Preprint
Deep Representation Learning for Electronic Design Automation
04 May 2025
Abstract
Representation learning has become an effective technique utilized by
electronic design automation (EDA) algorithms, which leverage the natural
representation of workflow elements as images, grids, and graphs. By addressing
challenges related to the increasing complexity of circuits and stringent
power, performance, and area (PPA) requirements, representation learning
facilitates the automatic extraction of meaningful features from complex data
formats, including images, grids, and graphs. This paper examines the
application of representation learning in EDA, covering foundational concepts
and analyzing prior work and case studies on tasks that include timing
prediction, routability analysis, and automated placement. Key techniques,
including image-based methods, graph-based approaches, and hybrid multimodal
solutions, are presented to illustrate the improvements provided in routing,
timing, and parasitic prediction. The provided advancements demonstrate the
potential of representation learning to enhance efficiency, accuracy, and
scalability in current integrated circuit design flows.
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Details
- Title
- Deep Representation Learning for Electronic Design Automation
- Creators
- Pratik ShresthaSaran PhatharodomAlec AversaDavid BlankenshipZhengfeng WuIoannis Savidis
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; College of Engineering
- Other Identifier
- 991022052281204721