Technical solutions are described for improving efficiency of training a resistive processing unit (RPU) array using a neural network training methodology. An example method includes reducing asymmetric RPUs from the RPU array by determining an asymmetric value of an RPU from the RPU array, and burning the RPU in response to the asymmetry value being above a predetermined threshold. The RPU can be burned by causing an electric voltage across the RPU to be above a predetermined limit. The method further includes initiating the training methodology for the RPU array after the asymmetric RPUs from the RPU array are reduced.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A resistive processing unit (RPU) array control system comprising: an RPU array comprising: a set of conductive row wires; a set of conductive column wires configured to form a plurality of crosspoints at intersections between the set of conductive row wires and the set of conductive column wires; and a plurality of RPUs, wherein an RPU is located at each of the plurality of crosspoints, wherein a conduction state of the RPU identifies a weight of a training methodology applied to said RPU; and a controller configured to: measure asymmetry values of each of the RPUs by: measuring a first change in the conduction state of the RPU caused by a positive pulse; measuring a second change in the conduction state of the RPU caused by a negative pulse; and determining the asymmetry value of the RPU as a difference between the first change and the second change; and burn a selected subset of RPUs from the plurality of the RPUs based on the corresponding asymmetry values. 2. The resistive processing unit (RPU) array of claim 1, wherein burning the selected subset of RPUs comprises sending one or more electric signals that cause a voltage above a predetermined limit at the selected subset of RPUs. 3. The resistive processing unit (RPU) array of claim 2, wherein burning an RPU from the selected subset of RPUs comprises: sending a first encoded signal above a first predetermined limit to a first terminal if the RPU and sending a second encoded signal below a second predetermined limit to a second terminal of the RPU. 4. The resistive processing unit (RPU) array of claim 2, wherein the RPU array receives the one or more electric signals that burn the selected subset of RPUs before initiating the training methodology. 5. The resistive processing unit (RPU) array of claim 2, wherein the one or more electric signals are directed to the selected subset of RPUs based on corresponding crosspoints between the set of conductive row wires and the set of conductive column wires. 6. The resistive processing unit (RPU) array of claim 1, wherein the selected subset of RPUs is selected based on corresponding asymmetry values being above a predetermined asymmetry threshold. 7. The resistive processing unit (RPU) array of claim 1, wherein the selected subset of RPUs is selected based on corresponding asymmetry values being above outside a predetermined standard deviation of a mean of the asymmetry values of the plurality of RPUs. 8. The resistive processing unit (RPU) array of claim 1, wherein the change in the conduction state comprises a non-linear change based on at least one first encoded signal applied to a first terminal and at least one second encoded signal applied to a second terminal of the RPU. 9. The resistive processing unit (RPU) array of claim 1, wherein each RPU comprises: a first terminal; a second terminal; and an active region having the conduction state; wherein the active region is configured to locally perform a data storage operation of the training methodology; and wherein the active region is further configured to locally perform a data processing operation of the training methodology. 10. A neuron control system facilitating training a resistive processing unit (RPU) array, the neuron control system comprising: the RPU array, which comprises a plurality of RPUs; and a processor configured to reduce asymmetric RPUs from the RPU array by: measuring asymmetry values of each of the RPUs by: measuring a first change in a conduction state of an RPU caused by a positive pulse; measuring a second change in the conduction state of the RPU caused by a negative pulse; and determining the asymmetry value of the RPU as a difference between the first change and the second change; and burning a selected subset of RPUs from the plurality of the RPUs, the subset of RPUs selected based on the corresponding asymmetry values. 11. The neuron control system of claim 10, wherein each RPU comprises: a first terminal; a second terminal; and an active region having a conduction state; and wherein the active region is configured to effect a non-linear change in the conduction state based on at least one first encoded signal applied to the first terminal and at least one second encoded signal applied to the second terminal. 12. The neuron control system of claim 11, wherein the processor burns the RPU by sending the first encoded signal above a first predetermined limit and sending the second encoded signal below a second predetermined limit. 13. The neuron control system of claim 10, wherein the RPU array comprises: a set of conductive row wires; a set of conductive column wires configured to form a plurality of crosspoints at intersections between the set of conductive row wires and the set of conductive column wires; and a two-terminal RPU at each of the plurality of crosspoints. 14. The neuron control system of claim 13, wherein the RPU that is burned is a first RPU from the RPU array, and wherein the RPU array further comprises a second RPU that is not burned, the second RPU comprising an active region having a conduction state, wherein the active region is further configured to locally perform a data storage operation of a training methodology based at least in part on a non-linear change in the conduction state; and wherein the active region is further configured to locally perform a data processing operation of the training methodology based at least in part on the non-linear change in the conduction state. 15. The neuron control system of claim 10, wherein the processor is further configured to initiate training methodology for the RPU array, wherein the processor reduces the asymmetric RPUs from the RPU array prior to initiating the training methodology. 16. The neuron control system of claim 10, wherein the selected subset of RPUs is selected based on corresponding asymmetry values being above a predetermined asymmetry threshold. 17. The neuron control system of claim 10, wherein the selected subset of RPUs is selected based on corresponding asymmetry values being above outside a predetermined standard deviation of a mean of the asymmetry values of the plurality of RPUs. 18. A non-transitory computer program product for training a resistive processing unit (RPU) array, the computer program product comprising computer readable storage medium with computer executable instructions embedded therein, wherein the computer readable storage medium comprises instructions to: reduce asymmetric RPUs from the RPU array by: measuring asymmetry values of each of the RPUs by: measuring a first change in a conduction state of an RPU caused by a positive pulse; measuring a second change in the conduction state of the RPU caused by a negative pulse; and determining the asymmetry value of the RPU as a difference between the first change and the second change; and burning a selected subset of RPUs from the plurality of the RPUs, the subset of RPUs selected based on the corresponding asymmetry values; and initiate a training methodology for the RPU array after the asymmetric RPUs from the RPU array are reduced. 19. The non-transitory computer program product of claim 18, wherein the RPU is burned by sending a first encoded signal above a first predetermined limit and sending a second encoded signal below a second predetermined limit to a first terminal and a second terminal of the RPU respectively. 20. The non-transitory computer program product of claim 18, wherein the selected subset of RPUs is selected based on corresponding asymmetry values being above outside a predetermined standard deviation of a mean of the asymmetry values of the plurality of RPUs.