Natural language processing using a CNN based integrated circuit
US10083171B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 19, 2017 |
| Grant date | Sep 25, 2018 |
| Priority date | — |
| Expiry date | Sep 19, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A string of natural language texts is received and formed a multi-layer 2-D symbol in a computing system. The 2-D symbol comprises a matrix of N×N pixels of K-bit data representing a “super-character”. The matrix is divided into M×M sub-matrices with each sub-matrix containing (N/M)×(N/M) pixels. K, N and M are positive integers, and N is preferably a multiple of M. Each sub-matrix represents one ideogram defined in an ideogram collection set. “Super-character” represents a meaning formed from a specific combination of a plurality of ideograms. The meaning of the “super-character” is learned by classifying the 2-D symbol via a trained convolutional neural networks model having bi-valued 3×3 filter kernels in a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.