Neural network processor based on application specific synthesis specialization parameters
US11556762B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 21, 2018 |
| Grant date | Jan 17, 2023 |
| Priority date | — |
| Expiry date | Jul 24, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0495
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Neural network processors that have been customized based on application specific synthesis specialization parameters and related methods are described. Certain example neural network processors and methods described in the present disclosure expose several major synthesis specialization parameters that can be used for specializing a microarchitecture instance of a neural network processor to specific neural network models including: (1) aligning the native vector dimension to the parameters of the model to minimize padding and waste during model evaluation, (2) increasing lane widths to drive up intra-row-level parallelism, or (3) increasing matrix multiply tiles to exploit sub-matrix parallelism for large neural network models.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.