Xilinx ML Suite v1.3

The Xilinx Machine Learning (ML) Suite provides users with the tools to develop and deploy Machine Learning applications for Real-time Inference. It provides support for many common machine learning frameworks such as Caffe, Tensorflow, and MXNet.

The ML Suite is composed of three basic parts:

1. xDNN IP - High Performance general CNN processing engine.
2. xfDNN Middleware - Software Library and Tools to Interface with ML Frameworks and optimize them for Real-time Inference.
3. ML Framework and Open Source Support - Support for high level ML Frameworks and other open source projects.

Learn More: ML Suite Overview
Watch: Webinar on Xilinx FPGA Accelerated Inference
Forum: ML Suite Forum

See What's New

• Release Notes

Getting Started

1. Clone ML Suite
   git clone https://github.com/Xilinx/ml-suite.git
2. Download Overlays and Pre-Trained Models from ML Suite Lounge
   • Overlays: Download and unzip desired overlays into the ml-suite/overlaybins/ dir, for example: ml-suite/overlaybins/alveo-u200
   • Pre-Trained Models: Download and unzip to the /ml-suite/ dir.
3. Install Anaconda2
   • Note: Ensure that you ran the fix_caffe_opencv_symlink.sh script

References

• ML Suite Overview
• User Guides:
  • Compiler Python API
  • Quantizer Python API
  • xfDNN Python API
• Tutorials and Examples:
  • Jupyter Notebooks
  • Precompiled Examples

Recommended System Requirements

• OS: Ubuntu 16.04.2 LTS, Ubuntu 18.04 LTS, CentOS 7.4
• CPU: 6 Cores (Intel/AMD/Power9)
• Memory: 8 GB

Supported Platforms

Cloud Services

• Amazon AWS EC2 F1
• Nimbix

On Premise Platforms (Visit ML Suite Lounge for Details)

• Alveo U200 Data Center Accelerator Card
• Xilinx Virtex UltraScale+ FPGA VCU1525 Acceleration Development Kit

Release Notes

• Release Notes

Questions and Support

• FAQ
• AWS F1 Application Execution on Xilinx Virtex UltraScale Devices
• ML Suite Forum
• Performance Whitepaper