EdgeAI: A Vision for Deep Learning in IoT Era

IEEE DESIGN AND TEST 1 EdgeAI: A Vision for Deep Learning in IoT Era Kartikeya Bhardwaj, Member, IEEE, Naveen Suda, Member, IEEE, and Radu Marculescu, Fellow, IEEE 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. Index Terms --EdgeAI, Deep Networks, Knowledge Distillation, Learning from Small Data.null 1 I NTRODUCTION D EEP learning has indeed pushed the frontiers of progress for many computer vision, speech recognition, and natural language processing applications. However, due to their enormous computational complexity, deploying such models on constrained devices has emerged as a critical bottleneck for large-scale adoption of intelligence at the IoT edge.