Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three
years. Compared with other deep learning modalities, high-performance hardware …

The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have
dramatically escalated the imperative for specialized AI accelerators. Nonetheless …

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning
model. However, it can be notoriously challenging to inference GCNs over large graph …

The rapidly-changing deep learning landscape presents a unique opportunity for building
inference accelerators optimized for specific datacenter-scale workloads. We propose Full …

Graph Neural Networks (GNNs) have shown great success in many applications such as
recommendation systems, molecular property prediction, traffic prediction, etc. Recently …

Graph neural networks (GNNs) are emerging for machine learning research on graph-
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …

Graph Neural Networks (GNNs) have emerged as the state-of-the-art (SOTA) method for
graph-based learning tasks. However, it still remains prohibitively challenging to inference …

Deep learning on point clouds has attracted increasing attention in the fields of 3D computer
vision and robotics. In particular, graph-based point-cloud deep neural networks (DNNs) …

Recently, Graph Neural Networks (GNNs) have become state-of-the-art algorithms for
analyzing non-euclidean graph data. However, to realize efficient GNN training is …

Mini-batch inference of Graph Neural Networks (GNNs) is a key problem in many real-world
applications. In this paper, we develop a computationally efficient mapping of GNNs onto …