IP Library › Patent Application 15985212
Patent Application
App. No. 15/985,212

DEEP LEARNING IN BIPARTITE MEMRISTIVE NETWORKS

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Quick Facts
Patent No.
US None
App. No.
15/985,212
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.

Claims (69)

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.