Getting Started with Xilinx ML Suite
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Haffon and kamranjk Update (#56)
* Update

fix "broswer" to "browser"

* spell correction

transfomer-> transformer
calbration -> calibration

* fixing __future__ imports error

SyntaxError: from __future__ imports must occur at the beginning of the file
Latest commit 33dadbb Nov 16, 2018

Xilinx ML Suite

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 Xilinx ML Suite currently features xDNNv2. xDNNv3 will be available in November 2018. xDNNv3 will bring higher throughput, at lower latency. For more information on the benefits of xDNNv3, please see the whitepaper here.

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

Getting Started

  1. Clone ML Suite
    git clone
  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 script


Recommended System Requirements

  • OS: Ubuntu 16.04.2 LTS, CentOS 7.4
  • CPU: 4 Cores (Intel/AMD)
  • Memory: 8 GB

Supported Platforms

Cloud Services

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

Questions and Support