# A Survey of Research in Large Language Models for Electronic Design Automation

arXiv: https://arxiv.org/abs/2501.09655 · HTML: https://mlsanity.com/paper/2501.09655/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 64 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Builds on (most cited first, 30 of 44)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [Learning to Compress Prompts with Gist Tokens](https://mlsanity.com/paper/2304.08467/) (2023)
- [VerilogEval: Evaluating Large Language Models for Verilog Code Generation](https://mlsanity.com/paper/2309.07544/) (2023)
- [VeriGen: A Large Language Model for Verilog Code Generation](https://mlsanity.com/paper/2308.00708/) (2023)
- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [ChipNeMo: Domain-Adapted LLMs for Chip Design](https://mlsanity.com/paper/2311.00176/) (2023)
- [CodeGemma: Open Code Models Based on Gemma](https://mlsanity.com/paper/2406.11409/) (2024)
- [Chip-Chat: Challenges and Opportunities in Conversational Hardware Design](https://mlsanity.com/paper/2305.13243/) (2023)
- [ChatEDA: A Large Language Model Powered Autonomous Agent for EDA](https://mlsanity.com/paper/2308.10204/) (2023)
- [ChipGPT: How far are we from natural language hardware design](https://mlsanity.com/paper/2305.14019/) (2023)
- [RTLFixer: Automatically Fixing RTL Syntax Errors with Large Language Models](https://mlsanity.com/paper/2311.16543/) (2023)
- [BetterV: Controlled Verilog Generation with Discriminative Guidance](https://mlsanity.com/paper/2402.03375/) (2024)
- [VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool](https://mlsanity.com/paper/2408.08927/) (2024)
- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [AutoChip: Automating HDL Generation Using LLM Feedback](https://mlsanity.com/paper/2311.04887/) (2023)
- [RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution](https://mlsanity.com/paper/2312.08617/) (2023)
- [Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism](https://mlsanity.com/paper/2305.18438/) (2023)

## Built on by (most cited first, 30 of 41)

- [LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls](https://mlsanity.com/paper/2511.09148/) (2025)
- [AMSbench: A Comprehensive Benchmark for Evaluating MLLM Capabilities in AMS Circuits](https://mlsanity.com/paper/2505.24138/) (2025)
- [AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs](https://mlsanity.com/paper/2508.02518/) (2025)
- [POET: Power-Oriented Evolutionary Tuning for LLM-Based RTL PPA Optimization](https://mlsanity.com/paper/2603.19333/) (2026)
- [TuRTLe: A Unified Evaluation of LLMs for RTL Generation](https://mlsanity.com/paper/2504.01986/) (2025)
- [MCP4EDA: LLM-Powered Model Context Protocol RTL-to-GDSII Automation with Backend Aware Synthesis Optimization](https://mlsanity.com/paper/2507.19570/) (2025)
- [Revolution or Hype? Seeking the Limits of Large Models in Hardware Design](https://mlsanity.com/paper/2509.04905/) (2025)
- [HWE-Bench: Benchmarking LLM Agents on Real-World Hardware Bug Repair Tasks](https://mlsanity.com/paper/2604.14709/) (2026)
- [AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents](https://mlsanity.com/paper/2508.01012/) (2025)
- [Dr. RTL: Autonomous Agentic RTL Optimization through Tool-Grounded Self-Improvement](https://mlsanity.com/paper/2604.14989/) (2026)
- [VFocus: Better Verilog Generation from Large Language Model via Focused Reasoning](https://mlsanity.com/paper/2511.02285/) (2025)
- [NotSoTiny: A Large, Living Benchmark for RTL Code Generation](https://mlsanity.com/paper/2512.20823/) (2025)
- [CircuitLM: A Multi-Agent LLM-Aided Design Framework for Generating Circuit Schematics from Natural Language Prompts](https://mlsanity.com/paper/2601.04505/) (2026)
- [CROP: Circuit Retrieval and Optimization with Parameter Guidance using LLMs](https://mlsanity.com/paper/2507.02128/) (2025)
- [Automated QoR improvement in OpenROAD with coding agents](https://mlsanity.com/paper/2601.06268/) (2026)
- [CktEvo: Repository-Level RTL Code Benchmark for Design Evolution](https://mlsanity.com/paper/2603.08718/) (2026)
- [HarmChip: Evaluating Hardware Security Centric LLM Safety via Jailbreak Benchmarking](https://mlsanity.com/paper/2604.17093/) (2026)
- [EARL: Entropy-Aware RL Alignment of LLMs for Reliable RTL Code Generation](https://mlsanity.com/paper/2511.12033/) (2025)
- [Bench4HLS: End-to-End Evaluation of LLMs in High-Level Synthesis Code Generation](https://mlsanity.com/paper/2601.19941/) (2026)
- [SafeTune: Mitigating Data Poisoning in LLM Fine-Tuning for RTL Code Generation](https://mlsanity.com/paper/2604.27238/) (2026)
- [HAVEN: Hybrid Automated Verification ENgine for UVM Testbench Synthesis with LLMs](https://mlsanity.com/paper/2604.27643/) (2026)
- [Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development](https://mlsanity.com/paper/2605.20456/) (2026)
- [ProtocolLLM: RTL Benchmark for SystemVerilog Generation of Communication Protocols](https://mlsanity.com/paper/2506.07945/) (2025)
- [AutoFSM: A Multi-agent Framework for FSM Code Generation with IR and SystemC-Based Testing](https://mlsanity.com/paper/2512.11398/) (2025)
- [LLMs in Digital EDA: A perspective on shifting roles from Generation to Orchestration](https://mlsanity.com/paper/2608.27184/) (2026)
- [AutoPPA: Automated Circuit PPA Optimization via Contrastive Code-based Rule Library Learning](https://mlsanity.com/paper/2604.18445/) (2026)
- [Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation Models](https://mlsanity.com/paper/2604.21952/) (2026)
- [RuC: HDL-Agnostic Rule Completion Benchmark Generation](https://mlsanity.com/paper/2604.27780/) (2026)
- [From Mirage to Grounding: Towards Reliable Multimodal Circuit-to-Verilog Code Generation](https://mlsanity.com/paper/2604.27969/) (2026)
- [Complexity Horizons of Compressed Models in Analog Circuit Analysis](https://mlsanity.com/paper/2605.02285/) (2026)

## Abstract (arXiv)

Within the rapidly evolving domain of Electronic Design Automation (EDA), Large Language Models (LLMs) have emerged as transformative technologies, offering unprecedented capabilities for optimizing and automating various aspects of electronic design. This survey provides a comprehensive exploration of LLM applications in EDA, focusing on advancements in model architectures, the implications of varying model sizes, and innovative customization techniques that enable tailored analytical insights. By examining the intersection of LLM capabilities and EDA requirements, the paper highlights the significant impact these models have on extracting nuanced understandings from complex datasets. Furthermore, it addresses the challenges and opportunities in integrating LLMs into EDA workflows, paving the way for future research and application in this dynamic field. Through this detailed analysis, the survey aims to offer valuable insights to professionals in the EDA industry, AI researchers, and anyone interested in the convergence of advanced AI technologies and electronic design.