ELECTRONIC DEVICE AND CONTROLLING METHOD OF ELECTRONIC DEVICE

    公开(公告)号:US20220405561A1

    公开(公告)日:2022-12-22

    申请号:US17893450

    申请日:2022-08-23

    Abstract: An electronic device and a controlling method of an electronic device are provided. An electronic device recursively determines a plurality of layers of a neural network model. Weight data of first model information is recursively quantized to obtain a second neural network model. The recursive quantization begins with the weight data and determines an iteration count of a recursion. The recursion operates on error data, quantized weight data, scale data and quantized error data to obtain the iteration count. A first bit-width of the weight data is reduced to a second bit-width of the quantized weight data. The recursion may be performed on a per-layer basis. The weight data may be formulated in a floating-point format and the quantized weight data may be formulated in a fixed point format with an integer number of bits.

    DATA PROCESSING METHOD AND DATA PROCESSING DEVICE USING SUPPLEMENTED NEURAL NETWORK QUANTIZATION OPERATION

    公开(公告)号:US20240412052A1

    公开(公告)日:2024-12-12

    申请号:US18811302

    申请日:2024-08-21

    Abstract: A data processing method for neural network quantization, includes: obtaining a quantized weight by quantizing a weight of a neural network; obtaining a quantization error that is a difference between the weight and the quantized weight; obtaining input data with respect to the neural network; obtaining a first convolution result by performing convolution on the quantized weight and the input data; obtaining a second convolution result by performing convolution on the quantization error and the input data; obtaining a scaled second convolution result by scaling the second convolution result based on bit shifting; and obtaining output data by using the first convolution result and the scaled second convolution result.

    APPARATUS AND METHOD FOR TRAINING DEVICE-TO-DEVICE PHYSICAL INTERFACE

    公开(公告)号:US20250094375A1

    公开(公告)日:2025-03-20

    申请号:US18965046

    申请日:2024-12-02

    Abstract: A method of training a physical interface between a first device and a second device includes performing a first training of the physical interface by communicating with the second device by using a first candidate group of lanes from among a plurality of lanes; performing a second training of the physical interface by communicating with the second device by using a second candidate group of lanes from among the plurality of lanes, the second candidate group being different from the first candidate group; determining a lane group based on a result of the first training and a result of the second training; and setting the second device so that the determined lane group is used for the physical interface.

    APPARATUS AND METHOD FOR TRAINING DEVICE-TO-DEVICE PHYSICAL INTERFACE

    公开(公告)号:US20220121592A1

    公开(公告)日:2022-04-21

    申请号:US17475705

    申请日:2021-09-15

    Abstract: A method of training a physical interface between a first device and a second device includes performing a first training of the physical interface by communicating with the second device by using a first candidate group of lanes from among a plurality of lanes; performing a second training of the physical interface by communicating with the second device by using a second candidate group of lanes from among the plurality of lanes, the second candidate group being different from the first candidate group; determining a lane group based on a result of the first training and a result of the second training; and setting the second device so that the determined lane group is used for the physical interface.

    ELECTRONIC APPARATUS AND CONTROL METHOD THEREOF

    公开(公告)号:US20210365779A1

    公开(公告)日:2021-11-25

    申请号:US17171290

    申请日:2021-02-09

    Abstract: An electronic apparatus is provided. The electronic apparatus includes a memory configured to store an artificial intelligence (AI) model including a plurality of layers and a processor, and the AI model may include a plurality of weight values that are scaled based on shift scaling factors different by a plurality of channels included in each of the plurality of layers and quantized by the plurality of layers, and the processor may, based on receiving input data, in a neural network computation process for the input data, compute a channel-wise computation result with an inverse-scaled composite scale parameter based on a shift scaling factor corresponding to each channel.

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