8 papers
Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need
Runzhen Xue, Hao Wu, Mingyu Yan +4
Design Space Exploration (DSE) is essential to modern CPU design, yet current frameworks struggle to scale and generalize in high-dimensional architectural spaces. As the dimension…
TLV-HGNN: Thinking Like a Vertex for Memory-efficient HGNN Inference
Dengke Han, Duo Wang, Mingyu Yan +2
Heterogeneous graph neural networks (HGNNs) excel at processing heterogeneous graph data and are widely applied in critical domains. In HGNN inference, the neighbor aggregation sta…
Accelerating GNN Training through Locality-aware Dropout and Merge
Gongjian Sun, Mingyu Yan, Dengke Han +4
Graph Neural Networks (GNNs) have demonstrated significant success in graph learning and are widely adopted across various critical domains. However, the irregular connectivity bet…
Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs
Taiyan Zhang, Renchi Yang, Yurui Lai +3
Graph neural networks (GNNs) have become the preferred models for node classification in graph data due to their robust capabilities in integrating graph structures and attributes.…
ITERTL: An Iterative Framework for Fine-tuning LLMs for RTL Code Generation
Peiyang Wu, Nan Guo, Xiao Xiao +3
Recently, large language models (LLMs) have demonstrated excellent performance, inspiring researchers to explore their use in automating register transfer level (RTL) code generati…
MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration
Runzhen Xue, Hao Wu, Mingyu Yan +3
Cross-workload design space exploration (DSE) is crucial in CPU architecture design. Existing DSE methods typically employ the transfer learning technique to leverage knowledge fro…