公开(公告)号：US20250094822A1

公开(公告)日：2025-03-20

申请号：US18963964

申请日：2024-11-29

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yinchuan Li ,  Yunfeng Shao

IPC: G06N3/096 ,  G06N3/045

Abstract: This application discloses a tensor-based continual learning method and apparatus. The method includes: obtaining input data; and inputting the input data into a first neural network to obtain a data processing result. After training of an ith task ends, the neural network includes A tensor cores, the A tensor cores are divided into B tensor layers, and each of the B tensor layers includes data of all of the A tensor cores in a same dimension. In training of an (i+1)th task, C tensor cores and/or D tensor layers are added to the first neural network, and parameters in the C tensor cores and/or parameters at the D tensor layers are updated. According to this application, an anti-forgetting capability of a model can be effectively improved, and an increase in a scale of the model is small, to effectively reduce storage and communication overheads.

公开(公告)号：US20240211816A1

公开(公告)日：2024-06-27

申请号：US18597011

申请日：2024-03-06

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yinchuan Li ,  Yunfeng Shao ,  Jun Wu

IPC: G06N20/20

CPC classification number: G06N20/20

Abstract: A method includes a server delivering a random quantization instruction to a plurality of terminals. The plurality of terminals perform random quantization on training update data based on the random quantization instruction and upload, to the server, training update data on which random quantization has been performed. After aggregating the training update data on which random quantization has been performed, the server may eliminate an additional quantization error introduced by random quantization.