Method and system for opportunistic load balancing in neural networks using metadata
US10970120B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 26, 2018 |
| Grant date | Apr 6, 2021 |
| Priority date | — |
| Expiry date | Jun 28, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods and systems for opportunistic load balancing in deep neural networks (DNNs) using metadata. Representative computational costs are captured, obtained or determined for a given architectural, functional or computational aspect of a DNN system. The representative computational costs are implemented as metadata for the given architectural, functional or computational aspect of the DNN system. In an implementation, the computed computational cost is implemented as the metadata. A scheduler detects whether there are neurons in subsequent layers that are ready to execute. The scheduler uses the metadata and neuron availability to schedule and load balance across compute resources and available resources.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.