PPT-Probabilistic Bug-Masking Analysis for Post-Silicon Tests i

Doowon Lee Tom Kolan Arkadiy Morgenshtein Vitali Sokhin Ronny Morad Avi Ziv Valeria Bertacco University of Michigan IBM Research Haifa example of processor die. (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. ECE . 7502 Class Discussion . B. en Calhoun. Thursday January 22, 2015. Requirements. Specification. Architecture. Logic / Circuits. Physical Design. Fabrication. Manufacturing Test. Packaging Test. PCB Test. Shou-pon. Lin. Advisor: Nicholas F. . Maxemchuk. Department. . of. . Electrical. . Engineering,. . Columbia. . University,. . New. . York,. . NY. . 10027. . Problem: . Markov decision process or Markov chain with exceedingly large state space. Reviewer: . Shuo-Ren. , Lin. 2012/5/11. ALCom. 1. Abstract. Post-silicon functional validation challenges. Light overhead associated with the testing procedures. High-quality validation. Post-silicon functional exerciser. Chapter 1: An Overview of Probabilistic Data Management. 2. Objectives. In this chapter, you will:. Get to know what uncertain data look like. Explore causes of uncertain data in different applications. Indranil Gupta. Associate Professor. Dept. of Computer Science, University of Illinois at Urbana-Champaign. Joint work with . Muntasir. . Raihan. . Rahman. , Lewis Tseng, Son Nguyen, . Nitin. . Vaidya. Silicon wafer. www.guardian.co.uk. http://. mrsec.wisc.edu. en.wikipedia.org. Wafers are cut from . boules. , . which are large . logs . of uniform . silicon.. Looking at this picture, . where. do you think silicon . Silicon wafer. www.guardian.co.uk. http://. mrsec.wisc.edu. en.wikipedia.org. Wafers are cut from . boules. , . which are large . logs . of uniform . silicon.. Looking at this picture, . where. do you think silicon . Bagrada. . hilaris. A Wide-Ranging Pest of . Brassicas. Thomas M. Perring and Darcy Reed. Department of Entomology. Univ. of California-Riverside. Geographic Origin of . B. . hilaris. Africa and the Middle East. A . Hybrid Approach. Abigail . Goldsteen. , . Ksenya. . Kveler. , Tamar . Domany. , Igor . Gokhman. , Boris . Rozenberg. , Ariel . Farkash. Information Privacy and Security, IBM Research – Haifa. Presented by . Jordan . Radice. jordanra@buffalo.edu. Advanced . VLSI. Spring 2015. Dr. Ram Sridhar. Background. Moore’s Law (As we all know): Doubling of transistors every 1.5 ~ 2.0 years.. Dennard scaling works in conjunction with Moore’s law in that we maintain the overall power density of the chip area despite the exponential increase in transistors.. Cedar Mountain post acute rehabilitation center provide a therapeutic environment for our residents Yucaipa. Individualized treatment programs are developed with the interdisciplinary team of nursing, social services, Healthcare and dietary in consultation with your physicians. CS772A: Probabilistic Machine Learning. Piyush Rai. Course Logistics. Course Name: Probabilistic Machine Learning – . CS772A. 2 classes each week. Mon/. Thur. 18:00-19:30. Venue: KD-101. All material (readings etc) will be posted on course webpage (internal access). Sarat Buasai, Alexander McMahon, Yi Jie Wu. Advisors: Adam C. Powell, N. Aaron Deskins, Walter Towner. 01. Background. Project Goal. Methods. 02. 03. 04. 05. 06. Results and Discussion. Conclusion. Acknowledgement.