Runtime-throttleable neural networks
US11494626B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 11, 2019 |
| Grant date | Nov 8, 2022 |
| Priority date | — |
| Expiry date | Feb 20, 2041 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02D10/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In general, the disclosure describes techniques for creating runtime-throttleable neural networks (TNNs) that can adaptively balance performance and resource use in response to a control signal. For example, runtime-TNNs may be trained to be throttled via a gating scheme in which a set of disjoint components of the neural network can be individually “turned off” at runtime without significantly affecting the accuracy of NN inferences. A separate gating neural network may be trained to determine which trained components of the NN to turn off to obtain operable performance for a given level of resource use of computational, power, or other resources by the neural network. This level can then be specified by the control signal at runtime to adapt the NN to operate at the specified level and in this way balance performance and resource use for different operating conditions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.