In a neural network component, a transformation matrix for each of several parallel processing units is defined by the formula $\mathbf{M} \in \mathbb{R}^{d \times \frac{d}{h}}$, where $d$ is the model's primary data representation dimension and $h$ is the number of parallel units. If a system architect decides to double the number of parallel units ($h$) while keeping the primary dimension ($d$) constant, what is the resulting effect on the dimensions of the matrix $\mathbf{M}$ for each individual unit?

Analysis of Transformation Matrix Dimensions

Calculating Transformation Matrix Dimensions