2 citations · 2 across the 5 of their papers we have counts for
6 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)…
A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification
Jacob Fein-Ashley, Sachini Wickramasinghe, Bingyi Zhang +2
Image classifiers for domain-specific tasks like Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) and chest X-ray classification often rely on convolutional neural n…
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…
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…