**Quantization Aware Training**
[Post-training quantization](https://texonom.com/post-training-quantization-ee4a0b4f02184b1193a68073dc60800e) 


## QAT
학습 진행 시점에 inference 시 quantization 적용에 의한 영향을 미리 시뮬레이션을 하는 방식이고 그걸 기반으로 Back Propagation
소형 모델에서도 성능하락 적다
![Red node is fake quantization node (act means activation, wt means weight)](https://www.notion.so/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2Fb8ec40a4-602a-4401-b830-8370b1508620%2FUntitled.png?table=block&id=382f2a9f-94b7-45ed-93e8-2a6e56201701&cache=v2)


> [Quantization aware training  |  TensorFlow Model Optimization](https://www.tensorflow.org/model_optimization/guide/quantization/training)

> [Inside Quantization Aware Training](https://towardsdatascience.com/inside-quantization-aware-training-4f91c8837ead)

> [딥러닝의 Quantization (양자화)와 Quantization Aware Training](https://gaussian37.github.io/dl-concept-quantization/)





