# Vitis-AI

**Repository Path**: qgzln/Vitis-AI

## Basic Information

- **Project Name**: Vitis-AI
- **Description**: Vitis AI is Xilinx’s development stack for AI inference on Xilinx hardware platforms, including both edge devices and Alveo cards.
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-04-10
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<div align="center">
  <img width="100%" height="100%" src="doc/img/Vitis-AI.png">
</div>

<br />
Vitis AI is Xilinx’s development stack for AI inference on Xilinx hardware platforms, including both edge devices and Alveo cards. It consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease of use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP.  
<br />
<br />

<div align="center">
  <img width="45%" height="45%" src="doc/img/Vitis-AI-arch.png">
</div>

<br />
Vitis AI is composed of the following key components:

* **AI Model Zoo**  - A comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices.
* **AI Optimizer** - An optional model optimizer that can prune a model by up to 90%. It is seperately available with commercial licenses.
* **AI Quantizer** - A powerful quantizer that supports model quantization, calibration, and fine tuning.
* **AI Compiler** - Compiles the quantized model to a high-efficient instruction set and data flow. 
* **AI Profiler** - Perform an in-depth analysis of the efficiency and utilization of AI inference implementation.
* **AI Library** - Offers high-level yet optimized C++ APIs for AI applications from edge to cloud.
* **DPU** - Efficient and scalable IP cores can be customized to meet the needs for many different applications


**Learn More:** [Vitis AI Overview](https://www.xilinx.com/products/design-tools/vitis/vitis-ai.html)  


## [See What's New](doc/release-notes/1.x.md)
 - [Release Notes](doc/release-notes/1.x.md)
 - Alveo U50 support with DPUv3E, a throughput optimized CNN overlay
 - Tensorflow 1.15 support
 - VART (Vitis AI Runtime) with unified API and samples for Zynq, ZU+ and Alveo
 - Vitis AI library fully open source
 - Whole Application Acceleration example on Alveo 
  
## Getting Started

Two options are available for installing the containers with the Vitis AI tools and resources. 

 - Pre-built containers on Docker Hub: [/xilinx/vitis-ai](https://hub.docker.com/r/xilinx/vitis-ai/tags)
 - Build containers locally with Docker recipes: [Docker Recipes](docker/README.MD)
 
 
### Installation 
 - [Install Docker](doc/install_docker/README.md) - if Docker not installed on your machine yet
 
 - [Ensure your linux user is in the group docker](https://docs.docker.com/install/linux/linux-postinstall/)

 - Clone the Vitis-AI repository to obtain the examples, reference code, and scripts. 
    ```
    git clone https://github.com/Xilinx/Vitis-AI  
    
    cd Vitis-AI
    ``` 
 - [Run Docker Container](doc/install_docker/load_run_docker.md)  
    
   1) Run the CPU image from docker hub
   ```
   ./docker_run.sh xilinx/vitis-ai
   ```
   or
   
   2) build the CPU image locally and run it
   ```
   cd docker
   ./docker_build_cpu.sh
   
   # After build finished
   cd ..
   ./docker_run.sh xilinx/vitis-ai-cpu:latest
   ```
   or 
   
   3) build the GPU image locally and run it 
   ```
   cd docker
   ./docker_build_gpu.sh

   # After build finished
   cd ..
   ./docker_run.sh xilinx/vitis-ai-gpu:latest
   ```
 - Get started with examples
    - [VART samples](VART/README.md)
    - [Alveo](alveo/README.md)
    - [Vitis AI DNNDK samples](mpsoc/README.md)
   

## Programming with Vitis AI

Vitis AI offers a unified set of high-level C++/Python programming APIs to run AI applications across edge-to-cloud platforms, including DPUv1 and DPUv3 for Alveo,  
and DPUv2 for Zynq Ultrascale+ MPSoC and Zynq-7000. It brings the benefits to easily port AI applications from cloud to edge and vice versa. 
7 samples in [VART Samples](VART/samples) are available to help you get familiar with the unfied programming APIs.


| ID | Example Name          | Models              | Framework  | Notes                                                                     |
|----|-----------------------|---------------------|------------|---------------------------------------------------------------------------|
| 1  | resnet50              | ResNet50            | Caffe      | Image classification with VART C\+\+ APIs\.                   |
| 2  | resnet50\_mt\_py      | ResNet50            | TensorFlow | Multi\-threading image classification with VART Python APIs\. |
| 3  | inception\_v1\_mt\_py | Inception\-v1       | TensorFlow | Multi\-threading image classification with VART Python APIs\. |
| 4  | pose\_detection       | SSD, Pose detection | Caffe      | Pose detection with VART C\+\+ APIs\.                         |
| 5  | video\_analysis       | SSD                 | Caffe      | Traffic detection with VART C\+\+ APIs\.                      |
| 6  | adas\_detection       | YOLO\-v3            | Caffe      | ADAS detection with VART C\+\+ APIs\.                         |
| 7  | segmentation          | FPN                 | Caffe      | Semantic segmentation with VART C\+\+ APIs\.                  |

For more information, please refer to [Vitis AI User Guide](https://www.xilinx.com/html_docs/vitis_ai/1_1/zkj1576857115470.html)

 
## References 
- [Vitis AI Overview](https://www.xilinx.com/products/design-tools/vitis/vitis-ai.html)
- [Vitis AI User Guide](https://www.xilinx.com/html_docs/vitis_ai/1_1/zkj1576857115470.html)
- [Vitis AI Model Zoo with Performance & Accuracy Data](https://github.com/Xilinx/Vitis-AI/tree/master/AI-Model-Zoo)
- [Vitis AI Tutorials](https://github.com/Xilinx/Vitis-AI-Tutorials)
- [Developer Articles](https://developer.xilinx.com/en/get-started/ai.html)
- [Performance Whitepaper][]

## [System Requirements](doc/system_requirements.md)

## Questions and Support
- [FAQ](doc/faq.md)
- [Vitis AI Forum](https://forums.xilinx.com/t5/AI-and-Vitis-AI/bd-p/AI)

[models]: docs/models.md
[Amazon AWS EC2 F1]: https://aws.amazon.com/marketplace/pp/B077FM2JNS
[Xilinx Virtex UltraScale+ FPGA VCU1525 Acceleration Development Kit]: https://www.xilinx.com/products/boards-and-kits/vcu1525-a.html
[AWS F1 Application Execution on Xilinx Virtex UltraScale Devices]: https://github.com/aws/aws-fpga/blob/master/SDAccel/README.md
[Release Notes]: docs/release-notes/1.x.md
[UG1023]: https://www.xilinx.com/support/documentation/sw_manuals/xilinx2017_4/ug1023-sdaccel-user-guide.pdf
[FAQ]: docs/faq.md
[ML Suite Overview]: docs/ml-suite-overview.md
[Webinar on Xilinx FPGA Accelerated Inference]: https://event.on24.com/wcc/r/1625401/2D3B69878E21E0A3DA63B4CDB5531C23?partnerref=Mlsuite
[Vitis AI Forum]: https://forums.xilinx.com/t5/AI-and-Vitis-AI/bd-p/AI
[ML Suite Lounge]: https://www.xilinx.com/products/boards-and-kits/alveo/applications/xilinx-machine-learning-suite.html
[Models]: https://www.xilinx.com/products/boards-and-kits/alveo/applications/xilinx-machine-learning-suite.html#gettingStartedCloud
[whitepaper here]: https://www.xilinx.com/support/documentation/white_papers/wp504-accel-dnns.pdf
[Performance Whitepaper]: https://www.xilinx.com/support/documentation/white_papers/wp504-accel-dnns.pdf