45 citations · 46 across the 9 of their papers we have counts for
6 papers · 1 filter
Incremental Risk Assessment of Progressive Elder Financial Scams via Instruction-Tuned Small Language Models
Parviz Ghafariasl, Weimin Fu, Xiaolong Guo +1
Financial scams targeting older adults increasingly occur through text and voice channels such as email, SMS, and phone calls, unfolding over multiple conversational turns that beg…
FinHardBench: Can LLMs Generate Latency-Aware Hardware for Financial Computing?
Weimin Fu, Hejia Zhang, Minghao Shao +6
Can large language models generate not just correct, but fast hardware? This paper investigates the question in financial FPGA design, where 5-10 nanoseconds of latency determines…
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…
HarmChip: Evaluating Hardware Security Centric LLM Safety via Jailbreak Benchmarking
Zeng Wang, Minghao Shao, Weimin Fu +6
The integration of large language models (LLMs) into electronic design automation (EDA) workflows has introduced powerful capabilities for RTL generation, verification, and design…
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…