English(EN) Sustainable Edge Vision via Empirically Calibrated DVFS: Eliminating Thermal Throttling on Passively Cooled Hardware

新的DVFS调度器消除了被动冷却硬件上边缘AI的热节流

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员开发了一种新的动态电压和频率缩放（DVFS）调度器，旨在防止运行深度神经网络的被动冷却边缘设备出现热节流。该调度器利用时域保护和绝对温度边界来管理热量，通过在每帧功耗更低的情况下实现更高的帧率，其性能优于仅基于温度的基线。虽然主动冷却仍能提供更高的吞吐量，但所提出的方法表明，在特定的运行范围内，可以无需机械冷却即可实现持续的边缘推理。 AI

影响使得在无主动冷却的低功耗边缘设备上实现更可持续、更可靠的AI推理成为可能。

排序理由学术论文，详细介绍了一种解决特定问题的新技术方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Aayush Marasini, Zhaoxian Zhou · 2026-09-07 04:00

通过经验校准的DVFS实现可持续边缘视觉：消除被动冷却硬件上的热节流

arXiv:2609.04705v1 Announce Type: cross Abstract: Passive cooling eliminates the energy overhead and mechanical failure modes of fans, making it attractive for edge deployment, yet sustained Deep Neural Network (DNN) inference on passively cooled edge Systems-on-Chip (SoCs) is bo…

本文如何被排名

报道来源 [1]

通过经验校准的DVFS实现可持续边缘视觉：消除被动冷却硬件上的热节流

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