公开(公告)号：US11010308B2

公开(公告)日：2021-05-18

申请号：US16428748

申请日：2019-05-31

Applicant: LG ELECTRONICS INC.

Inventor： Jaewon Kim ,  Thi Huong Giang Nguyen

IPC: G06F12/126 ,  G06N20/10 ,  G06N3/063

Abstract: Embodiments of the present disclosure include method for optimizing an internal memory for calculation of a convolutional layer of a convolutional neural network (CNN), the method including determining a computation cost of calculating the convolutional layer using each combination of a memory management scheme of a plurality of memory management schemes and data partition sizes of input feature map (IFM) data, kernel data, and output feature map (OFM) data to be loaded in the internal memory; identifying one combination of a memory management scheme and data partition sizes having a lowest computation cost for the convolutional layer; and implementing the CNN to use the one combination for calculation of the convolutional layer.

公开(公告)号：US20200050555A1

公开(公告)日：2020-02-13

申请号：US16428748

申请日：2019-05-31

Applicant: LG ELECTRONICS INC.

Inventor： Jaewon KIM ,  Thi Huong Giang Nguyen

IPC: G06F12/126 ,  G06N3/063 ,  G06N20/10

Abstract: Embodiments of the present disclosure include method for optimizing an internal memory for calculation of a convolutional layer of a convolutional neural network (CNN), the method including determining a computation cost of calculating the convolutional layer using each combination of a memory management scheme of a plurality of memory management schemes and data partition sizes of input feature map (IFM) data, kernel data, and output feature map (OFM) data to be loaded in the internal memory; identifying one combination of a memory management scheme and data partition sizes having a lowest computation cost for the convolutional layer; and implementing the CNN to use the one combination for calculation of the convolutional layer.