公开(公告)号：US11537892B2

公开(公告)日：2022-12-27

申请号：US16632215

申请日：2017-08-18

Applicant: Intel Corporation

Inventor： Shoumeng Yan ,  Jianguo Li ,  Zhuang Liu

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/063 ,  G06T1/20

Abstract: A mechanism is described for facilitating slimming of neural networks in machine learning environments. A method of embodiments, as described herein, includes learning a first neural network associated with machine learning processes to be performed by a processor of a computing device, where learning includes analyzing a plurality of channels associated with one or more layers of the first neural network. The method may further include computing a plurality of scaling factors to be associated with the plurality of channels such that each channel is assigned a scaling factor, wherein each scaling factor to indicate relevance of a corresponding channel within the first neural network. The method may further include pruning the first neural network into a second neural network by removing one or more channels of the plurality of channels having low relevance as indicated by one or more scaling factors of the plurality of scaling factors assigned to the one or more channels.

公开(公告)号：US20200234130A1

公开(公告)日：2020-07-23

申请号：US16632215

申请日：2017-08-18

Applicant: Intel Corporation

Inventor： Shoumeng Yan ,  Jianguo Li ,  Zhuang Liu

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/063 ,  G06T1/20

Abstract: A mechanism is described for facilitating slimming of neural networks in machine learning environments. A method of embodiments, as described herein, includes learning a first neural network associated with machine learning processes to be performed by a processor of a computing device, where learning includes analyzing a plurality of channels associated with one or more layers of the first neural network. The method may further include computing a plurality of scaling factors to be associated with the plurality of channels such that each channel is assigned a scaling factor, wherein each scaling factor to indicate relevance of a corresponding channel within the first neural network. The method may further include pruning the first neural network into a second neural network by removing one or more channels of the plurality of channels having low relevance as indicated by one or more scaling factors of the plurality of scaling factors assigned to the one or more channels.