Event-Based Power Manager for Embedded Systems
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Solution Overview
Problem
Conventional power management techniques in embedded processing systems are inefficient due to reliance on predetermined workload information and fixed sampling intervals, leading to suboptimal frequency selection and potential performance degradation, especially in systems without task managers or accurate workload predictions.
Innovation Solution
An event-based power manager that dynamically adjusts power management based on inferred workload models, synchronizing with task periods and handling asynchronous events, allowing for adaptive and efficient power configuration without requiring explicit workload definitions or task manager interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If time-sliced control with fixed periodic sampling is used, then power management can be implemented independently from task management, but the sampling interval must be manually tuned and may not correlate with actual workload timing, leading to suboptimal frequency selection
Solution Approach 1:
The power manager autonomously infers workload characteristics by monitoring processor events directly, eliminating the need for manual sampling interval configuration or task manager intervention. The system self-adjusts its observation strategy based on detected event patterns, achieving both independence and accuracy simultaneously.
Solution Approach 2:
The system continuously monitors processor events and uses this feedback to dynamically adjust frequency selections. By establishing a closed-loop control where workload measurements directly inform power management decisions, the system achieves accurate adaptation without manual tuning.
2Loss of energy
If the sampling interval is made longer to reduce overhead, then power management becomes less reactive to workload variations, but shorter intervals cause inconsistent workload measurements and degrade performance
Solution Approach 1:
The system transitions from static periodic sampling to dynamic event-driven monitoring. The observation rate adapts automatically based on workload characteristics, allowing the system to be highly responsive during active periods while minimizing overhead during idle periods through intelligent sampling strategies.
Solution Approach 2:
The system performs preliminary analysis of event patterns to identify workload characteristics before making frequency selection decisions. By pre-processing event data and establishing workload models in advance, the system can make accurate frequency decisions without requiring continuous high-rate sampling.
3Productivity
If workload information is explicitly declared by the programmer, then DVFS algorithms can optimize frequency selection, but this increases programming effort and system overhead
Solution Approach 1:
The power manager autonomously infers workload characteristics by monitoring processor events directly, eliminating the need for manual sampling interval configuration or task manager intervention. The system self-adjusts its observation strategy based on detected event patterns, achieving both independence and accuracy simultaneously.
Solution Approach 2:
The system uses processor events as an intermediary to indirectly measure workload characteristics without requiring explicit programmer input. By observing event timing and patterns, the power manager derives workload information automatically, serving as a mediator between hardware execution and power management decisions.
4Use of energy by moving object
If the lowest frequency is selected to maximize energy efficiency, then static power consumption increases due to leakage current, but higher frequencies increase dynamic power consumption
Solution Approach 1:
The system dynamically adjusts operating parameters (frequency and voltage) based on inferred workload characteristics. By changing these parameters adaptively rather than using fixed low-frequency operation, the system optimizes the balance between static and dynamic power consumption while meeting performance requirements.
Solution Approach 2:
The system transitions from static low-frequency operation to dynamic frequency scaling. The operating frequency is continuously adjusted based on real-time workload assessment, allowing the system to operate at higher frequencies when needed (reducing static power proportion) and lower frequencies when idle (reducing dynamic power), thereby optimizing total energy consumption.
Data Source
AI summary
A technique for managing power in an embedded processing system includes generating a workload model for the embedded processing system in response to a control signal, an event status signal, and a reference clock signal. The control signal is received from a peripheral device. The event status signal is received from an event processor configured to control execution of tasks by a processor core of the embedded processing system. The technique includes providing power configuration information to the processor core. The power configuration information corresponds to an operating point selected based on the control signal, the event status signal, the reference clock signal, the workload model, and a predetermined energy model.