Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …

Graph convolutional networks (GCNs) have received significant attention from various
research fields due to the excellent performance in learning graph representations. Although …

Transformer model architectures have recently received great interest in natural language,
machine translation, and computer vision, where attention mechanisms are their building …

This survey overviews recent Graph Convolutional Networks (GCN) advancements,
highlighting their growing significance across various tasks and applications. It underscores …

Transformer neural networks (TNNs) are being applied across a widening range of
application domains, including natural language processing (NLP), machine translation, and …

Graph convolutional networks (GCNs) have been applied successfully in social networks
and recommendation systems to analyze graph data. Unlike conventional neural networks …

Graph Neural Networks (GNNs) have revolutionized many Machine Learning (ML)
applications, such as social network analysis, bioinformatics, etc. GNN inference can be …

The Graph Attention Networks (GATs) exhibit outstanding performance in multiple
authoritative node classification benchmark tests (including transductive and inductive). The …

Synthetic Aperture Radar (SAR) automatic target recognition (ATR) is a key technique for
remote-sensing image recognition. In real-world applications, massive SAR images are …

While there have been many studies on hardware acceleration for deep learning on images,
there has been a rather limited focus on accelerating deep learning applications involving …