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Conference proceeding
EDA-schema: A Graph Datamodel Schema and Open Dataset for Digital Design Automation
GLSVLSI '24: Proceedings of the Great Lakes Symposium on VLSI 2024, pp 69-77
12 Jun 2024
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
The growing complexity of very large-scale integrated (VLSI) circuits due to CMOS technology scaling has led to an increased interest in utilizing machine learning (ML) techniques for design automation. However, the lack of available datasets and established standards for the representation of datasets presents substantial challenges within the research community. Of particular concern is the lack of interoperability and comparability of ML-driven research in the design of digital circuits, which effectively limits collaboration. In this paper, EDA-schema, an open and comprehensive graph schema, is introduced to address such challenges by providing a structured framework for representing datasets for digital design automation. The schema represents the physical attributes and quality-of-results (QoR) metrics of a circuit across various stages of the physical design flow, including logical synthesis, floorplanning, placement, clock network synthesis, and global and local routing. Utilizing the Skywater 130 nm process design kit (PDK) and the OpenROAD toolset, a dataset of physical designs is generated and analyzed based on the circuits from the IWLS’05 benchmark suite. The dataset is made publicly available, anticipating contributions that further advance the field of ML-driven digital design.
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Details
- Title
- EDA-schema: A Graph Datamodel Schema and Open Dataset for Digital Design Automation
- Creators
- Pratik Shrestha (Corresponding Author) - Drexel University, College of EngineeringAlec AversaSaran PhatharodomIoannis Savidis - Drexel University, Electrical and Computer Engineering
- Publication Details
- GLSVLSI '24: Proceedings of the Great Lakes Symposium on VLSI 2024, pp 69-77
- Conference
- GLSVLSI '24: Great Lakes Symposium on VLSI 2024 (Clearwater, Florida, 12 Jun 2024–14 Jun 2024)
- Publisher
- Association for Computing Machinery
- Number of pages
- 9
- Resource Type
- Conference proceeding
- Academic Unit
- Electrical and Computer Engineering; College of Engineering
- Web of Science ID
- WOS:001253690000012
- Scopus ID
- 2-s2.0-85195504201
- Other Identifier
- 991021883715504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Hardware & Architecture
- Computer Science, Information Systems
- Computer Science, Software Engineering
- Computer Science, Theory & Methods