mlsanityData to 7 Oct 2026

Rome was Not Built in a Single Step: Hierarchical Prompting for LLM-based Chip Design

arXiv 2407.18276 ↗First posted 23 Jul 2024cs.AR cs.AIcited by 24

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Abstract (arXiv)

Large Language Models (LLMs) are effective in computer hardware synthesis via hardware description language (HDL) generation. However, LLM-assisted approaches for HDL generation struggle when handling complex tasks. We introduce a suite of hierarchical prompting techniques which facilitate efficient stepwise design methods, and develop a generalizable automation pipeline for the process. To evaluate these techniques, we present a benchmark set of hardware designs which have solutions with or without architectural hierarchy. Using these benchmarks, we compare various open-source and proprietary LLMs, including our own fine-tuned Code Llama-Verilog model. Our hierarchical methods automatically produce successful designs for complex hardware modules that standard flat prompting methods cannot achieve, allowing smaller open-source LLMs to compete with large proprietary models. Hierarchical prompting reduces HDL generation time and yields savings on LLM costs. Our experiments detail which LLMs are capable of which applications, and how to apply hierarchical methods in various modes. We explore case studies of generating complex cores using automatic scripted hierarchical prompts, including the first-ever LLM-designed processor with no human feedback. Tools for the Recurrent Optimization via Machine Editing (ROME) method can be found at https://github.com/ajn313/ROME-LLM

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 23 Jul 2024.

Builds on

11 of 19 references.

References · 11

  1. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
  2. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 890
  3. 2022InCoder: A Generative Model for Code Infilling and Synthesiscited by 492
  4. 2023VerilogEval: Evaluating Large Language Models for Verilog Code Generationcited by 246
  5. 2023Chip-Chat: Challenges and Opportunities in Conversational Hardware Designcited by 136
  6. 2023Self-planning Code Generation with Large Language Modelscited by 121
  7. 2022Benchmarking Large Language Models for Automated Verilog RTL Code Generationcited by 112
  8. 2024Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Codecited by 106
  9. 2023AutoChip: Automating HDL Generation Using LLM Feedbackcited by 80
  10. 2023RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solutioncited by 69
  11. 2023Pushing the Limits of Machine Design: Automated CPU Design with AIcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 16; 2026: 5.

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  1. 2024A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languagescited by 65
  2. 2025HDLCoRe: A Training-Free Framework for Mitigating Hallucinations in LLM-Generated HDLcited by 17
  3. 2025A Survey of Circuit Foundation Model: Foundation AI Models for VLSI Circuit Design and EDAcited by 17
  4. 2025VeriThoughts: Enabling Automated Verilog Code Generation using Reasoning and Formal Verificationcited by 15
  5. 2024HiVeGen -- Hierarchical LLM-based Verilog Generation for Scalable Chip Designcited by 13
  6. 2025VeriMind: Agentic LLM for Automated Verilog Generation with a Novel Evaluation Metriccited by 12
  7. 2025Can Reasoning Models Reason about Hardware? An Agentic HLS Perspectivecited by 9
  8. 2025TuRTLe: A Unified Evaluation of LLMs for RTL Generationcited by 8
  9. 2025Large Language Model for Verilog Code Generation: Literature Review and the Road Aheadcited by 7
  10. 2025LLM-VeriPPA: Power, Performance, and Area Optimization aware Verilog Code Generation with Large Language Modelscited by 5
  11. 2025Rethinking the Outlier Distribution in Large Language Models: An In-depth Studycited by 4
  12. 2025VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Codecited by 3
12 more
  1. 2025Large Language Models (LLMs) for Electronic Design Automation (EDA)cited by 3
  2. 2025CorrectHDL: Agentic HDL Design with LLMs Leveraging High-Level Synthesis as Referencecited by 3
  3. 2024Watermarking Large Language Models and the Generated Content: Opportunities and Challengescited by 2
  4. 2025HDLxGraph: Bridging Large Language Models and HDL Repositories via HDL Graph Databasescited by 1
  5. 2025MAHL: Multi-Agent LLM-Guided Hierarchical Chiplet Design with Adaptive Debuggingcited by 1
  6. 2025Automating Hardware Design and Verification from Architectural Papers via a Neural-Symbolic Graph Frameworkcited by 1
  7. 2025QiMeng-CRUX: Narrowing the Gap Between Natural Language and Verilog via Core Refined Understanding eXpression for Circuit Designcited by 1
  8. 2026Report for NSF Workshop on AI for Electronic Design Automationcited by 1
  9. 2026GUIDE: GenAI Units In Digital Design Educationcited by 1
  10. 2026VeriGraphi: A Multi-Agent Framework of Hierarchical RTL Generation for Large Hardware Designscited by 1
  11. 2026The New Compiler Stack: A Survey on the Synergy of LLMs and Compilerscited by 0
  12. 2026Agentic Electronic Design Automation: A Handoff Perspectivecited by 0