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Panel Statement: EDA Needs at Advanced Technology Nodes

Published: 12 March 2024 Publication History

Abstract

Improvements in EDA technology become more urgent with the loss of traditional scaling levers and the growing complexity of leading-edge designs. The roadmap for device and cell architectures, power delivery, multi-tier integration, and multiphysics signoffs presents severe challenges to current optimization frameworks. In future advanced technologies, the quality of results, speed, and cost of EDA will be essential components of scaling. Each stage of the design flow must deliver predictable results for given inputs and targets. Optimization goals must stay correlated to downstream and final design outcomes.
The EDA and design ecosystem must achieve an aligned interest in scalable, efficient optimization. End-case solvers and core engines should become commoditized, freeing researchers and EDA suppliers to address new automation requirements. A rearchitected, rebuilt EDA 2.0 must achieve results in less time (multithreading, GPU) and better results in same time (cloud-native, sampling), while enabling AI/ML that steers and orchestrates optimizers within the design process. To deliver high-value system-technology pathfinding and implementation tools in time to meet industry needs, EDA must develop (learn) new optimization objectives, hierarchical solution strategies, multiphysics reduced-order models, and conditioning of instances at the interstices between chained optimizations. Last, as a community, EDA will meet the challenges of advanced technologies by investing in research and workforce development, via open infrastructure for ML EDA, and via proxy research enablements.

References

[1]
A. B. Kahng, "Solvers, Engines, Tools and Flows: The Next Wave for AI/ML in Physical Design", Proc. ISPD, 2024.
[2]
https://vlsicad.ucsd.edu

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  1. Panel Statement: EDA Needs at Advanced Technology Nodes

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    cover image ACM Conferences
    ISPD '24: Proceedings of the 2024 International Symposium on Physical Design
    March 2024
    286 pages
    ISBN:9798400704178
    DOI:10.1145/3626184
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 12 March 2024

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    Author Tags

    1. eda
    2. machine learning
    3. optimization
    4. physical design
    5. scaling

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    • DARPA

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    Overall Acceptance Rate 62 of 172 submissions, 36%

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    International Symposium on Physical Design
    March 16 - 19, 2025
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