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English(EN) A 25-$\mu$s/inf Event-driven Graph Neural Network Processor with Spatiotemporal Caching and Spline Convolution for Ultra-low-latency AI at the Edge

新型 EV-GNN 加速器在边缘实现 25μs 延迟

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 ETHEREAL，这是一种新颖的事件驱动图神经网络 (EV-GNN) 加速器，专为边缘的超低延迟人工智能处理而设计。该系统解决了处理来自动态视觉传感器 (DVS) 摄像机的数据所带来的挑战，这些摄像机以高时间分辨率生成事件。ETHEREAL 利用邻域并行三次样条卷积引擎和具有时空缓存的专用内存层次结构，实现了低至 25.6 微秒的端到端推理延迟和每事件 1.7 微焦耳的能耗。 AI

影响 通过显著降低延迟和功耗，在边缘实现实时人工智能应用。

排序理由 该集群包含一篇详细介绍新型人工智能处理硬件加速器的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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

新型 EV-GNN 加速器在边缘实现 25μs 延迟

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该集群包含一篇详细介绍新型人工智能处理硬件加速器的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Adrian Kneip, Martin Lefebvre, Daniel Gehrig, Victoria Catal\'an Pastor, Davide Scaramuzza, Marian Verhelst, Charlotte Frenkel ·

    一款具有时空缓存和样条卷积的 25-μs/inf 事件驱动图神经网络处理器，用于超低延迟边缘 AI

    arXiv:2609.15241v1 Announce Type: new Abstract: Dynamic-vision-sensor (DVS) cameras generate events on a per-pixel basis with a $\mu$s-level temporal resolution, calling for new algorithm-hardware co-design approaches compared to standard frame-based vision. While event-driven gr…

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