AI-ML Solutions for Low-Power Edge Platforms - Challenges and Opportunities

AI-ML Solutions for Low-Power Edge Platforms - Challenges and Opportunities

tinyML via YouTube Direct link

Kixo

29 of 32

29 of 32

Kixo

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AI-ML Solutions for Low-Power Edge Platforms - Challenges and Opportunities

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  1. 1 Introduction
  2. 2 Reminder
  3. 3 tinyML India Chapter
  4. 4 Amit Mate Introduction
  5. 5 GMAC Introduction
  6. 6 Challenges
  7. 7 Workflow
  8. 8 Performance Comparison
  9. 9 Challenges in AlwaysOn AI
  10. 10 Example Use Case
  11. 11 Video Attendance
  12. 12 Face Recognition Attendance
  13. 13 How to leverage tinyML
  14. 14 Questions
  15. 15 Network acceleration
  16. 16 Multicore DSPs
  17. 17 Hardware accelerators
  18. 18 Cnn
  19. 19 Story time
  20. 20 Lowpower devices
  21. 21 Practical problems
  22. 22 Unique algorithms
  23. 23 Nested for loop
  24. 24 Edge AI trends
  25. 25 Is there a niche for tinyML
  26. 26 Future of Edge AI
  27. 27 Deeplight
  28. 28 Edge Impulse
  29. 29 Kixo
  30. 30 Reality AI
  31. 31 October 27th
  32. 32 Closing

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