why AI chips need advance cooling?
Introduction
Artificial intelligence chip refer to a new generation of microprocessors designed to efficiently accelerate AI task manager while reducing data center energy consumption. As their high density computing continues to increase, traditional cooling methods are no longer sufficient, making integrated thermal management essential. These solutions combine advances in materials with system-level optimization. Currently, AI chips play a critical role in emerging fields, with their adoption expanding across autonomous vehicle, intelligent home systems, robotics, and other advanced technologies.he rapid commercialization of AI accelerators has also influenced capital markets, where companies leading in GPU and AI chip innovation are closely tracked through indicators such as nvidia stock, reflecting broader industry confidence in AI infrastructure growth. In addition, AI accelerators are driving market expansion in ai data center by enabling high-speed computing, system integration, and increased bandwidth. The complexity and contradiction in architecture of these systems require effective cooling solutions to dissipate heat, ensuring that performance, ai power consumption, and thermal constraints are jointly optimized across the entire computer stack. An advance cooling system helps maintain stable operating temperature and significantly improves overall efficiency of the system.
Why AI Chips Generate So Much Heat
what is a data center
AI chips deployed in ai data centers generate significantly more heat than traditional processors due to their high density computing and intensive workloads. Data centers serve as the backbone of modern computer network infrastructure, responsible for collecting, storing, and processing large volumes of data for a wide range of applications. As the demand for remote data services continues to grow, both computational workloads and energy consumption in ai data centers have increased rapidly. A major contributor to this heat-generating is the architectural design of ai accelerator, particularly GPUs. GPUs feature a highly parallel architecture with a large number of core process, enabling efficient execution of data-parallel tasks commonly used in artificial intelligence and high-performance computing. In contrast, CPUs allocate more silicon area to control logic and registers, while GPUs integrate a higher concentration of arithmetic logic unit (ALUs) to maximize parallel computing. This design significantly increases power density, resulting in higher generating heat.
Consequently, thermal expansion has become a critical challenge in data center cooling. Cooling system service are essential for maintaining appropriate temperature operation and ensuring reliable chip performance, accounting for approximately 30% of total data center energy consumption. Among various cooling approaches, air cooling remains the most widely adopted solution in data centers due to its relatively low operating cost and mature infrastructure.
Chiplet technology
In order to meet high usage of ai chip, more and more circuits embed in chips. Meanwhile, CPU and GPU die sizes grew annually by about 14% and 8%, respectively, during the period from 2006 to 2020[1]. An increase in size die is accompanied by a decrease in yield, leading to high cost. Producing microchips and integrate them is a way to keep production scale. Traditionally, multiple chip module are connected using printed circuit boards design(PCBs). However, PCB-based materials struggle to support high-density inter-chip connections due to limitations such as warpage, large solder bump pitch, and long interconnect lengths, which increase power consumption and limit signal frequency [2]. To address these challenges while sustaining performance scaling at reasonable cost, chiplet technology partitions a monolithic chip into smaller dies and integrates them into a system in package (SiP), enabling improved scalability and reduced manufacturing cost. Chiplet-based many-core systems rely heavily on interconnection network to enable efficient data exchange both inside individual chiplets and across multiple chiplets. Performance improvements in multi-core architectures have been achieved by integrating more mosfet transistor within a constrained chip area. However, as transistor dimensions decrease, efficiency in power drops significantly, which has a substantial impact on the performance of multi-core architectures. The increasing compute density and dense integration of multiple chiplets significantly elevate local power density, leading to severe thermal hotspots and strong thermal coupling within the package. As a result, efficient thermal management becomes a critical challenge in chiplet-based accelerator design.
Advanced Process Nodes
In chiplet-based systems, dense on-package interconnects and hbm memory communication fabrics intensify effects of crosstalk noise become increasingly critical, making signal and power integrity a key design concern at both the chip and package levels[3]. Crosstalk arises from capacitive coupling between adjacent interconnects, where switching activity on an aggressor net induces unwanted voltage disturbances on a neighboring victim net. As chiplets continue to scale in integral density and operating frequency, mitigating crosstalk while maintaining power efficiency and thermal reliability requires careful co-design of interconnect structures, materials, and packaging technologies.
Consequences of Inadequate Cooling
When the placement spans a large number of regions, more resources across the chip are involved, leading to increased routing overhead. This overhead results in longer clock and signal interconnects, higher routing capacitance, and increased leakage current. The operating speed of AI chips is a key metric for enabling simultaneous processing of multiple task. In chip design, achieving high performance computing and low response latency is critical. Insufficient cooling capability can significantly degrade performance, prolonging AI training time and ultimately slowing the pace of technological iteration. Further more, Component lifespan is strongly dependent on thermal state. Under long-term heavy workload, insufficient heat removal leads to thermal accumulation, increasing temperature of junction. Elevated temperature degrades performance management systems and accelerates temperature-dependent failure mechanisms, thereby reducing device lifetime. This thermal sensitivity is not limited to processors alone; it also impacts power delivery subsystems and energy storage units, where effective thermal management plays a crucial role in preserving battery lifespan and ensuring long-term system reliability.
Advanced Cooling Technologies
Air cooling technology
Conventional data center air cooling suffer from several inherent limitations. Due to the low convective heat transfer coefficient of air, significant temperature rise within the data center leads to large temperature gradients across servers. Moreover, inadequate airflow management allows hot exhaust air from ICT equipment to mix with supplied cold air, resulting in localized hotspots within and around server rack.
Despite these limitations, fan-based air cooling remains the most widely adopted cooling technique for CPUs. However, liquid cooling solutions employing micro heat exchanger–based heat sinks provide higher heat removal rate efficiency and have emerged as viable alternatives for high performance processors. Hotspots can be mitigated through airflow regulation at the cooling level, as well as workload management techniques at the ICT level, including load balancing, workload migration, and dynamic component deactivation.
Liquid cooling technology
Liquid cooling is suit for high performance equipment, which can be implement direct and indirect. Indirect liquid cooling dissipates heat through liquid-cooled heat sinks without direct contact between the coolant and the heat sources. While cold plate and water block represent conventional indirect liquid cooling approaches, recent studies prioritize microchannel heat sinks because of their significantly enhanced heat transfer characteristics. Liquid cooling leverages coolants with superior thermophysical properties to mitigate thermal constraints and lower data center energy consumption. However, this approach compromises system flexibility, as it requires sealed enclosures and additional piping infrastructure at both the rack and server levels[4]. In direct liquid cooling method customize system according to facility layout and special requirements. In direct liquid cooling systems, dielectric liquid coolant are brought into direct contact with electronic components pcb, providing effective heat removal while maintaining electrical insulation. This approach adapt in many scenario. On the other hand,the absence of sealed enclosures lowers maintenance requirements and enables support for plug-in boards.
Role of Packaging and Substrate in Thermal Management
Ceramic substrates are widely applied in microelectronics packaging, sensing, actuation, and passive components.
AlN substrate, in particular, exhibit high conduction thermal and a coefficient of thermal expansion (CTE) that closely matches that of silicon. This thermal expansion compatibility significantly reduces thermally induced mechanical stress at the chip–substrate interface during high-power operation and thermal cycling. Consequently, ceramic substrates help mitigate cracking, delamination, and solder joint fatigue, thereby improving long-term reliability under sustained AI workload.
Despite its long industrial history, silicon carbide has only recently become a leading semiconductor materials for high power and extreme-environment applications. Among SiC power modules, MOSFETs are regarded as strategically important due to their majority-carrier conduction and low gate current capability at voltage rating close to 5 kV[5]. silicon carbide (SiC) offers a combination of high thermal conductivity materials and strong thermal shock resistance, making it suitable for high heat-flux environments. Its thermal conductivity remains high over a wide temperature range and exhibits uniform behavior across samples, indicating good material homogeneity. Owing to its high strength, low thermal expansion, and favorable elastic properties, SiC demonstrates superior resistance to thermal shock compared with most conventional materials. Compared to diamond, SiC provides a more practical balance between performance, cost, and manufacturability, making it a preferred choice for high-power AI-related hardware and advanced l components.
