Deep learning has gained superior accuracy on Euclidean structure data in neural networks.
As a result, non-Euclidean structure data, such as graph data, has more sophisticated …

Graph Neural Networks (GCNs) have attracted wide attention and are applied to the real
world. However, due to the ever-growing graph data with significant irregularities, off-chip …

The adoption of ultrawideband (UWB) radar technology in IoT and healthcare applications
for respiration detection is rapidly expanding, opening up a wide array of potential use …

Recently, in light of the success of quantum computers, research teams have actively
developed quantum-inspired computers using classical computing technology. One notable …

The emergence of 5G and edge computing hardware has brought about a significant shift in
artificial intelligence, with edge AI becoming a crucial technology for enabling intelligent …

The graph attention network (GAT) has demonstrated its advantages via local attention
mechanism but suffered from low energy and latency efficiency when implemented on …

Graph convolutional network (GCN) has gained great success in a diverse range of
intelligent tasks. However, the hardware performance of GCNs is often bounded by random …

As the size of real-world graphs continues to grow at an exponential rate, performing the
Graph Convolutional Network (GCN) inference efficiently is becoming increasingly …

Graph neural networks (GNNs) have emerged as powerful approaches to learn knowledge
about graphs and vertices. The rapid employment of GNNs poses requirements for …

Hardware accelerated inference is a promising solution for exploiting graph convolutional
networks (GCN) in latency-sensitive applications. Existing accelerators overlook an …