Neural network output layer for machine learning
US11106976B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Jul 2, 2019 |
| Grant date | Aug 31, 2021 |
| Priority date | — |
| Expiry date | Jul 2, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/088
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques for a neural network output layer for machine learning are disclosed. A plurality of processing elements within a reconfigurable fabric is configured to implement a data flow graph, where the data flow graph implements a neural network. The data flow graph can include machine learning or deep learning. A layer is implemented, within the neural network, that maps a first vector of real values to a second vector of real values bounded by zero and one, where the second vector sums to a value of one using fixed-point calculations. The layer can include a final layer within the neural network. The layer that maps the first vector includes a Softmax function. Results of the neural network are classified based on a value of the second vector. The classifying can include part of a machine learning or a deep learning process.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.