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公开(公告)号:US11568303B2
公开(公告)日:2023-01-31
申请号:US16153135
申请日:2018-10-05
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Kyoung-hoon Kim , Young-hwan Park , Dong-soo Lee , Dae-hyun Kim , Han-su Cho , Hyun-jung Kim
IPC: G06N20/00
Abstract: An electronic apparatus is provided. The electronic apparatus includes a first memory configured to store a first artificial intelligence (AI) model including a plurality of first elements and a processor configured to include a second memory. The second memory is configured to store a second AI model including a plurality of second elements. The processor is configured to acquire output data from input data based on the second AI model. The first AI model is trained through an AI algorithm. Each of the plurality of second elements includes at least one higher bit of a plurality of bits included in a respective one of the plurality of first elements.
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公开(公告)号:US11568323B2
公开(公告)日:2023-01-31
申请号:US16650083
申请日:2018-05-16
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Kyoung-hoon Kim , Young-hwan Park , Dong-kwan Suh , Keshava Prasad Nagaraja , Dae-hyun Kim , Suk-jin Kim , Han-su Cho , Hyun-jung Kim
Abstract: Disclosed is an electronic device. The An electronic device including a storage, and a processor configured to perform convolution processing on target data and kernel data based on stride information that indicates an interval at which the kernel data is applied to the target data stored in the storage, in which the processor is further configured to divide the target data into a plurality of pieces of sub-data based on first stride information, perform the convolution processing on the plurality of pieces of sub-data and a plurality of pieces of sub-kernel data respectively corresponding to the plurality of pieces of sub-data based on second stride information that is different from the first stride information, and combine a plurality of processing results, the plurality of pieces of sub-kernel data are obtained by dividing the kernel data based on the first stride information, and the second stride information indicates that the interval at which the kernel data is applied to the target data is 1.
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公开(公告)号:US11450086B2
公开(公告)日:2022-09-20
申请号:US16617967
申请日:2018-06-05
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Han-su Cho , Kyung-hoon Kim , Young-hwan Park , Suk-jin Kim , Hyun-jung Kim , Dong-wook Kwon
Abstract: Disclosed is an electronic device and method for controlling same. The electronic device comprises: a memory; and a processor which checks an operation instruction for filtering input data of a neural network for each filter of a main pattern selected from a plurality of filters generated according to learning by the neural network, and stores the checked operation instruction in the memory.
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公开(公告)号:US11093439B2
公开(公告)日:2021-08-17
申请号:US16143922
申请日:2018-09-27
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Kyoung-hoon Kim , Young-hwan Park , Dong-kwan Suh , Keshava prasad Nagaraja , Suk-jin Kim , Han-su Cho , Hyun-jung Kim
Abstract: A processor for performing deep learning is provided herein. The processor includes a processing element unit including a plurality of processing elements arranged in a matrix form including a first row of processing elements and a second row of processing elements. The processing elements are fed with filter data by a first data input unit which is connected to the first row processing elements. A second data input unit feeds target data to the processing elements. A shifter composed of registers feeds instructions to the processing elements. A controller in the processor controls the processing elements, the first data input unit and second data input unit to process the filter data and target data, thus providing sum of products (convolution) functionality.
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公开(公告)号:US11907826B2
公开(公告)日:2024-02-20
申请号:US15934341
申请日:2018-03-23
Applicant: Samsung Electronics Co., Ltd.
Inventor: Kyoung-Hoon Kim , Young-hwan Park , Ki-seok Kwon , Suk-jin Kim , Chae-seok Im , Han-su Cho , Sang-bok Han , Seung-won Lee , Kang-jin Yoon
Abstract: An electronic apparatus for performing machine learning a method of machine learning, and a non-transitory computer-readable recording medium are provided. The electronic apparatus includes an operation module configured to include a plurality of processing elements arranged in a predetermined pattern and share data between the plurality of processing elements which are adjacent to each other to perform an operation; and a processor configured to control the operation module to perform a convolution operation by applying a filter to input data, wherein the processor controls the operation module to perform the convolution operation by inputting each of a plurality of elements configuring a two-dimensional filter to the plurality of processing elements in a predetermined order and sequentially applying the plurality of elements to the input data.
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公开(公告)号:US11675997B2
公开(公告)日:2023-06-13
申请号:US16163772
申请日:2018-10-18
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Kyoung-hoon Kim , Young-hwan Park , Dong-kwan Suh , Keshava Prasad , Dae-hyun Kim , Suk-jin Kim , Han-su Cho , Hyun-jung Kim
CPC classification number: G06N3/04 , G06F12/06 , G06F17/15 , G06F21/52 , G06N3/045 , G06N3/063 , G06N5/046
Abstract: Provided are a method and apparatus for processing a convolution operation in a neural network. The apparatus may include a memory, and a processor configured to read, from the memory, one of divided blocks of input data stored in a memory; generate an output block by performing the convolution operation on the one of the divided blocks with a kernel; generate a feature map by using the output block, and write the feature map to the memory.
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公开(公告)号:US20190114542A1
公开(公告)日:2019-04-18
申请号:US16031565
申请日:2018-07-10
Applicant: Samsung Electronics Co., Ltd.
Inventor: Kyoung-hoon Kim , Young-hwan Park , Dong-kwan Suh , Keshava Prasad Nagaraja , Dae-hyun Kim , Suk-jin Kim , Han-su Cho , Hyun-jung Kim
Abstract: An electronic apparatus and method thereof are provided for performing deep learning. The electronic apparatus includes a storage configured to store target data and kernel data; and a processor including a plurality of processing elements that are arranged in a matrix shape. The processor is configured to input, to each of the plurality of processing elements, a first non-zero element from among a plurality of first elements included in the target data, and sequentially input, to each of a plurality of first processing elements included in a first row from among the plurality of processing elements, a second non-zero element from among the plurality of elements included in the kernel data. Each of the plurality of first processing elements is configured to perform an operation between the input first non-zero element and the input second non-zero element, based on depth information of the first non-zero element and depth information of the second non-zero element.
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