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. 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. A pivotal component within GNNs is the Sparse General Matrix-Matrix Multiplication (SpGEMM) kernel, known for its computational intensity and irregular memory access patterns. In this thesis, we address the challenges posed by SpGEMM by implementing a highly optimized hashing-based SpGEMM kernel tailored for a custom accelerator. Synthesizing these insights and optimizations, we design state-of-the-art hardware accelerators capable of efficiently handling various GNN workloads. Our accelerator architectures are built on our characterization of GNN computational demands, providing clear motivation for our approaches. 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（GNN）的出现彻底改变了机器学习领域，为图结构数据的学习提供了一种新颖的范式。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与依赖，这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的应用场景。GNN凭借其独特的结构与运算方式，相比传统神经网络带来了新的计算挑战。这需要对GNN进行全面基准测试和深入表征，以洞悉其计算需求并识别潜在的性能瓶颈。在本论文中，我们旨在更好地理解GNN如何与底层硬件交互，并将利用这一知识来设计专用加速器并开发新的优化方法，从而实现更高效、更快速的GNN计算。GNN中的一个关键组成部分是稀疏通用矩阵-矩阵乘法（SpGEMM）内核，该内核以其计算密集性和不规则的内存访问模式而著称。在本论文中，我们通过为定制加速器实现一个高度优化的基于哈希的SpGEMM内核，以应对SpGEMM带来的挑战。综合这些洞见与优化，我们设计了能够高效处理各类GNN工作负载的先进硬件加速器。我们的加速器架构基于对GNN计算需求的表征而构建，这为我们的方法提供了明确的依据。通过对新颖模型的探索，我们强调了自身的全面研究路径，致力于开发不仅性能卓越，而且具备多功能性、能够适应图计算领域不断演变格局的加速器。