The relentless pursuit of artificial intelligence performance is driving radical innovation in chip design. A key bottleneck in modern AI systems is the speed at which data can move between processing units and memory. Now, researchers are exploring a daring solution: stacking high-bandwidth memory (HBM) directly on top of the GPU itself. But this approach isn’t without significant hurdles, primarily concerning heat dissipation. Recent simulations by Imec, presented at the 2025 IEEE International Electron Device Meeting (IEDM), initially revealed a doubling of operating temperatures – a potentially fatal flaw. However, Imec’s team, led by James Myers, uncovered a series of optimizations that could bring this ambitious design back from the brink.
The Challenge of 3D Chip Stacking: Balancing Performance and Thermal Limits
Current high-performance GPUs, like those from AMD and Nvidia, utilize a “2.5D” packaging approach. The GPU and HBM chips reside side-by-side on an interposer – a silicon substrate containing thousands of microscopic copper interconnects. This minimizes distance and maximizes bandwidth. While effective, this configuration limits future scalability, particularly regarding GPU-to-GPU connections within a single package. 3D stacking, placing HBM directly atop the GPU, promises increased bandwidth, reduced latency, and a smaller footprint, but at a steep thermal cost.
Initial simulations showed a stark reality: simply stacking HBM on the GPU resulted in a GPU temperature soaring to 140°C, far exceeding the typical 80°C limit. This dramatic increase is due to the concentrated heat generated by both the GPU and the stacked memory. However, Imec’s research demonstrates that clever engineering can mitigate these thermal challenges.
Deconstructing the HBM Stack for Thermal Efficiency
A crucial insight stemmed from understanding the internal architecture of HBM. HBM isn’t a single monolithic chip; it’s a stack of up to 12 ultra-thin DRAM dies connected by tiny solder balls. This stack is then connected to a “base die” which acts as a data multiplexer, managing the flow of information to the GPU. But when HBM is stacked directly on the GPU, the base die becomes redundant.
Removing this unnecessary layer of silicon offered a modest initial temperature reduction of less than 4°C. More importantly, it unlocked a significant opportunity to improve bandwidth. Without the base die’s multiplexing function, data could flow directly between the HBM and the GPU, dramatically increasing data transfer rates. This increased bandwidth, in turn, allowed the team to explore another counterintuitive optimization: slowing down the GPU’s clock speed.
Large language models are often “memory-bound,” meaning their performance is limited not by processing power, but by the speed at which data can be accessed from memory. Imec’s simulations showed that the fourfold increase in bandwidth from 3D stacking could offset a 50% reduction in GPU clock speed, resulting in an overall performance gain while simultaneously lowering temperatures by over 20°C. Further refinement showed that even increasing the clock frequency to 70% only resulted in a 1.7°C temperature increase.
Optimizing HBM Architecture and Cooling
Beyond eliminating the base die and adjusting clock speeds, Imec explored further optimizations to the HBM stack itself. Merging four HBM stacks into two wider stacks eliminated heat-trapping regions. Thinning the top die of the stack and filling surrounding space with thermally conductive silicon further improved heat dissipation. These changes brought the operating temperature down to approximately 88°C.
The final, critical step involved enhancing cooling. While current AI data centers increasingly rely on liquid cooling to remove heat from the top of the package, Imec found that adding similar cooling to the underside of the stacked chips resulted in a final temperature drop of 17°C, bringing the overall operating temperature back to around 70°C – a safe and sustainable level.
Did You Know?
While these simulations demonstrate the feasibility of HBM-on-GPU stacking, Myers emphasizes that it’s not necessarily the optimal solution. “We are simulating other system configurations to help build confidence that this is or isn’t the best choice,” he explains. An alternative approach, “GPU-on-HBM,” places the GPU beneath the HBM, potentially bringing it closer to the cooling solution, but at the cost of increased design complexity.
What impact will these advancements in chip packaging have on the future of AI-powered applications? And how will the industry balance the demands of performance with the challenges of thermal management?
Frequently Asked Questions About 3D Chip Stacking
What is the primary challenge of stacking HBM on top of a GPU?
The primary challenge is managing the significant increase in heat generated by concentrating both the GPU and HBM in a smaller space. Initial simulations showed temperatures exceeding safe operating limits.
How did Imec address the overheating issue with HBM stacking?
Imec employed several optimizations, including removing a redundant silicon layer, slowing down the GPU clock speed (leveraging increased bandwidth), optimizing the HBM stack architecture, and enhancing cooling on both sides of the package.
What is the role of the “base die” in traditional HBM configurations?
The base die acts as a data multiplexer, managing the flow of information between the HBM stack and the GPU. However, it becomes unnecessary when HBM is stacked directly on the GPU, allowing for a more direct data path.
What is meant by a “memory-bound” problem in the context of AI computing?
A memory-bound problem is one where the performance is limited not by the processing power of the GPU, but by the speed at which data can be accessed from memory. Increasing memory bandwidth is crucial for improving performance in these scenarios.
Is HBM-on-GPU stacking the only potential solution for improving chip packaging?
No, another approach being explored is “GPU-on-HBM” stacking, which places the GPU beneath the HBM. While potentially offering better cooling, it presents greater design complexities.
What is an interposer and how does it relate to 2.5D chip packaging?
An interposer is a silicon substrate containing microscopic interconnects that connect the GPU and HBM chips in 2.5D packaging. It allows for close proximity and high bandwidth communication between the chips.
Disclaimer: This article provides information for educational purposes only and should not be considered professional advice. Consult with qualified experts for specific applications.
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