Method, device, and computer program for operating a deep neural network

Patent metadata

Field	Value
Publication number	US-2022019874-A1
Application number	US-202017429105-A
Country	US
Kind code	A1
Filing date	Mar 24, 2020
Priority date	Apr 9, 2019
Publication date	Jan 20, 2022
Grant date	—

Abstract

A method for operating a deep neural network having at least one skip connection. The method includes: selecting a first path through the deep neural network along the specifiable sequence, using the skip connection; propagating an input variable along the first path; checking whether the output variable corresponds to a specifiable criterion, such that if the specifiable criterion is not met a further path through the deep neural network is selected that is longer by at least one layer than the first path, and the input variable is thereupon propagated along the second path with reuse of the intermediate results of the first path. A computer program, a device for carrying out the method, and a machine-readable storage element on which the computer program is stored, are also described.

First claim

1 - 10 . (canceled)

11 . A method for operating a deep neural network that has at least one skip connection, the method comprising the following steps:
selecting a first path through the deep neural network along the skip connection; ascertaining an output variable by propagating an input variable of the deep neural network along the first path; checking whether the output variable meets a specifiable criterion; and selecting, based on the specifiable criterion not being met, a second path through the deep neural network that differs from the first path, and ascertaining the output variable by propagating the input variable of the deep neural network along the second path.

12 . The method as recited in claim 11 , wherein those layers of the neural network that are not required for a respective path used being deactivated during the propagation of the input variable along the first and second paths, and not being activated until that layer is required for the respective path.

13 . The method as recited in claim 11 , wherein intermediate variables of those layers that were ascertained during the propagation along the first path are reused in the propagation of the input variable along the second path.

14 . The method as recited in claim 13 , wherein only the intermediate variables of those layers by which the second path differs from the first path are ascertained during the propagation along the second path, and those layers of the second path that are connected to more than one preceding layer and were also contained in the first path ascertaining, during the propagation along the second path, their intermediate variables as a function of a provided intermediate variable of the respective immediately preceding connected layer of the second path and of a preceding provided intermediate variable of the immediately preceding connected layer of the first path.

15 . The method as recited in claim 11 , further comprising the following steps:
providing a plurality of different paths through the deep neural network; selecting at least the first path and the second path from the plurality of different paths; ascertaining the output variable by propagating the input variable of the deep neural network simultaneously along at least the first path and the second path, intermediate variables of common layers of the first path and second path being first ascertained, in succession, up to the layer starting from which a sequence of the layers of the first and second paths differ; ascertaining intermediate variables of those remaining layers of the first path and of those remaining layers of the second path that have a same position in the sequence of layers of the first and second paths, respectively ascertained in parallel; checking the specifiable criterion when the output variable of the first path is outputted; and continuing the propagation when the criterion is not met until the output variable of the second path is outputted.

16 . The method as recited in claim 11 , wherein: (i) the selection of the first and second paths is a function of the input variable and/or a function of a specifiable energy/time contingent that has a maximum permissible consumption level in order to propagate the input variable through the deep neural network, and/or (ii) the specifiable criterion characterizes a specifiable minimum accuracy or reliability of the output variable.

17 . The method as recited in claim 11 , wherein each path of the deep neural network is trained separately from one another, and/or at least one group of paths is trained in common.

18 . A device configured to operate a deep neural network that has at least one skip connection, the device configured to:
select a first path through the deep neural network along the skip connection; ascertain an output variable by propagating an input variable of the deep neural network along the first path; check whether the output variable meets a specifiable criterion; and select, based on the specifiable criterion not being met, a second path through the deep neural network that differs from the first path, and ascertain the output variable by propagating the input variable of the deep neural network along the second path.

19 . A non-transitory machine-readable storage element on which is stored a computer program for operating a deep neural network that has at least one skip connection, the computer program, when executed by a computer, causing the computer to perform the following steps:
selecting a first path through the deep neural network along the skip connection; ascertaining an output variable by propagating an input variable of the deep neural network along the first path; checking whether the output variable meets a specifiable criterion; and selecting, based on the specifiable criterion not being met, a second path through the deep neural network that differs from the first path, and ascertaining the output variable by propagating the input variable of the deep neural network along the second path.

Assignees

• Bosch Gmbh Robert

Inventors

• Pfeil Thomas
• Fischer Volker

Classifications

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/082

  modifying the architecture, e.g. adding, deleting or silencing nodes or connections · CPC title

• G06N3/0495

  Quantised networks; Sparse networks; Compressed networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/092

  Reinforcement learning · CPC title