Dynamic AI Model Fragmentation for Edge Resource Adaptation
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Solution Overview
Problem
Existing AI models are not adaptive to dynamic changes in resource availability in edge environments, leading to inefficient operation or failure when resources decrease and sub-optimal performance when resources increase.
Innovation Solution
A system that monitors system resources and performance preferences, selects model fragments based on these conditions, and automatically modifies the running AI model by adding or removing fragments to optimize resource usage and performance metrics such as accuracy, speed, and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models use more resources, then performance and accuracy improve, but resource consumption increases
Solution Approach 1:
The AI model is divided into multiple independent model fragments that can be selectively loaded and executed. This segmentation allows the system to load only the necessary fragments based on available resources and performance requirements, rather than loading the entire model, thus reducing resource consumption while maintaining necessary performance.
Solution Approach 2:
The system dynamically adjusts the composition and size of the AI model by adding or removing model fragments based on real-time resource availability and performance preferences. This dynamic adaptation enables the model to optimize its resource usage and performance balance according to current system conditions.
2Stability of the object's composition
If AI models are fixed in structure, then stability is maintained, but adaptability to changing resources decreases
Solution Approach 1:
By segmenting the model into independent fragments, the system maintains structural stability of individual fragments while enabling flexible reconfiguration of the overall model composition. The fragments can be selectively combined and discarded without affecting the integrity of each fragment, achieving both stability and adaptability.
Solution Approach 2:
The model fragments are designed to be universal components that can be used in different combinations to serve various performance requirements. Each fragment can function independently or in conjunction with others, allowing the system to adapt to different resource conditions while maintaining consistent, reliable components.
3Productivity
If the entire AI model is loaded, then performance is optimized, but memory usage increases
Solution Approach 1:
The model is segmented into fragments that can be loaded selectively into memory based on performance requirements and available memory. This allows the system to maintain high performance by loading essential fragments while avoiding the memory overhead of loading the complete model, thus optimizing the performance-to-memory ratio.
4Use of energy by moving object
If model size is reduced, then resource consumption decreases, but performance and accuracy deteriorate
Solution Approach 1:
By segmenting the model into functional fragments, the system can load only the fragments necessary for the current task, reducing resource consumption while maintaining accuracy for that specific task. The system can load additional fragments if higher accuracy is required, providing a flexible trade-off between resource usage and performance.
Solution Approach 2:
Different model fragments can be selected based on local requirements and available resources. The system applies the appropriate level of model complexity locally rather than uniformly, using lighter fragments when resources are constrained and more comprehensive fragments when resources are abundant, thus maintaining accuracy while reducing overall resource consumption.
Data Source
AI summary
An example system includes a processor to monitor system resources and performance preferences. The processor is to select model fragments based on the system resources and the performance preferences. The processor is to also construct a running artificial intelligence (AI) model from the selected model fragments. The processor is to further automatically modify the running AI model using the model fragments in response to detecting a change in the system resources or a change in the performance preferences.