mlsanityData to 7 Oct 2026

LLM4SecHW: Leveraging Domain Specific Large Language Model for Hardware Debugging

arXiv 2401.16448 ↗First posted 28 Jan 2024cs.AR cs.AIcited by 18

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Abstract (arXiv)

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 software development tasks, their application in the hardware security domain has been limited due to the constraints of commercial LLMs and the scarcity of domain specific data. To address these challenges, we propose a unique approach to compile a dataset of open source hardware design defects and their remediation steps, utilizing version control data. This dataset provides a substantial foundation for training machine learning models for hardware. LLM4SecHW employs fine tuning of medium sized LLMs based on this dataset, enabling the identification and rectification of bugs in hardware designs. This pioneering approach offers a reference workflow for the application of fine tuning domain specific LLMs in other research areas. We evaluate the performance of our proposed system on various open source hardware designs, demonstrating its efficacy in accurately identifying and correcting defects. Our work brings a new perspective on automating the quality control process in hardware design.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 13 of its 39 references that are in the map. First posted on arXiv 28 Jan 2024.

Builds on

13 of 39 references.

References · 13

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2023PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallelcited by 641
  5. 2023LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Modelcited by 602
  6. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 382
  7. 2018The Adverse Effects of Code Duplication in Machine Learning Models of Codecited by 123
  8. 2022Benchmarking Large Language Models for Automated Verilog RTL Code Generationcited by 112
  9. 2023nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Modelscited by 57
  10. 2023(Security) Assertions by Large Language Modelscited by 31
  11. 2023Fixing Hardware Security Bugs with Large Language Modelscited by 24
  12. 2019The Cost of Application-Class Processing: Energy and Performance Analysis of a Linux-ready 1.7GHz 64bit RISC-V Core in 22nm FDSOI Technologycited by 16
1 more
  1. 2021Delving into Macro Placement with Reinforcement Learningcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 11; 2026: 3.

18 papers.

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  1. 2024A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languagescited by 65
  2. 2024LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trustcited by 18
  3. 2025SymRTLO: Enhancing RTL Code Optimization with LLMs and Neuron-Inspired Symbolic Reasoningcited by 16
  4. 2025From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verificationcited by 8
  5. 2024SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generationcited by 5
  6. 2025MARVEL: Multi-Agent RTL Vulnerability Extraction using Large Language Modelscited by 5
  7. 2025SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Modelscited by 5
  8. 2025HLSDebugger: Identification and Correction of Logic Bugs in HLS Code with LLM Solutionscited by 4
  9. 2025FVDebug: An LLM-Driven Debugging Assistant for Automated Root Cause Analysis of Formal Verification Failurescited by 4
  10. 2026CktEvo: Repository-Level RTL Code Benchmark for Design Evolutioncited by 3
  11. 2025Distilled Large Language Model in Confidential Computing Environment for System-on-Chip Designcited by 2
  12. 2026VeriCWEty: Embedding enabled Line-Level CWE Detection in Verilogcited by 2
6 more
  1. 2024Natural Language to Verilog: Design of a Recurrent Spiking Neural Network using Large Language Models and ChatGPTcited by 1
  2. 2025HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph Databasescited by 1
  3. 2025TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designscited by 1
  4. 2025Advancing AI-assisted Hardware Design with Hierarchical Decentralized Training and Personalized Inference-Time Optimizationcited by 0
  5. 2025DUET: Agentic Design Understanding via Experimentation and Testingcited by 0
  6. 2026SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detectioncited by 0