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Edge-Adaptive Dynamic Scalable Convolution for Efficient Remote Mobile Pathology Analysis
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AI
DOI：10.1145/3732781.png)
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Abstract
En 中文
With the emergence of edge computing, there is a growing need for advanced technologies capable of realtime, efficient processing of complex data on edge devices, particularly in mobile health systems handling pathological images. On edge computing devices, the lightweighting of models and reduction of computational requirements not only save resources but also increase inference speed. Although many lightweight models and methods have been proposed in recent years, they still face many common challenges. This article introduces a novel convolution operation, Dynamic Scalable Convolution (DSC), which optimizes computational resources and accelerates inference on edge computing devices. DSC is shown to outperform traditional convolution methods in terms of parameter efficiency, computational speed, and overall performance, through comparative analyses in computer vision tasks like image classification and semantic segmentation. Experimental results demonstrate the significant potential of DSC in enhancing deep neural networks, particularly for edge computing applications in smart devices and remote healthcare, where it addresses the challenge of limited resources by reducing computational demands and improving inference speed. By integrating advanced convolution technology and edge computing applications, DSC offers a promising approach to support the rapidly developing mobile health field, especially in enhancing remote healthcare delivery through mobile multimedia communication.
Keywords:
Internet of things
edge computing
dynamic scalable convolution
deep learning
lightweight
Journal
A
IF:
2.1
Papers:
24
Citations:
378
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