# EdgeAI: A Vision for Deep Learning in IoT Era

arXiv: https://arxiv.org/abs/1910.10356 · HTML: https://mlsanity.com/paper/1910.10356/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things](https://mlsanity.com/paper/2408.14528/) (2024)
- [A Comparative Study of CNN Optimization Methods for Edge AI: Exploring the Role of Early Exits](https://mlsanity.com/paper/2604.14789/) (2026)

## Abstract (arXiv)

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.