Efficient matrix data format applicable for artificial neural network
US10860293B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 27, 2019 |
| Grant date | Dec 8, 2020 |
| Priority date | — |
| Expiry date | Mar 14, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/063
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Many computing systems process data organized in a matrix format. For example, artificial neural networks (ANNs) perform numerous computations on data organized into matrices using conventional matrix arithmetic operations. One such operation, which is commonly performed, is the transpose operation. Additionally, many such systems need to process many matrices and/or matrices that are large in size. For sparse matrices that hold few significant values and many values that can be ignored, transmitting and processing all the values in such matrices is wasteful. Thus, techniques are introduced for storing a sparse matrix in a compressed format that allows for a matrix transpose operation to be performed on the compressed matrix without having to first decompress the compressed matrix. By utilizing the introduced techniques, more matrix operations can be performed than conventional systems.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.