ML Power Agent Algorithm for Processor Idle Time Prediction
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Solution Overview
Problem
Current power management algorithms for processors cannot accurately predict user behavior, leading to inefficient entry and exit from low power states like S0i4, resulting in wasted energy and reduced battery life.
Innovation Solution
A machine-learning based power agent algorithm that learns user habits and system telemetry to determine when to enter deeper power states, using a High Water Mark of dirty cache lines to inform decisions between S0i3 and S0i4 states, optimizing energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the processor enters deeper power states (S0i4) more frequently, then energy savings increase, but the risk of false entry and wasted energy increases
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and system telemetry data before making power state entry decisions. By pre-processing and learning from historical data, the machine learning model can predict future idle periods with higher accuracy, reducing false entries into deep power states while maximizing energy savings during genuine idle periods.
Solution Approach 2:
The system continuously monitors actual power state transitions and user behavior, feeding this information back to refine the machine learning model. This feedback loop allows the system to learn from past predictions and improve its accuracy over time, adjusting to changing user patterns while maintaining reliable energy savings.
2Measurement precision
If machine learning models are trained locally on user devices, then prediction accuracy improves, but computational resources and energy consumption increase
Solution Approach 1:
The system implements a hybrid approach where basic power management decisions use simple heuristics and thresholds, while more complex predictions leverage the machine learning model. This partial application of ML computation reduces energy consumption compared to full ML processing, while still achieving improved prediction accuracy over traditional methods.
Solution Approach 2:
The patent introduces an intermediary layer that processes and summarizes telemetry data before feeding it to the machine learning model. This intermediary preprocessing step reduces the computational burden on the ML model while preserving the essential patterns needed for accurate prediction, thereby reducing energy consumption.
3Measurement precision
If more telemetry data is collected for training, then model accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts and focuses on the most relevant telemetry data features that have the highest impact on power state prediction accuracy. By selectively extracting only the critical features (such as user activity patterns, application usage, and system state transitions) rather than processing all available data, the system maintains high model accuracy while reducing overall system complexity.
Data Source
AI summary
A machine-learning (ML) scheme running a software driver stack to learn user habits of entry into low power states, such as Modern Connect Standby (ModCS), and duration depending on time of day, and/or system telemetry. The ML creates a High Water Mark (HWM) number of dirty cache lines (DL) as a hint to a power agent. A power agent algorithm uses these hints and actual system's number of DL to inform the low power state entry decision (such as S0i4 vs. S0i3 entry decision) for a computing system.