English(EN) VLA-HWBench: Where the Time Goes When VLA Models Run on Edge Hardware

新基准测试揭示边缘VLA模型的延迟瓶颈

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

一项新的基准测试VLA-HWBench已被开发出来，用于分析视觉-语言-动作（VLA）模型在边缘硬件上的性能。研究发现，动作生成策略对延迟的影响比模型大小更大，某些策略会导致延迟变化超过150倍。使用4位权重进行内核融合可以显著提速，但尚未广泛应用，并且优化工作可能会将瓶颈转移到CPU预处理等其他阶段。最终，要实现低延迟的边缘VLA性能，需要高效的动作生成、更广泛地采用推理优化以及具有高内存带宽和快速主机CPU的硬件。 AI

影响识别边缘AI推理的关键瓶颈，指导硬件和软件优化，以实现实时机器人应用。

排序理由该集群包含一篇学术论文，详细介绍了新的基准测试以及对边缘硬件上AI模型性能的分析。[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 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Borui Xu, Hongshi Tan, Wenqi Jiang, Bingsheng He, Ming Wu, Jason Zeng, Michael Heinrich · 2026-10-27 04:00

VLA-HWBench：VLA模型在边缘硬件上运行时时间去哪儿了

arXiv:2610.34717v1 Announce Type: cross Abstract: Vision--language--action (VLA) models increasingly run on edge devices aboard robots, where inference latency constrains closed-loop robot control. However, it remains unclear how to design VLA models, their inference software and…

本文如何被排名

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VLA-HWBench：VLA模型在边缘硬件上运行时时间去哪儿了

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