Power Prediction Using Machine Learning for Voltage Frequency Scaling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Power management systems suffer from imprecise predictions of power consumption and slow reactions to changes in power consumption patterns, making it difficult to efficiently adjust voltage and frequency for optimal energy efficiency.
Innovation Solution
A predictive model, such as a neural network, is used to forecast future power consumption based on past measurements, allowing for real-time adjustments in voltage and frequency scaling to match changing power demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional power management systems use real-time power measurements to adjust voltage and frequency, then they can react to power consumption changes, but the reaction is slow and the predictions are imprecise
Solution Approach 1:
The system performs preliminary actions by training a machine learning model offline to predict future power consumption patterns. The model is trained on historical power measurement data and use case characteristics, enabling the system to proactively adjust voltage and frequency settings before actual power consumption changes occur, rather than reacting after the fact.
Solution Approach 2:
The system implements feedback by continuously monitoring actual power consumption measurements and comparing them with predicted values. The model is retrained periodically using new measurement data, allowing the system to adapt to changing usage patterns and improve prediction accuracy over time, creating a closed-loop control system.
2Productivity
If power management systems are tuned for specific use cases using traditional methods, then they can optimize for particular scenarios, but the tuning process takes many months of effort
Solution Approach 1:
The system creates simplified representations (copies) of complex use cases by extracting key characteristics and patterns from historical data. The machine learning model learns from these patterns and can generalize to predict power consumption for new, unseen use cases without requiring manual tuning, effectively copying the optimization behavior across different scenarios.
Solution Approach 2:
The system changes the approach from manual parameter tuning to automated parameter learning. Instead of experts manually adjusting voltage and frequency parameters for each use case, the machine learning model automatically learns optimal parameter settings from training data, dramatically reducing the time and expertise required for optimization.
3Stability of the object's composition
If power management systems make voltage and frequency adjustments based on current power consumption, then they can maintain stability, but they cannot proactively optimize for future power demands
Solution Approach 1:
The system performs preliminary actions by predicting future power consumption patterns before they actually occur. Based on these predictions, the system proactively adjusts voltage and frequency settings in advance, allowing it to optimize for upcoming power demands while maintaining system stability through controlled transitions.
Solution Approach 2:
The system implements dynamic adjustments by continuously adapting voltage and frequency settings based on predicted power consumption patterns. The machine learning model enables the system to dynamically respond to changing usage patterns, transitioning from static, reactive power management to dynamic, proactive optimization.
Data Source
AI summary
Methods, systems, and apparatus, for predicting power measurements on a device using predictive models. One of the methods includes continually generating, by a power sensor of a power management system of a device, a respective measure of power consumed at each of a plurality of time points. A power prediction module receives a sequence of N power measurements consumed for N previous time points and generates a predicted power measurement at a future time point based on a trained predictive model.