# Recent Developments in Transformer Inference Deployment on FPGA Platforms: A Survey

arXiv: https://arxiv.org/abs/2609.01212 · HTML: https://mlsanity.com/paper/2609.01212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 67 references that are in the map.
- First posted on arXiv 1 Sep 2026.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [HAWQ: Hessian AWare Quantization of Neural Networks with Mixed-Precision](https://mlsanity.com/paper/1905.03696/) (2019)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs](https://mlsanity.com/paper/2401.03868/) (2024)
- [A Survey of Techniques for Optimizing Transformer Inference](https://mlsanity.com/paper/2307.07982/) (2023)
- [Maximizing CNN Accelerator Efficiency Through Resource Partitioning](https://mlsanity.com/paper/1607.00064/) (2016)
- [Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions](https://mlsanity.com/paper/1803.05900/) (2018)
- [Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies](https://mlsanity.com/paper/2503.02891/) (2025)
- [A Survey on Hardware Accelerators for Large Language Models](https://mlsanity.com/paper/2401.09890/) (2024)
- [IANUS: Integrated Accelerator based on NPU-PIM Unified Memory System](https://mlsanity.com/paper/2410.15008/) (2024)
- [Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism](https://mlsanity.com/paper/2111.10770/) (2021)
- [BETA: Binarized Energy-Efficient Transformer Accelerator at the Edge](https://mlsanity.com/paper/2401.11851/) (2024)
- [Field-Programmable Gate Array Architecture for Deep Learning: Survey & Future Directions](https://mlsanity.com/paper/2404.10076/) (2024)
- [SWAT: Scalable and Efficient Window Attention-based Transformers Acceleration on FPGAs](https://mlsanity.com/paper/2405.17025/) (2024)
- [Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT](https://mlsanity.com/paper/2407.11041/) (2024)
- [On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration](https://mlsanity.com/paper/2504.17376/) (2025)
- [An Efficient FPGA-Based Accelerator for Swin Transformer](https://mlsanity.com/paper/2308.13922/) (2023)
- [PIVOT- Input-aware Path Selection for Energy-efficient ViT Inference](https://mlsanity.com/paper/2404.15185/) (2024)
- [Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml](https://mlsanity.com/paper/2409.05207/) (2024)
- [An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT](https://mlsanity.com/paper/2403.20230/) (2024)
- [UbiMoE: A Ubiquitous Mixture-of-Experts Vision Transformer Accelerator With Hybrid Computation Pattern on FPGA](https://mlsanity.com/paper/2502.05602/) (2025)
- [COBRA: Algorithm-Architecture Co-optimized Binary Transformer Accelerator for Edge Inference](https://mlsanity.com/paper/2504.16269/) (2025)
- [Hardware Acceleration of LLMs: A comprehensive survey and comparison](https://mlsanity.com/paper/2409.03384/) (2024)
- [A Runtime-Adaptive Transformer Neural Network Accelerator on FPGAs](https://mlsanity.com/paper/2411.18148/) (2024)
- [ProTEA: Programmable Transformer Encoder Acceleration on FPGA](https://mlsanity.com/paper/2409.13975/) (2024)

## Abstract (arXiv)

With the rapid and continuous growth in the incorporation of machine learning models based on the Transformer architecture, capable deployment is in high demand. In this context, capable deployment refers to operational performance aspects, e.g., throughput and latency, as well as efficiency aspects, e.g., energy consumption. When it comes to the task of inference using such models, purpose-built hardware accelerators provide a lucrative alternative to common deployment choices, such as Central Processing Units (CPUs) and Graphics Processing Units (GPUs). The Field Programmable Gate Array (FPGA) platforms category is an example of such alternative accelerators, promising implementation flexibility, energy efficiency, improved latency and suitability for on-site deployment. We investigate the most recent advances, trends, and design choices for Transformer inference on FPGA platforms. We perform a systematic literature review, extracting and delving into preferred techniques for implementation and optimisation. This study and the provided taxonomy of topics could act as a guide for researchers from the academia and industry alike.