  Neural networks are increasingly evolving towards training large models with
big data, a method that has demonstrated superior performance across many
tasks. However, this approach introduces an urgent problem: current deep
learning models are predominantly serial, meaning that as the number of network
layers increases, so do the training and inference times. This is unacceptable
if deep learning is to continue advancing. Therefore, this paper proposes a
deep learning parallelization strategy based on the Universal Approximation
Theorem (UAT). From this foundation, we designed a parallel network called
Para-Former to test our theory. Unlike traditional serial models, the inference
time of Para-Former does not increase with the number of layers, significantly
accelerating the inference speed of multi-layer networks. Experimental results
validate the effectiveness of this network.
