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English(EN) Relay Buffer Independent Communication over Pooled HBM for Efficient MoE Inference on Ascend

新的MoE推理设计利用汇聚式HBM降低Ascend上的通信延迟

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员为Ascend系统上的混合专家（MoE）推理开发了一种新的通信设计，旨在减少令牌交换中的瓶颈。该方法通过直接将数据放入目标专家窗口并从远程窗口读取，消除了中间中继和重新排序缓冲区。该系统利用全局汇聚式高带宽内存（HBM）和对称内存分配，从而提高了MoE工作负载的首个令牌时间和每输出令牌的竞争力。 AI

影响 这项研究可能导致在特定硬件平台上对大型MoE模型进行更高效的推理。

排序理由 这是一篇详细介绍MoE推理优化新颖技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的MoE推理设计利用汇聚式HBM降低Ascend上的通信延迟

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这是一篇详细介绍MoE推理优化新颖技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tianlun Hu, Tiancheng Hu, Shengsheng Litang, Sheng Wang, Xiaoming Bao, Yuxing Li, Wei Wang, Zhongzhe Hu, Lijun Li, Hongwei Sun, Jingbin Zhou\\ ·

    基于Ascend的HBM池化独立通信缓冲器，实现高效MoE推理

    arXiv:2605.06055v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) inference requires large-scale token exchange across devices, making dispatch and combine major bottlenecks in both prefill and decode. Beyond network transfer, routing-driven layout transformation, tempor…

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