ICCAD Logo LLM4HWDesign

ICCAD Contest on LLM-Assisted Hardware Code Generation

(LLM4HWDesign @ ICCAD 2024)

Welcome to the ICCAD Contest on LLM-Assisted Hardware Code Generation (LLM4HWDesign @ ICCAD 2024)!

Large Language Models (LLMs) have demonstrated impressive capabilities in generating high-quality content, sparking interest in their application to hardware design. By assisting in the translation of human instructions into hardware designs (e.g., hardware code), LLMs have the potential to streamline the labor-intensive process of hardware development. Unfortunately, the development of LLMs for hardware design is severely hindered by the scarcity of high-quality, publicly accessible hardware code datasets. Specifically, the lack of adequate datasets prevents effective fine-tuning of LLMs, a critical method for equipping them with hardware domain knowledge and mitigating their limited exposure to hardware-specific data during pretraining. This shortage thus significantly impedes progress in LLM-assisted hardware code generation.

The LLM4HWDesign contest aims to harness community efforts to develop an open-source, large-scale, and high-quality dataset for hardware code generation, igniting an ImageNet-like revolution in LLM-based hardware code generation. To achieve this goal, the LLM4HWDesign contest encourages participants to gather data samples and develop innovative data cleaning and labeling techniques that can effectively enhance the scale and quality of datasets for hardware code generation.

The detailed description of the contest problem can be found at Problem page.

If there are any questions that are not addressed in the FAQ page, please feel free to contact us at llm4hwdesign@groups.gatech.edu.

News

▪ 10/17 We are hosting the LLM4HWDesign Contest session at ICCAD 2024 in Salons A-C at the Newark Liberty International Airport Marriott on October 28th at 10:00 AM. Please join us for the award ceremony and a detailed overview of the contest and our collected dataset!
▪ 10/16 SDUAES, UMD, and VeriBest (ranked in alphabetical order) are among the top three winners in our contest! Detailed results for all other teams are released in the Results page!
▪ 09/07 We are now accepting submission for Phase I! Please check the detailed submission instructions in the Submission page!
▪ 08/27 We are thrilled to announce that Lambda is now sponsoring our contest by providing participants with cloud computing credits and faciliating our official evaluation process! Thank you Lambda Lab! #BuiltonLambda
▪ 08/10 The Phase II of our contest is officially started! Please check out the problem page and the starting toolkit for more information!
▪ 08/09 We have released the fine-tuning code we will use in our starting toolkit! Please feel free to check it out!
▪ 07/30 [Important Update] We are extending Phase I until Sep. 15 while keeping the Phase II schedule unchanged. However, the target dataset for Phase II has been updated to the original MG-Verilog dataset. For more details, please refer to the schedule and problem description.
▪ 07/07 The contest is officially kicked off!
▪ 07/07 We have released starting toolkit
▪ 07/01 We have released the base dataset we will be using!
▪ 06/22 Our website is online!

Tentative Schedule

▪ Contest Release Date: Jun.22, 2024
▪ Registration Deadline: Jul. 30, 2024
▪ Phase I - Data Sample Collection: Jul. 07, 2024 ~ Sep. 15, 2024
▪ Phase I Submission Deadline: 11:59:59 PM AoE, Sep. 15, 2024
▪ Phase II - Automatic Labeling Technique Exploration: Aug. 20, 2024 ~ Oct. 04, 2024
▪ Phase II Submission Deadline: 11:59:59 PM AoE, Oct. 04, 2024
▪ Top Three Teams Notified: Oct. 10, 2024
▪ Winner Announced: To be announced during ICCAD

Tentative Awards

▪ 1st Place Award: 1 × RTX 4080 GPU + US $2000 per team
▪ 2nd Place Award: 1 × RTX 4080 GPU + US $1000 per team
▪ 3rd Place Award: 1 × RTX 4070 GPU + US $500 per team
▪ Honorable Mention: Top three teams in each phase

Sponsors

Contest Organizers

Zhongzhi Yu

Zhongzhi Yu
Georgia Tech

Chaojian Li

Chaojian Li
Georgia Tech

Yongan Zhang

Yongan Zhang
Georgia Tech

Zhongzhi Yu

Mingjie Liu
Nvidia Corporation

Zhongzhi Yu

Nathaniel Pinckney
Nvidia Corporation

Zhongzhi Yu

Wenfei Zhou
Nvidia Corporation

Zhongzhi Yu

Haoyu Yang
Nvidia Corporation

Zhongzhi Yu

Rongjian Liang
Nvidia Corporation

Zhongzhi Yu

Mark Ren
Nvidia Corporation

Zhongzhi Yu

Yingyan (Celine) Lin
Georgia Tech


For any questions, please contact us at llm4hwdesign@groups.gatech.edu