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公开(公告)号:US20200234131A1
公开(公告)日:2020-07-23
申请号:US16727323
申请日:2019-12-26
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Dongsoo LEE , Sejung KWON , Parichay KAPOOR , Byeoungwook KIM
Abstract: An electronic apparatus is provided. The electronic apparatus includes sample data and memory storing a first matrix included in an artificial intelligence model trained based on sample data, and a processor configured to prunes each of a plurality of first elements included in the first matrix based on a first threshold, and acquire a first pruning index matrix that indicates whether each of the plurality of first elements has been pruned with binary data, factorize the first matrix to a second matrix of which size was determined based on the number of rows and the rank, and a third matrix of which size was determined based on the rank and the number of columns of the first matrix, prunes each of a plurality of second elements included in the second matrix based on a second threshold, and acquire a second pruning index matrix that indicates whether each of the plurality of second elements has been pruned with binary data, prunes each of a plurality of third elements included in the third matrix based on a third threshold, and acquire a third pruning index matrix that indicates whether each of the plurality of third elements has been pruned with binary data, acquire a final index matrix based on the second pruning index matrix and the third pruning index matrix, and update at least one of the second pruning index matrix or the third pruning index matrix by comparing the final index matrix with the first pruning index matrix.
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公开(公告)号:US20230244441A1
公开(公告)日:2023-08-03
申请号:US18131164
申请日:2023-04-05
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Byeoungwook KIM , Dongsoo LEE , Sejung KWON , Yeonju RO , Baeseong PARK , Yongkweon JEON
CPC classification number: G06F5/01 , G06F7/4876 , G06F7/485 , G06F7/5443
Abstract: An electronic device and a control method therefor are disclosed. An electronic device of the present disclosure includes a processor, which quantizes weight data with a combination of sign data and scaling factor data to obtain quantized data, and may input the first input data into a first module to obtain second input data in which exponents of input values included in the first input data are converted to the same value; input the second input data and the sign data into a second module to determine the signs of input values and perform calculations between the input values of which signs are determined to obtain first output data; input the first output data into a third module to normalize output values included in the first output data; and perform a multiplication operation on data including the normalized output values and the scaling factor data to obtain second output data.
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公开(公告)号:US20200074283A1
公开(公告)日:2020-03-05
申请号:US16555331
申请日:2019-08-29
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Parichay KAPOOR , Saehyung LEE , Dongsoo LEE , Byeoungwook KIM
Abstract: An electronic apparatus is provided. The electronic apparatus includes a storage storing a matrix included in an artificial intelligence model, and a processor. The processor divides data included in at least a portion of the matrix by one of rows and columns of the matrix to form groups, clusters the groups into clusters based on data included in each of the groups, and quantizes data divided by the other one of rows and columns of the matrix among data included in each of the clusters.
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公开(公告)号:US20250103288A1
公开(公告)日:2025-03-27
申请号:US18818742
申请日:2024-08-29
Applicant: Samsung Electronics Co., Ltd.
Inventor: Jae Hun JANG , Hong Rak SON , Dong-Min SHIN , JongYoon YOON , Younho JEON , Sejung KWON , Byeoungwook KIM , Baeseong PARK , Mankeun SEO , Byungmin AHN , Dongsoo LEE
Abstract: Disclosed is an accelerator performing an accumulation operation on a plurality of data, each being a floating point type. A method of operating the accelerator includes loading first data, finding a first exponent, which is a maximum value among exponents of the first data, generating aligned first fractions by performing a bit shift on first fractions of the first data based on the first exponent, and generating a first accumulated value by an accumulation operation on the aligned first fractions, loading second data, finding a second exponent, which is a maximum value among exponents of the second data, and generating a first aligned accumulated value by a bit shift on the first accumulated value, generating aligned second fractions by a bit shift on second fractions of the second data, and generating a second accumulated value by an accumulation operation on the aligned second fractions and the first aligned accumulated value.
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公开(公告)号:US20210027168A1
公开(公告)日:2021-01-28
申请号:US16892730
申请日:2020-06-04
Applicant: Samsung Electronics Co., Ltd.
Inventor: Dongsoo LEE , Sejung KWON , Byeoungwook KIM
Abstract: An electronic apparatus is provided. The electronic apparatus includes a memory configured to store one instruction or more and a processor configured to obtain output data by inputting input data to an artificial intelligence model including a plurality of layers by executing the instruction, and the artificial intelligence model is configured to output the output data based on operation through the plurality of layers and the processor is configured to encode operation data output from one of the plurality of layers and store the encoded operation data in the memory, obtain recovery data corresponding to the operation data by decoding the encoded operation data stored in the memory, and provide the obtained recovery data to another layer from among the plurality of layers.
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公开(公告)号:US20250117440A1
公开(公告)日:2025-04-10
申请号:US18817733
申请日:2024-08-28
Applicant: Samsung Electronics Co., Ltd.
Inventor: Jae Hun JANG , Hong Rak SON , Dong-Min SHIN , JongYoon YOON , Jihoon LIM , Younho JEON , Dongsoo LEE , Sejung KWON , Byeoungwook KIM , Baeseong PARK
Abstract: At least one embodiment provides a computing device including: a controller that receives first input data of a first data type and second input data of a second data type different from the first data type, and outputs a first signal representing the first data type, a second signal representing the second data type, and a clock signal based on the number of bits of the first input data and the second input data, and a computing circuit that performs a multiplication computation the first input data and the second input data based on the first signal, the second signal, and the clock signal and generates output data.
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公开(公告)号:US20200373946A1
公开(公告)日:2020-11-26
申请号:US16854285
申请日:2020-04-21
Applicant: Samsung Electronics Co., Ltd.
Inventor: Dongsoo LEE , Sejung KWON , Byeoungwook KIM , Parichay KAPOOR , Baeseong PARK
Abstract: A decompression apparatus is provided. The decompression apparatus includes a memory configured to store compressed data decompressed and used in neural network processing of an artificial intelligence model, a decoder configured to include a plurality of logic circuits related to a compression method of the compressed data, decompress the compressed data through the plurality of logic circuits based on an input of the compressed data, and output the decompressed data, and a processor configured to obtain data of a neural network processible form from the data output from the decoder.
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公开(公告)号:US20210279589A1
公开(公告)日:2021-09-09
申请号:US17258617
申请日:2019-05-10
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Dongsoo LEE , Parichay KAPOOR , Byeoungwook KIM
Abstract: Disclosed is an electronic device. The electronic device comprises a storage in which sample data and a matrix included in an artificial intelligence model which is trained on the basis of the sample data are stored, and a processor, wherein the processor is configured to: on the basis of the sizes of a plurality of elements included in the matrix, obtain a first matrix pruned by converting values of elements in the number corresponding to a first proportion to zero values; on the basis of test data, obtain first accuracy of an artificial intelligence model including the first matrix; if the first accuracy is within a preset range with respect to a preset value, retrain the artificial intelligence model including the first matrix on the basis of the sample data; and, on the basis of the sizes of a plurality of elements included in the retrained first matrix, obtain a second matrix pruned by converting values of elements in the number corresponding to a second proportion, which is greater than the first proportion, to zero values.
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公开(公告)号:US20210271981A1
公开(公告)日:2021-09-02
申请号:US17171582
申请日:2021-02-09
Applicant: Samsung Electronics Co., Ltd.
Inventor: Dongsoo LEE , Baeseong PARK , Byeoungwook KIM , Sejung KWON , Yongkweon JEON
Abstract: An electronic apparatus performing an operation of a neural network model is provided. The electronic apparatus includes a memory configured to store weight data including quantized weight values of the neural network model; and a processor configured to obtain operation data based on input data and binary data having at least one bit value different from each other, generate a lookup table by matching the operation data with the binary data, identify operation data corresponding to the weight data from the lookup table, and perform an operation of the neural network model based on the identified operation data.
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公开(公告)号:US20210111741A1
公开(公告)日:2021-04-15
申请号:US17130538
申请日:2020-12-22
Applicant: Samsung Electronics Co., Ltd.
Inventor: Dongsoo LEE , Sejung KWON , Byeoungwook KIM , Parichay KAPOOR , Baeseong PARK
Abstract: A decompression apparatus is provided. The decompression apparatus includes a memory configured to store compressed data decompressed and used in neural network processing of an artificial intelligence model, a decoder configured to include a plurality of logic circuits related to a compression method of the compressed data, decompress the compressed data through the plurality of logic circuits based on an input of the compressed data, and output the decompressed data, and a processor configured to obtain data of a neural network processible form from the data output from the decoder.
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