The Heat is On: Can 3D Chip Stacking Unlock the Next Generation of AI Performance?
The relentless demand for more processing power in artificial intelligence is pushing chip designers to the absolute limits of what’s physically possible. A recent exploration by Imec, detailed at the 2025 IEEE International Electron Device Meeting (IEDM), reveals a critical challenge: simply stacking High-Bandwidth Memory (HBM) directly on top of GPUs – a move promising massive performance gains – initially doubles the operating temperature, rendering the system unusable. But the story doesn’t end there. Imec’s research demonstrates that with clever engineering, this thermal bottleneck isn’t insurmountable, potentially paving the way for a new era of AI chip architecture.
The 2.5D Status Quo and Its Limitations
Today’s most advanced AI accelerators, like those from AMD and Nvidia, rely on a 2.5D packaging approach. The GPU and HBM chips sit side-by-side on an interposer – a silicon substrate packed with thousands of tiny copper interconnects. This proximity minimizes the distance data needs to travel, crucial for reducing latency and maximizing bandwidth. Current systems, cooled by increasingly sophisticated liquid cooling solutions, typically operate around 70°C. However, this configuration isn’t scalable. As Yukai Chen, a senior researcher at Imec, explained, it “blocks two sides of the GPU, limiting future GPU-to-GPU connections inside the package,” hindering further performance improvements.
The Allure – and Initial Failure – of 3D Stacking
3D stacking, placing the HBM directly on top of the GPU, offers a tantalizing solution. It promises even shorter data paths, lower latency, and a significantly smaller package footprint. However, the initial simulations were alarming. A straightforward stacking approach resulted in a GPU temperature soaring to 140°C – far beyond its operational limits. This dramatic temperature increase stems from the concentrated heat generated by both the GPU and HBM in a much smaller volume.
Engineering a Cooler Future: Imec’s Optimizations
Imec’s team didn’t abandon the 3D stacking concept. Instead, they systematically explored a range of optimizations. One key insight revolved around the architecture of HBM itself. HBM isn’t a single monolithic chip; it’s a stack of up to 12 ultra-thin DRAM dies connected by solder balls, sitting atop a ‘base die’ that manages data flow to the GPU.
Eliminating Redundancy: The Base Die’s Role
With HBM directly integrated onto the GPU, the base die becomes redundant. Bits can flow directly into the processor, bypassing the need for a data ‘pump.’ Removing this layer shaved off a modest 4°C, but more importantly, it freed up space and bandwidth. This bandwidth boost was then leveraged in a counterintuitive, yet effective, optimization: slowing down the GPU.
The Power of Bandwidth: Trading Speed for Efficiency
Large language models (LLMs) are often “memory bound,” meaning their performance is limited by the speed at which data can be accessed from memory. 3D stacking is projected to quadruple memory bandwidth. With this massive increase, Imec found that reducing the GPU clock speed by 50% still resulted in a performance win, while simultaneously lowering the temperature by over 20°C. Even a smaller reduction, increasing the clock frequency to 70%, only increased the temperature by 1.7°C.
Optimizing the HBM Stack Itself
Further temperature reductions came from optimizing the HBM stack’s physical design. Merging four stacks into two wider stacks eliminated heat-trapping regions. Thinning the top die of the stack and filling surrounding space with thermally conductive silicon also contributed to improved heat dissipation. Finally, adding cooling to the underside of the stack, in addition to the standard top-side cooling, brought the temperature down to around 70°C – a viable operating range.
Beyond HBM-on-GPU: Exploring Alternative Architectures
While Imec’s research demonstrates the feasibility of HBM-on-GPU, it’s not necessarily the optimal solution. The team is actively exploring other configurations, including “GPU-on-HBM,” which would place the GPU directly on top of the HBM stack. This approach could bring the GPU closer to the cooling solution, but presents its own design complexities, requiring power and data to flow vertically through the HBM.
The future of AI chip design is undoubtedly complex, demanding innovative approaches to overcome thermal limitations. Imec’s work highlights the critical interplay between chip architecture, thermal management, and software optimization. As AI models continue to grow in size and complexity, these advancements will be essential to unlocking the next generation of performance. What are your predictions for the future of chip stacking and AI acceleration? Share your thoughts in the comments below!