Edge AI

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

Overview

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 different approaches to address these Edge applications based on user trends.


Analytics and Machine Learning

Edge AI Platform

Edge AI Platform

Over the course of the past four years, extensive work has been done by Xilinx to develop a complete end to end flow which allows software developers, hardware developers and data scientists to leverage the existing AI/ML ecosystem.  In this paradigm, we have designed tools (Vitis AI) which enable our customers to directly parse the model graph and trained weights that are saved out of popular ML frameworks.  Today, this includes Caffe, Pytorch and TensorFlow.  We have developed pruning, quantization tools, compiler, runtime and efficient programmable IP which allows us to deploy your network on a variety of platforms, but on the edge or in popular cloud and server architectures.

PYNQ - Python on Zynq

PYNQ - Python on Zynq

Python powered control, edge analytics and machine learning enabled by PYNQ. PYNQ is a software-hardware framework for Xilinx Adaptive 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 like Numpy, Scikit-Learn, and Pandas etc.


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 Adaptive 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.

AWS IoT

Industrial IoT is also rapidly accelerating the opportunity for cloud connected and collaborative control systems that can unlock the next set of capabilities of the industrial asset using machine learning. Industrial control system providers are realizing this vision and the need for integrated edge to cloud solutions that will accelerate their time to market. Xilinx with AWS IoT provides differentiated and collaborative edge-to-cloud machine learning capabilities.

  • 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

Microsoft Azure IoT

Industrial IoT is also rapidly accelerating the opportunity for cloud connected and collaborative control systems that can unlock the next set of capabilities of the industrial asset using machine learning. Industrial control system providers are realizing this vision and the need for integrated edge to cloud solutions that will accelerate their time to market. Xilinx with Microsoft Azure IoT provide differentiated and collaborative edge-to-cloud machine learning capabilities.

  • Azure IoT Hub: Connect, monitor and manage billions of IoT assets
  • Azure IoT Edge: Extend cloud intelligence and analytics to the edge devices
  • Azure IoT Digital Twins: Build next-generation IoT spatial intelligence Solutions

SoC Integration

Vitis AI

  • Supports mainstream frameworks
  • Provides a comprehensive set of pre-optimized models
  • Efficient and scalable IP cores can be customized

Vivado ML

  • ML-Based Design Optimization
  • Collaborative Design Environment
  • New Advanced DFX Features

Solutions

Solution Provider Description Device Support
Xilinx Kria Vision AI Starter Kit – KV260 Xilinx Kria K26 SOM
Xilinx Kria Ecosystem Kria App Store Xilinx Kria K26 SOM
Xilinx Why Xilinx AI?  
Xilinx - Edge AI Platform Vitis AI Edge
Edge White Paper
Xilinx Adaptive SoCs
Kria K26 SOM
Versal AI Edge
Xilinx - PYNQ PYNQ Homepage
PYNQ Community Projects
Xilinx Adaptive SoCs
Kria K26 SOM
Versal AI Edge
AWS IoT AWS Certified Xilinx Products
AWS IoT
Xilinx – AWS Workshop
Xilinx Adaptive SoCs
Kria K26 SOM
Versal AI Edge
Azure IoT Azure IoT Xilinx Adaptive SoCs
Kria K26 SOM
Versal AI Edge
Xilinx Tools Vitis software platform
Vivado HLx
Xilinx Adaptive SoCs
Kria K26 SOM
Versal AI Edge
Xilinx SPYN Design Files
Community Portal
Xilinx Adaptive SoCs
Kria K26 SOM
Versal AI Edge
Documentation
Solution Stack
iiot-hc-solutions-stack

Some Industrial and Healthcare IoT products need all elements of the Xilinx IIoT and HcIoT Solutions Stack, all need some. The Xilinx IIoT and HcIoT 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 or Healthcare IoT system. Minimize development time and cost and maximize design reuse on your next Industrial or Healthcare IoT platform by exploring the different elements of the Xilinx IIoT and HcIoT Solutions Stack.