Neural network-based cooling design for high-performance processors.

Area of Science:

• Electronics cooling
• Thermal management
• Computational fluid dynamics

Background:

• Future high-performance systems will exceed 1-2kW/cm² power densities, overwhelming conventional cooling.
• Emerging methods like liquid cooling, thermoelectric coolers (TECs), and vapor chambers offer potential solutions.
• Optimal selection and parameter tuning for these advanced cooling solutions remain challenging and computationally intensive.

Purpose of the Study:

• To develop a computationally efficient method for optimizing chip cooling solutions.
• To identify the best cooling strategy and its parameters for specific chip designs and power loads.
• To enable rapid and accurate convergence to optimal thermal management.

Main Methods:

• A novel deep learning-based optimization framework is proposed.
• The framework integrates chip floorplan and power profiles into the cooling design process.
• Machine learning models are employed to navigate the vast solution space.

Main Results:

• The deep learning approach rapidly converges to optimal cooling solutions.
• Accurate identification of both the optimal cooling method and its parameters is achieved.
• Demonstrates feasibility for complex, high-power-density chip thermal management.

Conclusions:

• Deep learning offers a powerful tool for optimizing advanced electronics cooling.
• This method addresses the computational expense of traditional design exploration.
• Enables efficient thermal management for next-generation high-performance computing.