公开(公告)号：US20230206075A1

公开(公告)日：2023-06-29

申请号：US17991077

申请日：2022-11-21

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Ji LIU ,  Zhihua WU ,  Danlei FENG ,  Minxu ZHANG ,  Xinxuan WU ,  Xuefeng YAO ,  Beichen MA ,  Dejing DOU ,  Dianhai YU ,  Yanjun MA

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/082 ,  G06N3/04

Abstract: A method for distributing network layers in a neural network model includes: acquiring a to-be-processed neural network model and a computing device set; generating a target number of distribution schemes according to network layers in the to-be-processed neural network model and computing devices in the computing device set, the distribution schemes including corresponding relationships between the network layers and the computing devices; according to device types of the computing devices, combining the network layers corresponding to the same device type in each distribution scheme into one stage, to obtain a combination result of each distribution scheme; obtaining an adaptive value of each distribution scheme according to the combination result of each distribution scheme; and determining a target distribution scheme from the distribution schemes according to respective adaptive value, and taking the target distribution scheme as a distribution result of the network layers in the to-be-processed neural network model.

公开(公告)号：US20240394190A1

公开(公告)日：2024-11-28

申请号：US18696757

申请日：2022-09-27

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Minxu ZHANG ,  Haifeng WANG ,  Fan ZHANG ,  Xinxuan WU ,  Xuefeng YAO ,  Danlei FENG ,  Zhihua WU ,  Zhipeng TAN ,  Jie DING ,  Dianhai YU

IPC: G06F12/0873 ,  G06F12/0815 ,  G06F15/80

Abstract: The present application provides a method of training a deep learning model. A specific implementation solution of the method of training the deep learning model includes: determining, according to first training data for a current training round, a first target parameter required to be written into a target memory in a first network parameter required by an embedding of the first training data, wherein the target memory is a memory contained in a target processor; determining a remaining storage slot in the target memory according to a first mapping relationship between a storage slot of the target memory and a network parameter; and writing, in response to the remaining storage slot meeting a storage requirement of the first target parameter, the first target parameter into the target memory so that a computing core contained in the target processor adjusts the first network parameter according to the first training data.