Heat accumulation in AI computing modules in high-density computing scenarios has become a core challenge hindering their performance and stability. As deep learning model parameter sizes exceed hundreds of billions, the surge in power consumption per module has rendered traditional air cooling solutions ineffective, forcing multi-dimensional, systematic innovation in thermal design. This transformation requires not only breakthroughs in materials and structures but also coordinated optimization across the entire supply chain, from chip architecture to system integration.
Chip-level thermal innovation is fundamental to addressing heat accumulation. AI computing modules commonly utilize three-dimensional stacking technology, vertically integrating multiple computing cores and storage units. While this increases computing power density, it also exacerbates thermal coupling between layers. To address this issue, companies such as TSMC have developed integrated micro-cooler solutions. These solutions embed copper pillar arrays and organic interposers within the packaging layer, enabling direct water cooling on the backside of the chip. This design deeply integrates the heat dissipation manifold with the computing unit, shortening the heat conduction path and reducing thermal resistance. Furthermore, the combination of through-silicon via (TSV) technology and micro-fin heat sinks creates three-dimensional liquid channels, allowing heat from the core logic layer to be directly transferred through the silicon pillars, significantly improving cooling capacity.
The widespread adoption of liquid cooling technology is a key path to breaking through the bottleneck of air cooling. Traditional air cooling, due to its low thermal conductivity and high energy consumption, is no longer sufficient for the needs of AI computing modules. Direct liquid cooling involves contacting the chip surface with a cold plate, leveraging the liquid's high specific heat capacity to rapidly absorb heat before transferring it to an external heat sink through a circulation system. For higher-density scenarios, immersion liquid cooling immerses the entire server in a dielectric coolant, achieving uniform heat dissipation across all components. This solution not only eliminates localized hot spots but also reduces system noise and energy consumption by reducing fan usage. Rear-door heat exchangers, a hybrid transition solution, install cooling panels at the rear of the rack. They are compatible with existing air cooling systems and gradually improve heat dissipation efficiency.
Innovations in thermal interface materials offer opportunities for micro-level optimization of heat dissipation design. In AI computing modules, the presence of air in the tiny gaps between the chip and heat sink can cause a surge in thermal resistance. Thermal interface materials fill these gaps, creating a low-resistance thermal conductivity path. Traditional silicone greases are gradually being phased out due to their poor flow and durability, replaced by newer solutions such as liquid metals and phase change materials. Liquid metal, with its high thermal conductivity, can significantly reduce interfacial thermal resistance. Phase-change materials undergo a phase change as temperature rises, filling microscopic bumps and depressions and enabling dynamic thermal adaptation. The use of these materials enables more efficient heat transfer from the chip to the cooling system.
System-level thermal management requires a balanced balance between computing power density and cooling resources. AI computing module deployments often involve thousands of nodes operating in parallel, and overheating of a single node can trigger a chain reaction. Using AI algorithms to monitor each module's temperature in real time and dynamically adjust task allocation and cooling resources is a key measure for ensuring system stability. For example, when training a large language model, the system can prioritize computing tasks to cooler modules while simultaneously adjusting fan speed or liquid cooling flow on nearby nodes, creating a thermally-aware task scheduling mechanism. Furthermore, cabinet-level thermal simulation technology simulates airflow and heat distribution to optimize module layout and air duct design, physically reducing the risk of heat accumulation.
Looking forward, AI computing module cooling design will evolve towards intelligent and sustainable approaches. Intelligent cooling systems integrate temperature, pressure, and flow sensors, combined with machine learning algorithms to predict thermal load fluctuations and automatically adjust cooling parameters. For example, they dynamically switch between liquid and air cooling modes based on chip workload, reducing energy consumption during low-load periods. Regarding sustainability, heat recovery technology utilizes waste heat generated by servers for heating or industrial heating, improving overall energy efficiency. Furthermore, the use of environmentally friendly solutions such as bio-based coolants and biodegradable thermal conductive materials will reduce the environmental impact of cooling systems.
From chip-level micro-cooling to system-level thermal management, the thermal design of AI computing modules has developed a multi-dimensional innovative system encompassing materials, structures, algorithms, and energy. These technological breakthroughs not only address the problem of heat accumulation under high-density computing power but also lay the physical foundation for the large-scale application of AI technology. As computing power demands continue to grow, thermal design will continue to be a core driver of AI hardware development, driving simultaneous improvements in computing efficiency and reliability.
