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English(EN) Going Beyond the Edge: Distributed Inference of Transformer Models on Ultra-Low-Power Wireless Devices

CATS 框架支持低功耗无线设备上的分布式 Transformer 推理

研究人员开发了 CATS 框架,支持在多个超低功耗无线设备上分布式推理大型 Transformer 模型。这种方法允许设备协同运行比单个设备能处理的模型大得多的模型,实验中模型大小可达 14 倍。CATS 利用一种名为 SomeGather 的新通信原语来减少带宽和内存使用,并采用一种能提高对不可靠无线连接鲁棒性的训练方法。 AI

影响 使先进的 AI 模型能够部署在资源受限的物联网设备上,将 AI 的应用范围扩展到新的领域。

排序理由 该集群包含一篇学术论文,详细介绍了用于 Transformer 模型分布式推理的新框架。[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 →

CATS 框架支持低功耗无线设备上的分布式 Transformer 推理

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该集群包含一篇学术论文,详细介绍了用于 Transformer 模型分布式推理的新框架。[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) · Sebastian Trimpe ·

    超越边缘:超低功耗无线设备上的Transformer模型分布式推理

    Transformer models are rapidly becoming a cornerstone of modern Internet of Things (IoT) applications, yet their computational and memory demands far exceed the capabilities of a single typical ultra-low-power IoT device. We present CATS, a framework for distributed transformer i…