Frequency Scaling for Mobile Processors
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Solution Overview
Problem
Existing frequency scaling methods in mobile devices, such as smartphones, fail to timely respond to performance changes, leading to inefficient power consumption and battery life reduction.
Innovation Solution
A frequency scaling method that predicts energy efficiency parameters for modules like CPU, GPU, and memory, selecting optimal frequency sets based on these parameters to ensure timely response to load changes and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If frequency scaling is performed level by level based on predicted CPU usage by using a window size as a period, then power consumption can be reduced, but the system cannot timely respond to a change of the performance requirement
Solution Approach 1:
The system performs preliminary frequency adjustment within the current window based on detected performance changes, rather than waiting for the window to complete. This allows the frequency to be adjusted in advance (preliminarily) when a performance change is detected, reducing the response delay while still maintaining the window-based power management approach
Solution Approach 2:
The system dynamically adjusts the frequency scaling behavior based on whether a performance change is detected. When no change is detected, the system follows the original window-based level-by-level scaling. When a change is detected, the system dynamically switches to an accelerated adjustment mode, making the frequency scaling process adaptive and dynamic rather than static
2Use of energy by moving object
If the frequency of the CPU is scaled based on a load using traditional methods, then power consumption can be reduced, but the frequency cannot quickly respond to a load requirement
Solution Approach 1:
The system continuously monitors performance indicators (such as frame rate, CPU usage, or other load metrics) and uses this feedback to detect when a performance change occurs. This feedback mechanism triggers the preliminary frequency adjustment, creating a closed-loop control system that responds to actual load conditions rather than relying solely on predetermined window-based timing
Solution Approach 2:
Upon detecting a performance change through feedback, the system performs preliminary frequency adjustment within the current window, anticipating the need for frequency change before the window completes. This preliminary action reduces the time delay between load requirement and frequency response while maintaining power efficiency
Data Source
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AI summary
This application provides a frequency scaling method. The method includes: predicting an energy efficiency parameter for processing a current frame of an image by at least one module; and selecting, from a plurality of frequency sets based on the predicted energy efficiency parameter, a first frequency set that meets an energy efficiency requirement, and scaling a working frequency of each of the at least one module for processing the current frame to a preset frequency corresponding to each of the at least one module. According to the technical solutions provided in the embodiments of this application, a load change requirement can be responded to in time in a frequency scaling process.