28 citations · 66 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Code Less, Align More: Efficient LLM Fine-tuning for Code Generation with Data Pruning
Yun-Da Tsai, Mingjie Liu, Haoxing Ren
Recent work targeting large language models (LLMs) for code generation demonstrated that increasing the amount of training data through synthetic code generation often leads to exc…
Assessing Economic Viability: A Comparative Analysis of Total Cost of Ownership for Domain-Adapted Large Language Models versus State-of-the-art Counterparts in Chip Design Coding Assistance
Amit Sharma, Teodor-Dumitru Ene, Kishor Kunal +3
This paper presents a comparative analysis of total cost of ownership (TCO) and performance between domain-adapted large language models (LLM) and state-of-the-art (SoTA) LLMs , wi…