45 citations · 46 across the 9 of their papers we have counts for
6 papers · 1 filter
Configuration Over Selection: Hyperparameter Sensitivity Exceeds Model Differences in Open-Source LLMs for RTL Generation
Minghao Shao, Zeng Wang, Weimin Fu +5
Benchmarking of open-source LLMs for hardware design focuses on which LLMs to use, while treating inference-time decoding configuration as a secondary concern. This work shows that…
From Natural Language to Silicon: The Representation Bottleneck in LLM Hardware Design
Weimin Fu, Zeng Wang, Minghao Shao +5
Edge applications increasingly demand custom hardware, yet Field-Programmable Gate Array (FPGA) design requires expertise that domain engineers lack. Large Language Models (LLMs) p…
Synthesis-in-the-Loop Evaluation of LLMs for RTL Generation: Quality, Reliability, and Failure Modes
Weimin Fu, Zeng Wang, Minghao Shao +5
RTL generation is more than code synthesis. Designs must be syntactically valid, synthesizable, correct, hardware-efficient. SOTA evaluations stop at functional correctness and do…
AnalogSAGE: Self-evolving Analog Design Multi-Agents with Stratified Memory and Grounded Experience
Zining Wang, Jian Gao, Weimin Fu +2
Analog circuit design remains a knowledge- and experience-intensive process that relies heavily on human intuition for topology generation and device parameter tuning. Existing LLM…
LLM4SecHW: Leveraging Domain Specific Large Language Model for Hardware Debugging
Weimin Fu, Kaichen Yang, Raj Gautam Dutta +2
This paper presents LLM4SecHW, a novel framework for hardware debugging that leverages domain specific Large Language Model (LLM). Despite the success of LLMs in automating various…
VGF: Value-Guided Fuzzing -- Fuzzing Hardware as Hardware
Ruochen Dai, Michael Lee, Patrick Hoey +6
As the complexity of logic designs increase, new avenues for testing digital hardware becomes necessary. Fuzz Testing (fuzzing) has recently received attention as a potential candi…