Embodiments are directed to a two-terminal resistive processing unit (RPU) having a first terminal, a second terminal and an active region. The active region effects a non-linear change in a conduction state of the active region 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. The active region is configured to locally perform a data storage operation of a training methodology based at least in part on the non-linear change in the conduction state. 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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A two-terminal resistive processing unit (RPU) comprising: a first terminal; a second terminal; and an active region having a conduction state; wherein the conduction state identifies a weight of a training methodology applied to the RPU; 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. 2. The two-terminal RPU of claim 1, wherein the data storage operation comprises a change in the conduction state that is based at least in part on a result of the data processing operation. 3. The two-terminal RPU of claim 2, wherein the change in the conduction state comprises a non-linear change 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. 4. The two-terminal RPU of claim 3, wherein: the active region is further configured to locally perform the data storage operation of the training methodology based at least in part on the non-linear change in the conduction state; and the active region is further configured to locally perform the data processing operation of the training methodology based at least in part on the non-linear change in the conduction state. 5. The two-terminal RPU of claim 1, wherein the training methodology comprises at least one of: an online neural network training; a matrix inversion; and a matrix decomposition. 6. A two-terminal resistive processing unit (RPU) comprising: a first terminal; a second terminal; and an active region having a conduction state; 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; wherein the active region is further configured to locally perform a data storage operation of a training methodology based at least in part on the 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. 7. The two-terminal RPU of claim 6, wherein the encoding of the at least one first encoded signal and the at least one second encoded signal comprises a stochastic sequence of pulses. 8. The two-terminal RPU of claim 6, wherein the encoding of the at least one first encoded signal and the at least one second encoded signal comprise s a magnitude modulation. 9. The two-terminal RPU of claim 6, wherein the non-linear change comprises a rectifying non-linear change or a saturating non-linear change. 10. The two-terminal RPU of claim 6, wherein the non-linear change comprises an exponential non-linear change. 11. A trainable crossbar 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 two-terminal resistive processing unit (RPU) at each of the plurality of crosspoints; wherein the RPU is configured to locally perform a data storage operation of a training methodology applied to the trainable crossbar array; wherein the RPU is further configured to locally perform a data processing operation of the training methodology. 12. The array of claim 11, wherein: the two-terminal RPU comprises a first terminal, a second terminal and an active region having a conduction state; and the conduction state identifies a weight of the training methodology applied to the RPU. 13. The array of claim 12, wherein: the data storage operation comprises a change in the conduction state that is based at least in part on a result of the data processing operation; and the change in the conduction state comprises a non-linear change 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. 14. The array of claim 13, wherein: the active region is further configured to locally perform the data storage operation of the training methodology based at least in part on the non-linear change in the conduction state; and the active region is further configured to locally perform the data processing operation of the training methodology based at least in part on the non-linear change in the conduction state. 15. The array of claim 11, wherein the training methodology comprises at least one of: an online neural network training; a matrix inversion; and a matrix decomposition. 16-25. (canceled)