Neural network training with decreased memory consumption and processor utilization
US11853897B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 9, 2022 |
| Grant date | Dec 26, 2023 |
| Priority date | — |
| Expiry date | Dec 9, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Bounding box quantization can reduce the quantity of bits utilized to express numerical values prior to the multiplication of matrices comprised of such numerical values, thereby reducing both memory consumption and processor utilization. Stochastic rounding can provide sufficient precision to enable the storage of weight values in reduced-precision formats without having to separately store weight values in a full-precision format. Alternatively, other rounding mechanisms, such as round to nearest, can be utilized to exchange weight values in reduced-precision formats, while also storing weight values in full-precision formats for subsequent updating. To facilitate conversion, reduced-precision formats such as brain floating-point format can be utilized.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.