Adaptation of deep learning models to resource constrained edge devices
US11928583B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 8, 2019 |
| Grant date | Mar 12, 2024 |
| Priority date | — |
| Expiry date | May 22, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0985
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for generating a set of Deep Learning (DL) models are described. An example method includes training an initial set of DL models using the training data, wherein a topology of each of the DL models is determined based on the parameters vector. The method also includes generating a set of estimate performance functions for each of the DL models in the initial set based on the set of edge-related metrics, and generating a plurality of objective functions based on the set of estimated performance functions. The method also includes generating a final DL model set based on the objective functions, receiving a user selection of a selected DL model from the final DL model set, and deploying the selected DL model to an edge device.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.