Efficient convolutional neural networks and techniques to reduce associated computational costs
US11157815B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 29, 2019 |
| Grant date | Oct 26, 2021 |
| Priority date | — |
| Expiry date | Oct 9, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/20084
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure provides systems and methods to reduce computational costs associated with convolutional neural networks. In addition, the present disclosure provides a class of efficient models termed “MobileNets” for mobile and embedded vision applications. MobileNets are based on a straight-forward architecture that uses depthwise separable convolutions to build light weight deep neural networks. The present disclosure further provides two global hyper-parameters that efficiently trade-off between latency and accuracy. These hyper-parameters allow the entity building the model to select the appropriately sized model for the particular application based on the constraints of the problem. MobileNets and associated computational cost reduction techniques are effective across a wide range of applications and use cases.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.