Dynamic Power Management Controller for Multicore Processors
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Solution Overview
Problem
Modern processors are optimized for performance but lack energy efficiency in power management, leading to increased energy consumption and inefficiency, especially in multicore systems where workload characteristics vary dynamically.
Innovation Solution
An intelligent multi-core power management controller that uses machine learning and reinforcement learning to dynamically adjust power configurations, including core activation, voltage, and frequency, based on workload behaviors, allowing for optimal performance per energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a static power management model is used during design stage, then the processor can be optimized for performance, but energy efficiency deteriorates because the model cannot adapt to dynamic workload characteristics
Solution Approach 1:
The patent transforms the static power management model into a dynamic one by enabling continuous updates of workload characteristics and power configuration parameters during processor operation. The model adapts to changing workload conditions by collecting performance data and recalculating optimal power states in real-time, resolving the contradiction between static optimization and dynamic adaptability.
Solution Approach 2:
The patent implements a feedback mechanism where performance data from actual processor operation is collected, analyzed, and used to update the power management model. This closed-loop system continuously refines the power configuration based on observed workload characteristics, enabling the processor to optimize energy efficiency while maintaining performance across diverse application scenarios.
2Reliability
If a conservative power management model is set to work across wide range of applications, then reliability is improved, but energy efficiency worsens because the model is not optimal in many situations
Solution Approach 1:
The patent performs preliminary characterization of workload characteristics during the design stage to establish initial power management parameters. This preliminary action provides a reliable baseline configuration that can be later refined through dynamic updates based on actual usage patterns, maintaining reliability while enabling energy optimization.
Solution Approach 2:
The patent enables dynamic modification of power management parameters including voltage, frequency, and core activation states based on observed workload characteristics. By changing these parameters in response to actual usage conditions rather than relying on fixed conservative settings, the system achieves better energy efficiency while maintaining reliable operation across diverse applications.
3Use of energy by moving object
If machine learning techniques are used to dynamically update power management models, then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the power management system automatically collects performance data, analyzes workload characteristics, and updates its own configuration parameters without external intervention. This autonomous operation reduces the need for complex external control systems while achieving energy optimization through adaptive learning from actual usage patterns.
Data Source
AI summary
In one embodiment, A processor includes a logic to receive performance monitoring information from at least some of a plurality of cores and determine, according to a power management model, a performance state for one or more of the plurality of cores based on the performance monitoring information, and a second logic to receive the performance monitoring information and dynamically update the power management model according to a reinforcement learning process. Other embodiments are described and claimed.