Fast deep neural network feature transformation via optimized memory bandwidth utilization
US10013652B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 29, 2015 |
| Grant date | Jul 3, 2018 |
| Priority date | — |
| Expiry date | Oct 28, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG10L2015/0635
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Deep Neural Networks (DNNs) with many hidden layers and many units per layer are very flexible models with a very large number of parameters. As such, DNNs are challenging to optimize. To achieve real-time computation, embodiments disclosed herein enable fast DNN feature transformation via optimized memory bandwidth utilization. To optimize memory bandwidth utilization, a rate of accessing memory may be reduced based on a batch setting. A memory, corresponding to a selected given output neuron of a current layer of the DNN, may be updated with an incremental output value computed for the selected given output neuron as a function of input values of a selected few non-zero input neurons of a previous layer of the DNN in combination with weights between the selected few non-zero input neurons and the selected given output neuron, wherein a number of the selected few corresponds to the batch setting.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.