PPT-Clock Clustering and IO Optimization for 3D Integration
Author : debby-jeon | Published Date : 2018-09-21
Samyoung Bang Kwangsoo Han Andrew B Kahng and Vaishnav Srinivas ECE and CSE Departments UC San Diego La Jolla CA 92093 Samsung Electronics Co Ltd Hwaseongsi
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Clock Clustering and IO Optimization for 3D Integration: Transcript
Samyoung Bang Kwangsoo Han Andrew B Kahng and Vaishnav Srinivas ECE and CSE Departments UC San Diego La Jolla CA 92093 Samsung Electronics Co Ltd Hwaseongsi South Korea. in . IC Implementation. Tuck-Boon Chan, Andrew B. Kahng, . Jiajia Li. . VLSI CAD LABORATORY, . UC. San Diego. Outline. Background and Motivation . Problem Statement. Our Methodologies. Experimental Setup and Results. . Kwangsoo. Han, Andrew B. Kahng, . Jongpil. Lee, . Jiajia Li. and Siddhartha Nath. VLSI CAD LABORATORY, . UC. San Diego. Outline. Motivation. Related Work. Our Optimization Framework. Experimental Setup and Results. Samyoung Bang*, Kwangsoo Han. ‡. ,. Andrew B. . . Kahng. ‡†. and Vaishnav Srinivas. ‡. ‡. ECE . and . †. CSE . Departments, UC San Diego, La Jolla, CA . 92093. *. Samsung . Electronics Co. Ltd, Hwaseong-si, South Korea. Greg . Ford. Introduction. Timing closure is a key component of all design flows.. Integrated into nearly every step and process.. Automation important given the complexity of timing analysis / results.. Tuck-Boon Chan, . Kwangsoo. Han, Andrew B. . Kahng. , Jae-. Gon. Lee and . Siddhartha . Nath. VLSI CAD LABORATORY, UC San Diego. Outline. Motivation and Previous Work. Our Approach. Experimental Setup. Andrew B. . Kahng. , . Jiajia Li. and . Lutong. Wang. . UC San Diego VLSI CAD Laboratory. Outline. Background and Motivation. Related Work. Our Methodology. Experimental Setup and Results. Conclusion. L. . T. . Biegler. Joint work with Alex Dowling, . Ravi . Kamath. , Ignacio Grossmann. June, 2014. Overview. Introduction. Process optimization . Formulation and solution strategies. Bilevel. Optimization . for . Adaptive . C. ircuit . D. esign. Ang. Lu, . Hao. He, and Jiang Hu. Introduction. Proposed Techniques. Experiment Result. Conclusion. Overview. 2. Design Challenges. Process Variations. Device Aging. Large Graphs. David . Hallac. , Jure . Leskovec. , Stephen . Boyd . Stanford . University. Presented by Yu Zhao. What is this paper about. Lasso problem. The lasso solution is unique when rank(X) = p, because the criterion is strictly convex.. Kadin Tseng. Boston University. Scientific Computing and Visualization. Outline. Introduction. Timing. Example Code. Profiling. Cache. Tuning. Parallel Performance. Code Tuning and Optimization. 2. Introduction. and Cluster Analysis. Dissertation Defense. Nan Li. Committee. : Dr. . Longin. Jan . Latecki. (Advisor). Dr. . Haibin. Ling. Dr. Slobodan . Vucetic. Tuck-Boon Chan, . Kwangsoo. Han, Andrew B. . Kahng. , Jae-. Gon. Lee and . Siddhartha . Nath. VLSI CAD LABORATORY, UC San Diego. Outline. Motivation and Previous Work. Our Approach. Experimental Setup. Based on Neutrosophic Set Theory. A. E. Amin. Department of Computer Science, Mansoura University, Mansoura 35516, Egypt. In this presentation, a new technique is used to an unsupervised learning image classification based on integration between . Collections through . Contextual Focal Points. Kai . Xu. , . Rui. Ma, . Hao. Zhang, . Chenyang. Zhu,. Ariel . Shamir,. . Daniel Cohen-Or,. . . Hui. . Huang. Shenzhen . VisuCA. Key Lab / .
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