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

arXiv: https://arxiv.org/abs/2408.05363 · HTML: https://mlsanity.com/paper/2408.05363/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Learning Structured Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1608.03665/) (2016)
- [Slimmable Neural Networks](https://mlsanity.com/paper/1812.08928/) (2018)
- [CLIP-It! Language-Guided Video Summarization](https://mlsanity.com/paper/2107.00650/) (2021)
- [MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge](https://mlsanity.com/paper/2110.14032/) (2021)
- [PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning](https://mlsanity.com/paper/2001.00138/) (2020)
- [SMART Frame Selection for Action Recognition](https://mlsanity.com/paper/2012.10671/) (2020)
- [YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design](https://mlsanity.com/paper/2009.05697/) (2020)
- [Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution](https://mlsanity.com/paper/2207.12577/) (2022)
- [Deep Unsupervised Key Frame Extraction for Efficient Video Classification](https://mlsanity.com/paper/2211.06742/) (2022)
- [Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices](https://mlsanity.com/paper/2102.06336/) (2021)
- [CoCoPIE: Making Mobile AI Sweet As PIE --Compression-Compilation Co-Design Goes a Long Way](https://mlsanity.com/paper/2003.06700/) (2020)
- [All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management](https://mlsanity.com/paper/2212.05122/) (2022)
- [Semi-supervised and Deep learning Frameworks for Video Classification and Key-frame Identification](https://mlsanity.com/paper/2203.13459/) (2022)

## Built on by (most cited first, 1 of 1)

- [QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge](https://mlsanity.com/paper/2503.16709/) (2025)

## Abstract (arXiv)

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.