Hardware Design and Verification with Large Language Models: A Scoping Review, Challenges, and Open Issues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Large Language Models (LLMs) are emerging as promising tools in hardware design and verification, with recent advancements suggesting they could fundamentally reshape conventional practices. Objective: This study examines the significance of LLMs in shaping the future of hardware design and verification. It offers an extensive literature review, addresses key challenges, and highlights open research questions in this field. Design: in this scoping review, we survey over 360 papers most of the published between 2022 and 2024, including 71 directly relevant ones to the topic, to evaluate the current role of LLMs in advancing automation, optimization, and innovation in hardware design and verification workflows. Results: Our review highlights LLM applications across synthesis, simulation, and formal verification, emphasizing their potential to streamline development processes while upholding high standards of accuracy and performance. We identify critical challenges, such as scalability, model interpretability, and the alignment of LLMs with domain-specific languages and methodologies. Furthermore, we discuss open issues, including the necessity for tailored model fine-tuning, integration with existing Electronic Design Automation (EDA) tools, and effective handling of complex data structures typical of hardware projects. Conclusions: this survey not only consolidates existing knowledge but also outlines prospective research directions, underscoring the transformative role LLMs could play in the future of hardware design and verification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it