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.
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J. Kim et. al, "Trends in Lightweight Kernel for Many core Based High-Performance Computing", Electronics and Telecommunications Trends. Vol. 32, No. 4, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
J. Sim et.al, “the Fourth Industrial Revolution and ICT – IDX Strategy for leading the Fourth Industrial Revolution”, ETRI Insight, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
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