To provide natural language processing using a CNN based integrated circuit in order to understand ideograms since some of the ideograms may not be understood unless a user is familiar with the ideograms.SOLUTION: A string of natural language texts includes a matrix of N×N pixels of K-bit data representing a 'super-character' and forms a multi-layer 2-D symbol. The matrix is divided into M×M sub-matrices each including (N/M)×(N/M) pixels. Each sub-matrix represents one ideogram defined in an ideogram collection set. The '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.SELECTED DRAWING: Figure 5A
NATURAL LANGUAGE PROCESSING USING CNN BASED INTEGRATED CIRCUIT