In other words: let's assume that your input data array (e.g. image) has M elements (i.e. patches), where M is large. In a standard Transformer model, a self-attention operation is performed for the M elements. The complexity of this operation is O(M^2). However, the Perceiver model creates a latent array of size N elements, where N << M, and performs two operations iteratively: