3 papers
cs.AR2026
LongRTL: Graph-Similarity-Guided LLM-driven Long Context RTL Optimization
Yuyang Ye, Che-Kuan Shen, Xiangfei Hu +5
Large Language Models (LLMs) show great promise in RTL code generation and optimization. However, real-world RTL designs are typically long, entangled, and poorly modularized, posi…
cs.CE2026
MappingEvolve: LLM-Driven Code Evolution for Technology Mapping
Rongliang Fu, Yi Liu, Qiang Xu +1
Technology mapping is a critical yet challenging stage in logic synthesis. While Large Language Models (LLMs) have been applied to generate optimization scripts, their potential fo…
cs.LG2025
PermLLM: Learnable Channel Permutation for N:M Sparse Large Language Models
Lancheng Zou, Shuo Yin, Zehua Pei +3
Channel permutation is a powerful technique for enhancing the accuracy of N:M sparse models by reordering the channels of weight matrices to prioritize the retention of important w…