponential function on the hardware side; a memory in the form of a configuration register for storing hyperparameters and node data of the data-based function model to be calculated; and a logic circuit for controlling, on the hardware side, the calculation sequence in the multiplication unit, the addition unit, the exponential function unit and the memory in order to ascertain the data-based function model.