Precise hardware performance models play a crucial role in code optimizations. They can
assist compilers in making heuristic decisions or aid autotuners in identifying the optimal …

Graph Neural Networks (GNNs) have achieved remarkable accuracy in cognitive tasks such
as predictive analytics on graph-structured data. Hence, they have become very popular in …

Scaling deep learning models has been at the heart of recent revolutions in language
modelling and image generation. Practitioners have observed a strong relationship between …

Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning
on graph-structured data. However, key limitations of l-step MPGNNs are that their" receptive …

With the recent popularity of neural networks comes the need for efficient serving of
inference workloads. A neural network inference workload can be represented as a …

Graphs are general data structures that can represent information from a variety of domains
(social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) …

Graphs are a powerful tool for representing and analyzing complex relationships in real-
world applications. Large Language Models (LLMs) have demonstrated impressive …

Precise hardware performance models play a crucial role in code optimizations. They can
assist compilers in making heuristic decisions or aid autotuners in identifying the optimal …

Graphs are fundamental mathematical abstractions, accurately modelling real-world
phenomena such as disease propagation, infrastructure organisation, and biological …