Hardware-implemented training of an artificial neural network
US11138501B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 22, 2018 |
| Grant date | Oct 5, 2021 |
| Priority date | — |
| Expiry date | Jul 25, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/09
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method for hardware-implemented training of a feedforward artificial neural network is provided. The method comprises: generating a first output signal by processing an input signal with the network, wherein a cost quantity assumes a first cost value; measuring the first cost value; defining a group of at least one synaptic weight of the network for variation; varying each weight of the group by a predefined weight difference; after the variation, generating a second output signal from the input signal to measure a second cost value; comparing the first and second cost values; and determining, based on the comparison, a desired weight change for each weight of the group such that the cost function does not increase if the respective desired weight changes are added to the weights of the group. The desired weight change is based on the weight difference times −1, 0, or +1.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.