In a method of operating a neural network device, a plurality of consecutive input data is received by an input layer. A delta data is generated by the input layer based on a difference between a current input data and a previous input data. A first current feature is generated by a first linear layer based on a first delta feature generated by performing a first linear operation on the delta data and a first previous feature. A second delta feature is generated by a first nonlinear layer based on a second current feature generated by performing a first nonlinear operation on the first current feature and a second previous feature. A third current feature is generated by a second linear layer based on a third delta feature generated by performing a second linear operation on the second delta feature and a third previous feature.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of operating a neural network device, the method comprising: receiving, by an input layer, a plurality of consecutive input data, wherein the neural network device comprises a neural network including the input layer, a first linear layer, a first nonlinear layer, and a second nonlinear layer; generating, by the input layer, a delta data based on a difference between a current input data and a previous input data from among the plurality of consecutive input data; generating, by the first linear layer, a first current feature, wherein the first current feature corresponds to a result of performing a first linear operation on the current input data based on a first delta feature and a first previous feature, the first delta feature is generated by performing the first linear operation on the delta data, and the first previous feature is stored in a first register in the first linear layer; generating, by the first nonlinear layer, a second delta feature based on a second current feature and a second previous feature, wherein the second current feature is generated by performing a first nonlinear operation on the first current feature, and the second previous feature is stored in a second register in the first nonlinear layer; and generating, by the second linear layer, a third current feature based on a third delta feature and a third previous feature, wherein the third current feature corresponds to a result of performing a second linear operation on the second current feature, the third delta feature is generated by performing the second linear operation on the second delta feature, and the third previous feature is stored in a third register in the second linear layer. 2. The method of claim 1, wherein the input layer sets a first input data from among the plurality of consecutive input data as the delta data when the input layer receives the first input data as the current input data, wherein the input layer generates the delta data by subtracting the previous input data from the current input data when the input layer receives each of the plurality of consecutive input data after receiving the first input data, wherein the previous input data is received before the current input data. 3. The method of claim 1, wherein the input layer provides only the delta data to the first linear layer. 4. The method of claim 1, wherein generating, by the first linear layer, the first current feature, comprises: generating the first delta feature by performing the first linear operation on the delta data; generating the first current feature by adding the first delta feature to the first previous feature stored in the first register; and updating the first register from the first previous feature to the first current feature by storing the first current feature in the first register. 5. The method of claim 1, wherein the first previous feature stored in the first register corresponds to a result of performing the first linear operation on the previous input data. 6. The method of claim 1, wherein the first linear layer corresponds to a convolution layer of the neural network. 7. The method of claim 1, wherein the first linear layer provides only the first current feature to the first nonlinear layer. 8. The method of claim 1, wherein generating, by the first nonlinear layer, the second delta feature comprises: generating the second current feature by performing the first nonlinear operation on the first current feature; generating the second delta feature by subtracting the second previous feature stored in the second register from the second current feature; and updating the second register from the second previous feature to the second current feature by storing the second current feature in the second register. 9. The method of claim 1, wherein the second previous feature stored in the second register corresponds to a result of performing the first nonlinear operation on a feature provided to the first nonlinear layer from the first linear layer during a previous cycle. 10. The method of claim 1, wherein the first nonlinear layer corresponds to one of an activation layer and a pooling layer of the neural network. 11. The method of claim 1, wherein the first nonlinear layer provides only the second delta feature to the second linear layer. 12. The method of claim 1, wherein generating, by the second linear layer, the third current feature comprises: generating the third delta feature by performing the second linear operation on the second delta feature; generating the third current feature by adding the third delta feature to the third previous feature stored in the third register; and updating the third register from the third previous feature to the third current feature by storing the third current feature in the third register. 13. The method of claim 1, wherein the third previous feature stored in the third register corresponds to a result of performing the second linear operation on the second previous feature stored in the second register of the first nonlinear layer. 14. The method of claim 1, wherein the second linear layer corresponds to a fully connected layer of the neural network. 15. The method of claim 1, further comprising: recognizing information included in the current input data using the third current feature. 16. The method of claim 1, wherein the plurality of consecutive input data corresponds to a plurality of frame data included in a video stream. 17. The method of claim 16, further comprising: recognizing an object included in a frame data corresponding to the current input data using the third current feature. 18. A neural network device, comprising: an input layer of a neural network configured to receive a plurality of consecutive input data, and generate a delta data based on a difference between a current input data and a previous input data from among the plurality of consecutive input data; a first linear layer of the neural network configured to generate a first delta feature by performing a first linear operation on the delta data, and generate a first current feature based on the first delta feature and a first previous feature, wherein the first current feature corresponds to a result of performing the first linear operation on the current input data, and the first previous feature is stored in a first register of the first linear layer; a first nonlinear layer of the neural network configured to generate a second current feature by performing a first nonlinear operation on the first current feature, and generate a second delta feature based on the second current feature and a second previous feature, wherein the second previous feature is stored in a second register of the first nonlinear layer; and a second linear layer of the neural network configured to generate a third delta feature by performing a second linear operation on the second delta feature, and generate a third current feature based on the third delta feature and a third previous feature, wherein the third current feature corresponds to a result of performing the second linear operation on the second current feature, and the third previous feature is stored in a third register of the second linear layer. 19.-22. (canceled) 23. The neural network device of claim 18, wherein the plurality of consecutive input data corresponds to a plurality of frame data included in a video stream. 24. (canceled) 25. A method of recognizing an object in a video stream, comprising: receiving a plurality of consecutive data frames included in the video stream using an input layer of a neural network; generating a delta data based on a difference between a current data frame and a previous data frame from among the plurality of consecutive data frames using the input layer of the neural network; generating a first current feature using a first linear layer of the neural network, wherein the first current feature corresponds to a result of performing a first linear operation on the current data frame based on a first delta feature and a first previous feature, the first delta feature is generated by performing the first linear operation on the delta data, and the first previous feature is stored in a first register in the first linear layer; generating a second delta feature based on a second current feature and a second previous feature using a first nonlinear layer of the neural network, wherein the second current feature is generated by performing a first nonlinear operation on the first current feature, and the second previous feature is stored in a second register in the first nonlinear layer; generating a third current feature based on a third delta feature and a third previous feature using a second linear layer of the neural network, wherein the third current feature corresponds to a result of performing a second linear operation on the second current feature, the third delta feature is generated by performing the second linear operation on the second delta feature, and the third previous feature is stored in a third register in the second linear layer; and recognizing the object in the video stream using the third current feature. 26.-29. (canceled)