Energy-efficient AI for production
Artificial intelligence is typically run in the cloud today—but for many industrial applications, that’s not an option. Long computation times, data privacy concerns, reliance on internet connections, and high energy consumption stand in the way. EdgeAI shifts the intelligence directly to the machine, thereby solving these problems.
In this training course, you’ll learn the basics of EdgeAI and understand why spiking neural networks (SNNs) are particularly well-suited for use on energy-efficient edge hardware. You’ll implement your first SNN for defect detection in ball bearings as an example of an industrial problem. A live demonstration of a neuromorphic system for object detection and tracking on a conveyor belt—using SNNs and event cameras—brings the benefits to life.
Register now for free!
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- Why Edge AI? An introduction to Edge AI, industrial applications, and advantages over Cloud AI
- From deep neural networks to spiking neural networks: What are DNNs, what are SNNs, and why are SNNs particularly suited for edge applications
- Hands-on: Implementing an example spiking neural network for detecting defective ball bearings – from data preparation to network definition and training
- Live demonstration: Conveyor belt with stereo event cameras and an SNN on a neuromorphic edge processor – real-time object detection and tracking with low computation time and low power consumption
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- You will understand when and why Edge AI is superior to cloud AI and be able to identify use cases within your own company
- You will learn the difference between traditional neural networks and spiking neural networks
- You will independently implement your first SNN and experience the technology firsthand through a live demonstration
- Upon request, we will be happy to issue you a certificate of participation for the training
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The event is aimed at technical specialists and managers from small and medium-sized enterprises. Basic programming skills – preferably in Python – as well as a basic understanding of machine learning are beneficial but not required.
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Date
September 29, 2026, 9:30 a.m. – 1:30 p.m.Location
fortiss GmbH, Guerickestraße 25, 80805 MunichSpeaker
Thomas HuberLanguage
GermanRegistration fee
Free
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The event is presented by fortiss GmbH and Mittelstand-Digital-Zentrum Augsburg.
