Invited Paper: LLM4HWDesign Contest: Constructing a Comprehensive Dataset for LLM-Assisted Hardware Code Generation with Community Efforts
Zhongzhi Yu, Chaojian Li, Yongan Zhang, Mingjie Liu, Nathaniel Pinckney, Wenfei Zhou, Haoyu Yang, Rongjian Liang, Haoxing Mark Ren, Yingyan Lin · 2024
Large Language Models (LLMs) show promise in streamlining hardware design, particularly in hardware code generation. However, the development of LLMs for this domain is severely hindered by the scarcity of large-scale, high-quality, and publicly accessible hardware code datasets. This shortage limits the effective fine-tuning of LLMs, impeding their ability to acquire hardware domain knowledge and generate practical designs. To address this challenge, we have organized the first-of-its-kind LLM4HWDesign contest, a community-driven initiative aimed at constructing a large-scale, high-quality dataset for hardware code generation. The contest adopts a two-phase approach, focusing on expanding the scale and quality of an existing hardware code generation dataset, respectively. By harnessing the collective efforts of the hardware design community, the LLM4HWDesign contest seeks to establish a critical resource for advancing LLM-assisted hardware design workflows. The primary goal of this initiative is to deliver a comprehensive dataset compiled from participants' submissions. We hope the released dataset will significantly advance the field of LLM-assisted hardware design and provide substantial benefits to the broader hardware community.