An FPGA provides an extremely low-latency, flexible architecture that enables deep learning acceleration in a power-efficient solution. Learn how to deploy a computer vision application on a CPU, and then accelerate the deep learning inference on the FPGA. Next, learn how to take that application and use Docker* containers to scale the application across multiple nodes in a cluster using Kubernetes*.
By the end of this course, students will have practical knowledge of:
What convolutional neural networks are and how they are built
How to build a deep learning computer vision application
What an FPGA is from a software developer's perspective, and why FPGAs are so well suited for accelerating real-time machine learning applications
The components of the Intel® FPGA Deep Learning Acceleration Suite
What constitutes a computer vision application that uses deep learning to extract patterns from data
How to use the Intel® Distribution of OpenVINO™ toolkit to target convolutional neural network (CNN) based inferencing on Intel® CPUs and FPGAs
How the Acceleration Stack for Intel® Xeon® CPUs with FPGAs enables higher level cloud and data center software applications to leverage the FPGA seamlessly
The course is structured around five weeks of lectures and exercises. Each week requires three hours to complete. The exercises are implemented in Python*.