AI in EE

AI IN DIVISIONS

AI in Circuit Division

AI in EE

AI IN DIVISIONS

AI in Circuit Division ​

AI in Circuit Division

Yonghwi Kwon, Daijoon Hyun, Giyoon Jung, and Youngsoo Shin, “Dynamic IR drop prediction using image-to-image translation neural network," Proc. Int'l Symp. on Circuits and Systems (ISCAS), May 2021.

Dynamic IR drop analysis is very time consuming, so it is only applied in signoff stage before tapeout. U-net model, which is an image-to-image translation neural network, is employed for quick analysis of dynamic IR drop. A number of feature maps are used for u-net input: a map of effective PDN resistance seen from each gate, a map of current consumption of each gate (in particular time instance), and a map of relative distance to nearest power supply pad. A layout is partitioned into a grid of regions and IR drop is predicted region-by-region. For fast prediction, (1) analysis is performed only in time windows which are estimated to cause high IR drop, and (2) effective PDN resistance is approximated through a proposed simplification method. Experiments with a few test circuits demonstrate that dynamic IR drop is predicted 20 times faster than commercial analysis package with 15% error.