This tutorial targets z-score normalization that scales elements of a frame, making the frame output have $\mu=0$ and $\sigma=1$ distributions (mean=0, deviation=1).
Assume the input frame is a COL * ROW
matrix (data is stored column first). For each element in a frame, it computes the corresponding element as:
$$ {x^{‘}}={\frac{x-\mu}{\sigma}} $$
Where:
$$ {\mu}=\sum_{i=0}^{ROW}\sum_{i=0}^{COL}{x} / {(ROW*COL)} $$
$$\sigma=\sqrt{{\sum_{i=0}^{ROW}\sum_{i=0}^{COL}{{(x-\mu)}^2}} / {(ROW*COL-1)}} \approx \sqrt{\sum_{i=0}^{ROW}\sum_{i=0}^{COL}{{(x-\mu)}^2} / {(ROW*COL)}} $$
For designs in the tutorial, following specifications are chosen:
COL=256
ROW=384
Data type: bfloat16