9 papers
FABRICA: Agentic CUDA-to-CSL Translation and Optimization for Wafer-Scale Systems
Yuebo Luo, Eliu Huerta, Venkatram Vishwanath +3
Porting GPU kernels across architectures requires architectural remapping, not syntax substitution. CUDA encodes decomposition, locality, and synchronization through threads, block…
HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation
Yuebo Luo, Ahmad Sedigh Baroughi, Philip Stachura +4
Application-specific FPGA accelerators offer substantial performance and energy-efficiency gains across many application domains, but developing them is costly, often requiring mon…
CUDAHercules: Benchmarking Hardware-Aware Expert-level CUDA Optimization for LLMs
Shiyang Li, Zijian Zhang, Guangyan Sun +5
Large language models show promise for automated CUDA programming, however even the strongest coding models (e.g., Claude-Opus-4.6) may still fall short of expert-level, architectu…
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…
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning
Shiyang Li, Zijian Zhang, Winson Chen +3
Modern machine learning (ML) workloads increasingly rely on GPUs, yet achieving high end-to-end performance remains challenging due to dependencies on both GPU kernel efficiency an…
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…