What is Operator fusion, quantisation, post-training quantisation, quantisation-aware training?

Operator fusion computes several consecutive operations (a matrix product, a bias, an activation, a rescaling) in one pass over the data. Quantisation represents weights and activations as small integers with scale factors, so that inference runs in integer arithmetic (Jacob and co-authors, 2018). Post-training quantisation derives the integers and scales from a trained float model and a calibration sample; quantisation-aware training fine-tunes the model with the rounding simulated in the forward pass, so that it learns weights that survive it (Nagel and co-authors, 2021).