# Deep Learning at the Edge

arXiv: https://arxiv.org/abs/1910.10231 · HTML: https://mlsanity.com/paper/1910.10231/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 9 of 9)

- [A Review of Deep Transfer Learning and Recent Advancements](https://mlsanity.com/paper/2201.09679/) (2022)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks](https://mlsanity.com/paper/2409.02134/) (2024)
- [Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems](https://mlsanity.com/paper/2305.07135/) (2023)
- [Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs](https://mlsanity.com/paper/2111.15481/) (2021)
- [EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning](https://mlsanity.com/paper/2205.10356/) (2022)
- [Decoding the Alphabet Soup of Degrees in the United States Postsecondary Education System Through Hybrid Method: Database and Text Mining](https://mlsanity.com/paper/2309.13050/) (2023)
- [Convex Distillation: Efficient Compression of Deep Networks via Convex Optimization](https://mlsanity.com/paper/2410.06567/) (2024)
- [Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space](https://mlsanity.com/paper/2607.01689/) (2026)

## Abstract (arXiv)

The ever-increasing number of Internet of Things (IoT) devices has created a new computing paradigm, called edge computing, where most of the computations are performed at the edge devices, rather than on centralized servers. An edge device is an electronic device that provides connections to service providers and other edge devices; typically, such devices have limited resources. Since edge devices are resource-constrained, the task of launching algorithms, methods, and applications onto edge devices is considered to be a significant challenge. In this paper, we discuss one of the most widely used machine learning methods, namely, Deep Learning (DL) and offer a short survey on the recent approaches used to map DL onto the edge computing paradigm. We also provide relevant discussions about selected applications that would greatly benefit from DL at the edge.