EdgeAI: A Vision for Deep Learning in IoT Era

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.

To appear in IEEE Design and Test

References in corpus (4)

• Distilling the Knowledge in a Neural Network
• Data-Free Knowledge Distillation for Deep Neural Networks
• Dream Distillation: A Data-Independent Model Compression Framework
• Knowledge distillation using unlabeled mismatched images

Cited by in corpus (1)

• New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design