Machine learning
Implementing machine learning
This year, MathWorks' focus at Embedded World was on the implementation of machine learning and deep learning algorithms in embedded systems. On 100 square meters, the company showed several examples from different application areas of machine learning, such as the recognition and classification of objects and language for autonomous systems.
According to the company, machine learning and deep learning are powerful methods for solving complex modeling tasks in a wide range of industries. The core principle is that engineers and scientists develop models that learn independently from data. In this way, for example, machine failures can be predicted through predictive maintenance and avoided accordingly, or vehicles can steer themselves using autonomous systems. But how does intelligence get into the machine? In order to be able to use machine learning algorithms productively, implementation in embedded systems or in a cloud infrastructure is suitable. Depending on the model, there are different preferences for the hardware used. For example, microcontrollers are primarily used for machine learning methods such as "support vector machines", while GPUs have become established for the implementation of convolutional neural networks (CNN) in the field of deep learning. In both cases, the developed model must be converted into less abstract code, for example in C, in order to be transferred to the hardware. sw
You might also be interested in
ESTIA aims to strengthen Europe's cloud
New tech alliance for EU sovereignty
Twelve companies, including Dassault Systèmes, are founding the European Sovereign Tech Industry Alliance (ESTIA). The initiative aims to strengthen Europe's technological independence and define requirements for sovereign cloud services. The start...
read more...Deutsche Telekom and Nvidia launch one of Europe's largest AI factories
With the Industrial AI Cloud in Munich, Deutsche Telekom and NVIDIA are creating one of the largest AI factories in Europe. Companies can train AI models, carry out simulations and access 10,000 GPUs.
read more...Leveraging data potential with AI and streaming
Huge data treasures are still lying dormant in many manufacturing companies. And yet it is high time that these treasures were unlocked. There is often a lack of resources and capacity to make data usable in real time. Streaming production data can...
read more...More awareness of noise
Dematic offers solutions that are tailored to different cloud environments and reduce environmental impact through reduced material consumption and improved energy efficiency.
read more...Generative AI in the automation environment
The mechanical engineering and automation industry is undergoing profound change. This transformation is being driven by more volatile global trade, geopolitical crises and increasing market pressure, which demands greater agility, technological...
read more...Rigid data structures have had their day
The traditional data center is no longer the center of data. A hybrid cloud storage approach enables manufacturing companies to both meet the current requirements of data-intensive processes and future-proof their infrastructure.
read more...Software as a service in three stages
CIM Database Cloud is a new out-of-the-box solution from Contact for end-to-end digital product development.
read more...How production can benefit from AI
Together with AI technology, IIoT networking makes it possible to better control machine parameters and optimize quality with predictive quality. Downtimes and set-up times can also be further minimized. Cloud platforms also make these technologies...
read more...More expertise for digital engineering
Altair, a computational science and artificial intelligence (AI) company, has acquired OmniV, a technology from Michigan, USA-based software company XLDyn.
read more...