5 papers
GSR-GNN: Training Acceleration and Memory-Saving Framework of Deep GNNs on Circuit Graph
Yuebo Luo, Shiyang Li, Yifei Feng +3
Graph Neural Networks (GNNs) show strong promise for circuit analysis, but scaling to modern large-scale circuit graphs is limited by GPU memory and training cost, especially for d…
GROOT: Graph Edge Re-growth and Partitioning for the Verification of Large Designs in Logic Synthesis
Kiran Thorat, Hongwu Peng, Yuebo Luo +8
Traditional verification methods in chip design are highly time-consuming and computationally demanding, especially for large scale circuits. Graph neural networks (GNNs) have gain…
CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization
Zijian Zhang, Rong Wang, Shiyang Li +3
Developing efficient CUDA kernels is increasingly critical for AI applications such as large-scale LLM training. However, manual kernel design is both costly and time-consuming, mo…
DR-CircuitGNN: Training Acceleration of Heterogeneous Circuit Graph Neural Network on GPUs
Yuebo Luo, Shiyang Li, Junran Tao +6
The increasing scale and complexity of integrated circuit design have led to increased challenges in Electronic Design Automation (EDA). Graph Neural Networks (GNNs) have emerged a…
RTop-K: Ultra-Fast Row-Wise Top-K Selection for Neural Network Acceleration on GPUs
Xi Xie, Yuebo Luo, Hongwu Peng +1
Top-k selection algorithms are fundamental in a wide range of applications, including high-performance computing, information retrieval, big data processing, and neural network mod…