Workload-aware data encoding
US11907250B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 22, 2022 |
| Grant date | Feb 20, 2024 |
| Priority date | — |
| Expiry date | Jul 22, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/2462
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are described for executing machine learning models trained for specific operators with feature values that are based on the actual execution of a workload set. The machine learning models generate an estimate of benefit gain/cost for executing operations on data portions in the alternative encoding format. Such data potions may be sorted based on the estimated benefit, in an embodiment. Using cost estimation machine learning models for memory space, the data portions with the most benefits that comply with the existing memory space constraints are recommended and/or are automatically encoded into the alternative encoding format.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.