Year: 2024
Type: conference-paper
Abstract: The electronic design automation (EDA) community has recently begun recognizing the potential of generative artificial intelligence (AI) in chip design. However, its full potential is not fully exploited due to the limited availability of publicly accessible datasets crucial for advancing research in EDA. This paper highlights the dual role of generative AI; in particular, it showcases (i) BeGAN, the use of a generative AI strategy to create thousands of realistic benchmarks for power grid synthesis and analysis to advance power-related research, and (ii) EDA Corpus---an expert-curated and generative AI-enhanced dataset to serve research and development of EDA tool assistants. These two case studies emphasize the ability of generative methods to create and utilize datasets to advance research and lower the barriers to entry in EDA.
Authors: Vidya A. Chhabria, Bing-Yue Wu, Utsav Sharma, Kishor Kunal, Austin Rovinski, Sachin S. Sapatnekar
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4409285473