E2EdgeAI: Energy-Efficient Edge Computing for Deployment of Vision-Based DNNs on Autonomous Tiny Drones

Author:

Navardi Mozhgan1,Humes Edward1,Mohsenin Tinoosh1

Affiliation:

1. University of Maryland Baltimore County,Center for Real-time Distributed Sensing and Autonomy (CARDS),Computer Science and Electrical Engineering Department,USA

Publisher

IEEE

Reference30 articles.

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Dynamic task offloading edge-aware optimization framework for enhanced UAV operations on edge computing platform;Scientific Reports;2024-07-16

2. Neuromorphic Computing Architectures for EnergyEfficient Edge Devices in Autonomous Vehicles;2024 Third International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS);2024-03-14

3. Lossless Neural Network Model Compression Through Exponent Sharing;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2023-11

4. TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation;2023 IEEE SENSORS;2023-10-29

5. HAC-POCD: Hardware-Aware Compressed Activity Monitoring and Fall Detector Edge POC Devices;2023 IEEE Biomedical Circuits and Systems Conference (BioCAS);2023-10-19

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