Enabling Accelerators for Graph Computing
Kaustubh Shivdikar
The advent of Graph Neural Networks (GNNs) has revolutionized the field of
machine learning, offering a novel paradigm for learning on graph-structured
data. Unlike traditional neural networks, GNNs are capable of capturing complex
relationships and dependencies inherent in graph data, making them particularly
suited for a wide range of applications including social network analysis,
molecular chemistry, and network security. The impact of GNNs in these domains
is profound, enabling more accurate models and predictions, and thereby
contributing significantly to advancements in these fields.
  GNNs, with their unique structure and operation, present new computational
challenges compared to conventional neural networks. This requires
comprehensive benchmarking and a thorough characterization of GNNs to obtain
insight into their computational requirements and to identify potential
performance bottlenecks. In this thesis, we aim to develop a better
understanding of how GNNs interact with the underlying hardware and will
leverage this knowledge as we design specialized accelerators and develop new
optimizations, leading to more efficient and faster GNN computations.
  Synthesizing these insights and optimizations, we design a state-of-the-art
hardware accelerator capable of efficiently handling various GNN workloads. Our
accelerator architecture is built on our characterization of GNN computational
demands, providing clear motivation for our approach. Furthermore, we extend
our exploration to emerging GNN workloads in the domain of graph neural
networks. This exploration into novel models underlines our comprehensive
approach, as we strive to enable accelerators that are not just performant, but
also versatile, able to adapt to the evolving landscape of graph computing.