# VTR: An Optimized Vision Transformer for SAR ATR Acceleration on FPGA

arXiv: https://arxiv.org/abs/2404.04527 · HTML: https://mlsanity.com/paper/2404.04527/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 6 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Vision Transformer for Small-Size Datasets](https://mlsanity.com/paper/2112.13492/) (2021)
- [Accurate, Low-latency, Efficient SAR Automatic Target Recognition on FPGA](https://mlsanity.com/paper/2301.01454/) (2023)
- [Global in Local: A Convolutional Transformer for SAR ATR FSL](https://mlsanity.com/paper/2308.05464/) (2023)
- [Benchmarking Deep Learning Classifiers for SAR Automatic Target Recognition](https://mlsanity.com/paper/2312.06940/) (2023)
- [A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification](https://mlsanity.com/paper/2402.00564/) (2024)
- [Graph Neural Network for Accurate and Low-complexity SAR ATR](https://mlsanity.com/paper/2305.07119/) (2023)

## Built on by (most cited first, 1 of 1)

- [FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review](https://mlsanity.com/paper/2506.03938/) (2025)

## Abstract (arXiv)

Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is a key technique used in military applications like remote-sensing image recognition. Vision Transformers (ViTs) are the current state-of-the-art in various computer vision applications, outperforming their CNN counterparts. However, using ViTs for SAR ATR applications is challenging due to (1) standard ViTs require extensive training data to generalize well due to their low locality; the standard SAR datasets, however, have a limited number of labeled training data which reduces the learning capability of ViTs; (2) ViTs have a high parameter count and are computation intensive which makes their deployment on resource-constrained SAR platforms difficult. In this work, we develop a lightweight ViT model that can be trained directly on small datasets without any pre-training by utilizing the Shifted Patch Tokenization (SPT) and Locality Self-Attention (LSA) modules. We directly train this model on SAR datasets which have limited training samples to evaluate its effectiveness for SAR ATR applications. We evaluate our proposed model, that we call VTR (ViT for SAR ATR), on three widely used SAR datasets: MSTAR, SynthWakeSAR, and GBSAR. Further, we propose a novel FPGA accelerator for VTR, in order to enable deployment for real-time SAR ATR applications.