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English(EN) EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI

EdgeCompress框架为边缘设备大幅削减CNN计算量

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了EdgeCompress，一个新颖的框架，旨在显著降低卷积神经网络（CNN）的计算需求，以便在资源受限的边缘设备上部署。该框架采用动态图像裁剪来将计算集中在显著的前景对象上，并采用复合收缩技术协同压缩网络深度、宽度和分辨率。此外，EdgeCompress利用动态推理方法，将不同复杂度的模型级联起来，根据输入的识别难度自适应地处理输入，从而进一步提高效率。 AI

影响 支持在嵌入式硬件上部署先进的CNN，提高推理效率。

排序理由 该集群包含一篇详细介绍模型压缩新方法的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

EdgeCompress框架为边缘设备大幅削减CNN计算量

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Hao Kong, Di Liu, Shuo Huai, Xiangzhong Luo, Ravi Subramaniam, Christian Makaya, Qian Lin, Weichen Liu ·

    EdgeCompress：多维模型压缩与动态推理的边缘AI耦合

    arXiv:2607.06982v1 Announce Type: cross Abstract: Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of CNNs hinders the deployment of CNNs onto resource-constrained embedded device…

  2. arXiv cs.CV TIER_1 English(EN) · Weichen Liu ·

    EdgeCompress：多维模型压缩与动态推理的边缘AI耦合

    Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of CNNs hinders the deployment of CNNs onto resource-constrained embedded devices. To address this issue, we propose EdgeCompress,…

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