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

US11531888B2 · kind B2 · utility

Assignee

• Robert Bosch GmbH · DE

Inventors

• Jan Achterhold · Tübingen, DE
• Jan Mathias Koehler · Stuttgart, DE
• Tim Genewein · London, GB

Key dates

• Filing date: Oct 15, 2018
• Grant date: Dec 20, 2022
• Expiry date: Nov 5, 2039

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/09
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

A method for creating a deep neural network. The deep neural network includes a plurality of layers and connections having weights, and the weights in the created deep neural network are able to assume only predefinable discrete values from a predefinable list of discrete values. The method includes: providing at least one training input variable for the deep neural network; ascertaining a variable characterizing a cost function, which includes a first variable, which characterizes a deviation of an output variable of the deep neural network ascertained as a function of the provided training input variable relative to a predefinable setpoint output variable, and the variable characterizing the cost function further including at least one penalization variable, which characterizes a deviation of a value of one of the weights from at least one of at least two of the predefinable discrete values; training the deep neural network.