top of page


1st IEEE International Workshop on LLM-Aided Design

June 28-29 2024, Almaden, CA 

(In cooperation with ACM SIGDA)


This new international workshop will focus on how to use LLM (Large Language Model) as a methodology to help design circuits, software, and computing systems with improved quality, productivity, robustness, and cost. It is the first of its kind international workshop in the community that will focus on discussing results that leverage the significant advancement and innovation captured by the generative AI and LLM technology to offer new methods and solutions for design automation targeting various applications. The workshop will be a timely venue that will host leading researchers and thought leaders in this fast-growing area and will provide a forum for researchers and practitioners to present their latest results, contribute open-source LLM models, datasets, tool flows, and offer benchmarking, testing and validation methods and solutions. Topics of interest include but are not limited to new methodologies, datasets, and benchmarks, pertaining to:

  • LLM-aided hardware/software design specification and code generation

  • System-level design methodology development with LLMs

  • Security and robustness of LLM-generated designs

  • LLM-aided security verification and bug-fixing

  • Finetuning of large foundation models for specialization in design automation

  • New Datasets and Benchmarks of relevance to LLM-aided design

  • LLMs for EDA, including HLS, physical design, and EDA scripting

  • LLMs for reasoning and math used in design process 

  • Computational efficiency of LLM-aided design tools

  • Privacy, copyright and other regulatory concerns around LLM-aided design

  • Data science and data analytics for LLM-aided design

  • Evaluation and testing of LLM-aided design models and methods



The workshop invites 4-page regular papers in the IEEE Conference format. Page limits do not include referencesor a well-marked appendix. Papers should be anonymized for double-blind peer review. We strongly encourage papers with a commitment to open and reproducible research, including datasets and methods. Papers with open-source implementations will be highlighted at the workshop. All papers will be published on IEEEXplore. Papers can be submitted via OpenReview ( starting March 1st, 2024. See the workshop website ( for more details.


LAD'24 welcomes papers describing new Datasets and Benchmarks of relevance to LLM-Aided Design community. Papers describing new datasets and benchmarks must follow the exact same rules and procedures as regular 4 page papers, will be peer reviewed and published in the proceedings. Datasets and Benchmark papers must include an explicit commitment to releasing all artifacts publicly if accepted. The commitment should be added to the Conclusion section of the paper.  Accepted papers will be highlighted at the workshop, and separate Best Dataset and Benchmarks paper prizes will be awarded.


Full paper submission:             April 8th, 2024, Anywhere on Earth (AoE) (final deadline)

Notification of acceptance:     May 10th, 2024

Camera ready paper due:       May 26th, 2024



Authors should follow the recommended IEEE Conference format for their submissions ( to ensure compatibility with IEEEXplore. Manuscripts should be blinded so as to not disclose author identities. LAD’24 encourages open-source and reproducible research. Authors can provide anonymized URLs to their datasets and methods in the paper, or commit to open release on paper acceptance. However, this is not mandatory.




LAD’24 expects previously unpublished papers describing original research. Accepted LAD’24 papers will appear on IEEEXplore and count as formal, archival publications. LAD’24 papers can be enhanced and submitted for publication in other conferences or journals; the enhancements should be significant and consistent with the policies of these venues.



General Chairs                                               Ruchir Puri (IBM), Deming Chen (UIUC)

Technical Program Committee Chairs         Siddharth Garg (NYU), Haoxing (Mark) Ren (NVidia)

Finance Chair                                                 Callie Hao (GaTech) 

Special/Invited Sessions Chair                     Azalia Mirhoseini (Stanford) 

Open Community Chair                               Yingyan (Celine) Lin (GaTech)

Industrial Liaison                                           Yong Liu (Cadence)

Local Arrangements                                      Ehsan Degan (IBM)

Publicity Chair                                                Jeff Goeders (BYU)

Industry Outreach Chair                                David Z Pan (UT Austin)

Publications Chair                                          Kanad Basu (UT Dallas)

Webmaster                                                     Kaiwen Cao (UIUC)

bottom of page