Machine Learning Thermal Management for Functional Circuit Units
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Solution Overview
Problem
The challenge in modern data processing systems is to optimize performance while maintaining thermal and power budgets, especially with compute-intensive applications like machine learning workloads, where conventional rule-based or table-lookup methods are inadequate.
Innovation Solution
A system utilizing a functional circuit unit with a cooling device and power converter, along with sensors and machine learning models to generate control signals for optimizing performance, including a first machine learning model for generating control signals for the cooling device and power converter, and optionally a second model for predicting the state representation of the functional circuit unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional rule-based or table-lookup methods are used for power control, then device complexity is reduced, but performance optimization capability deteriorates
Solution Approach 1:
The patent replaces conventional rule-based mechanical control systems with machine learning models that use sensor data to dynamically optimize power and cooling control, enabling adaptive performance optimization without fixed lookup tables
Solution Approach 2:
The system employs self-learning machine learning models that continuously improve their control strategies by processing sensor data and learning from operational patterns, enabling the system to optimize itself without external intervention
2Productivity
If compute-intensive applications are utilized to increase performance, then productivity is improved, but thermal and power consumption increase
Solution Approach 1:
The patent implements dynamic control of power and cooling systems based on real-time sensor data and machine learning predictions, allowing the system to adapt power consumption levels to actual computational needs rather than operating at fixed high-power states
Solution Approach 2:
The system uses multiple sensors to continuously monitor operational parameters and feeds this data back to the machine learning models, which then adjust power and cooling control signals to maintain optimal performance while minimizing energy consumption
3Productivity
If machine learning models are deployed for real-time control, then performance optimization is improved, but computational overhead increases
Solution Approach 1:
The patent performs extensive machine learning model training offline before deployment, so that during real-time operation only inference is required, significantly reducing computational overhead and energy consumption during actual control operations
Solution Approach 2:
The system separates the computationally intensive training phase from the lightweight inference phase, allowing complex learning to occur offline while real-time control uses simplified model evaluation that requires minimal computational resources
Data Source
AI summary
Described aspects include a system for optimizing performance of a functional circuit unit, a method of optimizing performance of a functional circuit unit, and a computer program product. In one embodiment, the system may include a functional circuit unit having an associated cooling device and power converter, one or more sensors for the functional circuit unit, the one or more sensors including a power sensor and a temperature sensor, and a first machine learning model. The first machine learning model may be adapted to receive temperature data and power data from the one or more sensors, and to generate control signals for the cooling device and the power converter to optimize performance of the functional circuit unit.