Convolutional neural network using a binarized convolution layer
US9563825B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 20, 2014 |
| Grant date | Feb 7, 2017 |
| Priority date | — |
| Expiry date | May 26, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/19173
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A convolutional neural network is trained to analyze input data in various different manners. The convolutional neural network includes multiple layers, one of which is a convolution layer that performs a convolution, for each of one or more filters in the convolution layer, of the filter over the input data. The convolution includes generation of an inner product based on the filter and the input data. Both the filter of the convolution layer and the input data are binarized, allowing the inner product to be computed using particular operations that are typically faster than multiplication of floating point values. The possible results for the convolution layer can optionally be pre-computed and stored in a look-up table. Thus, during operation of the convolutional neural network, rather than performing the convolution on the input data, the pre-computed result can be obtained from the look-up table.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.