Artificial Intelligence Workshop with Intel at Simon Fraser University

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Location

Applied Science Building, Room 10908

888 University Drive

Simon Fraser University

Burnaby, BC

Canada

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Description

How to leverage data with Artificial Intelligence is on everyone’s mind. At this one-day workshop you will learn about AI methodologies as well as AI optimization tools and techniques, that can be applied to Intel Xeon® processors as well as learn about some of Intel’s upcoming products that can be leveraged for AI. Benefit from live demos, followed by guided hands-on experience where you will learn how to optimize deep learning codes to accelerate computing applications.

By attending this workshop, you will learn about:

  • AI Methodologies leveraging Intel® hardware and software portfolio for AI
  • New capabilities to analyze large data sets
  • How to accelerate existing or planned deep learning projects
  • Performance optimization techniques using frameworks and tools

Agenda:

8:30am Registration

9am – 12pm Sessions

  • Intel HPC, AI FPGA, and Nervana Neural Network Processor roadmaps
  • Optimized Software Frameworks and Performance
  • Intel® OpenVINO™, Nauta

12pm-1pm Lunch (provided)

1pm - 3pm Use cases of imaging with large memory

Live demos for inference, distributed training on multi-nodes, Brain tumor detection, Intel ® Movidius (Neural Compute Stick 2) for inference

3pm – 4:00pm AI solutions hands-on session

4:00-4:15pm Wrap up

Intel Presenters:

Anthony (Tony) Reina – Dr. Tony Reina is a Deep Learning Data Scientist at Intel. He is a medical doctor by trade who has previously worked on brain-computer interfaces and surgical telementoring devices. He currently works in high-performance computing algorithms for optimizing deep neural networks in imaging. He’s authored several papers dealing with Distributed DL Training, Federated Learning, Telemedicine, and Brain-Computer Interfaces.

Luke Ge – Luke is an artificial intelligence Technical Solution Specialist at Intel. His focus is on design, validation, and test of AI solutions and optimizing AI workloads, both training and inference, on Intel Platform. This includes hardware (Xeon, Optane NVMe, persistent memory, FPGA, Omni-Path) to software (Intel optimized Tensorflow/PyTorch, MKL-DNN, DAAL, BigDL/Spark, OpenVINO, Movidius SDK, nGraph, docker container, etc.).

Whitney Gross - Whitney has been with Intel for more than 15 years and has had roles including technical specialist, technical marketing engineering and technical program management. In his current role as HPC technical specialist, he focuses on High Performance Computing engagement and helps industry partners, universities, labs and end users understand Intel HPC technologies and performance.

Dan Nitta Mackay - Dan is an Industry Technical Specialist supporting the Canadian AI ecosystem and has been responsible for FPGA based research engagements with the Montreal Institute of Learning Algorithms (MILA) and McGill University Neuro Lab. Over his last two years he has been focused on introducing Programmable Acceleration Cards based on FPGA across various industries. Prior to Intel Dan spent 17 years at Texas Instruments holding various responsibilities predominantly in the Telecom and Mobile market spaces. Dan graduated from the University of Ottawa with a Bachelor’s Degree in Electrical Engineering.

Date and Time

Location

Applied Science Building, Room 10908

888 University Drive

Simon Fraser University

Burnaby, BC

Canada

View Map

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