Abstract: A device and method for machine learning using an artificial neural network. For a calculation hardware for the artificial neural network, a layer description is provided, which defines at least one part of a layer of the artificial neural network, the layer description defining a tensor for input values of at least one part of this layer, a tensor for weights of at least one part of this layer, and a tensor for output values of at least one part of this layer, in particular of its starting address. A message that includes a start address of the tensor for the input values, or of the tensor for the weighs, or of the tensor for the output values is sent by the calculation hardware for transfer of the input values, or the weights, or the output values, is sent by the calculation hardware.

Abstract: A method for ascertaining a performance of a machine learning system on a processing unit. The method includes: creating a hardware model of the processing unit from a provided technical specification of the processing unit and creating a simulation graph based on the machine learning system; simulating an implementation of the machine learning system on the processing unit using the hardware model and the graph, the simulation being an event-based simulation, and ascertaining the performance based on the result of the simulation.

Abstract: A device and a computer-implemented method for a neural architecture search. A first set of values is provided for parameters that define at least one part of an architecture for an artificial neural network, wherein the part of the architecture encompasses a plurality of layers of the artificial neural network and/or a plurality of operations of the artificial neural network, wherein a first value of a function is determined for the first set of values for the parameters, said first value characterizing a property of a target system when the target system executes a task for the part of the artificial neural network that is defined by the first set of values for the parameters.

Abstract: A device and method for machine learning using an artificial neural network. For a calculation hardware for the artificial neural network, a layer description is provided, which defines at least one part of a layer of the artificial neural network, the layer description defining a tensor for input values of at least one part of this layer, a tensor for weights of at least one part of this layer, and a tensor for output values of at least one part of this layer, in particular of its starting address. A message that includes a start address of the tensor for the input values, or of the tensor for the weighs, or of the tensor for the output values is sent by the calculation hardware for transfer of the input values, or the weights, or the output values, is sent by the calculation hardware.