Reinforcement Learning for Adaptive Computing Capacity Adjustment
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Solution Overview
Problem
Current computing systems face challenges in managing the balance between performance and power consumption, particularly in high-load, long-duration applications like gaming, where thermal throttling often leads to performance degradation.
Innovation Solution
A reinforcement learning-based system that dynamically adjusts computing capacity by collecting real-time environment information, determining reward values based on power consumption, and using these to calculate adjustment actions that optimize performance and power balance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If thermal throttling is engaged to prevent overheating, then power consumption and temperature are reduced, but application performance decreases
Solution Approach 1:
The patent implements dynamic computing capacity adjustment through reinforcement learning, allowing the system to adaptively modify performance levels in real-time based on thermal conditions and application requirements. The agent continuously learns optimal policies for balancing temperature control with performance maintenance, enabling the system to dynamically respond to changing thermal states without fixed throttling thresholds.
Solution Approach 2:
The system employs reinforcement learning with reward signals that provide feedback on thermal management effectiveness. The agent receives reward values based on temperature reduction and power consumption metrics, using this feedback to iteratively improve its policy for computing capacity adjustment. This feedback mechanism enables the system to learn optimal balancing acts between thermal control and performance preservation over time.
2Duration of action of stationary object
If computing capacity is reduced to extend time before thermal throttling, then power consumption is lowered, but application responsiveness decreases
Solution Approach 1:
The system dynamically adjusts computing capacity based on real-time thermal conditions and application state, rather than using static performance levels. The reinforcement learning agent continuously optimizes the timing and magnitude of performance adjustments, extending thermal headroom while maintaining application responsiveness through adaptive rather than fixed capacity reduction.
Solution Approach 2:
The patent changes key system parameters including CPU frequency, GPU clock speed, and memory bandwidth dynamically based on learned policies. The agent modifies these parameters in real-time to optimize the balance between extending thermal endurance and maintaining application performance, using parameter adjustment rather than binary on/off throttling.
3Device complexity
If static computing capacity configuration is used, then system simplicity is maintained, but power consumption and performance optimization are insufficient
Solution Approach 1:
The system implements self-service through autonomous reinforcement learning, where the agent independently learns optimal computing capacity adjustment policies without external intervention. The agent autonomously explores the state space, learns from reward signals, and develops its own control strategy for thermal management, eliminating the need for complex manual configuration while optimizing power consumption.
Solution Approach 2:
The reinforcement learning framework provides continuous feedback to the agent based on thermal conditions, power consumption, and application state. This feedback loop enables the system to automatically optimize its own operation without external configuration, with the agent using reward signals to iteratively improve its policy for energy-efficient computing capacity management.
Data Source
AI summary
A reinforcement learning-based system for adaptively adjusting computing capacity is provided. The system includes an environment module and an agent module. The environment module is configured to collect environment information, including the actual power consumption and one or more power-related metrics, from an application environment. The environment module is further configured to determine a reward value based on the actual power consumption and the expected power consumption, and determine state data based on the one or more power-related metrics. The agent module is configured to receive the reward value and the state data from the environment module, and determine an adjustment action based on the reward value and the state data. The adjustment action involves adjusting the computing capacity and is dynamically executed by the application environment.