  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.
