Buffered Sensor Array Gating for Low-Energy Multi-Modal Sensing
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Solution Overview
Problem
Multi-modal computing (M2C) systems in AIoT scenarios face high energy consumption due to numerous sensors, leading to energy budget constraints that hinder deployment in real-world applications, and existing efficiency optimization methods fail to manage modality processing effectively without prior knowledge of data.
Innovation Solution
An adaptive modality gating (AMG) system with a decoupled sensor architecture and smart power management strategy that throttles energy-hungry modules while maintaining situation awareness, using a buffer for analog signal storage and selective processing, and a detection and prediction controller for optimal modality ordering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple modality sensors are integrated to achieve multi-modal computing, then measurement precision and reliability are improved, but use of energy deteriorates significantly
Solution Approach 1:
The patent implements dynamic sensor activation where the sensor array selectively activates only the necessary modality sensors based on the current task requirements and environmental context. Instead of keeping all sensors continuously active, the system dynamically adjusts which sensors are operational, thereby maintaining measurement precision when needed while significantly reducing energy consumption during idle or low-demand periods.
Solution Approach 2:
The patent applies local quality by differentiating the operational state of individual sensor modules within the array. Different sensor modalities (e.g., camera, microphone, other sensors) can be in different states (active, semi-active, or inactive) based on their specific utility for the current task. This allows the system to optimize energy distribution across different sensor types rather than treating all sensors uniformly.
2Reliability
If all modality data is processed to ensure complete information, then reliability is improved, but use of energy and loss of energy increase
Solution Approach 1:
The patent extracts and processes only the essential modality data required for the current task rather than processing all available modality data. The system identifies and extracts the most relevant sensor modalities based on task requirements and environmental context, discarding or deferring processing of less critical data. This extraction approach maintains reliability by ensuring necessary information is processed while avoiding the energy waste of processing redundant or unnecessary modality data.
3Use of energy by moving object
If sensor processing is reduced to save energy, then use of energy is improved, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent implements dynamic processing intensity adjustment where the level of sensor data processing is adaptively modified based on task requirements and energy availability. During high-energy periods or critical tasks, the system increases processing intensity to maintain measurement precision. During low-energy periods or less critical tasks, it reduces processing intensity to conserve energy. This dynamic adjustment allows the system to optimize the trade-off between energy consumption and data quality rather than using a fixed processing level.
Data Source
AI summary
The present disclosure provides a sensor system comprising a sensor array including a plurality of sensors and is configured to generate analog signals; an amplifier coupled to the sensor array, the amplifier configured to amplify the received analog signal; a buffer in communication with the amplifier, the buffer is configured to receive the amplified analog signal from the amplifier and cache the amplified analog signal therein; an analog-to-digital converter coupled to the amplifier and the buffer, and a signal selector configured to control delivery of the amplified analog signal from the amplifier.