Generating Routing-Driven Power Distribution Networks with Machine-Learning Technique

Li-De Chen
Li-De Chen
Chien-Hsueh Lin
Chien-Hsueh Lin
Szu-Pang Mu
Szu-Pang Mu
Cheng-Hong Tsai
Cheng-Hong Tsai
Yen-Chih Chiu
Yen-Chih Chiu

ISPD'16: International Symposium on Physical Design Santa Rosa California USA April, 2016, pp. 145-152, 2016.

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Abstract:

As technology node keeps scaling and design complexity keeps increasing, power distribution networks (PDNs) require more routing resource to meet IR-drop and EM constraints. This paper presents a design flow to generate a PDN that can result in minimal overhead for the routing of the underlying standard cells while satisfying both IR-drop...More

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