Technical Meeting: GPUs from Gaming to Deep Learning & Cancer Research


NVIDIA GPUs: A Transition from Gaming to Autonomous Vehicle & Cancer Research


The history of NVIDIA and Graphical Processing Units (GPUs) is indeed a fascinating story. Once just a mere assistant to hobby then later mainstream game consoles, now GPUs have become a major player in accelerators for an entire spectrum of computing and beyond. In the world of big data, machine learning, artificial intelligence, vision, etc., the power of GPUs is viewed by all as being the prime driver and very central to making the GPU architecture both ubiquitous and very pervasive. The talk will trace this transition from Artificial Intelligence to Machine Learning to Deep Learning and Neural Networks. NVIDIA will introduce to us in great detail all about Deep Learning (DL) and Deep Neural Networks   and all the associated fundamentals. A stunning variety of different use cases will be presented, as well as how one can get started in this arena.

 



  Date and Time

  Location

  Contact

  Registration


  • EC 116, Engineering Center
  • Oakland University
  • Rochester, Michigan
  • United States 48309-4479
  • Building: Engineering Center
  • Room Number: EC 116
  • Click here for Map

Staticmap?size=250x200&sensor=false&zoom=14&markers=42.6719751%2c-83
  • sharan.kalwani@ieee.org

    Phone: +1 (248) 980-UNIX

    OR

    ganesan@oakland.edu

    Phone: +1 (248) 635-5890

  • Co-sponsored by Subramaniam Ganesan
  • Registration closed


  Speakers

Ty McKercher

Ty McKercher of NVIDIA

Topic:

GPUs: A transition from gaming to Deep learning & Cancer Reserach

The history of NVIDIA and Graphical Processing Units (GPUs) is indeed a fascinating story. Once just a mere assistant to hobby then later mainstream game consoles, now GPUs have become a major player in accelerators for an entire spectrum of computing and beyond. In the world of big data, machine learning, artificial intelligence, vision, etc., the power of GPUs is viewed by all as being the prime driver and very central to making the GPU architecture both ubiquitous and very pervasive. The talk will trace this transition from Artificial Intelligence to Machine Learning to Deep Learning and Neural Networks. NVIDIA will introduce to us in great detail all about Deep Learning (DL) and Deep Neural Networks   and all the associated fundamentals. A stunning variety of different use cases will be presented, as well as how one can get started in this arena.

Biography:

Over the last 9 Years, Ty has integrated NVIDIA technologies to help transform customer workflows. Ty enjoys solving challenging customer problems that intersect HPC, Visualization, and Virtualization. Lately, Ty is helping customers’ measure value from emerging technology evaluations in the field of Artificial Intelligence using Deep Neural Networks. Ty is leading a team of NVIDIA Solution Architects for DGX-1 server deployments, and has co-authored a CUDA programming book, plus a chapter in an OpenACC compiler directives book.





Agenda

6:00 PM - Welcome and Introductions, Chapter business update

6:15 PM - Technical Talk

7:15 PM - Q & A

7:45 PM - Wrap Up

7:45 to close - Networking 

 



A Joint Oakland University/IEEE Computer Society SEM Chapter Presentation