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REVIEW 3 major objections 5 minor 1 cited by

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

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This survey claims LLMs now touch every major stage of chip design, with RTL generation the most developed application, while physical design and analog automation remain comparatively open.

desk verdict A useful but sloppy survey of LLM-for-EDA that deserves peer review mainly so the authors can fix the factual errors, template artifacts, and the unverifiable meta-study corpus. read the letter →

arxiv 2501.09655 v1 pith:LWSKXCSV submitted 2025-01-16 cs.LG

Jingyu Pan , Guanglei Zhou , Chen-Chia Chang , Isaac Jacobson , Jiang Hu , Yiran Chen This is my paper · ORCID
classification cs.LG
keywords largelanguagemodelselectronicdesignautomationhardwareRTLcodegenerationVerilogdomainadaptationautonomousagentssurvey
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a survey of how large language models have been applied to electronic design automation (EDA) between 2022 and 2024. It claims to provide a comprehensive map of the field, organized by design stage — system-level design, RTL design, logic synthesis and physical design, and analog circuit design — and by the LLM technique used, such as prompt engineering, fine-tuning, retrieval-augmented generation, and autonomous agents. On the evidence it assembles, RTL code generation is the most active and most mature area, while backend physical design and analog design are comparatively open. The survey also identifies where progress comes from: domain-adapted and quality-weighted fine-tuning now outperform prompt engineering in RTL generation, and closed-source models such as GPT-4 currently lead autonomous agent frameworks.

What carries the argument

The organising machinery of the survey is a two-axis taxonomy: the EDA design-stage pipeline (system-level design, RTL design, synthesis and physical design, analog design) crossed with a set of LLM techniques (prompt engineering, domain-adaptive pre-training and supervised fine-tuning, retrieval-augmented generation, and autonomous agent frameworks). This taxonomy is what lets the paper turn a list of papers into claims about which stages are well-explored, which techniques work, and where the gaps are. The quantitative anchor is a small set of shared benchmarks — VerilogEval, RTLLM, and VerilogEval-Human — that let the survey compare functional correctness across models and methods in RTL generation.

What would settle it

Re-run the meta-study with the venue list and inclusion criteria made explicit and count regular papers per design stage; if the resulting distribution differs materially from the survey's reported trends — for instance, if physical design or analog papers are substantially undercounted — the survey's central map is falsified.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central result is an organized account of a young field: LLMs are being deployed across the whole EDA pipeline, but very unevenly. The RTL design stage dominates the literature, and within it three technique families have emerged — prompt engineering for commercial models, domain adaptation through supervised fine-tuning and custom datasets, and autonomous agent loops that let the model call tools and react to compiler or simulation feedback. The paper reports two quantitative patterns from its meta-study and benchmark comparisons: naive supervised fine-tuning improves with model size, while state-of-the-art customization methods that score or discriminatively guide generation (RTLCoder, BetterV) beat both naive fine-tuning and GPT-4 prompt engineering on VerilogEval; and autonomous agent methods raise VerilogEval-Human pass rates substantially, with GPT-4-based agents outperforming open-source ones. For the remaining stages, the paper concludes that script generation and documentation Q&A are established LLM niches, multi-modal and graph-based circuit representations are largely unexplored, and analog design work is only beginning.

Load-bearing premise

The survey's comprehensiveness depends on its unstated choice of which venues and papers its 2022–2024 meta-study covers; if that selection is narrower or biased, the reported distribution of work across design stages will not reflect the field as a whole.

Editorial extensions

If this is right

  • RTL code generation is the near-term payoff area: quality-weighted fine-tuning and controlled generation, not larger base models alone, are what currently push functional correctness past GPT-4 prompt engineering.
  • Backend physical design and analog layout remain the biggest open spaces; the survey names netlist generation, layout feature encoding, and timing estimation as specific targets for future LLM work.
  • Scaling LLMs for EDA is bottlenecked by data, not architecture: with VerilogEval at roughly 8,000 samples, expanding benchmark datasets is a precondition for benefiting from larger models.
  • Closed-source models like GPT-4 are currently the stronger choice for autonomous agent frameworks, but the gap is not fixed; open models improve when given planning and tool-integration scaffolds.
  • Multi-modal input — graph encoders for netlists and images for layouts — is the survey's main stated next frontier, and the field has barely started it.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial extension: because the meta-study never names its selected venues, the survey's 'well-explored versus unexplored' map is only as trustworthy as an unstated venue choice; a re-run with explicitly defined venues could shift the balance between RTL and physical design.
  • Editorial extension: the paper's finding that naive SFT tracks model size, while quality-weighted methods outperform at smaller sizes, suggests that data quality weighting and controlled decoding may be a cheaper route to EDA-specific models than raw parameter scaling.
  • Editorial extension: a testable prediction of the survey's own outlook is that the next wave of LLM-EDA work will focus on graph- and image-based circuit representations; if that does not materialize, the survey's identified gap may not have been the binding constraint.
  • Editorial extension: the paper's emphasis on trust, verification, and proprietary-data leakage implies that industrial adoption of LLM-generated RTL will hinge on verification tooling and privacy-preserving training at least as much as on pass-rate improvements.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper surveys applications of large language models in electronic design automation. It organizes the literature by design stage (system-level design, RTL design, logic synthesis and physical design, and analog circuit design), then discusses LLM methodology (model architecture and size, customization techniques, and multimodal feature representation), and closes with academic infrastructure, application bottlenecks, and ethics/security/efficiency considerations. The paper claims to provide a comprehensive understanding of the current state and future potential of LLM applications in EDA, and Section 3 presents itself as a meta-study reviewing all LLM-for-CAD publications in selected venues from 2022 to 2024.

Significance. If the claims were fully supported, the survey would be a useful organizational reference for a fast-moving area: it collects a broad set of recent results, provides comparative tables (Tables 1 and 2) and a dataset overview (Table 3), and identifies concrete gaps such as the small size of VerilogEval and the limited exploration of multi-modal circuit representations. The survey makes no machine-checked derivations and ships no code, so its value is inherently organizational; for that kind of contribution, auditability of the corpus selection and accuracy of the attributed claims are the central quality criteria. The paper has no obvious internal circularity, since its own prior works [8,9] are cited in the body, but the central comprehensiveness claim does not depend on those citations.

major comments (3)
  1. [Section 3] Section 3 states that the meta-study 'reviews all publications in the selected venues' for 2022-2024, but it never names those venues and gives no search protocol, database, or inclusion/exclusion log. The only stated criteria are negative ('excludes... works that use LLMs as an application' and 'Only regular papers are considered while invited papers are excluded'). The de facto corpus in Tables 1-3 includes arXiv preprints, workshop papers, and at least one non-CAD item ([53], JavaScript unit-test generation), so the corpus does not transparently match the stated criteria. Because the trend claims in Section 3 are derived from this unlisted corpus, they are currently unfalsifiable; the authors should name the venues, give the search and screening protocol, and report the screening decisions, or revise the claim to describe a curated selection rather than 'all publications' in those venues.
  2. [Section 4.1] Section 4.1 asserts, without citation, that 'encoder-only models are shown to be more effective compared to encoder-decoder ones in recent research advancement.' This is unsupported and disconnected from the EDA evidence presented elsewhere in the paper: the survey's own tables and text focus on decoder-only and encoder-decoder-style generation models, and no EDA-specific encoder-only comparison is given. This claim is load-bearing for the paper's model-selection guidance, so it should either be removed or replaced with a cited, EDA-relevant comparison.
  3. [Section 3.2, Figure 2] The text uses Figure 2 to support the central trend claim that 'State-of-the-art customization methods have outperformed the best of prompt engineering methods with GPT-4', but the figure and its surrounding discussion do not state the exact evaluation configuration used (e.g., VerilogEval-machine versus VerilogEval-Human, pass@1 versus pass@k, temperature, or number of samples). Without this information, the quantitative comparison among RTLCoder, BetterV, VerilogEval, and prompt-engineering baselines cannot be checked or reproduced. Please specify the evaluation setup and the version of each benchmark, and state which curves in the figure support each qualitative conclusion.
minor comments (5)
  1. [Throughout] The manuscript contains clear template artifacts that must be removed: the running header repeatedly reads 'Trovato et al.', the ACM Reference Format line gives a 2018 date and placeholder DOI, the Additional Key Words are the placeholder sentence 'Do, Not, Us, This, Code, Put, the, Correct, Terms, for, Your, Paper', and the footer says 'Manuscript submitted to ACM' on every page.
  2. [Section 2] The text identifies 'Perplexity's Claude3' and cites [3], but reference [3] is Anthropic's Model Card for Claude 3; the model is Anthropic's, not Perplexity's, and the attribution should be corrected.
  3. [Section 3.1] The discussion of LCDA reports a '25x speedup compared to state-of-the-art methods' without specifying the baseline method or the exact design point; please state the comparison target used in [65].
  4. [Section 4.1] There are several wording and typographical issues in this section, including 'close-sourced' for 'closed-source' and the sentence 'Encoder-only model [16] is primarily used for language prediction task, e.g., sentiment prediction', which misdescribes BERT-style models and should be rewritten.
  5. [Tables 1-3] The tables would be more actionable if they included a unified column for quantitative evaluation metrics (e.g., pass rate or version of the benchmark); currently the same project can appear with different descriptions in different tables and the metric information is scattered in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey is descriptive and its literature-based trend claims do not reduce to self-citations or fitted inputs.

full rationale

The paper is an expository survey, not a derivation: it contains no equations that map inputs to predictions, no fitted parameters renamed as results, and no uniqueness theorem invoked to force a choice. The two author-overlapping references (DRC-Coder [8] and LaMAGIC [9]) are used as descriptive examples in the taxonomy (Sections 3.4, 4.2, and 4.3), and the survey's organizational claims do not depend on their correctness or on any result unique to them. The Section 3 meta-study states it 'reviews all publications in the selected venues' without naming the venues; that is a transparency and auditability defect in the comprehensiveness claim, but it is not circularity because the survey's trend statements are reports about an external literature, not consequences of the survey's own definitions or assumptions. No step in the paper reduces to its own input, so the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The survey is a literature review, so it introduces no free parameters or invented entities. It relies on two unstated assumptions: the representativeness of the unnamed 'selected venues' and the completeness of its design-stage taxonomy. These assumptions are necessary for the comprehensiveness claim to hold.

assumptions (2)
  • domain assumption The unspecified 'selected venues' for the meta-study are representative of all significant LLM-for-EDA research.
    Section 3 states the review covers 'all publications in the selected venues' but never lists the venues, so the survey's comprehensiveness depends on an unstated representative sampling assumption.
  • domain assumption The design-stage taxonomy (system-level, RTL, synthesis/physical, analog) is a standard and complete partition of EDA.
    The survey organizes all works into four stages in Section 3, but does not justify that this partition covers the full design flow or that the categories are mutually exclusive.

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Cite this review

Pith. "Pith review of A Survey of Research in Large Language Models for Electronic Design Automation." pith.science (2026). https://pith.science/paper/LWSKXCSV

@misc{pith2026250109655,
  author       = {Pith},
  title        = {Pith review of: A Survey of Research in Large Language Models for Electronic Design Automation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LWSKXCSV}},
  note         = {Machine review of arXiv:2501.09655}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2501.09655 by the authors.

Figure 1
Figure 1. Overview of Large Language Models for EDA applications. Data sources comprise circuit representations from different design [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Comparing the functional correctness of Verilog code generation on VerilogEval-machine [ [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CircuitLM: A Multi-Agent LLM-Aided Design Framework for Generating Circuit Schematics from Natural Language Prompts

    cs.AI 2026-01 reject novelty 5.0 of 10

    A five-stage multi-agent pipeline with retrieval from a component database generates CircuitJSON schematics from natural-language prompts, achieving high ERC pass rates but much lower LLM-judge pass rates.

Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.