FlexPie: Accelerate Distributed Inference on Edge Devices with Flexible Combinatorial Optimization[Technical Report]
Fuente:
arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910838123659264 |
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| author | Zhang, Runhua Jiang, Hongxu Geng, Jinkun Ma, Yuhang Zhu, Chenhui Wang, Haojie |
| author_facet | Zhang, Runhua Jiang, Hongxu Geng, Jinkun Ma, Yuhang Zhu, Chenhui Wang, Haojie |
| contents | The rapid advancement of deep learning has catalyzed the development of novel IoT applications, which often deploy pre-trained deep neural network (DNN) models across multiple edge devices for collaborative inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15312 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FlexPie: Accelerate Distributed Inference on Edge Devices with Flexible Combinatorial Optimization[Technical Report] Zhang, Runhua Jiang, Hongxu Geng, Jinkun Ma, Yuhang Zhu, Chenhui Wang, Haojie Distributed, Parallel, and Cluster Computing The rapid advancement of deep learning has catalyzed the development of novel IoT applications, which often deploy pre-trained deep neural network (DNN) models across multiple edge devices for collaborative inference. |
| title | FlexPie: Accelerate Distributed Inference on Edge Devices with Flexible Combinatorial Optimization[Technical Report] |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2502.15312 |