Developing Xilinx AI Solutions for Cloud-based Applications

This course describes how to use Xilinx machine learning solutions for data center and cloud-based applications.

The emphasis of this course is on:

  • Utilizing DNN algorithms, models, inference and training, and frameworks in the cloud
  • Using the xfDNN software stack to optimize the trained model
  • Optimizing the xDNN instructions for the xDNN processing engine in a cloud application

You can also take the full assessment for the entire course.

1 Introduction to Xilinx Machine Learning Solutions for Cloud and Edge Applications
Describes Xilinx machine learning solutions.
2 Overview of ML Concepts
Overview of ML concepts such as DNN algorithms, models, inference and training, and frameworks.
3 xDNN Architecture Overview
Describes the architectural features of the Xilinx Deep Neural Network (xDNN) processing engine and how the engine can be optimized for cloud applications.
4 xfDNN Middleware
Describes the xfDNN middleware, a high-performance software library with a well-defined API, which acts as a bridge between deep learning frameworks such as Caffe, MXNet, TensorFlow, and the xDNN IP running on an FPGA
5 ML Suite-Supported Frameworks
Describes the support for many common machine learning frameworks such as Caffe, MXNet, and TensorFlow as well as Python and RESTful APIs.
6

Using ML Suite for Custom Applications with the Alveo Card
Describes the environment to set up the Xilinx ML Suite and how to create a custom application and deploy the design.

You can also view a video demonstration illustrating the above concepts.