# Deep Learning on Mobile Devices - A Review

arXiv: https://arxiv.org/abs/1904.09274 · HTML: https://mlsanity.com/paper/1904.09274/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2019.

## Built on by (most cited first, 16 of 16)

- [Transformer-Lite: High-efficiency Deployment of Large Language Models on Mobile Phone GPUs](https://mlsanity.com/paper/2403.20041/) (2024)
- [Computation-efficient Deep Learning for Computer Vision: A Survey](https://mlsanity.com/paper/2308.13998/) (2023)
- [GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity](https://mlsanity.com/paper/2108.11033/) (2021)
- [Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning](https://mlsanity.com/paper/2102.02638/) (2021)
- [Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey](https://mlsanity.com/paper/2309.15467/) (2023)
- [To Store or Not? Online Data Selection for Federated Learning with Limited Storage](https://mlsanity.com/paper/2209.00195/) (2022)
- [DAPPER: Label-Free Performance Estimation after Personalization for Heterogeneous Mobile Sensing](https://mlsanity.com/paper/2111.11053/) (2021)
- [Involution Fused ConvNet for Classifying Eye-Tracking Patterns of Children with Autism Spectrum Disorder](https://mlsanity.com/paper/2401.03575/) (2024)
- [Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching](https://mlsanity.com/paper/2109.08346/) (2021)
- [Device-friendly Guava fruit and leaf disease detection using deep learning](https://mlsanity.com/paper/2209.12557/) (2022)
- [In-flight Novelty Detection with Convolutional Neural Networks](https://mlsanity.com/paper/2112.03765/) (2021)
- [Estimating the Power Consumption of Heterogeneous Devices when performing AI Inference](https://mlsanity.com/paper/2207.06150/) (2022)
- [Deep learning pipeline for image classification on mobile phones](https://mlsanity.com/paper/2206.00105/) (2022)
- [Towards Implementing Energy-aware Data-driven Intelligence for Smart Health Applications on Mobile Platforms](https://mlsanity.com/paper/2302.00514/) (2023)
- [Unveiling the frontiers of deep learning: innovations shaping diverse domains](https://mlsanity.com/paper/2309.02712/) (2023)
- [SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group Sparsity](https://mlsanity.com/paper/2310.19509/) (2023)

## Abstract (arXiv)

Recent breakthroughs in deep learning and artificial intelligence technologies have enabled numerous mobile applications. While traditional computation paradigms rely on mobile sensing and cloud computing, deep learning implemented on mobile devices provides several advantages. These advantages include low communication bandwidth, small cloud computing resource cost, quick response time, and improved data privacy. Research and development of deep learning on mobile and embedded devices has recently attracted much attention. This paper provides a timely review of this fast-paced field to give the researcher, engineer, practitioner, and graduate student a quick grasp on the recent advancements of deep learning on mobile devices. In this paper, we discuss hardware architectures for mobile deep learning, including Field Programmable Gate Arrays, Application Specific Integrated Circuit, and recent mobile Graphic Processing Units. We present Size, Weight, Area and Power considerations and their relation to algorithm optimizations, such as quantization, pruning, compression, and approximations that simplify computation while retaining performance accuracy. We cover existing systems and give a state-of-the-industry review of TensorFlow, MXNet, Mobile AI Compute Engine, and Paddle-mobile deep learning platform. We discuss resources for mobile deep learning practitioners, including tools, libraries, models, and performance benchmarks. We present applications of various mobile sensing modalities to industries, ranging from robotics, healthcare and multi-media, biometrics to autonomous drive and defense. We address the key deep learning challenges to overcome, including low quality data, and small training/adaptation data sets. In addition, the review provides numerous citations and links to existing code bases implementing various technologies.