Abstract
The field of artificial intelligence (AI) has gained increasing importance in recent years due to its potential to sustain growth and prosperity in a disruptive way. However, the role of special hardware for AI is still underdeveloped, and dedicated AI-capable hardware is crucial for effective and efficient processing. Moreover, hardware aspects are often neglected in university teaching, which emphasizes theoretical foundations and algorithmic implementations. As a result, there is a need for courses that focus on AI hardware development and its diverse applications. In response to this need, the BB-KI Chips consortium aims to develop a series of hardware-oriented courses with real-world AI applications. This consortium includes the Technical University of Munich (TUM) and the University of Potsdam (UP), which both offer a wide range of courses that focus on AI basics, AI algorithmic development, general computer architectures, chip design, and as well applications of AI. In the BB-KI-CHIPS project, these different capacities are planned to be tightly integrated into a unified curriculum covering knowledge from chip design over AI algorithms and techniques to applications.
| Original language | English |
|---|---|
| Pages (from-to) | 75-81 |
| Number of pages | 7 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 10 |
| Issue number | 5/W1-2023 |
| DOIs | |
| State | Published - 23 May 2023 |
| Event | 2023 International Conference on Geomatics Education - Challenges and Prospects, ICGE 2023 - Hong Kong SAR, China Duration: 10 May 2023 → 12 May 2023 |
Keywords
- AI Detection for Natural Hazards
- BB-KI
- Chip Design for AI
- Hands on Teaching
- On Board CNN Detection
- University Curriculum