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EdgeAI: A Vision for Deep Learning in IoT Era (1910.10356v1)

Published 23 Oct 2019 in cs.LG, cs.CV, and stat.ML

Abstract: The significant computational requirements of deep learning present a major bottleneck for its large-scale adoption on hardware-constrained IoT-devices. Here, we envision a new paradigm called EdgeAI to address major impediments associated with deploying deep networks at the edge. Specifically, we discuss the existing directions in computation-aware deep learning and describe two new challenges in the IoT era: (1) Data-independent deployment of learning, and (2) Communication-aware distributed inference. We further present new directions from our recent research to alleviate the latter two challenges. Overcoming these challenges is crucial for rapid adoption of learning on IoT-devices in order to truly enable EdgeAI.

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Authors (3)
  1. Kartikeya Bhardwaj (21 papers)
  2. Naveen Suda (13 papers)
  3. Radu Marculescu (49 papers)
Citations (12)

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