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Developing Xilinx AI Solutions for Edge-based Applications

This course describes how to use DNN algorithms, models, inference and training, and frameworks on an edge computing platform.

The emphasis of this course is on:

  • Using the architectural features of the Deep Learning Processor Unit (DPU)
  • Optimizing a model for an edge application using the Deep Neural Network Development Kit (DNNDK)
  • Setting up an edge platform
  • Creating custom applications
  • Deploying the design

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 DPU Architecture Overview
Describes the DPU architecture, supported CNN operations, DPU data flow, and design considerations.
4 Deep Neural Network Development Kit (DNNDK) Software Stack
Covers the DNNDK tool flow. With the DNNDK tool, deep learning algorithms can deploy in the DPU, which is an efficient hardware platform, running on a Xilinx FPGA.
5 DNNDK-Supported Frameworks
Describes the support for many common machine learning frameworks such as Caffe and TensorFlow.

Using DNNDK for Custom Applications with Xilinx SoCs
Describes steps such as generating the trained model, optimizing the trained model, and creating an application that uses the optimized model to accelerate the design.

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

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