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.

翻译：图神经网络（GNNs）的出现彻底改变了机器学习领域，为图结构数据的处理提供了一种全新范式。与传统神经网络不同，GNN能够捕捉图数据中固有的复杂关系和依赖性，使其特别适用于社交网络分析、分子化学和网络安全等广泛领域。GNN在这些领域的影响深远，它能够构建更准确的模型和预测，从而显著推动这些领域的技术进步。相较于传统神经网络，GNN独特的结构和运算方式带来了新的计算挑战。这要求对GNN进行全面基准测试与深入特征分析，以洞察其计算需求并识别潜在性能瓶颈。本论文旨在深入理解GNN与底层硬件的交互机制，并利用这一认知设计专用加速器并开发新型优化方法，最终实现更高效、更快速的GNN计算。通过综合这些见解与优化方案，我们设计了一款能够高效处理多种GNN工作负载的先进硬件加速器。该加速器架构基于我们对GNN计算需求的精确特征分析，为设计方法论提供了明确依据。此外，我们将探索范围扩展至图神经网络领域的新兴GNN工作负载。这类针对新型模型的研究体现了我们的综合性设计理念——致力于实现不仅性能卓越，且具备高度灵活性以适配图计算领域持续演进的技术形态的加速器。