{"id":118447,"date":"2021-10-11T21:26:53","date_gmt":"2021-10-11T12:26:53","guid":{"rendered":"http:\/\/175.125.95.178\/ai-in-circuit\/18447\/"},"modified":"2026-04-05T23:35:56","modified_gmt":"2026-04-05T14:35:56","slug":"18447","status":"publish","type":"ai-in-circuit","link":"http:\/\/ee.presscat.kr\/en\/ai-in-circuit\/18447\/","title":{"rendered":"Yonghwi Kwon, Daijoon Hyun, Giyoon Jung, and Youngsoo Shin, \u201cDynamic IR drop prediction using image-to-image translation neural network,&quot; Proc. Int&#039;l Symp. on Circuits and Systems (ISCAS), May 2021."},"content":{"rendered":"<p><span lang=\"EN-US\" style=\"font-size:10.0pt\"><span style=\"line-height:107%\"><span style=\",serif\"><span style=\"color:black\">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.<\/span><\/span><\/span><\/span><\/p>\n<p><span lang=\"EN-US\" style=\"font-size:10.0pt\"><span style=\"line-height:107%\"><span style=\",serif\"><span style=\"color:black\"><\/p>\n<div class=\"\"><img decoding=\"async\" class=\"\" src=\"\/wp-content\/uploads\/drupal\/AI in EE \uc2e0\uc601\uc218\uad50\uc218\ub2d8 \uc5f0\uad6c\uc2e42.png\" alt=\"\" title=\"\"><\/div>\n<p><\/span><\/span><\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>706<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-118447","ai-in-circuit","type-ai-in-circuit","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-circuit\/118447","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-circuit"}],"about":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/types\/ai-in-circuit"}],"wp:attachment":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/media?parent=118447"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}