Method and apparatus for reducing the parameter density of a deep neural network (DNN)
US11887001B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 26, 2016 |
| Grant date | Jan 30, 2024 |
| Priority date | — |
| Expiry date | Mar 19, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/044
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.