Deep learning in bipartite memristive networks

Abstract

A bipartite memristive network and method of teaching such a network is described herein. In one example case, the memristive network can include a number of nanofibers, wherein each nanofiber comprises a metallic core and a memristive shell. The memristive network can also include a number of electrodes deposited upon the nanofibers. A first set of the number of electrodes can include input electrodes in the memristive network, and a second set of the number of electrodes can include output electrodes in the memristive network. The memristive network can be embodied as a bipartite memristive network and trained according to the method of teaching described herein.

Therefore, the following is claimed: 
     
         1 . A method to train a memristive network comprising a number of input nodes and a number of output nodes, comprising:
 applying an input voltage or current to an input node among the number of input nodes;   grounding an output node among the number of output nodes;   measuring an output current or voltage at the output node;   comparing the output current or voltage to a target current or voltage to determine an error delta; and   applying a threshold voltage or current to the output node for a time period proportional to a magnitude of the error delta.   
     
     
         2 . The method of  claim 1 , wherein, when the error delta is negative, applying the threshold voltage or current to the output node comprises:
 applying a positive threshold voltage or current to the output node for the time period proportional to the error delta; and   applying a negative threshold voltage or current to the output node for the time period proportional to the error delta.   
     
     
         3 . The method of  claim 1 , wherein, when the error delta is positive, applying the threshold voltage or current to the output node comprises:
 reversing a polarity of the input voltage or current applied to the input node;   applying a positive threshold voltage or current to the output node for the time period proportional to the error delta; and   applying a negative threshold voltage or current to the output node for the time period proportional to the error delta.   
     
     
         4 . The method of  claim 1 , further comprising:
 transforming the error delta into an error delta voltage or current;   applying the error delta voltage or current to the output node; and   applying the threshold voltage or current to the input node for a second time period proportional to an absolute value of the error delta voltage or current.   
     
     
         5 . The method of  claim 4 , wherein, when the input voltage or current applied to the input node was positive, applying the threshold voltage or current to the input node comprises:
 applying a positive threshold voltage or current to the input node for the second time period proportional to the absolute value of the error delta voltage or current; and   applying a negative threshold voltage or current to the input node for the second time period proportional to the absolute value of the error delta voltage or current.   
     
     
         6 . The method of  claim 4 , wherein, when the input voltage or current applied to the input node was negative, applying the threshold voltage or current to the input node comprises:
 reversing a polarity of the error delta voltage or current applied to the output node;   applying a positive threshold voltage or current to the input node for the second time period proportional to the absolute value of the error delta voltage or current; and   applying a negative threshold voltage or current to the input node for the second time period proportional to the absolute value of the error delta voltage.   
     
     
         7 . The method of  claim 1 , wherein the memristive network comprises a bipartite memristive network. 
     
     
         8 . The method of  claim 1 , wherein the method reproduces a backpropagation algorithm for training the memristive network. 
     
     
         9 . A memristive network, comprising:
 a number of nanofibers, wherein each nanofiber comprises a metallic core and a memristive shell;   a number of electrodes deposited upon the nanofibers, wherein the number of electrodes comprise a number of input nodes and a number of output nodes; and   a training processor configured to:
 apply an input voltage or current to an input node among the number of input nodes; 
 ground an output node among the number of output nodes; 
 measure an output current or voltage at the output node; 
 compare the output current or voltage to a target current or voltage to determine an error delta; and 
 apply a threshold voltage or current to the output node for a time period proportional to a magnitude of the error delta. 
   
     
     
         10 . The memristive network according to  claim 9 , wherein, when the error delta is negative, the training processor is further configured to:
 apply a positive threshold voltage or current to the output node for the time period proportional to the error delta; and   apply a negative threshold voltage or current to the output node for the time period proportional to the error delta.   
     
     
         11 . The memristive network according to  claim 9 , wherein, when the error delta is positive, the training processor is further configured to:
 reverse a polarity of the input voltage or current applied to the input node;   apply a positive threshold voltage or current to the output node for the time period proportional to the error delta; and   apply a negative threshold voltage or current to the output node for the time period proportional to the error delta.   
     
     
         12 . The memristive network according to  claim 9 , wherein the training processor is further configured to:
 transform the error delta into an error delta voltage or current;   apply the error delta voltage or current to the output node; and   apply the threshold voltage or current to the input node for a second time period proportional to an absolute value of the error delta voltage or current.   
     
     
         13 . The memristive network according to  claim 12 , wherein, when the input voltage or current applied to the input node was positive, the training processor is further configured to:
 apply a positive threshold voltage or current to the input node for the second time period proportional to the absolute value of the error delta voltage or current; and   apply a negative threshold voltage or current to the input node for the second time period proportional to the absolute value of the error delta voltage or current.   
     
     
         14 . The memristive network according to  claim 12 , wherein, when the input voltage or current applied to the input node was negative, the training processor is further configured to:
 reverse a polarity of the error delta voltage or current applied to the output node;   apply a positive threshold voltage or current to the input node for the second time period proportional to the absolute value of the error delta voltage or current; and   apply a negative threshold voltage or current to the input node for the second time period proportional to the absolute value of the error delta voltage.   
     
     
         15 . The memristive network according to  claim 9 , wherein the memristive network comprises a bipartite memristive network. 
     
     
         16 . A method to train a memristive network comprising a number of input nodes and a number of output nodes, comprising:
 applying an input voltage to an input node among the number of input nodes;   grounding an output node among the number of output nodes;   measuring an output current at the output node;   comparing the output current to a target current to determine an error delta; and   applying a threshold voltage to the output node for a time period proportional to a magnitude of the error delta.   
     
     
         17 . The method of  claim 16 , wherein, when the error delta is negative, applying the threshold voltage to the output node comprises:
 applying a positive threshold voltage to the output node for the time period proportional to the error delta; and   applying a negative threshold voltage to the output node for the time period proportional to the error delta.   
     
     
         18 . The method of  claim 16 , wherein, when the error delta is positive, applying the threshold voltage to the output node comprises:
 reversing a polarity of the input voltage applied to the input node;   applying a positive threshold voltage to the output node for the time period proportional to the error delta; and   applying a negative threshold voltage to the output node for the time period proportional to the error delta.   
     
     
         19 . The method of  claim 16 , wherein the memristive network comprises a bipartite memristive network. 
     
     
         20 . The method of  claim 16 , wherein the method reproduces a backpropagation algorithm for training the memristive network.