21 citations · 27 across the 10 of their papers we have counts for
11 papers
Accelerating ViT Inference on FPGA through Static and Dynamic Pruning
Dhruv Parikh, Shouyi Li, Bingyi Zhang +3
Vision Transformers (ViTs) have achieved state-of-the-art accuracy on various computer vision tasks. However, their high computational complexity prevents them from being applied t…
GCV-Turbo: End-to-end Acceleration of GNN-based Computer Vision Tasks on FPGA
Bingyi Zhang, Rajgopal Kannan, Carl Busart +1
Graph neural networks (GNNs) have recently empowered various novel computer vision (CV) tasks. In GNN-based CV tasks, a combination of CNN layers and GNN layers or only GNN layers…
VTR: An Optimized Vision Transformer for SAR ATR Acceleration on FPGA
Sachini Wickramasinghe, Dhruv Parikh, Bingyi Zhang +3
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is a key technique used in military applications like remote-sensing image recognition. Vision Transformers (ViTs)…
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks
Tian Ye, Rajgopal Kannan, Viktor Prasanna +1
Adversarial attacks have demonstrated the vulnerability of Machine Learning (ML) image classifiers in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems. An…
PAHD: Perception-Action based Human Decision Making using Explainable Graph Neural Networks on SAR Images
Sasindu Wijeratne, Bingyi Zhang, Rajgopal Kannan +2
Synthetic Aperture Radar (SAR) images are commonly utilized in military applications for automatic target recognition (ATR). Machine learning (ML) methods, such as Convolutional Ne…
Benchmarking Deep Learning Classifiers for SAR Automatic Target Recognition
Jacob Fein-Ashley, Tian Ye, Rajgopal Kannan +2
Synthetic Aperture Radar SAR Automatic Target Recognition ATR is a key technique of remote-sensing image recognition which can be supported by deep neural networks The existing wor…
Realistic Scatterer Based Adversarial Attacks on SAR Image Classifiers
Tian Ye, Rajgopal Kannan, Viktor Prasanna +2
Adversarial attacks have highlighted the vulnerability of classifiers based on machine learning for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) tasks. An adve…
Performance of Graph Neural Networks for Point Cloud Applications
Dhruv Parikh, Bingyi Zhang, Rajgopal Kannan +2
Graph Neural Networks (GNNs) have gained significant momentum recently due to their capability to learn on unstructured graph data. Dynamic GNNs (DGNNs) are the current state-of-th…
Graph Neural Network for Accurate and Low-complexity SAR ATR
Bingyi Zhang, Sasindu Wijeratne, Rajgopal Kannan +2
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is the key technique for remote sensing image recognition. The state-of-the-art works exploit the deep convolution…
Accurate, Low-latency, Efficient SAR Automatic Target Recognition on FPGA
Bingyi Zhang, Rajgopal Kannan, Viktor Prasanna +1
Synthetic aperture radar (SAR) automatic target recognition (ATR) is the key technique for remote-sensing image recognition. The state-of-the-art convolutional neural networks (CNN…
Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA
Hongkuan Zhou, Bingyi Zhang, Rajgopal Kannan +2
Temporal Graph Neural Networks (TGNNs) are powerful models to capture temporal, structural, and contextual information on temporal graphs. The generated temporal node embeddings ou…