公开(公告)号：US11544549B2

公开(公告)日：2023-01-03

申请号：US16106703

申请日：2018-08-21

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Junhaeng Lee ,  Hyunsun Park ,  Yeongjae Choi

IPC: G06N3/08 ,  G06N3/063 ,  G06F7/483

Abstract: A processor-implemented neural network method includes calculating individual update values for a weight assigned to a connection relationship between nodes included in a neural network; generating an accumulated update value by accumulating the individual update values in an accumulation buffer; and training the neural network by updating the weight using the accumulated update value in response to the accumulated update value being equal to or greater than a threshold value.

公开(公告)号：US10909418B2

公开(公告)日：2021-02-02

申请号：US16884232

申请日：2020-05-27

Applicant: Samsung Electronics Co., Ltd. ,  Korea Advanced Institute of Science and Technology

Inventor： Sehwan Lee ,  Leesup Kim ,  Hyeonuk Kim ,  Jaehyeong Sim ,  Yeongjae Choi

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/063

Abstract: A processor-implemented neural network method includes: obtaining, from a memory, data of an input feature map and kernels having a binary-weight, wherein the kernels are to be processed in a layer of a neural network; decomposing each of the kernels into a first type sub-kernel reconstructed with weights of a same sign, and a second type sub-kernel for correcting a difference between a respective kernel, among the kernels, and the first type sub-kernel; performing a convolution operation by using the input feature map and the first type sub-kernels and the second type sub-kernels decomposed from each of the kernels; and obtaining an output feature map by combining results of the convolution operation.

公开(公告)号：US10699160B2

公开(公告)日：2020-06-30

申请号：US16110664

申请日：2018-08-23

Applicant: Samsung Electronics Co., Ltd. ,  Korea Advanced Institute of Science and Technology

Inventor： Sehwan Lee ,  Leesup Kim ,  Hyeonuk Kim ,  Jaehyeong Sim ,  Yeongjae Choi

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/063

Abstract: A processor-implemented neural network method includes: obtaining, from a memory, data an input feature map and kernels having a binary-weight, wherein the kernels are to be processed in a layer of a neural network; decomposing each of the kernels into a first type sub-kernel reconstructed with weights of a same sign, and a second type sub-kernel for correcting a difference between a respective kernel, among the kernels, and the first type sub-kernel; performing a convolution operation by using the input feature map and the first type sub-kernels and the second type sub-kernels decomposed from each of the kernels; and obtaining an output feature map by combining results of the convolution operation.