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Home > Home > 3D Integration Reduces Embodied-Emissions Impact Of GPU Scaling (imec, VUB)

tag: high-bandwidth memory

3D Integration Reduces Embodied-Emissions Impact Of GPU Scaling (imec, VUB)

By Technical Paper Link - 02 Nov, 2026 - Comments: 0

Researchers at imec and Vrije Universiteit Brussels published “Embodied Emissions of 2.5D and 3D Scaling in Chipletized GPUs for Low-Carbon AI.” Abstract "GPU compute resources are scaling to support growing AI workloads, leading to further rises in the climate change impact of hyperscale data centers that rely on high-performance GPUs. To model how packaging decisions impact the carbon... » read more

Vertical DRAM Stacking Expands Thermal Design Space Beyond HBM-on-GPU (imec)

By Technical Paper Link - 26 Oct, 2026 - Comments: 0

Researchers at imec published “Beyond HBM-on-GPU: Thermal Design Envelope for 3D Volumetric DRAM-on-GPU Integration.” Summary: Researchers evaluated a 3D memory architecture that rotates DRAM dies vertically above a GPU and places cooling cavities between them as an alternative to conventional 2.5D HBM and direct-stacked HBM-on-GPU. Package-level thermal simulations examined stack heigh... » read more

Characterization of HBM Power Consumption (ETH Zurich, NYU)

By Technical Paper Link - 14 Oct, 2026 - Comments: 0

Researchers at ETH Zürich and New York University published a technical paper titled “Understanding the Power Consumption of Modern High Bandwidth Memory: Experimental Analysis and Modeling Using Real HBM2 DRAM Chips." Abstract Excerpt: “We perform the first comprehensive experimental characterization of power consumed by real HBM2 DRAM chips. Our extensive characterization yields 1)... » read more

HBF for High-Throughput LLM Serving (UC Berkeley, FuriosaAI)

By Technical Paper Link - 02 Oct, 2026 - Comments: 0

Researchers at the UC Berkeley and FuriosaAI published a technical paper titled “Characterizing High Bandwidth Flash for LLM Serving.” Abstract “Large language model (LLM) serving requires substantial memory to store model weights and KV caches. As models grow larger and contexts become longer, memory capacity and bandwidth increasingly become bottlenecks for serving performance... » read more

Concurrent HBM And Host Memory Access Improves LLM Inference Throughput (Georgia Tech, Nvidia, Stanford)

By Technical Paper Link - 21 Sep, 2026 - Comments: 0

Researchers at Georgia Tech, Nvidia Research, and Stanford University published a technical paper titled “BOOST: Concurrent Access to Host Memory and HBM to Accelerate LLM Inference.” Abstract Excerpt: “This paper presents BOOST, the first runtime system that provides concurrent and proportional access to both GPU memory tiers, extracting the combined bandwidth of host m... » read more

Reducing HBM ECC Controller Overhead For AI Inference (RPI, IBM)

By Technical Paper Link - 11 Sep, 2026 - Comments: 0

Researchers at Rensselaer Polytechnic Institute and IBM T.J. Watson Research Center published a technical paper titled “REACH: Controller-Managed Long-Span ECC for HBM AI Inference.” Abstract Excerpt: “High-Bandwidth Memory (HBM) cost motivates stronger controller protection that can support a wider range of device error rates.High-Bandwidth Memory (HBM) cost motivates stro... » read more

Workload-Driven HBF Substrate For Capacity-Scalable LLM Inference (Huawei, ETH Zurich, HUST)

By Technical Paper Link - 31 Aug, 2026 - Comments: 0

Researchers at Huawei, ETH Zürich, and HUST published a technical paper titled “FLINT: Efficiently Leveraging High Bandwidth Flash for Capacity-Scalable LLM Inference Acceleration.” Abstract: “LLM inference is increasingly constrained by accelerator memory capacity rather than compute throughput. This constraint is especially acute in single-accelerator and small-node i... » read more

Hybrid HBM-HBF Architecture in LLM Inference (University of Oxford)

By Technical Paper Link - 30 Aug, 2026 - Comments: 0

Researchers at the University of Oxford published a technical paper titled “Hardware-Managed Heterogeneous High-Bandwidth Memory and Flash in LLM Inference Systems.” Abstract Excerpt: “ High-Bandwidth Flash (HBF) offers a denser alternative, providing 16x more capacity per stack at comparable bandwidth. In this work, we show that while replacing HBM with HBF can address the capac... » read more

Issues Stack Up With More HBM Layers

By Ed Sperling - 27 Aug, 2026 - Comments: 0

Key Takeaways: HBM scaling is running into physical limitations. More bandwidth translates into higher area and capacity costs. AI demand is intensifying the DRAM shortage. DRAM is under stress, and the fallout is affecting the entire chip industry. Pressed by large systems companies to move data in and out of memory faster, the big three DRAM vendors — Micron, Samsung,... » read more

Near-memory Dequantization Architecture In Custom HBM for LLM inference (SK hynix)

By Technical Paper Link - 13 Jul, 2026 - Comments: 0

Researchers from SK hynix published a technical paper titled “StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration.” The paper proposes StreamDQ for “a lightweight architectural enhancement that enables on-the-fly dequantization in the memory subsystem for high-throughput, large-batch LLM inference,” and reports “up to 7.08× speedup and ... » read more

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