公开(公告)号：US11887001B2

公开(公告)日：2024-01-30

申请号：US16328182

申请日：2016-09-26

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Yiwen Guo ,  Lin Xu ,  Yan Lin ,  Yurong Chen

IPC: G06N3/08 ,  G06N3/082 ,  G06N3/084 ,  G06N3/02 ,  G06N3/045 ,  G06F17/16 ,  G06N3/04 ,  G06N3/044

CPC classification number: G06N3/082 ,  G06F17/16 ,  G06N3/02 ,  G06N3/04 ,  G06N3/045 ,  G06N3/084 ,  G06N3/044

Abstract: An apparatus and method are described for reducing the parameter density of a deep neural network (DNN). A layer-wise pruning module to prune a specified set of parameters from each layer of a reference dense neural network model to generate a second neural network model having a relatively higher sparsity rate than the reference neural network model; a retraining module to retrain the second neural network model in accordance with a set of training data to generate a retrained second neural network model; and the retraining module to output the retrained second neural network model as a final neural network model if a target sparsity rate has been reached or to provide the retrained second neural network model to the layer-wise pruning model for additional pruning if the target sparsity rate has not been reached.