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Using AI efficiently and in a sustainable way
Artificial intelligence is increasingly being run directly on devices, machines, and sensors to reduce latency, meet data privacy requirements, and decrease reliance on cloud infrastructures. At the same time, limited computing resources and energy availability place high demands on the efficiency of AI systems.
The workshop provides an overview of methods and technologies for energy-efficient AI at the edge. Building on classical approaches such as model pruning, quantization, binary neural networks, and resource-efficient network architectures for embedded systems, the workshop presents current strategies for reducing memory requirements, computational effort, and energy consumption. In addition, the limitations of today’s deep learning methods will be examined, and their implications for future AI systems will be discussed.
Building on this, the workshop introduces the fundamentals of neuromorphic computing. Participants will learn about the principles of event-driven information processing, spiking neural networks, and neuromorphic hardware, and will gain insight into current application areas as well as the technological maturity of this promising technology.
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Overview & general conditions
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The following questions form the central theme of the workshop and will be addressed in the individual agenda items:
- How can we bring AI to small, energy-efficient devices?
- Why are traditional approaches insufficient in the long term?
- What might future biologically inspired AI systems look like?
After the workshop, participants will be able to classify various approaches to efficient edge AI, evaluate their potential applications, and understand the role neuromorphic systems can play in the next generation of intelligent, energy-efficient applications.
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This event is aimed at specialists involved in
- Embedded Development
- AI Development
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- Location
fortiss GmbH
Room Ada (2. floor)
Guerickestr. 25
80805 Munich - Format: Workshop
- Duration: 4 hours
- Language: German
- Fee: free, sponsored by edih dina
- What to bring: your own laptop
- Location
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Time Topic 9:00 to 9:30 h - Introduction and discussion of your areas of interest
9:30 to 9:45 h - Motivation: Why efficient AI?
9:45 to 10:15 h - Traditional edge AI
- Model compression
- Specialized network architectures
- Hardware for edge AI
- Limitations of traditional edge AI
10:15 to 10:45 h - Tutorial: Edge AI: An example of anomaly detection using IMU sensors (Initial Measurement Unit – motion sensors)
10:45 to 11:00 h - Neuromorphic computing and spiking neural networks
11:00 to 12:00 h - Hands-on coding: Neuromorphic AI using the example of IMU anomaly detection
12:00 to 12:15 h - Additional practical applications and demos
12:15 to 12:30 h - Discussion and application to individual problems
- Outlook -> potential analysis
