Deep Learning from Idea to Embedded Hardware

Scientists and engineers are challenged on how to quickly prototype a complete algorithm—including pre- and post-processing logic around deep neural networks (DNNs) —to get a sense of timing and performance on standalone CPUs and GPUs.

Traditionally, they would hand translate the complete algorithm into C/C++ or CUDA^® and compile it with the NVIDIA^® toolchain. With MATLAB, they can short-circuit the standard process by generating CUDA code that compiles on modern NVIDIA GPUs.

MATLAB provides an integrated framework to develop, prototype, and deploy the complete algorithm workflow. It automates labelling of ground truth data and accelerates training models in HPC without changing your code. MATLAB supports the Open Neural Network Exchange (ONNX™) framework that scientists and engineers can use to improve team collaboration. Key capabilities include:

• Test and debug deep neural networks (DNNs) architectures interactively with Deep Network Designer
• Quickly prototype a complete workflow in MATLAB running on desktop GPUs
• Generate code, cross-compile, and run software-in-the-loop (SIL) testing on any modern NVIDIA GPUs, from Tesla^® to DRIVE to Jetson AGX Xavier platforms

Mixed-Signal Design

The design and analysis of Analog Mixed-Signal Designs have become increasingly challenging with analog impairment effects in modern semiconductor technology nodes and with the integration of complex embedded digital signal processing and control algorithms. Thus, accurate modeling and rapid system-level simulation have become extremely important to verify these designs before they go into production. Simulink^®, Simscape™, and MATLAB allow designers to build models of their complex design of Phase Lock Loops (PLL) and Analog to Digital Converter (ADC), to visualize them faster, and to do a comprehensive design space exploration.

Visit this showcase area to see and discuss how our tools are used to:

• Co-simulate a PLL with Cadence Virtuoso^®.
• Design Serializer-Deserializer and test new algorithms swiftly
• Build, simulate, and verify PLLs and ADCs models using dedicated libraries

Load Forecasting System

By connecting distributed devices, the Internet of Things (IoT) creates the possibility to collect large amounts of data from a wide range of sources. This is transforming the way businesses operate in many fields. A prime example is how the IoT helps power system operators accurately predict electricity demand to schedule the dispatch of power plants. Recently, this task has become much more complicated due to the continued integration of many distributed sources. However, using IoT technology, power systems operators now also have unprecedented access to streams of real-time measurement and weather data. Deep Learning can be used to find trends and predict the electricity demand based on the collected data.

In this demo, a neural network is trained on a desktop MATLAB environment to continuously forecast electrical load in the State of New York, and then deployed as a cloud application using the MATLAB Production Server™.

Visit this showcase area to see and discuss how MATLAB is used for:

• End-to-end data analytics workflow
• Multiple predictive modeling techniques, including neural networks and machine learning
• Cloud-based deployment

Triplex Pump with Condition Monitoring

This demo will showcase how a digital twin could be used to validate a conditioning monitoring algorithm developed with machine learning. The device considered in the demo is a triplex pump with an output pressure sensor. The digital twin of the pump is created using an intuitive physical model that can be configured to inject degraded behavior such as seal leakage, blocked inlets, bearing wear, and broken motor windings. Actual or simulated output sensor data can then be used to develop and validate a condition-monitoring algorithm that can detect which parts of the pump are failing.

Visit this showcase area to see and discuss how MATLAB/Simulink is used to:

• Model the multidomain physics of a hydraulic pump system
• Import physical parameters and visualization from CAD designs
• Test a condition=monitoring algorithm based on machine learning

Systems Engineering

Learn how System Composer™ can help you design and analyze architectures and compositions for model-based systems engineering and software design. With System Composer, you allocate requirements while refining an architecture model that can then be designed and simulated in Simulink.

Visit this showcase area to learn how to:

• Represent system architecture using components, ports, and interfaces
• Associate architecture models with Simulink models and requirements
• Add custom properties to architectural elements using stereotypes

Mechatronic System Design, Simulation, and Validation

Mechatronic systems require expertise from multiple domain experts. Simulating motors, electronics, and sensors together with mechanical and control systems is critical to optimizing system performance. System engineers need to perform trade-off studies to choose the correct design and implementation based on requirements.

To ensure that testing is efficient, MathWorks offers several ways to easily balance the trade-off of model fidelity and simulation speed, as well as the ability to import data from various sources, from CAD to measurement files as well as technical datasheets. The ability to generate C code from both the controller and the system model enables engineers to test the design in real-time.

• Use comprehensive component libraries and industrial examples to model your mechatronic system
• Perform trade-off studies and sensitivity analysis to reach optimal design
• Validate software design early by simulating it with the system model in one unified environment

MathWorks Consulting Services

With deep product knowledge and broad technical experience, we guide your team to apply best practices to your development projects. We offer “from-the-source” insights into our products and exclusive access to our in-house subject matter experts. Our worldwide presence means we live and work where you do, speak your language, and understand local customs and business practices. Wherever you are, we are there to help.

• MathWorks consultants combine real-world industry experience with MATLAB and Simulink expertise
• Industry best practices for application and model quality, optimal technology adoption, and accelerated development
• MATLAB and Simulink insider knowledge help realise the full value of your technology investments

MathWorks Training Services

For MATLAB and Simulink instruction, MathWorks provides an expert team with exclusive product knowledge.

Each course is designed to help participants quickly master necessary skills and workflows. A hands-on approach allows participants to practice, apply, and evaluate their knowledge in the classroom. There are a variety of training paths available for users with specific job roles or application areas, which means new and advanced users of all application areas can find training that suits their needs.

Discover how courses from MathWorks Training Services can accelerate your use of MATLAB and Simulink. Come by the booth to:

• Learn about training offerings—from public training throughout Europe to online self-paced training at a time and location that suits you best, we can tailor training to your needs
• See training materials for various courses
• Interact with online self-paced materials, including the free Onramp trainings, which enable new users to get up to speed in only a few hours