# A survey on FPGA-based accelerator for ML models

arXiv: https://arxiv.org/abs/2412.15666 · HTML: https://mlsanity.com/paper/2412.15666/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 205 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs](https://mlsanity.com/paper/1803.06305/) (2018)
- [REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs](https://mlsanity.com/paper/1909.13396/) (2019)
- [MaxEVA: Maximizing the Efficiency of Matrix Multiplication on Versal AI Engine](https://mlsanity.com/paper/2311.04980/) (2023)
- [DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference](https://mlsanity.com/paper/2304.06831/) (2023)
- [Reuse Kernels or Activations? A Flexible Dataflow for Low-latency Spectral CNN Acceleration](https://mlsanity.com/paper/2310.10902/) (2023)

## Built on by (most cited first, 7 of 7)

- [Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs](https://mlsanity.com/paper/2505.17662/) (2025)
- [Frugal Machine Learning for Energy-efficient, and Resource-aware Artificial Intelligence](https://mlsanity.com/paper/2506.01869/) (2025)
- [Learning Before Filtering: Real-Time Hardware Learning at the Detector Level](https://mlsanity.com/paper/2506.11981/) (2025)
- [Beyond the GPU: The Strategic Role of FPGAs in the Next Wave of AI](https://mlsanity.com/paper/2511.11614/) (2025)
- [Position: The Need for Ultrafast Training](https://mlsanity.com/paper/2602.02005/) (2026)
- [Ultrafast On-Chip Online Learning via Spline Locality in Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2602.02056/) (2026)
- [GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA](https://mlsanity.com/paper/2604.21290/) (2026)

## Abstract (arXiv)

This paper thoroughly surveys machine learning (ML) algorithms acceleration in hardware accelerators, focusing on Field-Programmable Gate Arrays (FPGAs). It reviews 287 out of 1138 papers from the past six years, sourced from four top FPGA conferences. Such selection underscores the increasing integration of ML and FPGA technologies and their mutual importance in technological advancement. Research clearly emphasises inference acceleration (81%) compared to training acceleration (13%). Additionally, the findings reveals that CNN dominates current FPGA acceleration research while emerging models like GNN show obvious growth trends. The categorization of the FPGA research papers reveals a wide range of topics, demonstrating the growing relevance of ML in FPGA research. This comprehensive analysis provides valuable insights into the current trends and future directions of FPGA research in the context of ML applications.