Natural language processing using a CNN based integrated circuit
First Claim
1. A method of natural language processing using a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit, the method comprising:
- receiving a string of natural language texts in a computing system;
forming, with a two-dimensional symbol creation module installed in the computing system, a multi-layer two-dimensional (2-D) symbol from the received string of natural language texts based on a set of 2-D symbol creation rules, the 2-D symbol being a matrix of N×
N pixels of K-bit data that contains a super-character, wherein the matrix is divided into M×
M sub-matrices with each of the sub-matrices containing (N/M)×
(N/M) pixels, said each of the sub-matrices representing one ideogram defined in an ideogram collection set, and the super-character representing a meaning formed from a specific combination of a plurality of ideograms, where K, N and M are positive integers or whole numbers, and N is a multiple of M; and
learning the meaning of the super-character 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.
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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.
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Citations
19 Claims
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1. A method of natural language processing using a Cellular Neural Networks or Cellular Nonlinear Networks (CNN) based integrated circuit, the method comprising:
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receiving a string of natural language texts in a computing system; forming, with a two-dimensional symbol creation module installed in the computing system, a multi-layer two-dimensional (2-D) symbol from the received string of natural language texts based on a set of 2-D symbol creation rules, the 2-D symbol being a matrix of N×
N pixels of K-bit data that contains a super-character, wherein the matrix is divided into M×
M sub-matrices with each of the sub-matrices containing (N/M)×
(N/M) pixels, said each of the sub-matrices representing one ideogram defined in an ideogram collection set, and the super-character representing a meaning formed from a specific combination of a plurality of ideograms, where K, N and M are positive integers or whole numbers, and N is a multiple of M; andlearning the meaning of the super-character 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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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Specification