3 citations · 3 across the 4 of their papers we have counts for
4 papers
Verilog-Evolve: Feedback-Driven and Skill-Evolving Verilog Generation
Zehua Pei, Hui-Ling Zhen, Yu Zhang +3
Large language models (LLMs) have improved Verilog generation from natural-language specifications, but most pipelines still treat generation as isolated sampling followed by funct…
MOSS: Efficient and Accurate FP8 LLM Training with Microscaling and Automatic Scaling
Yu Zhang, Hui-Ling Zhen, Mingxuan Yuan +1
Training large language models with FP8 formats offers significant efficiency gains. However, the reduced numerical precision of FP8 poses challenges for stable and accurate traini…
MixPE: Quantization and Hardware Co-design for Efficient LLM Inference
Yu Zhang, Mingzi Wang, Lancheng Zou +4
Transformer-based large language models (LLMs) have achieved remarkable success as model sizes continue to grow, yet their deployment remains challenging due to significant computa…
DiLA: Enhancing LLM Tool Learning with Differential Logic Layer
Yu Zhang, Hui-Ling Zhen, Zehua Pei +4
Considering the challenges faced by large language models (LLMs) in logical reasoning and planning, prior efforts have sought to augment LLMs with access to external solvers. While…