Training neural network accelerators using mixed precision data formats
US11676003B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 18, 2018 |
| Grant date | Jun 13, 2023 |
| Priority date | — |
| Expiry date | Oct 31, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Technology related to training a neural network accelerator using mixed precision data formats is disclosed. In one example of the disclosed technology, a neural network accelerator is configured to accelerate a given layer of a multi-layer neural network. An input tensor for the given layer can be converted from a normal-precision floating-point format to a quantized-precision floating-point format. A tensor operation can be performed using the converted input tensor. A result of the tensor operation can be converted from the block floating-point format to the normal-precision floating-point format. The converted result can be used to generate an output tensor of the layer of the neural network, where the output tensor is in normal-precision floating-point format.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.