LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation

• We would like to maintain a list of resources that utilize Large Language Models to solve problems in Electronic Design Automation
• LLM4EDA Paper Link
• Also see our maintaining list for Awesome Artificial Intelligence for Electronic Design Automation
• Maintained by members in SJTU-Thinklab: Ruizhe Zhong, Xingbo Du

Assistant Chatbot

• Users can interact with LLMs for knowledge acquisition and Q&A, providing user-friendly and easy-interactively assistant chatbot and bring us new interaction paradigm with EDA software.

1. ChipNeMo: Domain-Adapted LLMs for Chip Design
2. New Interaction Paradigm for Complex EDA Software Leveraging GPT
3. From English to PCSEL: LLM helps design and optimize photonic crystal surface emitting lasers
4. RapidGPT: Your Ultimate HDL Pair-Designer
5. EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD

HDL and Script Generation

• Given language format specification and requirements, LLMs will generate RTL codes and EDA controlling scripts.
• Besides, how to evaluate the quality of generated codes remains an open research focus, including syntax correctness, functionality equivalence, PPA, and security issues.

1. ChatEDA: A Large Language Model Powered Autonomous Agent for EDA
2. ChipNeMo: Domain-Adapted LLMs for Chip Design
3. ChipGPT: How far are we from natural language hardware design
4. CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
5. An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation
6. RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model
7. GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
8. AutoChip: Automating HDL Generation Using LLM Feedback
9. Chip-Chat: Challenges and Opportunities in Conversational Hardware Design
10. VeriGen: A Large Language Model for Verilog Code Generation
11. Generating Secure Hardware using ChatGPT Resistant to CWEs
12. The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platform
13. A Deep Learning Framework for Verilog Autocompletion Towards Design and Verification Automation
14. RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution
15. VerilogEval: Evaluating Large Language Models for Verilog Code Generation
16. Benchmarking Large Language Models for Automated Verilog RTL Code Generation
17. SpecLLM: Exploring Generation and Review of VLSI Design Specification with Large Language Model
18. Zero-Shot RTL Code Generation with Attention Sink Augmented Large Language Models
19. Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS
20. From English to ASIC Hardware Implementation with Large Language Model
21. EDA Corpus: A Large Language Model Dataset for Enhanced Interaction with OpenROAD
22. CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation
23. Evaluating LLMs for Hardware Design and Test
24. AnalogCoder: Analog Circuit Design via Training-Free Code Generation
25. Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework
26. SynthAI: A Multi Agent Generative AI Framework for Automated Modular HLS Design Generation
27. Evaluating LLMs for Hardware Design and Test

Code Analysis and Verification

• We also investigate LLMs' wide application in code analysis, such as bug detecting & fixing, code summarization and security checking.
• Besides, LLMs have also demonstrated strong ability for verification, e.g. Assertion Based Verification.

1. ChipNeMo: Domain-Adapted LLMs for Chip Design
2. LLM4SecHW: Leavering Domain-Specific Large Language Model for Hardware Debugging
3. Unlocking Hardware Security Assurance: The Potential of LLMs
4. RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models
5. LLM-assisted Generation of Hardware Assertions
6. Using LLMs to Facilitate Formal Verification of RTL
7. DIVAS: An LLM-based End-to-End Framework for SoC Security Analysis and Policy-based Protection
8. Fixing Hardware Security Bugs with Large Language Models (On Hardware Security Bug Code Fixes By Prompting Large Language Models)
9. LLM for SoC Security: A Paradigm Shift
10. The Power of Large Language Models for Wireless Communication System Development: A Case Study on FPGA Platform
11. A Deep Learning Framework for Verilog Autocompletion Towards Design and Verification Automation
12. SpecLLM: Exploring Generation and Review of VLSI Design Specification with Large Language Model
13. AssertLLM: Generating and Evaluating Hardware Verification Assertions from Design Specifications via Multi-LLMs
14. Self-HWDebug: Automation of LLM Self-Instructing for Hardware Security Verification
15. Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework
16. LLMs for Hardware Security: Boon or Bane?

Large Circuit Models (LCMs)

• A multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, RTL designs, circuit netlists, and physical layouts.

1. The Dawn of AI-Native EDA: Promises and Challenges of Large Circuit Models

LLMs as Agent

• LLMs are capable of functioning as agents, emulating human thought and action.
• The LLM agent can perform task planning and execute tools to generate or refine design outcomes.
• Based on the evaluation of the current design, the LLM agent utilizes these evaluation metrics as feedback to decide whether to refine the current result or to terminate the chip design process.

1. ChatPattern: Layout Pattern Customization via Natural Language
2. Large Language Model (LLM) for Standard Cell Layout Design Optimization

Citation

LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation

If you find this repo useful, please cite our paper.

@article{zhong2023llm4eda,
  title={LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation},
  author={Zhong, Ruizhe and Du, Xingbo and Kai, Shixiong and Tang, Zhentao and Xu, Siyuan and Zhen, Hui-Ling and Hao, Jianye and Xu, Qiang and Yuan, Mingxuan and Yan, Junchi},
  journal={arXiv preprint arXiv:2401.12224},
  year={2023}
}