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 different approaches to address these Edge applications based on user trends.
Analytics and Machine Learning
Edge AI Platform
Over the course of the past three 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 ML ecosystem. In this paradigm, we have designed tools 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 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
Python powered control, edge analytics and machine learning enabled by PYNQ. 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 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:
- Physical assets demand low latency decisions/actions closest to the data acquisition point (typically under 10 ms)
- 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.
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
*AWS Robomaker: AWS RoboMaker is a service that makes it easy to develop, test, and deploy intelligent robotics applications at scale
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 Sphere: Securely connect MCU-powered devices from silicon to Cloud
*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
*under development, contact local FAE for latest updates
SoC Integration
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
Vitis™ unified software platform
- Unified design methodology for all Xilinx platforms
- Familiar software development experience
- Software-defined whole application acceleration
- Leverage integration with high-level frameworks, develop in C, C++, or Python using accelerated libraries or use RTL-based accelerators & low-level runtime APIs for more fine-grained control over implementation
Solutions
| Solution Provider | Description | Device Support |
|---|---|---|
| Xilinx | Why Xilinx AI? | |
| Xilinx - Edge AI Platform | Edge AI Developer Hub DNNDK Edge White Paper |
Zynq UltraScale+ Zynq 7000 |
| Xilinx - PYNQ | PYNQ Homepage PYNQ Community Projects |
Zynq UltraScale+ Zynq 7000 |
| AWS IoT | AWS Certified Xilinx Products AWS IoT Xilinx – AWS Workshop |
Zynq UltraScale+ Zynq 7000 |
| Azure IoT | Azure IoT | Zynq UltraScale+ Zynq 7000 |
| Xilinx Tools | Vitis software platform 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 |
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.
