Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.05363 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911983724396544 |
|---|---|
| author | Wu, Chao Gong, Yifan Liu, Liangkai Li, Mengquan Wu, Yushu Shen, Xuan Li, Zhimin Yuan, Geng Shi, Weisong Wang, Yanzhi |
| author_facet | Wu, Chao Gong, Yifan Liu, Liangkai Li, Mengquan Wu, Yushu Shen, Xuan Li, Zhimin Yuan, Geng Shi, Weisong Wang, Yanzhi |
| contents | Object detection on the edge (Edge-OD) is in growing demand thanks to its ever-broad application prospects. However, the development of this field is rigorously restricted by the deployment dilemma of simultaneously achieving high accuracy, excellent power efficiency, and meeting strict real-time requirements. To tackle this dilemma, we propose AyE-Edge, the first-of-this-kind development tool that explores automated algorithm-device deployment space search to realize Accurate yet power-Efficient real-time object detection on the Edge. Through a collaborative exploration of keyframe selection, CPU-GPU configuration, and DNN pruning strategy, AyE-Edge excels in extensive real-world experiments conducted on a mobile device. The results consistently demonstrate AyE-Edge's effectiveness, realizing outstanding real-time performance, detection accuracy, and notably, a remarkable 96.7% reduction in power consumption, compared to state-of-the-art (SOTA) competitors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05363 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AyE-Edge: Automated Deployment Space Search Empowering Accuracy yet Efficient Real-Time Object Detection on the Edge Wu, Chao Gong, Yifan Liu, Liangkai Li, Mengquan Wu, Yushu Shen, Xuan Li, Zhimin Yuan, Geng Shi, Weisong Wang, Yanzhi Computer Vision and Pattern Recognition Object detection on the edge (Edge-OD) is in growing demand thanks to its ever-broad application prospects. However, the development of this field is rigorously restricted by the deployment dilemma of simultaneously achieving high accuracy, excellent power efficiency, and meeting strict real-time requirements. To tackle this dilemma, we propose AyE-Edge, the first-of-this-kind development tool that explores automated algorithm-device deployment space search to realize Accurate yet power-Efficient real-time object detection on the Edge. Through a collaborative exploration of keyframe selection, CPU-GPU configuration, and DNN pruning strategy, AyE-Edge excels in extensive real-world experiments conducted on a mobile device. The results consistently demonstrate AyE-Edge's effectiveness, realizing outstanding real-time performance, detection accuracy, and notably, a remarkable 96.7% reduction in power consumption, compared to state-of-the-art (SOTA) competitors. |
| title | AyE-Edge: Automated Deployment Space Search Empowering Accuracy yet Efficient Real-Time Object Detection on the Edge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.05363 |