Abstract: A computer-implemented method for performing a code generation for calculating a Softmax function of a neural network includes (i) providing a displacement s and a multiplier for the quantized representation of the input tensors of the Softmax function of the neural network, (ii) creating a second lookup table to replace a nested function to calculate the EXP_ON_NEG (MUL_SAT ( )) function from a CMSIS NN library depending on the displacement and the multiplier, wherein element values of an input tensor normalized to a negative value range between 0 and a minimum value are used as arguments, wherein 0 is assigned to a maximum possible element value of the input tensor and the minimum value is assigned to the smallest possible element value of the input tensor, and (iii) implementing an access to the second lookup table in a code generated to calculate the Softmax function, so that it replaces the function call of the EXP_ON_NEG (MUL_SAT ( )) function.

Abstract: A computer-implemented method for performing a code generation for determining a recalibratable code for computing a neural network includes (i) providing a source code for implementing the neural network that does not contain the network parameters defining the neural network, i.e. their configuration and their parameterization, as constants, wherein the source code is configured to access a parameter dataset with predetermined network parameters, in which all parameters necessary for recalibration of the neural network are contained, (ii) compiling the source code so that a program code for the desired hardware environment is generated, (iii) providing the network parameters to be accessible by the program code, and (iv) implementing the generated program code in the hardware environment.

Abstract: A computer-implemented method for performing memory planning for code generation to generate code for computing a neural network in a hardware environment is disclosed.

Abstract: A computer-implemented method for performing memory scheduling for code generation to determine a code for computing a neural network in a hardware environment includes (i) providing successive computation steps of the neural network, wherein a plurality of the computation steps provide for the computation of a weight prefetching layer for which weight prefetching is applicable, (ii) assigning possible weight prefetching to at least a portion of the plurality of weight-prefetching layers in order to obtain multiple different combinations of weight-prefetching layers, wherein the weight prefetching provides for preloading of network parameters into a working memory of the hardware environment for the respective weight-prefetching layer, (iii) performing memory scheduling for the code to be generated for the plurality of different combinations, wherein the respective memory scheduling is carried out for the successive computation steps of the neural network while taking into account the respective combination o

Abstract: A method is for performing memory planning for code generation to determine a code for calculating a neural network for use in a hardware environment. The method includes providing successive calculation steps of the neural network. A size of at least one input data block and at least one output data block is determined for each calculation step. The method further includes determining a maximum overlap area between an input data block and an output data block for each calculation step. The method includes performing memory planning in which the memory area of the respective input data block and output data block is determined in the working memory, depending on the maximum overlap area for each calculation step.

Abstract: A computer-implemented method for performing memory planning for code generation to determine a code for calculating a neural network in a hardware environment includes (i) providing successive calculation steps of layers of the neural network, wherein for each calculation step the size of an input data block, an output data block and, depending on the type of calculation step, one or more model parameter blocks is determined, wherein the one or more model parameter blocks have model parameters for a respective calculation step, (ii) determining a memory planning rule for each specific calculation step that requires the use of model parameters, wherein the rule specifies that the model parameters are loaded into a contiguous memory space in a working memory of the hardware environment, and (iii) performing memory planning, in which the memory space of the respective input data block, output data block, and model parameter block is determined in the working memory for each calculation step, taking into account

Abstract: A computer-implemented method for performing memory planning for a code generation for determining a code for computing a neural network includes (i) providing successive calculation steps of the neural network, wherein for each calculation step the size of an input data block and an output data block is determined, (ii) determining a condition for the memory planning for each specific computing step, in which an input data block in a memory area is at least partially contained in the output data block, wherein the condition indicates that the memory area associated with the output data block of the calculation step preceding the specific calculation step is contained in the memory area of the output data block of the specific calculation step, and (iii) performing memory planning in which the memory area of the respective input data block and output data block in the working memory is determined for each calculation step, taking into account the determined conditions.

Abstract: In a method for operating a household steam treatment appliance which includes an evaporator with a heatable water holding chamber and a fill level sensor with at least two measurement electrodes, which are disposed over one another at a distance from each other in the heatable water holding chamber, a water detection measured value provided for indicating wetting of the at least two measurement electrodes with water introduced into the water holding chamber is adapted in an adaptation process to an electrical conductivity of the water in the water holding chamber.

Abstract: An apparatus and computer-implemented method for determining a memory plan for executing operations, in particular of an artificial neural network. A list of memory areas required for executing the operations is created, wherein, depending on the list, it is determined for the operations which memory areas must be present in a first memory for executing the particular operation and which memory areas may be present in a second memory during the execution of the particular operation, wherein the memory plan is determined depending on whether a memory area must be present in the main memory for execution or may be present in the secondary memory.

Abstract: A method is for parallelized calculation of two convolutions of a filter having first and second receptive fields of the filter on input data. The first and second receptive fields correspond to a filter shifted by one step on the input data. The method includes initializing first and second output variables each having an initial value, and executing a loop for each line of the filter. The loop performs loading a first kernel element of the filter of the line and the corresponding data values to the first kernel element of the first and second receptive fields of the input data.

Abstract: In a method for determining a volumetric variable of food treated in a treatment chamber of a household cooking appliance, images are captured from the treatment chamber in chronological order. Image points belonging to the food are identified in one image of a sequence of images, and a movement direction and a movement speed of the image points using an optical flow method are calculated for subsequent images. Movement directions of previously identified image points are classified into classes for different movement directions and numbers of image points that fall into respective ones of the classes are counted. A variable relating to the volume of the food is counted and the household cooking appliance varies an operating parameter of the household cooking appliance influencing the treatment of the food, when the calculated variable satisfies a specified criterion.

Abstract: A method for generating program code which, when executed on a hardware platform, creates a neural network having a given architecture. In the method: for at least one layer and/or group of neurons, a non-linear activation function of the neurons in that layer and/or group is ascertained from the given architecture; possible values that can be assumed by the activation function are precalculated and stored in a lookup table; program code is generated which, for all neurons in the layer and/or group respectively: aggregates the inputs of the respective neuron to form an argument of the activation function in accordance with the given architecture, ascertains an index from this argument, under which the associated value of the activation function is stored in the lookup table for the respective layer or group, and ascertains the output of the neuron by retrieving the value from the lookup table with this index.

Abstract: A method for providing a neural network on a data processing device. The method includes: ascertaining, from a set of implementation variants of the neural network, a subset with a plurality of implementation variants of the neural network, wherein each implementation variant of the subset cannot be improved with respect to any of main memory requirement, non-volatile memory requirement, and execution time, when executed on the data processing device, without impairing at least one of the other two, and the subset for each of main memory requirement, non-volatile memory requirement and execution time, when executed on the data processing device, contains at least one particular implementation variant that is optimal in this respect from the set of implementation variants; selecting one of the ascertained implementation variants according to a user input that specifies a selection from the subset; and storing the selected implementation variant in the data processing device.

Abstract: A computer-implemented method, includes: providing temporally sequential global traffic scenarios as temporally sequential frames in a global coordinate system; characterizing all objects in the global traffic scenarios with various markers; determining the ego pose of the ego vehicle in the temporally sequential frame; transforming each of the frames with the marked objects on the basis of the determined ego pose into a local coordinate system as a local traffic scenario, wherein the transformed frames up to a first point in time used as historic frames, and the transformed frames from the first point in time up to a second point in time used as ground truth frames; and training the machine learning process on the basis of the historic frames (1a, . . . ,1e) for determining future local traffic scenarios up to a second point in time as future frames and comparing the future frames with the corresponding ground truth frames (2a, . . . ,2e).

Abstract: method for querying a data storage includes receiving queries for execution in the data storage, and deriving from the queries one or more table-scans over one or more tables of the data storage. Candidate sparse indexes, for pruning the table-scans, are specified based on the queries. Pruning-power measures, indicative of as effectiveness of the candidate sparse indexes in pruning the table-scans, are evaluated. One or more of the candidate sparse indexes are converted into actual sparse indexes, depending on the pruning-power measures. The queries are executed by performing the table-scans, including pruning the table-scans using the actual sparse indexes.

Abstract: In a method for operating a household steam treatment appliance which includes an evaporator with a heatable water holding chamber and a fill level sensor with at least two measurement electrodes, which are disposed over one another at a distance from each other in the heatable water holding chamber, a water detection measured value provided for indicating wetting of the at least two measurement electrodes with water introduced into the water holding chamber is adapted in an adaptation process to an electrical conductivity of the water in the water holding chamber.

Abstract: A method for querying a data storage includes receiving queries for execution in the data storage, and deriving from the queries one or more table-scans over one or more tables of the data storage. Candidate sparse indexes, for pruning the table-scans, are specified based on the queries. Pruning-power measures, indicative of an effectiveness of the candidate sparse indexes in pruning the table-scans, are evaluated. One or more of the candidate sparse indexes are converted into actual sparse indexes, depending on the pruning-power measures. The queries are executed by performing the table-scans, including pruning the table-scans using the actual sparse indexes.

Abstract: A cooking appliance that uses steam to heat a food item includes a cooking chamber; a steam generator that generates steam to heat the food item; a heating element that generates heat to heat the food item; an outlet that fluidly connects the cooking chamber to a first external environment outside of the cooking chamber; and an active inlet that fluidly connects the cooking chamber to a second external environment outside of the cooking chamber. The active inlet has a blocking member that prevents a fluid from the second external environment from entering the cooking chamber though the active inlet, and an actuator that moves the blocking member between a closed position in which the blocking member prevents the fluid from entering the cooking chamber through the active inlet, and an open position in which the blocking member allows the fluid to enter the cooking chamber through the active inlet.

Abstract: A cooking appliance that uses steam to heat a food item includes a cooking chamber; a steam generator that generates steam to heat the food item; a heating element that generates heat to heat the food item; an outlet that fluidly connects the cooking chamber to a first external environment outside of the cooking chamber; and an active inlet that fluidly connects the cooking chamber to a second external environment outside of the cooking chamber. The active inlet has a blocking member that prevents a fluid from the second external environment from entering the cooking chamber though the active inlet, and an actuator that moves the blocking member between a closed position in which the blocking member prevents the fluid from entering the cooking chamber through the active inlet, and an open position in which the blocking member allows the fluid to enter the cooking chamber through the active inlet.

Abstract: In a method for operating a vaporizer of a domestic appliance, the vaporizer is filled at least to a predetermined filling level. The vaporizer can be emptied using an emptying arrangement. A time measurement is started, when a filling level falls below the predetermined filling level. A heating system of the vaporizer is switched on, when the filling level falls below the predetermined filling level. The time measurement is stopped, when the heating system has overheated, and a delivery output is calculated during emptying of the vaporizer, from a duration for emptying that is determined by the time measurement.