Generative Methods in EDA: Innovations in Dataset Generation and EDA Tool Assistants

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

Institutions

Publication Details

Published
2024-10-27
DOI
https://doi.org/10.1145/3676536.3697122
Citations
1
Primary Topic
Machine Learning and Data Classification
Type
article
Field-Weighted Citation Impact
0.35
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generative Methods in EDA: Innovations in Dataset Generation and EDA Tool Assistants

Austin Rovinski, Kishor Kunal, Sachin S. Sapatnekar, Vidya A. Chhabria et al.
1 citations
Machine Learning and Data Classification
0.35
article

Generative Methods in EDA: Innovations in Dataset Generation and EDA Tool Assistants

Austin Rovinski, Kishor Kunal, Sachin S. Sapatnekar, Vidya A. Chhabria, Utsav Sharma, Bing-Yue Wu
article en
1 citations

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.

University of Minnesota (US), Arizona State University (US), New York University (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 29%
Machine Learning and Data Classification
0.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.