Tag: IoT and Embedded

Key Takeaways:

• Pruning of deep neural networks enables their efficient use on edge devices
• The most relevant and important data and workloads are prioritized
• Optimized models, as a result of deep neural network pruning, are ideal for real time processing, sustainable AI and efficient computing
• It can also ensure AI insight generation is secure and compliant

Optimizing Neural Networks for Edge Devices: The Power of Deep Neural Network Pruning

Neural networks – and deep neural networks in particular – are driving machine learning that is closer than ever to the thought processes of the human brain. Their ability to learn and drive decisions from data makes them ideal candidates to use the information gathered by edge devices.

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Key Takeaways:

• Digital twins are an instrumental part of smarter manufacturing
• Making manufacturing faster, more cost-effective, safer and more sustainable
• The same principle can extend through quality control and the supply chain
• Several key technologies need to come together to make it a practical reality

10 Ways Digital Twin Software Is Transforming Industry 4.0

Digitization is helping businesses develop new products and exploit opportunities in their marketplaces in so many different ways. It’s proving especially useful for organizations who want to get their concepts to market as quickly and cost-effectively as possible, without compromising on quality or testing.

Digital twin technology is one of the main ways in which product development can be expedited – and it’s no surprise that its global market size is predicted to rise from under $18 billion in 2024 to as much as $260 billion by 2032. This blog explores how Digital Twin technology works, how it’s influencing Industry 4.0, and how it can make a real practical difference to your development processes.

What is Digital Twin technology?

Digital twin technology refers to replications of physical items or assets in a virtual environment. Its features, functionality and capabilities can be simulated in real time, based on data collected from embedded IoT sensors in the real product. This can help make development, testing and maintenance a faster and more cost-effective exercise than it would be using physical products themselves.

Digital twins have gained real traction in recent years, across a variety of use cases. For example, fashion retailers have created virtual versions of clothing items that they sell, so that online shoppers can accurately assess fit and size on their own bodies before they commit to a purchase. In the energy sector, digital twins of wind farms combined with predictive analytics are enabling proactive maintenance and balancing of energy output. And automotive businesses can simulate and test new engine designs using digital twins, leveraging real-time monitoring to reduce the downtime of maintenance.

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Continue reading “The Future of Embedded Systems: Trends and Innovations”

 

Continue reading “Five challenges of embedded technology systems & why experts are the key to success”