Offline combination of convolutional/deconvolutional and batch-norm layers of convolutional neural network models for autonomous driving vehicles
US11308391B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 6, 2017 |
| Grant date | Apr 19, 2022 |
| Priority date | — |
| Expiry date | Dec 8, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/94
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In one embodiment, a system to accelerate batch-normalized convolutional neural network (CNN) models is disclosed. The system extracts a plurality of first groups of layers from a first CNN model, each group of the first groups having a first convolutional layer and a first batch-norm layer. For each group of the plurality of first groups, the system calculates a first scale vector and a first shift vector based on the first batch-norm layer, and generates a second convolutional layer representing the corresponding group of the plurality of first groups based on the first convolutional layer and the first scale and the first shift vectors. The system generates an accelerated CNN model based on the second convolutional layer corresponding to the plurality of the first groups, such that the accelerated CNN model is utilized subsequently to classify an object perceived by an autonomous driving vehicle (ADV).
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.