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