Optimization of graphics processing unit memory for deep learning computing
US12154025B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 13, 2018 |
| Grant date | Nov 26, 2024 |
| Priority date | — |
| Expiry date | Mar 11, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T1/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods are provided for optimizing GPU memory allocation for high-performance applications such as deep learning (DL) computing. For example, a DL task is executed using GPU resources (GPU device and GPU memory) to process a DL model having functional layers that are processed in a predefined sequence. A current functional layer of the DL model is invoked and processed using the GPU device. In response to the invoking, a data compression operation is performed to compress data of a previous functional layer of the DL model, and store the compressed data in the GPU memory. Responsive to the invoking, compressed data of a next functional layer of the DL model is accessed from the GPU memory and a data decompression operation is performed to decompress the compressed data for subsequent processing of the next functional layer of the DL model by the GPU device.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.