Apparatus and method for monitoring and controlling of a neural network using another neural network implemented on one or more solid-state chips
A device implemented on solid-state chips for an autonomous machine with sensors. The device includes a neural network on the autonomous machine, trained with a first training data set that includes training data generated by a sensor located remote from the autonomous machine, and configured to generate output data after processing input data. The device also includes a processor coupled to the neural network, and a detector to receive the output data and determine whether the output data breaches a predetermined condition, and a neural network manager coupled to the neural network and adapted to re-train the first neural network using another training data set if the detector determines the output data breach the first predetermined condition; and another neural network structured and trained identical to the first neural network to generate a second output data by processing the set of input data, wherein the neural networks are executed simultaneously.
1. A control system for an autonomous vehicle having a plurality of sensors, the control system comprising:
a controller, including
a first neural network with at least one hidden layer implemented on first one or more solid-state chips, deployed on the autonomous vehicle, the first neural network trained with a first training data set that includes training data generated by a sensor located remote from the autonomous vehicle, and configured to generate first output data after processing a set of input data;
a second neural network with at least one hidden layer implemented on second one or more solid-state chips, the second neural network structured identical to the first neural network and trained with the first training data set, the second neural network configured to generate a second output data by processing the set of input data, wherein the first and second neural networks are executed simultaneously,
a first processor coupled to the first neural network, the first processor including:
i) a detector adapted to receive the first output data and to determine whether the first output data breach a first predetermined condition; and
ii) a neural network manager coupled to the first neural network and adapted to place the first neural network offline and into a training mode, and re-train the first neural network when the first neural network is offline using a second training data set if the detector determines the first output data breach the first predetermined condition, the second training set different than the first training data set; and
wherein the first processor is further configured to operate using the second output from the second neural network for controlling the autonomous vehicle while the first neural network is offline.
2. The control system of claim 1 , wherein the second training data set includes data generated by a sensor on the autonomous vehicle.
3. The control system of claim 1 , further comprising a database management system coupled to the detector and constructed to receive and store the input data and the corresponding first output data if the detector determines the corresponding first output data does not breach the first predetermined condition, wherein the database management system is further adapted to retrieve the stored input data and the corresponding output data to form the second training data set.
4. The control system of claim 1 , wherein the predetermined condition is defined at least in part with machine recognizable human speech.
5. The control system of claim 1 , wherein the first predetermined condition is unchangeable after an initial installation onto the control system.
6. The control system of claim 1 , further comprising a second processor configured to execute the first and second neural networks.
7. A method of controlling an autonomous vehicle using a controller configured with one or more processors, the method comprising:
generating first output data after processing a set of input data using a first neural network with at least one hidden layer implemented on first one or more solid-state chips located on the autonomous vehicle, wherein the first neural network is trained with a first training data set that includes training data generated by a sensor located remote from the autonomous vehicle;
executing, simultaneously with generating the first output data using the first neural network, a second neural network with at least one hidden layer implemented on second one or more solid-state chips, the second neural network structured identical to and trained with the first training data set, the second neural network configured to generate a second output data by processing the set of input data value;
processing the first output data and to determine whether the first output data breach a first predetermined condition;
in response to determining the first output data breach the first determined condition, placing the first neural network in an offline operational state, re-training the first neural network using a second training data set while the first neural network is in the offline operational state, the second training data set being different from the first training data set, and using the second output data to control the autonomous vehicle while the first neural network is an the offline operational state.
8. The method of claim 7 , wherein the second training data set includes data generated by a sensor on the autonomous vehicle.
9. The method of claim 7 , further comprising:
storing the input data and the corresponding first output data if the detector determines the corresponding first output data does not breach the first predetermined condition, and
retrieving the stored input data and the corresponding output data to form the second training data set.
10. The method of claim 7 , further comprising:
executing the first and second neural networks on a first thread on the one or more processors; and
executing the steps of processing the first output data and re-training the first neural network on a second thread of the one or more processors.
11. The method of claim 7 , further comprising:
executing the first neural network on a first thread on the one or more processors; and
executing the second neural network on a second thread of the one or more processors.
12. The method of claim 7 , wherein the first predetermined condition is defined with a machine recognizable human speech part.
13. The method of claim 7 , wherein the first predetermined condition is unchangeable after an initial installation onto the control system.
14. A control system, for an autonomous vehicle having a plurality of sensors, the control system comprising:
a controller, including
a first neural network with at least one hidden layer implemented on first one or more solid-state chips, deployed on the autonomous vehicle, trained with a first training data set that includes training data generated by a sensor located remote from the autonomous vehicle, and configured to generate first output data after processing a set of input data;
a second neural network with at least one hidden layer implemented on second one or more solid-state chips, structured identical to the first neural network and initially trained with the first training data set, the second neural network configured to generate second output data by processing the set of input data, wherein the controller executes the first and second neural networks simultaneously; and
a first processor coupled to the first neural network and the second neural network, including:
i) a detector means for receiving the first output data and to determine whether the first output data breach a first predetermined condition;
ii) means for managing a neural network coupled to the first neural network, the neural network managing means configured for re-training the first neural network when the first neural network is in an offline mode using a second training data set if the detector determines the first output data breach the first predetermined condition, the second data set being different than the first training data set;
wherein the controller operates the autonomous vehicle using the second output from the second neural network when the first neural network is in an offline mode.
15. The controller of claim 14 , wherein the second training data set includes data generated by a sensor on the autonomous vehicle.
16. The controller of claim 14 , the controller further comprises a database management system coupled to the detector, the database management system configured to receive and store the input data and the corresponding first output data if the detector determines the corresponding first output data does not breach the first predetermined condition, the database management system further configured to retrieve the stored input data and the corresponding output data to form the second training data set.
17. The control system of claim 1 , wherein the autonomous vehicle is a land vehicle.
18. The control system of claim 1 , wherein the autonomous vehicle is a drone.
19. The control system of claim 7 , wherein the autonomous vehicle is a land vehicle.
20. The control system of claim 14 , wherein the autonomous vehicle is a land vehicle.