Edge Microelectronic Device for Real-Time Sensor Data Analysis
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Solution Overview
Problem
Conventional machine learning systems are disjointed in data collection and analysis, leading to 'stale' data at the time of consumption, limiting their applicability to real-time data analysis and lacking essential analysis, assessment, and control capabilities.
Innovation Solution
Integration of sensors and compute fabric components within a microelectronic device, enabling data collection and processing at the edge, with the compute fabric performing machine learning processes, statistical analyses, and generating identifying information for calibration and security applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection and analysis are performed in separate systems, then the analysis can be done with dedicated computing resources, but the data becomes stale by the time it reaches the analysis system
Solution Approach 1:
The patent merges data collection and analysis functions into a single integrated edge device. The microelectronic device includes both sensors for data collection and a compute fabric for analysis, eliminating the separation between data collection and analysis systems. This integration ensures data is analyzed immediately at the point of generation, maintaining freshness while reducing the complexity of distributed system integration.
Solution Approach 2:
The patent introduces a new architectural dimension by embedding compute fabric directly within the sensor system. Instead of traditional client-server or centralized cloud architectures, the analysis capability is dimensionally integrated into the data collection layer itself, enabling real-time processing at the edge without requiring complex network infrastructure.
2Adaptability or versatility
If purpose-built hardware is used for machine learning analysis, then computing power is sufficient, but the system lacks essential analysis and control capabilities
Solution Approach 1:
The compute fabric is designed as a universal processing platform that can perform multiple functions including machine learning inference, statistical analysis, data filtering, and control operations. Rather than dedicated hardware for each function, the programmable compute fabric can be configured for different analysis tasks, providing versatility while managing hardware complexity through software-defined functionality.
Solution Approach 2:
The system employs dynamic resource allocation where the compute fabric can adaptively adjust its processing capabilities based on the specific analysis requirements. The microelectronic device can dynamically switch between different computational modes and allocate processing resources according to the immediate needs of the analysis task, enhancing adaptability without requiring over-engineered hardware.
3Speed
If data is collected and then transmitted to a remote system for analysis, then centralized processing can be achieved, but real-time analysis is not possible
Solution Approach 1:
The system performs preliminary analysis actions directly at the edge device before any data transmission occurs. The compute fabric immediately processes the raw data from sensors, performing filtering, feature extraction, and initial analysis locally. This preliminary action ensures that by the time data is transmitted to remote systems, only essential information needs to be sent, maintaining real-time analysis capability while reducing network bandwidth requirements.
Data Source
AI summary
A microelectronic device for generating analysis results from data received from a sensor.