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3 May 2023 Performance optimization of target detection based on edge-to-cloud deep learning
ZhongKui Fan, YePeng Guan
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Proceedings Volume 12644, International Workshop on Frontiers of Graphics and Image Processing (FGIP 2022); 1264403 (2023) https://doi.org/10.1117/12.2668891
Event: International Workshop on Frontiers of Graphics and Image Processing (FGIP 2022), 2022, Beijing, China
Abstract
With the development of mobile internet, real-time target detection using mobile devices has wide application prospects, but the computing power of the terminal greatly limits the speed and accuracy of target detection. Edge-cloud collaborative computing is the main method to solve the lack of computing power of mobile terminals. The current method can't settle the problem of computation scheduling in the edge-cloud collaboration system. Given the existing problems, this paper proposes the pruning technology of classical target detection deep learning networks; training and prediction offloading strategy of edge-to-cloud deep learning network; dynamic load balancing migration strategy based on CPU, memory, bandwidth, and disk state-changing in cluster. After testing, the edge-to-cloud deep learning method can reduce the inference delay by 50% and increase the system throughput by 40%. The maximum waiting time for operation can be reduced by about 20%. The efficiency and accuracy of target detection are effectively improved.
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ZhongKui Fan and YePeng Guan "Performance optimization of target detection based on edge-to-cloud deep learning", Proc. SPIE 12644, International Workshop on Frontiers of Graphics and Image Processing (FGIP 2022), 1264403 (3 May 2023); https://doi.org/10.1117/12.2668891
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KEYWORDS
Clouds
Convolution
Target detection
Deep learning
Distributed computing
Performance modeling
Computing systems
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