See Vitis™ Development Environment on xilinx.comSee Vitis-AI™ Development Environment on xilinx.com |
Start here! This tutorial series will help to get you the lay of the land working with the Vitis AI toolchain and machine learning on Xilinx devices.
| Tutorial | Description |
| Introduction to Vitis AI | This tutorial puts in practice the concepts of FPGA acceleration of Machine Learning and illustrates how to quickly get started deploying both pre-optimized and customized ML models on Xilinx devices. |
These tutorials illustrate end-to-end design concepts or workflows using Vitis AI.
| Tutorial | Description |
| Quantization and Pruning of AlexNet CNN trained in Caffe with Cats-vs-Dogs dataset (UG1336) | Train, prune, and quantize a modified version of the AlexNet convolutional neural network (CNN) with the Kaggle Dogs vs. Cats dataset in order to deploy it on the Xilinx® ZCU102 board. |
| MNIST Classification using Vitis? AI and TensorFlow (UG1337) | Learn the Vitis AI TensorFlow design process for creating a compiled ELF file that is ready for deployment on the Xilinx DPU accelerator from a simple network model built using Python. This tutorial uses the MNIST test dataset. |
| Using DenseNetX on the Xilinx DPU Accelerator (UG1340) | Learn about the Vitis AI TensorFlow design process and how to go from a Python description of the network model to running a compiled model on the Xilinx DPU accelerator. |
| Deep Learning with Custom GoogleNet and ResNet in Keras and Xilinx Vitis AI (UG1381) | Quantize in fixed point some custom CNNs and deploy them on the Xilinx ZCU102 board, using Keras and the Xilinx7Vitis AI tool chain based on TensorFlow (TF). |
| FCN8 and UNET Semantic Segmentation with Keras and Xilinx Vitis AI (UG1445) | Train the FCN8 and UNET Convolutional Neural Networks (CNNs) for Semantic Segmentation in Keras adopting a small custom dataset, quantize the floating point weights files to an 8-bit fixed point representation, and then deploy them on the Xilinx ZCU102 board using Vitis AI. |
| Using DenseNetX on the Xilinx Alveo U50 Accelerator Card (UG1472) | Implement a convolutional neural network (CNN) and run it on the DPUv3E accelerator IP. |
| Vitis AI YOLOv4 | Learn how to train, evaluate, convert, quantize, compile, and deploy YOLOv4 on Xilinx devices using Vitis AI. |
| TensorFlow2 and Vitis AI design flow | Learn about the TF2 flow for Vitis AI. In this tutorial, you'll be trained on TF2, including conversion of a dataset into TFRecords, optimization with a plug-in, and compiling and execution on a Xilinx ZCU102 board or Xilinx Alveo U50 Data Center Accelerator card. |
| PyTorch flow for Vitis AI | Learn how to use Vitis AI by using PyTorch. You'll use a simple `get-you-started` example to get started, and then be trained on quantization with a plug-in, and then compiling and execution on a Xilinx ZCU102 board or Xilinx Alveo U50 Data Center Accelerator card. |
| RF Modulation Recognition with TensorFlow 2 | Machine learning applications are certainly not limited to image processing! Learn how to apply machine learning with Vitis AI to the recognition of RF modulation from signal data. |
Feature tutorials illustrate specific workflows or stages within Vitis AI.
| Tutorial | Description |
| Freezing a Keras Model for use with Vitis AI (UG1380) | Freeze a Keras model by generating a binary protobuf (.pb) file. |
| Profiling a CNN Using DNNDK or VART with Vitis AI (UG1487) | Profile a CNN application running on the ZCU102 target board with Vitis AI. |
| Moving Seamlessly between Edge and Cloud with Vitis AI (UG1488) | Compile and run the same identical design and application code on either the Alveo U50 data center accelerator card or the Zynq UltraScale+™ MPSoC ZCU102 evaluation board. |
| TensorFlow AI Optimizer Example Using Low-level Coding Style (UG1512) | Use AI Optimizer for TensorFlow to prune an AlexNet CNN by 80% while maintaining the original accuracy. |
| Partitioning Vitis AI SubGraphs on CPU/DPU | Learn how to deploy a CNN on the Xilinx VCK190 board using Vitis AI. |
| Fine-Tuning TensorFlow2 quantized models | Learn how to implement the Vitis-AI quantization fine-tuning for TensorFlow2.3. |
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