FIELD OF THE INVENTION

The present invention relates to the calculation of functional models in a separate hardwired model calculation unit, in particular, for calculating multi-layer perceptron models.

BACKGROUND INFORMATION

Functions of technical systems controls such as, for example, internal combustion engines, electric drives, storage batteries and the like are frequently implemented using models, which represent a mathematical copy of the real system. However, physical models lack the requisite calculation accuracy, in particular, in the case of complex correlations, and it is generally difficult in the case of modern processing capacities to calculate such models within the real time demands required for a control unit. For such cases, it is envisaged to use data-based models, which describe the correlations between an output variable and input variables exclusively on the basis of training data obtained with the aid of a test bench or the like. Data-based models are particularly suited for modeling complex correlations, in which multiple input variables, between which interrelations exist, are suitably taken into consideration in the model. The modeling, with the aid of data-based models, also offers the possibility of supplementing the model by adding individual input variables.

Data-based functional models are generally based on a large number of nodes in order to achieve a modeling accuracy sufficient for the respective application. Due to the high number of nodes, a high processing capacity is required for calculating a model value using a data-based functional model such as, for example, a Gauss process model or a multi-layer perceptron model. Thus, in order to be able to calculate such a data-based functional model in a control unit application in real time, model calculation units based on a hardware configuration may be provided.

SUMMARY OF THE INVENTION

According to the present invention, a method is provided 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 according to the description herein, as well as a model calculation unit, a control unit and a use of the control unit according to the other descriptions herein.

Additional embodiments are also specified and described herein.

According to a first aspect, a method is provided 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 or neuron layer of the perceptron model being calculated with the aid of an activation function, the activation function including a simplified sigmoid function and/or a simplified tan h function, the simplified sigmoid function and tan h function being formed by zero-point mirroring of the negative definition range of an exponential function.

One idea of the aforementioned method is to provide a calculation rule for calculating a simplification of the sigmoid function or of the tan h function as the entire activation function or as part of an activation function in a hardware-based calculation unit. The calculation rule is intended to utilize, in particular, merely the exponential function as a special function and otherwise to use only multiplications and additions in order to avoid a time-consuming and resource-consuming calculation of a division. This makes it possible to utilize a hardware calculation unit suitable for calculating a multi-layer perceptron model without providing additional operational blocks. To achieve this, the sigmoid function or the tan h function may be calculated by utilizing the zero-point mirroring of the negative definition range of the exponential function and by utilizing easily read-out sign bits of the value to be acted on with the activation function.

In addition, an output variable may be calculated for each neuron for a neuron layer of the perceptron model that includes a number of neurons as a function of one or of multiple input variables of an input variable vector, of a weighting matrix having weighting factors and of an offset value predefined for each neuron, a sum of the values of the input values weighted with a weighting factor determined by the neuron and the input variable being acted on by the offset value predefined for the neuron, in particular, added, and the result being transformed with the activation function in order to obtain the output variable for the neuron.

It may be provided to select the simplified sigmoid function or the simplified tan h function as the activation function in accordance with a variable.

It may be provided that a functional value of the simplified sigmoid function is calculated for a value by calculating an exponential function based on a negative absolute value of the value, the functional value being calculated in the case of a positive sign of the value as a sum of “1” and of the product of “−0.5” and of the result of the exponential function, and as the product of “0.5” and of the result of the exponential function in the case of a negative sign. In this way, the result of the exponential function is multiplied by 0.5 and acted on with the result of a step function.

Alternatively, a functional value of the simplified tan h function may be calculated for a value by calculating an exponential function based on a negative absolute value of the value, the functional value being calculated in the case of a positive sign of the value as a sum of “1” and of the negative result of the exponential function, and as a sum of “−1” and of the result of the exponential function in the case of a negative sign of the value. In this way, the simplified tan h function is defined by zero-point mirroring of the negative definition range of the exponential function and by subsequent application of a sign function.

According to another aspect, a model calculation unit is provided 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, the processor core being configured to calculate an output variable for each neuron for a neuron layer of a multi-layer perceptron model having a number of neurons as a function of one or multiple input variables of an input variable vector, of a weighting matrix having weighting factors and of an offset value predefined for each neuron, a sum of the values of the input variables weighted with a weighting factor determined by the neuron and the input variable and with the offset value predefined for the neuron being calculated for each neuron, and the result being transformed with an activation function in order to obtain the output variable for the neuron, the activation function including a simplified sigmoid function or a simplified tan h function and being calculated using a zero-point mirroring of the negative definition range of the exponential function acted on by the result of a step function or sign function.

The processor core may also include a state machine, a memory for storing the one or multiple input variables of the input variable vector, the weighting matrix, the offset values predefined for each neuron and the output values for each neuron, additionally, one or multiple processing operation blocks, in particular, a MAC block and an activation function calculation block.

The processor core may also be configured in a surface area of an integrated chip.

According to another aspect, a control unit that includes a microprocessor and the aforementioned model calculation unit, is provided. The control unit may be configured, in particular, as an integrated circuit.

According to another aspect, a use of the aforementioned control unit is provided as a control unit for controlling an engine system in a motor vehicle.

Specific embodiments are discussed in greater detail below with reference to the appended drawings.

