Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

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

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 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 …

With the increasing size of DNN models and the growing discrepancy between compute
performance and memory bandwidth, fusing multiple layers together to reduce off-chip …

Sparsity is a growing trend in modern DNN models. Existing Sparse-Sparse Matrix
Multiplication (SpMSpM) accelerators are tailored to a particular SpMSpM dataflow (ie, Inner …

The widespread use of Sparse Matrix Dense Matrix Multiplication (SpMM) and Sampled
Dense Matrix Dense Matrix Multiplication (SDDMM) kernels makes them candidates for …

Attention mechanisms, primarily designed to capture pairwise correlations between words,
have become the backbone of machine learning, expanding beyond natural language …

Graph Neural Networks (GNNs) are becoming increasingly popular for vision-based
applications due to their intrinsic capacity in modeling structural and contextual relations …