MATLAB and Simulink Get a Major Deep Learning Facelift
MathWorks has revamped MATLAB and Simulink with a host of deep learning development tools—and they say these updates will be a major boon for automotive and wireless designers.
To help engineers develop AI systems, MathWorks has added deep learning capabilities to its latest update of MATLAB and Simulink. The update, called R2020A, includes a "Deep Network Designer" app, which is said to help engineers train neural networks. Designers can also manage several deep learning experiments at a time in another app, Experiment Manager.
Users will have more network options when creating deep learning code.
Experiment Manager allows you to organize multiple deep learning experiments. Screenshot used courtesy of MathWorks
What else is included in this update and how will it benefit engineers in various fields?
Core Updates for AI Development
The latest update from MathWorks includes a range of updates for AI systems, including code conversions, deployable web apps, and managers. Many of these are included in an updated Deep Learning Toolbox.
The Deep Learning Toolbox teaches designers to construct and train long short-term memory (LSTM) networks. Image used courtesy of MathWorks
As mentioned, the two major updates of R2020A is the Deep Network Designer app, which allows users to train neural networks, and the Experiment Manager, which allows users to organize multiple experiments. The Experiment Manager also allows users to track experimental data, like core variables and results, providing a comprehensive view of a deep learning project.
Deep learning tools have also dramatically improved over a range of applications to help engineers with common AI tasks. AI involved with video and image data now includes features such as a signal labeler, pixel label datastore, audio datastore, and image datastore. In the network realm, MathWorks now allows users to build advanced network architectures, such as GANs, Siamese networks, attention networks, and variational auto-encoders.
Deep Learning Frameworks
MathWorks Deep Learning now supports a new range of other deep learning frameworks such as MobileNet-v2, ResNet-101, Inception-v3, SqueezeNet, NASNet-Large, and Xception. When it comes to the deployment of AI systems MathWorks now supports Arm Mali GPUs and includes a GPU Coder. The update is said to enable automatic deployment to Jetson AGX Xavier and Jetson Nano Platforms.
A glimpse at the GPU Coder app. Image used courtesy of MathWorks
MathWorks can also generate code for networks such as YOLO V2 object detector, DeepLab-v3+, MobileNet-v2, Xception, DenseNet-201, and recurrent networks. AI algorithms for reinforcement learning are now included for systems for walking and driving. Other generic robotics use algorithms including DQN, DDPG, A2C, and PPO.
Classes and Interface Upgrades
The R2020A update does not just affect deep learning applications; it also includes features that can help designers interface their designs with other systems and improve organization. For example, MathWorks now offers C++ classes from MATLAB classes. It also offers new messaging protocols.
The update allows users to generate C++ classes from MATLAB. Screenshot used courtesy of MathWorks
The R2020A update allows MathWorks users to work with UTF-8 characters and includes new graphic options for graphs. Users can also access a live editor tool, which allows them to view tasks currently being executed.
Example of the Simulink Compiler in action. Image used courtesy of MathWorks
To help designers generate compact code, the Motor Control Blockset offers a library of motor control algorithms. It also provides "out-of-the-box support" for a number of motor control hardware kits.
With the Simulink Compiler, designers can use Simulink models to create software, web apps, and standalone applications that will run simulations without actually installing Simulink.
Finally, the last major product—MATLAB Web App server—gives engineers organizational control over MATLAB web apps across one's organization from a web browser.
A Deeper Understanding of Designs
MathWorks says the tools included with R2020A will not only help engineers continue to develop AI systems but also help them to better understand their designs. For example, because users can now label signals in video applications, they can visualize stimuli; likewise, datastores allows them to manage audio and visual data.
By targeting more AI frameworks, MathWorks extends its reach and therefore widens the deployment possibilities of a trained system. The new debugging features will allow designers to better understand why their AI systems produce the results they do (i.e. explain their decisions), while the inclusion of reinforcement algorithms allows for engineers to explore new methods for training systems.
A Useful Asset for Automotive and Wireless Designers
MathWorks asserts that the automotive tools included with R2020A will dramatically improve the automotive sector, with tools like the HD map to road data converter. These features are said to decrease the time needed to set up a traffic simulation environment and define road lengths and intersections.
Automotive designers can generate driving scenarios from road data. Screenshot used courtesy of MathWorks
The update allows automotive designers to read high-definition road maps to create road data for use in traffic simulations. Optimized shift schedules can then be used with fuel economy and emissions analysis.
The R2020A update supports waveform generation and cell detection. Screenshot used courtesy of MathWorks
The update may also give designers in wireless communication a leg up with better support for wireless and 5G signal analysis, including waveform generation and cell detection.
A "Comprehensive" AI Platform
David Rich, MATLAB's marketing director, says that this R2020A update gives designers a comprehensive platform for building an AI-focused system. He explains, “We’ve taken three decades of product, consulting and support experiences and applied it to an AI workflow that empowers engineers and scientists to clean data, build models and deploy them in production IT or embedded systems."
Have you seen deep learning become a more central part of your workplace? If so, how? Share your experiences in the comment below.