Pith. sign in

REVIEW 3 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

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

classification cs.LG
keywords designelectronicmodelssurveyautomationcapabilitiesinsightslanguage
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

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

  1. Complexity Horizons of Compressed Models in Analog Circuit Analysis

    cs.AI 2026-05 unverdicted novelty 5.0

    Prerequisite graphs map compressed LLM performance boundaries in analog circuit analysis to allow selecting the smallest viable model for a given task complexity.

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

    cs.AI 2026-01 reject novelty 5.0

    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.

  3. Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development

    cs.SE 2026-05 unverdicted novelty 4.0

    Agentic Agile-V uses Agile-V as backbone and a Specify-Constrain-Orchestrate-Prove-Evolve-Verify loop to convert AI agent conversations into traceable engineering artifacts with acceptance evidence.