ARCHITECTURE FOR IMPLEMENTING AN IMPROVED NEURAL NETWORK

• US 20140337263A1
• Filed: 05/06/2014
• Published: 11/13/2014
• Est. Priority Date: 05/07/2013
• Status: Active Grant

• Current Assignee: Iotelligent Technology Limited Incorporated
• Original Assignee: Iotelligent Technology Limited Incorporated
• Inventors: ZHU, Theodore
• Granted Patent: US 10,235,621 B2
• Time in Patent Office: Days
• US Class Current: 706/41
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/38 for collecting sensor information
• CPC Class Codes: H04W 4/80 Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

Disclosed is an improved approach to implement artificial neural networks. According to some approaches, an advanced neural network is implemented using an internet-of-things methodology, in which a large number of ordinary items having RFID technology are utilized as the vast infrastructure of a neural network.

Claims

1. An architecture for implementing a neural network, comprising:

a plurality of artificial neurons;

at least some of the plurality of artificial neurons comprising a control device and a plurality of memory devices, in which the control device comprises an reader mechanism and at least one sensor, and the memory devices comprise a tag device having a memory component that is within communications range of the control device; and

the plurality of the artificial neurons being networked together in a peer-to-peer network.

2. The architecture of claim 1, in which the peer-to-peer network comprises a mesh network.

3. The architecture of claim 1, in which the tag device comprises a RFID tag.

4. The architecture of claim 1, in which the peer-to-peer network comprises an ad hoc pairings between two artificial neurons.

5. The architecture of claim 4, in which each artificial neuron is capable of pairing with another artificial neuron within communications range.

6. The architecture of claim 5, in which the pairing is implemented wirelessly using a wireless communications protocol.

7. The architecture of claim 6, in which the wireless communications operates using a 900 MHz band using the 6lowpan protocol.

8. The architecture of claim 5, in which the pairing is implemented using a CDMA air interface.

9. The architecture of claim 8, in which the CDMA air interface is a TD-SCDMA interface with an operating frequency between 840 MHz and 845 MHz.

10. The architecture of claim 4, in which two artificial neurons can have different numbers of network pairings.

11. The architecture of claim 1, in which two artificial neurons can have different numbers or types of sensors.

12. The architecture of claim 1, in which the at least one sensor comprises a MEMS sensor.

13. The architecture of claim 1, in which the at least one sensor performs sensing for at least one of speed, acceleration, temperature, moisture, sound, light/image/video capture, chemical detection, or GPS location.

14. The architecture of claim 1, in which the control device for the artificial neuron comprises a communications infrastructure that interfaces with the tag device using a wireless communications protocol.

15. The architecture of claim 14, in which the wireless communications is implemented at the 900 MHz frequency.

16. The architecture of claim 14, in which the wireless communications is implemented using an RFID protocol.

17. The architecture of claim 14, in which the control device for the artificial neuron further comprises a communications infrastructure that interfaces with one or more sensor systems using a wireless communications protocol.

18. The architecture of claim 17, in which the control device interfaces with the one or more sensor systems using a ZigBee protocol.

19. The architecture of claim 17, in which the sensor systems are part of or integral with the tag device, such that the control device is able to interface with the sensor systems using an RFID protocol.

20. The architecture of claim 14, in which the control device and the memory device are integrated into a single device with both controlling and memory functions.

21. The architecture of claim 20, in which the single device having both the control device and the memory device is implemented as a single IC chip having the reader mechanism and the at least one sensor.

22. The architecture of claim 1, in which the reader mechanism and the at least one sensor are implemented as separate components in the control device.

23. The architecture of claim 1, in which the control device and the tag devices are distributed as objects in an internet-of-things architecture.

24. The architecture of claim 1, in which the control device and the tag devices are distributed in consumer items.

25. The architecture of claim 1, in which the neural network implements decision-making tools, pattern-matching algorithms, learning systems, or control systems.

26. A method for implementing a neural network, comprising:

distributing a plurality of artificial neurons as distributed devices, wherein the plurality of artificial neurons comprise short range communications technology; and

networking the artificial neurons together as part of an adaptable peer-to-peer network.

27. The method of claim 26, in which at least some of the plurality of artificial neurons comprising a control device and a plurality of memory devices, in which the control device comprises an RFID reader mechanism and at least one sensor, and the memory devices comprise a RFID tag having a memory component that is within communications range of the control device.

28. The method of claim 27, in which the control device discovers the RFID tag within the communications range and uses the RFID tag as its associated memory device.

29. The method of claim 26, in which the artificial neurons forms the peer-to-peer network to implement processing functions.

30. The method of claim 29, in which the peer-to-peer network comprises a mesh network.

31. The method of claim 26, in which two artificial neurons can have different numbers of network pairings.

32. The method of claim 26, in which the artificial neurons forms pairings based upon input received through a sensor.

33. The method of claim 32, in which the artificial neurons can have different numbers or types of sensors.

34. The method of claim 33, in which at least one sensor comprises a MEMS sensor.

35. The method of claim 33, in which at least one sensor performs sensing for at least one of speed, acceleration, temperature, moisture, sound, light/image/video capture, chemical detection, or GPS location.

36. The method of claim 26, in which the artificial neurons are distributed as objects in an internet-of-things architecture.

37. The method of claim 26, in which the artificial neuron comprises a control device and RFID tags that are distributed in consumer items.

38. The method of claim 26, in which the neural network implements decision-making tools, pattern-matching algorithms, learning systems, or control systems.