Provided is a method for operating a convolutional neural network. The method includes performing learning on weights between neural network nodes by using input data, removing an adaptive parameter that performs learning using the input data after removing a weight having a size less than a threshold value among weights, and mapping remaining weights in the removing of the adaptive parameter to a plurality of representative values.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for operating a convolutional neural network, the method comprising: performing learning on weights between neural network nodes by using input data; removing an adaptive parameter that performs learning using the input data after removing a weight having a size less than a threshold value among weights; and mapping remaining weights in the removing of the adaptive parameter to a plurality of representative values. 2. The method of claim 1, wherein in the performing of the learning, the learning is performed in a state including all nodes of the neural network and weights that learn connections between all the nodes are generated. 3. The method of claim 1, wherein the removing of the adaptive parameter comprises: determining an initial threshold value for each of all layers of the neural network; removing a weight using the initial threshold value and performing learning; and removing a weight using an upper threshold value having a value greater than the initial threshold value and performing learning. 4. The method of claim 3, wherein in the determining of the initial threshold value, an initial threshold value of each of all the layers is applied by sequentially varying a threshold value adjusted in a state maintaining a connection of other layers, and a threshold value becoming lower than a standard accuracy is determined as the initial threshold value of each of the layers. 5. The method of claim 3, wherein the removing of the weight using the initial threshold value and the performing of the learning comprise: removing weights having a size less than the initial threshold value among weights of each of all the layers; and performing learning on remaining weights where weights having a size less than the initial threshold value are removed. 6. The method of claim 5, wherein the removing of the weights having the size less than the initial threshold value and the performing of the learning on the remaining weights configure an iterative loop and the iterative loop is repeated until the weights having the size less than the initial threshold value are removed. 7. The method of claim 3, wherein the removing of the weight using the upper threshold value having the value greater than the initial threshold value and the performing of the learning comprise: removing weights having a size less than the upper threshold value among remaining weights; and performing learning on weights having a size equal to or greater than the upper threshold value. 8. The method of claim 7, wherein the removing of the weights having the size less than the upper threshold value and the performing of the learning on the weights having the size equal to or greater than the upper threshold value configure an iterative loop and the iterative loop is repeated until weights having a size less than the upper threshold value are removed. 9. The method of claim 1, wherein in the sharing of the adaptive weight, a plurality of representative values are determined as a centroid value of the remaining weights. 10. The method of claim 9, wherein the centroid value is redefined through re-learning of the remaining weight. 11. A convolutional neural network system comprises: an input buffer configured to buffer input data; an operation unit configured to learn a parameter between a plurality of neural network nodes by using the input data; an output buffer configured to store and update a learning result of the operation unit; a parameter buffer configured to deliver a parameter between the plurality of neural network nodes to the operation unit and update the parameter according to a result of the learning; and a control unit configured to control the parameter buffer to remove weights having a size less than a threshold value among weights between the neural network nodes and map remaining weights among the weights to at least one representative value. 12. The system of claim 11, wherein the control unit performs learning on weights between neural network nodes by using the input data and performs re-learning after removing a weight having a size less than a threshold value among the weights, and maps the remaining weights to a plurality of representative values. 13. The system of claim 12, wherein the control unit determines the threshold value for each of all layers of the neural network. 14. The system of claim 13, wherein the control unit executes a first iterative loop for performing learning by removing weights less than a first threshold value among the weights so as to generate first remaining weights, and executes a second iterative loop for removing weights less than a second threshold value among the first remaining weights by using the second threshold value greater than the first threshold value so as to generate the remaining weight. 15. The system of claim 14, wherein the representative value corresponds to a centroid. 16. The system of claim 14, wherein the control unit stores a mapping table of a centroid index to which the representative value is mapped and the centroid and exchanges the centroid index as the remaining weight with an external memory.