Add to ORKGGraph convolutional neural networks (GCNs) have emerged as a key technology in various application domains where the input data is relational. A unique property of GCNs is that its two primary execution stages, aggregation and combination, exhibit drastically different dataflows. Consequently, prior GCN accelerators tackle this research space by casting the aggregation and combination stages as a series of sparse-dense matrix multiplication. However, prior work frequently suffers from inefficient data movements, leaving significant performance left on the table. We present GROW, a GCN accelerator based on Gustavson's algorithm to architect a row-wise product based sparse-dense GEMM accelerator. GROW co-designs the software/hardware that str...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse...
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse...
Graph Convolutional Networks (GCNs) have shown great results but come with large computation costs a...
In the past two years, various graph convolution neural networks (GCNs) accelerators have emerged, e...
Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large ...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning model. Howev...
Graph neural networks (GNNs) have received great attention due to their success in various graph-rel...
We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of ...
Graph neural networks (GNNs) can extract features by learning both the representation of each object...
Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN...
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. ...
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solv...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse...
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse...
Graph Convolutional Networks (GCNs) have shown great results but come with large computation costs a...
In the past two years, various graph convolution neural networks (GCNs) accelerators have emerged, e...
Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large ...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning model. Howev...
Graph neural networks (GNNs) have received great attention due to their success in various graph-rel...
We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of ...
Graph neural networks (GNNs) can extract features by learning both the representation of each object...
Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN...
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. ...
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solv...
Graph Neural Networks (GNNs) are an important tool for extracting value from relational and unstruct...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse...
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse...