AyE-Edge: Automated Deployment Space Search Empowering Accuracy yet Efficient Real-Time Object Detection on the Edge

• URL: http://arxiv.org/abs/2408.05363v1
• Date: Thu, 25 Jul 2024 16:17:08 GMT
• Title: AyE-Edge: Automated Deployment Space Search Empowering Accuracy yet Efficient Real-Time Object Detection on the Edge
• Authors: Chao Wu, Yifan Gong, Liangkai Liu, Mengquan Li, Yushu Wu, Xuan Shen, Zhimin Li, Geng Yuan, Weisong Shi, Yanzhi Wang,
• Abstract summary: AyE-Edge is the first-of-this-kind development tool that explores automated device deployment space. AyE-Edge excels in extensive real-world experiments conducted on a mobile device. A remarkable 96.7% reduction in power consumption, compared to state-of-the-art (SOTA) competitors.
• Score: 43.53507577737864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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