3 citations · 7 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
LLM-VeriPPA: Power, Performance, and Area Optimization aware Verilog Code Generation with Large Language Models
Kiran Thorat, Jiahui Zhao, Yaotian Liu +5
Large Language Models (LLMs) are gaining prominence in various fields, thanks to their ability to generate high- quality content from human instructions. This paper delves into the…
RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models
Can Jin, Hongwu Peng, Anxiang Zhang +8
In an Information Retrieval (IR) system, reranking plays a critical role by sorting candidate passages according to their relevance to a specific query. This process demands a nuan…