Advances in Quantization for Efficient On-Device Neural Network Inference

Advances in Quantization for Efficient On-Device Neural Network Inference

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Outliers in Transformers

10 of 10

10 of 10

Outliers in Transformers

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Advances in Quantization for Efficient On-Device Neural Network Inference

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  1. 1 Intro
  2. 2 Low-precision numerical formats
  3. 3 INT8 and FP8 have the same number of values but different distributions.
  4. 4 INT8 and FP8 accuracy
  5. 5 Challenges in using integer quantization
  6. 6 Introduction to Quantization-Aware Training (QAT)
  7. 7 Oscillating weights in QAT
  8. 8 MobileNetV2 - comparison to literature
  9. 9 Why do outliers occur?
  10. 10 Outliers in Transformers

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