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Conference Paper Efficient Resource Augmentation of Resource Constrained UAVs Through EdgeCPS
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Authors
Sangil Ha, Euteum Choi, Dongbeom Ko, Sungjoo Kang, Seongjin Lee
Issue Date
2023-03
Citation
Symposium on Applied Computing (SAC) 2023, pp.679-682
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1145/3555776.3577846
Project Code
23ZS1300, Research on High Performance Computing Technology to overcome limitations of AI processing, Kim Kang Ho
Abstract
We propose an efficient Resource Augmentation Framework (RAF) for resource-constrained UAVs through EdgeCPS. Typical UAVs with small form factors have limited computation power which hinders their ability to perform critical or computation-intensive missions. By exploiting EdgeCPS, UAVs can get computational support from the EdgeCPS and diversity its missions. Existing solutions allow exploiting the EdgeCPS; however, the network overhead is too great that it cannot be adopted in resource-constrained UAVs. The proposed framework, RAF, provides Task Management Module (TMM) and Offloading Inference Module (OIM) to solve the issue. Using Raspberry Pi 4 as the mission computer for the UAV, RAF shows an inference performance of 11.9 FPS in the ResNet-18 model, whereas the existing work shows about 6 FPS.
KSP Keywords
Computation power, Form factor, Network overhead, Resource-constrained, Task Management, raspberry pi, resource augmentation