2 citations · 10 across the 13 of their papers we have counts for
13 papers
Model-Driven Discipline for Multi-Agent LLMs: Requirement-to-Verification Generation of Traceable System Models
Ran Wei, Le Zhu, Haochi Wang +6
Software complexity is a long-standing challenge for system engineers. Model-Driven Engineering (MDE) addresses it by treating models as first-class artefacts, but a typical MDE pr…
UVMarvel: an Automated LLM-aided UVM Machine for Subsystem-level RTL Verification
Junhao Ye, Dingrong Pan, Hanyuan Liu +7
Verification presents a major bottleneck in Integrated Circuit (IC) development, consuming nearly 70% of total effort. While the Universal Verification Methodology (UVM) improves r…
AkiraRust: Re-thinking LLM-aided Rust Repair Using a Feedback-guided Thinking Switch
Renshuang Jiang, Yichong Wang, Pan Dong +6
Eliminating undefined behaviors (UBs) in Rust programs requires a deep semantic understanding to enable accurate and reliable repair. While existing studies have demonstrated the p…
ISAAC: Intelligent, Scalable, Agile, and Accelerated CPU Verification via LLM-aided FPGA Parallelism
Jialin Sun, Yuchen Hu, Dean You +6
Functional verification is a critical bottleneck in integrated circuit development, with CPU verification being especially time-intensive and labour-consuming. Industrial practice…
ARSP: Automated Repair of Verilog Designs via Semantic Partitioning
Bingkun Yao, Ning Wang, Xiangfeng Liu +5
Debugging functional Verilog bugs consumes a significant portion of front-end design time. While Large Language Models (LLMs) have demonstrated great potential in mitigating this e…
VeriDebug: A Unified LLM for Verilog Debugging via Contrastive Embedding and Guided Correction
Ning Wang, Bingkun Yao, Jie Zhou +4
Large Language Models (LLMs) have demonstrated remarkable potential in debugging for various programming languages. However, the application of LLMs to Verilog debugging remains in…
Insights from Verification: Training a Verilog Generation LLM with Reinforcement Learning with Testbench Feedback
Ning Wang, Bingkun Yao, Jie Zhou +4
Large language models (LLMs) have shown strong performance in Verilog generation from natural language description. However, ensuring the functional correctness of the generated co…
From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification
Junhao Ye, Yuchen Hu, Ke Xu +8
Verification presents a major bottleneck in Integrated Circuit (IC) development, consuming nearly 70% of the total development effort. While the Universal Verification Methodology…
Insights from Rights and Wrongs: A Large Language Model for Solving Assertion Failures in RTL Design
Jie Zhou, Youshu Ji, Ning Wang +7
SystemVerilog Assertions (SVAs) are essential for verifying Register Transfer Level (RTL) designs, as they can be embedded into key functional paths to detect unintended behaviours…
UVLLM: An Automated Universal RTL Verification Framework using LLMs
Yuchen Hu, Junhao Ye, Ke Xu +11
Verifying hardware designs in embedded systems is crucial but often labor-intensive and time-consuming. While existing solutions have improved automation, they frequently rely on u…
MaxMind: A Memory Loop Network to Enhance Software Productivity based on Large Language Models
Yuchen Dong, XiaoXiang Fang, Yuchen Hu +2
The application of large language models to facilitate automated software operations and tool generation (SOTG), thus augmenting software productivity, mirrors the early stages of…
Large Language Model for Verilog Generation with Code-Structure-Guided Reinforcement Learning
Ning Wang, Bingkun Yao, Jie Zhou +3
Recent advancements in large language models (LLMs) have sparked significant interest in the automatic generation of Register Transfer Level (RTL) designs, particularly using Veril…
MEIC: Re-thinking RTL Debug Automation using LLMs
Ke Xu, Jialin Sun, Yuchen Hu +4
The deployment of Large Language Models (LLMs) for code debugging (e.g., C and Python) is widespread, benefiting from their ability to understand and interpret intricate concepts.…