Aerospace Edge ML Deployment via Central Model Distribution
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Solution Overview
Problem
Deploying, monitoring, and updating machine learning models on a large number of peripheral devices in aerospace systems is hindered by communication bandwidth and power limitations, making it difficult to efficiently utilize edge ML.
Innovation Solution
A central device coordinates the distribution of machine learning models to peripheral devices and receives output data, enabling convenient deployment, maintenance, and monitoring of these models without extensive manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine learning models are implemented at edge devices for real-time processing, then latency is reduced and processing speed is improved, but deployment, monitoring and updating becomes difficult due to communication bandwidth and power limitations
Solution Approach 1:
A central device acts as an intermediary between external sources and peripheral edge devices. It receives machine learning model information from external sources, manages model updates, and distributes models to multiple peripheral devices. This intermediary approach enables efficient deployment and monitoring of ML models across the aerospace system without requiring direct complex communications between each device and external sources, thus resolving the contradiction between improved processing speed at edge and ease of model deployment.
2Adaptability or versatility
If large volumes of data are gathered and forwarded to remote processors for analysis, then sophisticated machine learning models with large memory and processing requirements can be executed, but energy consumption and communication infrastructure requirements increase
Solution Approach 1:
The system segments the machine learning processing function across multiple peripheral devices distributed throughout the aerospace system. Each peripheral device can independently execute machine learning models locally on gathered data, eliminating the need to forward all raw data to a single remote processor. This segmentation reduces communication bandwidth requirements and energy consumption while maintaining the capability to run sophisticated models at the edge.
Solution Approach 2:
The patent transitions from centralized remote processing to distributed edge processing across spatial dimensions. Instead of all data flowing to one remote location, processing is distributed across multiple peripheral devices at different locations within the aerospace system. This dimensional change in processing architecture reduces communication infrastructure requirements and energy consumption while enabling sophisticated ML model execution.
3Loss of information
If data is gathered and forwarded to remote processors for analysis, then useful insights can be produced, but long delays occur between data gathering and insight production
Solution Approach 1:
Machine learning models are pre-deployed to peripheral devices before data gathering occurs. This preliminary action enables immediate local processing of data as it is collected, eliminating the time delay associated with forwarding data to remote processors. The models are ready to execute instantly, producing insights in real-time without communication delays, while maintaining high-quality information processing capabilities.
Data Source
Figure 1~2
AI summary
An aerospace system 100 is provided, comprising a plurality of peripheral devices 102 and a central device 104 operable to communicate with the plurality of peripheral devices. The central device is arranged to receive information describing one or more machine learning models from an external source 106 and distribute said one or more machine learning models to the plurality of peripheral devices. Each peripheral device is arranged to obtain input data, apply a machine learning model of the one or more machine learning models to the input data to produce output data, and send said output data to the central device.