公开(公告)号：US20190130255A1

公开(公告)日：2019-05-02

申请号：US16033796

申请日：2018-07-12

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Han-young Yim ,  Do-yun Kim ,  Byeoung-su Kim ,  Nak-woo Sung ,  Jong-han Lim ,  Sang-hyuck Ha

IPC: G06N3/04 ,  G06N3/08

Abstract: A method of generating a fixed-point type neural network by quantizing a floating-point type neural network, includes obtaining, by a device, a plurality of post-activation values by applying an activation function to a plurality of activation values that are received from a layer included in the floating-point type neural network, and deriving, by the device, a plurality of statistical characteristics for at least some of the plurality of post-activation values. The method further includes determining, by the device, a step size for the quantizing of the floating-point type neural network, based on the plurality of statistical characteristics, and determining, by the device, a final fraction length for the fixed-point type neural network, based on the step size.

公开(公告)号：US11694073B2

公开(公告)日：2023-07-04

申请号：US16196131

申请日：2018-11-20

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Han-young Yim ,  Do-yun Kim ,  Byeoung-su Kim ,  Nak-Woo Sung ,  Jong-han Lim ,  Sang-hyuck Ha

IPC: G06N3/08 ,  G06F7/483 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06F7/483 ,  G06N3/04

Abstract: A method and apparatus for generating a fixed point neural network are provided. The method includes selecting at least one layer of a neural network as an object layer, wherein the neural network includes a plurality of layers, each of the plurality of layers corresponding to a respective one of plurality of quantization parameters; forming a candidate parameter set including candidate parameter values with respect to a quantization parameter of the plurality of quantization parameters corresponding to the object layer; determining an update parameter value from among the candidate parameter values based on levels of network performance of the neural network, wherein each of the levels of network performance correspond to a respective one of the candidate parameter values; and updating the quantization parameter with respect to the object layer based on the update parameter value.

公开(公告)号：US11373087B2

公开(公告)日：2022-06-28

申请号：US16033796

申请日：2018-07-12

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Han-young Yim ,  Do-yun Kim ,  Byeoung-su Kim ,  Nak-woo Sung ,  Jong-han Lim ,  Sang-hyuck Ha

IPC: G06N3/08 ,  G06N20/00 ,  G06N3/04

Abstract: A method of generating a fixed-point type neural network by quantizing a floating-point type neural network, includes obtaining, by a device, a plurality of post-activation values by applying an activation function to a plurality of activation values that are received from a layer included in the floating-point type neural network, and deriving, by the device, a plurality of statistical characteristics for at least some of the plurality of post-activation values. The method further includes determining, by the device, a step size for the quantizing of the floating-point type neural network, based on the plurality of statistical characteristics, and determining, by the device, a final fraction length for the fixed-point type neural network, based on the step size.

公开(公告)号：US11275986B2

公开(公告)日：2022-03-15

申请号：US16008275

申请日：2018-06-14

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Do-yun Kim ,  Han-young Yim ,  In-yup Kang ,  Byeoung-su Kim ,  Nak-woo Sung ,  Jong-Han Lim ,  Sang-hyuck Ha

IPC: G06N3/04 ,  G06N3/063 ,  G06N3/08

Abstract: A method of quantizing an artificial neural network includes dividing an input distribution of the artificial neural network into a plurality of segments, generating an approximated density function by approximating each of the plurality of segments, calculating at least one quantization error corresponding to at least one step size for quantizing the artificial neural network, based on the approximated density function, and determining a final step size for quantizing the artificial neural network based on the at least one quantization error.

公开(公告)号：US11823028B2

公开(公告)日：2023-11-21

申请号：US16044163

申请日：2018-07-24

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Do-yun Kim ,  Han-young Yim ,  Byeoung-su Kim ,  Nak-woo Sung ,  Jong-han Lim ,  Sang-hyuck Ha

IPC: G06N3/048 ,  G06F17/18 ,  G06N3/08

CPC classification number: G06N3/048 ,  G06F17/18 ,  G06N3/08

Abstract: An artificial neural network (ANN) quantization method for generating an output ANN by quantizing an input ANN includes: obtaining second parameters by quantizing first parameters of the input ANN; obtaining a sample distribution from an intermediate ANN in which the obtained second parameters have been applied to the input ANN; and obtaining a fractional length for the sample distribution by quantizing the obtained sample distribution.

公开(公告)号：US20190180177A1

公开(公告)日：2019-06-13

申请号：US16196131

申请日：2018-11-20

Applicant: SAMSUNG ELECTRONICS CO., LTD.

Inventor： Han-young Yim ,  Do-yun Kim ,  Byeoung-su Kim ,  Nak-woo Sung ,  Jong-han Lim ,  Sang-hyuck Ha

IPC: G06N3/08 ,  G06N3/04 ,  G06F7/483

Abstract: A method and apparatus for generating a fixed point neural network are provided. The method includes selecting at least one layer of a neural network as an object layer, wherein the neural network includes a plurality of layers, each of the plurality of layers corresponding to a respective one of plurality of quantization parameters; forming a candidate parameter set including candidate parameter values with respect to a quantization parameter of the plurality of quantization parameters corresponding to the object layer; determining an update parameter value from among the candidate parameter values based on levels of network performance of the neural network, wherein each of the levels of network performance correspond to a respective one of the candidate parameter values; and updating the quantization parameter with respect to the object layer based on the update parameter value.