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10 May 2019 How to practically deploy deep neural networks to distributed network environments for scene perception
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Recently, intelligent machine agents, such as a deep neural network (DNN), have been showing unparalleled capabilities in recognizing visual patterns, objects, semantic activities/events embedded in real-world images and videos. Hence, there has been an increasing need to deploy DNNs, to a battlefield to provide the Solider with realtime situational understanding by capturing a holistic view of battlespace. Soldiers engaged in tactical operations can greatly benefit from leveraging advanced at-the-point-of-need data analytics running on multimodal and heterogeneous platforms in distributed and constrained network environments. The proposed work aims to decompose DNNs and then distribute over edge nodes in such a way that a trade-off between resources available in the constrained network and recognition performance can be optimized. In this work, we decompose DNNs into two stages: an initial stage on an edge device and the remaining portion running on an edge cloud. To effectively and efficiently divide DNNs into two separate stages, we will rigorously analyze multiple widely used DNN architectures with respect to its memory size and FLOPs (Floating Point Operations) per each layer. Based on these analyses, we will develop advanced splitting strategies for DNNs to handle various network constraints.
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Hyungtae Lee and Heesung Kwon "How to practically deploy deep neural networks to distributed network environments for scene perception", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061R (10 May 2019);

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