Thermal Runtime Prediction Using Polynomial Curve Fitting
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Solution Overview
Problem
Predicting the remaining operating time before an electronic device reaches a critical shutdown temperature is challenging due to variable factors like processing load, screen brightness, ambient temperature, and airflow, and existing techniques fail to accurately account for interactions between components.
Innovation Solution
An on-device logger periodically logs temperature sensor readings and power consumption, using predefined functions such as fitted quadratic curves to predict temperature change based on power consumption, thereby estimating the remaining thermal runtime until shutdown.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple threshold techniques are used for thermal prediction, then device complexity is reduced, but prediction accuracy deteriorates leading to premature throttling or unexpected shutdowns
Solution Approach 1:
The system transitions from simple threshold-based temperature monitoring to a multi-parameter prediction model that incorporates temperature, power consumption, and time variables. By changing the parameters from single-threshold checks to continuous multi-variable tracking, the system achieves higher prediction accuracy while maintaining reasonable complexity through the use of polynomial functions.
Solution Approach 2:
The patent replaces complex fluid dynamics simulations (computationally intensive mechanical/physical modeling) with polynomial curve fitting and mathematical functions. This substitution maintains prediction accuracy while dramatically reducing computational complexity and resource requirements, allowing real-time prediction on resource-constrained devices.
2Measurement precision
If fluid dynamics simulations are used for thermal prediction, then prediction accuracy is improved, but computational intensity increases
Solution Approach 1:
The system replaces computationally intensive fluid dynamics simulations with polynomial curve fitting and mathematical functions. By substituting complex physical simulations with simplified mathematical models that capture the essential thermal behavior, the system maintains adequate prediction accuracy while dramatically reducing computational power consumption and processing requirements.
Solution Approach 2:
The patent uses lightweight polynomial functions and curve fitting approaches that are computationally inexpensive compared to full fluid dynamics simulations. These simpler mathematical models consume significantly less computational resources and can be executed efficiently on resource-constrained devices, providing a practical alternative to heavy simulations.
3Measurement precision
If multiple parameters are monitored to improve prediction accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses a unified polynomial prediction model that handles multiple parameters (temperature, power, time) simultaneously through a single mathematical framework. This multi-functional approach allows the system to process various input parameters and generate predictions without requiring separate complex analysis for each parameter, thereby improving accuracy while controlling overall system complexity.
Data Source
AI summary
A method and system for predicting remaining thermal runtime in an electronic device is disclosed. The method involves obtaining the device's current processor power consumption and temperature sensor reading, selecting a predefined function relating temperature and time at that power level, determining the position of the current temperature on the function, calculating the time position for a shutdown temperature, and determining the time difference to estimate remaining runtime. In some examples, the functions are quadratic curves fitted to empirical time-temperature data collected at different processor power levels. In one embodiment, the prediction is implemented in augmented reality glasses and continuously adjusted using updated power and temperature data. The runtime prediction enables the device to warn the user prior to shutdown and perform graceful shutdown procedures. By accounting for diverse operating conditions, the method provides an accurate estimate of remaining time before the device reaches critical temperatures.