A neural network device may include an input unit suitable for applying input signals to corresponding first lines, a calculating unit including memory elements cross-connected between the first lines and second lines, wherein the memory elements have respective weight values and generate product signals of input signals of corresponding first lines from among the plurality of first lines and weights to output the product signals to corresponding second lines from among the second lines, a drop-connect control unit including switches connected between the plurality of first lines and the plurality of memory elements, and suitable for randomly dropping a connection of an input signal applied to a corresponding memory element from among the plurality of memory elements, and an output unit connected to the plurality of second lines, and suitable for selectively activating signals of the plurality of second lines to apply the activated signals to the input unit and performing an output for the activated signals when the calculating unit performs generating of the product signals a set number of times.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A neural network device comprising: an input unit suitable for applying a plurality of input signals to a plurality of first lines; a calculating unit including a plurality of memory elements cross-connected between the plurality of first lines and a plurality of second lines, wherein the plurality of memory elements have respective weight values and generate product signals of input signals of corresponding first lines from among the plurality of first lines and weights to output the product signals to corresponding second lines from among the plurality of second lines; a drop-connect control unit including switches coupled between the plurality of first lines and the plurality of memory elements, and suitable for randomly dropping a connection of an input signal applied to a corresponding memory element from among the plurality of memory elements; and an output unit coupled to the plurality of second lines, and suitable for selectively activating signals of the plurality of second lines to apply the activated signals to the input unit and performing an output for the activated signals when the calculating unit performs generating of the product signals a set number of times. 2. The neural network device of claim 1, wherein the calculating unit comprises: a plurality of memristors cross-connected between the switches and the second lines, and wherein the memristors include resistance values respectively corresponding to weight values, and generate currents corresponding to products of the input signals and the weights to output the currents to the second lines, and the currents are combined in the second lines to generate a sum of products (SOP). 3. The neural network device of claim 2, wherein the drop-connect control unit comprises: a random signal generating unit suitable for generating a random signal; and switches respectively connected between the first lines and the memristors, and wherein, based on the random signal, a corresponding switch among the switches is switching-controlled to drop a connection of the input signal applied to the memristors. 4. The neural network device of claim 3, wherein the random signal generating unit includes a Fibonacci linear feedback shift register. 5. The neural network device of claim 4, wherein the drop-connect control unit drops connections of input signals of a preset ratio from among input signals applied to the memory elements. 6. The neural network device of claim 3, wherein the output unit comprises: a converting unit coupled to the second lines and suitable for converting a current to a voltage; and an activating unit suitable for performing an activating operation on an output signal of the converting unit. 7. The neural network device of claim 6, wherein the activating unit includes a comparator. 8. A neural network device comprising: an input unit suitable for applying a plurality of input signals to a plurality of first lines; a calculating unit including a plurality of memory elements cross-connected between the plurality of first lines and a plurality of second lines, wherein the plurality of memory elements have respective weight values and generate product signals of input signals of corresponding first lines from among the plurality of first lines and weights to output the product signals to a corresponding second line from among the plurality of second lines; a drop-connect control unit including switches connected between the plurality of first lines and the plurality of memory elements and suitable for randomly dropping a connection of an input signal applied to a corresponding memory element from among the plurality of memory elements; and a dropout control unit including switches connected to the plurality of second lines and suitable for randomly dropping out a signal of at least one second line among the plurality of second lines; and an output unit connected to the plurality of second lines, and suitable for selectively activating signals of the plurality of second lines to apply the activated signal to the input unit and performing an output for the activated signals when the calculating unit performs generating of the product signals a set number of times. 9. The neural network device of claim 8, wherein the calculating unit comprises: a plurality of memristors cross-connected between the plurality of first lines and the plurality of second lines, each of the plurality of memristors has resistance values respectively corresponding to weight values and generate currents corresponding to products of the input signals and the weights to output the currents to the second lines, and the currents are combined in the second lines to be generated as a sum of products (SOP). 10. The neural network device of claim 9, wherein the drop-connect control unit comprises: a first random signal generating unit suitable for generating a first random signal; and first switches respectively connected between the first lines and the memristor elements, and wherein, based on the first random signal, a corresponding first switch is switching-controlled to drop a connection of the input signal applied to the memristors. 11. The neural network device of claim 10, wherein the dropout control unit comprises: a second random signal generating unit suitable for generating a second random signal; and second switches respectively connected between the second lines and the output unit, wherein the corresponding second switch is switching-controlled by the second random signal to drop the SOP of the second line that is applied to the output unit. 12. The neural network device of claim 11, wherein the first random signal generating unit and the second random signal generating unit includes Fibonacci linear feedback shift registers. 13. The neural network device of claim 12, wherein the output unit comprises: a converting unit connected to the second lines and suitable for converting a current to a voltage; and an activating unit suitable for performing an activating operation on an output signal of the converting unit. 14. A method for operating a neural network device, the method comprising: applying a plurality of input signals to a plurality of first lines of memory elements cross-connected between the plurality of first lines and a plurality of second lines and having respective resistance values corresponding to weight values; performing a drop-connect operation in which a corresponding part of first switches from among first switches connected to the plurality of first lines and the memory elements are switching-controlled to drop connections of input signals applied to the memory elements; combining, in the second lines, current signals generated by corresponding input signals and resistance values in the memory elements to generate a signal of a node; performing an output operation of activating signals of the second lines by an activation function and feeding the activated signals back to the input signal. 15. The method of claim 14, wherein the combining of the current signals comprises: generating currents corresponding to products of the input signals and weights on a basis of resistance values of memristors cross-connected between the plurality of first lines and the plurality of second lines and having unique resistance values; and combining the currents generated by the memristors connected to the second lines to generate a sum of product signals (SOP) of the corresponding node. 16. The method of claim 15, wherein performing of the drop-connect operation comprises dropping the connections of a preset ratio of input signals from among the input signals applied to the memristors. 17. The method of claim 16, wherein performing of the output operation comprises: converting currents from the second lines to voltages; and determining whether to activate an output signal by a set bias voltage. 18. The method of claim 17, wherein the determining of whether to activate comprises comparing the converted voltage signals with a set reference voltage and determining whether to activate the output signal. 19. The method of claim 15, further comprising switching-controlling a corresponding part of second switches connected between the second lines and the output unit and dropping out the SOP applied to the output unit. 20. The method of claim 19, wherein performing of the output operation comprises: converting currents from the second lines to voltages; and determining whether an output signal is activated by a set bias voltage. 21. A neural network device comprising: an input unit suitable for applying input signals to a plurality of first lines; a calculating unit including a plurality of memory elements cross-connected between the plurality of first lines and a plurality of second lines, wherein the plurality of memory elements have respective weight values and generate product signals of input signals of corresponding first lines from among the plurality of first lines and weights to output the product signals to a corresponding second line from among the plurality of second lines; a dropout control unit suitable for dropping out a signal of at least one second line from among the plurality of second lines; and an output unit coupled to the plurality of second lines, and suitable for selectively activating corresponding signals of the plurality of second lines to apply the activated signals to the input unit and performing an output for the activated signals when the calculating unit performs generating of the product signals a set number of times. 22. The neural network device of claim 21, wherein the calculating unit comprises a plurality of memristors cross-connected between the plurality of first lines and the plurality of second lines, and wherein each of the plurality of memristors has a resistance value corresponding to a weight value and generates a current corresponding to a product of the input signal and the weight on the basis of the resistance value. 23. The neural network device of claim 22, wherein the calculating unit generates, as combined product signals, in the second lines, current signals of the plurality of memristors connected to the first lines. 24. The neural network device of claim 22, wherein the dropout control unit comprises: a random signal generating unit suitable for generating a random signal; and switches respectively connected between the second lines and the output unit, and wherein the random signal drops out a part of signals of the plurality of second lines. 25. The neural network device of claim 24, wherein the random signal generating unit includes a Fibonacci linear feedback shift register. 26. The neural network device of claim 24, wherein the dropout control unit drops out a preset ratio of nodes from among the nodes. 27. The neural network device of claim 24, wherein the output unit comprises: a converting unit coupled to the second lines and suitable for converting a current to a voltage; and an activating unit suitable for performing an activating operation on an output signal of the converting unit. 28. The neural network device of claim 27, wherein the activating unit includes a comparator. 29. A neural network device comprising: an input unit suitable for inputting signals to a plurality of first lines; a dropout control unit suitable for dropping out a signal of at least one first line from among the plurality of first lines; a calculating unit including a plurality of memory elements cross-connected between the plurality of first lines and a plurality of second lines, wherein the plurality of memory elements have respective weight values and generate product signals of corresponding input signals and weights to output the product signals to a corresponding second line from among the plurality of second lines; and an output unit coupled to the plurality of second lines, and suitable for selectively activating signals of the plurality of second lines to apply the activated signals to the input unit and performing an output for the activated signals when the calculating unit performs generating of the product signals a set number of times. 30. The neural network device of claim 29, wherein the calculating unit comprises a plurality of memristors cross-connected between the plurality of first lines and the plurality of second lines, wherein each of the plurality of memristors has a resistance value corresponding to a weight value and generates a current corresponding to a product of the input signal and the weight on the basis of the resistance value. 31. The neural network device of claim 30, wherein the calculating unit generates, as combined product signals in the second lines, current signals of the plurality of memristors connected to the first lines. 32. The neural network device of claim 30, wherein the dropout control unit comprises: a random signal generating unit suitable for generating a random signal; and switches respectively connected between the input unit and the first lines, and wherein the random signal drops out a part of input signals of the plurality of first lines. 33. The neural network device of claim 32, wherein the output unit comprises: a converting unit connected to the second lines and suitable for converting a current to a voltage; and an activating unit suitable for performing an activating operation on an output signal of the converting unit. 34. A method for operating a neural network device, the method comprising: applying a plurality of input signals to a plurality of first lines of memory elements cross-connected between the plurality of first lines and a plurality of second lines and having respective resistance values corresponding to weight values; generating a signal of a node by combining, in the second lines, current signals generated by corresponding input signals and resistance values in the memory elements; dropping out a signal of a second line selected from among the second lines; and performing an output operation of selectively activating signals of the second lines and feeding the activated signals back to the input signal. 35. The method of claim 34, wherein the generating of the signal of the node comprises: generating currents corresponding to products of the input signals and weights based on resistance values of memristors cross-connected between the plurality of first lines and the plurality of second lines and having the unique resistance values; and combining currents generated by the memristors coupled to the second lines to generate a sum of products (SOP) signal of a corresponding node. 36. The method of claim 35, wherein the dropping out comprises: generating a random signal; and dropping out, by the random signal, a part of the plurality of second line signals. 37. The method of claim 36, wherein the random signal is generated by a Fibonacci linear feedback shift register. 38. The method of claim 37, wherein dropping out of the signal comprises dropping out SOP signals of second lines with a preset ratio among the second lines. 39. The method of claim 37, wherein performing of the output operation comprises: converting currents from the second lines to voltages; and determining whether to activate an output signal by a set bias voltage. 40. The method of claim 39, wherein the determining of whether to activate comprises comparing the converted voltages with a set reference voltage to determine whether to activate.