Training a neural network using a non-homogenous set of reconfigurable processors
US11893424B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 24, 2022 |
| Grant date | Feb 6, 2024 |
| Priority date | — |
| Expiry date | Jul 16, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system for training parameters of a neural network includes a processing node with a processor reconfigurable at a first level of configuration granularity and a controller reconfigurable at a finer level of configuration granularity. The processor is configured to execute a first dataflow segment of the neural network with training data to generate a predicted output value using a set of neural network parameters, calculate a first intermediate result for a parameter based on the predicted output value, a target output value, and a parameter gradient, and provide the first intermediate result to the controller. The controller is configured to receive a second intermediate result over a network, and execute a second dataflow segment, dependent upon the first intermediate result and the second intermediate result, to generate a third intermediate result indicative of an update of the parameter.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.