ELECTRONIC APPARATUS AND CONTROL METHOD THEREOF

    公开(公告)号:US20200234131A1

    公开(公告)日:2020-07-23

    申请号:US16727323

    申请日:2019-12-26

    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.

    ELECTRONIC DEVICE AND CONTROL METHOD THEREFOR

    公开(公告)号:US20230244441A1

    公开(公告)日:2023-08-03

    申请号:US18131164

    申请日:2023-04-05

    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.

    ELECTRONIC APPARATUS AND CONTROL METHOD THEREOF

    公开(公告)号:US20200074283A1

    公开(公告)日:2020-03-05

    申请号:US16555331

    申请日:2019-08-29

    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.

    ELECTRONIC APPARATUS AND CONTROLLING METHOD THEREOF

    公开(公告)号:US20210027168A1

    公开(公告)日:2021-01-28

    申请号:US16892730

    申请日:2020-06-04

    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.

    ELECTRONIC DEVICE AND CONTROL METHOD THEREOF

    公开(公告)号:US20210279589A1

    公开(公告)日:2021-09-09

    申请号:US17258617

    申请日:2019-05-10

    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.

    ELECTRONIC APPARATUS AND METHOD FOR CONTROLLING THEREOF

    公开(公告)号:US20210271981A1

    公开(公告)日:2021-09-02

    申请号:US17171582

    申请日:2021-02-09

    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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