Power Management Temperature Control for Cryogenic Devices
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Solution Overview
Problem
Existing power management technologies fail to optimize system power while maintaining performance in a straightforward manner, particularly in low-temperature environments, by not considering the interplay between operating power and cooling power.
Innovation Solution
A method and apparatus that determines a target temperature using a machine learning model trained on cumulative operating data, adjusting the operating temperature and voltage to optimize system power by balancing operating and cooling power, especially in low-temperature ranges below 150 Kelvin.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the device operates at lower temperature to reduce operating power, then operating power is reduced, but cooling power increases
Solution Approach 1:
The system dynamically adjusts the operating temperature parameter based on the operating frequency to optimize the balance between operating power and cooling power. By changing the temperature parameter in response to frequency changes, the system achieves minimum total power consumption at each operating point.
Solution Approach 2:
The patent implements dynamic temperature adjustment based on real-time operating frequency. The target temperature is not fixed but dynamically determined through machine learning models that adapt to changing operating conditions, allowing the system to continuously optimize the power balance.
2Productivity
If the device operates at higher frequency to improve performance, then productivity increases, but both operating power and cooling power increase
Solution Approach 1:
The system adjusts multiple parameters including operating frequency, voltage, and temperature to optimize performance while minimizing total power consumption. The machine learning model determines optimal voltage and temperature settings for each frequency level, enabling efficient high-performance operation.
Solution Approach 2:
The system pre-determines optimal operating parameters including frequency, voltage, and temperature combinations through machine learning models trained on cumulative operating data. This preliminary optimization allows the system to quickly select the most efficient operating point for given performance requirements.
3Ease of operation
If traditional power management is used without considering cooling power, then device operation is simplified, but system power optimization is insufficient
Solution Approach 1:
The system uses machine learning models trained on cumulative operating data to automatically determine optimal operating parameters without requiring complex manual configuration. The models self-adapt to changing conditions and autonomously optimize the balance between operating power and cooling power, maintaining simplicity while achieving advanced optimization.
Solution Approach 2:
The system continuously monitors operating conditions and uses feedback from cumulative operating data to refine machine learning models. This feedback mechanism enables the system to learn from past operations and continuously improve its power optimization decisions while maintaining ease of operation.
Data Source
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AI summary
A method with power management includes: determining an operating frequency of a device; in response to the device operating at the operating frequency, determining a target temperature that improves system power corresponding to a combination of operating power for an operation of the device and cooling power for cooling the device; and adjusting an operating temperature of the device based on the target temperature.