Bi-scaled deep neural networks
US11263518B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 4, 2019 |
| Grant date | Mar 1, 2022 |
| Priority date | — |
| Expiry date | Aug 14, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/08
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method is provided for forming a Deep Neural Network (DNN). The method includes quantizing deep learning data structures of the DNN into at least two modes using at least two scale factors, respectively. Each of the at least two modes corresponds to a respective one of the at least two scale factors. The method further includes identifying which of the at least two scale factors to use for a given one of the data structures based on a data distribution of the given one of the data structures. The quantizing step includes identifying when a tail of the given one of the data structures starts by (i) building a histogram of values in the given one of the data structures using successive bins; (ii) identifying a ratio of density between the successive bins; and (iii) checking whether the ratio of density is greater than a ratio of density threshold.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.