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Computational CXL-Memory Solution for Accelerating Memory-Intensive Applications

This work proposes a novel CXL-based memory disaggregation architecture with a real-world prototype demonstration, which overcomes the bandwidth limitation of the CXL interface using near-data processing.
IEEE (opens in a new tab)

Accelerating Sparse Matrix-Matrix Multiplication on GPUs with Processing Near HBMs

A novel custom near-memory processing approach to optimizing SpGEMM on GPU and the Acceleration of Indirect Memory Access (AIA) technique is presented, a novel custom near-memory processing approach to optimizing SpGEMM on GPU HBM that demonstrates significant performance improvements over state-of-the-art methods.
arXiv (opens in a new tab)

StreamDQ: HBM-Integrated On-the-Fly DeQuantization via Memory Load for Large Language Models

StreamDQ is proposed, a lightweight architectural enhancement for cloud-scale LLM inference that enables on-the-fly dequantization within the memory subsystem by integrating compact DeQuantization Blocks (DQBs) into the base-die of high-bandwidth memory (HBM).
IEEE (opens in a new tab)

Computational CXL-Memory Solution for Accelerating Memory-Intensive Applications

This work proposes a novel CXL-based memory disaggregation architecture with a real-world prototype demonstration, which overcomes the bandwidth limitation of the CXL interface using near-data processing.
IEEE (opens in a new tab)
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