Neural Network Power Management for Automatic Frequency Tuning
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Solution Overview
Problem
Existing power management systems for processors rely on manual selection of performance counters and tuning of control system coefficients, which is prone to errors and labor-intensive.
Innovation Solution
A neural network-based system using reinforcement learning to automatically adjust processing frequency and voltage by receiving performance characteristics, calculating reward values, and modifying weights until convergence conditions are met, thereby eliminating the need for manual tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection and tuning of performance counters and control coefficients is used, then the system can achieve desired performance and power metrics, but the process is labor intensive and prone to error
Solution Approach 1:
The system uses reinforcement learning to enable the power management system to automatically tune its own control coefficients and select performance counters without human intervention. The neural network learns optimal tuning parameters through self-service by interacting with the system environment and receiving feedback in the form of rewards based on performance and power consumption outcomes.
Solution Approach 2:
The invention changes the parameters of the control system by replacing manual coefficient selection with dynamically learned coefficients from a neural network. The system continuously adapts control parameters based on learned patterns from reinforcement learning, allowing the parameters to change according to system state and workload conditions rather than remaining fixed or manually adjusted.
2Productivity
If manual tuning of control system parameters is performed, then desired performance metrics can be achieved, but the process requires significant time and labor
Solution Approach 1:
The system performs preliminary action by pre-training the neural network offline using reinforcement learning to learn optimal control coefficients and performance counter selections. This preliminary training phase allows the system to accumulate knowledge and experience before deployment, so that during actual operation, the power management decisions can be made quickly without requiring time-consuming manual tuning.
Solution Approach 2:
The invention substitutes the mechanical process of manual tuning with an automated computational system. The neural network replaces the manual iterative adjustment process with automated learning and decision-making, eliminating the need for human operators to spend time manually adjusting coefficients and selecting performance counters.
3Measurement precision
If a neural network with many weights is used for frequency modification decisions, then decision accuracy improves, but system complexity increases
Solution Approach 1:
The system extracts the complexity of neural network training and configuration into a separate offline development phase. The complex process of determining optimal network architecture, number of weights, and training data selection is taken out from the operational system and performed during system development and deployment. This extraction allows the operational system to use the pre-determined network configuration without bearing the burden of managing its own complexity.
Solution Approach 2:
The patent introduces an intermediary layer between the neural network complexity and the power management system. The trained neural network weights and architecture serve as an intermediary that has already processed and resolved the complexity issues during training. This intermediary provides simplified, pre-processed decision-making capabilities to the power management system without exposing it to the underlying complexity of neural network configuration and training.
Data Source
AI summary
Configuring a power management system using reinforcement learning, including: receiving data indicating, for an execution of a workload, a plurality of performance counters, a plurality of power consumptions, and a plurality of processing frequency modification decisions, wherein the plurality of processing frequency modification decisions are generated by a neural network; calculating, based on the plurality of performance counters, the plurality of power consumptions, and the plurality of processing frequency modification decisions, a reward value for the execution of the workload; and modifying one or more weights of the neural network based on the reward value.