Machine Learning Model Selection for Performance and Energy Trade-Offs
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Solution Overview
Problem
Existing machine learning models for tasks like speech-to-text conversion are resource-intensive, leading to high computational and energy consumption, without ensuring optimal performance thresholds are met.
Innovation Solution
A method for selecting a machine learning model that meets minimum performance thresholds while minimizing resource consumption by evaluating performance indicators and resource consumption indicators, allowing for energy-efficient and cost-effective task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing machine learning models are used for computational tasks like speech-to-text conversion, then task completion is achieved, but resource consumption and energy demand become excessively high
Solution Approach 1:
The system performs preliminary evaluation of multiple machine learning models before deployment, assessing their performance indicators and resource consumption characteristics. This advance analysis allows selection of models that meet performance thresholds while minimizing energy consumption, rather than deploying models and discovering high resource usage later.
Solution Approach 2:
The system changes key parameters including performance thresholds, resource consumption limits, and model complexity levels to identify optimal model configurations. By adjusting these parameters and evaluating multiple models against them, the system finds models that satisfy performance requirements with reduced energy consumption compared to existing models.
2Measurement precision
If complex machine learning models are deployed to ensure high performance, then accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system applies partial action by selecting models that provide sufficient accuracy to meet defined thresholds rather than maximizing accuracy indefinitely. This approach avoids the excessive computational complexity that would result from deploying overly sophisticated models when moderate accuracy levels are adequate for the task.
Solution Approach 2:
The system evaluates multiple machine learning models with varying complexity levels and selects those that provide adequate performance at lower computational cost. Rather than deploying a single complex high-accuracy model, the system chooses from multiple options including simpler models that meet the accuracy requirements with reduced complexity.
3Productivity
If multiple machine learning models are evaluated and selected based on performance and resource consumption, then optimal model selection is achieved, but selection process time increases
Solution Approach 1:
The system performs preliminary evaluation and pre-assessment of machine learning models before final deployment decisions. By conducting advance analysis of model performance indicators and resource consumption characteristics, the system prepares model recommendations in advance, reducing the time required for final selection and deployment.
Data Source
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AI summary
According to an embodiment, a computer-implemented method (100) for performing a computational task using a machine learning model comprises obtaining (101) an indication about a computational task to be performed; obtaining (102) at least one minimum performance threshold for the computational task; obtaining (103) a plurality of machine learning models, wherein each machine learning model in the plurality of machine learning models is associated with at least one performance indicator and at least one resource consumption indicator; choosing (104) a machine learning model out of the plurality of machine learning models based at least on the at least one minimum performance threshold for the computational task, the at least one performance indicator of each machine learning model in the plurality of machine learning models and the at least one resource consumption indicator of each machine learning model in the plurality of machine learning models; and performing (105) the computational task using the chosen machine learning model.