Some of these surfaces and interfaces are obvious while others are not.
The integrity of these surfaces can have a major impact on the long term reliability of the packaged die.
How Artificial Intelligence is Accelerating the Race to Self-Driving Cars by Danny Shapiro, Senior Director of NVIDIA's Automotive Business Unit Date: Wednesday, November 9, 2016 Time: pm - pm Location: NVIDIA Corp, 2880 Scott Blvd, Santa Clara 95050 (Meeting will be held in Bldg. - Marco Polo Conference Room) Admission: Open to all IEEE members and non-members for FREE Abstract: Virtually every automaker is working on driver assistance systems and self-driving cars.
Development of a truly autonomous car requires deep learning and artificial intelligence.With deep learning, the vehicle can be trained to have super-human levels of perception in order to navigate more safely than humanly possible.Breaks with snacks and drinks will be part of the seminar experience. This was largely due to the development of stronger vehicles, active and passive safety systems, and comprehensive reliability development and test programs to weed out reliability and safety issues before they affected customers.At the same time, the driver --the most unreliable part of the car-- underwent no improvements.Based on the same deep learning techniques used for detecting cancer and for beating the world champion Go player, artificial intelligence platforms are destined to enable fully autonomous vehicles.
This talk will cover the details of artificial intelligence in the automotive environment, and how a deep learning "supercomputer in a box" within the car itself will enable the dream of an autonomous vehicle.Given that 94% of traffic accidents involve human error, this is one place where we believe we really can really bring technology to bear.The self-driving car program is aimed squarely at replacing the human driver, and in doing so, making a step function improvement in automotive safety and reliability.Valeriy Sukharev, Technical Lead at the Design to Silicon Division (Calibre) of Mentor Graphics Corporation Co-Sponsor: IEEE Electron Devices Society and IEEE CPMT Chapter Date: Thursday, November 3, 2016 Time: pm - pm - Check-in pm - pm - Presentation with Q & A Location: Qualcomm Inc., 3165 Kifer Rd, Santa Clara, CA, 95051 (Meeting will be in the cafeteria, Building B) Admission: Open to all IEEE members and non-members for FREE Food sponsored by I. The growing need in a simulation-based design verification flow capable of analyzing and detecting across-die out-of- spec stress-induced variations in MOSFET/Fin FET electrical characteristics is addressed.A physics-based compact modeling methodology for multi-scale simulation of all contributing components of stress induced variability is described.This Webinar will explore the handful of best practices that we have found are almost always used by hardware-software development teams making products that have been demonstrated to be highly reliable.