Deriving a concordant software neural network layer from a quantized firmware neural network layer
US11556764B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 1, 2019 |
| Grant date | Jan 17, 2023 |
| Priority date | — |
| Expiry date | Nov 17, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/105
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for deriving a concordant software neural network layer are provided. A method includes receiving first instructions configured to, using a neural network processor (NNP), process a first set of data corresponding to a neural network layer, where the NNP is configured to quantize the first set of the data to generate a set of quantized data and then perform matrix-vector multiply operations on the set of quantized data using a matrix-vector-multiplier incorporated within hardware associated with the NNP to generate a first set of results. The method further includes processing the first instructions to automatically generate second instructions configured for use with at least one processor, different from the NNP, such that the second instructions, when executed by the at least one processor to perform matrix multiply operations, generate a second set of results that are concordant with the first set of results.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.