Power State Management via Driver Grouping
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Solution Overview
Problem
Computing devices face challenges in minimizing latency during power state transitions due to the need to manage various power states and communicate with multiple peripheral devices, leading to inefficiencies in reducing power consumption.
Innovation Solution
A system that detects power state transitions, collects historical callback information for drivers, sorts them into groups based on execution times, and executes power state instructions across multiple processors to minimize latency by optimizing the distribution of power state operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If the computing device initiates calls to drivers of peripheral devices to place the device in a reduced power consumption state, then power consumption is reduced, but the time required to transition between power states increases
Solution Approach 1:
The system performs preliminary actions by collecting historical callback information for each driver before the power state transition is actually needed. This historical data is stored and prepared in advance, so when a transition is required, the system already has the information needed to quickly determine the optimal execution sequence, thereby reducing the actual transition time while maintaining power savings.
Solution Approach 2:
The system dynamically adjusts the execution sequence of driver callbacks based on historical performance data. By analyzing past callback times and adapting the execution order to minimize latency, the system creates a dynamic power management approach that optimizes both power consumption and transition speed according to actual device behavior patterns.
2Adaptability or versatility
If the system manages multiple peripheral devices and drivers, then device functionality and versatility are improved, but the complexity of power state management increases
Solution Approach 1:
The system segments the power management task by dividing drivers into groups based on their historical callback information. Instead of managing all drivers uniformly, the system creates distinct groups that can be processed separately, with each group having its own optimized execution sequence. This segmentation reduces the overall management complexity while preserving support for multiple peripheral devices.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting and analyzing historical callback information from driver executions. This feedback loop allows the system to learn from past performance and automatically adjust the execution sequence, reducing the need for complex manual configuration and simplifying power state management while maintaining versatility.
3Loss of time
If the system collects and processes historical callback information for multiple drivers, then transition latency is reduced, but the initial processing overhead increases
Solution Approach 1:
The system applies partial action by selectively processing historical callback information only for drivers that are actually needed for the current power state transition. Instead of processing all driver information uniformly, the system identifies and processes only the relevant subset, reducing the processing overhead while still achieving low transition latency for the specific transition required.
Data Source
AI summary
In one example, a system for managing power states can include a processor to detect a power state transition operation and detect historical callback information for each of a plurality of drivers of the system. The processor can also sort the plurality of drivers into a plurality of driver groups based on the historical callback information and execute the power state transition operation by transmitting a plurality of power state instructions for each driver to a plurality of processors based on the plurality of driver groups.