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

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, exist...

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Veröffentlicht in:2023 60th ACM/IEEE Design Automation Conference (DAC) S. 1 - 2
Hauptverfasser: Hofmann, Simon, Walter, Marcel, Servadei, Lorenzo, Wille, Robert
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 09.07.2023
Schlagworte:
Automation
Deep learning
Design automation
Layout
Logic gates
Reinforcement learning
Scalability
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Zusammenfassung: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.
DOI:10.1109/DAC56929.2023.10247933