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English(EN) Accelerating Unified Multimodal Models with Core-Expansion Routing and Unified Computation Scheduling

新的CE-Router方法加速统一多模态模型

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为CE-Router的新方法，通过优化计算来加速统一多模态模型（UMMs）。该方法识别出一种非对称的核心扩展结构，将用于理解的稳定重要性组件与用于生成的依赖于进度的修正分开。CE-Router利用共享的核心评分器和特定于生成的扩展，并与统一计算调度协调，以在推理过程中实现高效的层跳过、剪枝和提前退出。实验表明，在性能损失极小的情况下，速度得到了显著提升。 AI

影响 该方法可能导致多模态人工智能系统更高效、更快速的推理。

排序理由 这是一篇详细介绍加速AI模型新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 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CE-Router方法加速统一多模态模型

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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wengyi Zhan, Chenqian Yan, Songwei Liu, Mingbao Lin, Rongrong Ji ·

    加速统一多模态模型：核心扩展路由与统一计算调度

    arXiv:2608.29291v1 Announce Type: new Abstract: Unified multimodal models jointly support understanding and generation, but incur substantial redundant computation across tokens, layers, and generation timesteps. Through token-importance probing, we identify an asymmetric core-ex…

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