mlsanityData to 13 Oct 2026

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

arXiv 2609.01212 ↗First posted 1 Sep 2026cs.LG cs.ARcited by 0

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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.

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References · 30

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18 more
  1. 2025Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategiescited by 24
  2. 2024A Survey on Hardware Accelerators for Large Language Modelscited by 16
  3. 2024IANUS: Integrated Accelerator based on NPU-PIM Unified Memory Systemcited by 13
  4. 2021Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanismcited by 9
  5. 2024BETA: Binarized Energy-Efficient Transformer Accelerator at the Edgecited by 6
  6. 2024Field-Programmable Gate Array Architecture for Deep Learning: Survey & Future Directionscited by 6
  7. 2024SWAT: Scalable and Efficient Window Attention-based Transformers Acceleration on FPGAscited by 6
  8. 2024Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoTcited by 6
  9. 2025On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Accelerationcited by 6
  10. 2023An Efficient FPGA-Based Accelerator for Swin Transformercited by 5
  11. 2024PIVOT- Input-aware Path Selection for Energy-efficient ViT Inferencecited by 5
  12. 2024Low Latency Transformer Inference on FPGAs for Physics Applications with hls4mlcited by 5
  13. 2024An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViTcited by 4
  14. 2025UbiMoE: A Ubiquitous Mixture-of-Experts Vision Transformer Accelerator With Hybrid Computation Pattern on FPGAcited by 4
  15. 2025COBRA: Algorithm-Architecture Co-optimized Binary Transformer Accelerator for Edge Inferencecited by 4
  16. 2024Hardware Acceleration of LLMs: A comprehensive survey and comparisoncited by 3
  17. 2024A Runtime-Adaptive Transformer Neural Network Accelerator on FPGAscited by 3
  18. 2024ProTEA: Programmable Transformer Encoder Acceleration on FPGAcited by 1

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