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    METHOD AND APPARATUS WITH NEURAL NETWORK OPERATION
    24.
    发明申请
    METHOD AND APPARATUS WITH NEURAL NETWORK OPERATION 有权

    公开(公告)号：US20220092394A1

    公开(公告)日：2022-03-24

    申请号：US17245144

    申请日：2021-04-30

    Applicant: SAMSUNG ELECTRONICS CO., LTD

    Inventor： Dongwook LEE ,  Changyong SON ,  Jinwoo SON ,  Jaehyoung YOO ,  Jaejoon HAN

    IPC: G06N3/063 ,  G06F9/50 ,  G06F9/54

    Abstract: Provided is a method and apparatus with neural network operation. The method includes generating a first intermediate operation result by performing a first-order partial operation of a neural network layer on a first input line of a first area of a frame, generating a second intermediate operation result by performing another first-order partial operation of the neural network layer on a second input line of the first area, and generating an objective operation result of the neural network layer with respect to the first area based on a second-order partial operation performed on the first intermediate operation result and the second intermediate operation result.

    METHOD AND APPARATUS FOR PROCESSING CONVOLUTION OPERATION ON LAYER IN NEURAL NETWORK
    25.
    发明申请
    METHOD AND APPARATUS FOR PROCESSING CONVOLUTION OPERATION ON LAYER IN NEURAL NETWORK 有权

    公开(公告)号：US20210279568A1

    公开(公告)日：2021-09-09

    申请号：US17015122

    申请日：2020-09-09

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Jaehyoung YOO ,  Jinwoo SON ,  Changyong SON ,  Seohyung LEE ,  Sangil JUNG ,  Changin CHOI

    IPC: G06N3/08 ,  G06N3/04 ,  G06F9/38

    Abstract: Disclosed are methods and apparatuses for processing a convolution operation on a layer in a neural network. The method includes extracting a first target feature vector from a target feature map, extracting a first weight vector matched with the first target feature vector from a first-type weight element, based on matching relationships for depth-wise convolution operations between target feature vectors of the target feature map and weight vectors of the first-type weight element, generating a first intermediate feature vector by performing multiplication between the first target feature vector and the first weight vector, generating a first hidden feature vector by accumulating the first intermediate feature vector and a second intermediate feature vector generated based on a second target feature vector, and generating a first output feature vector of an output feature map based on a point-wise convolution operation between the first hidden feature vector and a second-type weight element.

    OBJECT RECOGNITION METHOD AND APPARATUS
    27.
    发明申请
    OBJECT RECOGNITION METHOD AND APPARATUS 有权

    公开(公告)号：US20210056332A1

    公开(公告)日：2021-02-25

    申请号：US17089902

    申请日：2020-11-05

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： SeungJu HAN ,  SungUn PARK ,  JaeJoon HAN ,  Jinwoo SON ,  ChangYong SON ,  Minsu KO ,  Jihye KIM ,  Chang Kyu CHOI

    IPC: G06K9/00

    Abstract: An object recognition apparatus and method are provided. The apparatus includes a processor configured to verify a target image using an object model and based on reference intermediate data extracted by a partial layer of the object model as used in an object recognition of an input image, in response to a failure of a verification of the input image after a success of the object recognition of the input image, and perform an additional verification of the target image in response to the target image being verified in the verifying of the target image.

    CONVOLUTIONAL NEURAL NETWORK (CNN) PROCESSING METHOD AND APPARATUS
    29.
    发明申请
    CONVOLUTIONAL NEURAL NETWORK (CNN) PROCESSING METHOD AND APPARATUS 审中-公开

    公开(公告)号：US20180285715A1

    公开(公告)日：2018-10-04

    申请号：US15836988

    申请日：2017-12-11

    Applicant: Samsung Electronics Co., Ltd.

    Inventor： Jinwoo SON ,  Changyong SON ,  Jaejoon HAN ,  Chang Kyu CHOI

    IPC: G06N3/04 ,  G06N3/08

    Abstract: Disclosed is a convolutional neural network (CNN) processing apparatus and method, the apparatus configured to determine a loading space unit for at least one loading space in an input based on a height or a width for an input feature map of the input and an extent of a dimension of a kernel feature map, load target input elements corresponding to a target loading space, among the at least one loading space, from a memory and store the target input elements in an allocated input buffer having a size corresponding to the loading space unit, and perform a convolution operation between the target input elements stored in the input buffer and at least one kernel element of a kernel.

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