The Model Efficiency Pipeline: Enabling Deep Learning Inference at the Edge

The Model Efficiency Pipeline: Enabling Deep Learning Inference at the Edge

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23 of 42

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The Model Efficiency Pipeline: Enabling Deep Learning Inference at the Edge

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  1. 1 Introduction
  2. 2 About Qualcomm AI Research
  3. 3 Challenges with AI workloads
  4. 4 Model efficiency pipeline
  5. 5 Challenges
  6. 6 DONNA
  7. 7 Fourstep process
  8. 8 Example
  9. 9 Blocks
  10. 10 Models
  11. 11 Accuracy predictor
  12. 12 Yields
  13. 13 Linear regression
  14. 14 Evolutionary search
  15. 15 Evolutionary sampling
  16. 16 Finetuning
  17. 17 Results
  18. 18 Model pruning
  19. 19 Unstructured pruning
  20. 20 Structured compression
  21. 21 Main takeaway
  22. 22 Quantization research
  23. 23 Quantization
  24. 24 Recent papers
  25. 25 Adaptive rounding
  26. 26 AI model efficiency tool
  27. 27 Key results
  28. 28 Highlevel view
  29. 29 Mixed precision
  30. 30 Mixed precision on a chip
  31. 31 APQ
  32. 32 Running networks conditionally
  33. 33 Classification example
  34. 34 Multiscale dense nets
  35. 35 Semantic segmentation
  36. 36 Dynamic convolutions
  37. 37 Video processing
  38. 38 Skip convolutions
  39. 39 Video classification
  40. 40 Summary
  41. 41 Questions
  42. 42 Sponsors

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