28 citations · 66 across the 10 of their papers we have counts for
4 papers · 1 filter
Spec2RTL-Agent: Automated Hardware Code Generation from Complex Specifications Using LLM Agent Systems
Zhongzhi Yu, Mingjie Liu, Michael Zimmer +3
Despite recent progress in generating hardware RTL code with LLMs, existing solutions still suffer from a substantial gap between practical application scenarios and the requiremen…
FVEval: Understanding Language Model Capabilities in Formal Verification of Digital Hardware
Minwoo Kang, Mingjie Liu, Ghaith Bany Hamad +2
The remarkable reasoning and code generation capabilities of large language models (LLMs) have spurred significant interest in applying LLMs to enable task automation in digital ch…
CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair
Mingjie Liu, Yun-Da Tsai, Wenfei Zhou +1
Despite the significant progress made in code generation with large language models, challenges persist, especially with hardware description languages such as Verilog. This paper…
RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models
Yun-Da Tsai, Mingjie Liu, Haoxing Ren
This paper presents RTLFixer, a novel framework enabling automatic syntax errors fixing for Verilog code with Large Language Models (LLMs). Despite LLM's promising capabilities, ou…