NVIDIA and Arizona State University (ASU) School of Computing and Augmented Intelligence are hosting a GPU Tech Talk Day. NVIDIA wishes to recognize Professor Heni Ben Amor and the ASU Center for Embedded Systems for their support hosting this event. The day will focus on Embedded research at ASU along with the role NVIDIA GPUs are playing in Deep Learning and Embedded in Academia and industry.
Why you should attend: GPUs, Deep learning and Embedded Systems are a rapidly growing segment of artificial intelligence. They are increasingly used to deliver near-human level accuracy for image classification, voice recognition, natural language processing, sentiment analysis, recommendation engines, and more. Applications areas include facial recognition, scene detection, advanced medical and pharmaceutical research, and autonomous, self-driving vehicles.
Where: ASU Schwada Classroom Office Building 228 (SCOB 228)
9:30 AM Welcome & Introductions – Jon Saposhnik NVIDIA Senior Account Manager, Professor Heni Ben Amor ASU
9:45AM Various ASU Faculty and Research Talks (Check for updates)
– Robotics & Grasp manipulation
– Robotics Vision to Control
– Robotics , Machine Learning & Tensor Flow
12:00pm Lunch provided by NVIDIA (free)
1:00pm – 2:15pm The Jetson Embedded Platform harnessing the power of NVIDIA’s CUDA-enabled GPU cores
Barrett Wiliams: NVIDIA Technical Marketing Manager, Jetson/Tegra Embedded
The Jetson Embedded platform enables easy movement from concept stage all the way to product launch, enabling one to quickly develop and deploy compute-intensive systems for computer vision, robotics, medicine, and more. Through every stage of your product development process, beginning with Exploration and Evaluation, on to the Design of Hardware, Platform Software and Ap
plication Software, all the way through Production and Product Support, the resources you need are right at your fingertips.
This interactive discussion will introduce the audience to:
– The Jetson Embedded platform, TX1
– A overview of the Complete suite of development and profiling tools
– A summary of the out-of-the-box support for peripherals
– A Brief Introduction to the Deep Visualization Toolbox and VisionWorks
– Sample Demos and Tutorials
2:30 – 4:30 GPUs for Deep Learning
Allison Gray: NVIDIA Solution Architect
Today’s advanced deep neural networks use algorithms, big data, and the computational power of the GPU to change this dynamic. Machines are now able to learn at a speed, accuracy, and scale that are driving true artificial intelligence. Learn how GPUs can accelerate the training of Neural Networks and ways you can get started to explore this exciting domain in A.I.
– Introduction to Deep Learning
– Example use cases
– Introduction to Lab material for GPU Cloud based learning