35 hours (usually 5 days including breaks)
- Python programming experience
- Experience with pandas and scikit-learn
- Experience with deep learning and computer vision
- Data scientists
An FPGA (Field Programmable Gate Array) is an integrated circuit that can be used to accelerate deep learning computations. OpenVINO is an open source toolkit for optimizing Deep Learning models on Intel hardware.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale.
By the end of this training, participants will be able to:
- Install the OpenVINO toolkit.
- Accelerate a computer vision application using an FPGA.
- Execute different CNN layers on the FPGA.
- Scale the application across multiple nodes in a Kubernetes cluster.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Overview the Languages, Tools, and Libraries Needed for Accelerating a Computer Vision Application
Setting up OpenVINO
Overview of OpenVINO Toolkit and its Components
Understanding Deep Learning Acceleration GPU and FPGA
Writing Software That Targets FPGA
Converting a Model Format for an Inference Engine
Mapping Network Topologies onto FPGA Architecture
Using an Acceleration Stack to Enable an FPGA Cluster
Setting up an Application to Discover an FPGA Accelerator
Deploying the Application for Real World Image Recognition
Summary and Conclusion