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A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
Graph neural networks (GNNs) are emerging for machine learning research on graph-structured data.
ProgSG: Cross-Modality Representation Learning for Programs in Electronic Design Automation
In addition, these programs can be compiled and converted into a control data flow graph (CDFG), and the compiler also provides fine-grained alignment between the code tokens and the CDFG nodes.
Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks
High-level synthesis (HLS) has freed the computer architects from developing their designs in a very low-level language and needing to exactly specify how the data should be transferred in register-level.
SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation
While there have been many studies on hardware acceleration for deep learning on images, there has been a rather limited focus on accelerating deep learning applications involving graphs.
