Low Power Sensor Chip Architecture for Dynamic Processor Activation
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Solution Overview
Problem
Current computing devices face challenges in managing multiple sensors efficiently during low power states, leading to limited functionality and increased battery consumption, as existing methods do not effectively balance sensor usage and power conservation.
Innovation Solution
A method for managing processor analysis of multiple sensors by monitoring and comparing sensor signals with predetermined signatures, utilizing a low power core to activate sensors only when necessary, and partitioning functions among processors to reduce power consumption, allowing for accurate determination of user activities and environmental states while conserving battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all sensors are activated continuously to ensure accurate determination of user activities and environmental states, then measurement precision and reliability are improved, but power consumption increases significantly
Solution Approach 1:
The system dynamically adjusts sensor activation states based on device activity states. During active states, more sensors are activated to provide accurate data for user activities and environmental states. During low-power states, fewer sensors remain active, reducing power consumption while maintaining essential functionality. This dynamic adaptation resolves the contradiction between continuous measurement accuracy and power conservation.
Solution Approach 2:
The system changes operational parameters of sensors based on confidence levels and activity states. When confidence levels indicate sufficient accuracy from currently active sensors, the system reduces sensor activation to conserve power. When confidence levels drop below thresholds, the system activates additional sensors to restore measurement precision. This parameter adjustment strategy balances accuracy requirements with power consumption constraints.
2Reliability
If multiple sensors are monitored continuously to improve reliability of sensor data, then reliability is improved, but device complexity and power consumption increase
Solution Approach 1:
The system implements self-service mechanisms where the sensor hub processor automatically manages sensor activation, data collection, and confidence level assessment without requiring complex external control. The system monitors its own operational state and autonomously adjusts sensor activation based on predefined confidence thresholds and activity states, reducing the complexity burden on the overall device architecture while maintaining reliable data availability.
Solution Approach 2:
The system segments sensor management functions across multiple processors: the sensor hub processor handles low-power sensor monitoring and initial data collection, while the application processor handles complex data analysis when activated. This segmentation allows continuous reliable monitoring through the sensor hub while reducing overall device complexity by distributing management responsibilities and keeping the application processor in low-power states when full processing is not required.
3Loss of energy
If sensors are selectively activated based on confidence levels to reduce power consumption, then power efficiency is improved, but measurement precision may deteriorate
Solution Approach 1:
The system employs feedback mechanisms where confidence levels of sensor data are continuously assessed and fed back to control sensor activation decisions. When confidence levels meet predetermined thresholds, the system maintains reduced sensor activation to conserve power. When confidence levels fall below thresholds, the system activates additional sensors to restore measurement precision. This closed-loop feedback control ensures that power conservation decisions do not compromise measurement accuracy below acceptable levels.
Data Source
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AI summary
A method, device, system, or article of manufacture is provided for low-power management of multiple sensor chip architecture. In one embodiment, a method comprises, at a computing device that includes a first processor, a second processor and a third processor, receiving, at the first processor, first sensor data from a first sensor; determining, at the first processor, a motion state of the computing device using the first sensor data; in response to determining that the motion state corresponds to a predetermined motion state, activating the second processor; receiving, at the second processor, second sensor data from a second sensor; determining, by the second processor, that the motion state corresponds to the predetermined motion state using the second sensor data; and, in response to determining that the motion state corresponds to the predetermined motion state using the second sensor data, sending the motion state to the third processor.