PROS 2.0: A Plug-In for Routability Optimization and Routed Wirelength Estimation Using Deep Learning

Recently, the topic of how to utilize prior knowledge obtained by machine-learning (ML) techniques during the EDA flow has been widely studied. In this article, we study this topic and propose a practical plug-in named PROS for both routability optimization and routed wirelength estimation which can...

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Veröffentlicht in:	IEEE transactions on computer-aided design of integrated circuits and systems 2023-01, Vol.42 (1), p.164-177
Hauptverfasser:	Chen, Jingsong, Kuang, Jian, Zhao, Guowei, Huang, Dennis J.-H., Young, Evangeline F. Y.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Benchmark testing Benchmarks Circuit optimization Congestion Costs Deep learning design automation Estimation Machine learning Optimization Parameters Placement Plugs Routing Runtime Wires
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