GitHub

GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design

On the algorithm level, GCoD integrates a split and conquer training strategy to polarize the graphs to be either denser or sparser in local neighborhoods without compromising the model accuracy, ...
IEEE

MergePath-SpMM: Parallel Sparse Matrix-Matrix Algorithm for Graph Neural Network Acceleration

Abstract: Graph neural networks have seen tremendous adoption to perform complex predictive analytics on massive and unstructured real-world graphs. The trend in hardware accelerator designs has ...
GitHub

GNN-hardware-acceleration-paper-collect

Zeng, Hanqing, and Viktor Prasanna. "Graphact: Accelerating gcn training on cpu-fpga heterogeneous platforms." The 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. 2020 [PDF] ...
C&EN

Acceleration of Graph Neural Network-Based Prediction Models in Chemistry via Co-Design Optimization on Intelligence Processing Units

Graphcore, Kett House, Station Rd, Cambridge CB1 2JH, U.K.
IEEE

LSP: Acceleration of Graph Neural Networks via Locality Sensitive Pruning of Graphs

Abstract: Graph Neural Networks (GNNs) have emerged as highly successful tools for graph-related tasks. However, real- world problems involve very large graphs, and the compute resources needed to fit ...