In deep learning based model training, in general batch of inputs are passed. For example for training a deep learning model with [512] dimensional input feature vector, say for batch size= 4, we mainly pass [4,512] dimenional input. I am curious what are the logical significance of passing the same input after flattening the input across the batch and channel dimenions [2048]. Logically the locality structure will be destroyed but will it significanlty speed up my implementation? And can it affect the performance?