US 12,412,080 B2
Method and apparatus for forward computation of neural network, and computer-readable storage medium
Wen Ran Liu, Guangdong (CN); Hong Rui Chen, Guangdong (CN); Hao Yuan Li, Guangdong (CN); Qi Feng Chen, Guangdong (CN); and Feng Li, Guangdong (CN)
Assigned to TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED, Shenzhen (CN)
Filed by TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED, Guangdong (CN)
Filed on Oct. 21, 2021, as Appl. No. 17/507,127.
Application 17/507,127 is a continuation of application No. PCT/CN2020/098799, filed on Jun. 29, 2020.
Claims priority of application No. 201911294777.2 (CN), filed on Dec. 16, 2019.
Prior Publication US 2022/0044104 A1, Feb. 10, 2022
Int. Cl. G06N 3/063 (2023.01)
CPC G06N 3/063 (2013.01) 20 Claims
OG exemplary drawing
 
1. A method for forward computation of a neural network, performed by a computer device, for data processing at least one data processing layer in the neural network, the method comprising:
obtaining input data and weight data of the at least one data processing layer;
storing the input data by using a first texture storage structure to obtain first texture data, a plurality of data elements in the input data corresponding to a first index of the first texture data, and the computer device accessing data in the first texture data by using the first index as a unit;
determining a channel quantity of a second texture storage structure based on parameter data related to a data amount of the weight data and a dimension quantity of a second index of the second texture storage structure;
storing the weight data by using the second texture storage structure to obtain second texture data, a plurality of data elements in the weight data corresponding to the second index of the second texture data, and the computer device accessing data in the second texture data by using the second index as a unit; and
performing data processing of the at least one data processing layer based on the first texture data and the second texture data, to obtain output data of the at least one data processing layer.