Late Breaking Results from Hybrid Design Automation for Field-coupled Nanotechnologies

Simon Hofmann; Marcel Walter; Lorenzo Servadei; Robert Wille

doi:10.1109/DAC56929.2023.10247933

Late Breaking Results from Hybrid Design Automation for Field-coupled Nanotechnologies

Simon Hofmann, Marcel Walter, Lorenzo Servadei, Robert Wille

Chair of Design Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Recent breakthroughs in atomically precise manufacturing are paving the way for Field-coupled Nanocomputing (FCN) to become a real-world post-CMOS technology. This drives the need for efficient and scalable physical design automation methods. However, due to the problem's NP-completeness, existing solutions either generate designs of high quality, but are not scalable, or generate designs in negligible time but of poor quality. In an attempt to balance scalability and quality, we created and evaluated a hybrid approach that combines the best of established design methods and deep reinforcement learning. This paper summarizes the obtained results.

Original language	English
Title of host publication	2023 60th ACM/IEEE Design Automation Conference, DAC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350323481
DOIs	https://doi.org/10.1109/DAC56929.2023.10247933
State	Published - 2023
Event	60th ACM/IEEE Design Automation Conference, DAC 2023 - San Francisco, United States Duration: 9 Jul 2023 → 13 Jul 2023

Publication series

Name	Proceedings - Design Automation Conference
Volume	2023-July
ISSN (Print)	0738-100X

Conference

Conference	60th ACM/IEEE Design Automation Conference, DAC 2023
Country/Territory	United States
City	San Francisco
Period	9/07/23 → 13/07/23

Access to Document

10.1109/DAC56929.2023.10247933

Cite this

Hofmann, S., Walter, M., Servadei, L., & Wille, R. (2023). Late Breaking Results from Hybrid Design Automation for Field-coupled Nanotechnologies. In 2023 60th ACM/IEEE Design Automation Conference, DAC 2023 (Proceedings - Design Automation Conference; Vol. 2023-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DAC56929.2023.10247933

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title = "Late Breaking Results from Hybrid Design Automation for Field-coupled Nanotechnologies",

abstract = "Recent breakthroughs in atomically precise manufacturing are paving the way for Field-coupled Nanocomputing (FCN) to become a real-world post-CMOS technology. This drives the need for efficient and scalable physical design automation methods. However, due to the problem's NP-completeness, existing solutions either generate designs of high quality, but are not scalable, or generate designs in negligible time but of poor quality. In an attempt to balance scalability and quality, we created and evaluated a hybrid approach that combines the best of established design methods and deep reinforcement learning. This paper summarizes the obtained results.",

author = "Simon Hofmann and Marcel Walter and Lorenzo Servadei and Robert Wille",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 60th ACM/IEEE Design Automation Conference, DAC 2023 ; Conference date: 09-07-2023 Through 13-07-2023",

year = "2023",

doi = "10.1109/DAC56929.2023.10247933",

language = "English",

series = "Proceedings - Design Automation Conference",

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Hofmann, S , Walter, M, Servadei, L & Wille, R 2023, Late Breaking Results from Hybrid Design Automation for Field-coupled Nanotechnologies. in 2023 60th ACM/IEEE Design Automation Conference, DAC 2023. Proceedings - Design Automation Conference, vol. 2023-July, Institute of Electrical and Electronics Engineers Inc., 60th ACM/IEEE Design Automation Conference, DAC 2023, San Francisco, United States, 9/07/23. https://doi.org/10.1109/DAC56929.2023.10247933

Late Breaking Results from Hybrid Design Automation for Field-coupled Nanotechnologies. / Hofmann, Simon ; Walter, Marcel; Servadei, Lorenzo et al.
2023 60th ACM/IEEE Design Automation Conference, DAC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings - Design Automation Conference; Vol. 2023-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Recent breakthroughs in atomically precise manufacturing are paving the way for Field-coupled Nanocomputing (FCN) to become a real-world post-CMOS technology. This drives the need for efficient and scalable physical design automation methods. However, due to the problem's NP-completeness, existing solutions either generate designs of high quality, but are not scalable, or generate designs in negligible time but of poor quality. In an attempt to balance scalability and quality, we created and evaluated a hybrid approach that combines the best of established design methods and deep reinforcement learning. This paper summarizes the obtained results.

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M3 - Conference contribution

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Late Breaking Results from Hybrid Design Automation for Field-coupled Nanotechnologies

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NanoPlaceR: Placement and Routing for Field-coupled Nanocomputing (FCN) based on Reinforcement Learning

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