Fuzzy Logic Thermal Management for CPU-GPU Power Budgeting
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Solution Overview
Problem
Computer systems face thermal management challenges due to the high heat generated by CPUs and GPUs, which can lead to overheating and damage, and existing cooling systems often require complex designs and multiple sensors, limiting the choice of GPUs and increasing costs.
Innovation Solution
A closed-loop thermal management system implements a shared power budget for CPUs and GPUs, using integrated power over time as a metric to dynamically adjust their performance states and fan speeds, reducing overheating risks while allowing for a higher performance GPU in systems not designed for it, and conserving battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a high performance GPU is used, then processing power is improved, but thermal load increases causing overheating
Solution Approach 1:
The system dynamically adjusts the operational states of CPU and GPU based on real-time thermal conditions and power consumption patterns. The fuzzy logic controller continuously monitors system state and adjusts power allocation dynamically, allowing the system to operate at high performance when thermal conditions permit while preventing overheating through adaptive power management.
Solution Approach 2:
The system changes operational parameters (power states, frequency, voltage) of CPU and GPU based on thermal conditions and power budget constraints. By adjusting these parameters dynamically, the system can accommodate high-performance GPUs while maintaining thermal safety through controlled parameter modifications.
2Measurement precision
If traditional thermal management with multiple sensors is used, then temperature monitoring is improved, but system complexity and cost increase
Solution Approach 1:
The system extracts and monitors power consumption as the primary metric instead of relying on multiple temperature sensors. By taking out power measurement as the key monitoring parameter and using it to infer thermal conditions, the system reduces sensor requirements while maintaining effective thermal management through power-based predictions.
Solution Approach 2:
The system uses power consumption as an intermediary parameter to indirectly monitor thermal conditions. Instead of directly measuring temperature with multiple sensors, the fuzzy logic controller uses power measurements as a mediator to predict and manage thermal states, reducing hardware complexity while maintaining control effectiveness.
3Temperature
If power throttling is applied to reduce thermal load, then temperature control is improved, but processing performance decreases
Solution Approach 1:
The system dynamically adjusts power allocation between CPU and GPU based on real-time conditions rather than applying static throttling. The fuzzy logic controller continuously adapts power states to maintain optimal performance while controlling thermal load, allowing high performance when possible and applying minimal necessary throttling only when thermal constraints are reached.
Solution Approach 2:
The system changes power parameters adaptively based on thermal conditions and workload characteristics. By modifying operational parameters dynamically rather than applying fixed throttling, the system maintains processing performance when thermal conditions allow while effectively controlling temperature when necessary.
Data Source
AI summary
Metrics representing a combined measure of power used by a central processing unit (CPU) and power used by a graphics processing unit (GPU) are compared to a shared supply power and thermal power budgets. Power used by the CPU and power used by the GPU are regulated in tandem using a fuzzy logic control system that can implement fuzzy rules that describe the management within thermal and supply power design constraints of the platform.