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.
