Back to results
US 12,001,954 B2Granted
Method of performing learning of deep neural network and apparatus thereof
AssigneeSamsung Electronics Co., Ltd.
InventorsSungho Kang, Hyungdal Kwon, Cheon Lee, Yunjae Lim
Life of this patent
Estimating the term…
Abstract
An encoding apparatus connected to a learning circuit processing learning of a deep neural network and configured to perform encoding for reconfiguring connection or disconnection of a plurality of edges in a layer of the deep neural network using an edge sequence generated based on a random number sequence and dropout information indicating a ratio between connected edges and disconnected edges of a plurality of edges included in a layer of the deep neural network.
Claims20
An encoding apparatus comprising:
- a memory storing a random number sequence generated by a random number generator;
- and
- at least one processor configured to:
- receive dropout information of a deep neural network, the dropout information indicating a ratio between connected edges and disconnected edges of a plurality of first edges included in a first layer of the deep neural network,
- generate an edge sequence indicating connection or disconnection of a plurality of second edges included in a second layer of the deep neural network by modifying the random number sequence based on a comparison between the dropout information and a target ratio value, and
- output the edge sequence such that the connection or the disconnection of the plurality of second edges included in the second layer of the deep neural network are reconfigured,
- wherein the at least one processor is further configured to modify the random number sequence such that a ratio value of a number of bits having a value of 0 and 1 in an entirety of the deep neural network is the target ratio value.
4 more elements
An encoding method of an encoding apparatus, the encoding method comprising:
- storing a random number sequence generated by a random number generator;
- receiving dropout information of a deep neural network, the dropout information indicating a ratio between connected edges and disconnected edges of a plurality of first edges included in a first layer of the deep neural network;
- generating an edge sequence indicating connection or disconnection of a plurality of second edges included in a second layer of the deep neural network by modifying the random number sequence based on a comparison between the dropout information and a target ratio value;
- and
- outputting the edge sequence such that the connection or the disconnection of the plurality of second edges included in the second layer of the deep neural network are reconfigured,
- wherein the generating of the edge sequence comprises modifying the random number sequence such that a ratio value of a number of bits having a value of 0 and 1 in an entirely of the deep neural network is the target ratio value.
3 more elements
Dependencies are inferred from the claim text.