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Award ID contains: 1815047

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  1. ; ; ; ; (, International Conference on Management of Data)
    Full Text Available 
  2. ; ; ; (, IEEE Computer Architecture Letters)
    Full Text Available 
  3. (, Design Automation Conference (DAC))
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; (, IISWC)
    Full Text Available 
  5. ; ; ; (, Proceedings of the 56th Annual Design Automation Conference 2019)
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  6. ; ; ; (, Proceedings of the 56th Annual Design Automation Conference 2019)
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  7. (, CODES/ISSS '19: Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis Companion)
    Deep learning algorithms are an essential component of video analytics systems, in which the content of a video stream is analyzed. Although numerous studies target optimizing server-based video analysis, partially processing videos on edge devices is beneficial. Since edge devices are closer to data, they deliver initial insights on data before sending it to cloud. In this paper, we present an edge-tailored video analytics system by using a multi-stage network designed to run on heterogeneous computing resources. 
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    Accepted Manuscript Full Text Available
  8. ; ; ; ; (, IEEE Robotics and Automation Letters)
    Full Text Available