DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kwon, Yonghwi | ko |
dc.contributor.author | Shin, Youngsoo | ko |
dc.date.accessioned | 2022-11-25T01:00:25Z | - |
dc.date.available | 2022-11-25T01:00:25Z | - |
dc.date.created | 2022-11-22 | - |
dc.date.created | 2022-11-22 | - |
dc.date.issued | 2022-09-12 | - |
dc.identifier.citation | 4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022, pp.71 - 76 | - |
dc.identifier.uri | http://hdl.handle.net/10203/300936 | - |
dc.description.abstract | Recurrent U-Net (RU-Net) is employed for fast prediction of dynamic IR-drop when power distribution network (PDN) contains capacitor components. Each capacitor can be modeled by a resistor and a current source, which is a function of v(t-Δt) node voltages at time t - Δt allow the PDN to be solved at time t which then allows the analysis at t + Δt and so on. Provided that a quick prediction of IR-drop at one time instance can be done by U-Net, a image segmentation model, the analysis of PDN containing capacitors can be done by a number of U-Net instances connected in series, which become RU-Net architecture. Four input maps (effective PDN resistance map, PDN capacitance map, current map, and power pad distance map) are extracted from each layout clip, and are provided to RU-Net for IR-drop prediction. Experiments demonstrate that the proposed IR-drop prediction using the RU-Net is faster than a commercial tool by 16 times with about 12% error, while a simple U-Net-based prediction yields 19% error due to its inability to consider capacitors. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | Fast Prediction of Dynamic IR-Drop Using Recurrent U-Net Architecture | - |
dc.type | Conference | - |
dc.identifier.wosid | 000866282100013 | - |
dc.identifier.scopusid | 2-s2.0-85139194248 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 71 | - |
dc.citation.endingpage | 76 | - |
dc.citation.publicationname | 4th ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2022 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Snowbird Center | - |
dc.identifier.doi | 10.1145/3551901.3556477 | - |
dc.contributor.localauthor | Shin, Youngsoo | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.