# Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies

arXiv: https://arxiv.org/abs/2503.02891 · HTML: https://mlsanity.com/paper/2503.02891/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 112 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 30 of 58)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models](https://mlsanity.com/paper/2211.10438/) (2022)
- [AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration](https://mlsanity.com/paper/2306.00978/) (2023)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Scaling Vision Transformers to 22 Billion Parameters](https://mlsanity.com/paper/2302.05442/) (2023)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://mlsanity.com/paper/1812.00332/) (2018)
- [Learned Step Size Quantization](https://mlsanity.com/paper/1902.08153/) (2019)
- [Up or Down? Adaptive Rounding for Post-Training Quantization](https://mlsanity.com/paper/2004.10568/) (2020)
- [Post-Training Quantization for Vision Transformer](https://mlsanity.com/paper/2106.14156/) (2021)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [I-BERT: Integer-only BERT Quantization](https://mlsanity.com/paper/2101.01321/) (2021)
- [TinyViT: Fast Pretraining Distillation for Small Vision Transformers](https://mlsanity.com/paper/2207.10666/) (2022)
- [Low-bit Quantization of Neural Networks for Efficient Inference](https://mlsanity.com/paper/1902.06822/) (2019)
- [Chasing Sparsity in Vision Transformers: An End-to-End Exploration](https://mlsanity.com/paper/2106.04533/) (2021)
- [PTQ4ViT: Post-training quantization for vision transformers with twin uniform quantization](https://mlsanity.com/paper/2111.12293/) (2021)
- [SPViT: Enabling Faster Vision Transformers via Soft Token Pruning](https://mlsanity.com/paper/2112.13890/) (2021)
- [FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer](https://mlsanity.com/paper/2111.13824/) (2021)
- [LViT: Language meets Vision Transformer in Medical Image Segmentation](https://mlsanity.com/paper/2206.14718/) (2022)
- [Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer](https://mlsanity.com/paper/2210.06707/) (2022)
- [M3ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design](https://mlsanity.com/paper/2210.14793/) (2022)
- [TransMed: Transformers Advance Multi-modal Medical Image Classification](https://mlsanity.com/paper/2103.05940/) (2021)
- [Post-Training Piecewise Linear Quantization for Deep Neural Networks](https://mlsanity.com/paper/2002.00104/) (2020)

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

- [ThinkingViT: Matryoshka Thinking Vision Transformer for Elastic Inference](https://mlsanity.com/paper/2507.10800/) (2025)
- [EdgeWisePersona: A Dataset for On-Device User Profiling from Natural Language Interactions](https://mlsanity.com/paper/2505.11417/) (2025)
- [FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review](https://mlsanity.com/paper/2506.03938/) (2025)
- [RCPU: Rotation-Constrained Error Compensation for Structured Pruning of Large Language Models](https://mlsanity.com/paper/2510.07782/) (2025)
- [Vision-Language Model for Accurate Crater Detection](https://mlsanity.com/paper/2601.07795/) (2026)
- [EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices](https://mlsanity.com/paper/2506.11093/) (2025)
- [I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation](https://mlsanity.com/paper/2509.10334/) (2025)
- [Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers](https://mlsanity.com/paper/2507.21364/) (2025)
- [Region-Aware Deformable Convolutions](https://mlsanity.com/paper/2509.15436/) (2025)
- [Animal Re-Identification on Microcontrollers](https://mlsanity.com/paper/2512.08198/) (2025)
- [Do We Need Reformer for Vision? An Experimental Comparison with Vision Transformers](https://mlsanity.com/paper/2512.11260/) (2025)
- [Lightweight Transformer Architectures for Edge Devices in Real-Time Applications](https://mlsanity.com/paper/2601.03290/) (2026)
- [DSFlash: Comprehensive Panoptic Scene Graph Generation in Realtime](https://mlsanity.com/paper/2603.10538/) (2026)
- [HAViT: Historical Attention Vision Transformer](https://mlsanity.com/paper/2603.18585/) (2026)
- [Video Patch Pruning: Efficient Video Instance Segmentation via Early Token Reduction](https://mlsanity.com/paper/2604.00827/) (2026)
- [HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment](https://mlsanity.com/paper/2604.08435/) (2026)
- [Joint Architecture-Token-Bitwidth Multi-Axis Optimization of Vision Transformers for Semiconductor IC Packaging](https://mlsanity.com/paper/2605.01742/) (2026)
- [HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers](https://mlsanity.com/paper/2606.09960/) (2026)
- [Edge Physical AI Deployment of Vision Transformers on Heterogeneous Edge GPU Targeting Autonomous Vehicles](https://mlsanity.com/paper/2607.10942/) (2026)
- [Recursive transformers for semiconductor thermo-mechanical reliability](https://mlsanity.com/paper/2607.27251/) (2026)
- [MixFrag: Fragility-Guided Mixed-Precision Post-Training Quantization for Vision Transformers](https://mlsanity.com/paper/2607.28589/) (2026)
- [Recent Developments in Transformer Inference Deployment on FPGA Platforms: A Survey](https://mlsanity.com/paper/2609.01212/) (2026)
- [Real-World Perception for Autonomous Driving in Adverse Weather: Enhancing Standard Detectors via Foundation-Guided Auto-Annotation](https://mlsanity.com/paper/2609.25515/) (2026)
- [Momentum-Guided Federated Split Distillation for Personalized Temporal Edge Intelligence](https://mlsanity.com/paper/2609.31159/) (2026)

## Abstract (arXiv)

In recent years, vision transformers (ViTs) have emerged as powerful and promising techniques for computer vision tasks such as image classification, object detection, and segmentation. Unlike convolutional neural networks (CNNs), which rely on hierarchical feature extraction, ViTs treat images as sequences of patches and leverage self-attention mechanisms. However, their high computational complexity and memory demands pose significant challenges for deployment on resource-constrained edge devices. To address these limitations, extensive research has focused on model compression techniques and hardware-aware acceleration strategies. Nonetheless, a comprehensive review that systematically categorizes these techniques and their trade-offs in accuracy, efficiency, and hardware adaptability for edge deployment remains lacking. This survey bridges this gap by providing a structured analysis of model compression techniques, software tools for inference on edge, and hardware acceleration strategies for ViTs. We discuss their impact on accuracy, efficiency, and hardware adaptability, highlighting key challenges and emerging research directions to advance ViT deployment on edge platforms, including graphics processing units (GPUs), application-specific integrated circuit (ASICs), and field-programmable gate arrays (FPGAs). The goal is to inspire further research with a contemporary guide on optimizing ViTs for efficient deployment on edge devices.