Automatic hybrid quantization for deep neural network
US12412083B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 11, 2020 |
| Grant date | Sep 9, 2025 |
| Priority date | — |
| Expiry date | Feb 10, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0985
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods, computer program products, and/or systems are provided that perform the following operations: obtaining a target neural network structure and constraints for a target neural network; generating a meta learning network having an associated quantization function based, at least in part, on the target neural network structure; training the meta learning network based, at least in part, on providing a hybrid quantization vector as input to the meta learning network and providing a training dataset to the target neural network; obtaining a plurality of hybrid quantization vectors; determining a new hybrid quantization vector from the plurality of hybrid quantization vectors; and retraining the trained meta learning network based, at least in part, on providing the new hybrid quantization vector as input to the trained meta learning network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.