Robert Boschgmbh

Jaroslaw Topp

Ulrich Kersken

Andre Guntoro

Juergen Schirmer

Dieter Thoss

Robert Kornhaas

Sebastian Vogel

Christoph Schorn

Margit Mueller

Thomas Hartgen

Florian Kraemer

Armin Runge

Rainer Schittenhelm

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.

Method and device for operating a neural network in a memory-efficient manner

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.

Selective deactivation of processing units for artificial neural networks

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.

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

A method for operating a control unit of a motor vehicle. A status inquiry is transmitted by a watchdog unit to a first monitoring unit, which is implemented on a first processor core of a multicore processor. A status response is ascertained by the first monitoring unit as a function of the status inquiry. A fault is ascertained by the watchdog unit as a function of the status response.

Method for operating a control unit

A method is described for operating a microprocessor, in which a conversion software executed in the microprocessor carries out a binary translation, in the course of which a source instruction that is encoded according to a first instruction-set architecture is translated into a target instruction in a binary manner, which is encoded according to a second instruction-set architecture, and the target instruction translated by the translation software into the second instruction-set architecture being replicated, and in this replicated target instruction a memory area which is to be accessed in the course of the execution of the target instruction is replaced by a second memory area, and the target instruction and the copied target instruction is executed by the microprocessor. With the aid of the method, a temporal redundancy is achieved by a (temporally) parallel execution of the target instruction on a processor core and a local or regional redundancy by a parallel execution of the target instruction on different processor cores.

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Renningen, Germany

Jaroslaw Topp