CPU Wake-Up Prediction Using Dual Conditions
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Solution Overview
Problem
Current CPU low power mode (LPM) management algorithms, such as menu governor and ladder governor, struggle to balance CPU performance and power consumption effectively, leading to inaccurate predictions and suboptimal LPM selection.
Innovation Solution
A method that determines a first and second prediction condition to predict the CPU's wake-up moment, with the first condition using a timer value and the second condition using an interrupt moment, and adjusts based on accuracy rates to ensure accurate LPM selection, balancing power consumption and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the CPU enters a deeper low power mode (e.g., power-down mode) to reduce power consumption, then power consumption is reduced, but the response time increases and performance deteriorates
Solution Approach 1:
The patent implements dynamic LPM selection that adapts to changing system conditions. The governor algorithm continuously monitors CPU activity patterns, task characteristics, and system state to dynamically adjust LPM selection, transitioning between different LPMs based on real-time requirements rather than using a static approach. This allows the system to optimize the balance between power consumption and response time adaptively.
Solution Approach 2:
The patent uses predictive mechanisms to anticipate future CPU wake-up events and pre-select appropriate LPMs. By analyzing historical data, task schedules, and system patterns, the governor algorithm predicts when the CPU will need to wake up and proactively selects the optimal LPM in advance, ensuring that the CPU is in the correct power state before actually needed, thus minimizing both power consumption and response time penalties.
2Productivity
If the CPU remains in active state to ensure high performance, then running performance is guaranteed, but power consumption increases
Solution Approach 1:
The patent changes the operational parameters of the CPU by introducing multiple LPMs with different power consumption and performance characteristics. Instead of a binary active/sleep state, the system utilizes a spectrum of power states (WFI, suspend, power-down, etc.) and dynamically adjusts which state is active based on system requirements. This parameter change enables fine-grained control over the trade-off between performance and power consumption.
Solution Approach 2:
The system dynamically adjusts CPU power state based on real-time workload analysis. The governor algorithm continuously evaluates system state, task characteristics, and performance requirements to determine the optimal power state, transitioning between active and various LPMs as conditions change. This dynamic approach ensures high performance when needed while maximizing power savings during low-demand periods.
3Loss of energy
If traditional LPM management algorithms (menu governor or ladder governor) are used to predict wake-up moments, then power consumption is reduced, but prediction accuracy deteriorates leading to suboptimal LPM selection
Solution Approach 1:
The patent implements a feedback mechanism where the governor algorithm continuously monitors the accuracy of wake-up moment predictions and uses this information to adjust future predictions. By tracking actual versus predicted wake-up times and analyzing prediction errors, the system refines its prediction model over time. This feedback loop enables the system to maintain high prediction accuracy even as system conditions and workloads change, thereby selecting optimal LPMs consistently.
Solution Approach 2:
The governor algorithm performs self-optimization by automatically adjusting its prediction parameters and thresholds based on historical data and observed system behavior. Rather than requiring manual tuning or external intervention, the algorithm learns from past performance and autonomously improves its prediction accuracy. This self-service capability ensures that the system maintains optimal power management performance across varying operational conditions.
Data Source
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AI summary
A method for managing a central processing unit and related products are provided. The method includes the follows. A first prediction condition and a second prediction condition are determined. Both the first prediction condition and the second prediction condition are factors that affect a wake-up moment of a central processing unit (CPU). An accuracy rate of predicting the CPU entering a lower power mode (LPM) by using the first prediction condition is counted. The first prediction condition is continued to be used to predict a next wake-up moment of the CPU when the accuracy rate is greater than a predetermined threshold. The second prediction condition is used to predict the next wake-up moment of the CPU when the accuracy rate is not greater than the predetermined threshold. The next wake-up moment of the CPU can be predicted accurately, so that an appropriate LPM can be chosen by the CPU, which makes it possible to balance the power consumption and performance requirements of the CPU. Therefore, a higher CPU performance can be maintained while maintaining low power consumption.