METHOD AND APPARATUS WITH DATA LOADING

    公开(公告)号:US20230140239A1

    公开(公告)日:2023-05-04

    申请号:US17868361

    申请日:2022-07-19

    Abstract: A processor-implemented method with data loading includes: dividing a training data set into a plurality of subsets based on sizes of a plurality of data files included in the training data set; loading, from each of the plurality of subsets, a portion of data files in the subset to a plurality of processors based on a proportion of a number of data files of the plurality of subsets in the subset and a batch size of distributed training; and reallocating, based on sizes of data files loaded to processors in a same group among the plurality of processors, the loaded data files to the processors in the same group.

    METHOD AND APPARATUS FOR QUANTIZING PARAMETERS OF NEURAL NETWORK

    公开(公告)号:US20220092384A1

    公开(公告)日:2022-03-24

    申请号:US17192048

    申请日:2021-03-04

    Abstract: A method of quantizing parameters of a neural network includes acquiring a parameter of a floating-point format used in a process of inferring by the neural network, quantizing, based on statistics of a weight included in the parameter, the weight into a fixed-point format, determining, based on statistics of an activation of one or more layers configuring the neural network included in the parameter, a dynamic range of the activation, and quantizing, based on statistics of input data of the neural network, the input data into a fixed-point format.

    NEURAL NETWORK METHOD AND APPARATUS

    公开(公告)号:US20210081798A1

    公开(公告)日:2021-03-18

    申请号:US16835532

    申请日:2020-03-31

    Abstract: A method and apparatus for the pruning of a neural network is provided. The method sets a weight threshold value based on a weight distribution of layers included in a neural network, predicts a change of inference accuracy of a neural network by pruning of each layer based on the weight threshold value, determines a current subject layer to be pruned with a weight threshold value among the layers included in the neural network, and prunes a determined current subject layer.

Patent Agency Ranking