English(EN) Hardware-Algorithm Co-Optimization of Early-Exit Neural Networks for Multi-Core Edge Accelerators

新框架协同优化边缘设备的神经网络和硬件

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了一个新颖的框架，专门用于优化多核边缘加速器的早退神经网络（EENNs）。该框架联合优化网络架构、量化和硬件部署，以提高能效并降低延迟。通过将这些方面整合到统一的神经架构搜索（NAS）过程中，该系统可以识别高效的工作负载映射并提供准确的性能估算。在CIFAR-10上的实验表明，与静态基线相比，能耗-延迟乘积显著降低，凸显了动态推理在边缘设备上协同设计的重要性。 AI

影响这项研究可能带来更节能、更快速的边缘设备AI部署，从而在实时处理和物联网等领域实现新应用。

排序理由该集群包含一篇学术论文，详细介绍了一种优化边缘设备神经网络的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Alaa Zniber, Arne Symons, Ouassim Karrakchou, Marian Verhelst, Mounir Ghogho · 2026-10-02 04:00

面向多核边缘加速器的早退神经网络的硬件-算法协同优化

arXiv:2512.04705v3 Announce Type: replace-cross Abstract: The deployment of Early-Exiting Neural Networks (EENNs) on edge accelerators requires optimizing not only the network architecture but also its hardware deployment. Exit configuration, quantization, and hardware workload m…

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面向多核边缘加速器的早退神经网络的硬件-算法协同优化

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