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English(EN) CREST: Deployment-Realistic Hardware-in-the-Loop NAS for Embedded Sensing Systems

CREST框架为低功耗嵌入式系统优化AI模型

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了CREST，一个专为低功耗嵌入式传感系统设计的硬件在环神经架构搜索（NAS）框架。该框架通过考虑内存、延迟、能耗限制和传感调度等现实部署因素，而非仅仅依赖FLOPs或参数等静态代理，来解决现有方法的局限性。在Arm Cortex-M目标上的评估表明，与传统的NAS方法相比，CREST能够显著降低能耗并识别出更合适的模型架构。 AI

影响 能够更有效地在资源受限的嵌入式设备上部署AI模型。

排序理由 详细介绍一种用于嵌入式系统神经架构搜索新框架的研究论文。[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 →

CREST框架为低功耗嵌入式系统优化AI模型

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详细介绍一种用于嵌入式系统神经架构搜索新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Joseph Q. Zales, Pragya Sharma, Mani Srivastava ·

    CREST：面向嵌入式传感系统的部署现实硬件在环NAS

    arXiv:2606.15004v1 Announce Type: cross Abstract: Deploying neural networks on low-power microcontrollers (MCUs) requires selecting model architectures under tight memory, latency, and energy constraints. Existing workflows often simplify this process along one or more axes: stat…

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