CPU Frequency Scaling Using Load History and Power Budgets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current frequency scaling solutions in electronic devices achieve quick response to tasks but result in high power consumption overheads, leading to inefficiencies and potential undersupply or oversupply issues.
Innovation Solution
A frequency scaling method that dynamically adjusts the frequency scaling period and frequency based on historical load sequences and power consumption budgets, using a first-in first-out order for load sequence updates and incorporating temperature rise predictions to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If frequency scaling is implemented to achieve quick response to tasks, then response efficiency is improved, but power consumption increases
Solution Approach 1:
The patent applies dynamics by making the frequency scaling period adjustable rather than fixed. The system dynamically determines the scaling period based on historical load sequences and power consumption budgets, allowing the CPU frequency to be adjusted adaptively according to actual device conditions, thus achieving quick response when needed while reducing power consumption during normal operation
Solution Approach 2:
The patent changes the parameter of frequency scaling period from a static value to a dynamic value determined by historical load patterns and power budgets. By calculating the optimal scaling period based on actual device usage history, the system can adjust frequency scaling behavior to match real workload characteristics, improving response efficiency when needed while minimizing unnecessary power consumption
2Device complexity
If frequency scaling period is fixed, then implementation is simple, but it causes undersupply or oversupply of frequency
Solution Approach 1:
The patent implements feedback by using historical load sequence data to determine the frequency scaling period. The system continuously collects load information, analyzes historical patterns, and adjusts the scaling period accordingly. This feedback mechanism ensures that the frequency scaling period accurately reflects actual device needs, preventing both undersupply and oversupply while maintaining manageable implementation complexity through automated data collection and analysis
Data Source
AI summary
A frequency scaling method includes: in response to a first operation of a user, determining a frequency scaling frequency based on a first historical load sequence, where the first historical load sequence is a historical load sequence obtained by latest statistics collection in M historical load sequences, and M is a positive integer greater than or equal to 1; determining a frequency scaling period based on a first power consumption budget and the frequency scaling frequency, where the first power consumption budget is a power consumption budget that is obtained based on a temperature rise prediction and that corresponds to the first operation; and performing frequency scaling based on the frequency scaling period and the frequency scaling frequency.