PPT-TPL-aware displacement-driven detailed placement refinement

Tao Lin and Chris Chu Iowa State University 1 Outline Background Problem definition Proof of NPCompleteness MILP formulation Heuristic Algorithm Experimental results Conclusions 2 Background. David Chinnery. Joseph . Shinnerl. Vladimir Yutsis. ISPD . 2015. Detailed Routing-Driven Placement Contest. with Fence Regions . and Routing . Blockages. www.ispd.cc/contests/15/ispd2015_contest.html. Wing-Kai Chow. , Jian . Kuang. , Xu He, . Wenzan. . Cai. , Evangeline F.Y. Young. The Chinese University of Hong Kong. {. wkchow. , . jkuang. , . xhe. . wzcai. , . fyyoung. }@cse.cuhk.edu.hk. ISPD 2014, Petaluma, CA. Congestion Mitigation in Monolithic . 3D IC Designs. Shreepad Panth. 1. , Kambiz Samadi. 2. , Yang Du. 2. , and Sung Kyu Lim. 1. 1. Dept. of Electrical and Computer Engineering, Georgia Tech, Atlanta GA, USA. Caleb Serafy and . Ankur. Srivastava. Dept. ECE, University of Maryland. 3/31/2014. 1. 3D Integration. Vertically stack chips and integrate layers with vertical interconnects. Through Silicon . Vias. Andrew B. Kahng and . Hyein Lee. UC . San . Diego VLSI CAD Laboratory. Minimum Implant Area Constraint. Motivation. Prior Work. Minimum Implant Area-Aware . Placement and Sizing. Experimental Results. Sub-14nm . Constraints. Kwangsoo. Han. , Andrew B. . Kahng. and . Hyein. Lee. {. kwhan. , . abk. , . hyeinlee. }@. ucsd.edu. http://vlsicad.ucsd.edu/. ECE Department, UC San Diego. Outline. Motivation & Previous Work. Modern Algorithms. Modern global placement algorithms:. Can handle extremely large netlists. Use analytic techniques. 2. 3. Two . families: . Modern Algorithms. Quadratic . placers. Non-convex . optimization placers . Inference. Hiroshi Unno (University of Tsukuba). Joint work with: Naoki Kobayashi, . Tachio. Terauchi, . Ryosuke. Sato, Takuya . Kuwahara. , . Kodai. Hashimoto, . Sho. Torii. 2015/7/4. HOPA 2015. Routability. Prediction from Placement using Data Mining Techniques. Wei-Ting J. Chan. $. , Yang Du. +. , Andrew B. . Kahng. $. , . Siddhartha Nath. $*. and . Kambiz. . Samadi. +. $. UC San Diego. Prediction from Placement using Data Mining Techniques. Wei-Ting J. Chan. $. , Yang Du. . , Andrew B. . Kahng. $. , . Siddhartha Nath. $*. and . Kambiz. . Samadi. . $. UC San Diego. *. Synopsys Inc., Mountain View. Scalable Detailed Placement Legalization for Complex Sub-14nm Constraints Kwangsoo Han , Andrew B. Kahng and Hyein Lee { kwhan , abk , hyeinlee }@ ucsd.edu http://vlsicad.ucsd.edu/ ECE Department, UC San Diego Diffusion Break-Aware Leakage Power Optimization and Detailed Placement in Sub-10nm VLSI Sun ik Heo †, Andrew B. Kahng ‡, Minsoo Kim‡ and Lutong Wang ‡ ‡ UC San Diego, † Samsung Electronics Co., Ltd. Fig S2yLi EuA-DRietveld refinement of the SXRD patterns of Na099-xxAl1-xSi1xO4yLi 001EuNASO yLi Eu 0 y 015 samplesTable S1 Main parameters of processing and refinement of the NxASO 0 x 025 and NxASO Maryam . Rabiee. , . Siavash. . Mirarab. Introduction to INSTRAL. Species Tree Estimation. Adding a species to an existing dataset with a species tree. Start from scratch?. Add directly to species tree? .