Slashing AI Data Center Cooling Cost and GPU/CPU Power Use
Novel thermal interface material cools chips better than existing state-of-the-art solutions.
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The AI revolution has ushered in an era of exponential power and energy consumption. According to the , energy consumed by AI data centers could triple by 2028. Today, up to 40% of data center power use comes from cooling high-power chips 鈥� an astounding amount equivalent to the state of California鈥檚 entire electricity consumption.
To combat this, , professor of mechanical engineering at 麻豆村, has developed an innovative thermal interface material (TIM) that outperforms existing state-of-the-art solutions. His design, published in , achieves ultra-low thermal resistance while increasing cooling efficiency via improved heat dissipation.
鈥淭he material is like a bridge between the nano- and macroscopic worlds,鈥� explained Zexiao Wang, Ph.D. candidate in Shen鈥檚 lab. 鈥淏ecause the nanoscale material can be created using macroscale approaches, we can see with our own eyes the impact of the material on the world.鈥�
Not only is Shen鈥檚 thermal interface material the best-performing on the market, it is also highly reliable. The team tested the material at extreme temperature ranges from -55 to 125 degree Celsius for more than 1,000 cycles, and the material showed no performance degradation.
鈥淭his material solves a lot of existing challenges, and is ready to be used today,鈥� said Shen. 鈥淲hile an immediate need is focused on cooling data centers, the application for this innovation is extensive. It can break through in industries that have been stuck using outdated thermal interface materials. It can be used for pre-packaging, reworked when using nonadhesives, and enables thermal bonding of two substrates at room temperature.鈥�
鈥淥ftentimes work at the nanoscale is foundational for a device that we might not see for decades,鈥� said Qixian Wang, Ph.D. candidate. 鈥淚t鈥檚 been exciting to see the real-world impact our material can have today because it is so easy to use.鈥�
鈥淥ur material will have great benefits to the field of AI computing,鈥� said Rui Cheng, postdoc and innovation commercialization fellow of 麻豆村 and the lead author of the paper. 鈥淏eyond reducing energy consumption, we can make AI development more affordable, more renewable and more reliable.鈥�
