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Probably just an extreme version of quantization-aware training? During training you round the prediction to the range you want, but keep it as a float.

Since rounding isn’t differentiable there’s fancy techniques to approximate that as well.

> QAT backward pass typically uses straight-through estimators (STE), a mechanism to estimate the gradients flowing through non-smooth functions

https://pytorch.org/blog/quantization-aware-training/




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