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Edge AI

Lowest latency, power and cost for multi-sensor analytics and machine learning applications at the Industrial IoT edge


Analytics and Machine Learning encompass a tremendous field of industrial applications, for instance Predictive Maintenance, Digital Twin model based control, anomaly detection and many other use cases.  Xilinx and the Xilinx ecosystem offer multiple approaches to address these Edge applications based on user preferences.

Analytics and Machine Learning

Edge AI Platform

Edge AI Platform

The Xilinx Edge AI Platform offers unique and patented Deep Learning Acceleration techniques for AI inference. It includes tools for compression (pruning & quantization) as well as compilation of Deep Neural Network models. Pre-pruned reference models for popular networks are readily available for fast implementation. Networks are primarily focused on classification, segmentation, and detection. Supported deep learning frameworks are Caffe and TensorFlow.

PYNQ - Python on Zynq

PYNQ - Python on Zynq

PYNQ provides Python powered control, edge analytics and machine learning. PYNQ is a software-hardware framework for Zynq SoCs leveraging the programmable hardware to pre-process sensor and other types of data to make software analysis and manipulation highly efficient in an embedded processor. PYNQ supports all major python libraries including Numpy, Scikit-Learn, and Pandas.

Cloud Provider

The trend in Industrial is a partial shift of processing from the Cloud to the Edge driven by:

  1. Physical assets demand low latency decisions/actions closest to the data acquisition point (typically under 10 ms)
  2. Data generated is typically large in size and moving and storing all generated data is not desirable due to OPEX cost, time and privacy concerns

Xilinx provides the industry’s most capable single-chip Edge embedded processing platforms to address such trends. Furthermore, Xilinx’s SoC portfolio and ecosystem partnerships with leading cloud service providers enable distribution of tasks across the Cloud and Edge as well as mobilize applications from the Cloud to Edge.



Xilinx with AWS IoT provides differentiated and collaborative machine learning capabilities across Edge and Cloud

AWS IoT Greengrass: Seamlessly extends AWS to edge devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage

AWS FreeRTOS: Operating system for microcontrollers that makes small, low-power edge devices easy to program, deploy, secure, connect, and manage

*AWS Sagemaker: Fully-managed service that covers the entire machine learning workflow to label and prepare your data, choose an algorithm, train the algorithm, tune and optimize it for deployment, make predictions, and take action

*AWS Robomaker: AWS RoboMaker is a service that makes it easy to develop, test, and deploy intelligent robotics applications at scale

Microsoft Azure IoT

Xilinx with Azure IoT provides differentiated and collaborative machine learning capabilities across Edge and Cloud

*Azure Sphere: Securely connect Zynq-powered devices across Edge and Cloud

*Azure IoT Hub: Connect, monitor and manage entire fleet of assets

*Azure IoT Edge: Extend cloud intelligence and analytics to Edge devices

*Azure IoT Digital Twins: Build next-generation spatial intelligence solutions

*under development, contact local FAE for latest updates

SoC Integration

Xilinx SDK

  • Software Development Kit is the Integrated Design Environment for creating embedded applications on any of Xilinx's ARM-based SoCs or soft IP-based microprocessors
  • SDK is the first application IDE to deliver true homogenous and heterogeneous multi-processor design, debug, and performance analysis

Vivado HLS

  • Accelerates IP creation by enabling C, C++ and System C specifications to be directly targeted into Xilinx programmable devices without the need to manually create RTL
  • Faster verification using C/C++ test bench simulation, automatic VHDL or Verilog simulation and test bench generation



  • Software Defined System-on-Chip is a development environment tailored to tightly coupled hardware/software designs
  • Allows seamless integration of hardware and software
  • Automates and streamlines memory allocation, cache management, DMA, and device interaction


Solution Provider Description Device Support
Xilinx Why Xilinx AI?  
Xilinx - Edge AI Platform Edge AI Developer Hub
Edge White Paper
Zynq UltraScale+ Zynq 7000
Xilinx - PYNQ PYNQ Homepage
PYNQ Community Projects
Zynq UltraScale+
Zynq 7000
AWS IoT AWS Certified Xilinx Products
Xilinx – AWS Workshop
Zynq UltraScale+
Zynq 7000
Azure IoT Azure IoT Zynq UltraScale+
Zynq 7000
Xilinx Tools SDSoC
Vivado HLx
Zynq UltraScale+
Zynq 7000
Xilinx SPYN Design Files
Community Portal
Zynq UltraScale+
Zynq 7000

Ecosystem Solutions

Solution Provider Description Device Support
Kortiq AIScale – Small and Efficient CNN Accelerator Zynq UltraScale+
Zynq 7000
Silicon Software Visual Applets Zynq UltraScale+
Zynq 7000
Kintex 7000
Solution Stack

Some Industrial IoT products need all elements of the Xilinx IIoT Solutions Stack, all need some. The Xilinx IIoT Solutions Stack is comprised of optimized Xilinx and Ecosystem building blocks and solutions used across Industrial and Healthcare IoT platforms. Starting from scratch is never something you will have to do with a Xilinx-based Industrial IoT system. Minimize development time and cost and maximize design reuse on your next Industrial IoT platform by exploring the different elements of the Xilinx IIoT Solutions stack.

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