1 citations · 1 across the 4 of their papers we have counts for
5 papers
VClare: Resolving Imperfect Specifications in LLM-Based Verilog Generation
Zhuorui Zhao, Bing Li, Yu Li +2
Large language models (LLMs) have demonstrated promising capabilities in generating Verilog code from natural language specifications. However, human-written specifications often c…
VFocus: Better Verilog Generation from Large Language Model via Focused Reasoning
Zhuorui Zhao, Bing Li, Grace Li Zhang +1
Large Language Models (LLMs) have shown impressive potential in generating Verilog codes, but ensuring functional correctness remains a challenge. Existing approaches often rely on…
Large Language Models (LLMs) for Electronic Design Automation (EDA)
Kangwei Xu, Denis Schwachhofer, Jason Blocklove +10
With the growing complexity of modern integrated circuits, hardware engineers are required to devote more effort to the full design-to-manufacturing workflow. This workflow involve…
VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency
Zhuorui Zhao, Ruidi Qiu, Ing-Chao Lin +3
Large Language Models (LLMs) have demonstrated promising capabilities in generating Verilog code from module specifications. To improve the quality of such generated Verilog codes,…
LLM-Aided Efficient Hardware Design Automation
Kangwei Xu, Ruidi Qiu, Zhuorui Zhao +3
With the rapidly increasing complexity of modern chips, hardware engineers are required to invest more effort in tasks such as circuit design, verification, and physical implementa…