# Bringing AI To Edge: From Deep Learning's Perspective

arXiv: https://arxiv.org/abs/2011.14808 · HTML: https://mlsanity.com/paper/2011.14808/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 24 of 24)

- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle](https://mlsanity.com/paper/2303.01980/) (2023)
- [Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation](https://mlsanity.com/paper/2209.03355/) (2022)
- [PLiNIO: A User-Friendly Library of Gradient-based Methods for Complexity-aware DNN Optimization](https://mlsanity.com/paper/2307.09488/) (2023)
- [EigenLoRAx: Recycling Adapters to Find Principal Subspaces for Resource-Efficient Adaptation and Inference](https://mlsanity.com/paper/2502.04700/) (2025)
- [Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision](https://mlsanity.com/paper/2411.01431/) (2024)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Synthetic data generation method for data-free knowledge distillation in regression neural networks](https://mlsanity.com/paper/2301.04338/) (2023)
- [TransitReID: Transit OD Data Collection with Occlusion-Resistant Dynamic Passenger Re-Identification](https://mlsanity.com/paper/2504.11500/) (2025)
- [Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey](https://mlsanity.com/paper/2605.06714/) (2026)
- [A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices](https://mlsanity.com/paper/2205.07646/) (2022)
- [On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performance](https://mlsanity.com/paper/2403.17154/) (2024)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [HSCoNAS: Hardware-Software Co-Design of Efficient DNNs via Neural Architecture Search](https://mlsanity.com/paper/2103.08325/) (2021)
- [How Lightweight Can A Vision Transformer Be](https://mlsanity.com/paper/2407.17783/) (2024)
- [Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms](https://mlsanity.com/paper/2411.11406/) (2024)
- [Onboard Optimization and Learning: A Survey](https://mlsanity.com/paper/2505.08793/) (2025)
- [Toward Compact Parameter Representations for Architecture-Agnostic Neural Network Compression](https://mlsanity.com/paper/2111.10320/) (2021)
- [A Framework for Benchmarking Real-Time Embedded Object Detection](https://mlsanity.com/paper/2304.11580/) (2023)
- [Model compression using knowledge distillation with integrated gradients](https://mlsanity.com/paper/2506.14440/) (2025)
- [Exploring Deep-to-Shallow Transformable Neural Networks for Intelligent Embedded Systems](https://mlsanity.com/paper/2512.19731/) (2025)
- [EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI](https://mlsanity.com/paper/2607.06982/) (2026)
- [Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning](https://mlsanity.com/paper/2607.11473/) (2026)
- [On Hardware-Aware Design and Optimization of Edge Intelligence](https://mlsanity.com/paper/2607.16297/) (2026)

## Abstract (arXiv)

Edge computing and artificial intelligence (AI), especially deep learning for nowadays, are gradually intersecting to build a novel system, called edge intelligence. However, the development of edge intelligence systems encounters some challenges, and one of these challenges is the textitcomputational gap between computation-intensive deep learning algorithms and less-capable edge systems. Due to the computational gap, many edge intelligence systems cannot meet the expected performance requirements. To bridge the gap, a plethora of deep learning techniques and optimization methods are proposed in the past years: light-weight deep learning models, network compression, and efficient neural architecture search. Although some reviews or surveys have partially covered this large body of literature, we lack a systematic and comprehensive review to discuss all aspects of these deep learning techniques which are critical for edge intelligence implementation. As various and diverse methods which are applicable to edge systems are proposed intensively, a holistic review would enable edge computing engineers and community to know the state-of-the-art deep learning techniques which are instrumental for edge intelligence and to facilitate the development of edge intelligence systems. This paper surveys the representative and latest deep learning techniques that are useful for edge intelligence systems, including hand-crafted models, model compression, hardware-aware neural architecture search and adaptive deep learning models. Finally, based on observations and simple experiments we conducted, we discuss some future directions.