Systems and methods for a sensor hub system that accurately and efficiently performs sensory analysis across a broad range of users and sensors and is capable of recognizing a broad set of sensor-based events of interest using flexible and modifiable neural networks are disclosed. The disclosed solution consumes orders of magnitude less power than typical application processors. In one embodiment, a scalable sensor hub system for detecting sensory events of interest comprises a neural network and one or more sensors. The neural network comprises one or more dedicated low-power processors and memory storing one or more neural network programs for execution by the one or more processors. The output of the one or more sensors is converted into a spike signal, and the neural network takes the spike signal as input and determines whether a sensory event of interest has occurred.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A scalable sensor hub system for detecting sensory events of interest, comprising: a neural processing unit, wherein the neural processing unit comprises one or more dedicated low-power processors and memory storing one or more neural networks for execution by the one or more processors; and one or more sensors, wherein output of the one or more sensors are converted into a spike signal, and the neural processing unit takes the spike signal as input and determines whether a sensory event of interest has occurred. 2. The system of claim 1, wherein the one or more neural networks are Recurrent Spiking Neural Networks (RSNN). 3. The system of claim 2, wherein the RSNN comprises a Liquid State Machine (LSM). 4. The system of claim 1, wherein the one or more neural networks uses spiking Linear Integrated-and-Fire neurons. 5. The system of claim 4, further comprising an input converter that converts output of the one or more sensors into a spike signal input to the neural processing unit. 6. The system of claim 4, further comprising an output classifier that classifies the activity states of the neural processing unit. 7. The system of claim 6, wherein the output classifier is a linear threshold classifier. 8. The system of claim 4, wherein the event of interest occurred as a result of occurrence of a plurality of conditions and at least one of the plurality of conditions is detected by the neural processing unit. 9. The system of claim 8, wherein the plurality of conditions are detected in a prescribed time order. 10. The system of claim 9, wherein at least one of the one or more sensors is activated in response to detection of one of the plurality of conditions by the neural processing unit. 11. The system of claim 1, wherein at least one of the one or more sensors is always activated. 12. A method, performed by a scalable sensor hub system having one or more sensors, one neural processing unit comprising one or more dedicated low power processors and a memory storing one or more neural networks for execution by the one or more processors, the method comprising: converting output of the one or more sensors into a spike signal in response to an input signal to the one or more sensors; receiving the spike signal at the one or more dedicated low power processors; and determining by the neural processing unit whether a sensory event of interest has occurred. 13. The method of claim 12, wherein the determining comprises classifying the states of the neural processing unit. 14. The method of claim 13, wherein the classifying comprises performing linear threshold classification. 15. The method of claim 12, wherein the determining comprises detecting all of a plurality of conditions that together causes the event of interest to take place, and at least one of the plurality of conditions being detected by the neural network. 16. The method of claim 15, wherein the plurality of conditions are detected in a prescribed time order. 17. The method of claim 15, further comprising activating at least one of the one or more sensors in response to detecting one of the plurality of conditions. 18. A method, performed by a scalable sensor hub system having one or more sensors, one neural processing unit comprising one or more dedicated low power processors and a memory storing one or more neural networks using spiking Linear Integrated-and-Fire neurons for execution by the one or more processors, the method comprising: converting output of the one or more sensors into spike signal in response to an input signal to the one or more sensors; receiving the spike signal at the one or more dedicated low power processors; and determining by the neural processing unit whether a sensory event of interest has occurred. 19. The method of claim 18, wherein the determining comprises detecting all of a plurality of conditions that together causes the event of interest to take place, and at least one of the plurality of conditions being detected by the neural network. 20. The method of claim 18, wherein the plurality of conditions are detected in a prescribed time order. 21. The method of claim 18, further comprising activating at least one of the one or more sensors in response to detecting one of the plurality of conditions.