UPGRADE YOUR BROWSER

We have detected your current browser version is not the latest one. Xilinx.com uses the latest web technologies to bring you the best online experience possible. Please upgrade to a Xilinx.com supported browser:Chrome, Firefox, Internet Explorer 11, Safari. Thank you!

Overview

Data Center AI Platform

 

 

The Data Center AI Platform Supports industry-standard frameworks

You can bring your own trained model or start with one from our model zoo

Xilinx ML suite provides comprehensive optimization for optimal FPGA implementation, together with a runtime and hardware DSA

Target a Xilinx Alveo accelerator card, your own custom card, or FPGA-as-a-Service such as Amazon AWS

 

datacenter-tab1
ML Suite

ML Suite

 

 

The Data Center AI Platform software and hardware overlay is called ML Suite

ML Suite provides a comprehensive AI/ML solution allowing you to read in models from supported frameworks, optimize them and map them to Xilinx infrastructure

The provided runtime and DSA allow you to benefit from Xilinx hardware acceleration, without needing to be an FPGA expert

     

ai-ml-tab2-2

xfDNN Middleware

xfDNN middleware is a high-performance software library with a well-defined API which acts as a bridge between deep learning frameworks such as Caffe, MxNet, Tensorflow, and xDNN IP running on an FPGA.

xfDNN software is currently the only available method for programming and using xDNN IP and assumes a system running SDAccel reconfigurable acceleration stack compliant system.

xfDNN not only provides simple Python interfaces to connect to high level ML frameworks, but also provides tools for network optimization by fusing layers, optimizing memory dependencies in the network, and pre-scheduling the entire network removing CPU host control bottlenecks

 

box-diagram-1

Once these optimizations are completed per layer, the entire network is optimized for deployment in a "One-Shot" execution flow.

box-diagram-2

xfDNN Quantizer enables fast, high-precision calibration to lower precision deployments to INT8 and INT16. These Python tools are simple to use.

Frameworks

Frameworks

Xilinx Data Center AI Platform supports a number of industry-standard frameworks, highlighted in the table below.

Framework Description Availability
TensorFlow is an open-source framework developed by Google.
CAFFE is an open-source framework developed at UC Berkeley.
MXNet is an open-source framework developed by Apache.
Darknet is an open-source framework developed by Joseph Redmon.
Keras is an open source high-level API capable of running on top of several other frameworks.
Onnx is an open-source graph model and standardized operator definition. It works in conjunction with several frameworks. It was created by Facebook and Microsoft. Coming Soon
Models

Models

The Xilinx Data Center AI Platform supports the AI/ML Models as shown below.

Task Design Example & Descriptions
Image Classification GoogleNet
ResNet50
ResNet101
ResNet152
MobileNet 
VGG-16
SqueezeNet
Object Detection Yolo v3 (ADAS Detection)

We are working to bring the following models from the Edge AI Platform:

  • Pedestrian Attributes Recognition
  • Car Attributes Recognition
  • Car Logo Recognition
  • License Plate Recognition
Getting Started

Getting Started

For a full list of documentation, downloads and other useful resources, please navigate to the Developer Hub.

Page Bookmarked