Bibliographic Details
| Title: |
AyE-Edge: Automated Deployment Space Search Empowering Accuracy yet Efficient Real-Time Object Detection on the Edge |
| Authors: |
Wu, Chao, Gong, Yifan, Liu, Liangkai, Li, Mengquan, Wu, Yushu, Shen, Xuan, Li, Zhimin, Yuan, Geng, Shi, Weisong, Wang, Yanzhi |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2408.05363 |
| Accession Number: |
edsarx.2408.05363 |
| Database: |
arXiv |
