Compression techniques for data structures suitable for artificial neural networks
US11489541B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 30, 2019 |
| Grant date | Nov 1, 2022 |
| Priority date | — |
| Expiry date | Aug 21, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH03M7/3059
- WIPO fieldBasic communication processes
- WIPO sectorElectrical engineering
Abstract
In artificial neural networks, and other similar applications, there is typically a large amount of data involved that is considered sparse data. Due to the large size of the data involved in such applications, it is helpful to compress the data to save bandwidth resources when transmitting the data and save memory resources when storing the data. Introduced herein is a compression technique that selects elements with significant values from data and restructures them into a structured sparse format. By generating metadata that enforces the structured sparse format and organizing the data according to the metadata, the introduced technique not only reduces the size of the data but also consistently places the data in a particular format. As such, hardware can be simplified and optimized to process the data much faster and much more efficiently than the conventional compression techniques that rely on a non-structured sparsity format.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.