AI/ML Algorithm Management with Energy-Performance Comparison
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Solution Overview
Problem
Existing wireless communication systems face challenges in effectively managing artificial intelligence/machine learning (AI/ML) algorithms due to the lack of comprehensive monitoring and management of their energy consumption and performance, which affects lifecycle management.
Innovation Solution
A method and apparatus for algorithm management that involves obtaining and comparing energy consumption information of different algorithms, and using this information to manage AI/ML algorithms by combining it with monitoring information to optimize their lifecycle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI/ML algorithms are introduced into wireless communication systems to improve performance, then algorithm performance monitoring is enabled, but energy consumption increases
Solution Approach 1:
The patent implements a feedback mechanism where the first device monitors algorithm performance and energy consumption, then feeds this information back to the second device for lifecycle management decisions. The second device uses this feedback to determine whether to update algorithms based on performance gains versus energy consumption increases, creating a closed-loop control system that balances performance and energy efficiency.
Solution Approach 2:
The patent changes the parameters for algorithm selection and management by introducing energy consumption comparison information as a new decision criterion. Instead of solely optimizing for performance, the system now compares multiple algorithms based on both performance metrics and energy consumption parameters, allowing dynamic adjustment of algorithm selection to optimize the trade-off between reliability and energy efficiency.
2Extent of automation
If comprehensive monitoring of algorithm energy consumption and performance is implemented, then algorithm lifecycle management is improved, but system complexity increases
Solution Approach 1:
The patent segments the monitoring and management functions into distinct components: the first device handles local monitoring of algorithm performance and energy consumption, while the second device handles centralized lifecycle management decisions. This segmentation allows each device to focus on specific tasks, reducing overall system complexity while enabling comprehensive automated management.
Solution Approach 2:
The patent introduces energy consumption comparison information as an intermediary element that bridges performance monitoring and lifecycle management. This intermediary data structure simplifies the interaction between monitoring and management functions by providing a standardized format for comparing algorithms, reducing the complexity of direct integration between monitoring and management systems.
3Productivity
If multiple algorithms are compared and managed, then algorithm selection optimization is improved, but information processing requirements increase
Solution Approach 1:
The patent extracts the most critical information needed for algorithm selection by focusing on energy consumption comparison data and performance metrics. Instead of processing all possible algorithm parameters, the system extracts and processes only the essential comparison information (energy consumption, performance gain, threshold values), reducing information processing requirements while maintaining selection optimization capability.
Solution Approach 2:
The patent applies partial action by processing and comparing only the necessary algorithm parameters for lifecycle management decisions. The system processes energy consumption and performance information selectively based on management policies and thresholds, rather than processing all possible algorithm characteristics, thus reducing information processing load while maintaining effective algorithm selection.
Data Source
AI summary
This application relates to an algorithm management method and an apparatus. A first device obtains energy consumption comparison information of a first algorithm and a second algorithm, and sends first information to a second device based on the energy consumption comparison information, where the first information is used by the second device to perform algorithm management on the first device.